Abstract

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.

Highlights

  • Remote Sensing (RS) is one of the technologies that has been currently most employed in the forestry sector for monitoring, inventorying, and mapping purposes

  • Site A presented the highest vegetative growth conditions, site B an intermediate level, C and D, the lowest dimensions. Those different vegetative conditions directly affected the geometric estimation provided by the Unmanned Aerial Vehicle (UAV) data analysis, so it was necessary to divide the dataset into three different groups of trees according to age and size

  • In the context of the Circular Economy envisaged as a “regenerative system in which resource input and waste, emission, and energy leakage are minimized by slowing, closing and narrowing material and energy loops” [92], it is important to estimate the amounts of wastes available as by-products for industrial purposes

Read more

Summary

Introduction

Remote Sensing (RS) is one of the technologies that has been currently most employed in the forestry sector for monitoring, inventorying, and mapping purposes. RS platform as satellite systems, aircraft platforms and unmanned aerial vehicles (UAVs) have features that differ in terms of spatial resolution, surface covered, temporal resolution, operational procedures, and costs. Satellite solutions remain a fundamental tool for long-term and extensive monitoring and surveillance forestry activities against fire events [2], pests attack [3], illegal logging [4]. Aircraft platforms provide a better image resolution, returning a higher level of detail compared to satellite, against a higher effort in flight planning and relevant operational costs [6]. UAVs are flexible small platforms characterized by low operational costs, high spatial and temporal resolution [7] but suitable to cover only limited areas. Comparisons among different platforms have been made both in the agricultural [8] and in the forestry field [9]

Objectives
Discussion
Conclusion
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call