With global population projected to approach 9 billion by 2050, it has been estimated that a 40% increase in cereal production will be required to satisfy the worlds growing nutritional demands. Any such increases in agricultural productivity are likely to occur within a system that has limited room for growth and in a world with a climate that is different from that of today. Fundamental to achieving food and water security, is the capacity to monitor the health and condition of agricultural systems. While space-agency based satellites have provided the backbone for earth observation over the last few decades, many developments in the field of high-resolution earth observation have been advanced by the commercial sector. These advances relate not just to technological developments in the use of unmanned aerial vehicles (UAVs), but also the advent of nano-satellite constellations that offer a radical shift in the way earth observations are now being retrieved. Such technologies present opportunities for improving our description of the water, energy and carbon cycles. Efforts towards developing new observational techniques and interpretative frameworks are required to provide the tools and information needed to improve the management and security of agricultural and related sectors. These developments are one of the surest ways to better manage, protect and preserve national food and water resources. Here we review the capabilities of recently deployed satellite systems and UAVs and examine their potential for application in precision agriculture.
In this study, we present a geovisualization tool using Alpha-shapes to visualize class clusters in a remotely sensed image classification. An Alpha-shape is an accurate representation of the shape of a cluster of points in a 2D or 3D feature space. Traditionally, spheres and ellipsoids are used to represent class clusters in a classification. These shapes, however, are rough approximations of irregular shaped class clusters. In remote sensing classification we often have to deal with these irregular clusters (e.g. concavities, pockets and voids) and Alpha-shapes will improve visualization of these classes. We argue that Alpha-shapes will also improve insight into a classification process, and related uncertainty. Uncertainty can arise from ambiguity in the attribution of class labels to pixels. This ambiguity is often caused by overlapping classes. Visualization is helpful in communicating this ambiguity as Alpha-shapes clearly show where classes overlap. In this study, we also propose and implement a novel classification algorithm based on Alpha-shapes. Most classification algorithms cannot cope with irregular and concave cluster shapes in feature space. We apply our algorithm on a Landsat 7 image scene of a study area in Southern France. We show that good classification results can be obtained with Alpha-shapes.