Journal of Yangtze River Scientific Research Institute ›› 2023, Vol. 40 ›› Issue (10): 66-73.DOI: 10.11988/ckyyb.20220832

• Soil and Water Conservation and Ecological Restoration • Previous Articles     Next Articles

Vegetation Net Primary Productivity in Dabie Mountains in Recent Two Decades: Spatiotemporal Variation and Driving Factors

ZHU Peng-fan1, LIU Gang1,2, HE Jing1, DAI Tang-rui1   

  1. 1. College of Earth Sciences, Chengdu University of Technology, Chengdu 610059, China;
    2. State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, China
  • Received:2022-07-15 Revised:2022-09-27 Online:2023-10-01 Published:2023-10-13

Abstract: Being of former communist revolutionary base of China, the Dabie Mountains area emphasizes green development while promoting continuous economic growth. Ecological environment plays a vital role. In this study we focus on the net primary productivity (NPP) of vegetation and utilize the improved CASA (Carnegie-Ames-Stanford Approach) model to evaluate the NPP in the Dabie Mountains by integrating remote sensing and meteorological data from 2000 to 2018. We also analyzed the changing trends in vegetation NPP and its response to climate change. The findings reveal that 1) since 2000, the overall NPP in the Dabie Mountains has mainly ranged from 400 to 600 gC/(m2·a), experiencing a gradual 24.16% increase; 2) The NPP in the Dabie Mountains exhibits a correlation with meteorological factors, with temperature demonstrating the highest correlation (R2=0.79, p<0.05), followed by solar radiation (R2=0.70, p<0.05), while precipitation shows the lowest correlation (R2=0.51, p<0.05); 3) Land use/cover change (LUCC) has contributed to a total NPP increase of 6.23×10-2 TgC, primarily due to the conversion from cultivated land to forests; 4) The future changing trend of NPP in the research area is expected to continue increasing.

Key words: vegetation, NPP, CASA model, temporal and spatial changes, driving factors, Dabie Mountains

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