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@PhDThesis{Wiederkehr:2022:UsDaSa,
               author = "Wiederkehr, Natalia Cristina",
                title = "Uso de dados dos sat{\'e}lites ALOS/PALSAR-2 e Sentinel-1A para 
                         detec{\c{c}}{\~a}o de perdas de volume florestal por processo de 
                         corte seletivo em uma por{\c{c}}{\~a}o da Floresta Nacional do 
                         Tapaj{\'o}s",
               school = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                 year = "2022",
              address = "S{\~a}o Jos{\'e} dos Campos",
                month = "2022-08-19",
             keywords = "degrada{\c{c}}{\~a}o florestal, Amaz{\^o}nia, dados SAR, 
                         ALOS/PALSAR-2, sentinel-1A, forest degradation, Amazon, SAR 
                         data.",
             abstract = "O desmatamento e a degrada{\c{c}}{\~a}o florestal induzida pelas 
                         a{\c{c}}{\~o}es antr{\'o}picas, sobretudo pela 
                         extra{\c{c}}{\~a}o seletiva de madeira em regime n{\~a}o 
                         sustent{\'a}vel, pelos inc{\^e}ndios florestais e pelo efeito de 
                         bordas, contribuem significativamente para as emiss{\~o}es de 
                         di{\'o}xido de carbono (CO2), degrada{\c{c}}{\~o}es e perdas de 
                         florestas na Amaz{\^o}nia Brasileira. Identificar os impactos 
                         causados ao ambiente florestal torna-se extremamente importante, 
                         pois possibilita compreender as rela{\c{c}}{\~o}es de causa e 
                         efeito da perda de florestas, sobretudo, os ocasionados pela 
                         degrada{\c{c}}{\~a}o florestal, uma vez que ainda n{\~a}o 
                         existe um consenso sobre qual o limiar de perda da biomassa e a 
                         persist{\^e}ncia nessa perda que constitui a 
                         degrada{\c{c}}{\~a}o. Neste sentido, o sensoriamento remoto 
                         torna-se uma ferramenta muito importante, pois {\'e} uma maneira 
                         eficaz e economicamente vi{\'a}vel de monitorar as 
                         mudan{\c{c}}as na cobertura florestal, principalmente de grandes 
                         regi{\~o}es florestais como a Amaz{\^o}nia. O presente estudo 
                         teve como objetivo avaliar individualmente a capacidade das 
                         imagens polarim{\'e}tricas dos sat{\'e}lites ALOS/PALSAR-2 
                         (ALOS2) e Sentinel-1A (S1A) para detectar perdas de volume 
                         florestal por processo de extra{\c{c}}{\~a}o seletiva de madeira 
                         em regime sustent{\'a}vel. Quatro {\'a}reas com alta intensidade 
                         de explora{\c{c}}{\~a}o madeireira, entre 27 m ha-1 e 29 m 
                         ha-1, denominadas de Unidades de Produ{\c{c}}{\~a}o Anual (UPA), 
                         inseridas na Floresta Nacional do Tapaj{\'o}s, foram 
                         selecionadas. Para cada UPA, a explora{\c{c}}{\~a}o ocorreu em 
                         um ano espec{\'{\i}}fico: UPA 2015, UPA 2016, UPA 2017 e UPA 
                         2018. Os atributos extra{\'{\i}}dos a partir dos coeficientes de 
                         retroespalhamento ({\'a}lgebras, raz{\~o}es de bandas, 
                         {\'{\i}}ndices SAR de vegeta{\c{c}}{\~a}o, medidas de 
                         texturas) e de informa{\c{c}}{\~a}o de fase 
                         (decomposi{\c{c}}{\~a}o polarim{\'e}trica entropia-{\^a}ngulo 
                         alfa) das imagens ALOS2 e S1A, foram utilizados para detectar as 
                         perdas de volume florestal. A detec{\c{c}}{\~a}o foi realizada a 
                         partir das diferen{\c{c}}as entre os valores dos pixels das 
                         {\'a}reas exploradas e n{\~a}o exploradas. O teste 
                         n{\~a}o-param{\'e}trico de Wilcoxon, com um grau de 
                         confian{\c{c}}a de 95%, foi empregado para avaliar se as 
                         diferen{\c{c}}as encontradas eram significativas ou n{\~a}o. Os 
                         resultados obtidos pelos dados ALOS2 demonstraram um desempenho 
                         superior aos do S1A. Dentre os atributos associados ao ALOS2, 
                         destaca-se o Radar Normalized Difference Vegetation Index (RNDVI) 
                         que exibiu sensibilidade em detectar perdas de volume florestal 
                         devido {\`a} degrada{\c{c}}{\~a}o por processo de corte 
                         seletivo em todas as {\'a}reas investigadas. A 
                         polariza{\c{c}}{\~a}o HV, a raz{\~a}o de 
                         polariza{\c{c}}{\~a}o cruzada, a medida de textura denominada 
                         Contraste na polariza{\c{c}}{\~a}o HV e os atributos 
                         polarim{\'e}tricos entropia e {\^a}ngulo alfa tamb{\'e}m 
                         apresentaram potencial quando associados {\`a}s UPAs de 2015 e 
                         2016. Os atributos associados ao S1A apresentaram uma discreta 
                         diferen{\c{c}}a entre os valores de retroespalhamento, mesmo 
