Detection of Very Small Tree Plantations and Tree-Level Characterization Using Open-Access Remote-Sensing Databases
Highly fragmented land property hinders the planning and management of single species tree plantations. In such situations, acquiring information about the available resources is challenging. This study aims to propose a method to locate and characterize tree plantations in these cases. Galicia (Nor...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-07-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/12/14/2276 |
| _version_ | 1797562438691848192 |
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| author | Laura Alonso Juan Picos Guillermo Bastos Julia Armesto |
| author_facet | Laura Alonso Juan Picos Guillermo Bastos Julia Armesto |
| author_sort | Laura Alonso |
| collection | DOAJ |
| description | Highly fragmented land property hinders the planning and management of single species tree plantations. In such situations, acquiring information about the available resources is challenging. This study aims to propose a method to locate and characterize tree plantations in these cases. Galicia (Northwest of Spain) is an area where property is extremely divided into small parcels. European chestnut (<i>Castanea sativa</i>) plantations are an important source of income there; however, it is often difficult to obtain information about them due to their small size and scattered distribution. Therefore, we selected a Galician region with a high presence of chestnut plantations as a case study area in order to locate and characterize small plantations using open-access data. First, we detected the location of chestnut plantations applying a supervised classification for a combination of: Sentinel-2 images and the open-access low-density Light Detection and Ranging (LiDAR) point clouds, obtained from the untapped open-access LiDAR Spanish national database. Three classification algorithms were used: Random Forest (RF), Support Vector Machine (SVM), and XGBoost. We later characterized the plots at the tree-level using the LiDAR point-cloud. We detected individual trees and obtained their height applying a local maxima algorithm to a point-cloud-derived Canopy Height Model (CHM). We also calculated the crown surface of each tree by applying a method based on two-dimensional (2D) tree shape reconstruction and canopy segmentation to a projection of the LiDAR point cloud. Chestnut plantations were detected with an overall accuracy of 81.5%. Individual trees were identified with a detection rate of 96%. The coefficient of determination R<sup>2</sup> value for tree height estimation was 0.83, while for the crown surface calculation it was 0.74. The accuracy achieved with these open-access databases makes the proposed procedure suitable for acquiring knowledge about the location and state of chestnut plantations as well as for monitoring their evolution. |
| first_indexed | 2024-03-10T18:28:12Z |
| format | Article |
| id | doaj.art-f544f39f9f314504a3e94e8ec290760c |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T18:28:12Z |
| publishDate | 2020-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-f544f39f9f314504a3e94e8ec290760c2023-11-20T06:51:47ZengMDPI AGRemote Sensing2072-42922020-07-011214227610.3390/rs12142276Detection of Very Small Tree Plantations and Tree-Level Characterization Using Open-Access Remote-Sensing DatabasesLaura Alonso0Juan Picos1Guillermo Bastos2Julia Armesto3Forestry Engineering School, University of Vigo—A Xunqueira Campus, 36005 Pontevedra, SpainForestry Engineering School, University of Vigo—A Xunqueira Campus, 36005 Pontevedra, SpainForestry Engineering School, University of Vigo—A Xunqueira Campus, 36005 Pontevedra, SpainForestry Engineering School, University of Vigo—A Xunqueira Campus, 36005 Pontevedra, SpainHighly fragmented land property hinders the planning and management of single species tree plantations. In such situations, acquiring information about the available resources is challenging. This study aims to propose a method to locate and characterize tree plantations in these cases. Galicia (Northwest of Spain) is an area where property is extremely divided into small parcels. European chestnut (<i>Castanea sativa</i>) plantations are an important source of income there; however, it is often difficult to obtain information about them due to their small size and scattered distribution. Therefore, we selected a Galician region with a high presence of chestnut plantations as a case study area in order to locate and characterize small plantations using open-access data. First, we detected the location of chestnut plantations applying a supervised classification for a combination of: Sentinel-2 images and the open-access low-density Light Detection and Ranging (LiDAR) point clouds, obtained from the untapped open-access LiDAR Spanish national database. Three classification algorithms were used: Random Forest (RF), Support Vector Machine (SVM), and XGBoost. We later characterized the plots at the tree-level using the LiDAR point-cloud. We detected individual trees and obtained their height applying a local maxima algorithm to a point-cloud-derived Canopy Height Model (CHM). We also calculated the crown surface of each tree by applying a method based on two-dimensional (2D) tree shape reconstruction and canopy segmentation to a projection of the LiDAR point cloud. Chestnut plantations were detected with an overall accuracy of 81.5%. Individual trees were identified with a detection rate of 96%. The coefficient of determination R<sup>2</sup> value for tree height estimation was 0.83, while for the crown surface calculation it was 0.74. The accuracy achieved with these open-access databases makes the proposed procedure suitable for acquiring knowledge about the location and state of chestnut plantations as well as for monitoring their evolution.https://www.mdpi.com/2072-4292/12/14/2276small plantationsdetectioncharacterization<i>Castanea sativa</i>Sentinel-2LiDAR |
| spellingShingle | Laura Alonso Juan Picos Guillermo Bastos Julia Armesto Detection of Very Small Tree Plantations and Tree-Level Characterization Using Open-Access Remote-Sensing Databases Remote Sensing small plantations detection characterization <i>Castanea sativa</i> Sentinel-2 LiDAR |
| title | Detection of Very Small Tree Plantations and Tree-Level Characterization Using Open-Access Remote-Sensing Databases |
| title_full | Detection of Very Small Tree Plantations and Tree-Level Characterization Using Open-Access Remote-Sensing Databases |
| title_fullStr | Detection of Very Small Tree Plantations and Tree-Level Characterization Using Open-Access Remote-Sensing Databases |
| title_full_unstemmed | Detection of Very Small Tree Plantations and Tree-Level Characterization Using Open-Access Remote-Sensing Databases |
| title_short | Detection of Very Small Tree Plantations and Tree-Level Characterization Using Open-Access Remote-Sensing Databases |
| title_sort | detection of very small tree plantations and tree level characterization using open access remote sensing databases |
| topic | small plantations detection characterization <i>Castanea sativa</i> Sentinel-2 LiDAR |
| url | https://www.mdpi.com/2072-4292/12/14/2276 |
| work_keys_str_mv | AT lauraalonso detectionofverysmalltreeplantationsandtreelevelcharacterizationusingopenaccessremotesensingdatabases AT juanpicos detectionofverysmalltreeplantationsandtreelevelcharacterizationusingopenaccessremotesensingdatabases AT guillermobastos detectionofverysmalltreeplantationsandtreelevelcharacterizationusingopenaccessremotesensingdatabases AT juliaarmesto detectionofverysmalltreeplantationsandtreelevelcharacterizationusingopenaccessremotesensingdatabases |