Author
Listed:
- Zhujun Gu
(Pearl River Water Resources Research Institute, Pearl River Water Resources Commission, Guangzhou 510610, China
Key Laboratory of Water Security Guarantee in Guangdong-Hong Kong-Marco Greater Bay Area of Ministry of Water Resources, Guangzhou 510611, China
These authors contributed equally to this work and share first authorship.)
- Guanghui Liao
(Pearl River Water Resources Research Institute, Pearl River Water Resources Commission, Guangzhou 510610, China
Key Laboratory of Water Security Guarantee in Guangdong-Hong Kong-Marco Greater Bay Area of Ministry of Water Resources, Guangzhou 510611, China
These authors contributed equally to this work and share first authorship.)
- Qinghua Fu
(Pearl River Water Resources Research Institute, Pearl River Water Resources Commission, Guangzhou 510610, China)
- Jiasheng Wu
(Pearl River Water Resources Research Institute, Pearl River Water Resources Commission, Guangzhou 510610, China)
- Yanzi He
(Pearl River Water Resources Research Institute, Pearl River Water Resources Commission, Guangzhou 510610, China)
- Xianzhi Mai
(Pearl River Water Resources Research Institute, Pearl River Water Resources Commission, Guangzhou 510610, China)
- Jia Liu
(Pearl River Water Resources Research Institute, Pearl River Water Resources Commission, Guangzhou 510610, China)
- Qiuyin He
(Pearl River Water Resources Research Institute, Pearl River Water Resources Commission, Guangzhou 510610, China)
- Quanman Lin
(Pearl River Water Resources Research Institute, Pearl River Water Resources Commission, Guangzhou 510610, China)
Abstract
Understory shrub–grass coverage is a key indicator of forest ecosystem structure and function, and its accurate retrieval via remote sensing is essential for regional ecological assessments. To address the critical limitation in existing multi-angle remote sensing inversion methods: high-resolution images lack angular information while multi-angle datasets suffer from low spatial resolution, making it difficult to achieve large-scale and fine-grained inversion of understory shrub–grass coverage. Here, we developed an inversion method for estimating understory shrub–grass coverage by integrating multi-angle Moderate Resolution Imaging Spectroradiometer data with high-resolution Sentinel-2 imagery to produce 10 m resolution coverage maps; we then used this method to analyze spatiotemporal changes in Changting County from 2016 to 2025. The results demonstrated that the method achieved high accuracy ( R 2 = 0.8418, RMSE = 0.07), meeting the requirements for quantitative understory shrub–grass coverage estimation. Understory shrub–grass coverage exhibited a concentric decreasing pattern from the surrounding mountainous areas toward the central plain, with high-coverage zones concentrated primarily in the west, southwest, and south. Over the 2016–2025 period, understory shrub–grass coverage displayed a fluctuating upward trend: approximately 60% of the study area showed improvement, while 37.73% experienced slight degradation. The change persistence was dominated by positive trends, with an area proportion of 54.14%, which was close to that of the anti-persistent type (44.87%). This study provides technical support for the high-resolution inversion of understory vegetation structure.
Suggested Citation
Zhujun Gu & Guanghui Liao & Qinghua Fu & Jiasheng Wu & Yanzi He & Xianzhi Mai & Jia Liu & Qiuyin He & Quanman Lin, 2026.
"Remote Sensing Inversion and Spatiotemporal Evolution of Understory Shrub–Grass Coverage in Changting County by Fusing MODIS and Sentinel-2 Images,"
Sustainability, MDPI, vol. 18(6), pages 1-22, March.
Handle:
RePEc:gam:jsusta:v:18:y:2026:i:6:p:2987-:d:1898204
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