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 reflectance of vegetation) productivity assessments are a large improvement. We are now joining this new remote sensing product with productivity models to create a forecasting toolkit for the Southwest. Our larger goal is an online tool that integrates remote sensing, climate, and modeling data to visualize and forecast grassland and rangeland productivity for the upcoming growing season.
Principal Investigator : Sasha C Reed
Co-Investigator : Bill Smith, Justin Derner, Bill Parton, Brian Fuchs, Dannele Peck, Emilie Elias
Image caption: Analyses show that for these diverse dryland ecosystems, (a) NIRv remote sensing data are a better tool for assessing dryland plant productivity compared with (b) traditional remote sensing greenness indices (i.e., NDVI). Accordingly, the use of NIRv data products will significantly improve near-term forecasts of plant productivity for grasslands and rangelands of the Southwest.
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“NIRv remote sensing data versus NDVI”