1. U.S. Department of the Interior U.S. Geological Survey Chandra Giri Ying Zhong 7/15/2015 Cropland Extent Mapping in South America

2. U.S. Department of the Interior U.S. Geological Survey GOAL Cropland extent mapping for South America at 30m resolution for the year 2010.

3. U.S. Department of the Interior U.S. Geological Survey Post-processing Random Forest classification Examples: Tile 42, 56, 57 Training sample design Continent segmentation Cropland masking Cropland Definition

4. U.S. Department of the Interior U.S. Geological Survey “Cropland” “All cultivated plants harvested for food, feed, and fiber, including plantations.” (Teluguntla et al. 2009) Cropland: continuous, seasonal fallow, fallow under 5 years. Non-cropland: urban, native vegetation, barren, pasture, rangeland, timberland, long-abandoned cropland (>5 years), etc.

5. U.S. Department of the Interior U.S. Geological Survey Cropland masking Land cover map masking Giri, C., & Long, J. (2014)

6. U.S. Department of the Interior U.S. Geological Survey Cropland masking Biome Dinerstein et al. (1995)

7. U.S. Department of the Interior U.S. Geological Survey Continent Segmentation (stratification) Landsat tile & Biome segmentation (stratification) Dinerstein et al. (1995)

8. U.S. Department of the Interior U.S. Geological Survey Training sample selection Per-pixel based Selection system: a. Random sample strategy b. Manually added cropland samples Reference data Landsat image Google Earth 2009 GlobCover (Arino 2012) Time-series Landsat and NDVI composite Ground truth data – geo-wiki validation and geo-wiki competition

9. U.S. Department of the Interior U.S. Geological Survey Random Forest Classification Random Forest is an ensemble model of Decision Tree models. In a forest, large number of decision trees are constructed based on subsets of training sample dataset. When projecting the prediction of classification to the landscape, each object on the landscape gets its votes of classification from each tree in the forest. The classification that has the most votes among the trees wins and becomes the classification of that object. Why Random Forest ？  1. No assumption towards data distribution and high accuracy (Foody 1995; Friedl and Brodley, 1997)  2. “More accurate and robust to noise than single classifiers (Breiman, 1996; Dietterich, 2000)”  3. Evaluates importance of input variables.  4. Model error rate is evaluated internally.

10. U.S. Department of the Interior U.S. Geological Survey “Random subspace” and “Bagging” “If these predictors are aggregated – averaging in regression or voting in classification, then the resultant predictor can be considerably more accurate than the original predictor.” Error rate calculated based on out-of-bag samples is as accurate as based on an independent test dataset with the same sample size as training dataset. Out-of-bag (oob) error rate Breiman [1996]

11. U.S. Department of the Interior U.S. Geological Survey Tools Google Earth Engine randomForest package in R

12. U.S. Department of the Interior U.S. Geological Survey Input variables Landsat 5&7 band 3, 4, 5: growing season spanning from November 2009 to February 2010. NDVI Slope calculated from SRTMGL1 30m.

13. U.S. Department of the Interior U.S. Geological Survey Number of trees

14. U.S. Department of the Interior U.S. Geological Survey Examples – Tile 42 Venezuela, heterogeneous landscape Biome segments: Biome a: Tropical and subtropical moist broadleaf forest (Biome 1) & Tropical and subtropical dry broadleaf forest (Biome 2) Biome b: Tropical and subtropical grasslands, savannas, and shrublands (Biome 7) & Flooded grasslands and shrublands (Biome 9) Biome c: Deserts and xeric Shrublands (Biome 13)

15. U.S. Department of the Interior U.S. Geological Survey Examples - Tile 42 Maximum sample sizeTile 42Biome 1&2Biome 7&9Biome 13 Total600200 Non-cropland300100 Cropland300100

16. U.S. Department of the Interior U.S. Geological Survey Examples – Tile 57 East Brazil Biome segments: a. Deserts and Xeric Shrublands (Biome 13) b. Tropical and Subtropical Moist Broadleaf Forest (Biome 1)

17. U.S. Department of the Interior U.S. Geological Survey Examples – Tile 57 Maximum sample sizeTile 56Biome 1Biome 13 Total400200 Non-cropland200100 Cropland200100

18. U.S. Department of the Interior U.S. Geological Survey Examples – Tile 56 East Brazil Biome segments: a. Tropical and subtropical moist broadleaf forests (1) b. Deserts and xeric shrublands (13) c. Tropical and subtropical grasslands, savannas, and shrublands (7)

19. U.S. Department of the Interior U.S. Geological Survey Example – Tile 56 Maximum sample sizeTile 56Biome 1Biome 7Biome 13 Total420140 Non-cropland21070 Cropland21070

20. U.S. Department of the Interior U.S. Geological Survey Post-processing Mosaicking Filtering Manual correction

21. U.S. Department of the Interior U.S. Geological Survey Conclusions A training samples size of 70 per class for each biome is needed to gain stable and accurate cropland classification. Biome level stratification improves classification accuracy. Classification results need further improvement. -- refine training sample dataset -- adjust training sample size ratio between cropland and non-cropland classes.

22. U.S. Department of the Interior U.S. Geological Survey Plan for next 5 months Finish cropland extent mapping for 12 tiles in South America. Prepare documentation.

23. U.S. Department of the Interior U.S. Geological Survey Thank you!