Monitoring of vegetation productivity in areas of higher susceptibility to severe fires in mainland Portugal

Detalhes bibliográficos
Autor(a) principal: Barreirinha, André Miguel Labrincha
Data de Publicação: 2022
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10773/38599
Resumo: Extreme events such as wildfires and droughts deeply affect the global carbon cycle, since they cause massive degradation of the vegetation greenness. A global and continuous monitoring of vegetation dynamics is essential to analyse the impacts of such events in the vegetation and to understand how it recovers after strong disruptions. Remote Sensing provides information with a wide coverage, high spatial and temporal resolution, being therefore a valuable source of data for these studies. Using this type of data, we present here a study of the vegetation evolution in the Iberian Peninsula during the last 21 years, and give special attention to some large wildfires that occurred in Portugal. First, a climatological study of the Iberian Peninsula was done using MODIS MOD17A3 NPP data. The analysis of yearly and inter-annual mean NPP maps makes clear the areas with more vegetation productivity. A clustering analysis, using the k-means method on the inter-annual NPP variability, allowed the discrimination of 5 regions with distinct vegetation characteristics. The NPP integrated over 3 clusters, located in the northeastern and eastern parts of the Iberian Peninsula, showed a positive trend over the last 20 years. This seems to be associated with changes of the area of each land cover present in the clusters, as revealed by the analysis of the data in the MODIS MCD12Q1 land cover Type 2 product. The integrated NPP time series over Portugal’s mainland and over 4 regions (North, Central, Alentejo and Algarve regions) as well as the evolution of the land covers in the same areas were also analysed. Moreover, Portugal showed a decrease in Evergreen Needleleaf Forests and an increase in Grasslands were observed. A steep decrease in the integrated NPP over Portugal and associated with Evergreen Needleleaf Forest and an accentuated increase of the integrated NPP associated with Grassland were observed around 2017. These changes seemed to be coherent with the severe wildfire season of 2017. However, when the analysis was performed in each region, similar results were obtained in the North, Central, Alentejo and Algarve regions. This suggests more factors should be included to explain the identified changes. To estimate recovery times post-fire, a model that describe exponential decaying anomalies, which was previously applied to NDVI data, was here applied to the more time resolved GPP data from the MODIS MOD17A2 product. The results obtained for the burnt area of Monchique in 2003 are coherent with the values previously obtained when the model was applied to NDVI data. The application of the model to the burnt area of the more recent wildfires of 2017 estimates a recovery time of 39 months for the whole burnt area by the wildfires in October of 2017. When the recovery time of vegetation productivity was estimated as a function of the vegetation type that was present before the wildfire, the areas previously occupied by Savannas show a faster recovery (37 months) of vegetation productivity than the areas that were previously occupied by Evergreen Needleleaf Forests (45 months). Therefore, if the recovery time is estimated for different subareas of the global burnt area their values may differ because the land cover combinations for each subarea may be different. This was verified by applying the model to two subareas of the burnt area in the centre of Portugal in October 2017.
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spelling Monitoring of vegetation productivity in areas of higher susceptibility to severe fires in mainland PortugalGPPNPPVegetation recoveryDroughtsWildfiresExtreme events such as wildfires and droughts deeply affect the global carbon cycle, since they cause massive degradation of the vegetation greenness. A global and continuous monitoring of vegetation dynamics is essential to analyse the impacts of such events in the vegetation and to understand how it recovers after strong disruptions. Remote Sensing provides information with a wide coverage, high spatial and temporal resolution, being therefore a valuable source of data for these studies. Using this type of data, we present here a study of the vegetation evolution in the Iberian Peninsula during the last 21 years, and give special attention to some large wildfires that occurred in Portugal. First, a climatological study of the Iberian Peninsula was done using MODIS MOD17A3 NPP data. The analysis of yearly and inter-annual mean NPP maps makes clear the areas with more vegetation productivity. A clustering analysis, using the k-means method on the inter-annual NPP variability, allowed the discrimination of 5 regions with distinct vegetation characteristics. The NPP integrated over 3 clusters, located in the northeastern and eastern parts of the Iberian Peninsula, showed a positive trend over the last 20 years. This seems to be associated with changes of the area of each land cover present in the clusters, as revealed by the analysis of the data in the MODIS MCD12Q1 land cover Type 2 product. The