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Application of Remote Sensing Technology for Land Use and Land Cover Change Analysis

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A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainability in Geographic Science".

Deadline for manuscript submissions: closed (15 June 2023) | Viewed by 17320

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Special Issue Editors

Dr. Yang Zhang

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Guest Editor

College of Urban Economics and Public Administration, Capital University of Economics and Business, Beijing 100070, China
Interests: spatial planning; land use/cover change and simulation
Special Issues, Collections and Topics in MDPI journals

Dr. Zeying Lan

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Guest Editor

School of Management, Guangdong University of Technology, 169 Yinglong Road, Guangzhou 510520, China
Interests: remote sensing image classification; pattern optimization; sustainable development of land-use
Special Issues, Collections and Topics in MDPI journals

Dr. Yiyun Chen

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Guest Editor

School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China
Interests: land change science; soil security; sustainable development in the Anthropocene
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Earth has entered into a new geological epoch, the Anthropocene, which indicates that humans have changed the planet profoundly, and this may even alter the direction of its evolution. Land use/cover change (LUCC) is undoubtedly one of the most dominant pieces of evidence. LUCC is closely related to the sustainable development of humans. It exerts a direct or indirect influence on the quantity of the sources, such as land, water, food, fiber, etc. Moreover, it has a bearing on regional/global ecological security, food security, and the stability of regional society and economy. Here, we sincerely invite you to participate in the research of the topic “Land Use/Cover Change Based on Remote Sensing and GIS”. The involved topics are as follows ( not be limited to):

Land use/cover change based on remote sensing and GIS;
Research progress of land use/cover change;
Land use/cover change and ecological security;
Land use/cover change and food security;
Global land use/cover change;
Land use/cover change and driving forces;
Land use/cover change and simulation;
Land use/cover change and ecosystem service value;
Land use/cover change and socio-economic system coupling;
Land use/cover change and urbanization;
Thoughts on the study of land use/cover change under the background of the Anthropocene.

Dr. Yang Zhang
Dr. Zeying Lan
Dr. Yiyun Chen
Guest Editors

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Keywords

land use/cover change
ecological security
food security
ecosystem service value
urbanization
Anthropocene

Published Papers (12 papers)

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Research

25 pages, 10053 KiB

Open AccessArticle

Spatial-Temporal Pattern and Driving Forces of Fractional Vegetation Coverage in Xiong’an New Area of China from 2005 to 2019

by Zhiqing Huang, Haitao Qiu, Yonggang Cao, Adu Gong and Jiaxiang Wang

Sustainability 2023, 15(15), 11985; https://0-doi-org.brum.beds.ac.uk/10.3390/su151511985 - 04 Aug 2023

Viewed by 867

Abstract

The Xiong’an New Area was officially established in 2018 to construct a new, intelligent, and efficient urban area to alleviate Beijing’s non-capital functions. Using Landsat satellite images, we employed the dimidiate pixel model, band operation, and transition matrix to analyze the temporal and spatial variations in FVC (Fractional Vegetation Coverage) within the Xiong’an New Area in 2005, 2013, and 2019, respectively. Urbanization rate, precipitation, temperature, and population were considered potential driving forces, which we analyzed using grey relational analysis and linear regression to explore the correlation between FVC and these factors. The findings are as follows: from 2005 to 2019, overall improvement and significant degradation have been observed. In Baiyangdian, a part of the national key ecological area, water bodies and FVC have increased. Grey relational analysis revealed that precipitation had the highest grey relational value of 0.76. The average correlation among natural factors was 0.67, while that among human factors was 0.60. Generally, the Xiong’an New Area vegetation exhibited instability, while Baiyangdian demonstrated relatively stable FVC. Grey relational analysis indicates a strong potential for social and economic development in the Xiong’an New Area. Full article

(This article belongs to the Special Issue Application of Remote Sensing Technology for Land Use and Land Cover Change Analysis)

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31 pages, 10980 KiB

Open AccessArticle

Evaluation of Index-Based Methods for Impervious Surface Mapping from Landsat-8 to Cities in Dry Climates; A Case Study of Buraydah City, KSA

by Hussein Almohamad and Ibrahim Obaid Alshwesh

Sustainability 2023, 15(12), 9704; https://0-doi-org.brum.beds.ac.uk/10.3390/su15129704 - 17 Jun 2023

