Efficiently mapping large areas of olive trees using drones in Extremadura, Spain

Abstract

Remotely sensed multispectral (MS) images have been widely used in different areas of research in recent years, allowing their exploitation in many domains like precision agriculture. This study uses high-resolution RGB data, collected by an unmanned aerial vehicle (UAV/drone), equipped with a camera sensor and flown over an olive tree field in Extremadura (Spain), for land-cover classification purposes. Drone flights over large agricultural areas, collecting high spatial resolution images such as the scenario studied, can be tedious to process, requiring a large amount of random-access memory (RAM). To solve this issue, a new olive tree cover mapping approach is proposed by applying a spatial partitioning methodology that consists of splitting the image automatically, before processing. Each part (known as the raster window) is classified by using machine learning methods. Finally, a classification map is obtained after processing all windows. Among different supervised spectral classification methods, the following classifiers are considered: random forest (RF), support vector machine (SVM), multilayer perceptron (MLP), and k -nearest neighbor (kNN), with the ultimate goal of detecting olive trees and distinguishing them from other land-cover types available in the scene, such as soil, shadows, and weeds. In addition, our experimental results demonstrate that these classifiers can be used to avoid time-consuming fieldwork tasks, facilitating the analysis of agricultural fields with 'a la carte' UAV flights resulting in high classification accuracy.