Work

Comparison of Satellite-Derived Vegetation Indices for Assessing Vegetation Dynamics in Central Asia

Year: 2025

Type: article

Abstract: Each of the NDVI, EVI, NIRv, and kNDVI has varying strengths and weaknesses in terms of representing vegetation dynamics. Identifying the comparative advantages of these indices is crucial to objectively determine the dynamics of vegetation in dryland. In this study, Central Asia was selected as the research area, which is a typical drought-sensitive and ecologically fragile region. The Mann–Kendall trend test, coefficient of variation, and partial correlation analyses were used to compare the ability of these indices to express the spatiotemporal dynamics of vegetation, its heterogeneity, and its relationships with temperature and precipitation. Moreover, the composite vegetation index (CVI) was constructed by using the entropy weighting method and its relative advantage was identified. The results showed that the kNDVI exhibited a stronger capacity to express the relationship between the vegetation and the temperature and precipitation, compared with the other three indices. The NIRv best represented the spatiotemporal heterogeneity of vegetation in areas with a high vegetation coverage, while the kNDVI had the strongest expressive capability in areas with a low vegetation coverage. The critical value for distinguishing between areas with a high and low vegetation coverage was NDVI = 0.54 for temporal heterogeneity and NDVI = 0.50 for spatial heterogeneity. The CVI had no apparent comparative advantage over the other four indices in expressing the trends of changes in vegetation coverage and their correlations with the temperature and precipitation. However, it enjoyed a prominent advantage over these indices in terms of expressing the spatiotemporal heterogeneity of vegetation coverage in Central Asia.

Source: Water

DOI: 10.3390/w17050684

Authors: Qian Li, Junhui Cheng, Junjie Yan, Guangpeng Zhang, Hongbo Ling

Institutions: Xinjiang Agricultural University, Yili Normal University, Chinese Academy of Sciences, Xinjiang Institute of Ecology and Geography, State Key Laboratory of Desert and Oasis Ecology

Language: English

Topic: Remote Sensing in Agriculture

Subfield: Ecology

Field: Environmental Science

Domain: Physical Sciences

SDGs: Life on land, Climate action, Zero hunger

Open Access status: gold

Estimated APC paid: $3,137