Research on the mountain grassland aboveground biomass satellite remote sensing aspect-oriented modeling method

Jinxiang Shen; Jun Yin; Jianwei Duan; Guangtao Meng; Xiaoli Sun

doi:10.1117/12.3035475

10 July 2024 Research on the mountain grassland aboveground biomass satellite remote sensing aspect-oriented modeling method

Jinxiang Shen, Jun Yin, Jianwei Duan, Guangtao Meng, Xiaoli Sun

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Proceedings Volume 13223, Fifth International Conference on Geology, Mapping, and Remote Sensing (ICGMRS 2024); 1322308 (2024) https://doi.org/10.1117/12.3035475
Event: 2024 5th International Conference on Geology, Mapping and Remote Sensing (ICGMRS 2024), 2024, Wuhan, China

Abstract

Regarding the issue of variability in aboveground biomass (AGB) of plants caused by topographical factors in mountainous areas affecting water and thermal distribution, this paper explores an aspect-oriented modeling approach for AGB inversion using satellite remote sensing NDVI indices with support from ground-measured AGB data. Firstly, based on DEM slope analysis, three aspects—sunny slope, shady slope, and half-sunny half-shady slope—are classified. Secondly, the located sample data is overlaid with Sentinel-2 NDVI data and aspect data to obtain {aspect-NDVI-AGB} sample data. Subsequently, fitting models are established separately for the three aspects. Finally, with the support of aspect data, these models are used to calculate the overall AGB distribution data based on the input Sentinel-2 NDVI. Experimental results indicate that in Yunnan's Yuanmou County, the aspect-oriented inversion modeling method based on sunny slope, shady slope, and half-sunny half-shady slope achieves higher accuracy than a single modeling method. When modeling for the three aspects, exponential models exhibit higher fitting accuracy compared to linear models. The AGB remote sensing estimation model, developed based on remote sensing data, ground-measured biomass data, and different aspect terrain features, is feasible and provides technical reference for estimating regional grassland resources and even other plant AGB. Additionally, the consideration of incorporating more factors such as elevation and geographical location in classification modeling methods needs further exploration.

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

Citation Download Citation

Jinxiang Shen, Jun Yin, Jianwei Duan, Guangtao Meng, and Xiaoli Sun "Research on the mountain grassland aboveground biomass satellite remote sensing aspect-oriented modeling method", Proc. SPIE 13223, Fifth International Conference on Geology, Mapping, and Remote Sensing (ICGMRS 2024), 1322308 (10 July 2024); https://doi.org/10.1117/12.3035475

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KEYWORDS

Biomass

Remote sensing

Modeling

Data modeling

Satellites

Vegetation

Analytical research

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