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Article

Spatial Heterogeneity and the Increasing Trend of Vegetation and Their Driving Mechanisms in the Mountainous Area of Haihe River Basin

1
Key Laboratory of Agricultural Water Resources, Hebei Laboratory of Agricultural Water-Saving, Center for Agricultural Resources Research, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Shijiazhuang 050022, China
2
University of Chinese Academy of Sciences, Beijing 100049, China
3
College of Geographical Sciences, Hebei Normal University, Shijiazhuang 050024, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2024, 16(3), 587; https://doi.org/10.3390/rs16030587
Submission received: 24 December 2023 / Revised: 29 January 2024 / Accepted: 31 January 2024 / Published: 4 February 2024
(This article belongs to the Special Issue Remote Sensing of Mountain and Plateau Vegetation)

Abstract

In addition to serving as North China’s water supply and ecological barrier, the mountainous area of the Haihe River basin (MHRB) is a crucial location for the application of ecological engineering. Vegetation is an important component in the ecological conservation and eco-hydrological progress of the MHRB. A better understanding of regional vegetation growth can be achieved by a thorough investigation of vegetation indicators. In this research, the leaf area index (LAI) and gross primary productivity (GPP) were chosen as vegetation indicators. The characteristics and driving forces of the spatiotemporal variations of LAI and GPP in the MHRB were explored through Sen’s slope, the Mann–Kendall test, the optimal parameter-based geographical detector model, and correlation analysis. From 2001 to 2018, the annual LAI and GPP increased significantly on the regional scale. The areas with significantly increased vegetation accounted for more than 81% of the MHRB. Land use was the most influential element for the spatial heterogeneity of LAI and GPP, and the humidity index was the most crucial one among climate indicators. Non-linear enhancement or bivariate enhancement was discovered between any two factors, and the strongest interaction was from land use and humidity index. The lowest vegetation cover was found in dry regions with annual precipitation below 407 mm and the humidity index under 0.41; while in both forests and large undulating mountains, higher LAI and GPP were observed. About 87% of the significantly increased vegetation was found in areas with unaltered land use. The increase in vegetation in the MHRB from 2001 to 2018 was promoted by the increased precipitation and humidity index and the reduced vapor pressure deficit. The sensitivity of GPP to climate change was stronger than that of LAI. These findings can serve as a theoretical guide for the application of ecological engineering and ecological preservation in the MHRB.
Keywords: vegetation; optimal parameters-based geographical detector; land use; climate change; the mountainous area of Haihe River basin vegetation; optimal parameters-based geographical detector; land use; climate change; the mountainous area of Haihe River basin
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MDPI and ACS Style

Cao, B.; Wang, Y.; Zhang, X.; Shen, Y.-J. Spatial Heterogeneity and the Increasing Trend of Vegetation and Their Driving Mechanisms in the Mountainous Area of Haihe River Basin. Remote Sens. 2024, 16, 587. https://doi.org/10.3390/rs16030587

AMA Style

Cao B, Wang Y, Zhang X, Shen Y-J. Spatial Heterogeneity and the Increasing Trend of Vegetation and Their Driving Mechanisms in the Mountainous Area of Haihe River Basin. Remote Sensing. 2024; 16(3):587. https://doi.org/10.3390/rs16030587

Chicago/Turabian Style

Cao, Bo, Yan Wang, Xiaolong Zhang, and Yan-Jun Shen. 2024. "Spatial Heterogeneity and the Increasing Trend of Vegetation and Their Driving Mechanisms in the Mountainous Area of Haihe River Basin" Remote Sensing 16, no. 3: 587. https://doi.org/10.3390/rs16030587

APA Style

Cao, B., Wang, Y., Zhang, X., & Shen, Y.-J. (2024). Spatial Heterogeneity and the Increasing Trend of Vegetation and Their Driving Mechanisms in the Mountainous Area of Haihe River Basin. Remote Sensing, 16(3), 587. https://doi.org/10.3390/rs16030587

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