Decision Tree and Texture Analysis for Mapping Debris-Covered Glaciers in the Kangchenjunga Area, Eastern Himalaya
Abstract
:1. Introduction
2. Study Area
3. Data Sources and Methodology
3.1. Data Sources
3.2. Topographic and Multi-Spectral Analysis
3.3. Texture Measures
4. Results and Discussion
4.1. Clean Ice/Snow
4.2. Thermal Analysis Results
4.3. Decision Tree Results
- Clean ice and snow were delineated using the NDSI algorithm with a threshold of 0.7 (NDSI > 0.7 = snow/ice), as described in Section 4.1 above. Distinguishing snow from ice was not possible with NDSI alone, but both categories were excluded from potential debris at this step in the analysis.
- Clouds had a similar spectral response to ice in the visible bands (ASTER 1 and 2 in particular), but their reflectivity decreased at mid IR wavelengths. Band 4 of ASTER with a threshold of 90 successfully mapped all clouds (band 4 > 90 = clouds). However, we noted that some pixels corresponding bright moraine edges were inadequately mapped as clouds.
- Vegetation was mapped using the NDVI method with a threshold of 0.05 (NDVI < 0.05 = vegetation). The same threshold was obtained in a study conducted in the Alps [44]. Caution was exercised in choosing this threshold, because it is known that sparse vegetation can grow on some parts of the termini, covered with stagnant ice [32]. Some authors, for example, used the presence of vegetation on some parts of the debris-covered tongues during the spring time in the Kanchenjunga area as an indicator of debris to aid in the remote sensing mapping [78]. NDVI and slope variables were found to be two of the most important variables in predicting potential locations for rock glaciers in a different study [79].
- Shadows were delineated using ASTER band 3 with a threshold of 25 (band 3n < 25 = shadows). We note that shadows may occur over the debris-covered areas as well. However, the detection of debris-covered areas is not possible in areas of deep shadows anyway, unless the algorithm relies in big part on morphologic characteristics as pointed out in a few studies [32,36]. We included the shadow areas with a low threshold to exclude small, deep shadows that introduce too much noise in the final map.
- The HSV image generated from ASTER bands 2, 3 and 5 was useful for excluding bare rock and sand (including illuminated moraine) on the northern sides of the debris-covered tongues such as Zemu, using a threshold of 200 (Hue < 200 = periglacial moraine). Paul et al. [31] used the HSV image with a threshold of 126 to successfully map vegetated areas, which were then excluded from the potential debris-covered areas.
- Slope angle was calculated in GIS based on the ASTER DEM. Statistics showed that the slopes of the debris-covered areas ranged between 0 and 14 degrees. We chose a maximum value of 12 degrees in agreement with a previous studies [32], which showed that many debris cover tongues can be captured using this slope threshold. Another study, conducted in the Alps [31] used a much higher slope threshold of 24 degrees in their classification. However, we found that a higher slope threshold at this particular pixel size (30 m) included a lot of the steep slopes and rock walls with talus sheds, and were not suitable for the accumulation of debris.
- The temperature range was chosen based on bands 10 and 12 of ASTER following various experiments with all the thermal bands (section 4.2 above). Thresholding these two bands (band 10 > 70 K and band 12 < 90 K) captured most debris-covered tongues identified on the topographic map.
- Elevations outside the 4,000 m–5,600 m range were excluded from the map of potential debris-covered areas. The values are based on observations elsewhere in the Himalaya, which showed that the termini of debris-covered glaciers are generally situated between 4,000 and 5,000 m [30]. Modern ELA values were estimated to be 5,000–6,000 m in the Kangchenjunga area [79]. In a different study, we calculated a regional ELA value of ∼5,400 m for the Langtang Himalaya (in the same climatic zone as Kanchenjunga) based on ASTER imagery at the end of the ablation season [80]. The ELA was determined to be higher for debris-covered glaciers than clean glaciers for Nepal glaciers in the same climatic zone [81]. On the basis of these observations, we chose a slightly higher upper limit for the potential debris cover (5,600 m compared to 5,400 m estimated for Langtang), to minimize any exclusion of debris pixels higher in the ablation zone of glaciers.
4.3.1. Validation of the Decision Tree with High-Resolution Imagery
4.4. Texture Analysis Results
4.4.1. Grey-Level Co-Occurrence Measures
4.4.2. Filtering in Spatial and Frequency Domain
4.4.3. Geostatistics as a Texture Measure
4.4. Comparison of Texture Analysis with the Decision Tree Results
5. Uncertainty and Limitations
- Geolocation errors on the topographic map in the western part (Nepal) prevent an accurate assessment of the performance of the decision tree on these glacier tongues. Such errors are due to the use of different datums in this part of the world, most notably local datums (Everest 1956 or other), which introduce large errors when transformed to global datums (WGS84) [87].
- Profile curvature was shown to be important in previous studies to identify concavities in terrain, specific of debris-covered tongues [32,34,36,42]. Curvature was also found to be one of the most important predicting variables for debris-covered areas in other studies in the Andes [78,88]. In the current study, the profile and plan curvature calculated in GIS based on the ASTER DEM proved inconclusive in distinguishing debris-covered tongues from the surrounding terrain, as it produced noisy images. We estimate this to be caused by the noise inherent in the “relative” ASTER DEMs produced at LPDAAC, which lack post-processing steps such as terrain smoothing. Therefore, at this time, terrain curvature was not included in the decision tree.
- A quantitative assessment of the differences between the decision tree output and the old topographic map was not conducted, because of potential glacier changes that might have occurred since the 1960–1970s, and the unknown accuracy of the topographic map.
- Shadows over the debris-covered tongues remain a big problem in the delineation of these tongues. A more rigorous topographic analysis would be needed in order to capture all the debris-covered tongue independent of their degree of shadow.
