Integration and Analysis of Diverse Lidar and UAS Surveys to Study Coastal Sand Dune Evolution and Changes in Agricultural Field Microtopography.

Repeat airborne lidar surveys of coastal regions span over 20 years, creating a unique 3D record of coastal landscape evolution. During these years lidar technology has improved rapidly, followed by emergence of 3D mapping with UAS and Structure from Motion (SfM) and expanding the 3D data acquisition to regions with diverse landscapes. The resulting time series are extremely valuable but also challenging to work with because of their variability in coverage, resolution and accuracy. To address these challenges, we employed an open source GRASS GIS temporal framework TGRASS to process the time series and derive metrics characterizing landscape dynamics in two case studies. The first study combines photogrammetric, lidar and UAS data to analyze evolution of active coastal dune system within the Jockey's Ridge state park in North Carolina. Highly dynamic nature of the dunes and lack of distinct, stable features made co-registration of surveys spanning over 40 years a major challenge. Using TGRASS we were able to integrate the available data and quantify the dune evolution. Linear trend in the loss of peak elevation continued at a rate of 0.3 m/y since first observed in our 2005 study. Two periods of hurricane triggered large elevation losses followed by partial recovery were also captured in the data set. The core undisturbed volume of sand is shrinking, diminishing the preserved layers documenting the past periods of forest cover. This core is now only 30% of the total dune volume while 70% of sand was transported since 1974. The most striking change has been the continuing transformation of the crescentic dunes into several smaller parabolic dunes with accelerating southern migration. The second case study explores properties of multitemporal SfM data collected by 20+ UAS surveys of a sloping agricultural field over 3 years. Two recent airborne lidar surveys were also available for this area. We applied TGRASS to identify surveys with significant distortions that needed re-processing and to map changes in microtopography due to soil erosion, tillage and spatially variable crop growth. Our studies demonstrate the value and limitations of diverse multitemporal lidar and SfM data for characterization of land surface evolution in different environments and at different scales with applications for adaptive land management.