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Implementation of a Sentinel-2 Based Exploratory Workflow for the Estimation of Above Ground Biomass
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Resumo(s)
This work presents a Sentinel-2 based exploratory workflow for the estimation of Above Ground Biomass (AGB) in a Mediterranean forest. Up-to-date and reliable mapping of AGB has been increasingly required by international commitments under the climate convention, and in the last decades, remote sensing-based studies on the topic have been widely investigated. After the generation of several vegetation and topographic features, the proposed approach consists of 4 major steps: 1) Feature selection 2) AGB prediction with k-Nearest Neighbour (kNN), Random Forest (RF), Extreme Gradient Boosting (XGB), and Artificial Neural Networks (ANN); 3) hyper-parameters fine-tuning with Bayesian Optimization; and finally, 4) model explanation with the SHapley Additive exPlanations (SHAP) package. The following results were obtained: 1) before hyper-parameters optimization, the Deep Neural Network (DNN) yielded the best performance with a Root Mean Squared Error (RMSE) of 42.30 t/ha; 2) after hyper-parameters fine-tuning with Bayesian Optimization, the Extreme Gradient Boosting (XGB) model yielded the best performance with a RMSE of 37.79 t/ha; 3) model explanation with SHAP allowed for a deeper understanding of the features impact on the model predictions. Finally, the predicted AGB throughout the study area showed an average value of 83 t/ha, ranging from 0t/ha to 346.56 t/ha.
Descrição
Molisse, G., Emin, D., & Costa, H. (2022). Implementation of a Sentinel-2 Based Exploratory Workflow for the Estimation of Above Ground Biomass. In 2022 IEEE Mediterranean and Middle-East Geoscience and Remote Sensing Symposium, M2GARSS 2022 - Proceedings (pp. 74-77). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/M2GARSS52314.2022.9839897 ----Funding Information: This study was carried out in the framework of the MAIL project, which was funded by the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No 823805.
Palavras-chave
Above Ground Biomass Extreme Gradient Boosting Sentinel-2 SHAP Signal Processing Earth and Planetary Sciences (miscellaneous) Computers in Earth Sciences Earth-Surface Processes Management, Monitoring, Policy and Law Instrumentation SDG 13 - Climate Action
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Institute of Electrical and Electronics Engineers (IEEE)