- Snowfields outside the glacier caused an overestimation on the snow and ice area based on the NSDI, and had to be adjusted manually.
- Texture measures were not included in the decision tree due to the challenge of conducting texture classification and segmentation schemes. Supervised classification techniques have been used before as a visual clue to identify homogenous regions, with promising results [48]; however, these methods still require significant user input. More sophisticated methods such as structural methods, geometric methods or model-based methods also exist in other domains [46]. For debris-covered areas, we suggest that shape recognition techniques based on orientation or shading of objects may be useful to characterize the distribution of ice walls and supraglacial lakes on the texture surface. Geometrical methods such as the Voronoi polygons may also be useful for texture delimitation among regions, particularly the transition from debris cover to ice-free moraine. Voronoi polygons are areas with distinct shapes and texture derived based on texture tokens, i.e., points of high gradient or complex structure [47]. Such techniques have not yet yielded concluding results for our dataset, but will be explored in a future study.
6. Conclusions
Acknowledgments
References
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---|---|---|---|---|
ASTER | AST14DMO_00311272001045729_20071114145408 | 11-26-2001 | 15 m VIS 30 m SWIR 90 TIR | Some seasonal snow; clouds on the lower part of the image mostly outside glaciers |
Quickbird | 1010010004BD8700 1010010004BB8F00 | 1-6-2006 1-1-2006 | 2.4 m multispectral | Well contrasted; no clouds; minimal snow |
WorldView2 | 102001000FBA1D00 | 12-02-2010 | 0.5 m panchromatic | No clouds; good contrast |
Topographic map | N/A | ∼1960s–1970s | - | Exact date of the source aerial photography unknown; used as a priori knowledge to extract various classes. |
Criteria | ASTER Bands | Range | Threshold | Justification |
---|---|---|---|---|
Snow/ice | NDSI (bands 1 and 4) | 0.72–1 | 0.7 | Reflective in the VIS and less reflective in the NIR |
Clouds | Band 4 | 76–141 | 90 | Reflective in mid-IR where the reflectivity of snow and ice decreases |
Vegetation | NDVI (bands 2 and 3) | 0.05–0.56 | 0.05 | Highly reflective in the NIR, less in the VIS |
Shadows | Band 3 | 2–30 | 25 | Shadows are most distinguishable in band 3, where they have low reflectance values |
Hue | HSV (bands 235) | 210–258 | 200 | Useful for distinguishing between vegetated and non-vegetated land |
Slope | ASTER DEM | 0–14 | 12 | Debris-cover tongues generally have gentle slopes |
Temperature | AST08; bands 10–12 | 272.3–282.8 K | Band 10 > 700 and band 12 < 900 | Debris covered ice generally colder than lateral moraines for thin patchy debris |
Elevation | ASTER DEM | 4,000–5,400 | 4,000–5,400 | Debris-covered tongues exist between regional minimum termini elevation and below the regional ELA |
ID | Glacier | 2001 ASTER (Decision Tree) (km2) | 2001 ASTER Texture (Manual) (km2) | Area Difference (Texture-Decision Tree) (km2) | 2006 QB/WV2 (Manual) (km2) |
---|---|---|---|---|---|
1 | S. Lhonak | 0.9 | 1.2 | 0.3 | 0.8 |
2 | Broken | 2.6 | 3.0 | 0.4 | 0.2 |
3 | E. Langpo | 2.2 | 2.4 | 0.2 | 1.7 |
4 | Ramtang | 1.1 | 2.4 | 1.3 | 0.7 |
5 | Yalung | 13.2 | 16.9 | 3.7 | 9.0 |
6 | Zemu | 24.7 | 24.8 | 0.1 | 20.1 |
7 | Tongshuong | 4.0 | 2.2 | −1.8 | 2.2 |
8 | Talung | 9.9 | 10.1 | 0.2 | 7.7 |
9 | E. Rathong | 2.7 | 3.9 | 1.2 | 3.5 |
10 | Onglaktang | 2.9 | 2.9 | 0.0 | 2.0 |
Total | 64.1 | 69.9 | 5.7 | 47.9 |
Clean Ice | Debris Cover | Clouds | Bare Rock/Sand | |
---|---|---|---|---|
Variance | 0.06 | 15.27 | 0 | 2.01 |
Homogeneity | 0.99 | 0.22 | 1 | 0.52 |
Entropy | 0.01 | 2.13 | 0 | 1.79 |
Search Direction | Shape | Nugget (m) | Sill (m2) | Range (m) | |
---|---|---|---|---|---|
Debris | no | Exponential | 14.4 | 513.6 | 98.7 |
Debris | yes | Exponential | 90.8 | 457.2 | 201.2 |
Bare Rock/Sand | no | Exponential | 7.8 | 48.2 | 87 |
Clean ice | no | Spherical | 0.7 | 2.3 | 132 |
Share and Cite
Racoviteanu, A.; Williams, M.W. Decision Tree and Texture Analysis for Mapping Debris-Covered Glaciers in the Kangchenjunga Area, Eastern Himalaya. Remote Sens. 2012, 4, 3078-3109. https://doi.org/10.3390/rs4103078
Racoviteanu A, Williams MW. Decision Tree and Texture Analysis for Mapping Debris-Covered Glaciers in the Kangchenjunga Area, Eastern Himalaya. Remote Sensing. 2012; 4(10):3078-3109. https://doi.org/10.3390/rs4103078
Chicago/Turabian StyleRacoviteanu, Adina, and Mark W. Williams. 2012. "Decision Tree and Texture Analysis for Mapping Debris-Covered Glaciers in the Kangchenjunga Area, Eastern Himalaya" Remote Sensing 4, no. 10: 3078-3109. https://doi.org/10.3390/rs4103078