1. Land Cover Mapping for the Southwest Regional GAP Analysis Project Tenth Biennial Forest Service Remote Sensing Applications Conference, RS-2004, Salt Lake City, Utah John Lowry and R. Douglas Ramsey Remote Sensing/GIS Laboratory Utah State University Logan, Utah

2. Presentation Overview Project Background & Objectives Mapping Methodology Training Data Collection Approach Current Status & Preliminary Results

3. State-based vegetation classification systems (cover type legends) State-based mapping methods State-based mapping area I.Project Background & Objectives

4. 40 Mapping zones Spectrally consistent Eco-regionally distinct Labor divided among 5 state teams

5. NVC Formation NVC Alliance NVC Association Gap Analysis Program MRLC 2000 Proposal ~1,800 units National Park Mapping ~ NVC Class/Subclass ~10 units NatureServe Ecological Systems ~5,000 units ~700 units (Natural/Semi-natural types) ~300 units (Slide Courtesy Pat Comer, Nature Serve) Thematic Target Legend Developed with NatureServe

6. Groups of plant communities and sparsely vegetated habitats unified by similar ecological processes, substrates, and/or environmental gradients...and spectral characteristics. Ecological Systems

7. Elevation Landform Predictor Datasets: DEM derived

8. July-AugSept-Oct ETM Bands 5, 4, 3 Predictor Datasets: Imagery Derived

9. Data-mining software for decision-making and exploratory data analysis Identifies complex relationships between multiple independent variables to predict a single categorical class Predictor variables may be categorical or continuous Recursively “splits” the predictor data to create prediction rules or a decision tree. Software packages available: See5, SPLUS, CART II.Mapping Methods: Classification Trees

10. Mining the Predictor Layers Fall Brightness Summer NDVI Elevation Landform Etc…. Output table SAMPLE SITES Imagery: Landsat 7 ETM (1999- 2002) for spring, summer & fall NDVI, SAVI, Brightness,Greeness, Wetness, Landsat 7 Bands DEM: Elevation, Aspect, Slope, Landform Vector: Geology, Soils Meteorological : DAYMET

12. Simplified Example: Splits on 2 variables

13. Simplified Example: Tree output for 2 variables

14. Example: Rules Output See5 [Release 1.17] Wed Apr 23 13:42:02 2003 Options: Rule-based classifiers Class specified by attribute `dep' Read 7097 cases (10 attributes) from t3.data Rules: Rule 1: (17, lift 45.4) band01 = 1 band03 > 115 band03 <= 122 band05 <= 81 band06 <= 1419 -> class 1 [0.947] Rule 2: (9, lift 43.6) band01 = 1 band02 <= 102 band03 > 115 band03 <= 118 band04 <= 117 band06 <= 1419 -> class 1 [0.909] Rule 3: (6, lift 42.0) band01 = 13 band03 <= 110 band05 <= 73 band07 = 4 | Generated with cubistinput by EarthSat | Training samples : 10260 | Validation samples: 2565 | Minimum samples : 0 | Sample method : Random | Output format : See5 dep.|h:/mgzn_5/trainingdata/mrgpts1.img(:Layer_1) Xcoord:ignore. Ycoord:ignore. band01:1,2,-30 |h:/mgzn_5/img_files/sum30cl.img(:Layer_1) band02:continuous.|h:/mgzn_5/img_files/subrt.img(:Layer_1) band03:continuous.|h:/mgzn_5/img_files/sundvi.img(:Layer_1) band04:continuous.|h:/mgzn_5/img_files/fandvi.img(:Layer_1) band05:continuous.|h:/mgzn_5/img_files/fabrt.img(:Layer_1) band06:continuous.|h:/mgzn_5/img_files/elev.img(:Layer_1) band07:0,1,2,3,4,5,6,7,8,9,10. |h:/mgzn_5/img_files/landf.img(:Layer_1) dep:1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20. |h:/mgzn_5/trainingdata/mrgpts1

15. Boosting (iterative tree’s try to account for previous tree’s errors)—C5 Different over-fitting issues associated with each tree tend to be averaged out. Multiple Tree Approaches VOTEVOTE (Slide Courtesy Bruce Wylie, USGS EDC)

16. Imagine CART Module (USGS Eros Data Center)—See5-Imagine Integration

17. III.Training Data Collection Opportunistic, ground-based sampling, stratified by digital landform model

18. Percent ground cover by dominant species is recorded through ocular estimation. Only the top 4 species of each of 4 life forms are recorded

20. ~3000 Air Photo Interpretation Sites from USFS Photos

21. Regional Total ~ 93,000

22. IV. Current Status & Preliminary Results

23. Edge-matching between three mapping areas

24. Considered correctly classified if majority of pixels agree with sample polygon Accuracy Assessment with 20% withheld data:

25. Accuracy Assessment with 20% withheld data: Southern Wasatch Range

26. 1995 GAP 30 M2004 GAP 30 M1995 GAP Pub.1KM

27. Summary Approximately 100 Ecological Systems and 10 NLCD Land Use classes Generalized to 1 acre MMU Delivered via NBII data node Anticipated completion: 1 September, 2004

28. Acknowledgements