GC41A-0088 Integration of Classification Tree Analyses and Spatial Metrics to Assess Changes in Supraglacial Lakes in the Karakoram Himalaya Bulley, Henry N. N.; Michael P. Bishop; John F. Shroder; and Umesh K. Haritashya Department of Geography and Geology, University of Nebraska – Omaha, 6001 Dodge Street, Omaha, NE 68182 USA Contact: [email protected] OVERVIEW STUDY AREA Alpine glacier responses to climate change reveal increases in retreat with corresponding increases in production of glacier melt water and development of supraglacial lakes. The rate of occurrence and spatial extent of lakes in the Himalaya are difficult to determine because current spectral-based image analysis of glacier surfaces are limited through anisotropic reflectance and lack of high quality digital elevation models (DEM) (Bishop et al.,1998). Additionally, the limitations of mul- tivariate classification algorithms to adequately segregate glacier features in satellite imagery have led to an increased interest in nonparametric methods, such as classification and regression trees. We demonstrate the utility of a semi-automated approach that integrates classification-tree-based, image segmentation and object-oriented spatial analysis to differentiate supraglacial lakes from other glacier features. We used 2002 and 2004 ASTER VNIR and SWIR imagery to assess the Baltoro Glacier in the Karakoram Himalaya. Other input variables include the normalized difference water index (NDWI), normalized difference vegetation index (NDVI), normalized difference snow index Main Baltoro Glacier (NDSI) and band ratios. The classification tree was used to generate initial image segments and it was particularly effective in differentiating water from non-water features. Classification-tree results show => Preliminary water segments (blue) overlaid on August 2004 ASTER false color composite image of Baltoro glacier. that NDVI and NDWI were the most important variables for characterizing the glacier-surface features. => Water segments were extracted using classification tree analyses. The Karakoram Himalaya extends for approximately 500 km along the The object-oriented analysis was based on shape complexity metrics to distinguish lakes from ice K2 borders of Pakistan, India, and China. The range includes some of the => Input data that contributed to the segregation of water from non-water elements include NDVI, NDWI and IR/R, and ice cliffs. highest peaks in the world, e.g. K2 (8,611 m) and Gasherbrum II as well as ASTER bands G, R, & IR . (8,035 m). The Baltoro glacier is fed by K2 and Gasherbrum II, among other peaks. The Karakoram is part of South East Area of focus to Lake features extracted from both images show there were 142 lakes in 2002 as compared to 188 => The water segments comprise a minimum of 4 clumped pixels. assess alpine glacier conditions as part of the Global Land Ice Measurements from Space (GLIMS) project (Shroder et. al., 2007). lakes in 2004. We documented the formation of 46 new lakes and significant increases in lake MODIS FCC of Western Himalaya planimetric area from 2002 to 2004. It appears most of the increments occur in the lower part of the ablation zone, whereas the new lakes are formed mainly in the upper parts of the ablation zone. This RESULTS semi-automated approach has the potential of eliminating laborious visual image analysis of glacier => Number of lakes, at least 0.4 ha, in 2004 is 188. surface change, and can produce replicable supraglacial lake classification results needed to assess => Number of lakes, at least 0.4 ha, in 2002 is 142. alpine-glacier conditions in the Himalaya. => Significant lake area increases from 2002 to 2004 were observed. => There were 46 new lakes in 2004 compared to 2002. => Overall accuracy of the semi-automated supraglacial lake classification METHODS method, based on kappa statistic, is 84.93% (2004) and 84.52% (2002). 1. Flow chart: Semi-automated approach to supraglacial lake classification CONCLUSIONS The classification tree outputs are intuitive and the data-derived ASTER Image ASTER DEM (VNIR and SWIR) thresholds eliminate commonly subjective visual determination of threshold Image Ratios Indices 1st Derivatives values for indices such as NDVI, NDWI and NDSI. (IR/R, MIR/R,IR/IR) (NDWI, NDVI, NDSI) (Slope & Aspect) The semi-automated diagnostic lake classification method can potentially eliminate laborious visual multi-temporal analysis of glacier Composite Image (VNIR, SWIR, Ratio, Indices, st and 1 Derivatives) surface change. Therefore it is possible to generate consistent and replicable supraglacial Extract Image Objects Image Segmentation 2 lake classification results, needed to assess the trends of alpine-glacier (“Water” vs. “Non-water”) (Classification Tree) response to climate change in the Himalaya region and elsewhere. 