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Article

Detection of Very Small Tree Plantations and Tree-Level Characterization Using Open-Access Remote-Sensing Databases

Forestry Engineering School, University of Vigo—A Xunqueira Campus, 36005 Pontevedra, Spain
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Remote Sens. 2020, 12(14), 2276; https://doi.org/10.3390/rs12142276
Received: 12 June 2020 / Revised: 10 July 2020 / Accepted: 13 July 2020 / Published: 15 July 2020
(This article belongs to the Special Issue Forest Monitoring in a Multi-Sensor Approach)
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 R2 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. View Full-Text
Keywords: small plantations; detection; characterization; Castanea sativa; Sentinel-2; LiDAR; low density; open-access small plantations; detection; characterization; Castanea sativa; Sentinel-2; LiDAR; low density; open-access
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MDPI and ACS Style

Alonso, L.; Picos, J.; Bastos, G.; Armesto, J. Detection of Very Small Tree Plantations and Tree-Level Characterization Using Open-Access Remote-Sensing Databases. Remote Sens. 2020, 12, 2276. https://doi.org/10.3390/rs12142276

AMA Style

Alonso L, Picos J, Bastos G, Armesto J. Detection of Very Small Tree Plantations and Tree-Level Characterization Using Open-Access Remote-Sensing Databases. Remote Sensing. 2020; 12(14):2276. https://doi.org/10.3390/rs12142276

Chicago/Turabian Style

Alonso, Laura, Juan Picos, Guillermo Bastos, and Julia Armesto. 2020. "Detection of Very Small Tree Plantations and Tree-Level Characterization Using Open-Access Remote-Sensing Databases" Remote Sensing 12, no. 14: 2276. https://doi.org/10.3390/rs12142276

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