Combining Digital Imaging and Artificial Neural Networks for Individual Volumetric Estimation of Non-Straight Trees

Abstract

Tree volume models have become an important decision-making tool in forest resources management. The objective of this work was to create Artificial Neural Networks (ANN) to fit individual tree models from the Brazilian Savanna (the Cerrado biome), characterized by non-straight and very bifurcated tree stems. Data were collected in two different areas and through different methods: i) individual tree volume by digital photographs, and also by tree climbing measurements (both obtained in a non-destructive way); ii) tree volumes measured destructively by the traditional rigorous cubing procedure after thinning. Data obtained from climbing measurements were compared with the corresponding tree obtained from digital photographs, resulting in a reasonable operational linear relation of 0.80. Ten ANN structures were created with different combinations of forestry variables. Five ANN structures depicted predictive ability results above 0.80. Then, the results obtained by ANN were compared with those obtained by classic tree volumetric models, once again showing the predictive superiority of ANN. The present study indicates that tree volume obtained from digital photographs, achieved by popular mobiles, can be safely used for the estimation of very-crooked trees from the Brazilian Savanna and demonstrated the greater predictive ability of ANN in relation to the classic regression-based volumetric models in terms of lower modeling bias.