The boreal forests of interior Alaska are regulated by a disturbance regime dominated by large scale, high-intensity fires that tend to have homogeneous impacts on large areas of the landscape. This disturbance regime and the absence of other types of disturbance (except fluvial disturbance in riparian areas) results in patterns of forest succession that are well suited to identification from satellite imagery. The purpose of this project is to refine techniques from project completed in Fall 2004 that used Landsat ETM+ imagery to classify forests based on seral stage. Stand age is an important characteristic to understand because forests of different ages pose a spectrum of fire hazards based on available fuels for burning as well as important information regarding silvicultural and wildlife management.
The initial project conducted in Fall 2004 utilized a multitude of classifications to identify and categorize seral stages across the landscape. It appears that these classifications detect three seral stages that are unique from other land cover types (early seral stages) and one that is not unique (late seral stage). The areas classified as early seral stage designations matched up fairly well (within 10%) of the areas classified as these age classes by fire area estimates calculated by the Alaska Fire Service.
However, there remains much in the way of quantitative analysis to judge how well the classifications worked. Proposed work on this project in Spring 2005 includes assessments of classification accuracy using two different methods. The first is to do a point comparison of the classified images to USGS Alaska Vegetation Classification Maps and conduct an assessment of accuracy using the USGS land cover types as a benchmark. This technique will be applied to the image area used in the classifications produced last fall and will be applicable to most of the land cover types. The second type of accuracy assessment will be conducted by comparing classified land cover types with groundtruth data. I have GPS points with associated forest cover type and stand age for over 50 locations in interior Alaska. These points will serve as excellent benchmarks to test the accuracy of the classification. The downside of this method (that is countered by using the USGS data as a benchmark) is that the groundtruth points only represent a limited range of land cover types.
A second goal of this project will be to improve the accuracy of the classification. Once the accuracy assessments have been conducted, information regarding flaws in the classification will be used to more carefully define training regions or create new land cover types that were previously not represented. Additional improvements to the classifications also include utilizing the thermal bands and testing different filters during image processing.