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Urban Expansion Prediction and Land Use/Land Cover Change Modeling for Sustainable Urban Development

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A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Urban and Rural Development".

Deadline for manuscript submissions: closed (15 October 2022) | Viewed by 36855

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Special Issue Editors

Dr. Firoozeh Karimi

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Guest Editor

Geography, Environment, and Sustainability, University of North Carolina-Greensboro, Greensboro, NC 27412, USA
Interests: urban growth prediction; artificial intelligence; machine learning; deep learning

Prof. Dr. Selima Sultana

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Guest Editor

Geography, Environment, and Sustainability, University of North Carolina-Greensboro, Greensboro, NC 27412, USA
Interests: commuting/travel patterns and behaviors; school transportation; sustainable urban form/land use; accessibility and transportation equity; transportation and energy; geography of race/ethnicity; national parks
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Dear Colleagues,

The dynamic and convoluted process of urban expansion, which happens via the conversion of land covers to land uses, imposes adverse impacts on the natural environment, such as deforestation, habitat fragmentation, natural hazards, agricultural fields loss, and regional and global warming. Alleviating these impacts and maintaining sustainable urban development necessitates the development of reliable analytical methods for understanding the process of land conversion and predicting the location, extent, and intensity of urban expansion. Perceiving the urban expansion process and predicted probability maps can help practitioners and decision makers toward systematic urban planning, efficient management, and policies providing sustainable urban development. This requires modeling land use/land cover change to recognize the driving forces of land conversion and explore their relative importance, investigating change trends, predicting urban expansion patterns, and simulating a variety of urban expansion scenarios. Studies mainly focused on urban expansion prediction and land conversion modeling but did not cover the impacts on the natural environment and sustainable growth. This Special Issue invites submissions addressing urban expansion and land use/land cover change considering sustainable urban development. This includes but is not limited to:

Spatiotemporal land use/land cover change simulation;
Land use/land cover change detection and monitoring urban expansion;
Urban expansion impacts on the natural environment;
Urban expansion prediction using machine learning;
Urban expansion prediction using deep learning;
Scenario-based simulation for supporting sustainable urban expansion;
Causal factors of urban expansion and land conversion;
Urban expansion and climate change;
Urban expansion and public health.

Dr. Firoozeh Karimi
Prof. Dr. Selima Sultana
Guest Editors

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Keywords

urban expansion
land use/land cover change
urban sustainability
sustainable urban development
scenario-based simulation
prediction
modeling
GIS
remote sensing

Published Papers (13 papers)

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Editorial

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8 pages, 196 KiB

Open AccessEditorial

Urban Expansion Prediction and Land Use/Land Cover Change Modeling for Sustainable Urban Development

by Firoozeh Karimi and Selima Sultana

Sustainability 2024, 16(6), 2285; https://0-doi-org.brum.beds.ac.uk/10.3390/su16062285 - 09 Mar 2024

Viewed by 727

Abstract

Urban expansion, a defining feature of the contemporary era, presents both challenges and opportunities for sustainable development [...] Full article

(This article belongs to the Special Issue Urban Expansion Prediction and Land Use/Land Cover Change Modeling for Sustainable Urban Development)

Research

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26 pages, 9506 KiB

Open AccessArticle

Evaluation of Land Use Efficiency in Tehran’s Expansion between 1986 and 2021: Developing an Assessment Framework Using DEMATEL and Interpretive Structural Modeling Methods

by Safiyeh Tayebi, Seyed Ali Alavi, Saeed Esfandi, Leyla Meshkani and Aliakbar Shamsipour

Sustainability 2023, 15(4), 3824; https://0-doi-org.brum.beds.ac.uk/10.3390/su15043824 - 20 Feb 2023

