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

Detalhes bibliográficos
Autor(a) principal: Molisse, Giulia
Data de Publicação: 2021
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/113902
Resumo: Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies
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spelling Above ground biomass and carbon sequestration estimation -Implementation of a sentinel-2 based exploratory workflowAbove Ground BiomassCarbon SequestrationMedium resolutionRandom ForestExtremeGradient BoostingArtificial Neural Networkk-Nearest NeighbourFeatureSelectionBayesian SearchSentinel-2Dissertation 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.Costa, Hugo Alexandre Gomes daEmin, DzhanerPla Bañón, FilibertoRUNMolisse, Giulia2021-03-15T18:15:10Z2021-02-262021-02-26T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/113902TID:202673227enginfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T04:56:43Zoai:run.unl.pt:10362/113902Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:42:24.522181Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Above ground biomass and carbon sequestration estimation -Implementation of a sentinel-2 based exploratory workflow
title Above ground biomass and carbon sequestration estimation -Implementation of a sentinel-2 based exploratory workflow
spellingShingle Above ground biomass and carbon sequestration estimation -Implementation of a sentinel-2 based exploratory workflow
Molisse, Giulia
Above Ground Biomass
Carbon Sequestration
Medium resolution
Random Forest
Extreme
Gradient Boosting
Artificial Neural Network
k-Nearest Neighbour
Feature
Selection
Bayesian Search
Sentinel-2
title_short Above ground biomass and carbon sequestration estimation -Implementation of a sentinel-2 based exploratory workflow
title_full Above ground biomass and carbon sequestration estimation -Implementation of a sentinel-2 based exploratory workflow
title_fullStr Above ground biomass and carbon sequestration estimation -Implementation of a sentinel-2 based exploratory workflow
title_full_unstemmed Above ground biomass and carbon sequestration estimation -Implementation of a sentinel-2 based exploratory workflow
title_sort Above ground biomass and carbon sequestration estimation -Implementation of a sentinel-2 based exploratory workflow
author Molisse, Giulia
author_facet Molisse, Giulia
author_role author
dc.contributor.none.fl_str_mv Costa, Hugo Alexandre Gomes da
Emin, Dzhaner
Pla Bañón, Filiberto
RUN
dc.contributor.author.fl_str_mv Molisse, Giulia
dc.subject.por.fl_str_mv Above Ground Biomass
Carbon Sequestration
Medium resolution
Random Forest
Extreme
Gradient Boosting
Artificial Neural Network
k-Nearest Neighbour
Feature
Selection
Bayesian Search
Sentinel-2
topic Above Ground Biomass
Carbon Sequestration
Medium resolution
Random Forest
Extreme
Gradient Boosting
Artificial Neural Network
k-Nearest Neighbour
Feature
Selection
Bayesian Search
Sentinel-2
description Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies
publishDate 2021
dc.date.none.fl_str_mv 2021-03-15T18:15:10Z
2021-02-26
2021-02-26T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/113902
TID:202673227
url http://hdl.handle.net/10362/113902
identifier_str_mv TID:202673227
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv
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