Canopy Gap delineation using UAV data in Coniferous Forests using (Case Study: Arab Dagh Region in Golestan Province)
Extended Abstract Introduction and Objective: Forest canopy gaps play an important role in forest dynamics. Unmanned aerial vehicle (UAV) data provide demonstrated capacity to systematically and accurately detect and map canopy gaps and have been considered as an alternative way to describe the fore...
| Main Authors: | , , |
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| Format: | Article |
| Language: | fas |
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Sari Agricultural Sciences and Natural Resources University
2023-08-01
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| Series: | بومشناسی جنگلهای ایران |
| Subjects: | |
| Online Access: | http://ifej.sanru.ac.ir/article-1-468-en.pdf |
| _version_ | 1797350555685748736 |
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| author | zeynab khalili Asghar Fallah Shaban Shataee |
| author_facet | zeynab khalili Asghar Fallah Shaban Shataee |
| author_sort | zeynab khalili |
| collection | DOAJ |
| description | Extended Abstract
Introduction and Objective: Forest canopy gaps play an important role in forest dynamics. Unmanned aerial vehicle (UAV) data provide demonstrated capacity to systematically and accurately detect and map canopy gaps and have been considered as an alternative way to describe the forest stands. This study aims to extract canopy gaps using UAV data and compare the performance of different canopy gap extraction methods in a part of the replanted forest in the Arab Dagh Region, Golestan Province, Iran.
Material and Methods:After the acquisition of UAV images and initial preprocessing, the digital terrain model (DTM), digital surface model (DSM), Canopy height model (CHM), and orthophoto mosaic were produced. CHM classification performs to extract forest gaps by different methods of height thresholding on CHM, CHM slope thresholding, and object-based classification. For performance evaluation of used methods and accuracy assessment of the canopy gap maps, the central position and boundary of some gaps were measured by DGPS. Finally, the point and polygon base accuracy of delineated gaps were assessed for each of the methods..
Results: The results of the point accuracy assessment showed that the canopy gap map obtained by object-based classification method with applying the support vector machine (SVM) algorithm with 99% overall accuracy and 0.98 kappa coefficient had the best performance compared to other algorithms and methods. About area accuracy assessment, the best match between delineated gaps and ground truth polygons was achieved by using 3 m height thresholding.
Conclusion: The results showed that with aerial images of the UAV and its outputs, as well as the use of automated methods, the map of the canopy gap can be accurately extracted. Of course, the degree of accuracy depends on several factors such as the type of drone and cameras used, flight parameters and so on. Given the results, it is hoped that this approach will gradually be used as a cheap and accurate method in forest surveying. |
| first_indexed | 2024-03-08T12:46:37Z |
| format | Article |
| id | doaj.art-ab27f3f57a4d4319ae2a8c79665e48f3 |
| institution | Directory Open Access Journal |
| issn | 2423-7140 2676-4296 |
| language | fas |
| last_indexed | 2024-03-08T12:46:37Z |
| publishDate | 2023-08-01 |
| publisher | Sari Agricultural Sciences and Natural Resources University |
| record_format | Article |
| series | بومشناسی جنگلهای ایران |
| spelling | doaj.art-ab27f3f57a4d4319ae2a8c79665e48f32024-01-21T06:49:18ZfasSari Agricultural Sciences and Natural Resources Universityبومشناسی جنگلهای ایران2423-71402676-42962023-08-0111212439Canopy Gap delineation using UAV data in Coniferous Forests using (Case Study: Arab Dagh Region in Golestan Province)zeynab khalili0Asghar Fallah1Shaban Shataee2 Sari Agricultural Sciences and Natural Resources Sari Agricultural Sciences and Natural Resources Sari Agricultural Sciences and Natural Resources Extended Abstract Introduction and Objective: Forest canopy gaps play an important role in forest dynamics. Unmanned aerial vehicle (UAV) data provide demonstrated capacity to systematically and accurately detect and map canopy gaps and have been considered as an alternative way to describe the forest stands. This study aims to extract canopy gaps using UAV data and compare the performance of different canopy gap extraction methods in a part of the replanted forest in the Arab Dagh Region, Golestan Province, Iran. Material and Methods:After the acquisition of UAV images and initial preprocessing, the digital terrain model (DTM), digital surface model (DSM), Canopy height model (CHM), and orthophoto mosaic were produced. CHM classification performs to extract forest gaps by different methods of height thresholding on CHM, CHM slope thresholding, and object-based classification. For performance evaluation of used methods and accuracy assessment of the canopy gap maps, the central position and boundary of some gaps were measured by DGPS. Finally, the point and polygon base accuracy of delineated gaps were assessed for each of the methods.. Results: The results of the point accuracy assessment showed that the canopy gap map obtained by object-based classification method with applying the support vector machine (SVM) algorithm with 99% overall accuracy and 0.98 kappa coefficient had the best performance compared to other algorithms and methods. About area accuracy assessment, the best match between delineated gaps and ground truth polygons was achieved by using 3 m height thresholding. Conclusion: The results showed that with aerial images of the UAV and its outputs, as well as the use of automated methods, the map of the canopy gap can be accurately extracted. Of course, the degree of accuracy depends on several factors such as the type of drone and cameras used, flight parameters and so on. Given the results, it is hoped that this approach will gradually be used as a cheap and accurate method in forest surveying.http://ifej.sanru.ac.ir/article-1-468-en.pdfboundary geometric conformitycanopy gapobject-basedthresholdinguav |
| spellingShingle | zeynab khalili Asghar Fallah Shaban Shataee Canopy Gap delineation using UAV data in Coniferous Forests using (Case Study: Arab Dagh Region in Golestan Province) بومشناسی جنگلهای ایران boundary geometric conformity canopy gap object-based thresholding uav |
| title | Canopy Gap delineation using UAV data in Coniferous Forests using (Case Study: Arab Dagh Region in Golestan Province) |
| title_full | Canopy Gap delineation using UAV data in Coniferous Forests using (Case Study: Arab Dagh Region in Golestan Province) |
| title_fullStr | Canopy Gap delineation using UAV data in Coniferous Forests using (Case Study: Arab Dagh Region in Golestan Province) |
| title_full_unstemmed | Canopy Gap delineation using UAV data in Coniferous Forests using (Case Study: Arab Dagh Region in Golestan Province) |
| title_short | Canopy Gap delineation using UAV data in Coniferous Forests using (Case Study: Arab Dagh Region in Golestan Province) |
| title_sort | canopy gap delineation using uav data in coniferous forests using case study arab dagh region in golestan province |
| topic | boundary geometric conformity canopy gap object-based thresholding uav |
| url | http://ifej.sanru.ac.ir/article-1-468-en.pdf |
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