Cited by 3 | Viewed by 1568

Abstract

This paper aims to reveal the shortcomings of the land use efficiency assessment formula presented in SDG 11.3.1 Indicator and develop a framework that can provide urban planners with a more accurate understanding of the variables influencing and/or influenced by urban expansion. Based on the mentioned formula, Tehran never experienced urban shrinkage between 1986 and 2021, as shown by the relationship between land consumption and population growth. However, the research findings indicate that land allocation patterns have not only decreased most urban services per capita, but have also undermined ecosystem services during this period. In this paper, we propose a new assessment framework by which a dual aspect of urban planning is addressed, namely providing sustainable urban services while protecting natural resources, and using ecosystem services sustainably to support cost–beneficial urbanization. For this purpose, a total of ten mainly repeated contributing variables were collected in the categories of environmental, physical-spatial, and economic–social effects of urban expansion. A questionnaire based on these variables was prepared, and 14 urban planning experts collaborated to classify the variables and identify causal relationships between them. In the following, data obtained from the questionnaires were analyzed using DEMATEL and Interpretive Structural Modeling (ISM) methods to determine which variables influence and/or are influenced by urban expansion (and to what extent). Third-level variables that directly influence urban expansion include transportation (A6), infill development (A7), and entrepreneurship (A10). Spatial justice (A8) and housing and population attraction (A9) were identified as middle-level variables that both affect and are affected by urban expansion. Finally, land surface temperature (A1), air pollution (A2), sewage and waste (A3), water resources (A4), and vegetation (A5) were identified as first-level variables that are mainly affected by urban expansion. Full article

(This article belongs to the Special Issue Urban Expansion Prediction and Land Use/Land Cover Change Modeling for Sustainable Urban Development)

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17 pages, 3984 KiB

Open AccessArticle

Assessment of Machine Learning Methods for Urban Types Classification Using Integrated SAR and Optical Images in Nonthaburi, Thailand

by Niang Sian Lun, Siddharth Chaudhary and Sarawut Ninsawat

Sustainability 2023, 15(2), 1051; https://0-doi-org.brum.beds.ac.uk/10.3390/su15021051 - 06 Jan 2023

Cited by 2 | Viewed by 2603

Abstract

Urbanization and expansion in each city of emerging countries have become an essential function of Earth’s surface, with the majority of people migrating from rural to urban regions. The various urban category characteristics have emphasized the great importance of understanding and creating suitable land evaluations in the future. The overall objective of this study is to classify the urban zone utilizing building height which is estimated using Sentinel-1 synthetic aperture radar (SAR) and various satellite-based indexes of Sentinel-2A. The first objective of this research is to estimate the building height of the Sentinel-1 SAR in Nonthaburi, Thailand. A new indicator, vertical-vertical-horizontal polarization (VVH), which can provide a better performance, is produced from the dual-polarization information, vertical-vertical (VV), and vertical-horizontal (VH). Then, the building height model was developed using indicator VVH and the reference building height data. The root means square error (RMSE) between the estimated and reference height is 1.413 m. Then, the second objective is to classify three classes of urban types, which are composed of residential buildings, commercial buildings, and other buildings, including vegetation, waterbodies, car parks, and so on. Spectral indices such as normalized difference vegetation index (NDVI), normalized difference water index (NDWI), and normalized difference built up the index (NDBI) are extracted from the Sentinel-2A data. To classify the urban types, a three-machine learning classifier, support vector machine (SVM), random forest (RF), and k-nearest neighbor (KNN) were developed. The classification uses randomly trained data from each 500 m focus study which are divided into a 100 × 100 m grid. Different models are examined using different variables, for example, classification using only building height and only spectral indices. The indices and estimated building height were used to classify the urban types. Not only the average of various satellite-based indices and building height of 100 × 100 m grid was used, but also the minimum, maximum, mean, and standard deviation were calculated from NDVI, NDWI, NDBI, and building height. There are a total of 16 variables used in the model. Eventually, the principal components analysis (PCA) was used to reduce the variables and get better performance of the models. SVM showed better accuracy than the other two, RF and KNN. The accuracies of SVM, RF, and KNN are 0.86, 0.75, and 0.76, respectively. Full article

(This article belongs to the Special Issue Urban Expansion Prediction and Land Use/Land Cover Change Modeling for Sustainable Urban Development)

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17 pages, 4570 KiB

Open AccessArticle

Remote Sensing Data for Geological Mapping in the Saka Region in Northeast Morocco: An Integrated Approach

by Abdallah Elaaraj, Ali Lhachmi, Hassan Tabyaoui, Abdennabi Alitane, Antonietta Varasano, Sliman Hitouri, Yassine El Yousfi, Meriame Mohajane, Narjisse Essahlaoui, Hicham Gueddari, Quoc Bao Pham, Fatine Mobarik and Ali Essahlaoui

Sustainability 2022, 14(22), 15349; https://0-doi-org.brum.beds.ac.uk/10.3390/su142215349 - 18 Nov 2022

