Remote Sensing, Vol. 10, Pages 1134: 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

Publication Date:
2018-07-19
Publisher:
MDPI Publishing
Electronic ISSN:
2072-4292
Topics:
Architecture, Civil Engineering, Surveying
Geography
Published by:
http://www.mdpi.com/
_version_ 1836399008890748928
autor Hossein Vahidi; Brian Klinkenberg; Brian A. Johnson; L. Monika Moskal; Wanglin Yan
beschreibung Remote Sensing, Vol. 10, Pages 1134: 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 Sensing doi: 10.3390/rs10071134 Authors: Hossein Vahidi Brian Klinkenberg Brian A. Johnson L. Monika Moskal Wanglin Yan 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.
citation_standardnr 6306420
datenlieferant ipn_articles
feed_id 124526
feed_publisher MDPI Publishing
feed_publisher_url http://www.mdpi.com/
insertion_date 2018-07-19
journaleissn 2072-4292
publikationsjahr_anzeige 2018
publikationsjahr_facette 2018
publikationsjahr_intervall 7984:2015-2019
publikationsjahr_sort 2018
publisher MDPI Publishing
quelle Remote Sensing
relation http://www.mdpi.com/2072-4292/10/7/1134
search_space articles
shingle_author_1 Hossein Vahidi; Brian Klinkenberg; Brian A. Johnson; L. Monika Moskal; Wanglin Yan
shingle_author_2 Hossein Vahidi; Brian Klinkenberg; Brian A. Johnson; L. Monika Moskal; Wanglin Yan
shingle_author_3 Hossein Vahidi; Brian Klinkenberg; Brian A. Johnson; L. Monika Moskal; Wanglin Yan
shingle_author_4 Hossein Vahidi; Brian Klinkenberg; Brian A. Johnson; L. Monika Moskal; Wanglin Yan
shingle_catch_all_1 Remote Sensing, Vol. 10, Pages 1134: 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 Sensing, Vol. 10, Pages 1134: 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 Sensing doi: 10.3390/rs10071134 Authors: Hossein Vahidi Brian Klinkenberg Brian A. Johnson L. Monika Moskal Wanglin Yan 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.
Hossein Vahidi; Brian Klinkenberg; Brian A. Johnson; L. Monika Moskal; Wanglin Yan
MDPI Publishing
2072-4292
20724292
shingle_catch_all_2 Remote Sensing, Vol. 10, Pages 1134: 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 Sensing, Vol. 10, Pages 1134: 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 Sensing doi: 10.3390/rs10071134 Authors: Hossein Vahidi Brian Klinkenberg Brian A. Johnson L. Monika Moskal Wanglin Yan 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.
Hossein Vahidi; Brian Klinkenberg; Brian A. Johnson; L. Monika Moskal; Wanglin Yan
MDPI Publishing
2072-4292
20724292
shingle_catch_all_3 Remote Sensing, Vol. 10, Pages 1134: 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 Sensing, Vol. 10, Pages 1134: 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 Sensing doi: 10.3390/rs10071134 Authors: Hossein Vahidi Brian Klinkenberg Brian A. Johnson L. Monika Moskal Wanglin Yan 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.
Hossein Vahidi; Brian Klinkenberg; Brian A. Johnson; L. Monika Moskal; Wanglin Yan
MDPI Publishing
2072-4292
20724292
shingle_catch_all_4 Remote Sensing, Vol. 10, Pages 1134: 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 Sensing, Vol. 10, Pages 1134: 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 Sensing doi: 10.3390/rs10071134 Authors: Hossein Vahidi Brian Klinkenberg Brian A. Johnson L. Monika Moskal Wanglin Yan 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.
Hossein Vahidi; Brian Klinkenberg; Brian A. Johnson; L. Monika Moskal; Wanglin Yan
MDPI Publishing
2072-4292
20724292
shingle_title_1 Remote Sensing, Vol. 10, Pages 1134: 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
shingle_title_2 Remote Sensing, Vol. 10, Pages 1134: 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
shingle_title_3 Remote Sensing, Vol. 10, Pages 1134: 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
shingle_title_4 Remote Sensing, Vol. 10, Pages 1134: 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
timestamp 2025-06-30T23:36:09.227Z
titel Remote Sensing, Vol. 10, Pages 1134: 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
titel_suche Remote Sensing, Vol. 10, Pages 1134: 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
topic ZH-ZI
R
uid ipn_articles_6306420