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Open AccessArticle

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

1
EcoGIS Lab, Graduate School of Media and Governance, Keio University, Fujisawa, Kanagawa 252-0882, Japan
2
Lab for Advanced Spatial Analysis, University of British Columbia, Vancouver, BC V6T 1Z2, Canada
3
Natural Resources and Ecosystem Services Area, Institute for Global Environmental Strategies, Hayama, Kanagawa 240-0115, Japan
4
Remote Sensing and Geospatial Analysis Lab, University of Washington, Seattle, WA 98195-2100, USA
5
Faculty of Environmental and Information Studies, Keio University, Fujisawa, Kanagawa 252-0882, Japan
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(7), 1134; https://doi.org/10.3390/rs10071134
Received: 23 April 2018 / Revised: 5 July 2018 / Accepted: 15 July 2018 / Published: 18 July 2018
(This article belongs to the Special Issue Citizen Science and Earth Observation II)
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 F1 score (F1) = 0.918), and a fair positional accuracy (root mean square error(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. View Full-Text
Keywords: volunteered geographic information; very high resolution imagery; collective sensing; data quality; template matching; individual tree detection; urban orchard volunteered geographic information; very high resolution imagery; collective sensing; data quality; template matching; individual tree detection; urban orchard
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MDPI and ACS Style

Vahidi, H.; Klinkenberg, B.; Johnson, B.A.; Moskal, L.M.; Yan, W. 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. Remote Sens. 2018, 10, 1134.

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