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Remote Sensing Applications in Land Cover Changes and Associated Environmental Effects: Progress, Challenges, and Opportunities

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: closed (30 June 2025) | Viewed by 11086

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Prof. Dr. Mingjun Ding

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Key Laboratory of Poyang Lake Wetland and Watershed Research of Ministry of Education, School of Geography and Environment, Jiangxi Normal University, Nanchang 330028, China
Interests: land use and land cover; remote sensing; land resource management
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Dr. Lanhui Li

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School of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024, China
Interests: land cover change; human activity; climate change; remote sensing
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Dr. Linshan Liu

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Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
Interests: land cover mapping; satellite image processing; spatial analysis
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Dr. Haiyan Zhang

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Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
Interests: land cover change; human activity; climate change; remote sensing ecology and evolution
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Dr. Shoubao Geng

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Guangdong Provincial Key Laboratory of Remote Sensing and Geographical Information System, Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou 510070, China
Interests: land use; environmental management; remote sensing; ecosystem services
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Special Issue Information

Dear Colleagues,

Land cover change is an ongoing process intertwined with changes in population dynamics, climate, and socio-economic factors. Nearly four-fifths of the global land surface has been altered by direct human activities, significantly impacting land–atmosphere interactions, biodiversity, hydrological processes, the carbon cycle, and therefore human well-being. With the dual influences of climate change and human activities, land cover changes are becoming increasingly complex, emphasizing the importance of studying land use and land cover changes from global to regional scales and their environmental impacts. Satellite remote sensing technology has emerged as a powerful tool for monitoring the dynamics of land cover changes at various scales and exploring the mechanisms behind them. However, current research based on remote sensing still exhibits some discrepancies, and the associated monitoring and analysis methods present challenges.

This Special Issue of Remote Sensing, “Remote Sensing Applications in Land Cover Changes and Associated Environmental Effects: Progress, Challenges, and Opportunities,” aims to collect the latest advancements in remote sensing technologies and products for land cover change research and identify the impacts of human activities and climate change using various remote sensing techniques. The main areas include (but are not limited to) the following:

Land cover changes in forests, grasslands, and urban areas;
Vegetation degradation and biomass;
Integrating remote sensing with other data sources for land cover change analysis;
The impact of urbanization on environmental and climate change;
The impacts of human activities and climate change on land cover change.

Prof. Dr. Mingjun Ding
Dr. Lanhui Li
Dr. Linshan Liu
Dr. Haiyan Zhang
Dr. Shoubao Geng
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

land cover change
remote sensing
climate change
human activities
human well-being

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Related Special Issue

Remote Sensing Applications in Land Cover Changes and Associated Environmental Effects: Progress, Challenges, and Opportunities (Second Edition) in Remote Sensing

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19 pages, 11267 KiB

Open AccessArticle

Urban–Rural Differences in Cropland Loss and Fragmentation Caused by Construction Land Expansion in Developed Coastal Regions: Evidence from Jiangsu Province, China

by Jiahao Zhai and Lijie Pu

Remote Sens. 2025, 17(14), 2470; https://doi.org/10.3390/rs17142470 - 16 Jul 2025

Viewed by 286

Abstract

With the acceleration of global urbanization, cropland loss and fragmentation due to construction land expansion have become critical threats to food security and ecological sustainability, particularly in rapidly developing coastal regions. Understanding urban–rural differences in these processes is essential as divergent governance policies, socioeconomic pressures, and land use transition pathways may lead to uneven impacts on agricultural systems. However, past comparisons of urban–rural differences regarding this issue have been insufficient. Therefore, this study takes Jiangsu Province, China, as an example. Based on 30 m-resolution land use data, Geographic Information System (GIS) spatial analysis, and landscape pattern indices, it delves into the urban–rural differences in cropland loss and fragmentation caused by construction land expansion from 1990 to 2020. The results show that cropland in urban and rural areas decreased by 44.14% and 5.97%, respectively, while the area of construction land increased by 2.61 times and 90.14%, respectively. 94.36% of the newly added construction land originated from cropland, with the conversion of rural cropland to construction land being particularly prominent in northern Jiangsu, while the conversion of urban cropland to construction land is more pronounced in southern Jiangsu. The expansion of construction land has led to the continuous fragmentation of cropland, which is more severe in urban areas than in rural areas, while construction land is becoming increasingly agglomerated. There are significant differences in the degree of land use change between urban and rural areas, necessitating the formulation of differentiated land management policies to balance economic development with agricultural sustainability. Full article