Cited by 3 | Viewed by 2360

Abstract

Together with geological survey data, satellite imagery provides useful information for geological mapping. In this context, the aim of this study is to map geological units of the Saka region, situated in the northeast part of Morocco based on Landsat Oli-8 and ASTER images. Specifically, this study aims to: (1) map the lithological facies of the Saka volcanic zone, (2) discriminate the different minerals using Landsat Oli-8 and ASTER imagery, and (3) validate the results with field observations and geological maps. To do so, in this study we used different techniques to achieve the above objectives including color composition (CC), band ratio (BR), minimum noise fraction (MNF), principal component analysis (PCA), and spectral angle mapper (SAM) classification. The results obtained show good discrimination between the different lithological facies, which is confirmed by the supervised classification of the images and validated by field missions and the geological map with a scale of 1/500,000. The classification results show that the study area is dominated by Basaltic rocks, followed by Trachy andesites then Hawaites. These rocks are encased by quaternary sedimentary rocks and an abundance of Quartz, Feldspar, Pyroxene, and Amphibole minerals. Full article

(This article belongs to the Special Issue Urban Expansion Prediction and Land Use/Land Cover Change Modeling for Sustainable Urban Development)

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17 pages, 4336 KiB

Open AccessArticle

A Multi-Dimensional Deep Siamese Network for Land Cover Change Detection in Bi-Temporal Hyperspectral Imagery

by Seyd Teymoor Seydi, Reza Shah-Hosseini and Meisam Amani

Sustainability 2022, 14(19), 12597; https://0-doi-org.brum.beds.ac.uk/10.3390/su141912597 - 03 Oct 2022

Cited by 11 | Viewed by 1322

Abstract

In this study, an automatic Change Detection (CD) framework based on a multi-dimensional deep Siamese network was proposed for CD in bi-temporal hyperspectral imagery. The proposed method has two main steps: (1) automatic generation of training samples using the Otsu algorithm and the Dynamic Time Wrapping (DTW) predictor, and (2) binary CD using a multidimensional multi-dimensional Convolution Neural Network (CNN). Two bi-temporal hyperspectral datasets of the Hyperion sensor with a variety of land cover classes were used to evaluate the performance of the proposed method. The results were also compared to reference data and two state-of-the-art hyperspectral change detection (HCD) algorithms. It was observed that the proposed method relatively had higher accuracy and lower False Alarm (FA) rate, where the average Overall Accuracy (OA) and Kappa Coefficient (KC) were more than 96% and 0.90, respectively, and the average FA rate was lower than 5%. Full article

(This article belongs to the Special Issue Urban Expansion Prediction and Land Use/Land Cover Change Modeling for Sustainable Urban Development)

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18 pages, 4971 KiB

Open AccessArticle

Simulated Validation and Prediction of Land Use under Multiple Scenarios in Daxing District, Beijing, China, Based on GeoSOS-FLUS Model

by Xin Chen, Xinyi He and Siyuan Wang

Sustainability 2022, 14(18), 11428; https://0-doi-org.brum.beds.ac.uk/10.3390/su141811428 - 12 Sep 2022

Cited by 8 | Viewed by 1952

Abstract

Land-use changes in urban fringe areas are dramatic, and modelling and predicting land-use changes under different scenarios can provide a basis for urban development regulation and control. As an important part of Beijing’s urban fringe, Daxing District is representative of its land-use changes. Taking the Daxing District of Beijing as an example, this study selected two periods of land-use data in 2008 and 2018 and predicted land-use changes in 2028 and 2038 using the GeoSOS-FLUS model (geographical simulation and optimisation system–future land-use simulation) and Markov chain model, based on the simulation and validation of land use in Daxing District from 2008 to 2018. Meanwhile, three types of scenario simulations were carried out. The results in the future predictions show that: (1) under the natural development scenario, the area of construction land and grassland gradually increased, and the area of cultivated land, woodland and water bodies gradually decreased; (2) under the cultivated land protection scenario, the area of cultivated land remained largely unchanged, the area of grassland decreased before increasing, the expansion of construction land was curbed, and the area of woodland and water bodies increased slowly; and (3) under the ecological control scenario, the area of cultivated land, grassland, woodland and water bodies showed slowly increasing trends, with a small amount of cultivated land being converted to construction land. These results indicate that the setting of cultivated land protection and ecological control can limit the expansion of construction land to a certain extent. This study can provide a basis for the regulation of urban development in the Daxing District in the future. Full article

