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Resolving the Broccoli Problem: Identifying Optimal Computational Algorithms for the Accuracy Assessment of Tree Delineations from Remotely-sensed Data

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Resolving the Broccoli Problem: Identifying Optimal Computational Algorithms for the Accuracy Assessment of Tree Delineations from Remotely-sensed Data. / Murray, Jonathan; Gullick, David Stephen; Blackburn, George Alan; Whyatt, James Duncan; Edwards, Christopher James.

2018. 1 Paper presented at GISRUK 2018, Leicester, United Kingdom.

Research output: Contribution to conference - Without ISBN/ISSN Conference paper

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@conference{a5c4ba25e3b845f48ffc3eba0e95e4bc,
title = "Resolving the Broccoli Problem: Identifying Optimal Computational Algorithms for the Accuracy Assessment of Tree Delineations from Remotely-sensed Data",
abstract = "For many different investigative purposes, trees and forests are aerially scanned using light detection and ranging (LiDAR). Often, this also requires the manual measurement of ground reference (GR) plots within the LiDAR scan. Upon analysis, there is regularly a mismatch between the alignment of GR and LiDAR tree locations, crown areas and tree heights. This anomaly is frequently overlooked and under-reported in the current literature. This study investigates the suitability of match pairing algorithms for the quantification of misalignment errors between two datasets representing GR and LiDAR data, and recommends an algorithm for accurately quantifying match-pairing differences.",
author = "Jonathan Murray and Gullick, {David Stephen} and Blackburn, {George Alan} and Whyatt, {James Duncan} and Edwards, {Christopher James}",
year = "2018",
month = apr,
day = "19",
language = "English",
pages = "1",
note = "GISRUK 2018 : 26th GIScience Research UK Conference, University of Leicester ; Conference date: 17-04-2018 Through 20-04-2018",
url = "http://leicester.gisruk.org",

}

RIS

TY - CONF

T1 - Resolving the Broccoli Problem: Identifying Optimal Computational Algorithms for the Accuracy Assessment of Tree Delineations from Remotely-sensed Data

AU - Murray, Jonathan

AU - Gullick, David Stephen

AU - Blackburn, George Alan

AU - Whyatt, James Duncan

AU - Edwards, Christopher James

PY - 2018/4/19

Y1 - 2018/4/19

N2 - For many different investigative purposes, trees and forests are aerially scanned using light detection and ranging (LiDAR). Often, this also requires the manual measurement of ground reference (GR) plots within the LiDAR scan. Upon analysis, there is regularly a mismatch between the alignment of GR and LiDAR tree locations, crown areas and tree heights. This anomaly is frequently overlooked and under-reported in the current literature. This study investigates the suitability of match pairing algorithms for the quantification of misalignment errors between two datasets representing GR and LiDAR data, and recommends an algorithm for accurately quantifying match-pairing differences.

AB - For many different investigative purposes, trees and forests are aerially scanned using light detection and ranging (LiDAR). Often, this also requires the manual measurement of ground reference (GR) plots within the LiDAR scan. Upon analysis, there is regularly a mismatch between the alignment of GR and LiDAR tree locations, crown areas and tree heights. This anomaly is frequently overlooked and under-reported in the current literature. This study investigates the suitability of match pairing algorithms for the quantification of misalignment errors between two datasets representing GR and LiDAR data, and recommends an algorithm for accurately quantifying match-pairing differences.

M3 - Conference paper

SP - 1

T2 - GISRUK 2018

Y2 - 17 April 2018 through 20 April 2018

ER -