(This article belongs to the Special Issue Remote Sensing Applications in Land Cover Changes and Associated Environmental Effects: Progress, Challenges, and Opportunities)

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27 pages, 3599 KiB

Open AccessArticle

Progressive Shrinkage of the Alpine Periglacial Weathering Zone and Its Escalating Disaster Risks in the Gongga Mountains over the Past Four Decades

by Qiuyang Zhang, Qiang Zhou, Fenggui Liu, Weidong Ma, Qiong Chen, Bo Wei, Long Li and Zemin Zhi

Remote Sens. 2025, 17(14), 2462; https://doi.org/10.3390/rs17142462 - 16 Jul 2025

Viewed by 213

Abstract

The Alpine Periglacial Weathering Zone (APWZ) is a critical transitional belt between alpine vegetation and glaciers, and a highly sensitive region to climate change. Its dynamic variations profoundly reflect the surface environment’s response to climatic shifts. Taking Gongga Mountain as the study area, this study utilizes summer Landsat imagery from 1986 to 2024 and constructs a remote sensing method based on NDVI and NDSI indices using the Otsu thresholding algorithm on the Google Earth Engine platform to automatically extract the positions of the upper limit of vegetation and the snowline. Results show that over the past four decades, the APWZ in Gongga Mountain has exhibited a continuous upward shift, with the mean elevation rising from 4101 m to 4575 m. The upper limit of vegetation advanced at an average rate of 17.43 m/a, significantly faster than the snowline shift (3.9 m/a). The APWZ also experienced substantial areal shrinkage, with an average annual reduction of approximately 13.84 km², highlighting the differential responses of various surface cover types to warming. Spatially, the most pronounced changes occurred in high-elevation zones (4200–4700 m), moderate slopes (25–33°), and sun-facing aspects (east, southeast, and south slopes), reflecting a typical climate–topography coupled driving mechanism. In the upper APWZ, glacier retreat has intensified weathering and increased debris accumulation, while the newly formed vegetation zone in the lower APWZ remains structurally fragile and unstable. Under extreme climatic disturbances, this setting is prone to triggering chain-type hazards such as landslides and debris flows. These findings enhance our capacity to monitor alpine ecological boundary changes and identify associated disaster risks, providing scientific support for managing climate-sensitive mountainous regions. Full article

(This article belongs to the Special Issue Remote Sensing Applications in Land Cover Changes and Associated Environmental Effects: Progress, Challenges, and Opportunities)

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20 pages, 3970 KiB

Open AccessArticle

A Systematic Retrospection and Reflections on Main Glacial Hazards of the Tibetan Plateau

by Changjun Gu, Suju Li, Ming Liu, Bo Wei, Shengyue Jin, Xudong Guo and Ping Wang

Remote Sens. 2025, 17(11), 1862; https://doi.org/10.3390/rs17111862 - 27 May 2025

