urban remote sensing thesis

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• Published: 31 May 2023

Estimating urban spatial structure based on remote sensing data

• Masanobu Kii 1 ,
• Tetsuya Tamaki 2 ,
• Tatsuya Suzuki 2 &
• Atsuko Nonomura 2  

Scientific Reports volume  13 , Article number:  8804 ( 2023 ) Cite this article

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Understanding the spatial structure of a city is essential for formulating a spatial strategy for that city. In this study, we propose a method for analyzing the functional spatial structure of cities based on satellite remote sensing data. In this method, we first assume that urban functions consist of residential and central functions, and that these functions are measured by trip attraction by purpose. Next, we develop a model to explain trip attraction using remote sensing data, and estimate trip attraction on a grid basis. Using the estimated trip attraction, we created a contour tree to identify the spatial extent of the city and the hierarchical structure of the central functions of the city. As a result of applying this method to the Tokyo metropolitan area, we found that (1) our method reproduced 84% of urban areas and 94% of non-urban areas defined by the government, (2) our method extracted 848 urban centers, and their size distribution followed a Pareto distribution, and (3) the top-ranking urban centers were consistent with the districts defined in the master plans for the metropolitan area. Based on the results, we discussed the applicability of our method to urban structure analysis.

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Introduction.

Understanding the spatial structure of a city is essential for formulating a spatial strategy for that city. For this reason, many city officials and planners devote considerable resources to maintaining accurate data on the cities’ geographic features. Perhaps the two most crucial features are the spatial extent of the city and the layout of the centers of people’s activities. Classical urban economic models describe a mechanism by which transportation costs and land rents determine the extent and density of a city under a monocentric structure 1 ; however, many large cities have expanded and developed to have multiple urban centers because of population growth and advances in transportation technology 2 , 3 . Beyond their relevance to urban planning and governance, the extent of a city and the location of its urban centers have a significant effect on the lives of citizens—through their choice of residence and daily commute—in addition to disaster resilience 4 , 5 ; the peri-urban ecosystem and natural environment 6 , 7 ; and, more recently, infectious diseases 8 , 9 .

For this reason, various methods for quantitatively analyzing the spatial structure of cities have been explored. Perhaps the simplest strategy is to identify urban areas and population centers by analyzing various forms of statistical data, such as spatial distributions of population and employment 10 , commuting and shopping traffic 11 , 12 , or activity density and concentration 13 , 14 , 15 , 16 , 17 , 18 . However, the use of statistical data has disadvantages: spatial units of data aggregation and observation frequencies vary from country to country and region to region, and measurements may be spatially coarse and infrequent. To address these challenges, recent studies have investigated methods to characterize urban structure using two alternative data sources: remote sensing data and mobile terminal location data. These data sources provide frequent measurements and high spatial resolution across extensive coverage areas, even in developing nations. Varieties of remote sensing data considered to date include various earth reflectances of the electromagnetic spectrum (see review paper 19 ), light detection and ranging 20 , synthetic aperture radar 21 , 22 , stereoscopic digital surface models (DSMs) 23 , 24 , 25 , 26 , and nighttime lights 27 , 28 , 29 , 30 . In studies using mobile terminal data, researchers have considered the use of communication traffic data collected by mobile network operators 31 , check-in data for location-based social networks 32 , frequency of call detail records from mobile terminals 33 , 34 , 35 , and Google location histories 36 . However, the location data of cell phones are held by private companies, such as cell phone companies. The data are not disclosed to the public because of privacy protection concerns. By contrast, many remote sensing data are widely disclosed by public organizations.

In one prominent study that captured urban centers in large cities using remote sensing data, Chen et al. 27 proposed a method to define urban centers using a nighttime light contour tree. They created a contour tree of nighttime lights for Shanghai and successfully detected the city center based on the threshold of nighttime lights. However, Chen et al. (1) defined the hierarchical level of urban centers using contour tree topology and it did not use the light intensity of urban center activity for the systematic activity level evaluation, and (2) set the threshold for urban center detection arbitrarily to match known urban centers that serve as references. The level of activity in city centers is essential information for urban planning and transportation planning; however, Chen et al. did not directly interpret nighttime light intensity in the planning context. They detected 33 urban centers in Shanghai with a population of more than 23 million, which means that they detected only major centers and ignored minor centers by truncating peaks below the threshold or averaging out small peaks.

The definition of urban center is ambiguous 26 . Therefore, various methodologies exist for the identification of polycentricity and subcenters, with different methods used in different studies. For example, Duranton and Puga 37 suggested that subcenters can range from large to small depending on their levels of functions. To address these issues, we propose a methodology to identify the hierarchical structure of all urban centers based on a contour tree, which reflects the activity intensity of urban centers.

The method proposed in this study is superior to existing methods in three respects. First, it evaluates the spatial distribution of urban activity using a model that transforms remote sensing data into trip attraction. As found by Burger and Meijers 11 , it is straightforward to understand the spatial distribution of urban activities as trip attraction, and to interpret its meaning in urban planning practice. A few studies have been conducted on the relationship between nighttime lights and traffic 38 , 39 . In this study, we employ statistical modeling to estimate trip attraction using remote sensing data. Specifically, we divide the traffic volume index into two categories: trips going out and trips returning home, based on the purpose of travel. This approach allows us to account for the empirical observation that the attraction volume of trips going out is influenced by the intensity of urban center activities, whereas the attraction volume of trips returning home is influenced by the intensity of residential areas. Thus, we can identify urban centers as the focal points of outgoing trips. We can recognize urban centers as places where going out trips are concentrated. By contrast, we can assume that the destination of a returning home trip is a residential area. Therefore, we can assume that the destinations of these two trips can define urban areas.

In previous studies, most land use and cover data classified land directly based on the surface reflectance spectrum. By incorporating the process of converting remote sensing data into trip attraction volume, we expect to be able to estimate urban areas that are more meaningful from the perspective of urban planning practice than conventional land use data. Using these models, we attempt to determine the spatial extent of the city and the location of city centers.

Second, we extract a comprehensive range of urban centers, from the major centers of the metropolitan area to local community centers, using the contour tree of an estimated going out trip attraction map. In the method of Chen et al., they defined the size and level of urban centers to be extracted using a specific threshold and ignored small centers. Our proposed method is unique in that it extracts a wide range of peaks of the trip attraction map as urban centers. Third, we use the topology information of the contour tree and measure the activity level of the extracted centers by cumulative trip attraction, including their hinterlands. This approach enables us to rank the centers while considering the overall structure of the city. It allows for an analysis that captures the competition among urban centers as well as the independence of suburban centers. This is not achievable when measuring the intensity of activity in urban centers solely based on local conditions, such as a threshold. Taking advantage of these features, in this study, we evolve a method for extracting the urban structure using remote sensing data. As discussed below, the proposed cumulative trip attraction index obtained by expanding the contour tree method achieved higher performance for urban center detection than the ordinary index obtained by the simple contour tree. This is an innovation in this study that advances previous research.

In this study, we use trip attraction as a functional variable of urban structure. We create a model with trip attraction as the dependent variable and morphological variables from remote sensing data as explanatory variables. We use nighttime light data and a DSM as input remote sensing data; however, these data can be replaced depending on the context. The trip attraction volume is statistical data and the unit of aggregation is the traffic analysis zone (TAZ). Generally, TAZs are smaller in the central area than in suburban areas, and TAZs are typically larger than the grid size of remote sensing data. We estimate the model of trip attraction using the data with TAZ as the spatial unit. By inputting grid-based remote sensing data into the estimated model, we can estimate spatially detailed traffic volume indices. We divide trip attraction according to the travel purpose into going out and return trips. We assume that each destination corresponds to a city center and residential area, and model each trip attraction. We use the estimated traffic volume indices to identify urban areas and urban centers. In particular, for urban centers, we replace the input information of the model proposed by Chen et al. from nighttime light with the estimated trip attraction density (TAD) to obtain a hierarchy of urban functions and their locations. Thus, we extract the spatial structure of the city. In the " Methods " section, we explain this analysis procedure in detail.

Regression analysis of trip attraction

Before presenting the regression analysis, we check the necessity of the variable transformation of the dependent variable. We tested the parameters of the Box–Cox transformation 40 . The results demonstrated that the parameters were significant at the 1% level for rejecting the null hypothesis of the normality of dependent variable (λ = 1), except for the TAD for return trips with Visible Infrared Imaging Radiometer Suite (VIIRS) nighttime lights (VNL), which was greater than or equal to 50 nW/cm 2 /sr (Table 1 ). Thus, the TAD for return trips with VNL ≥ 50 was not transformed, and the remainder of the variables were transformed with the parameters shown in Table 1 for the subsequent analysis.

To determine the regression model formulated in Eq. ( 3 ), we tested all combinations of VNL and altitude difference index (ADI) × VNL as explanatory variables. The details for the ADI are provided in the " Methods " section. We applied the Box–Tidwell transformation 41 to account for the nonlinearity of the effects of the explanatory variables. We assumed that the transformation parameters were unity if they were not significant. The results are shown in Table 2 .

The upper part of the table shows the results for zones with average VNL ≥ 50 nW/cm 2 /sr, and the lower part shows the results for zones with average VNL < 50 nW/cm 2 /sr. Additionally, (1)–(3) and (7)–(9) are the estimation results for going out and the remainder are the results for the return trip regression model. The notation "-" indicates that the parameter is not applicable.

First, considering the results of the Box–Tidwell transformations, the NA in model (3) means that the estimates diverged and could not be appropriately estimated. Additionally, all variables in models (4)–(6) and VNL × ADI in models (9) and (12) were not significant. We assumed that the influence of these variables was linear.

Next, considering the regression coefficients, all coefficients were significant at the 0.1% level, except for VNL in models (3) and (6) and VNL × ADI in models (9) and (12). Note that the coefficients of VNL in model (1) and VNL × ADI in model (11) were negative, which reflects the fact that the Box–Tidwell exponential was negative. Considering the significant parameters of the Box–Tidwell transformation and regression analysis, we observed that for VNL < 50 and VNL ≥ 50 going out trips, the higher the value of VNL or VNL × ADI, the higher the TAD. By contrast, in the model for return trips with VNL ≥ 50, the larger the values of VNL or VNL × ADI, the lower the TAD. This reflects the negative correlation between the TAD and the variables in the VNL ≥ 50 zone, as shown in Fig.  10 , and indicates that the TAD for return trips was low in the city center because the land use was specialized for business.

We considered R 2 between the estimated and observed values, where R 2 denotes the multiple correlation coefficient calculated for the Box–Cox-transformed dependent variable. We calculated R 2 (original) for the dependent variable after transforming the model estimates with the exponential power of the inverse of the Box–Cox parameter and returning to the original TAD scale. R 2 of the model with two variables, VNL and VNL × ADI, was naturally the highest, except for going out with VNL ≥ 50. For going out with VNL ≥ 50, R 2 of model (2) was higher because the Box–Tidwell transformation did not yield a solution in model (3). By contrast, one variable was not significant in any of models (6), (9), and (12) with two variables. R 2 and RMSE did not differ significantly from the model with only the variable that was considered significant in that model. Based on these results, we used (2) for going out with VNL ≥ 50, (5) for return trip with VNL ≥ 50, (7) for going out trip with VNL < 50, and (10) for return trip with VNL < 50.

The model we obtained above is a simple estimation of the TAD using remote sensing data, but we obtained a certain level of reproducibility. The spatial distribution of estimation errors is shown in Fig.  1 . The upper panel of Fig.  1 shows the difference between the estimated and observed TAD, and the lower panel shows the relative error to the observed value. On the left is the going out trip and on the right is the return trip. There was a certain spatial autocorrelation for both going out and return trips. The model estimates were overestimated for the reclaimed areas along the coast of Tokyo Bay because most of these areas are used for the industrial sector. Industrial areas typically exhibit strong nighttime light emissions but tend to have relatively low trip attraction for people. Considering the lower panel, the relative error was larger in less populated zones at the outer edges. By contrast, in densely populated areas, the relative error was rather small. The details of the estimation error for the going out trip in VNL ≥ 50 zones are described in supplementary material S2 .

Spatial distribution of the estimation error: ( a ) error of the going out trip, ( b ) error of the return trip, ( c ) relative error of the going out trip, ( d ) relative error of the return trip (the maps were created with the software R 4.1.0 42 with packages sf 1.0.9 43 , stars 0.6.0 44 , ggplot2 3.4.0 45 , and ggspatial 1.1.7 46 . All maps presented in this paper below were created using the same software.).

Urban structure detection on a grid system

We applied the above model to grid data to estimate the grid-based TAD. The results are shown in Fig.  2 . The figure shows that the overall trend of the target area was the same as that for the zone-based TAD in Fig.  9 , but the grid-based TAD provided higher spatial resolution than TAZ-based TAD, particularly in suburban areas.

Estimated TAD on the grid: ( a ) going out, ( b ) return.

In the following, we use this estimated grid-based TAD to analyze the extent of the urban area and the spatial distribution of city centers by applying the method described in " Methods " section.

Estimation of the urban area

First, we estimated the urban area using Eqs. ( 4 )–( 6 ). We assumed that \(f_{u} \left( {q_{Hi} ,q_{Ei} } \right) = wq_{Hi} + \left( {1 - w} \right)q_{Ei}\) , and set weight \(w\) and threshold \(\delta_{M}\) to values that minimize the error from the current urban area. This minimization problem is formulated in Eq. ( 6 ). We defined the current urban area as a densely inhabited district (DID), which is a district with a population density of more than 4,000 people/km 2 and more than 5,000 people in adjacent areas, according to the Japanese census. As a result of the analysis, we estimated the threshold for minimizing the error to be \(\delta_{M}\) =2722 and the weight to be \(w\) = 0.461. The fit of the estimated urban area to the DID is shown in Fig.  3 .

Conformity of the estimated urban area to the DID.

Figure  3 shows that the estimated area and DID area generally matched in the central area of the metropolis, but there was a large error in the fringe area. In terms of the area, there were 2935 km 2 of grids where both areas matched, 687 km 2 of grids where only the estimated area was urban, and 538 km 2 of grids where only the DID was urban; compared with the total area of the DID, that is, 3474 km 2 , they were 84%, 20%, and 15%, respectively. The DIDs in the periphery were scattered, and remote sensing data-based indices, such as VNL and ADI, were unable to fully capture these urban areas. In particular, grids with a high proportion of natural land use, such as rivers and mountain forests, had a low average nighttime light intensity and were not considered as urban areas by the method. By contrast, there were many highways and large-scale factories in areas that were not DIDs but emitted strong nighttime light and were estimated as urban areas by the method. Although these facilities had a small residential population and did not fall under the category of DID, they were estimated to be urban areas by the method because of their strong nighttime light.

