Close
Metadata

@MastersThesis{Chagas::UsDaLi,
               author = "Chagas, Gabriel Oliveira",
                title = "Uso de dados LiDAR, SAR e Sentinel-2 para estimativa da biomassa 
                         florestal acima do solo na Floresta Nacional do Tapaj{\'o}s",
               school = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
                month = "2021-08-17",
             keywords = "sensoriamento remoto, Floresta Amaz{\^o}nica, 
                         degrada{\c{c}}{\~a}o florestal, modelagem de AGB, random forest, 
                         remote sensing, Amazon Rainforest, forest degradation, AGB 
                         modeling, random forest.",
             abstract = "A Floresta Amaz{\^o}nica, a maior {\'a}rea de floresta 
                         cont{\'{\i}}nua do mundo, tem um papel importante na 
                         regula{\c{c}}{\~a}o do clima local e regional, e na ciclagem da 
                         {\'a}gua. Al{\'e}m disso, a Amaz{\^o}nia det{\'e}m um grande 
                         estoque de carbono em sua {\'a}rea florestal, mas o 
                         desflorestamento, a extra{\c{c}}{\~a}o seletiva da madeira, os 
                         inc{\^e}ndios florestais e a fragmenta{\c{c}}{\~a}o florestal 
                         t{\^e}m contribu{\'{\i}}do para a redu{\c{c}}{\~a}o desse 
                         estoque na regi{\~a}o. Desta maneira, os dados de campo s{\~a}o 
                         fundamentais para mensurar a magnitude e a extens{\~a}o desses 
                         impactos na floresta e entender a din{\^a}mica das {\'a}reas 
                         degradadas. Esses dados tamb{\'e}m permitem a gera{\c{c}}{\~a}o 
                         de modelos preditivos de biomassa florestal acima do solo (AGB). A 
                         AGB permite identificar {\'a}reas priorit{\'a}rias para a 
                         atua{\c{c}}{\~a}o de projetos de conserva{\c{c}}{\~a}o 
                         florestal, visto que a biomassa est{\'a} relacionada com os 
                         estoques de carbono. Contudo, devido {\`a} extens{\~a}o da 
                         Floresta Amaz{\^o}nica, a extrapola{\c{c}}{\~a}o dos dados de 
                         campo {\'e} limitada pelo n{\'u}mero de amostras, pela {\'a}rea 
                         das parcelas de campo e pelo tempo gasto na amostragem de campo. 
                         Desse modo, o uso de dados de campo em conjunto com dados de 
                         sensoriamento remoto {\'e} uma alternativa para melhorar as 
                         estimativas de AGB. Todavia, devido {\`a} extens{\~a}o da 
                         Floresta Amaz{\^o}nica, {\`a} variabilidade de sua estrutura e 
                         aos diferentes graus de degrada{\c{c}}{\~a}o florestal, existem 
                         v{\'a}rias incertezas nas estimativas de AGB dispon{\'{\i}}veis 
                         na literatura. Assim, o presente trabalho teve como objetivo 
                         estimar a AGB para a Floresta Nacional do Tapaj{\'o}s e 
                         {\'a}reas adjacentes, considerando os diferentes graus de 
                         interven{\c{c}}{\~a}o humana, utilizando dados dos sensores 
                         LiDAR (GEDI e ALS), SAR (PALSAR-2) e {\'o}pticos (Sentinel-2). 
