Constructing DEM — LIDAR elevation data is typically distributed as a collection of (x,y,z) points, but most GIS algorithms are designed for either grid or triangulated irregular network (TIN) digital elevation models. We developed two methods for efficiently converting LIDAR point sets to more conventional formats:
Noise Removal — Even with high-resolution LIDAR data, there is some level of noise in DEMs derived from LIDAR. We are interested in methods of correcting noisy DEMs, particularly noise that impedes water flow along hydrological features. We present a method of computing a persistence score for topological features and use this persistence score to remove small topological features likely the result of noise while preserving larger features.
Hierarchical Watershed Decomposition — This project partitions a terrain into a hierarchy of nested watersheds using completely automated methods. Each node in the terrain is assigned a unique watershed label, that not only encodes its location in the watershed hierarchy but also allows users to determine upstream and downstream watersheds using only waterhsed labels.
Topographic Change — Because LIDAR can efficiently map large areas quickly, it is possible to acquire regular time series datasets of changing area. Detecting topographic change can quickly identify beach dunes damaged by hurricanes, monitor urban development or measure change in forest growth.