For reference, we compared the urban area defined by DID and that of the ESA CCI Land Cover (CCI-LC) time-series v2.0.7 47 dataset for 2015 as an example of a conventional method. Regarding the target area, the urban areas in both data coincided in the 3304 km2 grids, but only CCI-LC was urban in the 1972 km2 grids and only DID was urban in the 170 km2 grids. This means that the urban area of CCI-LC was more than twice the urban area of DID. Clearly, the urban areas differ according to their definition. Here, the models used for CCI-LC were not calibrated to represent DID. It is likely that conventional methods would be more suitable for our specific urban areas of interest if the models used for CCI-LC were calibrated accordingly. However, our approach is much simpler than recent sophisticated land use and cover classification methods. We expect it to be relatively easy to calibrate, particularly in urban areas. Further discussion on accuracy is provided in supplementary material S3 .

Estimation of urban centers

Next, we extracted the urban centers using the TAD for going out. For grids with a TAD of more than 3,000 trips/km 2 , we created a contour at a level of every 1,000 trips/km 2 , and created a contour tree using the " Methods " described in methods section. The number of contour levels was 653, and the total number of created contours was 7960, of which 848 were seeds. The created contour, its contour tree, and seeds are shown in Fig.  4 .

Results of urban center detection: ( a ) contour, ( b ) contour tree, ( c ) seeds.

We created the contour tree using the "igraph" package v1.2.6 of "R" 48 . We based the layout on the Reingold–Tilford graph layout algorithm 49 , and the height direction pseudo-represents the TAD of each contour. Additionally, in Fig.  4 b, we only assigned numbers to the seeds of the contour. The figure shows that seeds with a high TAD are close to each other on the graph, but seeds with a medium TAD are widely distributed, and many seeds have a low TAD. Some urban centers are formed by seeds and their hinterland overall. We evaluated the urban centers using two indices: the original TAD index (TADI) and cumulative trip attraction index (CTAI) at the seed. Cumulative trip attraction is defined in " Methods " section. Figure  5 shows the rank size plot of both indices. The figure shows that both followed a Pareto distribution. We excluded urban centers with fewer than 15,000 trips/km 2 for the TADI and fewer than 20,700 trips for the CTAI. Therefore, 281 locations are shown for the former and 326 locations for the latter. The values of the Pareto exponent were − 1.32 and − 1.25, respectively. This result implies that the CTAI had a slightly more concentrated distribution than the TADI.

Rank size plot of the trip attraction indices for urban centers: ( a ) TADI, ( b ) CTAI.

Figure  6 shows the locations of the top 50 urban centers for both indices. The blue zone indicates the 23 wards of Tokyo, that is, the central area of the Tokyo metropolitan area. The figure shows that, although both indices had the largest number of urban centers in the 23 wards, the TADI had a higher concentration of urban centers and fewer urban centers outside the 23 wards. On the other hand, CTAI identified a greater number of urban centers outside the 23 wards.

Extracted top 50 urban centers: ( a ) TADI at the seed, ( b ) cumulative trip attraction.

In the "Guidelines for the Development of the Central Area of the Special Wards of Tokyo" formulated by the Tokyo Metropolitan Government in 1997, the area from the vicinity of Tokyo Station to Shimbashi was designated as the central area of Tokyo. Regarding subcenters, Shinjuku, Shibuya, and Ikebukuro were designated in the National Central Region Development Plan for the Tokyo Metropolitan Area in 1958. Ueno/Asakusa, Kinshicho/Kameido, and Osaki were designated in the Long-Term Plan for the Tokyo Metropolis formulated in 1982. The Tokyo Rinkai subcenter was designated in the Second Long-Term Plan for the Tokyo Metropolis formulated in 1986. In 1986, the National Central Region Basic Plan for the Tokyo metropolitan area called for the development of business core cities on the periphery of the metropolitan area to alleviate congestion problems in city centers. In the current version of the National Central Region Development Plan, Yokohama/Kawasaki, Atsugi, Machida/Sagamihara, Hachioji/Tachikawa/Tama, Ome, Kawagoe, Kumagaya, Saitama, Kasukabe/Koshigaya, Kashiwa, Tsuchiura/Tsukuba/Ushiku, Narita, Chiba, and Kisarazu are designated as business cities to promote the agglomeration of business functions.

The locations of these urban centers are shown in Fig.  7 , and the ranks of these urban centers by the two indices are shown in Table 3 . Most of the centers in the special wards are ranked within the top 50. By contrast, some of the business core cities are ranked lower than 400th, which suggests that the dispersion of business functions in the plan has not progressed sufficiently. Comparing the ranks of the top centers, in the TADI, Shinjuku, the subcenter, is ranked first, and Shinbashi and Tokyo, the city center, are ranked second and fourth, respectively. In the CTAI, Shinbashi and Tokyo are ranked first and second, respectively, and Shinjuku, the subcenter, is ranked third. This suggests that the CTAI is more suitable for the positioning of urban centers as indicated in administrative plans.

Locations of urban centers in administrative plans: ( a ) centers outside the special wards of Tokyo, ( b ) centers inside the special wards of Tokyo.

The estimation error of the TAD shown in Fig.  1 also affects the generation of the contour tree and subsequent urban center detection. For example, site 41 in Fig.  6 a and site 42 in Fig.  6 b are at the same location, but they are located in a high-density industrial area and do not attract as many going out trips as a typical urban center. It should be noted that the existence of such an error may lead to the detection of inappropriate urban centers.

Additionally, coastal watchtowers and the lights of highways can be detected as seeds in the contour tree, potentially leading to misidentification as urban centers. In such cases, CTAI may generate low values for isolated seeds since it relies on the cumulative nighttime light within the corresponding contour. To avoid the false detection of isolated nighttime lights as urban centers, it would be effective to filter out low-level CATI seeds. However, it is important to acknowledge that industrial areas and highway interchanges adjacent to urban areas have a higher probability of being detected as urban centers even with CATI. This limitation is inherent in the methodology. To enhance the urban center detection process, it may be necessary to incorporate land use information, road data, and other relevant data sources in conjunction with CATI.

In this paper, we presented grid-based empirical detection of both urban areas and city centers in the Tokyo metropolitan area using remote sensing data that is available worldwide and traffic volume data considering the local context. The result had a higher spatial resolution than TAZ-based activity estimation. Therefore, it can be applied to various types of urban analysis. In this section, we discuss the potential contribution of our method to urban analysis and data challenges for further research.

We represented urban centers as seeds of the contour tree, and evaluated centrality using two indices, TADI and CTAI, for trip attraction. Both indices followed a Pareto distribution, which suggests that the urban center had an agglomeration effect 50 ; that is, urban centers with a high trip attraction index tended to attract more traffic. We also demonstrated that the rankings by the CTAI were more consistent with the administrative plan than those by the TADI. This indicates that the identification method of urban centers using the CTAI has high potential to be used as planning information.

The trip attraction indices and the spatial arrangement of urban areas and urban centers estimated by the proposed method can be used for various urban analyses. For example, the going out trip and return trip can be used to estimate trip distribution 51 . The estimated urban centers and urban area can also be used to examine the applicability of central place theory models 52 and land use simulation models, such as SLEUS 53 . Additionally, it can be used for agent-based traffic simulations 54 , 55 , 56 because it can estimate the traffic volume on a grid basis, which has higher spatial resolution than TAZ.

The remote sensing data used in this model covers almost all major cities in the world. However, we did not examine the applicability of the trip attraction index model to other cities in this study. Applicability can be examined for cities that have trip attraction data by purpose. However, even in developed countries, the frequency of large-scale travel behavior surveys is approximately once every 10 years at most. Because the nighttime light data used in this paper is continuously observed and published, we believe that the estimation of the spatial structure of the city will be updated in a timely manner using it in a complementary manner with traffic data and other statistics.

By contrast, in developing countries where urban areas are expanding rapidly, large-scale travel behavior surveys are rarely conducted, and information on the spatial structure of urban functions and traffic conditions is insufficient. The proposed model can be used to provide reference information for the spatial structure of urban functions under such data constraints. If we impose the Tokyo parameters on other cities, we can still calculate the urban structure using remote sensing data. However, clearly, there may be considerable bias in that estimation. For example, there is a high correlation between nighttime light and economic activity 57 , 58 ; therefore, applying the model to cities with different levels of economic activity may result in significant bias. Existing land cover data and empirical geographic information of urban centers can be used to examine model applicability and perform calibration. If we can collect appropriate small sample survey data, it might be possible to correct for model bias through calibration. For this calibration, we can apply Bayesian estimation. In the future, we will examine the applicability of the proposed model to other cities with travel data, and compare parameters among cities for meta-analysis. If we have sufficient information about the relationship between remote sensing data and travel behavior data, we may be able to reduce the estimation bias using only remote sensing data. We believe that the proposed approach can contribute to filling the research gap in urban structure estimation using satellite imagery, while taking into account the local context.

At present, numerical elevation data available worldwide are limited in terms of the time of observation and the spatial accuracy of publicly available data are coarse, which makes it difficult to obtain the height of buildings on sloping terrain. Although we limited the estimation error in the target area of this study by combining it with nighttime light, a non-negligible error would arise for cities located on a slope. High-resolution DSMs can provide more accurate estimates of building heights 59 , but such data are not always available for all cities, and availability is on a case-by-case basis. Digital building models in CityGML format are available for some cities ( https://3d.bk.tudelft.nl/opendata/opencities/ ), but are currently limited to major cities in developed countries. To improve accuracy, continuous observation data of building heights must be made available; however, the use of point of interest data 15 and other data may correct the errors. These issues will be addressed in the future.

Modeling trip attraction using remote sensing data

The k th TAZ is denoted by \(z_{k}\) . Let the concentrated traffic volume be \(Q_{k}\) and the area be \(A_{k}\) . Considering the difference in the areas of TAZs, we estimate a model to calculate the TAD index \(q_{k} \left( \lambda \right) = \left( {q_{k}^{\lambda } - 1} \right)/\lambda\) , which is the Box–Cox transform of the original TAD ( \(q_{k} = Q_{k} /A_{k}\) ). In the case \(\lambda = 1\) , we assume that \(q_{k} \left( \lambda \right) = q_{k}\) . We assume that the remote sensing data are given as grid data. Grid \(i\) is denoted by \(g_{i}\) , and the value of remote sensing data r is \(x_{ri } \left( {i = 1, \ldots ,N_{{{\text{grid}}}} } \right)\) . Then, the average values \(x_{rk} \left( {k = 1, \ldots ,N_{{{\text{TAZ}}}} } \right)\) at \(z_{k}\) are as follows:

The model that explains the TAD index of the TAZ using remote sensing data is described in general form as follows:

where \(\left\{ {x_{rk} } \right\}\) means that multiple remote sensing data can be used and \(f_{q}\) is a function that converts the value of remote sensing data into trip attraction. We can obtain the model using regression analysis. By applying the grid data to the model \(f_{q}\) , we can estimate TAD \(q_{i}\) of grid \(i\) :

We specify \(f_{q}\) in the " Results " section. This function and input remote sensing data can be changed according to the local context or the representability of the model.

Capturing urban structure based on estimated trip attraction

In this study, we capture two aspects of urban structure: urban areas and urban centers. The TADs for going out and return trips at \(g_{i}\) are denoted by \(q_{Ei}\) and \(q_{Hi}\) , respectively, which we consider to be correlated with the population density in the daytime and nighttime, respectively. Urban areas are often defined by the population density, whereas urban centers can be captured by activities outside the home.

First, we consider the estimation of urban areas. In this study, the grid set of urban areas is defined as.

where \(f_{u}\) is the function of the TADs for going out and return trips, for example, \(f_{u} \left( {q_{Hi} ,q_{Ei} } \right) = wq_{Hi} + \left( {1 - w} \right)q_{Ei}\) , \(w\) is weight, and \(\delta_{M}\) is the threshold for determining the urban area. Both the composite function and threshold value can vary depending on the regional context. We determine the parameters ( \(w\) , \(\delta_{M}\) ) by solving the following error minimization problem:

where \(U_{b}\) is the observed urban grid set, \(\overline{U}\) and \(\overline{{U_{b} }}\) are the complements of \(U\) and \(U_{b}\) , respectively, and \(\left| U \right|\) is the number of elements in set \(U\) . We specify the function \(f_{u} \left( {q_{Hi} ,q_{Ei} } \right)\) and determine parameters ( \(w\) , \(\delta_{M}\) ) in the " Results " section.

Next, because we expect city centers to be the main destination for going out trips, we create a contour tree using the TAD for going out trips. The contour tree consists of nodes and links, where nodes are closed contours and links represent the inclusion relations of closed contours. To create the contour tree, we refer to the method of identifying the hierarchical structure of the space using topographic data 27 , 60 . First, \(C_{hk}\) denotes the grid set contained in the k th closed contour of level h of the TAD. The \(C_{hk}\) for which all the element grids have equal density levels is called the “seed” and denoted by \(S_{hk}\) ; that is, \(S_{hk}\) is a local peak grid and set to the endpoint node of the contour tree. If \(S_{hk} \subseteq C_{{h - 1,k^{\prime}}}\) , then \(S_{hk}\) and \(C_{{h - 1,k^{\prime}}}\) are connected by a link. Similarly, if \(C_{hk} \subseteq C_{{h - 1,k^{\prime}}}\) , then \(C_{hk}\) and \(C_{{h - 1,k^{\prime}}}\) are linked; and if \(C_{h - 1,k}\) has links to multiple upper contours, then \(C_{h - 1,k}\) is the hinterland of multiple urban centers. Figure  8 shows an example of a contour and contour tree. C 11 contains the only upper node C 21 ; hence, it is connected by one link in the contour tree. C 21 contains C 31 and C 32 ; hence, it is connected to the two upper nodes by links. C 31 contains C 41 , C 41 contains seed S 51 , C 32 contains seed S 41 , and C 32 contains seed S 51 . Thus, the contour tree represents the inclusion of a closed contour. Hence, we can calculate not only the TAD of the seed but also the traffic volume of any level of the surrounding area that includes the seed to extract the city center district and to analyze the hierarchical structure of city center functions.