                         Inicialmente, a AGB foi calculada com uma equa{\c{c}}{\~a}o 
                         alom{\'e}trica, utilizando dados de campo, para compor a 
                         vari{\'a}vel dependente do modelo. As vari{\'a}veis 
                         independentes do modelo obtidas neste estudo foram: as 
                         vari{\'a}veis biof{\'{\i}}sicas da vegeta{\c{c}}{\~a}o e as 
                         m{\'e}tricas derivadas dos sensores remotos utilizados (altura do 
                         dado GEDI e as m{\'e}tricas que descrevem a nuvem de pontos do 
                         LiDAR ALS); as decomposi{\c{c}}{\~o}es polarim{\'e}tricas SAR; 
                         os coeficientes de retroespalhamento, os {\'{\i}}ndices e 
                         raz{\~o}es SAR dispon{\'{\i}}veis na literatura; os 
                         {\'{\i}}ndices de vegeta{\c{c}}{\~a}o do Sentinel-2 
                         dispon{\'{\i}}veis na literatura e as imagens fra{\c{c}}{\~a}o 
                         do modelo linear de mistura espectral (MLME). A seguir o algoritmo 
                         Random Forest foi usado para modelar a rela{\c{c}}{\~a}o entre 
                         vari{\'a}veis preditivas e a AGB. Os melhores modelos de AGB, com 
                         resolu{\c{c}}{\~o}es espaciais de 16,62 m, 50 m e 555,55 m, 
                         apresentaram RMSE de 60,35 Mg ha-1 (23,07%), 75,88 Mg ha-1 
                         (26,42%) e 89,87 Mg ha-1 (28,04%), e um Rē de 0,72, 0,66 e 0,57, 
                         respectivamente. De acordo com a an{\'a}lise das estimativas de 
                         AGB, os modelos {\'o}timos obtidos neste estudo foram capazes de 
                         descrever a AGB de acordo com o grau de degrada{\c{c}}{\~a}o 
                         florestal. Os resultados demonstram o potencial dos dados GEDI, 
                         LiDAR ALS, SAR e {\'o}pticos para estimar a AGB em {\'a}reas de 
                         floresta tropical de acordo com a variabilidade da estrutura 
                         florestal. ABSTRACT: The world's largest continuous forest area, 
                         the Amazon rainforest, plays an important role in regulating local 
                         and regional climate and water cycling. In addition, the Amazon 
                         rainforest has a large carbon stock in its forest area, but 
                         deforestation, logging, forest fires, and forest fragmentation 
                         have contributed to the reduction of its carbon stock. Thus, field 
                         data are essential to measure the magnitude and extent of these 
                         impacts on forests and to understand the dynamics of degraded 
                         areas. These data also allow the generation of forest aboveground 
                         biomass (AGB) predictive models. The AGB allows identifying 
                         priority areas for the development of forest conservation projects 
                         since biomass is related to carbon stocks. However, due to the 
                         extension of the Amazon rainforest, the extrapolation of field 
                         data is limited by the number of samples, the area of the sample 
                         plots, and the time spent in field sampling. Thereby, the use of 
                         field data together with remote sensing data is an alternative to 
                         improve AGB estimates. Although, due to the extension, variability 
                         of the Amazon rainforest structure, and different degrees of 
                         forest degradation, there are several uncertainties in the AGB 
                         estimates available in the literature. Thus, the present work 
                         aimed to estimate AGB for the Tapaj{\'o}s National Forest and 
                         adjacent areas, considering the different degrees of forest 
                         degradation, using data from the sensors LiDAR (GEDI and ALS), SAR 
                         (PALSAR-2), and optical (Sentinel-2). Initially, the AGB was 
                         calculated with an allometric equation using field data to compose 
                         the dependent variable of the model. The independent variables of 
                         the model obtained in this study were: the vegetation biophysical 
                         variables and the metrics derived from LiDAR sensors (height of 
                         the GEDI data and the metrics that describe the LiDAR ALS point 
                         cloud); polarimetric SAR decompositions; backscatter coefficients, 
                         SAR indexes, and ratios available in the literature; Sentinel-2 
                         vegetation indexes available in the literature and fraction images 
                         from linear spectral mixing model. Then, the Random Forest 
                         algorithm was used to model the relationship between predictive 
                         variables and AGB. The best AGB estimates with spatial resolution 
                         of 16.62m, 50m and 555.55m had RMSE of 60.35 Mg ha-1 (23.07%), 
                         75.88 Mg ha-1 (26.42%) and 89 .87 Mg ha-1 (28.04%), and an Rē of 
                         0.72, 0.66 and 0.57, respectively. According to the AGB estimates 
                         analysis, the optimal models obtained in this study were able to 
                         describe the AGB according to the degree of forest degradation. 
                         The results demonstrate the potential of GEDI, LiDAR ALS, SAR, and 
                         optical data to estimate AGB in tropical forest areas according to 
                         the variability of the forest structure.",
            committee = "Gama, F{\'a}bio Furlan (presidente) and Shimabukuro, Yosio Edemir 
                         (orientador) and Sano, Edson Eyji",
         englishtitle = "The use of LiDAR, SAR and Sentinel-2 data to estimate forest 
                         aboveground biomass in Tapaj{\'o}s National Forest",
             language = "pt",
                pages = "131",
                  ibi = "8JMKD3MGP3W34T/4593M6E",
                  url = "http://urlib.net/ibi/8JMKD3MGP3W34T/4593M6E",
           targetfile = "publicacao.pdf",
        urlaccessdate = "2021, Dec. 04"
}


Close