UAV Video-Based Approach to Identify Damaged Trees in Windthrow Areas

Abstract

Disturbances in forest ecosystems are expected to increase by the end of the twenty-first century. An understanding of these disturbed areas is critical to defining management measures to improve forest resilience. While some studies emphasize the importance of quick salvage logging, others emphasize the importance of the deadwood for biodiversity. Unmanned aerial vehicle (UAV) remote sensing is playing an important role to acquire information in these areas through the structure-from-motion (SfM) photogrammetry process. However, the technique faces challenges due to the fundamental principle of SfM photogrammetry as a passive optical method. In this study, we investigated a UAV video-based technology called full motion video (FMV) to identify fallen and snapped trees in a windthrow area. We compared the performance of FMV and an orthomosaic, created by the SfM photogrammetry process, to manually identify fallen and snapped trees, using a ground survey as a reference. The results showed that FMV was able to identify both types of damaged trees due to the ability of video to deliver better context awareness compared to the orthomosaic, although providing lower position accuracy. In addition to its processing being simpler, FMV technology showed great potential to support the interpretation of conventional UAV remote sensing analysis and ground surveys, providing forest managers with fast and reliable information about damaged trees in windthrow areas.

Plant phenotyping relevance

UAV動画による倒木・折損木という樹木状態の識別手法を開発・比較検証しており、植物状態の取得方法が中心である。

Code and data availability

The paper's UAV video, orthomosaic imagery, ground-survey GNSS feature data, and analysis are not deposited in any public repository. The Data Availability Statement reads 'Not applicable', and no author code, dataset, or model URL is provided; all cited URLs are generic tools (DJI, ESRI, Agisoft, R) or references.

No evidence-backed public reproduction asset is currently recorded.