Assessing the spatiotemporal dynamic of NPP in desert steppe and its response to climate change from 2003 to 2017: a case study in Siziwang banner

Xiaohua Zhu; Chuanrong Li; Lingli Tang

doi:10.1117/12.2535486

21 October 2019 Assessing the spatiotemporal dynamic of NPP in desert steppe and its response to climate change from 2003 to 2017: a case study in Siziwang banner

Xiaohua Zhu, Chuanrong Li, Lingli Tang

Author Affiliations +

Proceedings Volume 11149, Remote Sensing for Agriculture, Ecosystems, and Hydrology XXI; 111491I (2019) https://doi.org/10.1117/12.2535486
Event: SPIE Remote Sensing, 2019, Strasbourg, France

Abstract

Siziwang banner desert steppe is a typical arid inland region, which is also one of the most ecologically vulnerable regions in the Inner Mongolia plateau. In this article, the Siziwang banner is taken as a typical case, for studying the spatiotemporal dynamic of NPP in desert steppe based on the Carnegie-Ames-Stanford Approach (CASA) model from 2003 to 2017, and analyzing the response of grassland NPP to the change of key climate factor, including temperature and precipitation. The results indicate that, 1) in terms of time, the grassland NPP in Siziwang banner desert steppe decreases first and then rises, while in terms of space, it decreases from south to north. 2) the NPP is more sensitive to precipitation with average partial correlation coefficient of 64.74%, which is higher than that of temperature. 3) the average of lagged time for NPP responding to temperature is 1.78 months and the average of lagged time for NPP responding to precipitation is 3.17 months. Precipitation at the early stage of vegetation growing season is particularly important for the growth of grassland vegetation in desert steppe.

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Xiaohua Zhu, Chuanrong Li, and Lingli Tang "Assessing the spatiotemporal dynamic of NPP in desert steppe and its response to climate change from 2003 to 2017: a case study in Siziwang banner", Proc. SPIE 11149, Remote Sensing for Agriculture, Ecosystems, and Hydrology XXI, 111491I (21 October 2019); https://doi.org/10.1117/12.2535486

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KEYWORDS

Climatology

Vegetation

Climate change

Data modeling

Earth observing sensors

Landsat

Statistical analysis

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