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Predictive Models of Primary Tropical Forest Structure from Geomorphometric Variables Based on SRTM in the Tapajós Region, Brazilian Amazon

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posted on 2016-05-13, 09:57 authored by Polyanna da Conceição Bispo, João Roberto Dos Santos, Márcio de Morisson Valeriano, Paulo Maurício Lima de Alencastro Graça, Heiko Balzter, Helena França, Pitágoras da Conceição Bispo
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.


PolCB was supported by Universidade Federal do ABC (PNPD-CAPES, Council for Advanced Professional Training) in 2015 and currently is supported by European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 660020. JRS, MMV and PitCB were supported by CNPq (National Council for Scientific and Technological Development), PMLG was supported by Instituto Nacional de Ciência e Tecnologia dos Serviços Ambientais da Amazônia (SERVAMB) and HB was supported by Royal Society Wolfs on Research Merit Award (2011/R3) and by Natural Environment Research Council, which support the National Centre for Earth Observation. The São Paulo State University (UNESP, Brazil) funded the publication fee.



PLoS One, 2016, 11 (4), pp. e0152009

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/Organisation/COLLEGE OF SCIENCE AND ENGINEERING/Department of Geography/GIS and Remote Sensing


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