Construction and application of hilly remote sensing ecological index.

Q3 Environmental Science
Zhi-Yun Wang, Xiu-Juan Hu, Si-Yi Zheng, Xin-Yu Zou, Gui-Fen Su, Shun-Fa Lu
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引用次数: 0

Abstract

Mountainous and hilly regions are one of the mainstays of national ecological security barriers. To avoid the impact of terrain undulations on the ecological environment quality assessment, we used the normalized difference mountain vegetation index (NDMVI) as the greenness ecological factor, combined with the humidity, aridity, and thermal factors, to construct the improved the hilly remote sensing ecological index (HRSEI) for mountainous areas based on the remote sensing ecological index (RSEI). We assessed ecological quality in two typical mountai-nous and hilly areas, i.e., Changting County in Longyan City, Fujian Province, and Shanyang County in Shangluo City, Shaanxi Province. We compared the ecological quality grade transition paths of HRSEI and RSEI, and verified the applicability of HRSEI in mountainous areas. The results showed that NDMVI could extract more vegetation information in mountainous areas than NDVI. The greater the topographic relief, the stronger the ability of NDMVI to extract vegetation information. Verified through average correlation and stepwise regression equations, HRSEI was representative for the ecological quality assessment of mountainous and hilly areas. HRSEI mainly upgraded the vegetation ecological grade from good to excellent for some areas affected by shadows. Compared with the extraction results of RSEI, areas classified as excellent increased by 13.75 and 41.88 km2 in Changting and Shangyang, respectively. Combined with high-resolution imagery, the areas with improved ecological quality corresponded to high-vegetation-cover areas affected by mountain shadows, indicating that HRSEI could effectively improve the identification accuracy of high-vegetation-cover areas influenced by shadows, making it more practical.

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应用生态学报
应用生态学报 Environmental Science-Ecology
CiteScore
2.50
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0.00%
发文量
11393
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