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16 pages, 8686 KiB

Open AccessArticle

Urban Heat Island Effects on Megacities in Desert Environments Using Spatial Network Analysis and Remote Sensing Data: A Case Study from Western Saudi Arabia

by Mady Mohamed, Abdullah Othman, Abotalib Z. Abotalib and Abdulrahman Majrashi

Remote Sens. 2021, 13(10), 1941; https://doi.org/10.3390/rs13101941 - 16 May 2021

Cited by 21 | Viewed by 3724

Abstract

Contemporary cities continue to face significant geoenvironmental challenges due to constant rapid urbanization. Furthermore, the governments of cities worldwide are considering the green cities approach to convert their cities’ weaknesses into opportunities. The 2030 Saudi vision supports smart growth concepts, with a vision [...] Read more.

Contemporary cities continue to face significant geoenvironmental challenges due to constant rapid urbanization. Furthermore, the governments of cities worldwide are considering the green cities approach to convert their cities’ weaknesses into opportunities. The 2030 Saudi vision supports smart growth concepts, with a vision of speeding up economic growth while ensuring that natural assets strengthen the country’s foundations. The urban heat island (UHI) effect is a threatening phenomenon that increases the required cooling loads and negatively affects urban communities and the quality of life, especially in arid environments. This study integrates remote sensing and spatial network analysis to investigate the UHI using the distribution of land surface temperatures (LST) extracted from satellite data during both winter and summer seasons in Makkah city. We investigated and compared the UHIs in two districts, Al-Sharashef and AlEskan, representing the organic and deformed iron-grid with fragmented paralleled street networks, respectively. The spatial analysis of different LST maps, which were derived from Landsat-8 images revealed significant differences between the two case studies. The mean temperature for the AlEskan district was 1–1.5 °C higher than that of the Al-Sharshaf district. This difference can be attributed to the different urban fabrics between the two districts. Moreover, the zones that are currently under construction show relatively higher LST compared to residential zones. The research revealed that the organic/compact urban fabric is better than the deformed iron-grid urban fabric in mitigating the UHI. However, these results are specific to the test site; however, they emphasize the role of integration of remote sensing and spatial network analysis in urban planning. In light of these findings, we recommend integrating remote sensing-based LST analysis with spatial analysis of urban fabrics to better understand the causal effects of UHI, especially in cities located in desert environments. This can help mitigate the impact of projected global warming and contribute to improving the quality of urban life. Full article

(This article belongs to the Special Issue Understanding Urban Systems Using Remote Sensing)

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27 pages, 8510 KiB

Open AccessArticle

Remote Sensing Time Series Classification Based on Self-Attention Mechanism and Time Sequence Enhancement

by Jingwei Liu, Jining Yan, Lizhe Wang, Liang Huang, Haixu He and Hong Liu

Remote Sens. 2021, 13(9), 1804; https://doi.org/10.3390/rs13091804 - 06 May 2021

Cited by 5 | Viewed by 3358

Abstract

Nowadays, in the field of data mining, time series data analysis is a very important and challenging subject. This is especially true for time series remote sensing classification. The classification of remote sensing images is an important source of information for land resource [...] Read more.

Nowadays, in the field of data mining, time series data analysis is a very important and challenging subject. This is especially true for time series remote sensing classification. The classification of remote sensing images is an important source of information for land resource planning and management, rational development, and protection. Many experts and scholars have proposed various methods to classify time series data, but when these methods are applied to real remote sensing time series data, there are some deficiencies in classification accuracy. Based on previous experience and the processing methods of time series in other fields, we propose a neural network model based on a self-attention mechanism and time sequence enhancement to classify real remote sensing time series data. The model is mainly divided into five parts: (1) memory feature extraction in subsequence blocks; (2) self-attention layer among blocks; (3) time sequence enhancement; (4) spectral sequence relationship extraction; and (5) a simplified ResNet neural network. The model can simultaneously consider the three characteristics of time series local information, global information, and spectral series relationship information to realize the classification of remote sensing time series. Good experimental results have been obtained by using our model. Full article

(This article belongs to the Special Issue Understanding Urban Systems Using Remote Sensing)

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32 pages, 6782 KiB

Open AccessArticle

Integrated Mapping of Spatial Urban Dynamics—A European-Chinese Exploration. Part 1—Methodology for Automatic Land Cover Classification Tailored towards Spatial Allocation of Ecosystem Services Features

by Ellen Banzhaf, Wanben Wu, Xiangyu Luo and Julius Knopp

Remote Sens. 2021, 13(9), 1744; https://doi.org/10.3390/rs13091744 - 30 Apr 2021

Cited by 5 | Viewed by 2900

Abstract

Urbanisation processes inherently influence land cover (LC) and have dramatic impacts on the amount, distribution and quality of vegetation cover. The latter are the source of ecosystem services (ES) on which humans depend. However, the temporal and thematical dimensions are not documented in [...] Read more.

