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임정호

Im, Jungho
Intelligent Remote sensing and geospatial Information Science Lab.
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A novel transferable individual tree crown delineation model based on Fishing Net Dragging and boundary classification

Author(s)
Liu, TaoIm, JunghoQuackenbush, Lindi J.
Issued Date
2015-12
DOI
10.1016/j.isprsjprs.2015.10.002
URI
https://scholarworks.unist.ac.kr/handle/201301/17866
Fulltext
http://www.sciencedirect.com/science/article/pii/S0924271615002257
Citation
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, v.110, pp.34 - 47
Abstract
This study provides a novel approach to individual tree crown delineation (ITCD) using airborne Light Detection and Ranging (LiDAR) data in dense natural forests using two main steps: crown boundary refinement based on a proposed Fishing Net Dragging (FiND) method, and segment merging based on boundary classification. FiND starts with approximate tree crown boundaries derived using a traditional watershed method with Gaussian filtering and refines these boundaries using an algorithm that mimics how a fisherman drags a fishing net. Random forest machine learning is then used to classify boundary segments into two classes: boundaries between trees and boundaries between branches that belong to a single tree. Three groups of LiDAR-derived features-two from the pseudo waveform generated along with crown boundaries and one from a canopy height model (CHM)-were used in the classification. The proposed ITCD approach was tested using LiDAR data collected over a mountainous region in the Adirondack Park, NY, USA. Overall accuracy of boundary classification was 82.4%. Features derived from the CHM were generally more important in the classification than the features extracted from the pseudo waveform. A comprehensive accuracy assessment scheme for ITCD was also introduced by considering both area of crown overlap and crown centroids. Accuracy assessment using this new scheme shows the proposed ITCD achieved 74% and 78% as overall accuracy, respectively, for deciduous and mixed forest. © 2015 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS)
Publisher
ELSEVIER SCIENCE BV
ISSN
0924-2716
Keyword (Author)
Accuracy assessmentITCDLiDARPseudo waveformRandom forestWavelet analysis
Keyword
SPECIES CLASSIFICATIONFOREST BIOMASSLIDARAIRBORNESEGMENTATIONHEIGHTEXTRACTIONIMAGERYCANOPYFUSION

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