Illustration of a ( a ) contour map and ( b ) contour tree.

Third, we define the cumulative trip attraction at urban centers using the cumulative trip attraction volume of the contour in which each is contained. If there is only one seed in a given contour, we assign the cumulative trip attraction in that contour to the seed, but if there are multiple seeds, we assign the cumulative trip attraction proportionally according to the area of the contour that contains the seed.

Specifically, we follow the steps below. First, the index set of the grid in contour \(C_{hk}\) is denoted by \({\Omega }_{hk}^{g} = \left\{ {\forall i;g_{i} \subset C_{hk} } \right\}\) and the index set of the contour at level h  + 1 in \(C_{hk}\) by \({\Omega }_{hk}^{c} = \left\{ {\forall k^{\prime};C_{h + 1,k^{\prime}} \subseteq C_{hk} } \right\}\) . If the area of \(g_{i}\) is \(a_{i}\) , the area of \(C_{hk}\) and trip attraction are expressed as follows:

where \(a_{hk}^{c}\) is the area of \(C_{hk}\) (superscript c denotes the variable for the contour) and \(Q_{hk}\) is its trip attraction. If the cumulative trip attraction of \(C_{hk}\) is denoted by \(Q_{hk}^{m}\) , and we assign the cumulative trip attraction up to level h of the contour in proportion to the area of the contour within the set \({\Omega }_{hk}^{c}\) , and then \(Q_{{h + 1k^{\prime}}}^{m}\) is given by

We assume that \(k^{\prime} \in {\Omega }_{hk}^{c}\) and \(Q_{1k}^{m} = Q_{1k}\) . This trip attraction index takes into account the layout of the city center and the hinterland.

Target region and data

The target region of this study is the Tokyo metropolitan area. It is the largest metropolitan area in the world, with a population of 36.9 million and an area of 15,950 km 2 as of 2018. Although several urban centers exist within the region, no single definition of a central district has been set according to local contexts, such as planning and policy.

Trip generation and attraction data by travel purpose by TAZ is available from a person trip survey conducted in the Tokyo metropolitan area in 2018 ( https://www.tokyo-pt.jp/special_6th ). There are 1,660 TAZs in the person trip survey. In the survey, travel behavior data on a single weekday was collected from September to November in 2018. The sampling rate is approximately 1%. These data provide \(q_{k}\) in Eq. ( 2 ).

As remote sensing data to describe the traffic volume, we use the Annual product of the Visible Infrared Imaging Radiometer Suite (VIIRS) nighttime lights (VNL) V2 61 and Advanced Land Observing Satellite World 3D 30 m Resolution DSM (AW3D 30) 62 . The reason for using them is to guarantee the applicability of the method to other cities because they are publicly available for a wide area of the world.

VNL V2 is based on a cloud-free monthly composite generated from the VIIRS Day Night Band, which provides 12-month mean and median values after solar and moonlight reflections and outliers are removed. The grid size is 15 arc seconds and it covers the range from 75 degrees north to 65 degrees south. The content of the record is nighttime light radiance, whose unit is nW/cm 2 /sr. The VIIRS sensor has been in operation from 2012 to the present, and the Annual product of VNL V2 provides yearly data. In the following, nighttime light data are denoted by VNL.

AW3D30 is a set of DSMs released by JAXA in 2016 that were created using images from Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM) bands aboard the Advanced Land Observing Satellite. PRISM was in operation from 2006 to 2011, and AW3D30 is based on the images taken during that period. The grid size is 1 arc second and it covers the entire land area from 90 degrees north to 90 degrees south latitude. The purpose of this study is to estimate urban structure and we are interested in the height of buildings, therefore we used two methods, the minimum value filter 63 and slope-dependent filtering technique 24 , to create a digital terrain model (DTM) from the DSM. We created the altitude difference index (ADI) as the difference between the DSM and DTM. For validation, we used the level of detail 1 building data of the 23 wards of Tokyo in PLATEAU ( https://www.mlit.go.jp/plateau/ ), which is a 3D city model. We found that the minimum value filter provided the best fit. We averaged the obtained ADI into the same resolution raster using VNL. In addition, the data acquisition period is an important issue. In the case of Tokyo, the rate of urban development during the period under study is moderate compared with cities in emerging economies, and we assume that it is possible to use the two sets of data together. For more details, refer to supplementary material S1 .

The TADs for going out and return trips, VNL, and ADI are shown in Fig.  9 . The TAD for going out is a high value at the center of the city, which suggests the presence of multiple subcenters. The TAD for return trips tends to decrease from the central area to the suburbs, but the value is low in the central districts and coastal industrial area. VNL is grid data, and may be able to detect the location of subcenters more precisely than the TAZ system. The ADI estimated in this study indicates a higher value in hilly and mountainous areas than in flatland because of the influence of topography, whereas the values in the central part of the urban area are higher, which reflects the height of the buildings.

Tokyo metropolitan area data: ( a ) TAD for going out, ( b ) TAD for return, ( c ) VNL, ( d ) ADI.

Figure  10 is a scatter plot that shows the relationship between VNL, ADI, and product of the two, and the TADs. First, VNL and the TAD for going out are positively correlated, but the variation of the TAD is much larger in the zones where VNL is higher than approximately 50 nW/cm 2 /sr than in the zones where it is lower. Additionally, the TAD for return trips has a strong positive correlation with VNL in the zone below about 50 nW/cm 2 /sr, whereas we observe a moderate negative correlation above that. From both results, we can infer that a zone whose VNL is above 50 nW/cm 2 /sr is a zone in which urban center functions are significant, whereas a zone whose VNL is below 50 nW/cm 2 /sr is a zone in which the main land use is residential. Using a segmented linear regression model 64 , the maximum likelihood estimator of the break point is 45.7 (standard deviation = 1.20) for going out trips and 53.2 (standard deviation = 1.16) for return trips. Therefore, in the following analysis, we classify the region by VNL using 50 nW/cm 2 /sr as the threshold.

Relationship between trip attraction and indices: ( a ) VNL and going out trip, ( b ) VNL and return trip, ( c ) ADI and going out trip, ( d ) ADI and return trip, ( e ) ADI × VNL and going out trip, ( c ) ADI × VNL and return trip.

Next, we can observe that two types of correlated TAZs and uncorrelated TAZs exist in going out trips. The former indicates that the building volume is high in the zone with a high TAD. The latter results from the fact that the ADI is higher on slopes because of the characteristics of this index. Therefore, it is not appropriate to use the ADI as an explanatory variable for the TAD in a mountainous and hilly area, but it can be used as an explanatory variable for trip attraction in a central urban area. The TAD for return trips does not demonstrate a clear relationship with the ADI.

Considering the relationship between ADI × VNL and TAD, we can observe that the correlation is higher in the zone with a high TAD for the going out trip than in the case of VNL alone; that is, the ADI has the effect of increasing the accuracy of the estimation of the TAD in areas with high VNL, whereas low VNL reflects a low TAD in mountainous zones with a high ADI. Therefore, we can use these two variables in a complementary manner when describing the target region.

Based on the above summary, we consider using the ADI alone as an explanatory variable to be undesirable because of the large error in mountainous zones. Thus, we use VNL and ADI × VNL as explanatory variables in the regression analysis shown in the " Results " section.

Data availability

The Annual product of the Visible Infrared Imaging Radiometer Suite (VIIRS) nighttime lights (VNL) V2 was available from the Earth Observation Group website ( https://eogdata.mines.edu/products/vnl/ ). Advanced Land Observing Satellite World 3D 30 m Resolution DSM was obtained from JAXA website ( https://www.eorc.jaxa.jp/ALOS/en/dataset/aw3d30/aw3d30_e.htm ). Trip generation and attraction data by travel purpose by TAZ was obtained from the 6th Tokyo person trip survey committee website ( https://www.tokyo-pt.jp/special_6th ). Map for densely inhabited district (DID) was obtained from website of National Land Information Division, National Spatial Planning and Regional Policy Bureau, Ministry of Land, Infrastructure, Transport and Tourism of Japan ( https://nlftp.mlit.go.jp/index.html ).

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This work was supported by JSPS Grants-in-Aid for Scientific Research (KAKENHI) [grant numbers 16KK0013, 21H01456], and Science and Technology Research Partnership for Sustainable Development (SATREPS) [grant number JPMJSA1704]. We thank Homer Reid and Maxine Garcia for editing a draft of this manuscript.

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Home > Books > Urban Agglomeration - Extracting Lessons for Sustainable Development [Working Title]

Framework for Assessing the Impacts of Climate Change on Urban Agglomerations: A GIS and Remote Sensing Perspective

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As the specter of climate change looms over urban agglomerations, this concept chapter delves into the transformative potential of GIS and Remote Sensing techniques in dissecting and mitigating its impacts. By intricately analyzing land-cover and surface temperature data, we unveil the nuanced effects of climate change on land surface temperature (LST) across varied land-cover types. Leveraging the expansive spatial coverage of remote sensing data, especially satellite images, we can meticulously monitor urban structures, offering invaluable insights into impervious surfaces and vegetated areas. This trove of information not only enlightens the current state and evolution of urban structures but also becomes the bedrock for effective urban planning strategies and climate change adaptation measures. In tandem, the amalgamation of remote sensing with GIS techniques facilitates a granular exploration of the intra-urban thermal environment and the intricate spatial links between urban vulnerability and characteristics. By delving into these insights, GIS and remote sensing emerge as indispensable allies in the quantification and monitoring of climate change impacts on urban agglomerations, guiding decisive measures for sustainable urban development and climate adaptation.

• urban agglomerations
• climate change
• vulnerability
• disaster management
• remote sensing

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Rifaat abdalla *.

• Department of Earth Sciences, College of Science, Sultan Qaboos University, Al-Khoudh, Oman

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1. Introduction

Urban agglomerations are navigating an unprecedented era of challenges under the pervasive influence of climate change. The escalating temperatures, capricious alterations in precipitation patterns, and surges in extreme weather events pose formidable threats to the resilience and sustainability of urban areas worldwide [ 1 ]. In response, this chapter advocates the strategic deployment of Geographic Information Systems (GIS) and remote sensing techniques as powerful tools in comprehending and managing the multifaceted impacts of climate change on urban landscapes [ 2 ]. Our focus is on the confluence of land-cover and surface temperature data, unraveling the intricate relationships between different land-cover types and the ensuing shifts in land surface temperature (LST) [ 3 ]. The arsenal of remote sensing, particularly satellite imagery, comes to the forefront, affording us expansive spatial coverage and recurrently updated information essential for the vigilant monitoring of urban structures [ 4 ]. This chapter contends that this dynamic repository of data not only facilitates an understanding of the present urban milieu but becomes an indispensable asset in sculpting effective urban planning strategies and fortifying the adaptive measures against the onslaught of climate change.

The subsequent integration of GIS techniques with remote sensing adds another layer of sophistication to our analytical approach [ 5 ]. This union allows us to delve into the complex intra-urban thermal environment, deciphering the spatial variability of the connections between urban vulnerability and characteristics. As we navigate through these methodologies, the overarching aim is to provide a robust framework that quantifies, monitors, and analyzes the impacts of climate change on urban agglomerations [ 6 ]. This framework, fortified by GIS and remote sensing, is envisaged as a catalyst for informed decision-making processes, steering urban development toward sustainability and resilience. As we embark on this exploration, it becomes increasingly evident that GIS and remote sensing emerge not merely as technical tools but as indispensable allies in deciphering the intricate dance between climate change and urban agglomerations.

In this context, the integration of Geographic Information Systems (GIS) and remote sensing technologies has emerged as a powerful approach to assess and monitor the effects of climate change on urban landscapes. This amalgamation of advanced technologies forms the bedrock of our exploration, promising a comprehensive understanding of the intricate dynamics between climate change and urban agglomerations, which can be summarized in the following.

1.1 Utilizing land-cover and surface temperature data

GIS and remote sensing stand as pivotal tools in dissecting the impacts of climate change on land surface temperature (LST) under various land-cover types. Leveraging satellite imagery and cutting-edge remote sensing techniques, researchers gain access to robust datasets. This allows for a nuanced assessment of temperature variations across urban landscapes, a crucial insight for comprehending the contribution of different land-cover types to the urban heat island effect. The data becomes instrumental in formulating strategies to mitigate heat-related challenges in urban environments.

1.1.1 Monitoring urban structures

Remote sensing data, with its extensive spatial coverage and frequent updates, facilitates the meticulous monitoring of urban structures. This includes the dynamic tracking of changes in impervious surfaces like roads and buildings, alongside vegetated areas. The integration of GIS enhances this capability, enabling the creation of dynamic maps that vividly illustrate the evolution of urban structures over time. This real-time monitoring becomes a linchpin in supporting urban planning strategies, providing decision-makers with valuable insights into the current state and developmental trajectory of urban areas within municipal boundaries [ 7 ].

1.1.2 Supporting Urban Planning Strategies and Climate Change Adaptation

The synergy between GIS and remote sensing data offers indispensable information for supporting urban planning strategies and fortifying climate change adaptation measures. Decision-makers can leverage these tools to identify vulnerable areas, assess the impact of climate change on infrastructure, and devise targeted interventions to bolster urban resilience. The visualization of spatial data empowers planners to make informed decisions, steering the course toward sustainable urban development and the implementation of effective climate change adaptation measures [ 8 ].

1.1.3 Assessing intra-urban thermal environment

GIS techniques, in tandem with remote sensing, contribute significantly to the assessment of the intra-urban thermal environment. This involves unraveling the spatial variability of the links between urban vulnerability and characteristics. This nuanced understanding enables a more refined analysis of the impacts of climate change, guiding the development of localized adaptation strategies and the prioritization of resources based on the specific needs of different urban zones [ 9 ].

1.2 Research objectives

1.2.1 spatial analysis of urban heat islands.

Conduct an intricate spatial analysis utilizing GIS and remote sensing techniques to identify and characterize urban heat islands (UHIs) within different land-cover types. This involves assessing land surface temperature (LST) variations under various urban structures and natural features, providing insights into the intensity and distribution of heat islands.

1.2.2 Temporal monitoring of urban structures

Develop a comprehensive temporal monitoring system using remote sensing data to track changes in impervious surfaces and vegetated areas within urban agglomerations over time. This objective aims to unravel the dynamics of urban development and land-use changes, providing a basis for assessing the effectiveness of urban planning strategies in response to climate change.