Viewed by 1086

Abstract

The natural landscape is fast turning into impervious surfaces with the increase in urban density and the spatial extent of urbanized areas. Remote sensing data are crucial for mapping impervious surface area (ISA), and several methods for ISA extraction have been developed and implemented successfully. However, the heterogeneity of the ISA spectra and the high similarity of the ISA spectra to those of bare soil in dry climates were not adequately addressed. The objective of this study is to determine which spectral impervious surface index best represents impervious surfaces in arid climates using two seasonal Landsat-8 images. We attempted to compare the performance of various impervious surface spectral Index for ISA extraction in dry climates using two seasonal Landsat-8 data. Specifically, nine indices, i.e., band ratio for the built-up area (BRBA), built-up area extraction method (BAEM), visible red near infrared built-up index (VrNIR-BI), normalized ratio urban index (NRUI), enhanced normalized difference impervious surfaces index (ENDISI), dry built-up index (DBI), built-up land features extraction index (BLFEI), perpendicular impervious surface index (PISI), combinational biophysical composition index (CBCI), and two impervious surface binary methods (manual method and ISODATA unsupervised classification). According to the results, PISI and CBCI combined with the manual method had the best accuracy with 88.5% and 88.5% overall accuracy (OA) and 0.76 and 0.81 kappa coefficients, respectively, while DBI combined with the manual method had the lowest accuracy with 75.37% OA and 0.56 kappa coefficients. PISI is comparatively more stable than the other approaches in terms of seasonal sensitivity. The ability of PISI to discriminate ISA from soil and vegetation accounts for much of its good performance. In addition, spring is the ideal time of the year for mapping ISA from Landsat-8 images because the impervious surface is generally less likely to be confused with bare soil and sand at this time of year. Therefore, this study can be used to determine spectral indices for studying ISA extraction in drylands in conjunction with binary approaches and seasonal effects. Full article

(This article belongs to the Special Issue Application of Remote Sensing Technology for Land Use and Land Cover Change Analysis)

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17 pages, 3425 KiB

Open AccessArticle

Adaptability Evaluation of the Spatiotemporal Fusion Model of Sentinel-2 and MODIS Data in a Typical Area of the Three-River Headwater Region

by Mengyao Fan, Dawei Ma, Xianglin Huang and Ru An

Sustainability 2023, 15(11), 8697; https://0-doi-org.brum.beds.ac.uk/10.3390/su15118697 - 27 May 2023

Cited by 1 | Viewed by 1003

Abstract

The study of surface vegetation monitoring in the “Three-River Headwaters” Region (TRHR) relies on satellite data with high spatial and temporal resolutions. The spatial and temporal fusion method for multiple data sources can effectively overcome the limitations of weather, the satellite return period, and funding on research data to obtain data higher spatial and temporal resolutions. This paper explores the spatial and temporal adaptive reflectance fusion model (STARFM), the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM), and the flexible spatiotemporal data fusion (FSDAF) method applied to Sentinel-2 and MODIS data in a typical area of the TRHR. In this study, the control variable method was used to analyze the parameter sensitivity of the models and explore the adaptation parameters of the Sentinel-2 and MODIS data in the study area. Since the spatiotemporal fusion model was directly used in the product data of the vegetation index, this study used NDVI fusion as an example and set up a comparison experiment (experiment I first performed the band spatiotemporal fusion and then calculated the vegetation index; experiment II calculated the vegetation index first and then performed the spatiotemporal fusion) to explore the feasibility and applicability of the two methods for the vegetation index fusion. The results showed the following. (1) The three spatiotemporal fusion models generated high spatial resolution and high temporal resolution data based on the fusion of Sentinel-2 and MODIS data, the STARFM and FSDAF model had a higher fusion accuracy, and the R2 values after fusion were higher than 0.8, showing greater applicability. (2) The fusion accuracy of each model was affected by the model parameters. The errors between the STARFM, ESTARFM, and FSDAF fusion results and the validation data all showed a decreasing trend with an increase in the size of the sliding window or the number of similar pixels, which stabilized after the sliding window became larger than 50 and the similar pixels became larger than 80. (3) The comparative experimental results showed that the spatiotemporal fusion model can be directly fused based on the vegetation index products, and higher quality vegetation index data can be obtained by calculating the vegetation index first and then performing the spatiotemporal fusion. The high spatial and temporal resolution data obtained using a suitable spatial and temporal fusion model are important for the identification and monitoring of surface cover types in the TRHR. Full article

