|Title||Ecohydrology with Unmanned Aerial Vehicles|
|Publication Type||Journal Article|
|Year of Publication||2014|
|Authors||Vivoni E, Rango A., Anderson C.A., Pierini N.A., Schreiner-McGraw A.P, Saripalli S, Laliberte AS|
|Keywords||ecology, environmental sensor network, hydrology, remote sensing, robotics, scaling, unmanned aircraft systems|
High-resolution characterizations and predictions are a grand challenge for ecohydrology. Recent advances in flight control, robotics and miniaturized sensors using unmanned aerial vehicles (UAVs) provide an unprecedented opportunity for characterizing, monitoring and modeling ecohydrologic systems at high-resolution (<1 m) over a range of scales. How can the ecologic and hydrologic communities most effectively use UAVs for advancing the state of the art? This Innovative Viewpoints paper introduces the utility of two classes of UAVs for ecohydrologic investigations in two semiarid rangelands of the southwestern U.S. through two useful examples. We discuss the UAV deployments, the derived image, terrain and vegetation products and their usefulness for ecohydrologic studies at two different scales. Within a land-atmosphere interaction study, we utilize high-resolution imagery products from a rotary-wing UAV to characterize an eddy covariance footprint and scale up environmental sensor network observations to match the time-varying sampling area. Subsequently, in a surface and subsurface interaction study within a small watershed, we demonstrate the use of a fixed-wing UAV to characterize the spatial distribution of terrain attributes and vegetation conditions which serve as input to a distributed ecohydrologic model whose predictions compared well with an environmental sensor network. We also point to several challenges in performing ecohydrology with UAVs with the intent of promoting this new self-service (do-it-yourself) model for high-resolution image acquisition over many scales. We believe unmanned aerial vehicles can fundamentally change how ecohydrologic science is conducted and offer ways to merge remote sensing, environmental sensor networks and numerical models.