Abstract
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.
Highlights
Forestry policies rely strongly on the available knowledge about forest resources [1]
The goal of our research is to explore the potential of the combination of Sentinel-2 satellite images with airborne Light Detection and Ranging (LiDAR) data in the detection, mapping, and characterization of small plantations of trees on a large scale
All described processes are based on a combination of low-resolution multispectral data and low-resolution LiDAR point clouds
Summary
Forestry policies rely strongly on the available knowledge about forest resources [1]. At the European scale, one of the objectives of the Ministerial Conference on the Protection of Forests in Europe [4] is to update the tools for sustainable monitoring and assessing of forestry. One issue that forestry policy must deal with is fragmented rural ownership. This is one of the most significant obstacles for profitable rural management on a global scale [7,8,9]. Fragmented rural ownership is a very common phenomenon in developed countries [10], and it is gradually increasing due to the dissociation between forestry and small-scale farming, and to the limited involvement of landowners in forest management [11]. In 2010, Hirsch and Schmithüsen [12] reported that 61% of all European private forest holdings were less than one hectare
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