Spatial pattern analysis for monitoring protected areas

Abstract

The management of diverse biota within protected areas is affected by both land cover change within a protected area and habitat loss and fragmentation in the surrounding landscape. Satellite images provide a synoptic view of land cover patterns, but the use of such imagery requires careful consideration of sensor type, resolution, extent, and the metrics used to quantify ecologically significant change. We examined these factors for landscape monitoring applications in four small National Parks near Washington, DC: Antietam National Battlefield, Catoctin Mountain Park, Prince William Forest Park and Rock Creek Park. Using 4 m Ikonos, 10 m SPOT, 15 m pan-sharpened Landsat ETM+ and 30 m Landsat ETM+ imagery, the parks and surrounding areas were mapped to National Land Cover system classes. For each park, we examined four methods for defining map extent, including park administrative boundaries, two variable buffer widths, and watershed boundaries, and then analyzed patterns of forest habitat for the maps using a graph theoretic approach (critical dispersal threshold distance) and common landscape metrics (number of patches, percent forest, forest edge density, and forest area-weighted mean patch size). As expected, landscape metrics for maps derived at differing resolutions varied significantly, but map extent often yielded even larger differences. We found that for most applications, coarser scale data (e.g., 30 m Landsat) are adequate for characterizing landscape pattern, although ultimately data from multiple sensors may be appropriate or necessary based on different objectives of landscape monitoring (e.g., mapping single trees vs. forest stands) and the scale at which a resource of interest interacts with the larger landscape (e.g., birds vs. herptiles). Our results provide a strong caution regarding the practical issues associated with combining data sources from multiple satellite sensors for monitoring applications.