Estimating Aboveground Biomass of Wetland Plant Communities from Hyperspectral Data Based on Fractional-Order Derivatives and Machine Learning

Author:

Li Huazhe1234,Tang Xiying1234,Cui Lijuan1234,Zhai Xiajie1234,Wang Junjie56ORCID,Zhao Xinsheng1234,Li Jing1234,Lei Yinru1234,Wang Jinzhi1234,Wang Rumiao1234,Li Wei1234ORCID

Affiliation:

1. Institute of Wetland Research, Chinese Academy of Forestry, Beijing 100091, China

2. Beijing Key Laboratory of Wetland Services and Restoration, Beijing 100091, China

3. Institute of Ecological Conservation and Restoration, Chinese Academy of Forestry, Beijing 100091, China

4. Beijing Hanshiqiao National Wetland Ecosystem Research Station, Beijing 101399, China

5. College of Life Sciences and Oceanography, Shenzhen University, Shenzhen 518060, China

6. MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area, Guangdong Key Laboratory of Urban Informatics, Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen 518060, China

Abstract

Wetlands, as a crucial component of terrestrial ecosystems, play a significant role in global ecological services. Aboveground biomass (AGB) is a key indicator of the productivity and carbon sequestration potential of wetland ecosystems. The current research methods for remote-sensing estimation of biomass either rely on traditional vegetation indices or merely perform integer-order differential transformations on the spectra, failing to fully leverage the information complexity of hyperspectral data. To identify an effective method for estimating AGB of mixed-wetland-plant communities, we conducted field surveys of AGB from three typical wetlands within the Crested Ibis National Nature Reserve in Hanzhong, Shaanxi, and concurrently acquired canopy hyperspectral data with a portable spectrometer. The spectral features were transformed by applying fractional-order differentiation (0.0 to 2.0) to extract optimal feature combinations. AGB prediction models were built using three machine learning models, XGBoost, Random Forest (RF), and CatBoost, and the accuracy of each model was evaluated. The combination of fractional-order differentiation, vegetation indices, and feature importance effectively yielded the optimal feature combinations, and integrating vegetation indices with feature bands enhanced the predictive accuracy of the models. Among the three machine-learning models, the RF model achieved superior accuracy using the 0.8-order differential transformation of vegetation indices and feature bands (R2 = 0.673, RMSE = 23.196, RPD = 1.736). The optimal RF model was visually interpreted using Shapley Additive Explanations, which revealed that the contribution of each feature varied across individual sample predictions. Our study provides methodological and technical support for remote-sensing monitoring of wetland AGB.

Funder

China’s Special Fund for Basic Scientific Research Business of Central Public Research Institutes

Publisher

MDPI AG

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