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Land Cover Change Detection: Emerging Algorithms and Applications in Remote Sensing

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: 30 November 2026 | Viewed by 2042

Special Issue Editors

Dr. Lili Xu

E-Mail Website
Guest Editor
College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China
Interests: remote sensing of environment; land use and land cover change; precision agriculture; crop monitoring; time series analysis; fractional vegetation cover; food security
Special Issues, Collections and Topics in MDPI journals
Dr. Xu Ma

E-Mail Website
Guest Editor
College of Geography and Remote Sensing Science, Xinjiang University, Urumqi, China
Interests: mathematical modeling on remote sensing; radiative transfer of vegetation; inversion of fractional vegetation cover; monitoring of biological crusts; short-lived vegetation
Special Issues, Collections and Topics in MDPI journals
Dr. Na Chen

E-Mail Website
Guest Editor
Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
Interests: remote sensing; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Ran Huang

E-Mail Website
Guest Editor
School of Automation, Hangzhou Dianzi University, Hangzhou, China
Interests: thermal remote sensing; vegetation remote sensing; crop classification; smart agriculture
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Land cover change (LCC) detection is a central theme in remote sensing, with increasing importance for understanding global environmental dynamics and supporting sustainable development. Recent advances in algorithms—such as deep learning, spatio-temporal modeling, and multi-source data fusion (e.g., optical, SAR, LiDAR)—are reshaping how LCC is monitored across both global and regional scales. At the same time, diverse applications, including urban expansion, agricultural transitions, forest degradation, wetland and water dynamics, and desertification and land degradation, demonstrate the wide relevance of LCC studies to ecosystem management and policy. Moreover, linking LCC with climate change impacts, ecosystem services, and the UN Sustainable Development Goals (SDGs) highlights its broader significance.

We invite contributions on innovative algorithms and frameworks for LCC detection, validation, and interpretation, as well as studies showcasing real-world applications that provide insights into environmental change and management. Both methodological advances and application-oriented research are welcome, with a focus on integrating data, models, and policy-relevant outcomes. Papers are solicited in, but not limited to, the following topics:

  • Novel algorithms for land cover change detection
  • Multi-source and multi-scale data fusion
  • Cropland change detection
  • Urban expansion and land-use transitions
  • Forest, wetland, and water body dynamics
  • Desertification and land degradation monitoring
  • Accuracy assessment and uncertainty analysis
  • Linking land cover change to climate, ecosystem services, and SDGs

Dr. Lili Xu
Dr. Xu Ma
Dr. Na Chen
Dr. Ran Huang
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 250 words) can be sent to the Editorial Office for assessment.

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 detection
  • deep learning and machine learning
  • spatio-temporal analysis
  • multi-source data fusion (optical, SAR, LiDAR)
  • urban expansion and agricultural transitions
  • forest, wetland, and water dynamics
  • desertification and land degradation
  • climate change and ecosystem services