Urbanisation processes inherently influence land cover (LC) and have dramatic impacts on the amount, distribution and quality of vegetation cover. The latter are the source of ecosystem services (ES) on which humans depend. However, the temporal and thematical dimensions are not documented in a comparable manner across Europe and China. Three cities in China and three cities in Europe were selected as case study areas to gain a picture of spatial urban dynamics at intercontinental scale. First, we analysed available global and continental thematic LC products as a data pool for sample selection and referencing our own mapping model. With the help of the Google Earth Engine (GEE) platform and earth observation data, an automatic LC mapping method tailored for more detailed ES features was proposed. To do so, differentiated LC categories were quantified. In order to obtain a balance between efficiency and high classification accuracy, we developed an optimal classification model by evaluating the importance of a large number of spectral, texture-based indices and topographical information. The overall classification accuracies range between 73% and 95% for different time slots and cities. To capture ES related LC categories in great detail, deciduous and coniferous forests, cropland, grassland and bare land were effectively identified. To understand inner urban options for potential new ES, dense and dispersed built-up areas were differentiated with good results. In addition, this study focuses on the differences in the characteristics of urban expansion witnessed in China and Europe. Our results reveal that urbanisation has been more intense in the three Chinese cities than in the three European cities, with an 84% increase in the entire built-up area over the last two decades. However, our results also show the results of China’s ecological restoration policies, with a total of 963 km² of new green and blue LC created in the last two decades. We proved that our automatic mapping can be effectively applied to future studies, and the monitoring results will be useful for consecutive ES analyses aimed at achieving more environmentally friendly cities. Full article

(This article belongs to the Special Issue Understanding Urban Systems Using Remote Sensing)

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17 pages, 4713 KiB

Open AccessArticle

Monitoring of Urban Sprawl and Densification Processes in Western Germany in the Light of SDG Indicator 11.3.1 Based on an Automated Retrospective Classification Approach

by Gohar Ghazaryan, Andreas Rienow, Carsten Oldenburg, Frank Thonfeld, Birte Trampnau, Sarah Sticksel and Carsten Jürgens

Remote Sens. 2021, 13(9), 1694; https://doi.org/10.3390/rs13091694 - 27 Apr 2021

Cited by 29 | Viewed by 3309

Abstract

By 2050, two-third of the world’s population will live in cities. In this study, we develop a framework for analyzing urban growth-related imperviousness in North Rhine-Westphalia (NRW) from the 1980s to date using Landsat data. For the baseline 2017-time step, official geodata was [...] Read more.

By 2050, two-third of the world’s population will live in cities. In this study, we develop a framework for analyzing urban growth-related imperviousness in North Rhine-Westphalia (NRW) from the 1980s to date using Landsat data. For the baseline 2017-time step, official geodata was extracted to generate labelled data for ten classes, including three classes representing low, middle, and high level of imperviousness. We used the output of the 2017 classification and information based on radiometric bi-temporal change detection for retrospective classification. Besides spectral bands, we calculated several indices and various temporal composites, which were used as an input for Random Forest classification. The results provide information on three imperviousness classes with accuracies exceeding 75%. According to our results, the imperviousness areas grew continuously from 1985 to 2017, with a high imperviousness area growth of more than 167,000 ha, comprising around 30% increase. The information on the expansion of urban areas was integrated with population dynamics data to estimate the progress towards SDG 11. With the intensity analysis and the integration of population data, the spatial heterogeneity of urban expansion and population growth was analysed, showing that the urban expansion rates considerably excelled population growth rates in some regions in NRW. The study highlights the applicability of earth observation data for accurately quantifying spatio-temporal urban dynamics for sustainable urbanization and targeted planning. Full article

