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A GEE - based study on the temporal and spatial variations in the carbon source/sink function of vegetation in the Three - River Headwaters region.
- Source :
- Remote Sensing for Natural Resources; Mar2023, Vol. 35 Issue 1, p231-242, 12p
- Publication Year :
- 2023
-
Abstract
- Net ecosystem productivity ( NEP) represents the carbon sequestration capacity of a regional ecosystem. Based on the Google Earth Engine ( GEE) platform, this study analyzed the temporal and spatial variations in the NEP of the Three - River Headwaters Region ( TRHR) from 2001 to 2020 based on the Moderate Resolution Imaging Spectrometer ( MODIS) and meteorological data and revealed their relationships with climate factors. The results are as follows; 1 The TRHR had an important carbon sink function, with carbon sink areas accounting for 99. 89% ; The carbon source areas in the TRHR were primarily distributed in the northwest, accounting for only 0. 11%. The NEP of the TRHR decreased gradually from the southeast to the northwest and differed significantly among different ecological areas; 2 The NEP of the TRHR showed an upward trend overaU in the past 20 years, with an annual increasing rate of 1. 13 gC/(m ² • a), indicating huge carbon sequestration potential; 3 The area of zones whose NEP showed an upward trend accounted for 95. 05% of the total area. Ecological engineering construction significantly improved the NEP of vegetation. As a result, the carbon sink function gradually increased and was highly stable; 4 The TRHR had an annual average NEP of 120. 93 gC/( m ² • a), and the NEP was positively correlated with the annual precipitation but negatively correlated with average annual temperature and annual solar radiation. The warm, humid climate and the ecological engineering construction contributed to the carbon sink function of vegetation in the TRHR. This is of great significance for improving the carbon sink value of the terrestrial ecosystem and achieving the peak carbon dioxide emissions and carbon neutrality of China. [ABSTRACT FROM AUTHOR]
Details
- Language :
- Chinese
- ISSN :
- 2097034X
- Volume :
- 35
- Issue :
- 1
- Database :
- Complementary Index
- Journal :
- Remote Sensing for Natural Resources
- Publication Type :
- Academic Journal
- Accession number :
- 162740303
- Full Text :
- https://doi.org/10.6046/zrzyyg.2022042