(This article belongs to the Special Issue Urban Expansion Prediction and Land Use/Land Cover Change Modeling for Sustainable Urban Development)

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18 pages, 4376 KiB

Open AccessArticle

CD-TransUNet: A Hybrid Transformer Network for the Change Detection of Urban Buildings Using L-Band SAR Images

by Lei Pang, Jinjin Sun, Yancheng Chi, Yongwen Yang, Fengli Zhang and Lu Zhang

Sustainability 2022, 14(16), 9847; https://0-doi-org.brum.beds.ac.uk/10.3390/su14169847 - 09 Aug 2022

Cited by 10 | Viewed by 2468

Abstract

The change detection of urban buildings is currently a hotspot in the research area of remote sensing, which plays a vital role in urban planning, disaster assessments and surface dynamic monitoring. SAR images have unique characteristics compared with traditional optical images, mainly including abundant image information and large data volume. However, the majority of currently used SAR images for the detection of changes in buildings have the problems of missing the detection of small buildings and poor edge segmentation. Therefore, this paper proposes a new approach based on deep learning for changing building detection, which we called CD-TransUNet. It should be noted that CD-TransUNet is an end-to-end encoding–decoding hybrid Transformer model that combines the UNet and Transformer. Additionally, to enhance the precision of feature extraction and to reduce the computational complexity, the CD-TransUNet integrates coordinate attention (CA), atrous spatial pyramid pooling (ASPP) and depthwise separable convolution (DSC). In addition, by sending the differential images to the input layer, the CD-TransUNet can focus more on building changes over a large scale while ignoring the changes in other land types. At last, we verify the effectiveness of the proposed method using a pair of ALOS-2(L-band) acquisitions, and the comparative experimental results obtained from other baseline models show that the precision of the CD-TransUNet is much higher and the Kappa value can reach 0.795. Furthermore, the low missed alarms and the accurate building edge reflect that the proposed method is more appropriate for building changing detection tasks. Full article

(This article belongs to the Special Issue Urban Expansion Prediction and Land Use/Land Cover Change Modeling for Sustainable Urban Development)

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21 pages, 3631 KiB

Open AccessEditor’s ChoiceArticle

Examining the Use of Urban Growth Boundary for Future Urban Expansion of Chattogram, Bangladesh

by Pankaj Bajracharya and Selima Sultana

Sustainability 2022, 14(9), 5546; https://0-doi-org.brum.beds.ac.uk/10.3390/su14095546 - 05 May 2022

Cited by 2 | Viewed by 2117

Abstract

With the rapid and unregulated nature of urban expansion occurring in Chattogram, Bangladesh, the adoption of urban growth restriction mechanisms such as the urban growth boundary (UGB) can provide a robust framework necessary to direct the development of built-up areas in a way that curtails the growth in environmentally sensitive areas of the city. Using a support vector machine (SVM)-based urban growth simulation model, this paper examines the areas of future contiguous expansion of the city to aid in the delineation of the UGB. Utilizing landcover, topographic, and population density data from a variety of sources for the past twenty years, the SVM method with the radial basis function (RBF) kernel is used to develop a model based on fourteen predictor variables. A grid-search is used to tune the hyperparameters and determine the best performance combination of the hyperparameters for the RBF kernel function used in the SVM. The final SVM model using the best performance combination of the hyperparameters indicates a high percentage agreement of 91.79% and a substantial agreement for the Kappa coefficient of 0.7699. The developed SVM simulation model identifies potential areas that are more likely to undergo urban expansion in Chattogram in the next twenty years and provides aids for a stringent and strict delineation of UGB for this region. Full article

(This article belongs to the Special Issue Urban Expansion Prediction and Land Use/Land Cover Change Modeling for Sustainable Urban Development)

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22 pages, 6623 KiB

Open AccessEditor’s ChoiceArticle

Modeling Urban Growth and the Impacts of Climate Change: The Case of Esmeraldas City, Ecuador

by Carlos F. Mena, Fátima L. Benitez, Carolina Sampedro, Patricia Martinez, Alex Quispe and Melinda Laituri

Sustainability 2022, 14(8), 4704; https://0-doi-org.brum.beds.ac.uk/10.3390/su14084704 - 14 Apr 2022

