Grassroots September 2016, Vol. 16, No. 3 | Page 24

Feature requirements of spatial, spectral and temporal resolution at budget cost (Dunford et al., 2013). There are three general classes of RPA’s, close range (± 50 km), medium range (±200 km) and long range (>200 km) (Watts et al., 2012). Cost of medium and long range PRA’s amount to multimulti million Rand’s and may require complex and expensive ground control stations. Close range RPA’s provides lightweight, versatile and costcost effective opportunities to act as carriers of a variety of sensors, including NIR (near infra-red), infra RGB (red, green and blue bands), RE (red edge), multimulti and hyper spectral, LIDAR (Light Imaging, Detection and Ranging) and temperature measuring thermal sensors. Furthermore, different dif models of RPA’s are advantageous for different applications. Multi-copter copter RPA’s are more suitable for surveillance and mapping of small areas, while fixed wing RPA’s are more suited for mapping when extended flight times are required to cover longer distances. Potential applications of RPA’s in rangeland research and monitoring Figure 2: a) Remotely sensed image of a drainage line running down the middle of the image, b) Enhanced Vegetation Index calculated from Near Nearinfrared image captured by a RPA indicating, in white, photosynthetic activity of the vegetation layer in and along the drainage lines. With the introduction of affordable remotely piloted aircrafts (RPA’s), also known as unmanned aerial vehicles (UAV’s), one can now safely acquire timely hyper-resolution resolution aer aerial images at low altitudes (Whitehead et al., 2014; Giessel, 2015). Currently the National Aeronautics and Space Administration (NASA; http://www.nasa.gov/centers) as well as the National Oceanic and Atmospheric Administration (NOAA; http://uas.noaa.gov) have Unmanned Aircraft Systems Programs. RPA’s provide low operational complexity and costs, compared to manned aerial photography (Whitehead &Hugenholtz, 2014). Remote sensing sensors placed in RPA’s furthermore provides the capacity to combine high spatial al resolution and quick turnaround times in order to meet critical Grassroots The RPA’s have the potential to bridge the gap between ground-based based surveys and remote sensing data acquired from satellites or manned aircrafts (Whitehead et al., 2014). Ground surveys provide only the information required to develop high resolution maps and allow monitoring of vegetation changes in small areas. Remote sensing data acquired from satellites are sufficient to produce maps and monitor vegetation activity over areas of a much larger extent; however, limitations arise in terms of lower spatial resolution and operational flexibility (Whitehead &Hugenholtz, 2014). It becomes expensive to monitor features at a finerfiner scale from satellite derived remote sensing images image on a routinely basis. However, with fully autonomous GPS-guided guided RPA’s, mapping and monitoring changes on a routinely basis at an intermediate spatial scale is possible (Koh&Wich, 2012; Whitehead et al., 2014). The RPA is capable of producing high quality orthorectified and geogeo referenced images (Figure 1) which can be used for mapping purposes as well as a tool to verify course scale satellite data in order to conduct ground truthing on vegetation indexes calculated from near-infrared infrared images, such as; the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) (Figure 2; Laliberte et al., 2010). Satellite derived remote sensing has already been used in various previous studies to depict land-cover cover change (Stow et al., 2004, Shalaby&Tateishi, by&Tateishi, 2007; Koh&Wich, 2012), the impact of fires (Huesca et al., 2013; Wooster et al., September 2016 Vol 16 No. 3