Sarah L. Endres

Development of a Bi-National Great Lakes Coastal Wetland and Land Use Map Using Three-Season PALSAR and Landsat Imagery

Methods using extensive field data and three-season Landsat TM and PALSAR imagery were developed to map wetland type and identify potential wetland stressors (i.e., adjacent land use) for the United States and Canadian Laurentian coastal Great Lakes. The mapped area included the coastline to 10 km inland to capture the region hydrologically connected to the Great Lakes. Maps were developed in cooperation with the overarching Great Lakes Consortium plan to provide a comprehensive regional baseline map suitable for coastal wetland assessment and management by agencies at the local, tribal, state, and federal levels. The goal was to provide not only land use and land cover (LULC) baseline data at moderate spatial resolution (20–30 m), but a repeatable methodology to monitor change into the future. The prime focus was on mapping wetland ecosystem types, such as emergent wetland and forested wetland, as well as to delineate wetland monocultures (Typha, Phragmites, Schoenoplectus) and differentiate peatlands (fens and bogs) from other wetland types. The overall accuracy for the coastal Great Lakes map of all five lake basins was 94%, with a range of 86% to 96% by individual lake basin (Huron, Ontario, Michigan, Erie and Superior).

Identification of Woodland Vernal Pools with Seasonal Change PALSAR Data for Habitat Conservation

Woodland vernal pools are important, small, cryptic, ephemeral wetland ecosystems that are vulnerable to a changing climate and anthropogenic influences. To conserve woodland vernal pools for the state of Michigan USA, vernal pool detection and mapping methods were sought that would be efficient, cost-effective, repeatable and accurate. Satellite-based L-band radar data from the high (10 m) resolution Japanese ALOS PALSAR sensor were evaluated for suitability in vernal pool detection beneath forest canopies. In a two phase study, potential vernal pool (PVP) detection was first assessed with unsupervised PALSAR (LHH) two season change detection (spring when flooded—summer when dry) and validated with 268, 1 ha field-sampled test cells. This resulted in low false negatives (14%–22%), overall map accuracy of 48% to 62% and high commission error (66%). These results make this blind two-season PALSAR approach for cryptic PVP detection of use for locating areas of high vernal pool likelihood. In a second phase of the research, PALSAR was integrated with 10 m USGS DEM derivatives in a machine learning classifier, which greatly improved overall PVP map accuracies (91% to 93%). This supervised approach with PALSAR was found to produce better mapping results than using LiDAR intensity or C-band SAR data in a fusion with the USGS DEM-derivatives.