Rangelands cover about half of Earth's land surface, encompass considerable biodiversity, and provide pivotal ecosystem services. However, rangelands across the globe face degradation due to changes in climate, land use, and management. Moreover, since herbivory is fundamental to rangeland ecosystem dynamics, shifts in the distribution of herbivores lead to overgrazing and desertification. To better understand, predict, and prevent changes on rangelands it is important to monitor these landscapes in a timely and efficient manner. Remote sensing can be a viable tool for measuring such change. However, the high spatial and temporal variability of rangeland vegetation, high reflectance from soil background and senesced vegetation during prolonged parts of the year, present challenges to the application of remote sensing in these ecosystems. The goal of my dissertation is to address these challenges and advance the application of remote sensing and geographic information system (GIS) to quantify vegetation and herbivores on rangelands across the world. My dissertation aims to address the connections among three main components of rangelands: the landscape, herbivores and human factors. I first develop a method to characterize the rangeland landscape by measuring and mapping detailed vegetation variables in Etosha National Park, Namibia. Etosha is a 22,270 km2 semiarid savanna, which encompasses great diversity of flora and fauna. I then examine how landscape variables affect the movement patterns of a large mammalian herbivore that is a keystone species in Etosha, the African elephant (Loxodanta africana). Finally, I develop tools to monitor how herbivory affects the productivity of rangelands conservation easement in California. In the first chapter, I outline the importance of rangelands and the threats these ecosystems face. I review the main challenges of measuring change processes on rangelands and describe some of the remote sensing based approaches that have been used to address these challenges. In the second chapter, I show that time series analysis of Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indices can produce excellent results in predicting detailed field measurement of vegetation in Etosha. Using three innovative approaches I improve the prediction of both woody and herbaceous vegetation on the landscape, providing good measurements of vegetation cover, density, and biomass over large spatial extents. First, I develop field methodology that combines visual estimation of vegetation cover and vegetation type together with accurate field measurements. Second, by integrating time series of remote sensing data over six years and consolidating this information with partial least square regression, I achieve accurate models of vegetation measurements. Third, by using four different MODIS-based vegetation indices: Normalized Difference Vegetation Index (NDVI), Enhanced vegetation index (EVI), Leaf Area Index (LAI), and Fraction of Photosynthetically Active Radiation (FPAR), I measure different vegetation forms - grasses, shrubs, and trees, and thereby provide valuable information for monitoring and conserving Etosha's savanna vegetation. An exciting result from this chapter is the ability to transfer the application of these models in space, to other parts of the reserve, and in time, to other seasons and years. This emphasizes the validity of the models I have developed for predicting vegetation measurements and the ability to use these models in other locations. In the third chapter, I use the detailed vegetation maps I have created for Etosha National Park to understand resource selection of African elephants. I show how landscape variables affect both the speed and the direction of elephants' movements. Elephants prefer to move into areas with higher grass and shrub biomass, but lower tree biomass. Moreover, elephants prefer to be closer to water sources and, interestingly, to roads. Elephants' resource selection is influenced by the sex and the age of the individual. Importantly, temporal variation significantly influences the movement in response to the landscape: elephants choose different resources at different times of the day, which illustrates the behavior underpinnings of their resource selection. Moreover, they respond differently to resources at different times of the year, which highlights the ecological importance of these resources to the elephants. This chapter provides valuable information on how to manage resources in a manner that will promote the conservation of these magnificent keynote animals. In the fourth chapter, I use MODIS satellite data to monitor the effects of grazing on rangeland conservation easements in California, using as a study case the Simon Newman Ranch, a conservation property own by The Nature Conservancy. I use time series information of three vegetation indices to measure Residual Dry Matter (RDM), which is the dry grass matter left on the ground in the fall, at the end of the grazing season. RDM is a measure of grazing pressure; moderate levels of RDM are correlated with the health of rangeland ecosystem, soil stability, water retention and biodiversity of native plants and wildlife. Therefore RDM levels are used by The Nature Conservancy and other land managers as a conservation easement compliance measure. I develop a rapid, easy to use, efficient and robust methodology to predict RDM in the fall using spring maximum and annual sum of vegetation index values. My results show that MODIS-based Leaf Area Index (LAI) is the best measure of dry grass biomass. Most importantly, I demonstrate that MODIS data can be efficiently used by range managers and conservationists to estimate RDM easement compliance. In summary, in this dissertation I develop the use of quantitative spatial tools to measure both vegetation and herbivores on rangelands and to characterize landscapes on large spatial scales. I conduct interdisciplinary research connecting landscape ecology, remote sensing science and wildlife ecology. I demonstrate how freely available MODIS satellite imagery and open source software can be used by conservation managers to understand vegetation patterns and wildlife distribution in relatively easy, cost efficient, rapid and robust ways. The tools I develop in this dissertation identify and quantify change in rangelands. My results inform targeted management and conservation practices, and contribute to improve monitoring and to the understanding of these imperiled ecosystems.