Viewed by 433

Abstract

Glacial hazards pose significant threats to millions globally, especially with rapid climate warming drawing increased attention. Understanding past glacial hazards on both global and regional scales is crucial for early warning systems. This study quantified glacier and glacial lake changes on the Tibetan Plateau (TP) over recent decades and analyzed the spatial and temporal distribution of major glacial hazards. It also focused on glacial lakes that have experienced outburst events by reconstructing long-term data for 48 lakes. Key findings include: (1) TP glaciers have generally shrunk, with glacier area decreasing from 57,100 km² in the first inventory to 44,400 km² in the second, primarily in the middle and eastern Himalayas between 5000 and 6000 m. Meanwhile, the number of glacial lakes increased from 14,487 in 1990 to 16,385 in 2020, expanding towards higher elevations and glacier melt zones. (2) Since 1900, 283 glacial hazards have occurred, including 97 glacier surges, 36 glacier-related slope failures, and 150 glacial lake outburst floods (GLOFs). Hazard frequency increased post-2000, especially in the Karakoram and eastern Himalayas, during June to September. (3) Changes in glacier numbers contribute most to hazard frequency (11.56%), followed by July’s temperature change (10.24%). Slope and June’s temperature changes combined have the highest interaction effect (37.59%). (4) Of the 48 lakes studied, four disappeared after outbursts, 38 remained stable, and six expanded. These insights aid in monitoring, early warnings, and disaster management. Full article

(This article belongs to the Special Issue Remote Sensing Applications in Land Cover Changes and Associated Environmental Effects: Progress, Challenges, and Opportunities)

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21 pages, 23248 KiB

Open AccessArticle

Upper Elevational Limit of Vegetation in the Himalayas Identified from Landsat Images

by Bo Wei, Yili Zhang, Linshan Liu, Binghua Zhang, Dianqing Gong, Changjun Gu, Lanhui Li and Basanta Paudel

Remote Sens. 2025, 17(1), 78; https://doi.org/10.3390/rs17010078 - 28 Dec 2024

Cited by 1 | Viewed by 940

Abstract

Climate change has caused substantial shifts in species’ ranges and vegetation distributions in local areas of the Himalayas. However, the spatial patterns and dynamic changes of the vegetation lines in the Himalayas remain poorly understood due to the lack of comprehensive vegetation line dataset. This study developed a method to identify vegetation lines by combining the Canny edge detection algorithm with elevation parameters and produced comprehensive vegetation line datasets with 30 m resolution in the Himalayas. First, the Modified Soil-Adjusted Vegetation Index (MSAVI) was applied to indicate vegetation presence. The image was then smoothed by filling (or removing) small non-vegetated (or vegetated) patches scattered within vegetated (or unvegetated) areas. Subsequently, the Canny edge detection algorithm was applied to identify vegetation edge pixels, and elevation differences were utilized to determine the upper edges of the vegetation. Finally, Gaussian function-based thresholds were used across 24 sub-basins to determine the vegetation lines. Field surveys and visual interpretations demonstrated that this method can effectively and accurately identify vegetation lines in the Himalayas. The R² was 0.99, 0.93, and 0.98, respectively, compared with the vegetation line verification points obtained through three different ways. The mean absolute errors were 11.07 m, 29.35 m, and 13.99 m, respectively. Across the Himalayas, vegetation line elevations ranged from 4125 m to 5423 m (5th to 95th percentile), showing a trend of increasing and then decreasing from southeast to northwest. This pattern closely parallels the physics-driven snowline. The method proposed in this study enhances the toolkit for identifying vegetation lines across mountainous regions. Additionally, it provides a foundation for evaluating the responses of mountain vegetation to climate change in the Himalayas. Full article

(This article belongs to the Special Issue Remote Sensing Applications in Land Cover Changes and Associated Environmental Effects: Progress, Challenges, and Opportunities)

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20 pages, 10942 KiB

Open AccessArticle

Changes in Urban Green Spaces in the Pearl River Delta Urban Agglomeration: From the Perspectives of the Area, Spatial Configuration, and Quality

by Tianci Yao, Shengfa Li, Lixin Su and Hongou Zhang

Remote Sens. 2024, 16(23), 4369; https://doi.org/10.3390/rs16234369 - 22 Nov 2024

