Leaf area index of vegetation based on Gaofen-5 hyperspectral image inversion research

Ting Kang; Ying Han; Yongchang Wang; Qi Xue; Sairu Xu; Yingying Su

doi:10.1117/12.3020895

23 January 2024 Leaf area index of vegetation based on Gaofen-5 hyperspectral image inversion research

Ting Kang, Ying Han, Yongchang Wang, Qi Xue, Sairu Xu, Yingying Su

Proceedings Volume 12978, Fourth International Conference on Geology, Mapping, and Remote Sensing (ICGMRS 2023); 129780T (2024) https://doi.org/10.1117/12.3020895
Event: 2023 4th International Conference on Geology, Mapping and Remote Sensing (ICGMRS 2023), 2023, wuhan, China

Abstract

Using Gaofen-5 hyperspectral data and vegetation index analysis method, the leaf area index inversion model of the study area was established, the results show that: the correlation coefficient between enhanced vegetation index and leaf area index reached 0.7334, the linear regression inversion model of growing season in the study area was established, and the root mean square error was 0.1807; spatial analysis results show, In the southwest part of the study area, the leaf area index was larger and the vegetation growth was flourishing, while the leaf area index was smaller in the southeast and central part of the study area, and the vegetation growth was normal. This conclusion was consistent with the vegetation coverage.

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

Citation Download Citation

Ting Kang, Ying Han, Yongchang Wang, Qi Xue, Sairu Xu, and Yingying Su "Leaf area index of vegetation based on Gaofen-5 hyperspectral image inversion research", Proc. SPIE 12978, Fourth International Conference on Geology, Mapping, and Remote Sensing (ICGMRS 2023), 129780T (23 January 2024); https://doi.org/10.1117/12.3020895

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KEYWORDS

Vegetation

Data modeling

Remote sensing

Near infrared

Hyperspectral imaging

Mathematical modeling

Image enhancement

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