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

29 pages, 6565 KB  
Article
Urban Expansion-Driven Cropland NPP Change in the Beijing-Tianjin-Hebei Region, China (2001–2020): Spatiotemporal Patterns, Ecological Selectivity, and Spatially Varying Driver Effects
by Jiahua Liang, Huan Li, Ao Jiao, Haoyuan Lv and Zhongke Feng
Remote Sens. 2026, 18(6), 933; https://doi.org/10.3390/rs18060933 - 19 Mar 2026
Viewed by 483
Abstract
As the largest urban agglomeration and a critical grain production base in northern China, the Beijing–Tianjin–Hebei (BTH) region faces a sharp conflict between rapid urbanization and cropland conservation. Urban expansion inevitably leads to the loss of high-quality agricultural land, posing dual threats to [...] Read more.
As the largest urban agglomeration and a critical grain production base in northern China, the Beijing–Tianjin–Hebei (BTH) region faces a sharp conflict between rapid urbanization and cropland conservation. Urban expansion inevitably leads to the loss of high-quality agricultural land, posing dual threats to food security and the terrestrial carbon cycle. To accurately assess the ecological costs of this process, this study integrates the CASA model with a time-weighted cumulative model to quantify the spatiotemporal impacts of urban expansion on cropland NPP in the BTH region from 2001 to 2020. Furthermore, a Geographically Weighted Regression (GWR) model was employed to examine the spatially varying effects of key driving factors on cropland NPP loss. The results indicate that urban land in the BTH region expanded by 45.2% over the past two decades, with 91.04% originating from cropland. Despite an overall upward trend in regional cropland NPP driven by climate change and agricultural intensification, the time-weighted cumulative cropland NPP loss attributable to urban encroachment over 2001–2020 reached 29.24 Tg C, which is equivalent to 0.751× the annual total cropland NPP in 2020 (used as a reference benchmark). Crucially, this expansion exhibits distinct ecological selectivity toward high-quality cropland, meaning that urban development has disproportionately encroached upon highly productive land with productivity levels exceeding the regional average. This selective occupation has led to a structural decline in the region’s potential agricultural production capacity. Additionally, GWR results reveal significant spatial non-stationarity in the relationships between cropland NPP loss and its drivers, revealing differentiated response patterns between plains and mountainous areas in terms of socio-economic drivers and physical constraints. These findings expose the hidden threats of urban expansion to food security, providing a crucial scientific basis for formulating differentiated land management policies and coordinating regional urbanization with cropland protection. Full article
(This article belongs to the Special Issue Land Cover Change Detection: Emerging Algorithms and Applications in Remote Sensing)
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29 pages, 20383 KB  
Article
Driving Mechanisms and Adaptive Governance for Cultivated Land in Agro-Pastoral Ecotones: A 40-Year Empirical Study of Yulin City, China
by Hao Liu, Maosheng Zhang, Li Feng, Shaoqi Yun, Fan Zhang and Chuanbo Yang
Remote Sens. 2026, 18(5), 833; https://doi.org/10.3390/rs18050833 - 8 Mar 2026
Viewed by 455
Abstract
The northern agro-pastoral ecotone of China faces persistent trade-offs among cultivated land (CL) protection, energy development, water constraints, and ecological restoration, posing challenges for sustainable human–land interactions. Focusing on Yulin City from 1980 to 2020, this study develops an integrated diagnostic framework coupling [...] Read more.
The northern agro-pastoral ecotone of China faces persistent trade-offs among cultivated land (CL) protection, energy development, water constraints, and ecological restoration, posing challenges for sustainable human–land interactions. Focusing on Yulin City from 1980 to 2020, this study develops an integrated diagnostic framework coupling pattern–process–trend–mechanism modules to analyze the spatiotemporal evolution, transition pathways, and driving forces of CL change. Results show that CL dynamics over four decades were shaped by nonlinear interactions among natural conditions, policies, economic development, and technological progress. Spatially, CL changes exhibited a distinct divergence, with ecological-driven contraction in the southern region and sandy land-based compensation in the north. Temporally, the transformation evolved from a gradual, nature-dominated stage to a policy-intensive phase characterized by abrupt shifts, followed by a refined regulation stage with multi-factor synergies. Policy interventions and economic incentives emerged as dominant drivers of CL spatial heterogeneity, with interacting factors exerting bidirectional effects. Building on these findings, a Zoning–Optimization–Synergy (ZOS) framework is proposed to support adaptive land governance, emphasizing differentiated management and cross-sector coordination. This study offers a transferable diagnostic approach for understanding CL dynamics in fragile ecotones and provides insights for managing the water–energy–food nexus under ecological transition and climate change. Full article
(This article belongs to the Special Issue Land Cover Change Detection: Emerging Algorithms and Applications in Remote Sensing)
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20 pages, 4719 KB  
Article
Cropland Extraction Based on PlanetScope Images and a Newly Developed CAFM-Net Model
by Jianhua Ren, Yating Jing, Xingming Zheng, Sijia Li, Kai Li and Guangyi Mu
Remote Sens. 2026, 18(4), 646; https://doi.org/10.3390/rs18040646 - 19 Feb 2026
Viewed by 537
Abstract
Cropland constitutes a foundational resource for global food security and agricultural sustainability, and its accurate extraction from high-resolution remote sensing imagery is essential for agricultural monitoring and land management. However, existing deep learning-based segmentation methods often struggle to balance global contextual modeling and [...] Read more.
Cropland constitutes a foundational resource for global food security and agricultural sustainability, and its accurate extraction from high-resolution remote sensing imagery is essential for agricultural monitoring and land management. However, existing deep learning-based segmentation methods often struggle to balance global contextual modeling and fine-grained boundary representation, leading to boundary blurring and omission of small cropland parcels. To address these challenges, this study proposes a novel CNN–Transformer dual-branch fusion network, named CAFM-Net, which integrates a convolution and attention fusion module (CAFM) and an edge-assisted supervision head (EH) to jointly enhance global–local feature interaction and boundary delineation capability. Experiments were conducted on a self-built PlanetScope cropland dataset from Suihua City, China, and the GID public dataset to evaluate the effectiveness and generalization ability of the proposed model. On the self-built dataset, CAFM-Net achieved an overall accuracy (OA) of 96.75%, an F1-score of 96.80%, and an Intersection over Union (IoU) of 93.79%, outperforming mainstream models such as UNet, DeepLabV3+, TransUNet, and Swin Transformer by a clear margin. On the GID public dataset, CAFM-Net obtained an OA of 94.58%, an F1-score of 94.19%, and an IoU of 89.02%, demonstrating strong robustness across different data sources. Ablation experiments further confirm that the CAFM contributes most significantly to performance improvement, while the EH module effectively enhances boundary accuracy. Overall, the proposed CAFM-Net provides a quantitatively validated and robust solution for fine-grained cropland segmentation from high-resolution remote sensing imagery, with clear advantages in boundary precision and small-parcel detection. Full article
(This article belongs to the Special Issue Land Cover Change Detection: Emerging Algorithms and Applications in Remote Sensing)
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