UAV Based Estimation of Forest Leaf Area Index (LAI) through Oblique Photogrammetry

Abstract

As a key canopy structure parameter, the estimation method of the Leaf Area Index (LAI) has always attracted attention. To explore a potential method to estimate forest LAI from 3D point cloud at low cost, we took photos from different angles of the drone and set five schemes (O (0°), T15 (15°), T30 (30°), OT15 (0° and 15°) and OT30 (0° and 30°)), which were used to reconstruct 3D point cloud of forest canopy based on photogrammetry. Subsequently, the LAI values and the leaf area distribution in the vertical direction derived from five schemes were calculated based on the voxelized model. Our results show that the serious lack of leaf area in the middle and lower layers determines that the LAI estimate of O is inaccurate. For oblique photogrammetry, schemes with 30° photos always provided better LAI estimates than schemes with 15° photos (T30 better than T15, OT30 better than OT15), mainly reflected in the lower part of the canopy, which is particularly obvious in low-LAI areas. The overall structure of the single-tilt angle scheme (T15, T30) was relatively complete, but the rough point cloud details could not reflect the actual situation of LAI well. Multi-angle schemes (OT15, OT30) provided excellent leaf area estimation (OT15: R2 = 0.8225, RMSE = 0.3334 m2/m2; OT30: R2 = 0.9119, RMSE = 0.1790 m2/m2). OT30 provided the best LAI estimation accuracy at a sub-voxel size of 0.09 m and the best checkpoint accuracy (OT30: RMSE [H] = 0.2917 m, RMSE [V] = 0.1797 m). The results highlight that coupling oblique photography and nadiral photography can be an effective solution to estimate forest LAI.

Plant phenotyping relevance

UAV斜め写真測量と3D点群・ボクセル解析を用いて森林キャノピーのLAIを推定する手法を開発・比較検証しており、植物形態形質の取得方法が研究の中心である。

Code and data availability

The paper's authors publicly released the voxelization/LAI extraction code on GitHub; phenotype data (UAV images, point clouds, LAI-2200 measurements) are only available upon request.