Quantifying Carbon Use Efficiency: Unraveling the Impact of Climate Change and Ecological Engineering on Vegetation in the Three Rivers Source Region

Carbon use efficiency (CUE) was identified as a pivotal parameter for elucidating the carbon cycle within ecosystems. It signified the efficiency with which light energy was transformed into organic matter by vegetation. In light of the challenges posed by global warming, it was deemed essential to gain a comprehensive understanding of the fluctuations and determinants of CUE. Despite the significance of this topic, the current research on factors influencing CUE remained incomplete, notably lacking in exploration of the impacts of ecological engineering on CUE. The objective of this study is to elucidate the influences of climate change and ecological engineering on CUE, quantifying their effects using residual analysis. Additionally, it aims to analyze the primary factors contributing to the fluctuations in CUE. Our findings indicated an average CUE of 0.8536 (±0.0026) with minor interannual variation. In the Three Rivers Source region, CUE is jointly influenced by ecological engineering (30.88%) and climate change (69.12%). Notably, climatic factors predominantly regulate CUE, accounting for approximately 90.20% of its regional variations, with over 44.70% of areas exhibiting contributions exceeding 80%. Moreover, the impact of evapotranspiration on CUE surpasses that of precipitation and temperature, while factors such as elevation and vegetation types also play significant roles. This study showed the quantification of climate change and ecological engineering effects on CUE, which would hold substantial implications for predicting and evaluating global carbon cycling.

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Journal
Remote Sensing
Published
2024-08-09
DOI
https://doi.org/10.3390/rs16162909
Primary Topic
Remote Sensing in Agriculture
Type
article
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article

Quantifying Carbon Use Efficiency: Unraveling the Impact of Climate Change and Ecological Engineering on Vegetation in the Three Rivers Source Region

Jing Yu Pan, Yixia Luo, Daming Tan, Gao Xue et al.
Remote Sensing
Remote Sensing in Agriculture
article

Quantifying Carbon Use Efficiency: Unraveling the Impact of Climate Change and Ecological Engineering on Vegetation in the Three Rivers Source Region

Jing Yu Pan, Yixia Luo, Daming Tan, Gao Xue, Jilong Chen, Shengjun Wu, Hengyi Duan
article en

Abstract

Carbon use efficiency (CUE) was identified as a pivotal parameter for elucidating the carbon cycle within ecosystems. It signified the efficiency with which light energy was transformed into organic matter by vegetation. In light of the challenges posed by global warming, it was deemed essential to gain a comprehensive understanding of the fluctuations and determinants of CUE. Despite the significance of this topic, the current research on factors influencing CUE remained incomplete, notably lacking in exploration of the impacts of ecological engineering on CUE. The objective of this study is to elucidate the influences of climate change and ecological engineering on CUE, quantifying their effects using residual analysis. Additionally, it aims to analyze the primary factors contributing to the fluctuations in CUE. Our findings indicated an average CUE of 0.8536 (±0.0026) with minor interannual variation. In the Three Rivers Source region, CUE is jointly influenced by ecological engineering (30.88%) and climate change (69.12%). Notably, climatic factors predominantly regulate CUE, accounting for approximately 90.20% of its regional variations, with over 44.70% of areas exhibiting contributions exceeding 80%. Moreover, the impact of evapotranspiration on CUE surpasses that of precipitation and temperature, while factors such as elevation and vegetation types also play significant roles. This study showed the quantification of climate change and ecological engineering effects on CUE, which would hold substantial implications for predicting and evaluating global carbon cycling.

Remote SensingVol. 16(16)
Chinese Academy of Sciences (CN), Changjiang Water Resources Commission (CN), Tibet Academy of Agricultural and Animal Husbandry Sciences (CN), Chongqing Institute of Green and Intelligent Technology (CN), Chongqing Jiaotong University (CN)
Openalex Percentile: Top 87%
Remote Sensing in Agriculture
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