An allometric model for estimating DBH of isolated and clustered Eucalyptus trees from measurements of crown projection area
Owing to its relevance to remotely-sensed imagery of landscapes, this paper investigates the ability to infer diameter at breast height (DBH) for five species of Australian native 'Eucalyptus' from measurements of tree height and crown projection area. In this study regression models were developed for both single trees and clusters from 2 to 27 stems (maximum density 536 stems per ha) of 'Eucalyptus bridgesiana', 'Eucalyptus caliginosa', 'Eucalyptus blakelyi', 'Eucalyptus viminalis', and 'Eucalyptus melliodora'. Crown projection area and tree height were strongly correlated for single trees, and the log-transformed crown projection area explained the most variance in DBH (R² = 0.68, mean prediction error ±16 cm). Including tree height as a descriptor did not significantly alter the model performance and is a viable alternative to using crown projection area. The total crown projection area of tree clusters explained only 34% of the variance in the total (sum of) the DBH within the clusters. However average crown projection area per stem of entire tree clusters explained 67% of the variance in the average (per stem) DBH of the constituent trees with a mean prediction error ±8 cm. Both the single tree and tree cluster models were statistically similar and a combined model to predict average stem DBH yielded R² = 0.71 with a mean prediction error (average DBH per stem) of ±13 cm within the range of 0.28-0.84 m. A single model to infer DBH for both single trees and clusters comprising up to 27 stems offers a pathway for using remote sensing to infer DBH provided a means of determining the number of stems within cluster boundaries is included.
Tree Cover Extraction from 50 cm Worldview2 Imagery: A Comparison of Image Processing Techniques
High resolution remote sensing is a valuable tool for quantifying the distribution and density of trees with applications ranging from forest inventory, mapping urban parklands to understanding impacts on soil nutrient and carbon dynamics in farming land. The present study aims to compare the accuracy of different remote sensing techniques for delineating the tree cover in 50 cm resolution WorldView2 imagery of farmland. An image of farmland comprising pastures, remnant vegetation and woodland was initially classified into six classes, namely tree cover, bare soil, rock outcrop, natural pasture, degraded pasture and water body using different techniques. Pixel based classification based on all four available wavebands, were tested and an overall classification accuracy of 96.8% and 72.9 % were achieved for supervised and unsupervised techniques. Object based segmentation and subsequent classification yielded an improved overall classification accuracy of 98.3%. Addition of a fifth NDVI layer to the available wavebands did improve the accuracy but not significantly (98.1%, approx 1.3%). In addition to the improvements in overall classification accuracy, a visual inspections of results from the different methods indicated the object based method to yield a more 'realistic' result, avoiding the 'salt and pepper' effects apparent in the pixel-based methods. Overall, object based classification hence is considered more suitable for tree cover extraction from high resolution images.