Detection of Very Small Tree Plantations and Tree-Level Characterization Using Open-Access Remote-Sensing Databases
In: Remote Sensing, Vol 12, Iss 2276, p 2276 (2020, Jg. 12 (2020), Heft 2276, p 2276
academicJournal
Zugriff:
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 ( Castanea sativa ) 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 2 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 .
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Detection of Very Small Tree Plantations and Tree-Level Characterization Using Open-Access Remote-Sensing Databases
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Autor/in / Beteiligte Person: | Alonso, Laura ; Picos, Juan ; Bastos, Guillermo ; Armesto, Julia |
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Zeitschrift: | Remote Sensing, Vol 12, Iss 2276, p 2276 (2020, Jg. 12 (2020), Heft 2276, p 2276 |
Veröffentlichung: | MDPI AG ; Remote Sensing ; Enxeñaría dos recursos naturais e medio ambiente ; Economía financeira e contabilidade ; Enxeñería Agroforestal ; INARdesign ; Xeotecnoloxías Aplicadas, 2020 |
Medientyp: | academicJournal |
DOI: | 10.3390/rs12142276 |
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