integrated NPP time series over Portugal’s mainland and over 4 regions (North, Central, Alentejo and Algarve regions) as well as the evolution of the land covers in the same areas were also analysed. Moreover, Portugal showed a decrease in Evergreen Needleleaf Forests and an increase in Grasslands were observed. A steep decrease in the integrated NPP over Portugal and associated with Evergreen Needleleaf Forest and an accentuated increase of the integrated NPP associated with Grassland were observed around 2017. These changes seemed to be coherent with the severe wildfire season of 2017. However, when the analysis was performed in each region, similar results were obtained in the North, Central, Alentejo and Algarve regions. This suggests more factors should be included to explain the identified changes. To estimate recovery times post-fire, a model that describe exponential decaying anomalies, which was previously applied to NDVI data, was here applied to the more time resolved GPP data from the MODIS MOD17A2 product. The results obtained for the burnt area of Monchique in 2003 are coherent with the values previously obtained when the model was applied to NDVI data. The application of the model to the burnt area of the more recent wildfires of 2017 estimates a recovery time of 39 months for the whole burnt area by the wildfires in October of 2017. When the recovery time of vegetation productivity was estimated as a function of the vegetation type that was present before the wildfire, the areas previously occupied by Savannas show a faster recovery (37 months) of vegetation productivity than the areas that were previously occupied by Evergreen Needleleaf Forests (45 months). Therefore, if the recovery time is estimated for different subareas of the global burnt area their values may differ because the land cover combinations for each subarea may be different. This was verified by applying the model to two subareas of the burnt area in the centre of Portugal in October 2017.Eventos extremos como incêndios e secas afectam profundamente o ciclo global do carbono, uma vez que provocam degradação da vegetação. Por isso, uma monitorização global e contínua da dinâmica da vegetação é essencial para analisar os impactos desses eventos na vegetação e para compreender como esta recupera após fortes perturbações. A detecção remota fornece informação com uma ampla cobertura, alta resolução espacial e temporal, sendo por isso uma valiosa fonte de dados para estes estudos. Utilizando este tipo de dados, um estudo sobre a evolução da vegetação na Peninsula hibérica durante os últimos 21 anos é apresentado, onde é dada especial atenção a alguns incêndios que ocorreram em Portugal Primeiro, foi feito um estudo climatológico da Península Ibérica utilizando dados MODIS MOD17A3 NPP. A análise dos mapas anuais e interanuais médios de NPP evidenciou as áreas mais produtivas. Uma análise de clusters, utilizando o método k-means sobre a variabilidade inter-anual do NPP, permitiu a discriminação de 5 regiões com características de vegetação distintas. Os valores de NPP de 3 clusters, localizados nas partes nordeste e oriental da Península Ibérica, mostraram uma tendência positiva ao longo dos últimos 20 anos. Esta tendência positiva parece estar associada a alterações na área de cada cobertura terrestre presente nos clusters, como revelado pela análise dos dados MCD12Q1 land cover Type 2. Foram também analisadas as séries temporais de NPP sobre Portugal e sobre quatro regiões (Norte, Centro, Alentejo e Algarve), bem como a evolução da cobertura terrestre nas mesmas zonas. Além disso, em Portugal observou-se uma diminuição de Evergreen Needleaf Forests e um aumento das Graslands. Por volta de 2017 observou-se uma diminuição acentuada do NPP em Portugal associado a Evergreen Needleaf Forests e um aumento acentuado do NPP associado a Graslands. Estas alterações pareciam ser coerentes com a severa época de incêndios florestais de 2017. Contudo, aquando da análise por região, foram obtidos resultados semelhantes em todas. Isto sugere que deveriam ser incluídos mais factores para explicar as alterações identificadas. Para estimar os tempos de recuperação da vegetação no pós-fogo, foi aplicado um modelo que descreve anomalias de decaimento exponenciais, previamente aplicado a dados NDVI, aos dados MOD17A2 GPP, que têm maior resolução temporal. Os resultados obtidos para a área queimada de Monchique em 2003 são coerentes com os valores anteriormente obtidos quando o modelo foi aplicado aos dados NDVI. A aplicação do modelo à área ardida dos incêndios mais recentes de 2017 estima um tempo de recuperação de 39 meses para toda a área ardida em outubro desse ano. Quando o tempo de recuperação da produtividade da vegetação foi estimado em função do tipo de vegetação presente antes do incêndio, as áreas anteriormente ocupadas pelas Savannas mostram uma recuperação mais rápida (37 meses) da produtividade da vegetação do que as áreas anteriormente ocupadas por Evergreen Needleaf Forests (45 meses). Portanto, se o tempo de recuperação for estimado para diferentes subáreas da área ardida global, os seus valores podem ser diferentes devido às combinações de cobertura do solo presentes em cada sub-área. Isto foi verificado aplicando o modelo a duas sub-áreas da área ardida no centro de Portugal em outubro de 2017.2023-07-12T15:17:21Z2022-12-14T00:00:00Z2022-12-14info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10773/38599engBarreirinha, André Miguel Labrinchainfo: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:RCAAP2024-02-22T12:14:50Zoai:ria.ua.pt:10773/38599Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:08:48.613299Repositório 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 Monitoring of vegetation productivity in areas of higher susceptibility to severe fires in mainland Portugal
