Unverified paper record
Forest inventories for small areas using drone imagery without in-situ field measurements
Remote Sensing of Environment · 27 Dec 2019 · 10.1016/j.rse.2019.111404
Abstract
Drone applications are becoming increasingly common in the arena of forest management and forest inventories. In particular, the use of photogrammetrically derived drone-based image point clouds (DIPC) in individual tree detection has become popular. Use of an area-based approach (ABA) in small areas has also been considered. However, in-situ field measurements of sample plots substantially increase the cost of small area forest inventories. Therefore, we examined whether small-scale forest management inventories could be carried out without local field measurements. We used nationwide and regional ABA models for stem volumes fitted with airborne laser scanning (ALS) data to predict stem volumes using corresponding metrics calculated from DIPC data. The stem volumes were predicted at the cell level (15 × 15 m) and aggregated to test plots (30 × 30 m). Height metrics for the dominant tree layer from the DIPC data showed strong correlations with similar metrics computed from the ALS data. The ALS-based models applied with DIPC metrics performed well, especially if the ABA model was fitted in the same geographical area (regional model) and the inventory units were disaggregated to coniferous and deciduous dominated stands using auxiliary information from Multi-source National Forest Inventory data (root mean square error at 30 × 30 m level was 13.1%). The corresponding root mean square error associated with the nationwide ABA model was 20.0% with an overestimation (mean difference 9.6%).
Plant phenotyping relevance
ドローン画像由来の点群を用いて森林の個体・区画レベルの幹材積を推定し、ALSモデルとの比較および誤差評価を行う測定手法が研究の中心であるため、植物形質推定の方法適用・検証として含める。
abstractThe stem volumes were predicted at the cell level (15 × 15 m) and aggregated to test plots (30 × 30 m).
abstractThe corresponding root mean square error associated with the nationwide ABA model was 20.0% with an overestimation (mean difference 9.6%).
Code and data availability
The supplied blocks describe drone/ALS forest inventory methods but contain no authors' public deposit of the paper's field measurements, drone imagery, point clouds, models, or analysis code. All URLs mentioned are third-party data sources (National Land Survey LiDAR, Luke MS-NFI maps, Agisoft software, R) or generic/
No evidence-backed public reproduction asset is currently recorded.
This is an automatically classified, unverified record. Curator approval is required before any resource enters the Catalog.