(This article belongs to the Special Issue Understanding Urban Systems Using Remote Sensing)

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18 pages, 33199 KiB

Open AccessArticle

Exploring the Variation Trend of Urban Expansion, Land Surface Temperature, and Ecological Quality and Their Interrelationships in Guangzhou, China, from 1987 to 2019

by Jianhui Xu, Yi Zhao, Caige Sun, Hanbin Liang, Ji Yang, Kaiwen Zhong, Yong Li and Xulong Liu

Remote Sens. 2021, 13(5), 1019; https://doi.org/10.3390/rs13051019 - 08 Mar 2021

Cited by 21 | Viewed by 2619

Abstract

This study explored the model of urban impervious surface (IS) density, land surface temperature (LST), and comprehensive ecological evaluation index (CEEI) from urban centers to suburbs. The interrelationships between these parameters in Guangzhou from 1987 to 2019 were analyzed using time-series Landsat-5 TM [...] Read more.

This study explored the model of urban impervious surface (IS) density, land surface temperature (LST), and comprehensive ecological evaluation index (CEEI) from urban centers to suburbs. The interrelationships between these parameters in Guangzhou from 1987 to 2019 were analyzed using time-series Landsat-5 TM (Thematic Mapper), Landsat-8 OLI (Operational Land Imager), and TIRS (Thermal Infrared Sensor) images. The urban IS densities were calculated in concentric rings using time-series IS fractions, which were used to construct an inverse S-shaped urban IS density function to depict changes in urban form and the spatio-temporal dynamics of urban expansion from the urban center to the suburbs. The results indicated that Guangzhou experienced expansive urban growth, with the patterns of urban spatial structure changing from a single-center to a multi-center structure over the past 32 years. Next, the normalized LST and CEEI in each concentric ring were calculated, and their variation trends from the urban center to the suburbs were modeled using linear and nonlinear functions, respectively. The results showed that the normalized LST had a gradual decreasing trend from the urban center to the suburbs, while the CEEI showed a significant increasing trend. During the 32-year rapid urban development, the normalized LST difference between the urban center and suburbs increased gradually with time, and the CEEI significantly decreased. This indicated that rapid urbanization significantly expanded the impervious surface areas in Guangzhou, leading to an increase in the LST difference between urban centers and suburbs and a deterioration in ecological quality. Finally, the potential interrelationships among urban IS density, normalized LST, and CEEI were also explored using different models. This study revealed that rapid urbanization has produced geographical convergence between several ISs, which may increase the risk of the urban heat island effect and degradation of ecological quality. Full article

(This article belongs to the Special Issue Understanding Urban Systems Using Remote Sensing)

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20 pages, 43297 KiB

Open AccessArticle

Monitoring Land Surface Temperature Change with Landsat Images during Dry Seasons in Bac Binh, Vietnam

by Thanhtung Dang, Peng Yue, Felix Bachofer, Michael Wang and Mingda Zhang

Remote Sens. 2020, 12(24), 4067; https://doi.org/10.3390/rs12244067 - 11 Dec 2020

Cited by 17 | Viewed by 4549

Abstract

Global warming-induced climate change evolved to be one of the most important research topics in Earth System Sciences, where remote sensing-based methods have shown great potential for detecting spatial temperature changes. This study utilized a time series of Landsat images to investigate the [...] Read more.

Global warming-induced climate change evolved to be one of the most important research topics in Earth System Sciences, where remote sensing-based methods have shown great potential for detecting spatial temperature changes. This study utilized a time series of Landsat images to investigate the Land Surface Temperature (LST) of dry seasons between 1989 and 2019 in the Bac Binh district, Binh Thuan province, Vietnam. Our study aims to monitor LST change, and its relationship to land-cover change during the last 30 years. The results for the study area show that the share of Green Vegetation coverage has decreased rapidly for the dry season in recent years. The area covered by vegetation shrank between 1989 and 2019 by 29.44%. Our findings show that the LST increase and decrease trend is clearly related to the change of the main land-cover classes, namely Bare Land and Green Vegetation. For the same period, we find an average increase of absolute mean LST of 0.03 °C per year for over thirty years across all land-cover classes. For the dry season in 2005, the LST was extraordinarily high and the area with a LST exceeding 40 °C covered 64.10% of the total area. We expect that methodological approach and the findings can be applied to study change in LST, land-cover, and can contribute to climate change monitoring and forecasting of impacts in comparable regions. Full article