1.2.3 Integration of climate change impacts into urban planning

Investigate the integration of GIS and remote sensing data into urban planning processes to enhance climate change adaptation strategies. This includes assessing the vulnerability of urban structures to climate change impacts, such as increased temperatures and extreme weather events. Develop recommendations for incorporating spatial information into urban planning frameworks to foster resilient and sustainable urban development.

1.2.4 Intra-urban thermal environment analysis

Explore the spatial variability of the links between urban vulnerability and characteristics within municipal boundaries. Utilize GIS techniques to assess the intra-urban thermal environment and identify hotspots of vulnerability. This research objective aims to provide a nuanced understanding of how different urban zones are affected by climate change, guiding the development of targeted adaptation measures for specific areas within the urban agglomeration.

1.3 Research scope

This research seeks to comprehensively assess approaches for the impacts of climate change on urban agglomerations by employing GIS and remote sensing techniques. The first objective delves into a focused spatial analysis, identifying and characterizing urban heat islands (UHIs) within different land-cover types. Emphasis is placed on the relationship between land surface temperature (LST) variations and various urban structures. The second objective concentrates on developing a streamlined temporal monitoring system, utilizing remote sensing data to track changes in impervious surfaces and vegetated areas over time. This provides insights into the dynamic nature of urban development and land-use changes. The third objective explores the practical integration of GIS and remote sensing data into urban planning processes, enhancing climate change adaptation strategies. This involves vulnerability assessments and recommendations for incorporating spatial information into planning frameworks. Lastly, the fourth objective focuses on the spatial variability of links between urban vulnerability and characteristics within municipal boundaries, utilizing GIS techniques to assess the intra-urban thermal environment and identify vulnerable hotspots. The overarching goal is to guide the development of targeted adaptation measures for sustainable urban development.

2. Methodology

This methodology presents a generic methodology that provides an integrated approach utilizing remote sensing and GIS techniques to comprehensively assess the impacts of climate change on urban agglomerations. The research initiates with the acquisition of satellite images for identifying land transformations and monitoring changes in the Earth’s surface, with a specific focus on urbanization and land use/land-cover changes. The leverage of remote sensing data, particularly satellite images, stems from their extensive spatial coverage and regular availability, enabling effective monitoring of urban structures and climate indicators. GIS is subsequently employed to analyze and visualize spatial data extracted from remote sensing, facilitating the assessment of climate-related indicators in urban areas. The synergy of remote sensing and GIS allows for the extraction of intricate information from urban surfaces, including land surface temperature (LST), imperviousness, and vegetation vitality indicators. To enhance efficiency, advanced technologies such as Artificial Intelligence (AI) are integrated to automate the measurement and evaluation of climate adaptation indicators in urban areas. Climate change vulnerability and adaptation assessments are conducted through a fusion of remote sensing data, GIS analysis, and numerical modeling systems, allowing for an in-depth analysis of the urban climate and thermal bioclimatic in urban agglomerations. The incorporation of Local Climate Zones (LCZ) based on remote sensing data and GIS techniques facilitates the analysis of climate change impacts on LST under different land-cover types in urban agglomerations. This comprehensive methodology aspires to provide a robust framework for understanding and addressing the intricate dynamics of climate change in urban environments.

The research methodology entails systematic data collection and preprocessing to ensure the reliability and accuracy of the analysis. Satellite imagery and remote sensing data specific to the study area will be acquired, capturing pertinent information on land-cover, surface temperature, and urban structures. To introduce a temporal dimension, time-series data will be collected to monitor changes over defined periods. The collected data will undergo rigorous preprocessing, incorporating radiometric and atmospheric corrections, geometric adjustments, and image normalization. GIS tools will be employed to integrate diverse datasets, facilitating the creation of precise spatial representations. Quality control measures will address sensor artifacts, enhancing the overall reliability of the dataset. This comprehensive approach to data collection and preprocessing is pivotal for generating robust insights into the impacts of climate change on urban agglomerations, ensuring the validity of subsequent analyses and supporting informed decision-making in urban planning and climate change adaptation strategies ( Figure 1 ).

Methodology flowchart.

3. Discussion

This study attempts to unravel the complexities of climate change impacts on Urban Agglomerations. It employed a comprehensive methodology, integrating GIS and remote sensing techniques, to assess the multifaceted impacts of climate change on urban agglomerations [ 10 ]. The spatial analysis of urban heat islands (UHIs) unveiled intricate relationships between land surface temperature (LST) variations and different land-cover types, emphasizing the significance of understanding nuances within urban structures [ 11 ]. The temporal monitoring of urban structures uncovered dynamic patterns of change in impervious surfaces and vegetated areas, underscoring the evolving nature of urban development in response to climate influences. Integrating climate change impacts into urban planning processes proved to be a pivotal aspect, emphasizing the need for vulnerability assessments and the incorporation of spatial information for effective adaptation strategies [ 12 ]. The intra-urban thermal environment analysis further nuanced our understanding, highlighting specific vulnerable hotspots within municipal boundaries [ 13 ]. The success of the methodology relied heavily on robust data collection and preprocessing, ensuring the reliability of the analyses conducted ( Figure 2 ) [ 14 ].

Urban Head Islands effect, as it relates to urban agglomerations (after the City of Little Rock, Arkansas). https://www.littlerock.gov/city-administration/city-departments/public-works/sustainability/urban-heat-islands/

3.1 Preprocessing steps and data quality

The preprocessing steps, including radiometric and atmospheric corrections, geometric adjustments, and data fusion, played a critical role in enhancing the accuracy of the dataset. Quality control measures addressed potential artifacts, contributing to the overall credibility of the findings. By systematically addressing sensor-specific variations and atmospheric effects, radiometric corrections ensured that the data accurately reflected the true surface characteristics. Geometric corrections eliminated distortions, guaranteeing accurate spatial alignment crucial for meaningful spatial analyses. Data fusion techniques enriched the integrated dataset, providing a comprehensive view of urban structures and climate indicators. The meticulous approach to quality control reinforced the reliability of the dataset, enhancing the robustness of subsequent analyses.

3.2 Spatial representation and analysis

The spatial representation of integrated datasets facilitated a visually compelling depiction of the complex interactions within urban areas. Accurate spatial representations, including maps and layers, served as effective tools for conveying the nuanced relationships between urban structures, land-cover, and surface temperature. These visualizations were instrumental in communicating complex findings to diverse stakeholders, aiding in the interpretation of spatial patterns and trends. The analysis and modeling phase provided quantitative insights into spatial relationships, enabling the development of models that can inform future projections of climate change impacts on urban landscapes.

3.3 Decision support and implications

The results presented in this study contribute valuable information for decision-makers involved in urban planning and climate change adaptation. By unraveling the complexities of climate change impacts on urban agglomerations, this research provides a foundation for evidence-based decision-making. The findings emphasize the importance of spatially informed strategies for sustainable urban development, underscoring the need for targeted interventions in vulnerable areas. As we navigate an era of rapid urbanization and climate change, the integration of GIS and remote sensing techniques proves indispensable for shaping resilient and adaptive cities.

The study offers crucial managerial implications for decision-makers involved in urban planning and climate change adaptation. By unraveling the complexities of climate change impacts on urban areas, the research provides a foundation for evidence-based decision-making. Decision-makers can utilize the insights gained from GIS and remote sensing techniques to inform urban planning strategies effectively. The identification of vulnerable areas, conducted through spatial analysis, allows for targeted interventions, optimizing resource allocation for climate change adaptation. The study’s emphasis on spatially informed strategies underscores the need for decision-makers to integrate GIS and remote sensing data into planning frameworks. This integration ensures that climate change considerations are seamlessly embedded in urban development plans, promoting sustainable and resilient cities. The spatial analysis further aids in the identification of vulnerable hotspots within urban agglomerations, guiding decision-makers in prioritizing areas requiring immediate attention. Resource allocation becomes more effective as decision-makers gain an understanding of the spatial variability of climate change impacts, allowing for nuanced, tailored approaches to diverse urban zones. The study advocates for a proactive stance by decision-makers, leveraging real-time monitoring capabilities provided by GIS and remote sensing for swift responses to emerging challenges. Additionally, the findings stress the importance of community engagement and awareness, enabling decision-makers to foster collaboration and inclusivity in climate resilience initiatives. In essence, the research offers a comprehensive framework for decision-makers to navigate the complexities of urbanization and climate change, emphasizing the indispensable role of GIS and remote sensing in shaping resilient and adaptive cities for the future.

4. Conclusion

In conclusion, this study showcases the significance of employing GIS and remote sensing techniques to comprehensively understand and address the impacts of climate change on urban agglomerations. Moving forward, continuous advancements in technology, including the integration of Artificial Intelligence, offer opportunities to further enhance the precision and efficiency of climate change impact assessments. Future research endeavors should explore the incorporation of additional socio-economic and demographic factors to deepen our understanding of vulnerability within urban populations. Additionally, longitudinal studies could provide insights into the long-term effectiveness of implemented urban planning strategies. Overall, as urban areas continue to evolve in response to climate challenges, the integration of spatial technologies remains pivotal for informed decision-making and sustainable urban development.

5. Future directions

5.1 adaptation strategy for climate-resilient urban development.

In response to the identified vulnerabilities revealed through our GIS and remote sensing analysis, an adaptive strategy is proposed to enhance the resilience of urban agglomerations. This strategy encompasses targeted interventions addressing specific vulnerabilities, while also integrating broader measures related to urban heat island (UHI) mitigation, flood resilience, and sustainable urban planning.

5.1.1 Tailored vulnerability interventions

Green Infrastructure Implementation: Identify and prioritize vulnerable hotspots within the urban fabric, particularly areas experiencing elevated land surface temperatures. Implement green infrastructure initiatives, including the creation of urban green spaces and increased tree canopy coverage, to mitigate heat island effects and enhance overall urban resilience.

Localized Flood Management: Develop localized flood management strategies tailored to areas prone to flooding. This may include the implementation of permeable surfaces, green roofs, and strategically designed drainage systems to reduce surface runoff and enhance flood resilience.

5.1.2 Urban heat island mitigation

Cool Roof Initiatives: Encourage and incentivize the adoption of cool roofing technologies, especially in areas with high impervious surface concentrations. Cool roofs reflect more sunlight and absorb less heat, contributing to a reduction in surface temperatures and mitigating UHI effects.

Tree Canopy Expansion: Prioritize the expansion of urban tree canopy coverage, focusing on areas identified as UHI hotspots. Trees provide shade, enhance evapotranspiration, and contribute to a cooler microclimate, effectively countering the urban heat island effect.

5.1.3 Flood resilience measures

Elevated Infrastructure Design : In flood-prone areas, promote the construction of elevated or flood-resistant infrastructure. Elevating critical facilities and residential structures above flood levels reduces the impact of inundation and safeguards against potential damages.

Community Early Warning Systems: Implement community-based early warning systems to enhance preparedness for flood events. Disseminate timely information and evacuation protocols, ensuring that vulnerable populations are well-informed and can respond effectively to flood threats.

5.1.4 Sustainable urban planning

Compact and Mixed-Use Development: Encourage compact and mixed-use urban development patterns to minimize the expansion of impervious surfaces. This approach promotes walkability, reduces the urban heat island effect, and enhances the overall sustainability of urban areas.

Integrated Land-Use Planning: Integrate climate resilience considerations into land-use planning processes. Ensure that future urban development adheres to sustainable practices, minimizing environmental impact and promoting long-term resilience to climate change.

By implementing this adaptive strategy, we aim to create climate-resilient urban agglomerations that not only address specific vulnerabilities but also foster sustainable development practices. This multifaceted approach acknowledges the interconnected nature of climate change impacts, positioning urban areas for a more resilient and sustainable future. The success of this strategy hinges on collaborative efforts among policymakers, urban planners, and communities to enact and sustain these adaptive measures.

5.1.5 Forging resilient urban futures in the face of climate change

This research, employing a holistic approach integrating GIS and remote sensing techniques, has illuminated the intricate dynamics of climate change impacts on urban agglomerations. The spatial analysis uncovered the complex relationships between land surface temperature variations and diverse land-cover types, emphasizing the need for a nuanced understanding of urban structures. Temporal monitoring revealed the dynamic nature of urban development, urging the adoption of adaptive strategies to navigate evolving climate challenges. The integration of climate change impacts into urban planning processes underscored the necessity of vulnerability assessments and spatially informed strategies for resilient urban futures.

In addressing specific vulnerabilities, the proposed adaptive strategy encapsulates tailored interventions while aligning with broader goals of urban heat island mitigation, flood resilience, and sustainable urban planning. Initiatives such as green infrastructure implementation, cool roof adoption, and elevated infrastructure design aim to fortify urban areas against the impacts of a changing climate. These strategies not only mitigate specific vulnerabilities but also contribute to the overarching goal of fostering sustainable and climate-resilient urban development.

Crucially, the success of these adaptation measures hinges on robust data collection and preprocessing, ensuring the reliability of our analyses. Quality-controlled, integrated datasets form the foundation upon which informed decision-making can occur. The spatial representation of these datasets serves as a visual roadmap for identifying vulnerabilities and formulating targeted interventions.

As urbanization and climate change continue to shape our world, the findings of this research contribute substantively to the discourse on resilient urban futures. The proposed adaptive strategy provides a blueprint for cities globally, offering a flexible framework that can be tailored to diverse urban contexts. By acknowledging the interconnected nature of climate change impacts and embracing adaptive strategies, urban areas can forge resilient paths forward, fostering sustainable development in the face of an ever-changing climate. As we navigate the challenges of the twenty-first century, the fusion of advanced technologies, data-driven insights, and strategic planning emerges as a beacon guiding cities toward climate resilience and a sustainable urban future.

5.2 Future work: charting paths for continued research and action

While this research has provided valuable insights into the impacts of climate change on urban agglomerations and proposed an adaptive strategy, there remain avenues for future exploration and enhancement:

5.2.1 Fine-scale analysis

Future research could delve deeper into fine-scale analyses, examining micro-level variations in land surface temperature and vulnerabilities within urban areas. This could involve higher-resolution remote sensing data and more advanced GIS techniques to capture localized nuances.

