ArticleHeliyon2024
Spatiotemporal variation and GeoDetector analysis of NDVI at the northern foothills of the Yinshan Mountains in Inner Mongolia over the past 40 years.
Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- Spatiotemporal Mapping of Grazing Livestock Behaviours Using Machine Learning Algorithms.Sensors (Basel, Switzerland) · 2025Article
- Improving model performance in mapping black-soil resource with machine learning methods and multispectral features.Scientific reports · 2025Article
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Authors and funding
6 authors.
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Abstract
The study of spatiotemporal variation and driving forces of the normalized difference vegetation index (NDVI) is conducive to regional ecosystem protection and natural resource management. Based on the 1982-2022 GIMMS NDVI data and 26 influencing variables, by using the Theil-Sen median slope analysis, Mann-Kendall (M - K) test method and GeoDetector model, we analyzed the spatial and temporal characteristics of vegetation cover and the driving factors of its spatial differentiation in the northern foothills of the Yinshan Mountains in Inner Mongolia. The NDVI showed a significantly increasing trend during 1982-2022, with a growth rate of 0.0091 per decade. It is further predicted that future change in NDVI will continue the 1982-2022 trend, and sustainable improvement will dominate in the future; however, 17.69 % of vegetation will degrade, that is, NDVI will degrade instead of improvement. The spatial distribution of the NDVI in the northern foothills of the study area was generally characterized by high in the east and low in the west. Annual precipitation (Pre), evapotranspiration (Evp), relative humidity (Rhu) and sunshine hours (Ssd) had >70 % explanatory power (73.5, 79.9, 79.0, and 74.9 %, respectively). The explanatory power of edaphic factors was >30 %, whereas anthropogenic and topographic factors had little influence on the spatial variation of NDVI, with an explanatory power of <30 %. Thus, climatic factors were the dominant factors influencing the spatial variability of NDVI in the study area. The results of the interaction detector analysis showed nonlinear strengthening for any two factors, and the interaction between Rhu and barometric pressure had the highest explanatory power. There were optimal ranges or characteristics of each factor that promoted vegetation growth. This study investigated the differences in the explanatory power of different factors on the NDVI and the optimal range of individual factors to promote vegetation growth, which can provide a basis for the development of vegetation resource management programs.
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