## 5 New Management Applications  
In this Special Issue of _Remote Sensing_, we spotlight researchers who are applying remote sensing geospatial technologies and/or spatial data science in novel ways in the management of livestock and grazinglands. The first article by [PERSON] et al. [91] examines the application of a moving window behavior state classification method using time segments of varying length on accelerometers mounted to collars, ears, and legs on Merino ewes. These authors differentiate between grazing, standing, walking, and lying with up to 10-s moving window durations. [PERSON] et al. [97] outfitted cattle with GNSS collars and intravaginal devices to test the ability to detect birthing events, and potential behavioral changes due to birthing. They found that birth detection in these systems were lacking, most likely due to the length of time the devices must remain implanted. Alerts that were successful demonstrated that cattle are quite active leading up to and after birthing, indicating a need for better real-time alert systems than communication using the Iridium satellites followed by GSM and email alerts.  
[PERSON] et al. [66] conducted a pilot study to assess forage biomass using UAVs on a south Texas semi-arid rangeland. By combining field collected data with UAV-derived surface models, they estimate forage biomass at varying altitudes. [PERSON] and [PERSON] [99] developed a satellite-based aridity index (SbAI) using MODIS imagery and weather data to estimate water use and growth/expansion of grassland areas in China and Mongolia, providing a potential new tool to monitor the effects of climate change and desertification.  
[PERSON] and [PERSON] [100] observed grazing effects related to ecosystem resilience from wild and domesticated ungulates using NDVI-based phenology and rainfall accumulation in a Mediterranean ecosystem. They report that current pressure by herbivores lowers ecosystem resilience in the face of potential climatic changes. Likewise, [PERSON] et al. [101] matched field observations with ground-based, time-lapse phenological cameras (phenocams) and Landsat NDVI data to determine the phenological timing of vegetation in dry, mesic, and wet riparian meadows in the Central Great Basin. They reported strong correlations between NDVI, field measurements, and green chromatic coordinates from ground based phenocams based upon yearly precipitation and vegetation type.  
[PERSON] et al. [102] developed the expected ecosystem performance (EEP) model which is based upon climate, growth potential and variability, and greenness. By combining MODIS NDVI data and the USDA soil survey geographic (SSURO) database, they were able to correlate the model to ground-truthed biomass data. [PERSON] et al. [103] coupled remotely sensed vegetation indices with electronic identification of cattle via automatic weighing stations at watering points to show that cattle live weights, and live weight change can be modeled from a combination of vegetation indices, Julian day, and rainfall data. Finally, [PERSON] et al. [104] present a novel way to estimate crude protein content of forages using the MODIS platform. What distinguishes this work from previous attempts is that they focus mostly on the red and green portions of the electromagnetic spectrum, rather than relying solely upon NDVI. This information could be useful in determining feeding ration needs or prompt producers to sell cattle earlier to avoid additional feed costs.