Patents and Disclosures
Streamflow Presence in Arid Areas
We developed an algorithm for predicting the presence of water and streamflow regime in rivers and streams of arid and semiarid areas at high spatial and temporal resolution. With remote sensing datasets, this method can detect the presence of water in a channel and map the streamflow regime at any place along the river. The algorithm is robust and can be applied rivers or streams with ease and at a low cost. Additionally, this algorithm can be applied to detect other rapidly varying phenomena, for instance to monitor changes in agriculture, forestry, natural disasters, and maritime and coastal areas.
Adding Trees into Model Meshes
We developed a method for incorporating individual trees at very high spatial resolution into a multiple resolution mesh used for environmental models. With point cloud data from aerial or satellite imagery, the method identifies trees in a landscape, adds them into a triangulated irregular network, and builds a representation of a watershed that respects tree locations and other features such as channels and boundaries. Changes to tree characteristics, including removal through a natural or manmade disturbances, can be accounted for in the methodology. A multiple resolution mesh with the original trees or after tree removal can then serve as the domain for other software that conduct simulations of environmental processes.
Mapping Snow Cover in Variable Terrain
We developed a method to assess the spatial representativeness of a ground-based snow observation site in a variable terrain. By leveraging high-resolution snow cover maps and a deep learning model, the method evaluates the spatial variability of snow persistence at any location. By integrating ground and remotely sensed data, this approach identifies discrepancies between point-based measurements and the broader spatial variability in snow cover, particularly in regions with complex topography and vegetation influences. This is particularly useful in assessing the validity of existing snow networks and guiding future expansions to improve the accuracy and utility of ground-based snow monitoring.