Cited by 4 | Viewed by 3075

Abstract

This research has been developed in the city of Esmeraldas, which is one of the poorest urban centers of Ecuador. Historically, the economic dynamics of the city have been related to the extraction of natural resources, but little has been invested in local populations. The objectives of this paper are, first, to create a predictive scenario of urban growth linked to future climate projections for Esmeraldas, with a focus on vulnerability to landslides and flooding; and second, to generate methodological advances related to the linkage between urban growth simulation and the downscaling of global models for climate change. This paper is based on spatially explicit simulation, Cellular Automata (CA), to capture the dynamics of urban processes. CA is linked to the analysis of vulnerability to climate change based on socioeconomic conditions and is focused on flooding- and landslide-exposed areas. We found that the proportion of Afro-Ecuadorian people and the risk of landslides and flooding are positively related to urban growth. Based on our future scenarios, the urban growth area in Esmeraldas will increase 50% compared to the year 2016. Moreover, if the existing trends continue, natural vegetation—including mangroves—will be removed by that time, increasing the vulnerability to climate change. Full article

(This article belongs to the Special Issue Urban Expansion Prediction and Land Use/Land Cover Change Modeling for Sustainable Urban Development)

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24 pages, 17429 KiB

Open AccessArticle

Urban Spatial Development Based on Multisource Data Analysis: A Case Study of Xianyang City’s Integration into Xi’an International Metropolis

by Yiyi Hu, Yi He and Yanlin Li

Sustainability 2022, 14(7), 4090; https://0-doi-org.brum.beds.ac.uk/10.3390/su14074090 - 30 Mar 2022

Cited by 5 | Viewed by 1923

Abstract

The study of urban spatial development focuses on the process of urbanization, which involves the urban economy, population, the scale of urban construction land and the construction land’s structure. All this influences the economic structure, social structure and functional structure of the city. Taking Xianyang City, a core part of Xi’an international metropolis, as an example, this study, based on night light remote sensing data from 1992 to 2013, land use data from 1980 to 2015 (6 periods), AutoNavi Map (AMAP) Points of Interest (POI) data, and the patch-generated land use simulation model (PLUS), simulates the spatial–temporal pattern change characteristics of land use in Xianyang City from 2025 to 2035. The results show that: (1) During 1985–2015, urban land use showed a significant upward trend (p < 0.05); (2) From 1992 to 2013, the change in night light in the Xianyang City Administrative Region showed an upward trend. The gravitational center of Xianyang City’s built-up area moves southeast first and then northeast. After the beginning of 2010, the gravitational center of Xianyang City’s built-up area moved faster; (3) The distribution of different types of urban centers in Xianyang City is basically the same; (4) From 2005 to 2035, the overall land use in Xianyang City showed a trend of “multi polar explosive growth in construction land, slow growth in forest land, and first a decrease then an increase in wetland water body”. The urban spatial structure has changed from a single-center development model to a point–axis development model. The study of urban space development can provide some reference for the layout of urban construction in the future. Full article

(This article belongs to the Special Issue Urban Expansion Prediction and Land Use/Land Cover Change Modeling for Sustainable Urban Development)

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20 pages, 5037 KiB

Open AccessEditor’s ChoiceArticle

Evolution of Green Space under Rapid Urban Expansion in Southeast Asian Cities

by Amal Najihah Muhamad Nor, Hasifah Abdul Aziz, Siti Aisyah Nawawi, Rohazaini Muhammad Jamil, Muhamad Azahar Abas, Kamarul Ariffin Hambali, Abdul Hafidz Yusoff, Norfadhilah Ibrahim, Nur Hairunnisa Rafaai, Ron Corstanje, Jim Harris, Darren Grafius and Humberto L. Perotto-Baldivieso

Sustainability 2021, 13(21), 12024; https://0-doi-org.brum.beds.ac.uk/10.3390/su132112024 - 30 Oct 2021

