title: Predictive Models of Primary Tropical Forest Structure from Geomorphometric Variables Based on SRTM in the Tapajós Region, Brazilian Amazon.
abstract: Surveying primary tropical forest over large regions is challenging. Indirect methods of relating terrain information or other external spatial datasets to forest biophysical parameters can provide forest structural maps at large scales but the inherent uncertainties need to be evaluated fully. The goal of the present study was to evaluate relief characteristics, measured through geomorphometric variables, as predictors of forest structural characteristics such as average tree basal area (BA) and height (H) and average percentage canopy openness (CO). Our hypothesis is that geomorphometric variables are good predictors of the structure of primary tropical forest, even in areas, with low altitude variation. The study was performed at the Tapajós National Forest, located in the Western State of Pará, Brazil. Forty-three plots were sampled. Predictive models for BA, H and CO were parameterized based on geomorphometric variables using multiple linear regression. Validation of the models with nine independent sample plots revealed a Root Mean Square Error (RMSE) of 3.73 m2/ha (20%) for BA, 1.70 m (12%) for H, and 1.78% (21%) for CO. The coefficient of determination between observed and predicted values were r2 = 0.32 for CO, r2 = 0.26 for H and r2 = 0.52 for BA. The models obtained were able to adequately estimate BA and CO. In summary, it can be concluded that relief variables are good predictors of vegetation structure and enable the creation of forest structure maps in primary tropical rainforest with an acceptable uncertainty.
authors.affiliation: Ciência e Tecnologia Ambiental, Universidade Federal do ABC (UFABC), Santo André, São Paulo, Brazil.|Centre for Landscape and Climate Research, Department of Geography, University of Leicester, Leicester, United Kingdom.
authors.forename: Polyanna da Conceição
authors.initials: Pda C
authors.lastname: Bispo
authors.affiliation: Divisão de Sensoriamento Remoto, Instituto Nacional de Pesquisas Espaciais (INPE), São José dos Campos, São Paulo, Brazil.
authors.forename: João Roberto
authors.initials: JR
authors.lastname: Dos Santos
authors.affiliation: Divisão de Sensoriamento Remoto, Instituto Nacional de Pesquisas Espaciais (INPE), São José dos Campos, São Paulo, Brazil.
authors.forename: Márcio de Morisson
authors.initials: Mde M
authors.lastname: Valeriano
authors.affiliation: Coordenação de Dinâmica Ambiental, Instituto Nacional de Pesquisas da Amazônia (INPA), Manaus, Amazonas, Brazil.
authors.forename: Paulo Maurício Lima de Alencastro
authors.initials: PM
authors.lastname: Graça
authors.affiliation: Centre for Landscape and Climate Research, Department of Geography, University of Leicester, Leicester, United Kingdom.|National Centre for Earth Observation, University of Leicester, Leicester, United Kingdom.
authors.forename: Heiko
authors.initials: H
authors.lastname: Balzter
authors.affiliation: Ciência e Tecnologia Ambiental, Universidade Federal do ABC (UFABC), Santo André, São Paulo, Brazil.
authors.forename: Helena
authors.initials: H
authors.lastname: França
authors.affiliation: Departamento de Ciências Biológicas, Faculdade de Ciências e Letras de Assis, Universidade Estadual Paulista (UNESP), Assis, São Paulo, Brazil.
authors.forename: Pitágoras da Conceição
authors.initials: Pda C
authors.lastname: Bispo
mesh_terms: D001938:Brazil; D008954:Models, Biological; D065947:Rainforest; D014329:Tropical Climate