A Comparative Study to Evaluate Accuracy on Canopy Height and Density Using UAV, ALS, and Fieldwork

Che-young Oh,Cheonggil Jin,Chuluong Choi,Nkwain Wilfred Njungwi,Sanghyun Shin

doi:10.3390/f11020241

Abstract

Accurate measurement of the tree height and canopy cover density is important for forest biomass and management. Recently, Light Detection and Ranging (LIDAR) and Unmanned Aerial Vehicle (UAV) images have been used to estimate the tree height and canopy cover density for a forest stands. More so, UAV systems with autopilot functions, affordable Global Navigation Satellite System (GNSS) and Inertial Measurement Unit (IMU) have created new possibilities, aided by available photogrammetric programs. In this study, we investigated the possibility of data collection methods using an Aerial LIDAR Scanner (ALS) and an UAV together with a fieldworks to evaluate accurate the tree standard metrics in Singyeri, Gyeongjusi, and Gyeongsangbukdo province. The derived metrics via statistical analyses of the ALS and UAV data and validated by field measurements were compared to a published forest type map (scale 1:5000) by the Korea Forest Service; geared towards improving the forest attributes. We collected data and analyzed and compared them with existent the forest type map produced from an aerial photographs and a digital stereo plotter. The ALS data of around 19.5 points·m–2 were collected by an airplane, then processed and classified using the LAStools; while about 362 images of the UAV were processed via Structure from Motion algorithm in the Agisoft Metashape Pro. Thus, we calculated the metrics using the point clouds of both an ALS and an UAV, and then verified their similarity. The fieldwork was manually done on 110 sampled trees. Calculated heights of the UAV were 3.8~5.8 m greater than those for the ALS; and when correlated with the fieldwork, the UAV data overestimated, while the maximum height of the ALS data was more accurate. For the canopy cover, the ALS computed canopy cover was 10%~30% less than that of the UAV. However, the canopy cover above 2 m by an UAV was the best measurement for a forest canopy. Therefore, these results assert that the examined techniques are robust and can significantly complement methods of the conventional data acquisition for the forest type map.

Highlights

More than 63% of South Korea is a forest zone
We investigated an Aerial LIDAR Scanner (ALS) and an Unmanned Aerial Vehicle (UAV) data collection methods and compared them to the standard metrics, a diameter at breast height, a tree age in the field; used statistical analyses to validate and assess an ALS and an UAV data; and compared with the fieldwork likewise the forest type map
The ground sample distance (GSD) was set to 8.95 cm/pixel and 362 images were captured so that the average point canopy cover density was consistent for comparison between UAV and aerial Light Detection and Ranging (LIDAR) data

Summary

Introduction

More than 63% of South Korea is a forest zone. As such, these resources are both environmentally and economically important to the country. A diameter at breast height and a tree age can both be obtained from woodchip statistics established during a forest exploitation Considering all of these factors, and the fact that the forest type map has about 150~200 polygons per the forest type map (scale 1:5000), it can be difficult to judge the overall accuracy of the forest type map. We investigated an ALS and an UAV data collection methods and compared them to the standard metrics (a tree height and a canopy cover density based on the class definition in the Forest Type Map of South Korea), a diameter at breast height, a tree age in the field; used statistical analyses to validate and assess an ALS and an UAV data; and compared with the fieldwork likewise the forest type map

Study Area

Aerial

A Terrascan

Aerial Light

Research

UAV Digital Image and Data Processing

Field Forest Measurement

Discussion

Map m and

Conclusions