title Monitoring of vegetation productivity in areas of higher susceptibility to severe fires in mainland Portugal
spellingShingle Monitoring of vegetation productivity in areas of higher susceptibility to severe fires in mainland Portugal
Barreirinha, André Miguel Labrincha
GPP
NPP
Vegetation recovery
Droughts
Wildfires
title_short Monitoring of vegetation productivity in areas of higher susceptibility to severe fires in mainland Portugal
title_full Monitoring of vegetation productivity in areas of higher susceptibility to severe fires in mainland Portugal
title_fullStr Monitoring of vegetation productivity in areas of higher susceptibility to severe fires in mainland Portugal
title_full_unstemmed Monitoring of vegetation productivity in areas of higher susceptibility to severe fires in mainland Portugal
title_sort Monitoring of vegetation productivity in areas of higher susceptibility to severe fires in mainland Portugal
author Barreirinha, André Miguel Labrincha
author_facet Barreirinha, André Miguel Labrincha
author_role author
dc.contributor.author.fl_str_mv Barreirinha, André Miguel Labrincha
dc.subject.por.fl_str_mv GPP
NPP
Vegetation recovery
Droughts
Wildfires
topic GPP
NPP
Vegetation recovery
Droughts
Wildfires
description Extreme events such as wildfires and droughts deeply affect the global carbon cycle, since they cause massive degradation of the vegetation greenness. A global and continuous monitoring of vegetation dynamics is essential to analyse the impacts of such events in the vegetation and to understand how it recovers after strong disruptions. Remote Sensing provides information with a wide coverage, high spatial and temporal resolution, being therefore a valuable source of data for these studies. Using this type of data, we present here a study of the vegetation evolution in the Iberian Peninsula during the last 21 years, and give special attention to some large wildfires that occurred in Portugal. First, a climatological study of the Iberian Peninsula was done using MODIS MOD17A3 NPP data. The analysis of yearly and inter-annual mean NPP maps makes clear the areas with more vegetation productivity. A clustering analysis, using the k-means method on the inter-annual NPP variability, allowed the discrimination of 5 regions with distinct vegetation characteristics. The NPP integrated over 3 clusters, located in the northeastern and eastern parts of the Iberian Peninsula, showed a positive trend over the last 20 years. This seems to be associated with changes of the area of each land cover present in the clusters, as revealed by the analysis of the data in the MODIS MCD12Q1 land cover Type 2 product. The integrated NPP time series over Portugal’s mainland and over 4 regions (North, Central, Alentejo and Algarve regions) as well as the evolution of the land covers in the same areas were also analysed. Moreover, Portugal showed a decrease in Evergreen Needleleaf Forests and an increase in Grasslands were observed. A steep decrease in the integrated NPP over Portugal and associated with Evergreen Needleleaf Forest and an accentuated increase of the integrated NPP associated with Grassland were observed around 2017. These changes seemed to be coherent with the severe wildfire season of 2017. However, when the analysis was performed in each region, similar results were obtained in the North, Central, Alentejo and Algarve regions. This suggests more factors should be included to explain the identified changes. To estimate recovery times post-fire, a model that describe exponential decaying anomalies, which was previously applied to NDVI data, was here applied to the more time resolved GPP data from the MODIS MOD17A2 product. The results obtained for the burnt area of Monchique in 2003 are coherent with the values previously obtained when the model was applied to NDVI data. The application of the model to the burnt area of the more recent wildfires of 2017 estimates a recovery time of 39 months for the whole burnt area by the wildfires in October of 2017. When the recovery time of vegetation productivity was estimated as a function of the vegetation type that was present before the wildfire, the areas previously occupied by Savannas show a faster recovery (37 months) of vegetation productivity than the areas that were previously occupied by Evergreen Needleleaf Forests (45 months). Therefore, if the recovery time is estimated for different subareas of the global burnt area their values may differ because the land cover combinations for each subarea may be different. This was verified by applying the model to two subareas of the burnt area in the centre of Portugal in October 2017.
publishDate 2022
dc.date.none.fl_str_mv 2022-12-14T00:00:00Z
2022-12-14
2023-07-12T15:17:21Z
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