Remote sensing application to grassland monitoring

Application of remote sensing to the management of grassland resources, the role this plays in developing sustainable grassland farming systems and opportunities for further development are outlined. Use of remote sensing technologies in grassland monitoring has a history of more than 30 years. Both fine- and coarse-grained remote sensing techniques are used to monitor and study grasslands. Fine-grained techniques are used to study landscape scale processes through the use of sensors providing spatial resolution of a few meters, whereas coarse-grained techniques are used to study catchment scale areas, and even entire biomes, using satellite-based sensors with a spatial resolution of kilometers. Remote sensing information is obtained from aerial photography, radar systems, video systems, and satellite-based sensors including the Landsat satellites' Multispectral Scanner (MSS) and Thematic mapper (TM) and the National Oceanic and Atmospheric Administration (NOAA) polar orbiters' Advanced Very High Resolution Radiometer (AVHRR). Various normalized difference vegetation indices (NDVI) have been developed and used extensively with data from the Landsat sensors (MSS and TM) and NOAA's AVHRR. The NDVI has been used for grassland classification and inventory, monitoring grassland-use change, determination of site productivity and herbivore carrying capacity, water and soil conservation, integrated management of grassland pests, and suitability for recreational use and wild life protection. Special techniques have also been developed for monitoring where fires occur on grasslands. To date the remote sensing techniques have become a powerful tool for scientists, farmers and policy makers to study and manage grassland resources. World demand for sustainable development of grasslands will increase the reliance on remote sensing as a tool in grassland management. However, the adaptation of existing remote sensing technology in grassland management will require more scientists and technicians to be trained in both remote sensing and grassland science. Additional training programs targeting scientists in developing countries will be needed. System approaches will be required that lead to better understanding of the interfacing of ground and remote sensing data sets. There is also a need for research on low cost, high resolution systems to be flown from aircraft and helicopters using narrow filters for assessing the condition of grassland health.