Cited by 2 | Viewed by 1362

Abstract

Urban green spaces (UGSs) are integral to urban ecosystems, providing multiple benefits to human well-being. However, previous studies mainly focus on the quantity or quality of UGSs, with less emphasis on a comprehensive analysis. This study systematically examined the spatiotemporal UGS dynamics in the Pearl River Delta urban agglomeration (PRDUA) in China from the perspectives of the area, spatial configuration, and quality, using the high spatial resolution (30 m) Landsat-derived land-cover data and Normalized Difference Vegetation Index (NDVI) data during 1985–2021. Results showed the UGS area in both the old urban districts and expanded urban areas across all nine cities in the PRDUA has experienced a dramatic reduction from 1985 to 2021, primarily due to the conversion of cropland and forest into impervious surfaces. Spatially, the fragmentation trend of UGSs initially increased and then weakened around 2010 in nine cities, but with an inconsistent fragmentation process across different urban areas. In the old urban districts, the fragmentation was mainly due to the loss of large patches; in contrast, it was caused by the division of large patches in the expanded urban areas of most cities. The area-averaged NDVI showed a general upward trend in urban areas in nearly all cities, and the greening trend in the old urban districts was more prevalent than that in the expanded urban areas, suggesting the negative impacts of urbanization on NDVI have been balanced by the positive effects of climate change, urbanization, and greening initiatives in the PRDUA. These findings indicate that urban greening does not necessarily correspond to the improvement in UGS states. We therefore recommend incorporating the three-dimensional analytical framework into urban ecological monitoring and construction efforts to obtain a more comprehensive understanding of UGS states and support effective urban green infrastructure stewardship. Full article

(This article belongs to the Special Issue Remote Sensing Applications in Land Cover Changes and Associated Environmental Effects: Progress, Challenges, and Opportunities)

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20 pages, 8335 KiB

Open AccessArticle

Evaluating the Multidimensional Stability of Regional Ecosystems Using the LandTrendr Algorithm

by Lijuan Li, Jiaqiang Du, Jin Wu, Zhilu Sheng, Xiaoqian Zhu, Zebang Song, Guangqing Zhai and Fangfang Chong

Remote Sens. 2024, 16(20), 3762; https://doi.org/10.3390/rs16203762 - 10 Oct 2024

Cited by 1 | Viewed by 1338

Abstract

Stability is a key characteristic for understanding ecosystem processes and evolution. However, research on the stability of complex ecosystems often faces limitations, such as reliance on single parameters and insufficient representation of continuous changes. This study developed a multidimensional stability assessment system for regional ecosystems based on disturbances. Focusing on the lower reaches of the Yellow River Basin (LR-YRB), we integrated the remote sensing ecological index (RSEI) with texture structural parameters, and applied the Landsat-based detection of trends in disturbance and recovery (LandTrendr) algorithm to analyze the continuous changes in disturbances and recovery from 1986 to 2021, facilitating the quantification and evaluation of resistance, resilience, and temporal stability. The results showed that 72.27% of the pixels experienced 1–9 disturbances, indicating the region’s sensitivity to external factors. The maximum disturbances primarily lasted 2–3 years, with resistance and resilience displaying inverse spatial patterns. Over the 35-year period, 61.01% of the pixels exhibited moderate temporal stability. Approximately 59.83% of the pixels recovered or improved upon returning to pre-disturbance conditions after maximum disturbances, suggesting a strong recovery capability. The correlation among stability dimensions was low and influenced by disturbance intensity, underscoring the necessity for a multidimensional assessment of regional ecosystem stability based on satellite remote sensing. Full article

(This article belongs to the Special Issue Remote Sensing Applications in Land Cover Changes and Associated Environmental Effects: Progress, Challenges, and Opportunities)

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23 pages, 39394 KiB

Open AccessArticle

Fine-Scale Mangrove Species Classification Based on UAV Multispectral and Hyperspectral Remote Sensing Using Machine Learning

by Yuanzheng Yang, Zhouju Meng, Jiaxing Zu, Wenhua Cai, Jiali Wang, Hongxin Su and Jian Yang