(This article belongs to the Special Issue Application of Remote Sensing Technology for Land Use and Land Cover Change Analysis)

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27 pages, 16551 KiB

Open AccessArticle

A New Technique for Impervious Surface Mapping and Its Spatio-Temporal Changes from Landsat and Sentinel-2 Images

by Lizhong Hua, Haibo Wang, Huafeng Zhang, Fengqin Sun, Lanhui Li and Lina Tang

Sustainability 2023, 15(10), 7947; https://0-doi-org.brum.beds.ac.uk/10.3390/su15107947 - 12 May 2023

Cited by 3 | Viewed by 1465

Abstract

Accurately mapping and monitoring the urban impervious surface area (ISA) is crucial for understanding the impact of urbanization on heat islands and sustainable development. However, less is known about ISA spectra heterogeneity and their similarity to bare land, wetland, and high-rise-building shadows. This study proposes a feature-based approach using decision tree classification (FDTC) to map ISAs and their spatio-temporal changes in a coastal city in southeast China using Landsat 5 TM, Landsat 8 OLI/TIRS, and Sentinel-2 images from 2009 to 2021. Atmospheric correction using simplified dark object subtraction (DOS) was applied to Landsat imagery, which enabled faster computation. FDTC’s performance was evaluated with three sensors with different spectral and spatial resolutions, with parameter thresholds held constant across remote-sensing images. FDTC produces a high average overall accuracy (OA) of 94.53%, a kappa coefficient (KC) of 0.855, and a map-level image classification efficacy (MICE) of 0.851 for ISA mapping over the studied period. In comparison with other indices such as BCI (biophysical composition index), PISI (automated built-up extraction index), and ABEI (perpendicular impervious surface index), the FDTC demonstrated higher accuracy and separability for extracting ISA and bare land as well as wetland and high-rise buildings. The results of FDTC were also consistent with those of two open-source ISA products and other remote sensing indices. The study found that the ISA in Xiamen City increased from 16.33% to 26.17% over the past 13 years due to vegetation occupation, encroachment onto bare land, and reclamation of coastal areas. While the expansion significantly reduced urban vegetation in rapidly urbanizing areas of Xiamen, ambitious park greening programs and massive redevelopment of urban villages resulted in a modest but continuous increase in urban green space. Full article

(This article belongs to the Special Issue Application of Remote Sensing Technology for Land Use and Land Cover Change Analysis)

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18 pages, 5444 KiB

Open AccessArticle

The Impact of Land-Use Structure on Carbon Emission in China

by Hui Zhang, Pengcheng Gu, Genrong Cao, Dongquan He and Bofeng Cai

Sustainability 2023, 15(3), 2398; https://0-doi-org.brum.beds.ac.uk/10.3390/su15032398 - 29 Jan 2023

Cited by 5 | Viewed by 1577

Abstract

Research objectives: This paper discusses how to support the realization of carbon peak and carbon neutrality through the optimization of national spatial structures by establishing a relationship model between land-use structure and carbon emissions, and then provide theoretical and methodological support for the formulation of relevant policies and plans, as well as the evaluation of implementation effects. Research methods: grid analysis, GIS spatial analysis, double log linear regression model. Results: There is a strong correlation between the spatial structure of land and carbon emissions; the scale of construction land, especially industrial land, directly affects carbon emissions; if the area of construction land is doubled, CO₂ emissions will increase by about 1.7 times. Conclusions: The potential of controlling carbon emission intensity through land structure at the urban level is great, and it is feasible to control carbon emission intensity through territorial spatial planning system. The control elements can be divided into the following levels: land supply control, land structure adjustment, land intensity constraint, and function adjustment of existing land. Full article

(This article belongs to the Special Issue Application of Remote Sensing Technology for Land Use and Land Cover Change Analysis)

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28 pages, 34999 KiB

Open AccessArticle

A Comparison between Supervised Classification Methods: Study Case on Land Cover Change Detection Caused by a Hydroelectric Complex Installation in the Brazilian Amazon

by Alynne Almeida Affonso, Silvia Sayuri Mandai, Tatiana Pineda Portella, José Alberto Quintanilha, Luis Américo Conti and Carlos Henrique Grohmann

Sustainability 2023, 15(2), 1309; https://0-doi-org.brum.beds.ac.uk/10.3390/su15021309 - 10 Jan 2023