(This article belongs to the Special Issue Understanding Urban Systems Using Remote Sensing)

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21 pages, 7970 KiB

Open AccessArticle

Fusion of High- and Medium-Resolution Optical Remote Sensing Imagery and GlobeLand30 Products for the Automated Detection of Intra-Urban Surface Water

by Zhi Li and Xiaomei Yang

Remote Sens. 2020, 12(24), 4037; https://doi.org/10.3390/rs12244037 - 09 Dec 2020

Cited by 6 | Viewed by 2234

Abstract

Intra-urban surface water (IUSW) is an indispensable resource for urban living. Accurately acquiring and updating the distributions of IUSW resources is significant for human settlement environments and urban ecosystem services. High-resolution optical remote sensing data are used widely in the detailed monitoring of [...] Read more.

Intra-urban surface water (IUSW) is an indispensable resource for urban living. Accurately acquiring and updating the distributions of IUSW resources is significant for human settlement environments and urban ecosystem services. High-resolution optical remote sensing data are used widely in the detailed monitoring of IUSW because of their characteristics of high resolution, large width, and high frequency. The lack of spectral information in high-resolution remote sensing data, however, has led to the IUSW misclassification problem, which is difficult to fully solve by relying only on spatial features. In addition, with an increasing abundance of water products, it is equally important to explore methods for using water products to further enhance the automatic acquisition of IUSW. In this study, we developed an automated urban surface-water area extraction method (AUSWAEM) to obtain accurate IUSW by fusing GaoFen-1 (GF-1) images, Landsat-8 Operational Land Imager (OLI) images, and GlobeLand30 products. First, we derived morphological large-area/small-area water indices to increase the salience of IUSW features. Then, we applied an adaptive segmentation model based on the GlobeLand30 product to obtain the initial results of IUSW. Finally, we constructed a decision-level fusion model based on expert knowledge to eliminate the problem of misclassification resulting from insufficient information from high-resolution remote sensing spectra and obtained the final IUSW results. We used a three-case study in China (i.e., Tianjin, Shanghai, and Guangzhou) to validate this method based on remotely sensed images, such as those from GF-1 and Landsat-8 OLI. We performed a comparative analysis of the results from the proposed method and the results from the normalized differential water index, with average kappa coefficients of 0.91 and 0.55, respectively, which indicated that the AUSWAEM improved the average kappa coefficient by 0.36 and obtained accurate spatial patterns of IUSW. Furthermore, the AUSWAEM displayed more stable and robust performance under different environmental conditions. Therefore, the AUSWAEM is a promising technique for extracting IUSW with more accurate and automated detection performance. Full article

(This article belongs to the Special Issue Understanding Urban Systems Using Remote Sensing)

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23 pages, 16849 KiB

Open AccessFeature PaperArticle

Spatiotemporal Changes in 3D Building Density with LiDAR and GEOBIA: A City-Level Analysis

by Karolina Zięba-Kulawik, Konrad Skoczylas, Ahmed Mustafa, Piotr Wężyk, Philippe Gerber, Jacques Teller and Hichem Omrani

Remote Sens. 2020, 12(21), 3668; https://doi.org/10.3390/rs12213668 - 09 Nov 2020

Cited by 5 | Viewed by 4352

Abstract

Understanding how, where, and when a city is expanding can inform better ways to make our cities more resilient, sustainable, and equitable. This paper explores urban volumetry using the Building 3D Density Index (B3DI) in 2001, 2010, 2019, and quantifies changes in the [...] Read more.

Understanding how, where, and when a city is expanding can inform better ways to make our cities more resilient, sustainable, and equitable. This paper explores urban volumetry using the Building 3D Density Index (B3DI) in 2001, 2010, 2019, and quantifies changes in the volume of buildings and urban expansion in Luxembourg City over the last two decades. For this purpose, we use airborne laser scanning (ALS) point cloud (2019) and geographic object-based image analysis (GEOBIA) of aerial orthophotos (2001, 2010) to extract 3D models, footprints of buildings and calculate the volume of individual buildings and B3DI in the frame of a 100 × 100 m grid, at the level of parcels, districts, and city scale. Findings indicate that the B3DI has notably increased in the past 20 years from 0.77 m³/m² (2001) to 0.9 m³/m² (2010) to 1.09 m³/m² (2019). Further, the increase in the volume of buildings between 2001–2019 was +16 million m³. The general trend of changes in the cubic capacity of buildings per resident shows a decrease from 522 m³/resident in 2001, to 460 m³/resident in 2019, which, with the simultaneous appearance of new buildings and fast population growth, represents the dynamic development of the city. Full article

