1. Land Cover Mapping Background: Training Data and Classification Methods Southwest Regional GAP Project Arizona, Colorado, Nevada, New Mexico, Utah US-IALE 2004, Las Vegas, Nevada: Transdisciplinary Challenges in Landscape Ecology John Lowry, Douglas Ramsey, Jessica Kirby, Lisa Langs and Wendy Rieth Remote Sensing/GIS Laboratory Utah State University Logan, Utah

2. Presentation Overview I.Project Background & Objectives II.Mapping Methodology III.Training Data Collection Approach IV.Summary

3. Earlier GAP efforts: –State-based vegetation classification systems –State-based mapping methods –State-based mapping area Project Objectives: –Regionally consistent product –Improvements in Land Cover representation I.Project Background & Objectives

4. Mapping Zone Identification Began by Refining Bailey’s Ecoregions over a Color Shaded Relief Map

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

6. 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

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

8. Elevation Landform Predictor Datasets: DEM derived

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

10. 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

11. 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

13. Simplified Example: Splits on 2 variables

14. Simplified Example: Tree output for 2 variables

15. 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

16. 2) 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

17. Imagine CART Module (USGS Eros Data Center)—C5-Imagine Integration

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

19. 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. X THE FIELD SITE POLYGON IS DRAWN ONLY AROUND THE GENERAL AREA OF THE PERSON RECORDING FIELD DATA. THE SITE SHOULD BE AT 90 METERS SQUARED (3X3 PIXEL AREA) OR LARGER

22. Sub-sampling to account for positional error for point samples, and minimize size bias for polygon samples

25. IV.Summary Challenge to assure to regional consistency Challenge of developing tools & methods to be used by multiple analysts/teams Importance of training sample collection (quantity and quality) Primarily product oriented Many research questions…

26. Acknowledgements