Cited by 1 | Viewed by 2097

Abstract

The Volta Grande do Xingu (VGX) in the Amazon Forest of Brazil was chosen to analyze the land use and land cover changes (LULCC) from 2000 to 2017, with the aim of assessing the most suitable classification method for the area. Three parametric (Mahalanobis distance, maximum likelihood and minimum distance) and three non-parametric (neural net, random forest and support vector machine) classification algorithms were tested in two Landsat scenes. The accuracy assessment was evaluated through a confusion matrix. Change detection of the landscape was analyzed through the post-classification comparison method. While maximum likelihood was more capable of highlighting errors in individual classes, support vector machine was slightly superior when compared with the other non-parametric options, these being the most suitable classifiers within the scope of this study. The main changes detected in the landscape were from forest to agro-pasture, from forest/agro-pasture to river, and from river to non-river, resulting in rock exposure. The methodology outlined in this research highlights the usefulness of remote sensing tools in follow-up observations of LULCC in the study area (with the possibility of application to the entire Amazon rainforest). Thus, it is possible to carry out adaptive management that aims to minimize unforeseen or underestimated impacts in previous stages of environmental licensing. Full article

(This article belongs to the Special Issue Application of Remote Sensing Technology for Land Use and Land Cover Change Analysis)

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18 pages, 1932 KiB

Open AccessArticle

Study on Urban Land Ecological Security Pattern and Obstacle Factors in the Beijing–Tianjin–Hebei Region

by Wenying Peng, Yue Sun, Can Liu and Dandan Liu

Sustainability 2023, 15(1), 43; https://0-doi-org.brum.beds.ac.uk/10.3390/su15010043 - 20 Dec 2022

Cited by 4 | Viewed by 1298

Abstract

Land ecological security is the material basis of the sustainable development of human society. The coordinated development of the Beijing–Tianjin–Hebei region is a major national strategy of China. Land ecological security is of great significance to the coordinated development of the Beijing–Tianjin–Hebei region and the maintenance of China’s ecological security. In this paper, the pressure–state–response (PSR) model is used to construct an evaluation index system of land ecological security, an entropy-weight technique for order preference by similarity to ideal solution (TOPSIS) method is used to calculate the land ecological security index in the Beijing–Tianjin–Hebei region, and an obstacle degree model is used to reveal the obstacle factors. The results show that the overall level of land ecological security in the Beijing–Tianjin–Hebei region was low, and that the security level presented a pattern of “high in the north and low in the south”. The land ecological security level was mainly affected by the state subsystem and response subsystem, and the average index of the pressure subsystem was 0.543, which reached the safe state. The main obstacle factors are per capita grassland area, per capita forest area, green land rate of built-up area, urbanization rate, per capita cultivated land, etc. This study provides a theoretical basis for the construction of the land ecological security system, sustainable utilization of land resources and regional sustainable development in the Beijing–Tianjin–Hebei region and promotes the formation of a benign circulation pattern of land ecosystem and effective prevention and control of land ecological and environmental risks in the region. Full article

(This article belongs to the Special Issue Application of Remote Sensing Technology for Land Use and Land Cover Change Analysis)

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18 pages, 3794 KiB

Open AccessArticle

Identification of Key Areas for Ecosystem Restoration Based on Ecological Security Pattern

by Jiaquan Duan, Xuening Fang, Cheng Long, Yinyin Liang, Yue ‘e Cao, Yijing Liu and Chentao Zhou

Sustainability 2022, 14(23), 15499; https://0-doi-org.brum.beds.ac.uk/10.3390/su142315499 - 22 Nov 2022

Cited by 3 | Viewed by 1144

Abstract

Ecosystem degradation and conversion are leading to a widespread reduction in the provision of ecosystem services. It is crucial for the governance of regional land spaces to rapidly identify key areas for ecosystem restoration. Herein, we combined the InVEST Habitat Quality Model with the granularity inverse method to identify ecological sources in Jiashi county, China, based on the “source-corridor” ecological security pattern paradigm. The minimum cumulative resistance model and circuit theory were adopted to diagnose the ecological “pinch points”, barrier points, break points, and key restoration areas for land space. Our results show that: (1) the area of the ecological source and the total length of the ecological corridor were identified as 1331.13 km² and 316.30 km, respectively; (2) there were 164 key ecological “pinch points” and 69 key ecological barrier points in Jiashi county, with areas of 15.13 km² and 14.57 km², respectively. Based on the above ecological security pattern, recovery strategies are put forward to improve regional ecosystem health. This study describes the best practices which can be used to guide the planning and implementation of ecosystem restoration at the local landscape scale. Full article

