A Work Flow and Evaluation of Using Unmanned Aerial Systems for Deriving Forest Stand Characteristics in Mixed Hardwoods of West Virginia

Liebermann, Henry

doi:10.33915/etd.6083

Cited by 1 publication

(1 citation statement)

References 46 publications

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“…In the literature, several unsupervised segmentation approaches have been proposed: the most widely used is the watershed segmentation algorithm [20,23,34,[64][65][66][67] and its variants [16,54,68]. Other techniques are multiresolution segmentation algorithm [27,69], large-scale mean-shift algorithm [35], semantic-level segmentation using a Convolutional Neural Network (CNN) [70] and more complex approaches with two or more integrated algorithms [37,63,71]. Some authors associated the above-mentioned unsupervised approaches to manually drawn individual tree crown polygons from on-screen interpretation to compare and validate results or provide a reference for the accuracy assessment of an automatic procedure [72][73][74].…”

Section: Introductionmentioning

confidence: 99%

An Automatic UAV Based Segmentation Approach for Pruning Biomass Estimation in Irregularly Spaced Chestnut Orchards

Gennaro

Nati

Dainelli

et al. 2020

Forests

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The agricultural and forestry sector is constantly evolving, also through the increased use of precision technologies including Remote Sensing (RS). Remotely biomass estimation (WaSfM) in wood production forests is already debated in the literature, but there is a lack of knowledge in quantifying pruning residues from canopy management. The aim of the present study was to verify the reliability of RS techniques for the estimation of pruning biomass through differences in the volume of canopy trees and to evaluate the performance of an unsupervised segmentation methodology as a feasible tool for the analysis of large areas. Remote sensed data were acquired on four uneven-aged and irregularly spaced chestnut orchards in Central Italy by an Unmanned Aerial Vehicle (UAV) equipped with a multispectral camera. Chestnut geometric features were extracted using both supervised and unsupervised crown segmentation and then applying a double filtering process based on Canopy Height Model (CHM) and vegetation index threshold. The results show that UAV monitoring provides good performance in detecting biomass reduction after pruning, despite some differences between the trees’ geometric features. The proposed unsupervised methodology for tree detection and vegetation cover evaluation purposes showed good performance, with a low undetected tree percentage value (1.7%). Comparing crown projected volume reduction extracted by means of supervised and unsupervised approach, R2 ranged from 0.76 to 0.95 among all the sites. Finally, the validation step was assessed by evaluating correlations between measured and estimated pruning wood biomass (Wpw) for single and grouped sites (0.53 < R2 < 0.83). The method described in this work could provide effective strategic support for chestnut orchard management in line with a precision agriculture approach. In the context of the Circular Economy, a fast and cost-effective tool able to estimate the amounts of wastes available as by-products such as chestnut pruning residues can be included in an alternative and virtuous supply chain.

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