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Article

Assessing Habitat Quality on Synergetic Land-Cover Dataset Across the Greater Mekong Subregion over the Last Four Decades

1
School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
2
Shenzhen Environmental Monitoring Center of Guangdong Province, Shenzhen 518049, China
3
Laboratoire des Sciences du Climat et de l’Environnement, CEA-CNRS-UVSQ, Université Paris-Saclay, 91191 Gif-sur-Yvette, France
4
State Key Laboratory of Resource and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
5
University of Chinese Academy of Sciences, No. 19A, Yuquan Road, Beijing 100049, China
6
Department of Natural Resource Management, Ambo University, Ambo P.O. Box 19, Ethiopia
7
Jiangsu Mineral Resources and Geological Designed Research Institute, China National Administration of Coal Geology, Xuzhou 221006, China
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Remote Sens. 2025, 17(8), 1467; https://doi.org/10.3390/rs17081467
Submission received: 9 January 2025 / Revised: 23 March 2025 / Accepted: 16 April 2025 / Published: 20 April 2025

Abstract

In the face of rapid infrastructure expansion and escalating anthropogenic activities, it becomes imperative to prioritize the examination of long-term transformations in land cover and ecological quality within the Greater Mekong Subregion (GMS). We developed an ecological evaluation system integrating the land cover data assimilation framework (LCDAF) with the InVEST model to accomplish this goal. The LCDAF compensates for the disadvantages of weather interference, difficulty in recognizing complex scenes, and poor generalization in remote sensing image classification, and also adds temporal continuity that other fusion methods do not have. The synthesized land cover dataset demonstrates superior overall accuracy compared to five existing global products. This enhanced dataset provides a robust foundation for comprehensive analysis and decision making within the ecological evaluation system. We implemented a rigorous and quantitative assessment of changes in land cover and habitat quality spanning 1980 to 2020. The land cover analysis unveiled a noteworthy trend that surfaced in the dynamic interplay between forested areas and croplands, highlighting simultaneous processes of forest restoration and agricultural expansion, albeit at varying rates. Further analysis of habitat quality showed that the GMS generally sustained a moderate level with a slight downward trend observed over the period. Significantly, Laos attained the highest ranking in habitat quality, succeeded by Myanmar, China, Cambodia, Vietnam, and Thailand. In human factors, land use intensity and landscape fragmentation emerge as contributors with detrimental effects on habitat quality. Substantial progress was achieved in implementing forestland conservation measures, exemplified in regions such as Cambodia and Guangxi Province of China, where these endeavors proved effective in mitigating habitat degradation. Despite these positive endeavors, the GMS’s overall habitat quality did not significantly improve. It emphasizes the enduring challenges confronted by the region in terms of ecological management and habitat conservation.
Keywords: habitat quality; land use/land cover changes; data assimilation; the Greater Mekong Subregion habitat quality; land use/land cover changes; data assimilation; the Greater Mekong Subregion

Share and Cite

MDPI and ACS Style

Liu, S.; Sun, T.; Ciais, P.; Zhang, H.; Fang, J.; Fang, J.; Gemechu, T.M.; Chen, B. Assessing Habitat Quality on Synergetic Land-Cover Dataset Across the Greater Mekong Subregion over the Last Four Decades. Remote Sens. 2025, 17, 1467. https://doi.org/10.3390/rs17081467

AMA Style

Liu S, Sun T, Ciais P, Zhang H, Fang J, Fang J, Gemechu TM, Chen B. Assessing Habitat Quality on Synergetic Land-Cover Dataset Across the Greater Mekong Subregion over the Last Four Decades. Remote Sensing. 2025; 17(8):1467. https://doi.org/10.3390/rs17081467

Chicago/Turabian Style

Liu, Shu’an, Tianle Sun, Philippe Ciais, Huifang Zhang, Junjun Fang, Jingchun Fang, Tewekel Melese Gemechu, and Baozhang Chen. 2025. "Assessing Habitat Quality on Synergetic Land-Cover Dataset Across the Greater Mekong Subregion over the Last Four Decades" Remote Sensing 17, no. 8: 1467. https://doi.org/10.3390/rs17081467

APA Style

Liu, S., Sun, T., Ciais, P., Zhang, H., Fang, J., Fang, J., Gemechu, T. M., & Chen, B. (2025). Assessing Habitat Quality on Synergetic Land-Cover Dataset Across the Greater Mekong Subregion over the Last Four Decades. Remote Sensing, 17(8), 1467. https://doi.org/10.3390/rs17081467

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