Action: Map forest disturbance (harvest, insect damage, storm damage, fire, etc) across a Landsat frame (timing, intensity, location) for 1984 -2005 Biomass Atmospheric source Regrowth Dominated Disturbance rate -> Forest age -> Carbon Flux Disturbance Dominated Balanced area histograms Why? Interested in knowing forest carbon flux for North America, and how it is changing Carbon Consequences Biologic C Flux
Spatial Sample of US Disturbance ~25 Sample Sites Annual or Biennial Image Time Series (1972 -2004) Disturbance history / stand age
I. II. 1. 2. 3. 4. Search for leaf-on, cloud-free annual images for a given location; optimize scene selection for anniversary dates. Need to balance: - cloud avoidance - seasonality - data quality (e. g. L 7 vs L 5) - year selection II. Download/buy data (Landsat-7 data now free; Landsat-5 by end of year)
III. Orthorectify images to map base using GLS data set and SRTM digital topography; clip images to common X-Y spatial domain - image-image registration essential; orthorectification desirable (facilitates GIS integration with other data) IV. Calibrate each image to radiance, and apply atmospheric correction to convert images to surface reflectance - not essential for change detection, but useful for additional studies (e. g. integration with other sensor data; canopy reflectance modeling)
Orthorectified (2)
Landsat 1 Gs (2)
Atmospheric Correction 1990’s Landsat-5 mosaic TOA reflectance Surface reflectance 100 km BOREAS Study Region 100 km
V. Find a sample of “mature” forest in each scene either by visual inspection or automated histogram thresholding; obtain mean reflectance and standard deviation for this set of pixels, for each scene VI. Calculate per-pixel “Forestness Index” for each reflectance image according to: FI = 1/n * S [(ri – rmf_mean) / rmf_stdev] Where ri is the reflectance for band i (from 1, n), and rmf_mean, _stdev are the mean and standard deviations of the mature forest band for that image.
Time Trajectories of Forestness Index Indicate Forest Dynamics (b) Disturbance Forestness index (a) Permanent forest Year index (19 xx) (c) Thinning (d) Aforestation (e) Permanent non-forest
VII Use rule-based system and thresholds to identify disturbance events (e. g. “if FI increases > 8 and stays >6 for at least three consecutive years, then mark Year 1 as disturbance”). - agriculture identified by frequent large changes in FI value - clouds can be identified as large “single event” changes in FI VIII. Filter maps using sieve filter to remove speckle (single pixel changes).
Fig. 1. (a) Location of samples selected across the conterminous United States, where biennial time series stacks of Landsat images (LTSS) were acquired analyzed to map forest disturbance over the past three decades. The background forest group map shows that most of the forest groups in the United States have been represented by the samples. (b) An example disturbance map developed using the LTSS in a 28. 5 -kilometersquare area south of Lake Moultrie in South Carolina. Persisting forest, nonforest, and water are shown in green, gray, and blue, respectively. All other colors represent changes mapped in different years. (c) Percent of forest land disturbed annually, calculated according to the derived disturbance map for the entire South Carolina Landsat scene.
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