1. November 20, 2014 Mapping croplands using Landsat data with generalized classifier over large areas Aparna Phalke and Prof. Mutlu Ozdogan Nelson Institute for Environmental Studies University of Wisconsin - Madison

2. Updates on following  LDA model training and tuning  LDA results of sample footprints

3. Basic Definitions Model tuning is the process in which one or more parameters of a device or model are adjusted upwards or downwards to achieve improved or specified results The aim of LDA model tuning is to calibrate the parameters of propagation models and improve the key performance indicators.

4. Methodology R algorithm Divide training data in 75%-25% splits LDA model trained on 75% data and tested on 25% data This procedure repeated 1000 times with random sets of train and test LDA model accuracy check with train and test within scene or within footprint.

5. Results Group 1:

6. Results Group 2:

7. Results Group 3:

8. Results Group 4:

9. Results Group 5:

10. Results Group 6:

11. Results LDA model accuracy at different levels

12. LDA classified image sample result: MeanStd Min Variance Range Counts Slope Elevation Max Inputs

13. LDA classified image results turkeyGroup/zoneAll mean1.11E-036.10E-045.97E-04 sd-1.83E-03-1.58E-03-1.62E-03 max-8.13E-05-1.21E-04-1.23E-04 min-3.44E-05-3.46E-04-2.09E-04 var1.34E-071.82E-071.37E-07 range-7.63E-05-5.60E-051.13E-05 count1.99E-02-1.67E-021.02E-02 slope7.91E-029.21E-021.39E-01 elevation2.82E-031.03E-031.88E-04 LDA coefficients of sample example

14. Own Within group/zone All LDA classified image sample results

15. Conclusion Model tuning helped us in understanding dynamics of whole process, which gives a more accurate picture of how the model is behaving.

16. Thank you phalke@wisc.edu