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Rangeland systems are some of our nation’s largest providers of agro-ecological services, sustaining plant productivity that is highly variable across seasons and years. Although the ability to predict the upcoming growing season’s rangeland productivity would have enormous economic and management value – such as for making decisions about cattle stocking rates, fire, restoration, and wildlife – the ability to provide these forecasts has remained poor. New remote sensing and modeling technologies allow for dramatic improvements to near-term forecasts of rangeland productivity. With this project, our multi-disciplinary team has shown that, compared with traditional remote sensing greenness indices, NIRv-based (NIR...
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Rangeland ecosystems are one of the largest single providers of agro-ecological services in the U.S. The plant growth of these rangelands helps determine the amount of forage available for our livestock and for wildlife, as well as information about fire likelihood and restoration opportunities. However, every spring, ranchers and other rangeland managers face the same difficult challenge —trying to approximate how much and where grass will be available during the upcoming growing season. This project represents an innovative grassland productivity forecasting tool, named “Grass-Cast”, which we are developing for the US Southwest to help managers and producers in the region reduce this economically important source...
Fire has increased dramatically across the western U.S.and these increases are expected to continue. With this reality, it is critical that we improve our ability to forecast the timing, extent, and intensity of fire to provideresource managers and policy makers the information neededfor effective decisions. For example, an advanced, spatially-explicit prediction of the upcoming fire season would support the planning and prioritization of fire-fighting crews, the placement and abundance of fire breaks, and the amount and type of seed needed for post-fire restoration. While the Southwesthasseen exceptional increasesin fire, these drierecosystems are also notably difficult forfire predictions because ofuniqueremote...