## 6 Future Directions  
Remote sensing technologies have been a crucial tool in understanding and further developing grassland management systems. However, there is still ground left to cover. Approximately one-third of the earth's land mass is covered in grasslands, making up 70% of all agricultural areas [105; 106; 107], with grazing animals producing as much as one-third of the global human protein supply [108; 109]. Much of the published literature regarding grasslands and remote sensing come from North America, China, and Australia. Comparatively, literature for much of Africa and South America, two continents with large grassland regions [107], is limited. Also, current research tends to focus on climate change and production, compared to research conducted on management [107], when they may not be mutually exclusive. As climate change and desertification continues to be an ever-pressing concern, geospatial analyses focusing on their extent and effects upon grassland management will become increasingly important [13; 110; 111]. Research from the US has shown the potential for increased grazingland production in the northern Great Plains from increasing temperatures due to climate change, while the southwestern US will most likely face severe reductions in biomass production [112; 113]. Research from west Asia and north Africa indicate potential reductions of desirable species and an increase in less palatable species [114]. Moreover, a global analysis of desertification shows that it is being magnified in central North America, eastern Asia, the Pacific Rim, and central Australia [115]. As global food demands continue to rise over the next 50-100 years [116; 117; 118], remote sensing-based models will be critical to managing grazinglands, and to define, identify, and prioritize areas for rehabilitation to provide the critical ecosystem services of food and fiber production. An additional benefit of remote sensing technologies is that private and restricted-access lands can be included in regional and global surveys without need for physical access. Having accurate and timely data and models to inform land management decisions is vital to proper grazingland management [13]. As geospatial sciences and technologies continue to evolve, there has been much progress over the past 10 years in the development of free and open-source software for viewing and analyzing geospatial data. The market for geographical information systems (GIS) and remote sensing software was once completely dominated by a few expensive proprietary packages, but has given way to numerous freely available products and services such as QGIS ([[https://qgis.org/](https://qgis.org/)]([https://qgis.org/](https://qgis.org/)) accessed on 5 January 2022), GRASS ([[https://grass.osgeo.org/](https://grass.osgeo.org/)]([https://grass.osgeo.org/](https://grass.osgeo.org/)) accessed on 5 January 2022), the R ([[https://www.r-project.org/](https://www.r-project.org/)]([https://www.r-project.org/](https://www.r-project.org/)) accessed on 5 January 2022) and Python ([[https://www.python.org/](https://www.python.org/)]([https://www.python.org/](https://www.python.org/)) accessed on 5 January 2022) programming languages, Google Earth ([[https://www.google.com/earth/](https://www.google.com/earth/)]([https://www.google.com/earth/](https://www.google.com/earth/)) accessed on 5 January 2022), and Google Earth Engine ([[https://eartheengine.google.com/](https://eartheengine.google.com/)]([https://eartheengine.google.com/](https://eartheengine.google.com/)) accessed on 5 January 2022) [119; 120] just to name a few. Many of these alternatives have steep learning curves, but by making geospatial sciences and technologies available to a wider audience, scientists and managers now have access to affordable tools to conduct research, process data, perform decision support analyses, and collaborate globally in ways that were once unimaginable. Advances in cloud storage and computing will be a major asset as geospatial data continues to grow in both resolution and file size.