3 Spatial Metrics Image Classification 2a. CART icon integrates NLCD spatial sampling tool with SEE 5 classification tree functionality (Shape Complexity) (Object-Oriented) ACKNOWLEDGEMENT The work was funded by the National Aeronautics and Space Administration (NASA) grant (NNG04GL84G). Morphometric Rules (Lakes vs. Ice & Ice-cliffs) REFERENCES 2 Classification Tree Analysis: Bishop, M.P., J.F. Shroder, Jr., B.L. Hickman, and L. Copland, 1998. Scale-dependent analysis => The process involves a binary, recursive, non-parametric data partitioning that of satellite imagery for characterization of glacier surfaces in the Karakoram Himalaya. Special can account for non-linear relationships (Breiman et al., 1984; Quinlan 1986). volume on Remote Sensing in Geomorphology; eds. S.Walsh and D. Butler; Geomorphology, RESULTS RESULTS vol. 21, 217-232. 2a. NLCD sampling tool, obtained from USGS, was used to derive training A July 16, 2005 ground photograph Object-oriented classification samples for the SEE5 classification tree software (www.rulequest.com) Breiman, L., J.H. Friedman, R.A. Olshen, and C.J. Stone, 1984. Classification and Regression 2b. An example of SEE 5 classification Tree interface of a drained supraglacial lake at the Two examples of seven SEE5 derived Classification Trees generated (Squared Pixel metric, SqP) Trees. Wadsworth, Inc. Belmont, California, 358 pp. intersection between the Baltoro main => Decreases in SqP values up to -0.000833 reflects by adaptive boosting. Oval boxes represent non-terminal node, 2b. Adaptive boosting was used to enhance the SEE5 classification tree process potential for ice or ice cliffs. glacier and Liligo tributary glacier in Freund, Y. and R.E. Schapire, 1999. A Short Introduction to Boosting. Journal of Japanese while rectangular boxes indicate a terminal node. (Freund and Schapire, 1999; Friedman et al., 2000). the Karakoram Himalaya . The previous Society for Artificial Intelligence, 14(5):771-780. Trial 1 Trial 3 SqP Range Class Description => SqP values between -0.00554 and -0.0024 indicate water level is clearly depicted. (Photograph taken by Dr. Michael P. Bishop) -0.065321 — -0.055437 1 Orphan pixel a good chance to identify regular shaped Friedman, J., T. Hastie, and R. Tibshirani, 2000. Additive logistic regression: a statistical view 3 Shape Complexity Analysis: clumps features such as lakes -0.055437 — -0.047175 2 Lake of boosting. The Annals of Statistics. Vol. 28 (2): 337-407 => Perimeter (P)- to - Area (A) shape index or Squared Pixel metric (SqP) ( Frohn, 2006 ) was used to distinguish -0.047175 — -0.040851 2 Lake supraglacial lakes from other glacier surface features with similar spectral characteristics but different shape -0.040851 — -0.035011 2 Lake => Features with SqP values higher than -0.00554 Frohn, R. C., 2006. The use of landscape pattern metrics in remote sensing image Ice swallow hole -0.035011 — -0.030615 2 Lake complexity values, such as ice and ice cliffs. -0.030615 — -0.024934 2 Lake indicate “orphan” clumps of less than 4 pixels classification. International Journal of Remote Sensing. Vol. 27 (10): 2025-2032 -0.024934 — -0.018077 3 Ice & Ice cliffs => SqP is computed as: -0.018077 — -0.008333 3 Ice & Ice cliffs => Therefore higher SqP values imply regular shaped Quinlan, J.R., 1986. Induction of Decision Trees. Machine Learning. Vol. 1(1):81-106. -0.008333 — -0.0066667 0 Background (SqP) = 1- (4 x √A)/P Previous water level features such as lakes, whereas more irregularly Shroder, J.F., M.P. Bishop, H.N.N. Bulley, U.K. Haritashya and J.A. Olsenholler, 2007. (June 17, 2005) shaped features such as ice cliffs (concave) 1 Water Global Land Ice Monitoring from Space Project Regional Center for Southwest Asia. => The SqP values were then used in object-based discrimination of supraglacial lakes from ice and ice cliffs 1 Water 2 Non-water exhibit lower SqP values. 2 Non-water Elsevier’s Book on Share-ASIA Project. .