Automatic Extraction of Tree Crown for the Estimation of Biomass from UAV Imagery Using Neural Networks

The biomass content of vegetation has great significance for efficient balancing of biomass cycle. Estimation of biomass in vegetation requires quantification of tree canopy. Automatic extraction of tree canopy helps in faster and efficient estimation of the biomass content of a vegetative area in comparison to manual mapping methods. An agricultural farm at Papparapati Village in Dharmapuri, District, Tamil Nadu, with an Area of 1.2 sq.km was chosen as study area and multispectral UAV imagery at 4-cm spatial resolution with 4 Bands viz., NIR, Green, Red Edge, RED was acquired. In this study, an attempt has been made to automatically extract tree canopy using simple deep learning convolution neural network (CNN) using training sites as input from the band spectral information. The extracted tree canopy was refined using linear cluster algorithm to delineate the exact tree crown. The biomass of the tree is estimated using the tree properties (crown diameter, height) derived from UAV data and using the allometric equations. A field survey was conducted to validate the classification results achieved from UAV data of individual sample trees. It is observed that the accuracy of extracted tree properties was in agreement with the field measurements of sample trees.