(This article belongs to the Special Issue Understanding Urban Systems Using Remote Sensing)

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17 pages, 17571 KiB

Open AccessArticle

Urbanization-Driven Changes in Land-Climate Dynamics: A Case Study of Haihe River Basin, China

by Zhouyuan Li, Yanjie Xu, Yingbao Sun, Mengfan Wu and Bin Zhao

Remote Sens. 2020, 12(17), 2701; https://doi.org/10.3390/rs12172701 - 20 Aug 2020

Cited by 16 | Viewed by 3169

Abstract

Urbanization changes the land surface environment, which alters the regional climate system. In this study, we took the Haihe River Basin in China as a case study area, as it is highly populated and experienced rapid urbanization from 2000–2015. We investigated how land [...] Read more.

Urbanization changes the land surface environment, which alters the regional climate system. In this study, we took the Haihe River Basin in China as a case study area, as it is highly populated and experienced rapid urbanization from 2000–2015. We investigated how land use and cover change (LUCC) was driven by urban land development affects land-climate dynamics. From 2000–2015, we collected data from the land use and cover database, the remote sensing database of the Moderate Resolution Imaging Spectroradiometer (MODIS) series, and the meteorological database to process and generate regional datasets for LUCC maps. We organized data by years aligned with the selected indicators of land surface, normalized difference vegetation index (NDVI), albedo, and land surface temperature (LST), as well as of regional climate, cloud water content (CWC), and precipitation (P). The assembled datasets were processed to perform statistical analysis and conduct structural equation modelling (SEM). Based on eco-climatology principles and the biophysical process in the land-climate dynamics, we made assumptions on how the indicators connected to each other. Moreover, we testified and quantified them in SEM. LUCC results found that from 2000–2015 the urban area proportion increased by 214% (2.20–6.91%), while the agricultural land decreased by 7.2% (53.05–49.25%) and the forest increased by 4.3% (10.02–10.45%), respectively. This demonstrated how cropland intensification and afforestation happened in the urbanizing basin. SEM results showed that the forest had both positive and negative effects on the regional hydrological cycle. The agricultural land, grassland, and shrub had indirect effects on the P via different biophysical functions of LST. The overall effects of urbanization on regional precipitation was positive (pathway correlation coefficient = 0.25). The interpretation of how urbanization drives LUCC and alters regional climate were herein discussed in different aspects of socioeconomic development, biophysical processes, and urbanization-related atmospheric effects. We provided suggestions for further possible research on monitoring and assessment, putting forth recommendations to advance sustainability via land planning and management, including agricultural land conservation, paying more attention to the quality growth of forest rather than the merely area expansion, integrating the interdisciplinary approach, and assessing climatic risk for extreme precipitation and urban flooding. Full article

(This article belongs to the Special Issue Understanding Urban Systems Using Remote Sensing)

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17 pages, 9231 KiB

Open AccessFeature PaperArticle

Use of Remote Sensing in Comprehending the Influence of Urban Landscape’s Composition and Configuration on Land Surface Temperature at Neighbourhood Scale

by Ifeanyi R. Ejiagha, M. Razu Ahmed, Quazi K. Hassan, Ashraf Dewan, Anil Gupta and Elena Rangelova

Remote Sens. 2020, 12(15), 2508; https://doi.org/10.3390/rs12152508 - 04 Aug 2020

Cited by 23 | Viewed by 5139

Abstract

The spatial composition and configuration of land use land cover (LULC) in the urban landscape impact the land surface temperature (LST). In this study, we assessed such impacts at the neighbourhood level of the City of Edmonton. In doing so, we employed Landsat-8 [...] Read more.