5.2.2 Dynamic modeling

Developing dynamic models that incorporate predictive capabilities would enhance our ability to foresee the evolving impacts of climate change on urban landscapes. Incorporating climate change projections and urban development scenarios would offer a forward-looking perspective for more effective long-term planning.

5.2.3 Community engagement and social dynamics

Expanding the research scope to include community engagement and social dynamics would provide a more comprehensive understanding of resilience. Investigating how communities perceive and respond to climate change impacts can inform adaptive strategies that align with local needs and priorities.

5.2.4 Multi-criteria decision analysis

Integrate multi-criteria decision analysis (MCDA) approaches to prioritize and optimize adaptation measures. This could involve considering not only climate-related factors but also economic, social, and governance dimensions to ensure comprehensive and inclusive decision-making.

5.2.5 Technological advances

Embrace emerging technologies, such as advanced satellite sensors and machine learning algorithms, to enhance the accuracy and efficiency of data collection, analysis, and modeling. This could provide more detailed and real-time information for decision-makers.

5.2.6 Long-term monitoring

Establishing long-term monitoring programs to track the effectiveness of implemented adaptation measures is crucial. This would provide continuous feedback on the success of strategies, allowing for adaptive management in response to changing conditions.

5.2.7 Cross-disciplinary collaboration

Foster collaboration between climate scientists, urban planners, social scientists, and policymakers to create an integrated and holistic approach. Cross-disciplinary efforts can provide a more comprehensive understanding of the challenges and facilitate the development of effective solutions.

5.2.8 Global comparative studies

Undertake global comparative studies to assess the transferability and scalability of adaptation strategies across diverse urban contexts. Understanding how different cities respond to similar challenges can provide valuable insights for shared learning and best practices.

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Spatiotemporal non-stationarity analysis of urban environment using multi-source remote sensing in Chinese metropolitan areas

• Kaige Lei , Yan Li , +5 authors Tingting He
• Published in Journal of Cleaner Production 1 August 2024
• Environmental Science, Geography

97 References

Exploring the seasonal effects of urban morphology on land surface temperature in urban functional zones, improved human greenspace exposure equality during 21st century urbanization, evaluation and prediction of ecological carrying capacity in the qilian mountain national park, china., temporal and spatial heterogeneity of land use, urbanization, and ecosystem service value in china: a national-scale analysis, the spatio-temporal trends of urban green space and its interactions with urban growth: evidence from the yangtze river delta region, china, a new approach to peri-urban area land use efficiency identification using multi-source datasets: a case study in 36 chinese metropolitan areas, dynamics of urban land per capita in china from 2000 to 2016, principal component analysis, understanding seasonal contributions of urban morphology to thermal environment based on boosted regression tree approach, impacts of urban green space on land surface temperature from urban block perspectives, related papers.

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Urban High-Resolution Remote Sensing

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With urbanization as a global phenomenon, there is a need for data and information about these terrains. Urban remote sensing techniques provide critical physical input and intelligence for preparing base maps, formulating planning proposals, and monitoring implementations. Likewise these methodologies help with understanding the biophysical properties, patterns, and process of urban landscapes, as well as mapping and monitoring urban land cover and spatial extent.

Advanced sensor technologies and image processing methodologies such as deep learning, data mining, etc., facilitate the wide applications of remote sensing technology in urban areas. This book presents advanced image processing methods and algorithms focused on three very important roots of urban remote sensing: 3D urban modelling using different remotely sensed data, urban orthophotomap generation, and urban feature extraction, which are also today’s real challenges in high resolution remote sensing. Data generated by remote sensing, with its repetitive and synoptic viewing and multispectral capabilities, constitutes a powerful tool for mapping and monitoring emerging changes in the city's urban core, as well as in peripheral areas.

• Provides advances in emerging methods and algorithms in image processing and technology
• Uses algorithms and methodologies for handling high-resolution imagery from a ground sampling distance (GSD) less than 1.0 meter
• Focuses on 3D urban modelling, orthorectification methodologies, and urban feature extraction algorithms from high-resolution remotely sensed imagery
• Demonstrates how to apply up-to-date techniques to the problems identified and how to analyze research results
• Presents methods and algorithms for monitoring, analyzing, and modeling urban growth, urban planning, and socio-economic developments

In this book, readers are provided with valuable research studies and applications-oriented chapters in areas such as urban trees, soil moisture mapping, city transportation, urban remote sensing big data, etc.

TABLE OF CONTENTS

Section section i | 49  pages, introduction, chapter 1 | 7  pages, chapter 2 | 19  pages, urban remote sensing and urban studies, chapter 3 | 19  pages, advances in urban remote sensing, section section ii | 124  pages, information extroduction, chapter 4 | 23  pages, urban 3d surface information extraction from aerial image sequences, chapter 5 | 28  pages, urban 3d surface information extraction from linear pushbroom stereo imagery, chapter 6 | 25  pages, urban 3d building extraction through lidar and aerial imagery, chapter 7 | 11  pages, urban 3d building extraction from lidar and orthoimages, chapter 8 | 9  pages, vehicle extraction from high-resolution aerial images, chapter 9 | 12  pages, single tree canopy extraction from lidar point cloud data, chapter 10 | 10  pages, power lines extraction from aerial images, section section iii | 91  pages, urban orthophotomap generation, chapter 11 | 15  pages, the basic principle of urban true orthophotomap generation, chapter 12 | 13  pages, orthophotomap creation with extremely high buildings, chapter 13 | 20  pages, near real-time orthophotomap generation from uav video, chapter 14 | 19  pages, orthophotomap generation from satellite imagery without camera parameters, chapter 15 | 19  pages, building occlusion detection in an urban true orthophotomap, section section iv | 70  pages, advanced algorithms urban remote sensing application, chapter 16 | 17  pages, hierarchical spatial features learning for image classification, chapter 17 | 18  pages, surface soil moisture retrieval from cbers‑02b satellite imagery, chapter 18 | 17  pages, measuring control delay at signalized intersections using gps and video flow, chapter 19 | 14  pages, measurement of dry asphalt road surface friction using hyperspectral images.

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GIS-based multi‐criteria analysis for sustainable urban green spaces planning in emerging towns of Ethiopia: the case of Sululta town

• Eshetu Gelan 1  

Environmental Systems Research volume  10 , Article number:  13 ( 2021 ) Cite this article

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Urban green spaces are important components, contributing in different ways to the quality of human well-being. In the planning and management of urban centres, attention to the appropriate site selection of urban green spaces with regard to the importance that these spaces have from the perspectives of ecology, socioeconomic, mentality, etc., is an inevitable requirement. In present decades, land suitability mapping methods and GIS have been used to support urban green space planners in developed countries; however, its application and practices are limited in developing countries, like Ethiopia. Therefore, the aim of this study has to select potential sites for green spaces in Sululta town that assist an effective planning process of green areas in a sustainable way.

In this study, GIS-based Multi-criteria analysis (MCA) has been adopted to select suitable sites for urban green spaces. Existing land use, proximity to settlement, road and water body, population density, land ownership, topography, and scenic attractiveness were recognized as the key factor affecting urban green land suitability.

The results showed that 13.6%, 34%, 28%, and 18.9% of the study area are highly suitable, suitable, moderately suitable, and poorly suitable, respectively, for urban green spaces development. Furthermore, out of the total area of the study town 5.5% of the landmass is not suitable for urban green spaces development.

Conclusions

Therefore, the application of GIS-based MCA has provided an effective methodology to solve a complex decisional problem in urban green spaces site selection in the study town and urban planning all over the country.

Introduction

With more than 50 % of the global population now living in urban areas, the world has experienced unprecedented urban growth in recent decades (Wu  2014 ). The global urban population is projected to be 6.3 billion by 2050, almost double the global population of 3.5 billion urban dwellers in 2010 (SCBD  2012 ). This rapid urbanization has posed greater pressure on natural resources and the environment (Rees and Wackernagel  1996 ; Shi  2002 ) and the amount of land exploited for infrastructure development and buildings has increased at the expense of urban green spaces (Sandstrom  2002 ).

Urban green spaces are of crucial importance, especially in an urbanized world, as they are the key providers of ecosystem services and improve the quality of life of urban residents. For instance, by increasing water infiltration, it promotes the regulation of ecosystem services (Haase and Nuissl  2007 ; Pauleit and Duhme  2000 ) and has positive impacts on microclimate regulation (Gill et al. 2000 ; Hamada and Ohta  2010 ). It also provides benefits to city residents, such as exercise, socialization, interaction with nature and connection with places of rich cultural heritage (Crompton  2005 ; Cho et al.  2006 ; Sarev 2011). It is important to understand in this sense that green spaces are main components of urban environments (Tratalos et al. 2007 ) not only for their recreation but also for social contributions (Jones et al. 2013 ), health (Kimberlee et al. 2011) and environmental outcomes (Patel et al. 2009 ).

Despite the numerous aforementioned benefits, urban green spaces are unable to provide urban dwellers with the desirable facilities due to increased urbanization and unplanned urban growth (Wright and Nebel  2002 ), lack of proper site selection and planning and lack of attention to population thresholds (Ahmadi et al. 2016 ). As a result, both quality and quantity of urban green spaces are adversely affected and do not deliver what urban centres demand from urban green spaces as a living organism (Crompton  2001 ). Therefore, by taking into consideration environmental and social-economic factors, well planned, and well-designed green spaces within the reach of the community are mandatory in order to maximize the value that green spaces bring to urban residents and their environment in a sustainable way (Giles-Corti et al.  2005 ).

Land suitability analysis is vital in urban green spaces planning as it gives room for choosing the most suitable site from among various alternatives (Sahabo and Mohammed  2016 ). For suitable site selection, the multi-criteria analysis (MCA) approach that is integrated with the Geographical Information System (GIS) has been increasingly used (Uy and Nakagoshi  2008 ; Van Berkel et al.  2014 ; Ustaoglu, and Aydınoglu  2020 ). In order to determine different land problems considering the alternatives, MCA focuses on various parameters such as biophysical, socio-economic and policy-related factors in decision-making processes (Pramanik, 2016 ).

The MCA methods have been widely applied in both developed and developing countries to select agricultural sites, industrial sites, residential areas, landfill sites, wind farms, disaster area, health centres, and education centres (Rikalovic et al. 2014 ; Rahmati et al. 2016 ; Marsh et al. 2016 ; Demesouka et al. 2016 ; Vasileiou et al. 2017 ). However, the MCA methodology has not commonly used in developing countries such as Ethiopia to select suitable site for urban green spaces development and using MCA in urban planning, as decision-making tools are not practiced.

In parts of Europe, North America and Asia, MCA approach that is integrated with the GIS to identify suitable site for urban green spaces has been receiving more attention and it is considered as one of the essential tools for urban green spaces planning (Nowak et al. 2003 ; Ustaoglu and Aydinoglu  2020 ). In order to specifically analyse the characteristics of green areas and possible sites suitable for green spaces in either the European or overseas context, numerous studies were conducted (Kienast et al.  2012 ; La Rosa and Privitera  2013 ; Chandio et al.  2014 ; Morckel  2017 ; Merry et al.  2018 ; Ustaoglu and Aydınoglu  2020 ). However, in developing countries, while some green spaces studies have been performed, the available studies have concentrated largely on the evaluation of urban green spaces with less emphasis on the study of the suitability analysis for green spaces site selection. For instances, the studies in sub-Saharan African countries are primarily related to street trees’ abundance and composition (Kuruneri-Chitepo and Shackleton  2011 ), green space degradation (Mensah  2014 ), green space extent (McConnachie et al. 2008 ; McConnachie and Shackleton 2010 ) and planning aspects (Cilliers 2009 ; Fohlmeister et al. 2015 ).

This situation also occurs in the case of Ethiopia, which is one of the fastest growing countries in sub-Saharan Africa (Lamson-Hall et al. 2018 ), and studies have focused on the impacts of urban growth on green space (Abebe and Megento  2016 ; Gashu and Gebre-Egziabher  2018 ; Abo El Wafa et al.  2018 ), climate change adaptation (Lindley et al.  2015 ), the development of functional green infrastructure and ecosystem service (Woldegerima et al. 2017 ), planning aspect (Girma et al.  2019 ), green spaces depletion (Girma et al., 2019a ) and utilization pattern (Yeshewazerf  2017 ; Molla et al.  2017 ; Girma et al.  2019b ). However, the topic of suitability analysis for green space in the urban environment by using methods such as GIS-based Multi-criteria analysis has not discussed in these studies. This study therefore aimed to fill the existing research gap by applying GIS-based Multi-criteria analysis method to identify suitable sites for urban green space development in Sululta town.

Materials and methods

Description of the study area.

Sululta town is located in Sululta district of the previous North Shewa administrative zone of Oromia region, currently under Oromia special zone surrounding Finfinne. It is situated very close to the district capital town Chancho and Addis Ababa, which are far about 15 and 23 km in the north and south direction, respectively. Astronomically, the study area is located between 9° 30′ 00″ N to 9° 12′ 15″ N latitude and 38° 42′ 0″ E to 38° 46′ 45″ E longitude. The administrative area of the town is about 4471 hectares. Sululta has the same general climatologically characteristics as that of Addis Ababa. Globally it is a part of tropical humid climatic region, which is characterized by warm temperature and high rainfall. The soils of the zone are basically derived from mesozoic sedimentary and volcanic rocks. The major soil types of Suluta area are Chromic Luvisols (Fig. 1 ).

Map of the study area

Factor map to make suitability analysis for urban green space

Urban green spaces have continuously played a significant role in enhancing the quality of life of urban inhabitants and in supporting urban metabolism. However, urban green spaces have experienced a physical and social decline, while its heterogeneity and richness is often neglected and its contribution to the well-being of a community ignored within current urban planning instruments in Sululta town (Girma et al.  2019 ; Girma et al.  2019a ). Under this circumstance, GIS-based multi-criteria land suitability analysis is becoming critical in determining the land resource that is suitable for urban green spaces (Cetin 2015). Continued development and refinement of suitability analysis, particularly with GIS technology, can enable urban planners to create a suitable urban green spaces system in the urban environment (Manlum 2003 ).

Several literatures have stated that MCA components are used in only a few GIS programs (e.g. IDRISI and ILWIS) to select appropriate places for different functions (Lesslie et al. 2008 ; Chen et al. 2001 ; Ozturk and Batuk  2011 ). MCA has not yet been implemented in the standard functions, according to the literatures, while ArcGIS is one of the most popular GIS applications. In this study, MCA has incorporated ArcGIS 10.2 as a method to select an appropriate location for the development of urban green space.

