Climatic and Human Drivers of Forest Vegetation Index Changes in Mainland Southeast Asia: Insights from Protected and Non-Protected Areas
Forests’ dynamics have become increasingly complex under climate change and human activities. Mainland Southeast Asia (MSEA), with extensive forest cover and a mosaic of protected and non-protected areas, is an ideal region for examining forest responses to climate and anthropogenic activities. To ensure robust long-term monitoring, we retrieved EVI2 from daily AVHRR and MODIS surface reflectance data and performed pixel-wise assimilation of the two datasets, substantially reducing systematic bias and constructing a consistent 1982–2024 annual EVI2 dataset. Using this harmonized dataset, along with land use, climate, and protected area data, we analyzed over four decades of forest greenness trends, variability, and drivers in protected and non-protected zones. Results show that forests in MSEA maintained high coverage (mean EVI2 = 0.6253) and exhibited a clear greening trend (+0.014 per decade). Temperature was the dominant driver, contributing over 50% of the variation, followed by human activities (>30%), while precipitation played a smaller and mixed role. Forests in protected areas were 1.3 times more stable than those outside (CV = 4.1% vs. 5.2%), highlighting the buffering role of protection. These findings provide a reliable long-term benchmark for forest monitoring and inform targeted conservation and sustainable management strategies in MSEA. The constructed assimilated long-term EVI2 dataset is available to support future research on vegetation dynamics, climate impacts, and ecosystem sustainability.
Authors
- Jianxing Zhu (ORCID: https://orcid.org/0000-0003-4205-9417)
- Yue Xi (ORCID: https://orcid.org/0000-0003-3313-0576)
- Hao Wang (ORCID: https://orcid.org/0000-0002-8394-9558)
- Qiufeng Wang (ORCID: https://orcid.org/0000-0002-6196-3092)
Institutions
- Chinese Academy of Sciences (CN)
- Institute of Geographic Sciences and Natural Resources Research (CN)
- University of Chinese Academy of Sciences (CN)
Publication Details
- Journal
- Forests
- Published
- 2025-10-28
- DOI
- https://doi.org/10.3390/f16111645
- Primary Topic
- Remote Sensing in Agriculture
- Type
- article
- Field-Weighted Citation Impact
- 0.00