(This article belongs to the Special Issue Application of Remote Sensing Technology for Land Use and Land Cover Change Analysis)

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15 pages, 2371 KiB

Open AccessArticle

Study on the Spatio-Temporal Evolution of Land Use in Resource-Based Cities in Three Northeastern Provinces of China—An Analysis Based on Long-Term Series

by Qiang Li, Yuchi Pu and Yang Zhang

Sustainability 2022, 14(20), 13683; https://0-doi-org.brum.beds.ac.uk/10.3390/su142013683 - 21 Oct 2022

Cited by 1 | Viewed by 1352

Abstract

Land is the basis of development, and the unique patterns of the spatio-temporal evolution of land use in resource-based cities can reflect regional development, help land resources to be used efficiently and rationally, promote scientific regulation, and achieve high-quality development. Based on the land use data of resource-based cities in three northeastern provinces from 1980 to 2020, the spatio-temporal evolution characteristics and driving factors of land use in the sample study area were studied by the Markov transfer matrix and a parametric optimal geographic detector model. The results showed that: (1) From the perspective of time, the land use changes in the sample study area were active, mainly reflected in the continuous conversion of forest land transfer-out (11.66%) and arable land transfer-in (11.28%), and the dynamic attitude of forest land showed a trend of decreasing, then increasing and then decreasing, while the dynamic attitude of arable land showed a trend of increasing, then decreasing and then increasing. (2) Spatially, the areas where land conversion occurred were mainly concentrated in the northern part of the study area and the border area in the east, which is also the area where forest land was converted to arable land and grassland was converted to arable land, and the expansion of construction land was more common; (3) In terms of influencing factors, land conversion before 2000 was mainly influenced by socio-economic factors, and population quantity and urbanization rate had stronger explanatory power. The spatial and temporal evolution of forest land conversion to arable land was realized by the interaction of various factors, and the driver interactions were all non-linearly enhanced and bi-factor enhanced. (4) In terms of influencing factors, land conversion before 2000 was mainly influenced by socio-economics, with population quantity and urbanization rate having a stronger explanatory power; after 2000, land conversion was mainly influenced by physical geography, with precipitation and temperature having a stronger explanatory power. (5) The spatio-temporal evolution of forest land conversion to cropland was realized by the interaction between various factors, and the driving factor interactions all showed non-linear enhancement and bifactor enhancement. Full article

(This article belongs to the Special Issue Application of Remote Sensing Technology for Land Use and Land Cover Change Analysis)

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18 pages, 4362 KiB

Open AccessArticle

Characterizing the Long-Term Landscape Dynamics of a Typical Cloudy Mountainous Area in Northwest Yunnan, China

by Youjun Chen, Xiaokang Hu, Yanjie Zhang and Jianmeng Feng

Sustainability 2022, 14(20), 13488; https://0-doi-org.brum.beds.ac.uk/10.3390/su142013488 - 19 Oct 2022

Cited by 1 | Viewed by 926

Abstract

Detailed knowledge of landscape dynamics is crucial for many applications, from resource management to ecosystem service assessments. However, identifying the spatial distribution of the landscape using optical remote sensing techniques is difficult in mountainous areas, primarily due to cloud cover and topographic relief. Our study uses stable classification samples from mountainous areas to investigate an integrated approach that addresses large volumes of cloud-cover data (with associated data gaps) and extracts landscape time series (LTS) with a high time–frequency resolution. We applied this approach to map LTS in a typical cloudy mountainous area (Erhai watershed in northwestern Yunnan, China) using dense Landsat stacks, and then we also used the classified results to investigate the spatial–temporal landscape changes in the study area at biennial intervals. The overall accuracy of the landscape classification ranged from 81.75% to 88.18%. The results showed highly dynamic processes in the landscape throughout the study period. Forest was the main land cover type, covering approximately 39.19% to 41.68% of the total study area. Alpine meadow showed fluctuating trends, with a net loss of 11.22% and an annual reduction rate of −0.4%. Shrub cover increased by 1.26%, and water bodies showed a small decrease in area, resulting in an overall net change of −0.03%. Built-up land and farmland areas continued to expand, and their annual growth rates were 1.52% and 1.06%, respectively. Bare land showed the highest loss, with a net change of 228.97 km². In the Erhai watershed, all the landscape classes changed or transitioned into other classes, and a substantial decrease in bare land occurred. The biennial LTS maps allow us to fully understand the spatially and temporally complex change processes occurring in landscape classes; these changes would not be observable at coarse temporal intervals (e.g., 5–10 years). Our study highlights the importance of increasing the temporal resolution in landscape change studies to support sustainable land resource management strategies and integrate landscape planning for environmental conservation. Full article

