Individual Tree Detection and Classification with UAV-Based Photogrammetric Point Clouds and Hyperspectral Imaging

Olli Nevalainen,Teemu Hakala,Xiaowei Yu,Ilkka Pölönen,Nilton Imai,Eija Honkavaara,Antonio Tommaselli,Heikki Saari,Juha Hyyppä,Sakari Tuominen,Niko Viljanen

doi:10.3390/rs9030185

Abstract

Small unmanned aerial vehicle (UAV) based remote sensing is a rapidly evolving technology. Novel sensors and methods are entering the market, offering completely new possibilities to carry out remote sensing tasks. Three-dimensional (3D) hyperspectral remote sensing is a novel and powerful technology that has recently become available to small UAVs. This study investigated the performance of UAV-based photogrammetry and hyperspectral imaging in individual tree detection and tree species classification in boreal forests. Eleven test sites with 4151 reference trees representing various tree species and developmental stages were collected in June 2014 using a UAV remote sensing system equipped with a frame format hyperspectral camera and an RGB camera in highly variable weather conditions. Dense point clouds were measured photogrammetrically by automatic image matching using high resolution RGB images with a 5 cm point interval. Spectral features were obtained from the hyperspectral image blocks, the large radiometric variation of which was compensated for by using a novel approach based on radiometric block adjustment with the support of in-flight irradiance observations. Spectral and 3D point cloud features were used in the classification experiment with various classifiers. The best results were obtained with Random Forest and Multilayer Perceptron (MLP) which both gave 95% overall accuracies and an F-score of 0.93. Accuracy of individual tree identification from the photogrammetric point clouds varied between 40% and 95%, depending on the characteristics of the area. Challenges in reference measurements might also have reduced these numbers. Results were promising, indicating that hyperspectral 3D remote sensing was operational from a UAV platform even in very difficult conditions. These novel methods are expected to provide a powerful tool for automating various environmental close-range remote sensing tasks in the very near future.

Highlights

Knowing the tree species composition of a forest enables the estimation of the forest’s economic value and produces valuable information for studying forest ecosystems
This study investigated the ability of unmanned airborne vehicle (UAV) based photogrammetry and hyperspectral imaging in individual tree detection and classification in boreal forests
Eleven small forest test sites with 4151 reference trees were included in the analysis

Summary

Introduction

Knowing the tree species composition of a forest enables the estimation of the forest’s economic value and produces valuable information for studying forest ecosystems. 1 pts/m2 ) and aerial images (resolution typically 0.5 m) are used as inventory data. Using these approaches, species-specific diameter distributions have poor prediction accuracy and improvement in tree species detection is needed. Stand-level forest variables are typically an average or sum from the set of trees composing the stand. In calculating forest inventory variables such as the volume and biomass of the growing stock, tree-level models are typically used nowadays [3,4,5]. Very high resolution remote sensing data allows moving from the stand level to the level of individual trees, which has certain benefits, for example in precision forestry, forest management planning, biomass estimation and modeling forest growth [6]

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