                         considerando a alta intensidade de corte seletivo nas UPAs. Os 
                         melhores resultados foram obtidos pelos atributos \σVH 
                         associado {\`a} UPA 2015, Radar Ratio Vegetation Index (RRVI) na 
                         UPA 2016, medidas de texturas denominadas de Energia e M{\'a}xima 
                         Probabilidade na polariza{\c{c}}{\~a}o VH associados {\`a} UPA 
                         2017, {\^a}ngulo alfa, Contraste na polariza{\c{c}}{\~a}o VH, 
                         al{\'e}m do RRVI para a UPA 2018. ABSTRACT: Human-induced 
                         deforestation and forest degradation, especially by illegal 
                         logging, fires, and edge effects contribute significantly to 
                         carbon dioxide (CO2) emissions, degradation, and loss of forests 
                         in the Brazilian Amazon. Identifying the impacts caused to the 
                         forest environment becomes extremely important, as it makes 
                         possible to understand the cause and effect relationships of 
                         forest loss especially those caused by forest degradation, since 
                         there is still no consensus on the threshold of biomass loss and 
                         the persistence of this loss that constitutes degradation. In this 
                         sense, remote sensing becomes a very important tool, as it is an 
                         effective and economically viable way to monitor changes in forest 
                         cover, especially in large forest regions, such as the Amazon. In 
                         this context, this work aimed to evaluate the ALOS/PALSAR-2 
                         (ALOS2) and Sentinel-1A (S1A) polarimetric images to detect losses 
                         in forest volume through the process of selective logging in a 
                         sustainable regime. Four areas with high timber exploration 
                         (between 27 m ha-1 and 29 m ha-1), called Annual Production 
                         Units (APU), inserted in the Tapaj{\'o}s National Forest, were 
                         selected. For each APU, the exploration took place in a specific 
                         year: APU 2015, APU 2016, APU 2017, and APU 2018. The attributes 
                         derived from radar backscatter (algebras, band ratios, SAR 
                         vegetation indexes, texture measures) and phase information 
                         (entropyalpha angle) from ALOS2 and S1A images were used to detect 
                         forest volume losses. The detection was performed from the 
                         differences between the pixel values of the explored and 
                         unexplored areas. Wilcoxon's nonparametric test, with a confidence 
                         level of 95%, was used to assess whether the differences found 
                         were statistically significant or not. The results obtained by the 
                         ALOS2 data showed a superior performance in relation to the S1A 
                         datasets. Among the ALOS2 attributes, Radar Normalized Difference 
                         Vegetation Index (RNDVI) showed best potential to detect forest 
                         volume losses due to degradation by the selective logging process 
                         in all investigated areas. The HV polarization, the cross 
                         polarization ratio, the Contrast texture measure, and the entropy 
                         and alpha angle attributes also showed potential when associated 
                         with the 2015 and 2016 APUs. The S1A attributes showed a slight 
                         difference between the backscatter values, even considering the 
                         high intensity of selective logging in the APUs. The best results 
                         were obtained by the \σVH associated with the 2015 APU, 
                         Radar Ratio Vegetation Index (RRVI) in the APU 2016, Energy and 
                         Maximum Probability textural measures in the VH polarization 
                         associated with the 2017 APU, alpha angle, Contrast in the VH 
                         polarization, as well as by RRVI attribute for the APU 2018.",
            committee = "Shimabukuro, Yosio Edemir (presidente) and Gama, F{\'a}bio Furlan 
                         (orientador) and Bispo, Polyanna da Concei{\c{c}}{\~a}o 
                         (orientadora) and Mura, Jos{\'e} Cl{\'a}udio and Sano, Edson 
                         Eyji and Araujo, Luciana Spinelli",
         englishtitle = "Use of ALOS/PALSAR-2 and Sentinel-1A satellite data for the 
                         detection of forest volume losses by selective logging process in 
                         a portion of the Tapaj{\'o}s National Forest",
             language = "pt",
                pages = "353",
                  ibi = "8JMKD3MGP3W34T/47PQBTS",
                  url = "http://urlib.net/ibi/8JMKD3MGP3W34T/47PQBTS",
           targetfile = "publicacao.pdf",
        urlaccessdate = "31 mar. 2023"
}


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