Grassland botanical species identification based on MTMF model

Mengge Huang; Xinhong Wang; Lingling Ma; Guangzhou Ouyang; Wei Liu; Xiaohua Zhu; Yifang Niu; Lingli Tang

doi:10.1117/12.2680926

8 June 2023 Grassland botanical species identification based on MTMF model

Mengge Huang, Xinhong Wang, Lingling Ma, Guangzhou Ouyang, Wei Liu, Xiaohua Zhu, Yifang Niu, Lingli Tang

Proceedings Volume 12707, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023); 127074O (2023) https://doi.org/10.1117/12.2680926
Event: International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023), 2023, Changsha, China

Abstract

Grassland plays an important role in regional economic development and terrestrial ecosystem security. Remote sensing technology is fast, efficient and low cost. The use of remote sensing technology to discriminate grassland species is an important way to monitor the population dynamics and botanical community succession in grassland. Such information is conducive to the timely and accurate detection of changes in the grassland ecological environment and provides an important reference for the scientific management of grassland ecosystems and the construction of an ecologically aware civilization. In this paper, based on the UAV hyperspectral remote sensing image of natural grassland in Inner Mongolia and the linear spectral mixture model, the MTMF (mixture tuned matched filtering) model was used for grass species identification, and the results were verified with FVC (fractional vegetation cover) estimation. The results showed that this method could achieve effective identification and extraction of the dominant species of Leymus chinensis in the study area.

Citation Download Citation

Mengge Huang, Xinhong Wang, Lingling Ma, Guangzhou Ouyang, Wei Liu, Xiaohua Zhu, Yifang Niu, and Lingli Tang "Grassland botanical species identification based on MTMF model", Proc. SPIE 12707, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023), 127074O (8 June 2023); https://doi.org/10.1117/12.2680926

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