Therefore, this study proposed the application of GIS-based multi-criteria suitability analysis using analytical hierarchy process (AHP) to support the decision-making process on selecting an appropriate site for development urban green spaces. This approach will be used as a basis for the town’s administration and the planning authority to identify an appropriate and potential site for providing suitable, sufficient and accessible urban green spaces to the urban dwellers. Moreover, it will be used as a benchmark to guide the sustainable land use decision in the study area.

In this study, to select a suitable site for urban green spaces using GIS-based multi-criteria analysis the following five main steps were used:

Spatial and non-spatial data collection

Determination and rating of criteria and sub-criterion

Criteria standardization and factor map generation

Determination of weighting for factors and

Weighted overlay analysis.

Spatial and non‐spatial data collection

The primary data from the field survey were collected through interviews undertaken with different experts in the related field of study for identifying factors that are important for urban green spaces site selection. Various spatial data were also obtained from different secondary sources (Table  1 ). The data were analysed in ArcGIS 10.2 and ERDAS Imagine 2010 for further analysis and mapping purposes.

Determination and rating of criteria and sub‐criteria

In AHP process selection of criteria and their sub-criteria is a crucial stage as selection of criteria influences the judgment by segregating one criterion from other and at the same time, by giving more importance to one criterion over other (Ullah  2014 ). For urban green space planning, there were no universally agreed criteria and factors (Jabir and Arun 2014 ). Therefore, by synthesizing literature review, personal experiences, experts opinions and previous related studies conducted by different researchers (Manlun  2003 ; Uy and Nakagoshi  2008 ; Pantalone  2010 ; Ahmed et al.  2011 ; Kuldeep  2013 ; Heshmat et al.  2013 ; Elahe et al.  2014 ; Yousef et al. 2014 ; Abebe and Megento  2017 ; Li et al. 2018 ; Dagistanli et al.  2018 ; Ustaoglu and Aydinoglu 2020 ) 12 factors were considered for selection of suitable site for development of urban green spaces (Table  2 ). In this study, scientific standards review and personal experiences were used to ensure the reliability of the experts’ opinions.

Besides identifying appropriate criteria and sub-criteria to select a suitable site for urban green spaces the rating has been assigned for each factor. In order to assign a rating (score) for each criterion and sub-criteria, review of previous scientific experimental research findings and literature on parameters were undertaken. Furthermore, reviews were consolidated through consultations and discussion with experienced experts and researchers from various disciplines. Rating of factors has usually made in terms of five classes: highly suitable, suitable, moderately suitable, poorly suitable, and not suitable (FAO  2006 ).

Criteria standardization and factors map generation

In GIS-based multi-criteria decision-making analysis, there is a need to standardize the data in order to integrate the data measured in different units and mapped in different scale of measurement such as ordinal, interval, nominal and ratio scales (Pereira and Duckstein 1993 ). Even though there are different methods that can be used to standardize criterion maps, linear scale transformation is the most frequently used technique (Malczewski  2003 ). For criterion standardization in this study, all the vector maps of the criterion were converted to raster data formats. Afterward using the Spatial Analyst tool in ArcMap the raster maps were reclassified into five classes with the values that range from 1 to 5, where the value of 5 was taken as highly suitable while that of 1 was unsuitable for all factors considered. This approach will enable all measurements to have an equivalent value before any weights were applied. However, it was important to note that there were some variables that did not fulfil the whole range of the criteria. Once all the criteria maps were standardized, a weight of each criteria map was calculated using AHP.

Estimating weight for factors and sub‐factors

One component of GIS-Based multi-criteria decision-making analysis is assigning criteria weights for each factor maps. The purpose of weighing in this process is to express the importance or preference of each factor relative to another factor effect on urban green spaces. In this study, the AHP using pairwise comparison matrixes were used to calculate weights for the criteria maps. AHP is a widely used method in multi-criteria decision-making analysis and was introduced by Saaty ( 1980 ). In this study, the AHP was carried out in three steps. Firstly, pair-wise comparison of criteria was performed and results were put into a comparison matrix. A Pair-wise comparison is performed in the 9-degree preferences scale, which is suggested by Saaty ( 1980 ), each higher level of scale shows higher importance than the previous lower level (Table  3 ).

According to Saaty ( 1980 ), the values in the matrix need to be consistent, which means that if x is compared to y, it receives a score of 9 (strong importance), y to x should score 1/9 (little importance) and something compared to itself gets the score of 1 (equal importance). Experts are asked to rank the value of criterion map for pairwise matrix on a saaty’s scale. Moreover, the pairwise comparison matrices (Annexe 1) were developed by taking into account the information provided by the relevant literature (Uy and Nakagoshi  2008 ; Pantalone  2010 ; Elahe et al.  2014 ; Yousef et al. 2014 ; Abebe and Megento  2017 ; Dagistanli et al.  2018 ; Ustaoglu and Aydinoglu 2020 ).

The second step was calculating criterion weights, the weights are calculated by normalizing the eigenvector associated with the maximum eigenvalue of the (reciprocal) ratio matrix. In this study the computation of the criterion weights involves the following operations: (a) summing the values in each column of the pairwise comparison matrix (Annexe 1); (b) dividing each element in the matrix by its column total (the resulting matrix is referred to as the normalized pairwise comparison matrix, (Annexe 1)), and (c) computing the average of the elements in each row of the normalized matrix, that is, dividing the sum of normalized scores for each row by 12 (the number of criteria).

Once the pair-wise comparison was filled and the weight of the factor was determined, a consistency ratio (CR) was calculated to identify inconsistencies and develop the best-fit weights in the complete pair-wise comparison matrix. A consistency ratio was calculated for each pairwise comparison matrix to verify the degree of credibility of the relative weights, by using the following formula (Bunruamkaew and Yuji 2001).

where CR = Consistency ratio, CI = referred to as consistency index, RI = is the random inconsistency index whose value depends on the number (n) of factors being compared; as illustrated in Table  4 (Saaty 1980 ).

The consistency index (CI) was calculated by the following formula:

where n = the number of items being compared in the matrix, λ max  = Average value of the consistency vector.

Weighted overlay analysis

Once the criteria maps and weights have been developed and established, a decision rule of multi-criteria analysis was used. As pointed by Jiang and Eastman ( 2000 ) and Malczewski ( 2003 ) there are three common decision rules in multi-criteria analysis namely weighted linear overlay, Boolean overlay and ordered averaging. The weighted linear combination technique was applied to aggregate the standardized layers in this study. In weighted linear combination procedure, factors and parameters (Xi) are multiplied by the weight of the suitability parameters (Wi) to get composited weights and then summed. This can be expressed by using the following formula to derive the intended map i.e. urban green spaces suitability map for the towns.

where S = total suitability score, Wi = weight of the selected suitability criteria layer, Xi = assigned sub criteria score of suitability criteria layer i, n = total number of suitability criteria layer.

Result and discussion

Ahp weights.

The result of AHP shows that the derived factors have a different degree of influence on urban green spaces. As it is evident from Table  5 , the weight assigned to the factors reveals the relative importance of each parameter in exposing an area to urban green spaces evaluation. As a result shows, an area with high population density with the normalized weight of 0.22 has the highest priority. Proximity to settlement area with the weight of 0.21 is in the second priority. Slop with a normal weight of 0.13 has the third priority. Proximity to the road with a normal weight of 0/10 is in the fourth priority. Elevation with normal weight of 0/059 is of the fifth priority. The area with vegetation cover with normal weight of 0/048 is the next priority. The flood-prone area with the normal weight of 0/04 is in the low priority. Proximity to water sources, visibility and existing land with almost similar weight of 0/032, 0/032 and 0/039, respectively, have relatively lowest priority (Table  5 ). These imply that the higher the weight in the percentage of a factor, the more influence it has in suitable site selection for urban green spaces.

Saaty (2008) has shown that Consistency ratio of 0.1 or less is acceptable to continue the AHP analysis. But if it’s larger than 0.10, then there are inconsistencies in the evaluation process, and the AHP method may not yield a meaningful result. In this study, consistency ratio or CR of conducted comparisons has obtained 0.09, which is smaller than 0.1 and therefore the comparisons can be acceptable. The computation of consistency ratio is given in Table  5 , below.

Based on the result of this study, AHP is a highly efficient instrument for determining factor weights and is more beneficial than alternative approaches since the inconsistency of the factor weights’ pair-wise comparison matrix can be calculated and controlled by the Consistency Ratio (CR). In various studies (Dong et al.  2008 ; Tudes and Yigiter 2010 ; Kumar and Shaikh  2012 ; Bagheri et al.  2013 ; Romano et al.  2015 ; Abebe and Megento  2017 ; Ustaoglu and Aydinoglu 2020 ), this has been confirmed.

Suitability values of each factors

Studies have shown that current land use must be considered when choosing suitable sites for the development of urban green spaces and have identified the suitability of different land uses based on their use type (Uy and Nakagoshi  2008 ; Zhang et al. 2013 ; Malmir et al. 2016 ; Abebe and Megento 2017 ; Dagistanli et al. 2018 ). Open spaces and forest land were considered to be highly suitable for urban green spaces in this study, based on knowledge obtained from the analysis of literature and expert opinion. To rehabilitate the quarry area they are considered as suitable for urban green spaces. Additional, in this study, existing building area and water body has considered as moderately suitable for urban green spaces. In this study, agriculture is regarded as poorly suited to urban green spaces (Fig.  2 i; Table  2 ).

Various researchers have shown that low-slope areas are highly suitable for the development of urban green spaces (Heshmat et al. 2013 ; Mahdavi and Niknejad, 2014 ; Pramanik, 2016 ; Abebe and Megento, 2017 ; Dagistanli et al. 2018 ) and 0–10 slope areas are suitable for urban green spaces such as open spaces and parks. This study therefore considered the lower slope land to be more suitable than the higher slope land and area with slope of 0–5 %, 5–10 %, 10–15 % and 15–20 % has considered as highly suitable, suitable, moderately suitable, and poorly suitable, respectively, for identify suitable site for urban green spaces development. Area with the slope greater than 20 % considered as unsuitable for developing urban green spaces in this study (Fig.  2 d; Table  2 ).

In selecting suitable sites for urban green spaces, elevation have also significant role and should be considered as the major factor (Gül et al. 2006 ; Mahmoud and El-Sayed 2011 ; Li et al. 2018 ; Dagistanli et al. 2018 ). Based on the information acquired from literature review and expert opinion, in this study the elevations between 2550 and 2600m, 2600–26500m, 2650–2700m and 2700–2800m were considered as highly suitable, suitable, moderately suitable and poorly suitable, respectively. In this analysis, areas with elevations greater than 2800 m were considered to be unsuitable for the development of urban green spaces (Fig.  2 h; Table  2 ).

In any geographic analysis, proximity is always significant. Green spaces must be accessible to settlement areas in urban areas, since they have numerous ecological, social and economic benefits (Zhang et al. 2013 ; Malmir et al. 2016 ; Ustaoglu and Aydinoglu 2020 ). Hornsten and Fredman ( 2000 ) argued that a significant distance between settlement areas and green spaces had an adverse impact on users and reported that green spaces such as playground, parks and sport field closest to settlement areas are most popular. Therefore, the proximity of green spaces to the settlement area in terms of distance is very important to consider. In this research, the proximity of the settlement area has taken as a criterion. Based on this, areas that have identified within 500 m distances from the settlement area has considered as highly suitable by making Euclidian distances and the area with distances from 500 m to 1000 m has been considered suitable (Fig.  2 g; Table  2 ). In addition, the area with distances of 1000 m to 2000 m, 2000 m to 3000 m and greater than 3000 m form settlement area has considered to be moderately appropriate, poorly suited and unsuitable for the development of urban green spaces.

The road proximity also plays a vital role in providing convenient and feasible routes to the local population to reach local green areas in their surroundings (Bunruamkaew and Murayama 2011 ; Kienast et al. 2012 ; Morckel  2017 ). Elahe et al. ( 2014 ) and Ahmed et al. ( 2011 ) indicated that if it is situated at an acceptable distance from roads in order to access transport, the green space site is preferable. As a result, the road network proximity has been given due consideration as one aspect of infrastructural facilities in the mapping suitable site for urban green areas. Based on this, by making Euclidian distances, areas within the 400 m radius of the road network has considered as highly suitable, area within the 400 m-800 m range was considered suitable, and area within the 800 m-1000 m range was considered as moderately suitable. In addition, the area between 1000 m and 1500 m has considered as poorly suitable and the area more than 1500 m from the road network has considered as not suitable (Fig.  2 f; Table  2 ). Studies have also shown that the types of roads have an effect on the selection of suitable urban green spaces (Gül et al. 2006 ; 2011). Research conducted by Gül et al. ( 2006 ) and Chandio et al., ( 2011 ) found that areas with access to major roads are highly appropriate for the development of urban green spaces than areas with access to local roads such as gravel-soil roads, forest soil roads. Therefore, arterial and collector roads are considered to be highly suitable in this study for the selection of suitable locations for urban green spaces, as these types of roads are highly distributed in the town. In addition, main roads and local roads are regarded as suitable and moderately suitable, respectively (Fig.  2 j; Table  2 ).

Manlun ( 2003 ), Heshmat et al. ( 2013 ), Kuldeep ( 2013 ) and Abebe and Megento ( 2017 ) have noted that for the development of green space, lands closest to rivers, lakes and reservoirs are highly suitable. Therefore, on the basis of this claim, the distance less than 250 m from the river considered to be highly suitable and between 250 m and 500 m is considered as suitable in this study. Moreover, distances between 500 m and 1000 m and 1000 m to 1500 m is considered as moderately suitable and poorly suitable for urban green spaces, respectively. Whereas distance greater that 1500 m relatively considered as totally unsuitable (Fig.  2 e; Table  2 ).

Flood-prone areas have also introduced as parameters for the study of suitability. Studies found that the area within the lower flood-prone area has more suitable than the land with higher flood-prone area for urban green spaces development and they indicated that urban green spaces must be free from flood prone area as most as possible (Piran et al. 2013 ; Peng et al. 2016 ). Based on the information obtained from the literature review and expert opinion, high flood risk areas has considered as unsuitable for the development of urban green spaces in this study, and low and medium flood risk areas are considered as highly and moderately suitable (Fig.  2 a; Table  2 ).

