Michael Mitchell and Dale James, Ducks Unlimited, Southern Regional Office; Randy Wilson, U.S. Fish and Wildlife Service; Daniel Twedt, U. S Geological ...
Improved Forest Classification for Wildlife Conservation Michael Mitchell and Dale James, Ducks Unlimited, Southern Regional Office; Randy Wilson, U.S. Fish and Wildlife Service; Daniel Twedt, U. S Geological Survey, Patuxent Wildlife Research Center; Anne Mini, Lower Mississippi Valley Joint Venture & American Bird Conservancy; Keith McKnight and Blaine Elliott, Lower Mississippi Valley Joint Venture Forests are important to wildlife resources in the Mississippi Alluvial Valley (MAV). As the focus of bird conservation action for several decades, monitoring the status of bottomland hardwood forests allows assessment of progress towards conservation objectives and updates of existing decision support models. Previous efforts have classified forest cover using pixelbased processing within a Geographic Information System (GIS), but we recommend object-based processing due to its greater efficiency and ease of application for future classifications. Objective: Develop an object-based GIS processing method that is accurate, efficient, and replicable, to classify forest cover from satellite imagery. Methods (Fig. 1): • Create indices and band ratios from atmospheric corrected Landsat 5 TM satellite imagery. • Segment into image objects representing landscape features using a 2-step segmentation process (Fig. 2). • Define training samples based on band ratio and indices statistics. • Classify each TM scene using ‘Random Forest’ methodology1. • Merge classified scenes into a single MAV-wide mosaic.
Results (Fig. 3): • 7,463,345 acres (3,020,309 ha) classified as forested • We captured early growth forest but misclassifying as agriculture was noted (Fig. 4). • To add early growth forest missed by the classification we incorporated spatially explicit reforestation data from local, state, and federal partners.
Figure 4. Early-growth forest (e.g., restoration sites) may be misclassified as agriculture.
Figure 1. Classification model for Landsat scenes.
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Figure 2. Two-step segmentation process in which (a) all objects are initially segmented and (b) dissolved based on spectral difference.
Management Implications (Fig. 5): • Forest classification will be used to update ForestBreeding Bird Decision Support Model based on multiple criteria: 1) Forest cores of different size and their juxtaposition 2) Percent forest cover in local (10-km radius) landscapes 3) Total forest cover 4) Elevation as determined by flood frequency
Figure 5. Preliminary results of reforestation priorities for forest breeding birds.
Figure 3. Forest classification using object-based GIS processing methods.
