A comparative study of forest extraction methods in the yellow river basin (Gansu section) based on multi-source remote sensing data

Mingzhi Liu; Quanfu Niu; Zhenzhen Wang; Liu Bo

doi:10.1117/12.3011207

15 November 2023 A comparative study of forest extraction methods in the yellow river basin (Gansu section) based on multi-source remote sensing data

Mingzhi Liu, Quanfu Niu, Zhenzhen Wang, Liu Bo

Author Affiliations +

Proceedings Volume 12815, International Conference on Remote Sensing, Mapping, and Geographic Systems (RSMG 2023); 1281509 (2023) https://doi.org/10.1117/12.3011207
Event: International Conference on Remote Sensing, Mapping, and Geographic Systems (RSMG 2023), 2023, Kaifeng, China

Abstract

Forest is one of the most important ecosystems on earth, and it plays an important role in maintaining ecological balance and protecting biodiversity. Therefore, the protection and construction of forests is one of the important contents of the implementation of ecological engineering in the Yellow River Basin (Gansu section), and the rapid and accurate extraction of forest resources has also become a research hotspot in this area. This study compared and analyzed the results of forest information extraction from multi-source remote sensing data based on feature fusion and decision fusion. and Kappa values were as high as 95.4% and 0.97, respectively. The distribution of forests extracted in mountainous areas, built-up areas, and farmland areas is basically consistent with visual interpretation. It shows that the forest extraction method of decision-level fusion of remote sensing data has high accuracy and reliability, and is suitable for high-precision forest acquisition in this study area.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Mingzhi Liu, Quanfu Niu, Zhenzhen Wang, and Liu Bo "A comparative study of forest extraction methods in the yellow river basin (Gansu section) based on multi-source remote sensing data", Proc. SPIE 12815, International Conference on Remote Sensing, Mapping, and Geographic Systems (RSMG 2023), 1281509 (15 November 2023); https://doi.org/10.1117/12.3011207

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KEYWORDS

Remote sensing

Feature fusion

Data fusion

Feature extraction

Backscatter

Landsat

Short wave infrared radiation

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