Influence of Agisoft Metashape Parameters on UAS Structure from Motion Individual Tree Detection from Canopy Height Models

Wade T Tinkham,Neal C Swayze

doi:10.3390/f12020250

Abstract

Applications of unmanned aerial systems for forest monitoring are increasing and drive a need to understand how image processing workflows impact end-user products’ accuracy from tree detection methods. Increasing image overlap and making acquisitions at lower altitudes improve how structure from motion point clouds represents forest canopies. However, only limited testing has evaluated how image resolution and point cloud filtering impact the detection of individual tree locations and heights. We evaluate how Agisoft Metashape’s build dense cloud Quality (image resolution) and depth map filter settings influence tree detection from canopy height models in ponderosa pine forests. Finer resolution imagery with minimal filtering provided the best visual representation of vegetation detail for trees of all sizes. These same settings maximized tree detection F-score at >0.72 for overstory (>7 m tall) and >0.60 for understory trees. Additionally, overstory tree height bias and precision improve as image resolution becomes finer. Overstory and understory tree detection in open-canopy conifer systems might be optimized using the finest resolution imagery that computer hardware enables, while applying minimal point cloud filtering. The extended processing time and data storage demands of high-resolution imagery must be balanced against small reductions in tree detection performance when down-scaling image resolution to allow the processing of greater data extents.

Highlights

The monitoring of forest structure through remotely sensed individual tree observations has rapidly expanded through advancements in airborne light detection and ranging (LiDAR) [1,2] and unmanned aerial system (UAS) photogrammetry [3,4]
This study evaluates the influence of image resolution and intensity of of point filtering on point cloud generation and individual tree detection in filtering on Metashape UAS-structures from motion (SfM) point cloud generation and individual tree detection in aa ponderosa pine forest
Forests 2021, 12, x FOR PEER REVIEWcrowns and the representation of understory trees improve with increasing Quality/image resolution; this improvement is less noticeable between High and Ultra High Quality

Summary

Introduction

The monitoring of forest structure through remotely sensed individual tree observations has rapidly expanded through advancements in airborne light detection and ranging (LiDAR) [1,2] and unmanned aerial system (UAS) photogrammetry [3,4]. Modern UAS structures from motion (SfM) algorithms are proving capable of producing higher density point clouds (100 s points m−2 ) for characterizing forest canopy structure than current airborne LiDAR technology (10 s points m−2 ) [3] This increased point cloud density could improve fine resolution details within canopy height models (CHMs), potentially allowing for more accurate use of individual tree detection (ITD) algorithms that extract tree metrics by searching CHMs for local-maximums within a moving search window [5]. These individual tree techniques have been demonstrated across a range of conifer forest types to accurately characterize overstory tree locations and heights [1,6,7]. Despite the rapidly expanding use of ITD methods with UAS-SfM derived CHMs, there has been limited testing of how photogrammetric processing parameters influence point clouds and their derived products, such as CHMs and subsequent estimates of tree locations and heights

Results

Discussion

Conclusion