ASTER ratio indices for supraglacial terrain mapping

Keshri, Amit; Shukla, Anugya

doi:10.1080/01431160802385459

Cited by 78 publications

(72 citation statements)

References 6 publications

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“…Mapping glacier boundaries has been carried out successfully using various techniques: (1) photo-interpretation and manual delineation on colour composites (Williams et al 1997;Khromova et al 2006); (2) image segmentation (or thresholding) of principal components (Sidjak & Wheat 1999), single bands (e.g., TM3-Bronge & Bronge 1999), single band ratios (e.g., TM4/TM5- Bayr et al 1994;Paul et al 2002;Bolch 2007;TM3/TM5-Rott 1994;andreassen et al 2008;aSTeR 3/aSTeR4-Kääb et al 2003;aSTeR2/aSTeR4-Paul et al 2007), or spectral indices (e.g., NdSi-hall et al 2001 ;Krishna 2005;Silverio & Jaquet 2005;Racoviteanu et al 2008a;Keshri et al 2009); (3) unsupervised and supervised classifications (e.g., ISOdaTa, max. likelihood algorithms-aniya et al 1996;Li et al 1998).…”

Section: Image Classification and Ela Measurementsmentioning

confidence: 99%

Detecting the equilibrium‐line altitudes of New Zealand glaciers using ASTER satellite images

Mathieu

Chinn²,

Fitzharris

2009

New Zealand Journal of Geology and Geophysics

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The glaciers of the Southern Alps of New Zealand play an important role in understanding regional and global patterns of climate change. They constitute the third largest ice mass in the Southern Hemisphere after Antarctica and South America, and present unique glaciological features in the region: high sensitivity, high input-output systems that represent the temperate, maritime end of glacier process-behaviour continuum. Since 1977 equilibrium-line altitudes (ELA) of 48 New Zealand index glaciers have been surveyed using oblique aerial photographs as a low-cost proxy for monitoring glacier mass balance. Although this method yields good results, it is location-specific. The use of synoptic high resolution satellite imagery can potentially maximise opportunities to get a wider picture of glacier response to climate variability throughout the New Zealand Southern Alps. Hence, our objective was to investigate how well ELAs detected from 15 m ASTER satellite imagery compare with reference ELAs obtained from aerial photographs taken approximately at the same time, and thus determine whether this imagery could in the future provide a more comprehensive view of glacier wellbeing and trends throughout the Southern Alps. End-of-summer ASTER images were selected and orthorectified. Glaciers were extracted, and ice and snow patterns were classified, using supervised classification of principal components. ELAs were digitised and projected over a digital elevation model. The comparison of ELAs gave a mean height departure of 61 ± 55 m, or 14.4 ± 13.4% expressed relative to the glacier elevation range. We conclude that ASTER data could extend ELAs estimated by G08007; Online publication date 20 August 2009 Received 25 February 2008 accepted 11 July 2009 the current snowline program across the whole Southern Alps. However, small and steep glaciers with complex shapes generate large errors (dominant in the New Zealand context), and thus we suggest that only the largest glaciers can be monitored reliably. ASTER imagery cannot easily replace the annual aerial surveys, mainly because of the difficulty in obtaining the appropriate imagery. The short temporal window, high percentage of cloud cover within the area, and the potential for early unseasonable snowfall are significant limitations to the use of ASTER imagery, without mentioning the recent deterioration of the shortwave infrared ASTER instrument.

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Section: Image Classification and Ela Measurementsmentioning

confidence: 99%

Detecting the equilibrium‐line altitudes of New Zealand glaciers using ASTER satellite images

Mathieu

Chinn²,

Fitzharris

2009

New Zealand Journal of Geology and Geophysics

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“…Improvements are proposed in the algorithm itself, in the discrimination approach, in its resolution and in the extent of the area mapped. First, a different detection index, the NDSII-2 [28] is used to distinguish ice from water. Second, to achieve a 250 m resolution, a downscaling method developed at the Canadian Center for Remote Sensing (now known as the Canadian Center for Mapping and Earth Observation) using local regression between MODIS bands is applied [27,44].…”

Section: The Proposed Algorithm: Icemap250mentioning

confidence: 99%

“…A few snow and ice detection indices with potential for sea ice discrimination, other than the NDSI, exist in the literature, such as the Normalized Difference Snow and Ice Index-1 [45] and the Normalized Difference Snow and Ice Index-2 [28]. The NDSII-1 (Equation (2)) and NDSII-2 (Equation (3)) follow a logic similar to the NDSI but use different spectral bands to provide snow presence information.…”