Remote Sens. 2024, 16(16), 3093; https://doi.org/10.3390/rs16163093 - 22 Aug 2024

Cited by 10 | Viewed by 3630

Abstract

Mangrove ecosystems play an irreplaceable role in coastal environments by providing essential ecosystem services. Diverse mangrove species have different functions due to their morphological and physiological characteristics. A precise spatial distribution map of mangrove species is therefore crucial for biodiversity maintenance and environmental conservation of coastal ecosystems. Traditional satellite data are limited in fine-scale mangrove species classification due to low spatial resolution and less spectral information. This study employed unmanned aerial vehicle (UAV) technology to acquire high-resolution multispectral and hyperspectral mangrove forest imagery in Guangxi, China. We leveraged advanced algorithms, including RFE-RF for feature selection and machine learning models (Adaptive Boosting (AdaBoost), eXtreme Gradient Boosting (XGBoost), Random Forest (RF), and Light Gradient Boosting Machine (LightGBM)), to achieve mangrove species mapping with high classification accuracy. The study assessed the classification performance of these four machine learning models for two types of image data (UAV multispectral and hyperspectral imagery), respectively. The results demonstrated that hyperspectral imagery had superiority over multispectral data by offering enhanced noise reduction and classification performance. Hyperspectral imagery produced mangrove species classification with overall accuracy (OA) higher than 91% across the four machine learning models. LightGBM achieved the highest OA of 97.15% and kappa coefficient (Kappa) of 0.97 based on hyperspectral imagery. Dimensionality reduction and feature extraction techniques were effectively applied to the UAV data, with vegetation indices proving to be particularly valuable for species classification. The present research underscored the effectiveness of UAV hyperspectral images using machine learning models for fine-scale mangrove species classification. This approach has the potential to significantly improve ecological management and conservation strategies, providing a robust framework for monitoring and safeguarding these essential coastal habitats. Full article

(This article belongs to the Special Issue Remote Sensing Applications in Land Cover Changes and Associated Environmental Effects: Progress, Challenges, and Opportunities)

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Open AccessTechnical Note

Analysis of Changes in Forest Vegetation Peak Growth Metrics and Driving Factors in a Typical Climatic Transition Zone: A Case Study of the Funiu Mountain, China

by Jiao Tang, Huimin Wang, Nan Cong, Jiaxing Zu and Yuanzheng Yang

Remote Sens. 2024, 16(16), 2921; https://doi.org/10.3390/rs16162921 - 9 Aug 2024

Cited by 1 | Viewed by 1515

Abstract

Phenology and photosynthetic capacity both regulate carbon uptake by vegetation. Previous research investigating the impact of phenology on vegetation productivity has focused predominantly on the start and end of growing seasons (SOS and EOS), leaving the influence of peak phenology metrics—particularly in typical climatic transition zones—relatively unexplored. Using a 24-year (2000–2023) enhanced vegetation index (EVI) dataset from the Moderate Resolution Imaging Spectroradiometer (MODIS), we extracted and examined the spatiotemporal variation for peak of season (POS) and peak growth (defined as EVI_max) of forest vegetation in the Funiu Mountain region, China. In addition to quantifying the factors influencing the peak phenology metrics, the relationship between vegetation productivity and peak phenological metrics (POS and EVI_max) was investigated. Our findings reveal that POS and EVI_max showed advancement and increase, respectively, negatively and positively correlated with vegetation productivity. This suggested that variations in EVI_max and peak phenology both increase vegetation productivity. Our analysis also showed that EVI_max was heavily impacted by precipitation, whereas SOS had the greatest effect on POS variation. Our findings highlighted the significance of considering climate variables as well as biological rhythms when examining the global carbon cycle and phenological shifts in response to climate change. Full article

(This article belongs to the Special Issue Remote Sensing Applications in Land Cover Changes and Associated Environmental Effects: Progress, Challenges, and Opportunities)

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