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
Hossein Vahidi; Brian Klinkenberg; Brian A. Johnson; L. Monika Moskal; Wanglin Yan
MDPI Publishing
Published 2018
MDPI Publishing
Published 2018
Publication Date: |
2018-07-19
|
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Publisher: |
MDPI Publishing
|
Electronic ISSN: |
2072-4292
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Topics: |
Architecture, Civil Engineering, Surveying
Geography
|
Published by: |
_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 |