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GIS and Remote Sensing Methods for Mapping Forests: Lessons from the Western Oregon Vegetation Mapping Project

Western Oregon Vegetation Mapping Project. NASA Land-Cover Land-Use Change (LCLUC) and Terrestrial Ecology ProgramUSDA Forest Service PNW Research StationOregon State UniversityH. J. Andrews LTER siteMapping forest cover and stand replacement disturbance patternsCreating carbon flux models to c

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GIS and Remote Sensing Methods for Mapping Forests: Lessons from the Western Oregon Vegetation Mapping Project

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    1. GIS and Remote Sensing Methods for Mapping Forests: Lessons from the Western Oregon Vegetation Mapping Project Doug R. Oetter, Georgia College & State University Warren B. Cohen, USDA Forest Service Thomas K. Maiersperger, USDA Forest Service Maria Fiorella, Pacific Meridian Resources

    2. Western Oregon Vegetation Mapping Project NASA Land-Cover Land-Use Change (LCLUC) and Terrestrial Ecology Program USDA Forest Service PNW Research Station Oregon State University H. J. Andrews LTER site Mapping forest cover and stand replacement disturbance patterns Creating carbon flux models to characterize trends in carbon storage across major forest biomes (Pacific NW and NW Russia)

    3. Project Remote Sensing Component Forest Cover Forest Composition Forest Age/Size Harvesting/Fire Patterns

    4. Forest Mapping Methodology Develop reference information from ground sample and aerial photography sources Incorporate spatial data into GIS linked to plot data Extract spectral signatures from satellite images for each plot Regression modeling in core scene to infer explanatory relationships Test predictive models using independent data Extend models from core image to adjacent scenes Map generation

    5. Forest Mapping Study Design

    6. Landsat Thematic Mapper (TM) Scenes Core scene Path 46, Row 29 (1988) Subsequent scenes grouped by ecosystem Coast Range West Cascades Klamath Mountains

    7. Tasseled Cap Transformation Six TM visible and infra-red bands collapsed into three orthogonal bands Related to physical variation in Brightness, Greenness, and Wetness: Brightness: Amount of Spectral Reflection 10.3695 + .2909*(b1) + .2493*(b2) + .4806*(b3) + .5568*(b4) + .4438*(b5) + .1706*(b7) Greenness: Presence of Green Vegetation -.7310 - .2728*(b1) - .2174*(b2) - .5508*(b3) + .7221(b4) + .0733*(b5) - .1648*(b7) Wetness: Soil Moisture and Canopy Development -3.3828 + .1446*(b1) + .1761*(b2) + .3322*(b3) + .3396*(b4) - .6210*(b5) - .4186*(b7)

    8. Feature Space

    9. Ground Data Percent Forest Cover 1690 photo-interpreted polygons Conifer vs. Hardwoods USFS, BLM, ODOF photos (1986-89) Conifer Age 100 PNW Research Station field measurements 434 USFS, BLM field measurements and inventories Conifer Crown Diameter 560 photo-interpreted polygons USFS, BLM, ODOF photos (1986-89)

    10. Study Plots

    11. Statistical Analysis Scatter Plots Independent Variable Transformation Max R, Stepwise Regression Model Testing Outlier Removal Model Improvement

    12. Removal of Outliers Digitizing Errors Edge Pixels Temporal Cover Change between image and reference Clearcuts Grasslands Snow Incorrect Interpretation Data Omissions

    13. Core Scene Predictive Models Percent Total Cover TOTCOV = 9.8996*WET + 7.1073*GREEN - 0.0559*GW - 11.2856 Percent Conifer Cover CON = 1.5153*BRITE + 0.0420*WET2 - 0.0244*GW - 200.8083 Conifer Age Log10(AGE) = 56.0037 - 0.4194*WET - 1.0621*BRITE + 0.0082*BW Conifer Visible Crown Diameter Log10(VCD) = 16.0889 - 0.1259*WET -0.0009*BW + 0.0007*BG

    14. Scene Extension

    15. Independent Model Testing

    16. Western Oregon Vegetation Map

    17. Conifer Age Map

    18. Conifer Visible Crown Diameter Map

    19. Disturbance Methodology Multiple scenes from six dates: Multi-Spectral Sensor: 1972, 1977, 1984 Thematic Mapper: 1988, 1991, 1995 Divided WOV region into 21 scenes Tasseled cap (BG for MSS; BGW for TM) Layer stack into 15-band image Unsupervised classification captures change Filtering to remove edge and single pixels Manual re-coding of fires

    20. Stand Replacement Disturbance Map

    21. Potential Uses of Digital Forest Cover Images Wildlife Biology Stream Ecology Forest Planning Landscape Ecology Economic Modeling Regional Carbon Flux Models

    22. Transition to Southeastern Forests Methodologies developed for Western Oregon should apply to Southeast forests Necessary information: Field-based inventories Aerial photographs Satellite imagery Different environments ? Different approaches Improvements in regression methods and change detection strategies Satellite information can be very powerful when incorporated with a robust ground reference data set!

    23. Further Information Cohen, W B, T K Maiersperger, T A Spies, D R Oetter. 2001. Modeling forest cover attributes as continuous variables in a regional context with Thematic Mapper data. International Journal of Remote Sensing 22(12):2279-2310. Cohen, W B, T A Spies, R J Alig, D R Oetter, T K Maiersperger, M Fiorella. 2002. Characterizing 23 years (1972-1995) of stand replacement disturbance in western Oregon forests with Landsat imagery. Ecosystems 5:122-137. http://www.fsl.orst.edu/larse/wov/88wov.html

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