Above ground biomass and carbon sequestration estimation -Implementation of a sentinel-2 based exploratory workflow

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesThis work presents a Sentinel-2 based exploratory work ow for the estimation of Above Ground Biomass (AGB) and Carbon Sequestration (CS) in a subtropical forest. In the last decades, remote sensing-based studies on AGB have been widely investigated alongside with a variety of sensors, features and Machine Learning (ML) algorithms. Up-to-date and reliable mapping of such measures have been increasingly required by international commitments under the climate convention as well as by sustainable forest management practices. The proposed approach consists of 5 major steps: 1) generation of several Vegetation Indices (VI), biophysical parameters and texture measures; 2) feature selection with Mean Decrease in Impurity (MDI), Mean Decrease in Accuracy (MDA), L1 Regularization (LASSO), and Principal Component Analysis (PCA); 3) feature selection testing with k-Nearest Neighbour (kNN), Random Forest (RF), Extreme Gradient Boosting (XGB), and Arti cial Neural Network (ANN); 4) hyper-parameters ne-tuning with Grid Search, Random Search and Bayesian Optimization; and nally, 5) model explanation with the SHapley Additive exPlanations (SHAP) package, which to this day has not been investigated in the context of AGB mapping. The following results were obtained: 1) MDI was chosen as the best performing feature selection method by the XGB and the Deep Neural Network (DNN), MDA was chosen by the RF and the kNN, while LASSO was chosen by the Shallow Neural Network (SNN) and the Linear Neural Network (LNN); 2) 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; 3) after hyper-parameters ne-tuning with Bayesian Optimization, the XGB model yielded the best performance with a RMSE of 37.79 t=ha; 4) 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 0 t=ha to 346.56 t=ha. The related CS was estimated by using a conversion factor of 0.47

Implementation of a Sentinel-2 Based Exploratory Workflow for the Estimation of Above Ground Biomass

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.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.authorsversionpublishe