Urban green space suitability assessment is directly or indirectly correlated with different socio-economic factors. Population density is known to be one of the socio-economic factors influencing the appropriate selection of green space in urban areas. Places with a higher number of people with crowded places near the high population density required access to the open green spaces (Schipperijn et al. 2010 ). Some researchers (Gül et al. 2006 ; Pantalone 2010 ; Ahmed et al. 2011 ; Heshmat et al. 2013 ; Elahe et al. 2014 ; Dagistanli et al. 2018 ) recommend that areas that have high population density are highly suitable for developing green space. On the basis of this claim, the study area is densely populated in the northwest, north, south and southeast, and it is considered as highly suitable for the development of urban green space. The eastern portion is sparsely populated and believed to be insufficiently suited to urban green spaces development. As it has a medium population density, the central and western parts of the town has considered as moderately suitable for urban green spaces development (Fig.  2 b; Table  2 ).

Environmental criteria are the most significant and important criteria for the evaluation of urban green spaces in any locality. Factor like vegetation cover plays an important role (Gül et al.  2006 ; Mahmoud, & El-Sayed  2011 ; Li et al. 2018 ; Dagistanli et al. 2018 ). Based on the information obtained from the literature review and expert opinion, in this study area with high vegetation cover has considered as highly suitable for urban green space development. Moreover, area with medium and low vegetation cover has considered as moderately and poorly suitable, respectively (Fig.  2 k; Table  2 ).

The availability of land is often considered as significant factor in the selection of appropriate sites for urban green spaces. Studies have shown that public land is highly suitable for urban green space development as compared to private land (Chandio et al. 2011 ). The study undertaken by Wang and Chan ( 2019 ) suggest that the situation with initial public land ownership status backed up by regulatory instruments is more advantageous for providing urban green spaces than that with the initial private land ownership status relying on market-based instruments. On the basis of this claim, in this study public land is considered as highly suitable and private land has considered as moderately suitable for selecting optimal location for urban green spaces in the town (Fig.  2 g, i Table  2 ).

In this study, as suggested by Gül et al. ( 2006 ) and Nur ( 2017 ), scenic beauty is also considered to decide the best or potentially acceptable sites for urban green space development. Based on the information obtained from the literature review and expert opinion, in this study area with high, moderate and low scenic attractiveness has considered as highly, moderately and poorly suitable for appropriate site selection of urban green space development, respectively.

Final suability analysis for urban green spaces

After weighting the criteria, as regards the relative importance of each criterion as well as suitability index, all the criterion maps were overlaid and final urban green spaces suitability map was prepared. According to GIS-based multi-criteria analysis, the final suitability maps have five classes for the study town that are highly suitable, suitable, moderately suitable, poorly suitable and unsuitable. Suitability maps of Sululta towns are demonstrated in Fig.  3 .

Final suitability map for urban green spaces

According to the overall suitability map, southern, central and south eastern part of the study area is more adequate for urban green space such as playground, sport field, parks and the like development purposes. It is because the lands mass in this area are fall in suitable and highly suitable classes.

Based on Table  6 , out of the total area of the Sululta, town, about 13.6 % (610.7 ha) area fall under the highly suitable category. The suitable area covers an area of 34 % (1523.9 ha) of Sululta town. The area which is shaded by blue colour covers 28 % (1276.6 ha) of Sululta town representing the moderately suitable class. Moreover, based on the Table 6, out of the total area 18.9 % (813 ha) of Sululta towns have been covered by poorly suitable class. Out of the total area 5.5 % of Sululta towns land mass is not suitable for urban green spaces.

The final suitability maps show a series of spaces following a pattern and connectivity. These can be adapted to form the urban green spaces system, complete with corridors and hubs within the study area. This can increase opportunities for residents and biodiversity to enjoy the nature and benefits of urban green spaces. Moreover, as the maps show the town have a high potential for developing the urban green spaces such as playground, sport field, parks and the like as more than half of the town’s lands mass are suitable. Therefore, the planning authority and the towns’ administration can take this approach as a benchmark to provide suitable, accessible, interconnected and sufficient urban green spaces in town under study.

Literature shows that many studies have used multi-criteria analysis based on GIS for land use planning in different countries. Ustaoglu and Aydinoglu (2020), for example, performed a site suitability study for the development of green space in the Pendik district of Turkey. Similar to this study, they considered geophysical factors, accessibility, blue and green amenities, settlement centres and land use/cover as the key factors affecting urban green land suitability and they also concluded that undertaking suitability analysis for green space through GIS based multi criteria analysis is mandatory for optimising land use planning and decision support. Giordano and Riedel ( 2008 ) conducted land suitability assessment of greenways in the city of Rio Claro, Brazil. They combined the AHP method with GIS for the analysis of land suitability, similar to this study. Uy and Nakagoshi ( 2008 ) used the ecological threshold factor approach and GIS in Hanoi, Vietnam, for land suitability study for green areas. Their research considered the concepts of landscape-ecology in the organisation of urban green spaces. Chandio et al. ( 2011 ) used GIS-integrated AHP strategy to evaluate factors such as land availability, land price/value, accessibility and socio-economic factors for the development of public parks in Larkana City, Pakistan. Similar to this study, Abebe and Megento ( 2017 ) also considered land use/cover, density, road network and river as the main factor undertake to site suitability analysis of urban green space development for the city of Addis Ababa.

In general, the factors used in this study to select suitable site for urban green spaces such as parks, play grounds and sport filed development is compliant with different studies undertaken in different part of the world. Moreover, similar to studies conducted by Giordano and Riedel ( 2008 ), Uy and Nakagoshi ( 2008 ), Chandio et al. ( 2011 ), Abebe and Megento ( 2017 ) and Ustaoglu and Aydinoglu (2020) the methodology applied in this study provide a base for future studies focusing on land suitability assessments. GIS-based multi criteria analysis suitability assessment technique can be utilised to produce land suitability maps regarding other land uses such as industrial, residential, landfill, urban land, water management and forest development. Moreover, the methodologies are complementary with the other green land assessment methods, such as landscape metrics analysis, landscape connectivity analysis, accessibility and network analysis and therefore can be used in green spaces planning to specify and quantify the suitable sites in line with the other approaches.

In this study, GIS-based multi-criteria analysis has been used to support the site selection process for the development of urban green spaces. The study results are very significant in evaluating the feasibility of the use of GIS-based multi-criteria analysis for the development of urban green space. Since, by using appropriate analytical methods, the evaluation of urban green space is necessary to recognize their potential and to better select the most suitable land uses to improve their integrity and maintain the benefits obtained from them.

In the present study, the sub-criteria for site suitability for urban green spaces in order of importance were area with high population density (22 %), Proximity to settlement area (21 %), Slop (13 %), Proximity to the road (10 %), elevation (5.9 %), vegetation cover (4.8 %), Proximity to water sources, visibility and existing land (3.2 %) and flood prone area (4 %). The GIS-based multi-criteria analysis performed in this study found that, in the current situation, the larger land mass (47 %) of the town is suitable for developing urban green spaces. The town, therefore, has great potential to develop adequate urban green spaces.

GIS technologies can play a crucial role in urban green space planning, as shown in this study, and AHP has been shown to be a flexible and realistic tool for selecting areas for urban green spaces in the study area. This can be attributed to participation of experts in the determination of the criteria and sub criteria using AHP. Furthermore, GIS may be used to support spatial decision-making, as it has excellent spatial problem solving capabilities. Therefore, this study can provide a framework for the planning process using GIS and AHP for Ethiopian County planning and the results can be useful in the planning of urban green space and future land use planning in study town.

Finally, future research should focus on assessing the suitable site selection for each urban green spaces component such as park, playground, sport field, and the like, independently. In this study, the same criteria and sub criteria were considered to select suitable site for all components of urban green space. Therefore, considering criteria and sub criteria for each component separately are necessary in order to provide a complete understanding of urban green space suitability analysis.

Availability of data and materials

All data generated or analysed during this study are included in this published article.

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The author wishes to thank Sululta town municipality experts who participated in ranking the value of criterion map for pairwise matrix.

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Gelan, E. GIS-based multi‐criteria analysis for sustainable urban green spaces planning in emerging towns of Ethiopia: the case of Sululta town. Environ Syst Res 10 , 13 (2021). https://doi.org/10.1186/s40068-021-00220-w

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A remote sensing and GIS-based analysis of urban sprawl in Soran District, Iraqi Kurdistan

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Determination of spatial and temporal patterns of urban growth has become one of the most significant challenges in monitoring and assessing current and future trends of the urban growth issue. Soran district has witnessed very rapid growth in the last two decades, mostly because of its economic, commercial and social attractions. The aim of this work is to study the growth and sprawl dynamics through the land use and land cover (LULC) maps for the area at three different periods (1998, 2008, and 2018) particularly in the urban areas employing GIS and RS techniques. Three Landsat images, Enhanced Thematic Mapper plus in the 1998, Thematic Mapper in the 2008 and Landsat Operational Land (LOL) in the 2018 were used to assess the changes of urban encroachment. A supervised classification technique by maximum likelihood classifier has been employed to create a classified image and has been assessed based on Kappa index. The results obtained showed that the urbanized area increased from 4.51 to 14.93 km 2 from 1998 to 2018. This study demonstrated that the substantial changes in LULC in Soran district since the end of 1990s are directly related to and influenced by the main and secondary roads development on spatial expansion and land use change.

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1 Introduction

Urban sprawling refers to the growth of urban areas resulting from uncontrolled, uncoordinated and unplanned growth [ 29 ]. In most cities around the world, the urban growth phenomena have become unsustainable in many respects [ 2 ]. Moreover, urbanization itself is a common concern throughout the world where people leave rural areas and accumulate in major cities [ 3 ].

Urbanization either spreads radially around a well-established city or linearly along the highways, which is often referred to as sprawl dispersed development [ 37 ]. Normally, a settlement which is controlled by the natural factors including flat area, rivers, and mountains is described as the clustered settlement pattern [ 30 ]. Soran district urbanization pattern can be described as clustered settlement since it is surrounded by rivers and mountains.

Humans have influenced the Earth environment by changing the dynamics of land use/land cover [ 15 ]. In the last five decades, human activities around the world negatively affected most of land use land cover (LULC) categories [ 16 ]. The land has become scarce because of the enormous agricultural and population pressure [ 33 ]. The farmland displacement, urban sprawl, and deforestation, which leads to habitat destruction, loss of arable land, and to the decline of natural greenery areas are characteristics of rapid land cover change [ 39 ].

LULC rapid change is attributed to several direct and potential sprawling factors [ 17 ]. Direct factors would involve the infrastructure construction, settlement expansion, and industry development factors, whereas potential factors include technology, economy, population, policies, wars, and natural factors [ 9 ].

On the other hand, another relevant factor that supports growth and development in cities is the transportation infrastructure of primary and secondary road networks [ 5 ] that show the spatial structure of population distribution [ 43 ]. Consequently, urban expansion and transportation are essentially interrelated [ 23 ]. These landscape dynamics can be well understood using multi-temporal satellite imagery for digital change detection techniques [ 33 ].

The significance of this work highlights the challenges embedded in the analysis of an urban sprawl in the very rapid development of Soran district in Kurdistan following the 1990–2003 major postwar socio-political upheaval in Iraq, and secondly, it demonstrates that sprawl is a real and measurable phenomenon. Thus, analyzing the sprawl over a period of time through RS and GIS techniques will help in understanding the nature and growth of this phenomenon, which suggests the future directions and patterns of sprawling growth.

Satellite images are significant source for land use/land cover information as they offer rapid, periodic and accurate data acquisition from RS [ 40 ] system. Landsat data are widely used in the study of the LULC change. Information on LULC change and urban growth study is essential for urban planners and local governments futuristic plans for sustainable development in any area [ 38 ].

One of the most relevant factors that relates to RS is classification. Certain types of algorithms are used to provide suitable classification accuracy [ 28 ]. Maximum likelihood classification (MLC), the most commonly supervised method, was used. In this context, the training areas are used in supervised technique [ 13 ]. The mapping of LULC can be delineated from fine and coarse resolutions [ 31 ].

Landsat TM, ETM+ and LOL [ 14 ], Satellite Pour l’Observation de la Terre (SPOT) [ 11 ], Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) [ 1 ], Ikonos (Pereiraa and Caetanoa, [ 32 ]), Pleiades [ 12 ], Worldview [ 22 ], and aerial photographs [ 20 ] have been employed in the mapping analysis of land use classes and in the monitoring of their changes.

Soran district has expanded rapidly in the 1990s into a large city. It has gained real importance from 1991 onwards due to its strategic location on a major international commercial route, which resulted in considerable spread with the establishment of new residential and commercial areas. Furthermore, and following the imposition of food and petrol products embargo on Kurdistan region by the former Iraqi regime from October 1991 onwards, almost two-thirds of the United Nations (UN) budget for humanitarian operations in Iraq went to the three northern governorates: Erbil, Sulaymaniyah and Duhok. Thus, a ‘winterization plan’ was launched by UN, which involved food and kerosene distribution in these three Northern governorates. It is worth to mention that, the Non-Governmental Organizations (NGOs) such as UNICEF (United Nations Children’s Fund), the World Food Program (WFP), and CARE International had a significant role in goods distribution. As a result, these improvements encouraged the refugees to return back [ 25 ]. Therefore, Soran district was one of the cities that has enjoyed a period of economic development, enhanced political stability, and a growing local recognition especially after April 9, 2003 or Post-Saddam period [ 36 ].

2 Materials and methods

2.1 study area.

Soran district is located in the northeast of Erbil governorate about 100 km from the Erbil city center. It is surrounded by several major mountain ranges, including the mountains of Korek, Handren, Zozk, Hassan Bag, and Bradost. Thus, winter snowfall is common on these mountains. The study area was carried out on the Soran municipality with a geographical coordinates 36° 42′ 04′′ N to 36° 37′ 30′′ N latitude and 44° 30′ 01′′ to 44° 34′ 30′′ E longitude at an average altitude of 700 m above sea level, covering an administrative area spread over 43 square kilometers (Fig.  1 ). Soran district is a residential, industrial and agricultural area.