(This article belongs to the Special Issue Application of Remote Sensing Technology for Land Use and Land Cover Change Analysis)

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18 pages, 4736 KiB

Open AccessArticle

Monitoring and Assessing Land Use/Cover Change and Ecosystem Service Value Using Multi-Resolution Remote Sensing Data at Urban Ecological Zone

by Siqi Liu, Guanqi Huang, Yulu Wei and Zhi Qu

Sustainability 2022, 14(18), 11187; https://0-doi-org.brum.beds.ac.uk/10.3390/su141811187 - 07 Sep 2022

Cited by 1 | Viewed by 1278

Abstract

An urban ecological zone (UEZ) is an important part of a city, focusing on environmental conservation and ecological economic development simultaneously. During the past decade, the urban scale of Xi’an city in China has been expanding, and the population has been increasing rapidly. This dramatic change is a huge challenge to urban sustainability. It puts forward higher requirements for the construction of an UEZ. Under different spatial resolution scales, this study adopted Landsat8-OLI and gaofen-2 (GF-2) satellite high-resolution remote sensing data to interpret the land use/cover change (LUCC) of the Weihe River UEZ. The ecosystem service value (ESV) was assessed, and the ecological effect was analyzed based on LUCC. The results showed that the spatial distribution of land types in the Weihe River UEZ changed significantly from 2014 to 2020. The construction land gathered to the southeast. Especially, the vegetative land (i.e., forestland, grassland and other green land) and water body showed a slightly increasing trend since the official establishment of the UEZ in 2018. The cultivated land area gradually reduced, and the vegetative land area tended to be concentrated as well as expanded. Through the interpretation of GF-2 remote sensing data, the ESV at the Weihe River UEZ showed a downward trend in general. The high-value areas were mainly distributed in the Weihe River and its surrounding beach areas, which were greatly affected by river water scope. Construction land normally had low ESV, and it was affected by human activities obviously. Therefore, the development of urban construction had significant impacts on the Weihe River UEZ. Full article

(This article belongs to the Special Issue Application of Remote Sensing Technology for Land Use and Land Cover Change Analysis)

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15 pages, 4615 KiB

Open AccessArticle

Comparing Four Machine Learning Algorithms for Land Cover Classification in Gold Mining: A Case Study of Kyaukpahto Gold Mine, Northern Myanmar

by Tin Ko Oo, Noppol Arunrat, Sukanya Sereenonchai, Achara Ussawarujikulchai, Uthai Chareonwong and Winai Nutmagul

Sustainability 2022, 14(17), 10754; https://0-doi-org.brum.beds.ac.uk/10.3390/su141710754 - 29 Aug 2022

Cited by 11 | Viewed by 1997

Abstract

Numerous studies have been undertaken to determine the optimal land use/cover classification algorithm. However, there have not been many studies that have compared and evaluated the performance of maximum likelihood (ML), random forest (RF), support vector machine (SVM), and classification and regression trees (CART) using ASTER imagery, especially in a mining district. Therefore, this study aims to investigate land use/cover (LULC) change over three decades (1990–2020), comparing the performance of the ML, RF, SVM, and CART machine learning algorithms. The Landsat and ASTER data were retrieved using Google Earth Engine (GEE). Traditional ML classification was performed on ArcGIS 10.2 software while RF, SVM, and CART classification were undertaken on GEE. Then, thematic accuracy assessments were conducted for the four algorithms and their performances were compared. The results showed that the largest changes in area occurred in forest cover that decreased from 37.8 to 27.3 km² during the three decades. The remarkable expansion of gold mining occurred during 2005–2010 with the increases of 1.6%. The mining land rose by 2.9% during the study period whereas agricultural land increased significantly by 10.7% between 1990 and 2020. When comparing the four algorithms, the RF algorithm gives the highest accuracy with an overall accuracy of 95.85% while SVM follows RF with 91.69%. This study proved that RF is the best choice for optimal land use/cover classification, particularly in the mining district. Full article

(This article belongs to the Special Issue Application of Remote Sensing Technology for Land Use and Land Cover Change Analysis)

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