UAV-Based Automatic Detection and Monitoring of Chestnut Trees

Abstract

Unmanned aerial vehicles have become a popular remote sensing platform for agricultural applications, with an emphasis on crop monitoring. Although there are several methods to detect vegetation through aerial imagery, these remain dependent of manual extraction of vegetation parameters. This article presents an automatic method that allows for individual tree detection and multi-temporal analysis, which is crucial in the detection of missing and new trees and monitoring their health conditions over time. The proposed method is based on the computation of vegetation indices (VIs), while using visible (RGB) and near-infrared (NIR) domain combination bands combined with the canopy height model. An overall segmentation accuracy above 95% was reached, even when RGB-based VIs were used. The proposed method is divided in three major steps: (1) segmentation and first clustering; (2) cluster isolation; and (3) feature extraction. This approach was applied to several chestnut plantations and some parameters—such as the number of trees present in a plantation (accuracy above 97%), the canopy coverage (93% to 99% accuracy), the tree height (RMSE of 0.33 m and R2 = 0.86), and the crown diameter (RMSE of 0.44 m and R2 = 0.96)—were automatically extracted. Therefore, by enabling the substitution of time-consuming and costly field campaigns, the proposed method represents a good contribution in managing chestnut plantations in a quicker and more sustainable way.

Post-Fire Forestry Recovery Monitoring Using High-Resolution Multispectral Imagery From Unmanned Aerial Vehicles

“…These results are directly related with the GSD of the imagery since both flights performed by the multi-rotor UAV have a GSD lower than 5 cm while the fixed-wing UAV has a GSD of 12 cm. GSD values greater than 10 cm shown to be able to automatically detect and segment trees with leaves using CHMs and orthophoto mosaics (Marques et al, 2019).…”

Multispectral Mapping on 3D Models and Multi-Temporal Monitoring for Individual Characterization of Olive Trees

“…Individual Tree Crown Detection and Delineation (ITCD) algorithms have advanced through novel approaches by the integration of heterogeneous data sources [40,41]. Marques et al [42] proposed a fully automated process to monitor chestnuts plantations. This method is based on RGB and multispectral imagery for tree identification and counting as well as feature plant extraction.…”

Citrus Tree Segmentation from UAV Images Based on Monocular Machine Vision in a Natural Orchard Environment

“…Guo et al [18] segmented a forest canopy in a geometric point cloud model established by a UAV-light detection and ranging (LiDar) system and captured the forest canopy height, canopy coverage and terrestrial biomass of three ecosystems, namely, coniferous broad-leaved mixed, evergreen broad-leaf, and mangrove forests. Pedro et al [19] segmented chestnut trees in image data from a red/green/blue (RGB) + infrared camera UAV system through an object clustering extraction method and automatically monitored the trees according to their geometric features and the canopy coverage rate of the resulting area. Wu et al [20] segmented the area of trees with a watershed method and a polynomial fitting method in a 3D forest model established by a UAV-LiDar system and calculated the canopy coverage of a planted forest with a canopy height model and multiple linear regression model.…”