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Satellite Evidence for Divergent Forest Responses within Close Vicinity to Climate Fluctuations in a Complex Terrain

Authors :
Jing Wang
Wei Fang
Peipei Xu
Hu Li
Donghua Chen
Zuo Wang
Yuanhong You
Christopher Rafaniello
Source :
Remote Sensing, Vol 15, Iss 11, p 2749 (2023)
Publication Year :
2023
Publisher :
MDPI AG, 2023.

Abstract

Climate change has a significant impact on forest ecosystems worldwide, but it is unclear whether forest responses to climate fluctuations are homogeneous across regions. In this study, we investigated the impact of climatic fluctuations on forest growth in a complex terrain, in Anhui Province of China, using Enhanced Vegetation Index (EVI) data from the Moderate-Resolution Imaging Spectroradiometer (MODIS), while considering the impact of terrain characteristics and forest types. Our regional-scale analysis found that the forest response to climatic drivers in Anhui Province is not homogeneous, with only 69% of the forest area driven by temperature (TEM), while 11% is precipitation (PRE) driven and 20% is solar radiation (SWD) driven. We also found with random forest models that terrain traits (elevation and slope) contributed significantly (29.47% and 27.96%) to the spatial heterogeneity of forest response to climatic drivers, with higher elevation associated with a stronger positive correlation between the EVI and temperature (p < 0.001), a weaker positive correlation between the EVI with precipitation (p < 0.001), and a stronger negative correlation between the EVI with solar radiation (p < 0.01), while forest type contributed the least (4.21%). Our results also imply that in a warmer and dryer climate, some forest patches may switch from TEM driven to PRE driven, which could lead to a decrease in forest productivity, instead of an increase as predicted by existing climate models. These results highlight the heterogeneous response of forests within close vicinity to climate fluctuations in a complex terrain, which has important implications for climate-related risk assessments and local forest management.

Details

Language :
English
ISSN :
20724292
Volume :
15
Issue :
11
Database :
Directory of Open Access Journals
Journal :
Remote Sensing
Publication Type :
Academic Journal
Accession number :
edsdoj.82b47e49afa0489aa9f1103f6c8fa7bd
Document Type :
article
Full Text :
https://doi.org/10.3390/rs15112749