Cited by 23 | Viewed by 4542

Abstract

Globally, rapid urban expansion has caused green spaces in urban areas to decline considerably. In this study, the rapid expansion of three Southeast Asia cities were considered, namely, Kuala Lumpur City, Malaysia; Jakarta, Indonesia; and Metro Manila, Philippines. This study evaluates the changes in spatial and temporal patterns of urban areas and green space structure in the three cities over the last two decades. Land use land cover (LULC) maps of the cities (1988/1989, 1999 and 2014) were developed based on 30-m resolution satellite images. The changes in the landscape and spatial structure were analysed using change detection, landscape metrics and statistical analysis. The percentage of green space in the three cities reduced in size from 45% to 20% with the rapid expansion of urban areas over the 25-year period. In Metro Manila and Jakarta, the proportion of green space converted to urban areas was higher in the initial 1989 to 1999 period than over the latter 1999 to 2014 period. Significant changes in green space structure were observed in Jakarta and Metro Manila. Green space gradually fragmented and became less connected and more unevenly distributed. These changes were not seen in Kuala Lumpur City. Overall, the impact of spatial structure of urban areas and population density on green space is higher in Jakarta and Metro Manila when this is compared to Kuala Lumpur. Thus, the results have the potential to clarify the relative contribution of green space structure especially for cities in Southeast Asia where only a few studies in urban areas have taken place. Full article

(This article belongs to the Special Issue Urban Expansion Prediction and Land Use/Land Cover Change Modeling for Sustainable Urban Development)

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17 pages, 32911 KiB

Open AccessArticle

Urban Expansion Simulation Based on Various Driving Factors Using a Logistic Regression Model: Delhi as a Case Study

by Muhammad Salem, Arghadeep Bose, Bashar Bashir, Debanjan Basak, Subham Roy, Indrajit R. Chowdhury, Abdullah Alsalman and Naoki Tsurusaki

Sustainability 2021, 13(19), 10805; https://0-doi-org.brum.beds.ac.uk/10.3390/su131910805 - 28 Sep 2021

Cited by 37 | Viewed by 3641

Abstract

During the last three decades, Delhi has witnessed extensive and rapid urban expansion in all directions, especially in the East South East zone. The total built-up area has risen dramatically, from 195.3 sq. km to 435.1 sq. km, during 1989–2020, which has led to habitat fragmentation, deforestation, and difficulties in running urban utility services effectively in the new extensions. This research aimed to simulate urban expansion in Delhi based on various driving factors using a logistic regression model. The recent urban expansion of Delhi was mapped using LANDSAT images of 1989, 2000, 2010, and 2020. The urban expansion was analyzed using concentric rings to show the urban expansion intensity in each direction. Nine driving factors were analyzed to detect the influence of each factor on the urban expansion process. The results revealed that the proximity to urban areas, proximity to main roads, and proximity to medical facilities were the most significant factors in Delhi during 1989–2020, where they had the highest regression coefficients: −0.884, −0.475, and −0.377, respectively. In addition, the predicted pattern of urban expansion was chaotic, scattered, and dense on the peripheries. This pattern of urban expansion might lead to further losses of natural resources. The relative operating characteristic method was utilized to assess the accuracy of the simulation, and the resulting value of 0.96 proved the validity of the simulation. The results of this research will aid local authorities in recognizing the patterns of future expansion, thus facilitating the implementation of effective policies to achieve sustainable urban development in Delhi. Full article

(This article belongs to the Special Issue Urban Expansion Prediction and Land Use/Land Cover Change Modeling for Sustainable Urban Development)

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Review

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12 pages, 956 KiB

Open AccessReview

A Comprehensive Review on Land Use/Land Cover (LULC) Change Modeling for Urban Development: Current Status and Future Prospects

by Srishti Gaur and Rajendra Singh

Sustainability 2023, 15(2), 903; https://0-doi-org.brum.beds.ac.uk/10.3390/su15020903 - 04 Jan 2023

Cited by 19 | Viewed by 6442

Abstract

Land use land cover (LULC) modeling is considered as the best tool to comprehend and unravel the dynamics of future urban expansion. The present paper provides a comprehensive review of existing LULC modeling techniques and novel approaches used by the research community. Moreover, the review also compares each technique’s applications, utility, drawbacks, and broader differences. The rationale behind such a comparison is to highlight the strengths/weakness of individual techniques. The review further highlights the utility of the hybridization of different techniques (e.g., machine learning model combined with statistical models) to LULC modeling to complement their strengths. Although significant progress has been made in LULC modeling, the review highlights the need to incorporate the policy framework into LULC modeling for better urban planning and management. The present review will help researchers and policymakers to achieve better land management practices and ultimately assist in achieving Sustainable Development Goal-15 (SDG-15) (i.e., life on land). Full article

(This article belongs to the Special Issue Urban Expansion Prediction and Land Use/Land Cover Change Modeling for Sustainable Urban Development)

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