Section: Discrimination Between Ice and Water: The Ndsii-2 Indexmentioning

confidence: 99%

“…The proposed algorithm provides improved accuracy for sea ice mapping due to finer spatial resolution and an improved set of decision thresholds. These improvements will help index) [28]. The proposed algorithm provides improved accuracy for sea ice mapping due to finer spatial resolution and an improved set of decision thresholds.…”

Section: Introductionmentioning

confidence: 96%

“…MODIS shows great potential, providing a spatial resolution of 250-1000 m combined with daily global coverage. The automated process to map sea ice presence is inspired by the original IceMap algorithm [23] but including three improvements: (1) a finer, 250 m spatial resolution made possible by using the Canadian Centre for Remote Sensing [27] downscaling algorithm; (2) use of a hybrid cloud-masking approach; (3) use of a more efficient spectral ratio to detect sea ice (the NDSII-2 index) [28]. The proposed algorithm provides improved accuracy for sea ice mapping due to finer spatial resolution and an improved set of decision thresholds.…”

Section: Introductionmentioning

confidence: 99%

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IceMap250—Automatic 250 m Sea Ice Extent Mapping Using MODIS Data

et al. 2017

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Abstract:The sea ice cover in the North evolves at a rapid rate. To adequately monitor this evolution, tools with high temporal and spatial resolution are needed. This paper presents IceMap250, an automatic sea ice extent mapping algorithm using MODIS reflective/emissive bands. Hybrid cloud-masking using both the MOD35 mask and a visibility mask, combined with downscaling of Bands 3-7 to 250 m, are utilized to delineate sea ice extent using a decision tree approach. IceMap250 was tested on scenes from the freeze-up, stable cover, and melt seasons in the Hudson Bay complex, in Northeastern Canada. IceMap250 first product is a daily composite sea ice presence map at 250 m. Validation based on comparisons with photo-interpreted ground-truth show the ability of the algorithm to achieve high classification accuracy, with kappa values systematically over 90%. IceMap250 second product is a weekly clear sky map that provides a synthesis of 7 days of daily composite maps. This map, produced using a majority filter, makes the sea ice presence map even more accurate by filtering out the effects of isolated classification errors. The synthesis maps show spatial consistency through time when compared to passive microwave and national ice services maps.

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Geological and meteorological controls on icing (aufeis) dynamics (1985 to 2014) in subarctic Canada

Morse

Wolfe

2015

JGR Earth Surface

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Icings are widespread yet poorly understood winter hydrological phenomena that develop over the winter by freezing successive overflows of groundwater to the surface. Groundwater hydrology in arctic regions is constrained by geological setting and permafrost extent, and overflows are possibly driven by cold winters, winter warming intervals, high antecedent autumn rainfall, and low early winter snowfall. Consequently, icings are spatially recurrent but not necessarily annually nor to the same extent. We test the significance of identified meteorological forcing variables against a long‐term data set of icing dynamics and distribution we developed for the Great Slave region around Yellowknife, Northwest Territories. Climate is regionally consistent, but variable geology and permafrost create hydrological conditions representative of much of the subarctic. We mapped 5500 icings in the study area (21,887 km2) with a semiautomated approach utilizing late spring Landsat archival images (1985 to 2014). Individual icing size, ranging 3 orders of magnitude (1.8 × 10−3 km2 to 4.1 km2), is related to return frequency. Infrequent ice (25% return frequency) accounts for 94% of the total icing area (86 km2). Winter warming intervals (≥5°C; typically over 1–3 days) and autumn rainfall (September and October) explain 28% of icing density interannual variation overall. Interannual icing variation and significant meteorological forcing variables differ among ecoregions where varied geological settings and permafrost conditions influence the hydrological regime. Future icings may develop less frequently due to decreasing winter warming intervals, but increasing autumn rainfall may increase icing density where Canadian Shield leads to strong threshold‐mediated runoff generation processes.

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ASTER ratio indices for supraglacial terrain mapping

Cited by 78 publications

References 6 publications

Detecting the equilibrium‐line altitudes of New Zealand glaciers using ASTER satellite images

Detecting the equilibrium‐line altitudes of New Zealand glaciers using ASTER satellite images

IceMap250—Automatic 250 m Sea Ice Extent Mapping Using MODIS Data

Geological and meteorological controls on icing (aufeis) dynamics (1985 to 2014) in subarctic Canada

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