Autoria: OLIVEIRA, M. V. N. d'; OLIVEIRA, L. C. de

Resumo: Lidar data has been largely used to produce estimative on biomass and timber stocks in tropical forests. A major problem is the lidar flights costs, and the exhaustive and expensive ground plot data acquisition necessary to calibrate lidar data metrics. The use of ground information from previously established plots and the generalization of existent models to structurally similar forests should be a way to minimize these costs. In this work we study six forest in Acre state with similar structure and different disturbance history covered by lidar flights and forest inventories. We investigate whether the use of plots with different sizes violate the null hypothesis of the variance equality of the lidar metrics and tested the use of a lidar general model to estimate the biomass on the studied sites. We generated regression models to estimate above ground biomass for each area and compared them to a general model elaborated with the ground and lidar information of all areas together. The results showed that the null hypotheses of the variance was not violated to the variable selected to compose the models and no significant differences were found among the local and general models suggesting that in the absence of forest inventories, when forest were structurally similar, a general lidar model can be used to assess biomass stocks.

Ano de publicação: 2017

Tipo de publicação: Artigo em anais e proceedings

Unidade: Embrapa Acre

Palavras-chave: Aboveground biomass, Acre, Amazonia Occidental, Amazônia Ocidental, Análise estatística, Análisis de regresión, Análisis estadístico, Biomasa aérea, Biomassa, Bujari (AC), Embrapa Acre, Estimativa, Fazenda Bonal, Feijó (AC), Floresta Estadual do Antimary (AC), Geotécnica, Lidar, Lásers, Parte aérea, Porto Acre (AC), Projeto de Assentamento Dirigido Humaita (AC), Raio laser, Regression analysis, Regressão linear, Remote sensing, Rio Branco (AC), Sena Madureira (AC), Senador Guiomard (AC), Sensoriamento remoto, Statistical analysis, Teledetección, Terra Indígena Kaxinawá de Nova Olinda (AC), Western Amazon