The Forest Above Ground Biomass Estimation Based on Multi-Feature Combination Method Using Multi-Frequency SAR Data

• Main Authors: Ma, Yunmei, Zhao, Lei, Chen, Erxue, Li, Zengyuan, Fan, Yaxiong, Xu, Kunpeng
• Format: Conference Proceeding
• Language: English
• Subjects: Accuracy; Biomass; Estimation; Feature selection; Forest biomass estimation; Forestry; Geoscience and remote sensing; Machine learning; Multi-feature combination; Multi-frequency SAR data; Robustness
• ISSN: 2153-7003
• DOI: 10.1109/IGARSS53475.2024.10641961

In this paper, we studied the multi-feature combination estimation approach of forest above ground biomass (AGB) using X-band InSAR and P-band PolInSAR data. We focus on a crucial step of the estimation process, which is selection of the optimal feature combination. Firstly, the feature pool was acquired using multi-frequency SAR data, which includes optimized features (forest height and polarimetric interferometric feature) and original features (polarimetric features, intensity features, and texture features). Then, using machine learning method to select the optimal feature combination. Finally, the forest AGB was estimated based on multiple types of the features combination. The experimental results showed that the combination of optimized features with original features has the highest accuracy in forest AGB estimation, followed by the combination using only optimized features. The accuracy of forest AGB estimation is lower for the feature combination that does not include optimized features.