Seasonal analysis of spatial and temporal variations in NDVI and its driving factors in Inner Mongolia during the vegetation growing season (1999–2019)

Inner Mongolia’s vegetation dynamics are critical for understanding regional ecological responses to climate change. This study investigates the spatiotemporal patterns of the Normalized Difference Vegetation Index (NDVI) during the growing season (1999–2019) and explores its relationship with climatic factors to predict future vegetation changes. Using MODIS monthly NDVI products and meteorological data, we analyzed NDVI trends across Inner Mongolia’s distinct vegetation zones. Statistical methods (e.g., trend analysis, correlation tests) were applied to assess spatial-temporal NDVI variations and climate linkages, with significance thresholds set at p < 0.05 and p < 0.01. NDVI exhibited a northeast-to-southwest decline, reflecting regional vegetation gradients. Growing-season NDVI increased significantly ( p < 0.05), with summer NDVI rising markedly ( p < 0.01; +0.0022/yr). Precipitation significantly increased over 21 years, while temperatures remained stable. NDVI-climate correlations were spatially heterogeneous, with precipitation as the dominant vegetation growth factor. The findings highlight precipitation’s pivotal role in shaping Inner Mongolia’s vegetation dynamics. The stable temperature but increasing precipitation suggests a shift toward wetter conditions, potentially enhancing grassland productivity. These insights enable predictive modeling of vegetation responses to future climate variability, supporting regional ecological management.

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Publication Details

Journal
Frontiers in Forests and Global Change
Published
2025-04-04
DOI
https://doi.org/10.3389/ffgc.2025.1555385
Citations
6
Primary Topic
Remote Sensing and Land Use
Type
article
Field-Weighted Citation Impact
7.80

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article

Seasonal analysis of spatial and temporal variations in NDVI and its driving factors in Inner Mongolia during the vegetation growing season (1999–2019)

Narisu Narisu, Y. Jun Xu, Jinting Guo, Jingyuan Wang et al.
6 citations
Frontiers in Forests and Global Change
Remote Sensing and Land Use
7.80
article

Seasonal analysis of spatial and temporal variations in NDVI and its driving factors in Inner Mongolia during the vegetation growing season (1999–2019)

Narisu Narisu, Y. Jun Xu, Jinting Guo, Jingyuan Wang, Weijie Liao, Hasi Qimuge
article en
6 citations

Abstract

Inner Mongolia’s vegetation dynamics are critical for understanding regional ecological responses to climate change. This study investigates the spatiotemporal patterns of the Normalized Difference Vegetation Index (NDVI) during the growing season (1999–2019) and explores its relationship with climatic factors to predict future vegetation changes. Using MODIS monthly NDVI products and meteorological data, we analyzed NDVI trends across Inner Mongolia’s distinct vegetation zones. Statistical methods (e.g., trend analysis, correlation tests) were applied to assess spatial-temporal NDVI variations and climate linkages, with significance thresholds set at p < 0.05 and p < 0.01. NDVI exhibited a northeast-to-southwest decline, reflecting regional vegetation gradients. Growing-season NDVI increased significantly ( p < 0.05), with summer NDVI rising markedly ( p < 0.01; +0.0022/yr). Precipitation significantly increased over 21 years, while temperatures remained stable. NDVI-climate correlations were spatially heterogeneous, with precipitation as the dominant vegetation growth factor. The findings highlight precipitation’s pivotal role in shaping Inner Mongolia’s vegetation dynamics. The stable temperature but increasing precipitation suggests a shift toward wetter conditions, potentially enhancing grassland productivity. These insights enable predictive modeling of vegetation responses to future climate variability, supporting regional ecological management.

Frontiers in Forests and Global ChangeVol. 8
Inner Mongolia Normal University (CN), Inner Mongolia University (CN), Fanjingshan National Nature Reserve (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 2%
Remote Sensing and Land Use
7.80
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