Individual Tree Detection and Crown Delineation with 3D Information from Multi-view Satellite Images

TL;DR

This paper introduces a novel method for individual tree detection and crown delineation using 3D information from multi-view satellite images, significantly improving accuracy over traditional 2D spectral approaches.

Contribution

The study presents a new ITDD approach that combines 3D DSM data with 2D imagery and incorporates biological characteristics for enhanced accuracy.

Findings

Detection accuracy up to 89%

Effective extraction of treetops using morphological operations

Improved crown delineation with combined 2D and 3D data

Abstract

Individual tree detection and crown delineation (ITDD) are critical in forest inventory management and remote sensing based forest surveys are largely carried out through satellite images. However, most of these surveys only use 2D spectral information which normally has not enough clues for ITDD. To fully explore the satellite images, we propose a ITDD method using the orthophoto and digital surface model (DSM) derived from the multi-view satellite data. Our algorithm utilizes the top-hat morphological operation to efficiently extract the local maxima from DSM as treetops, and then feed them to a modi-fied superpixel segmentation that combines both 2D and 3D information for tree crown delineation. In subsequent steps, our method incorporates the biological characteristics of the crowns through plant allometric equation to falsify potential outliers. Experiments against manually marked tree plots on three representative regions have demonstrated promising results - the best overall detection accuracy can be 89%.