Accepted author manuscript, 433 KB, PDF document
Research output: Contribution to conference - Without ISBN/ISSN › Conference paper › peer-review
Research output: Contribution to conference - Without ISBN/ISSN › Conference paper › peer-review
}
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 -