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

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.

Automatic Detection of Aquatic Weeds: A Case Study in the Guadiana River, Spain

“…Several research efforts have considered digital aerial orthophotographs collected by PNOA for photointerpretation tasks, due to their high spatial resolution [44], [45], [46]. Some projects managed by the Ministry of Development (Spain) follow the INSPIRE Directive [47], which establishes a spatial data infrastructure for geographic data in Europe to provide geometric and temporal coherence of cartographic and geographic databases.…”

Hyperspectral and Lidar Data Applied to the Urban Land Cover Machine Learning and Neural-Network-Based Classification: A Review

“…In the classification approaches, Charaniya et al [145] included height variation, Bartels and Wei [146] calculated mean variance and standard derivation of the height in the first echo from lidar to measure the roughness, The nDSM represents the above-ground points that correspond to the actual heights of the object, omitting information about the objects which could complicate the classification, for example, the differentiation of buildings in lowland or hilly regions. The height information from lidar data helps differentiate between high and low vegetation [139], tree-level characterization applying the canopy height model (CHM) [140], and roads and buildings in the urban environment [8]. In addition, slope calculation (first derivative of any elevation product) and surface curvature (second derivative of the elevation surface) have been applied for detecting surface roughness [141,142] and changes in the normal vectors of the surface [143].…”

A Handheld Laser-Scanning-Based Methodology for Monitoring Tree Growth in Chestnut Orchards

“…Remote sensing techniques have been specifically tested in chestnut orchards using different scales and platforms. Alonso et al [ 17 ] used satellite images and low-density LiDAR to identify trees and estimate their heights. UAV imagery has been used for health monitoring and nutritional deficiency identification and even to calculate biomass in non-regular chestnut orchards [ 9 ].…”