Remote Sensing of Grassland Biodiversity

This section will be split into three broad categories. The first is based on the spectral variation hypothesis (SVH). This the most common method for mapping plant biodiversity and is centred on the premise that individual plant species absorb and reflect sunlight in unique ways, creating a distinct spectral signature (Figure 3). Where there are many distinct species in a grassland, the spectral diversity (SD) recorded by the remote sensing instrument will be greater than in areas with fewer species (Rocchini et al., 2004). This type of analysis can be performed with both multi- and hyper-spectral instruments, with measures of SD ranging from simple standard deviations of spectral bands to convex hull volume of the principal components of hundreds of hyperspectral bands and more. Studies utilising the SVH approach are the focus of 18 of the 37 biodiversity papers in this section, representing refinement of the methodology, application in different environments, as well as exploration of mediating factors and limitations. The second biodiversity section encompasses studies with a focus on machine learning. As in many scientific fields, remote sensing of grassland biodiversity has experienced and accelerated uptake in the use of machine learning in the last few years. Here, they account for 12 of the 38 papers presented. The third section will explore studies that focus on neither the SVH nor machine learning (although they form small parts of some studies) but include approaches from manual identification of species from UAV imagery to interdisciplinary research.