The spatial composition and configuration of land use land cover (LULC) in the urban landscape impact the land surface temperature (LST). In this study, we assessed such impacts at the neighbourhood level of the City of Edmonton. In doing so, we employed Landsat-8 Operational Land Imager (OLI) and Thermal Infrared Sensors (TIRS) satellite images to derive LULC and LST maps, respectively. We used three classification methods, such as ISODATA, random forest, and indices-based, for mapping LULC classes including built-up, water, and green. We obtained the highest overall accuracy of 98.53 and 97.90% with a kappa value of 0.96 and 0.92 in the indices-based method for the 2018 and 2015 LULC maps, respectively. Besides, we estimated the LST map from the brightness temperature using a single-channel algorithm. Our analysis showed that the highest contributors to LST were the industrial (303.51 K in 2018 and 295.99 K in 2015) and residential (303.47 K in 2018 and 296.56 K in 2015) neighbourhoods, and the lowest contributor was the riverine/creek (298.77 K in 2018 and 292.89 K in 2015) during the 2018 late summer and 2015 early spring seasons. We also found that the residential neighbourhoods exhibited higher LST in comparison with the industrial with the same LULC composition. The result was also supported by our surface albedo analysis, where industrial and residential neighbourhoods were giving higher and lower albedo values, respectively. This indicated that the rooftop materials played further role in impacting the LST. In addition, our spatial autocorrelation (local Moran’s I) and proximity (near distance) analyses revealed that the structural configurations would additionally play an important role in contributing to the LST in the neighbourhoods. For example, the cluster pattern with a small gap of minimum 2.4 m between structures in the residential neighbourhoods were showing higher LST in compared with the sparse pattern, with large gaps between structures in the industrial areas. The wide passages for wind flow through the large gaps would be responsible for cooling the LST in the industrial neighbourhoods. The outcomes of this study would help planners in planning and designing urban neighbourhoods, and policymakers and stakeholders in developing strategies to balance surface energy and mitigate local warming. Full article

(This article belongs to the Special Issue Understanding Urban Systems Using Remote Sensing)

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14 pages, 4822 KiB

Open AccessLetter

Influence of the Economic Efficiency of Built-Up Land (EEBL) on Urban Heat Islands (UHIs) in the Yangtze River Delta Urban Agglomeration (YRDUA)

by Zhicheng Shen and Xinliang Xu

Remote Sens. 2020, 12(23), 3944; https://doi.org/10.3390/rs12233944 - 02 Dec 2020

Cited by 9 | Viewed by 1836

Abstract

Currently, an urban agglomeration is a trend in global urbanization. With the continuous development of urban agglomerations, Chinese urban agglomerations have entered an era of high-quality development. Improving the economic efficiency of built-up land (EEBL) and maintaining a good ecological environment are important [...] Read more.

Currently, an urban agglomeration is a trend in global urbanization. With the continuous development of urban agglomerations, Chinese urban agglomerations have entered an era of high-quality development. Improving the economic efficiency of built-up land (EEBL) and maintaining a good ecological environment are important for promoting the high-quality development of urban agglomerations. Urban heat islands (UHIs) are one of the major ecological environmental problems affecting urban agglomerations. Therefore, it is meaningful to investigate the influence of the EEBL on UHIs in urban agglomerations. Based on the land-use data, MODIS land surface temperature (LST) data and gross domestic product (GDP) in the secondary and tertiary industries from 2000 to 2018, and electric power consumption data in 2018, this paper analyzed the influence of the EEBL on the surface urban heat island intensity (SUHII) in the Yangtze River Delta Urban Agglomeration (YRDUA). The results showed that most of cites in the YRDUA experienced rapid EEBL growth and a significant increase in heat island intensity from 2000 to 2018. There has been a significant positive correlation between the EEBL and the SUHII over the years, among which the EEBL had the strongest correlation (R = 0.76, p < 0.01) with the SUHII in 2000. Moreover, among the 27 cities in the YRDUA, 21 cities showed a significant uptrend of the SUHII with rising EEBL and the uptrend was significantly and negatively correlated with the electric power utilization efficiency (EPUE) of the secondary and tertiary industries (R = −0.6, p < 0.01). These results indicated that the EEBL of the secondary and tertiary industries had a significant influence on UHIs, which also reflected the significant influence of anthropogenic heat on UHIs to a certain extent. The findings of this paper can provide a scientific basis for mitigating the UHIs caused by the rapid economic development and promoting the high-quality development of urban agglomerations. Full article

(This article belongs to the Special Issue Understanding Urban Systems Using Remote Sensing)

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