Paper Info
Title
Mapping the Individual Trees in Urban Orchards by Incorporating Volunteered Geographic Information and Very High Resolution Optical Remotely Sensed Data: A Template Matching-Based Approach.
Abstract
This paper presents a collective sensing approach that integrates imperfect Volunteered Geographic Information (VGI) obtained through Citizen Science (CS) tree mapping projects with very high resolution (VHR) optical remotely sensed data for low-cost, fine-scale, and accurate mapping of trees in urban orchards. To this end, an individual tree crown (ITC) detection technique utilizing template matching (TM) was developed for extracting urban orchard trees from VHR optical imagery. To provide the training samples for the TM algorithm, remotely sensed VGI about trees including the crowdsourced data about ITC locations and their crown diameters was adopted in this study. A data quality assessment of the proposed approach in the study area demonstrated that the detected trees had a very high degree of completeness (92.7%), a high thematic accuracy (false discovery rate (FDR) = 0.090, false negative rate (FNR) = 0.073, and F-1 score (F-1) = 0.918), and a fair positional accuracy (root mean squareerror (RMSE) = 1.02 m). Overall, the proposed approach based on the crowdsourced training samples generally demonstrated a promising ITC detection performance in our pilot project.
Year
DOI
Venue
2018
10.3390/rs10071134
REMOTE SENSING
Keywords
Field
DocType
volunteered geographic information,very high resolution imagery,collective sensing,data quality,template matching,individual tree detection,urban orchard
Template matching,Data quality,Remote sensing,Volunteered geographic information,Geology
Journal
Volume
Issue
ISSN
10
7
2072-4292
Citations 
PageRank 
References 
1
0.38
33
Authors
5
Name
Order
Citations
PageRank
Hossein Vahidi110.38
Brian Klinkenberg210.38
Brian Johnson3658.83
L Monika Moskal414020.24
Wanglin Yan543.08