Study on Net Primary Productivity over Complicated Mountainous Area based on Multi-Source Remote Sensing Data
Abstract
Mountainous area hosts approximately a quarter of the global land surface, with complex climate and ecosystem conditions. More knowledge about mountainous ecosystem could highly advance our understanding of the global carbon cycle and climate change. Net Primary Productivity (NPP), the biomass increment of plants, is a widely used ecological indicator that can be obtained by remote sensing methods. However, limited by the defective characteristic of sensors, which cannot be long-term with enough spatial details synchronously, the mountainous NPP was far from being understood. In this study, a multi-sensor fusion framework was applied to synthesize a 1-km NPP series from 1982 to 2014 in mountainous southwest China, where elevation ranged from 76m to 6740m. The validation with field-measurements proved this framework greatly improved the accuracy of NPP (r=0.79, p<0.01). The detailed spatial and temporal analysis indicated that NPP variation trends changed from decreasing to increasing with the ascending elevation, as a result of a warmer and drier climate over the region. The correlation of NPP and temperature varied from negative to positive almost at the same elevation break-point of NPP trends, but the opposite for precipitation. This phenomenon was determined by the altitudinal and seasonally uneven allocation of climatic factors, as well as the downward run-off. What is more, it was indicated that the NPP variation showed three distinct stages at the year break-point of 1992 and 2002 over the region. The NPP in low-elevation area varied almost triple more drastic than the high-elevation area for all the three stages, due to the much greater change rate of precipitation. In summary, this study innovatively conducted a long-term and accurate NPP study on the not understood mountainous ecosystem with multi-source data, the framework and conclusions will be beneficial for the further cognition of global climate change.
- Publication:
-
AGU Fall Meeting Abstracts
- Pub Date:
- December 2017
- Bibcode:
- 2017AGUFM.B31D2000G
- Keywords:
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- 0439 Ecosystems;
- structure and dynamics;
- BIOGEOSCIENCES;
- 0480 Remote sensing;
- BIOGEOSCIENCES;
- 1622 Earth system modeling;
- GLOBAL CHANGE;
- 1640 Remote sensing;
- GLOBAL CHANGE