Spaceborne imaging spectroscopy advances modeling of grazer resource selection

Linking animal movement to underlying forage resources across large landscapes remains challenging because spatially explicit measurements of forage quantity and quality are difficult to obtain at broad spatial extents. Researchers have often relied on localized field studies, limiting the ability to generalize movement-resource relationships at landscape scales. Imaging spectroscopy offers a promising solution, yet its application in modeling animal resource selection has been constrained by the limited availability of fine spatial resolution data. We integrated airborne and spaceborne imaging spectroscopy with Global Positioning System telemetry from 28 free-ranging cattle monitored from April to July 2025 at the Tallgrass Prairie Preserve (TGPP), Oklahoma, USA, to evaluate how forage quantity (aboveground dry biomass) and quality (nitrogen concentration) shape movement in a grassland managed with prescribed fire and grazing. We assessed the capacity of imaging spectroscopy to predict forage characteristics using two datasets acquired during the peak growing season in June 2025, including a 1 m airborne dataset collected with an AISA Fenix imaging spectrometer and a fused 3 m product combining DLR Earth Sensing Imaging Spectrometer (DESIS) and PlanetScope imagery. Specifically, we estimated forage characteristics using partial least squares regression models calibrated and validated with field measurements of aboveground dry biomass and nitrogen concentration collected from 900 quadrats across 100 plots. Airborne data achieved the highest predictive performance for both aboveground dry biomass (R 2 = 0.32, RMSE = 53 g/m 2 , NRMSE = 19%) and nitrogen concentration (R 2 = 0.35, RMSE = 0.28%, NRMSE = 20%), while the fused DESIS-PlanetScope product achieved comparable biomass estimation (R 2 = 0.35, RMSE = 52 g/m 2 , NRMSE = 18%) but weaker nitrogen retrieval (R 2 = 0.19, RMSE = 0.29%, NRMSE = 22%), likely due to the absence of shortwave infrared bands in DESIS. We then incorporated these variables into an integrated step selection function to test whether imaging spectroscopy enables inference of grazer resource selection and how selection varies with fire history. Cattle consistently avoided areas with high aboveground dry biomass but selected nitrogen-rich vegetation, indicating a preference for high-quality regrowth. Selection varied with time since fire; recently burned patches showed higher relative selection strength, whereas older burns were increasingly avoided. Spatial patterns of resource selection were highly consistent between airborne and fused spaceborne datasets (concordance = 0.81 for both), indicating that data fusion is a scalable solution for transitioning from localized airborne surveys to global monitoring using spaceborne assets. These findings demonstrate that spaceborne imaging spectroscopy can facilitate routine, landscape-scale monitoring of herbivore-vegetation interactions, with broad implications for rangeland management, conservation planning, and the utilization of next-generation imaging spectroscopy missions.