Individual tree detection and characterization using 3D remote sensing

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Authors

Mikko Vastaranta , Ninni Saarinen , Tuomas Yrttimaa , Ville Kankare

Abstract

Here, we will cover individual tree detection and characterization using 3D remote sensing. Simply, it means that point clouds are collected over a forested area using airborne laser scanning (ALS) or created using photogrammetric image interpretation and further used to detect individual trees using different algorithms. After the tree detection, the attributes of interest are predicted for each tree. We try to consistently use the terms “individual tree detection and characterization” or “individual tree detection” and “individual tree characterization” separately referring to different methodological steps. In the scientific remote sensing literature, terms individual tree detection (ITD), individual tree crown approach (ITC) and single tree inventory (STI) are also often used and most often they refer to the same thing. We’ll start by discussing why we need information from single trees. Then we go through the methodological steps that are used in individual tree detection and characterization: 1) remote sensing and field data collection, 2) data types and processing, 3) tree detection algorithms, and 4) methods for prediction of tree attributes. The current methodological state-of-the-art in individual tree detection and characterization is described before we’ll finally present some example applications in biodiversity monitoring, urban forestry and wood procurement planning.

DOI

https://doi.org/10.31223/osf.io/h7asu

Subjects

Forest Sciences, Geographic Information Sciences, Geography, Life Sciences, Remote Sensing, Social and Behavioral Sciences

Keywords

LiDAR, forest sciences, geoinformatics

Dates

Published: 2020-04-11 15:50

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License

CC BY Attribution 4.0 International

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