Location map of Soran district in the Kurdistan Region of Iraq

Soran district has hot, dry summers, and cool to cold, damp, humid winters (Soran Iraqi kurdistan, [ 35 ]). The mean monthly temperature recorded in Soran station is 10 °C in January and 40 °C in July. Precipitation is highly seasonal and peaks during November and April.

Soran city stands as a strong example of explosive urban growth following a major political upheaval and demographic change. Kurdish refugees started to return to Soran district from Iran as early as the 1990s, having fled to Iran between 1974 and 1989 during the second Iraqi–Kurdish War and the Al-Anfal Campaign. As a result of this influx, the population grew in 1991 from 27,000 (Soran Iraqi kurdistan, [ 35 ]) to about 196,895 in 2018 according to Kurdistan Regional Statistics Office [ 24 ]. Consequently, Soran district passes through a tremendous urban pressure as a result of unusual rapid population growth after 2003.

Researchers have widely addressed the impacts of population growth on urban sprawl. El Garouani et al. [ 10 ] highlighted the acceleration rate of population growth that has generated a large urban sprawl of Fez city in Morocco. Hegazy and Kaloop [ 18 ] argued how Mansoura and Talkha cities in Daqahlia governorate in Egypt are expanding rapidly with varying growth rates and patterns, whereas Belal and Moghanm [ 7 ] showed the case of urban expansion that causes loss of productive agricultural lands in Tanta and Quttour districts in Egypt. Finally, measuring the LULC change is necessary for future urban planning at local and global level reported Singh and Singh [ 34 ].

This study analyzes the urban sprawl of Soran district in the past two decades and shows how various features have affected the spatial pattern of this urban sprawl.

2.2 Data used

Digital image processing of ETM+ in the 1998 (spatial resolution 30 m), TM acquired in the 2008 (30 m), and LOL in the 2018 (30 m) were treated by ENVI 5.3 and ARCGIS 10.3 software. The images were used to explore the land use/land cover change and to estimate physical extent of urban area of Soran district for three-time series during the same season. Table  1 shows the information related to the satellite images used in this study.

Three scenes during the dry season of Landsat imagery 30 m resolution were downloaded from Earth Explorer (USGS) [ 42 ]. The dry season provides a better view of the surface although distinguishing wetlands from other types of vegetation. The obtained images were clipped with Soran municipality boundaries. The land cover types found in the area included build-up areas, barren land, cultivated land and riparian zone.

The scenes were selected to be geometrically corrected and calibrated. The data of ground truth were adapted for each single classifier produced by its spectral signatures for producing series of classification maps.

2.3 Classification system

A modified version of the Anderson LULC classification system [ 4 ] was used in this study. Classification scheme and detailed descriptions are given in Table  2 . A MLC, which is a supervised classification system and is the most popularly used algorithm in image classification [ 17 , 19 ], was applied for LULC mapping from the three images. Six bands except thermal bands for Landsat 5, 7 and 8 were used as input for maximum likelihood classification system. A total of two hundred and forty sampling points, sixty sampling points for each class in the city of Soran district, were collected to compare the uses of existing situation of satellite images. Training sites were carefully selected using high resolution from images; worldview 2 image (50 cm) to compare image Landsat 5 TM 2008 and Pleiades (50 cm) to compare it with 2018 OLI satellite image, whereas for Landsat image 7 ETM+1998 an expert knowledge of the study was gained from the Municipality Department of Soran district. Thus, these images were used as base maps to maximize the accuracy of classification in the images.

Concerning the accuracy assessment, a confusion matrix, which is a table that shows correspondence between the result of a classification and a reference image, was used as the quantitative method of characterizing image classification accuracy. Pixel values were extracted from reference points, created an error matrix, calculated the overall percent accuracy, and the user’s and producer’s accuracies [ 26 ].

Soran district was divided into four zones from the central pixel based on spatial directions. The spatial directions approach is carried out according to known geographical trends, either four or eight. Four directions (instead of eight) were selected in this approach to quantitatively and spatially obtain additional details about the studied case for a realistic evaluation that would support planners and decision makers in Soran district create sustainable plans for the future [ 2 ].

3 Result visualization

3.1 classification accuracy assessment.

Tables  3 , 4 , 5 show the accuracy assessment with overall accuracy, producer’s accuracy, user’s accuracy, and Kappa coefficient for the classified maps for 1998, 2008, and 2018 [ 26 ]. Moreover, the overall classification accuracies for 1998, 2008 and 2018 are 74%, 86% and 88%, respectively. The Kappa coefficient for 1998, 2008 and 2018 images is 0.65, 0.81 and 0.84, respectively.

3.2 Landscape structure and dynamics

The classified images provide the information about the land use pattern of the study area. The red color represents the urban built-up area, light green color shows the cultivated land, dark green color shows the riparian zone, and orange color shows the barren land. The LULC maps are shown in (Fig.  2 ) for three points in time. The directions in this study were northeast (NE), southwest (SW), northwest (NW), and southeast (SE).

LULC distribution and zone maps of Soran district from classification using maximum likelihood classification for years 1998, 2008, and 2018

The increased expansion of road infrastructure has its effect on urban expansion (Fig.  3 ). Moreover, lands in Soran district are being consumed at a faster rate in the centre growth and toward the SE in correlation with road infrastructure expansion increased with increasing population in 2018. More line roads are being constructed in 2018 outward from urban centers, which is also a prominent characteristic of sprawl.

Urban growth map based on land use changes and road network expansion of Soran district from classification using maximum likelihood classification for years 1998, 2008, and 2018

3.3 Post classification comparison

The spatial distributions of the classes were extracted from each of the LULC distribution for the three images and are presented in Table  6 .

The urban built-up area class increased significantly from 10.49% in 1998 to 18.91% in 2008, then after to 34.73% in 2018. Cultivated land decreased from 21.38% in 1998 to 12.07% in 2008 and increased rapidly to 23.97% in 2018. A steady increase in riparian zone can be observed from 9.16% in 1998 to 10.63% in 2008, and then to 12.84% in 2018. On the other hand, barren land that existed in 1998 remained as it was in 2008, whereas the rate decreased sharply to 28.46% in 2018. Thus, the results show an expansion of urban/built-up areas in 2018, on the expense of barren areas decrease.

4 Discussion

4.1 urban sprawl expansion.

The results brought out significant urban expansion of the city for the period 1998–2018. Hence, the built-up land has expanded more than threefold during the last 20 years from 4.51 to 14.93 km 2 showing a positive trend over time. However, the barren land is under stress due to population pressure and the associated demand for urban expansion (Table  6 and Fig.  4 ).

The spatial changes of the urban expansion of Soran district in 1998, 2008, and 2018. Showing a steady increase in urban/built-up areas from 1998 to 2018

In order to identify, describe, and quantify differences between images of the same scene at different times, a GIS has been used to integrate urban/built-up areas class for the three images and generate a thematic map to examine dynamics of urban expansion. This analysis allowed to identify several changes occurring in different classes of the land use. The classified land use maps of built-up area and their spatial distributions for years 1998, 2008 and 2018 are shown in Fig.  4 .

During the three periods, the encroachment of urban/built-up areas occurred to the direction of SE from the center of Soran district since 1998 as shown in Table  7 , where the classified results indicate that the city is expanding and fanning out in the SE, NE and SW parts of the city. These areas are less hilly with no agricultural activities.

During the period 1998–2018, the NE zone grew by 2.33 km 2 , the SE zone grew by 5.64 km 2 which has the maximum growth zone, and the SW zone grew by 1.49 km 2 , while the NW zone grew by 0.86 km 2 (Table  8 ). Thus, the SE zone witnessed the largest growth, contrasted by the lowest NW growth marking the SE as general active growth direction. This growth orientation could partly be caused by the international Hamilton road, which is a spectacular winding mountain road from Erbil to Iran and one of the most strategically important roads.

Numerically, since 1998, urban built-up areas of Soran district have been expanding at a faster rate especially in the last decade (2008–2018). Built-up area of the town was about 4.51 km 2 in 1998, 8.13 km 2 in 2008, and 14.13 km 2 in 2018 (Table  6 ). On average, the rate of expansion of the town was 0.36 km 2 per annum in the first decade (1998–2008) and about 0.68 km 2 in the second period (2008–2018). Thus, change detection resulted in evolution of urban areas and its implication with barren land, which revealed that the development in Soran district has developed as sprawl area.

As can be observed from Table  6 in 1998 and 2008, the majority of the area (about 25.39 km 2 and 25.12 km 2 , out of 43 km 2 of the study area) in Soran district was barren land. Whereas in 2018 the build-up area dominated and covered 14.93 km 2 out of 43 km 2 , then barren land took second class and covered 12.13 km 2 . The urban expansion of Soran district was largely caused by the increasing built-up area of the town from 10.49% in 1998 to 34.73% in 2018. This in turn is due to the natural increase in the population and rural to urban migration. Furthermore, most of these returnees were unable to return to their original villages which had been destroyed by the Iraqi regime’s army or unwilling to return to locations, and instead settled in Soran district [ 14 ]. Moreover, push and pull factors explain the processes that attract migrants to the new location and clarify why people migrate from countryside to urban areas [ 16 ].

On the other hand, barren land has declined from 25.39 km 2 (58.97%) in 1998 to 12.13 km 2 (28.46%) in 2018. The decline of barren land was largest, and it was as high as 30.51%. Areas covered by riparian zone have increased by 0.66 km 2 in the first period (1998–2008) and by 2.21 km 2 in the second period (2008–2018). A significant change of cultivated land can be observed as it declined sharply in the first period from 21.38% in 1998 to 12.07% in 2008, but the change in second period was reversed, as it increased from 12.07% in 2008 to 23.97% in 2018. The expansions of build-up area and cultivation increase are directly related to the socioeconomic boom after the Fall of Baghdad in 2003. The people are increasingly trying to allocate comfort farms and summer houses, as a way of a more relaxed and prosperous way of life in the community.

The residential land use has the largest share of the built-up area in all times. Moreover, from 1998 to 2008, the built-up area increased by 8.02 km 2 . The next period of physical expansion of the city was between 2008 and 2018, when the built-up area expanded by 14.76 km 2 (Fig.  5 ).

Built-up area in 1998, 2008 and 2018 in km 2

In order to evaluate whether the build-up areas are growing equally, four zones of the study area were selected namely NE, SE, SW, and NW. As shown in Fig.  6 , the rate of expansion for these zones for the 1998–2008 period is 0.5, 2.42, 0.42 and 0.24 km 2 /year and 1.83, 3.22, 1.07 and 0.62 km 2 /year for the second period 2008–2018, respectively, which indicate that the SE zone witnessed the largest growth and NW witnessed the smallest growth, meaning that the study area grew mainly in a south easterly direction.

Rate of expansion of urban areas in the periods 1998–2008 and 2008–2018

The impact of urban sprawl is seen mainly on barren land that decreased from 25.39 to 12.13 km 2 ; thus, new urban development occurs mainly on barren land. The residential colonies have grown in the SE and NE, then SW parts, while the rest of the city still depicts dispersed built-up land patterns. The study had an overall classification accuracy of 81.7% and kappa coefficient of 0.722.

Researchers have focused on successful remotely sensed classification as an essential source for many application processes [ 28 ]. The classification technique used to derive the LULC maps for 1998, 2008 and 2018 was MLC. The kappa coefficient for year 1998 was rated as substantial agreement 0.65, while for images 2008 and 2018 had the Kappa coefficients of 0.81 and 0.84, respectively, reflecting almost perfect agreement ([ 6 ]; [ 8 ]). However, the supervised classification produced good results with overall accuracies of 74%, 86%, and 88% for the years 1998, 2008, and 2018, respectively. One possible reason for variance between accuracies 1998 with 2008 and 2018 is that the high-resolution images worldview 2 (50 cm) and Pleiades (50 cm), which were treated as the reference data for the 2008 and 2018 Landsat images, respectively, are more precise than Landsat image 1998 that the expert knowledge from the Municipality Department of Soran district was interpreted.

Additionally, the land cover updating using RS classification techniques is an essential task especially in a rapidly urbanizing region, where fast development makes it necessary to monitor land cover change in a timely manner [ 27 ]. The updating is based on the application of digital interpretation techniques of high-resolution satellite data. The data in a GIS have to be updated in order to maintain a valid representation of the ‘real world’ [ 21 ].

To conclude, the period 1998–2018 witnessed a rapid population growth, spatial expansion, land use change, advanced road construction and densification of central city, which usually mark the historical origins of growth and road infrastructure expansion [ 41 ].

5 Conclusions

Urban growth change and trend in Soran district were computed and assessed by using three different points 1998, 2008, and 2018 using GIS and remote sensing in order to evaluate the urban growth and sprawl patterns and to produce the land use and land cover map for the studied area. Rapid urbanization has led to significant changes. Urban expansion in Soran district during last 20 years effected the land resources, as well as a potential decrease in water quantity and quality in the town. It has pointed out that Soran district has experienced substantial changes in land cover and land use since the end of 1990s. These changes are induced by upgrading of many urban roads or construction of new road linked structures. Furthermore, the sprawl in Soran district particularly in the north and NE part occurs in disorderly and unplanned patterns, influenced by the proximity of villages and land rents. Further research is encouraged to use proper measures in accordance with scientific planning for the urban expansion of the city in the future.

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Acknowledgements

Many thanks to Dr. K. Kolo of the Scientific Research Centre (SRC) at Soran University for valuable comments on manuscript and to the (SRC) for providing the reference images.

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Hamad, R. A remote sensing and GIS-based analysis of urban sprawl in Soran District, Iraqi Kurdistan. SN Appl. Sci. 2 , 24 (2020). https://doi.org/10.1007/s42452-019-1806-4

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Geometric factor correction algorithm based on temperature and humidity profile lidar.

1. Introduction

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Zhang, B.; Fan, G.; Zhang, T. Geometric Factor Correction Algorithm Based on Temperature and Humidity Profile Lidar. Remote Sens. 2024 , 16 , 2977. https://doi.org/10.3390/rs16162977

Zhang B, Fan G, Zhang T. Geometric Factor Correction Algorithm Based on Temperature and Humidity Profile Lidar. Remote Sensing . 2024; 16(16):2977. https://doi.org/10.3390/rs16162977

Zhang, Bowen, Guangqiang Fan, and Tianshu Zhang. 2024. "Geometric Factor Correction Algorithm Based on Temperature and Humidity Profile Lidar" Remote Sensing 16, no. 16: 2977. https://doi.org/10.3390/rs16162977

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