Aboveground Biomass Retrieval in Tropical and Boreal Forests Using L-Band Airborne Polarimetric Observations
Author:
Wang Mengjin1, Zhang Wangfei1ORCID, Ji Yongjie2ORCID, Marino Armando3, Xu Kunpeng4, Zhao Lei4, Shi Jianmin1, Zhao Han1
Affiliation:
1. College of Forestry, Southwest Forestry University, Kunming 650224, China 2. School of Geography and Ecotourism, Southwest Forestry University, Kunming 650224, China 3. Biological and Environmental Sciences, The University of Stirling, Stirling FK9 4LA, UK 4. Institute of Forest Resources Information Technique, Chinese Academy of Forestry, Beijing 100091, China
Abstract
Forests play a crucial part in regulating global climate change since their aboveground biomass (AGB) relates to the carbon cycle, and its changes affect the main carbon pools. At present, the most suitable available SAR data for wall-to-wall forest AGB estimation are exploiting an L-band polarimetric SAR. However, the saturation issues were reported for AGB estimation using L-band backscatter coefficients. Saturation varies depending on forest structure. Polarimetric information has the capability to identify different aspects of forest structure and therefore shows great potential for reducing saturation issues and improving estimation accuracy. In this study, 121 polarimetric decomposition observations, 10 polarimetric backscatter coefficients and their derived observations, and six texture features were extracted and applied for forest AGB estimation in a tropical forest and a boreal forest. A parametric feature optimization inversion model (Multiple linear stepwise regression, MSLR) and a nonparametric feature optimization inversion model (fast iterative procedure integrated into a K-nearest neighbor nonparameter algorithm, KNNFIFS) were used for polarimetric features optimization and forest AGB inversion. The results demonstrated the great potential of L-band polarimetric features for forest AGB estimation. KNNFIFS performed better both in tropical (R2 = 0.80, RMSE = 22.55 Mg/ha, rRMSE = 14.59%, MA%E = 12.21%) and boreal (R2 = 0.74, RMSE = 19.82 Mg/ha, rRMSE = 20.86%, MA%E = 20.19%) forests. Non-model-based polarimetric features performed better compared to features extracted by backscatter coefficients, model-based decompositions, and texture. Polarimetric observations also revealed site-dependent performances.
Funder
National Natural Science Foundation of China Agriculture joint special project of Yunnan province
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