Spatial-temporal change analysis of gross primary productivity of Xilingol grassland based on remote sensing technology

Jun Zhao; Yungang Cao

doi:10.1117/12.3035816

10 July 2024 Spatial-temporal change analysis of gross primary productivity of Xilingol grassland based on remote sensing technology

Jun Zhao, Yungang Cao

Author Affiliations +

Proceedings Volume 13223, Fifth International Conference on Geology, Mapping, and Remote Sensing (ICGMRS 2024); 132230C (2024) https://doi.org/10.1117/12.3035816
Event: 2024 5th International Conference on Geology, Mapping and Remote Sensing (ICGMRS 2024), 2024, Wuhan, China

Abstract

As an important part of China's grassland ecosystem, it is crucial to use remote sensing for high-resolution, long-term ecological monitoring and management of the Xilingol Grassland. This study uses Landsat data from May to September for the years 2013 to 2022 to estimate GPP, with a spatial resolution of 30m, and utilizes Slope and F-test methods to discern the spatial-temporal variations of GPP in Xilingol Grassland. The results indicate that over the past decade: 1) The multi-year average GPP value for Xilingol Grassland stands at 458.55 g C∙m^-2∙year^-1, with high-value areas mainly located in the northeastern and southern parts of the grassland, and low-value areas mainly located in the southwestern region. 2) The inter-annual average changes in GPP in Xilingol Grassland showed a pattern of initial decline followed by an increase. Among them, the highest proportion without significant change is 54.92%. 3) In particular, the meadow grassland mainly shows an increase or no significant change, accounting for 37.28% and 52.82% respectively. The typical grassland mostly shows no significant change, accounting with 61.8%. And desert steppes mainly showed a decreasing trend with 62.83%.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Jun Zhao and Yungang Cao "Spatial-temporal change analysis of gross primary productivity of Xilingol grassland based on remote sensing technology", Proc. SPIE 13223, Fifth International Conference on Geology, Mapping, and Remote Sensing (ICGMRS 2024), 132230C (10 July 2024); https://doi.org/10.1117/12.3035816

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KEYWORDS

Ecosystems

Remote sensing

Data modeling

Vegetation

Landsat

Climatology

Carbon

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