Automated Generation of a Digital Elevation Model Over Steep Terrain in Antarctica From High-Resolution Satellite Imagery

Lee, Changno; Oh, Jaehong; Hong, Chang-Hee; Youn, Junhee

doi:10.1109/tgrs.2014.2335773

Cited by 18 publications

(7 citation statements)

References 18 publications

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“…The "Karakoram anomaly" that was first identified by Hewitt (2005), based on the observed unusual behaviour of glacier termini, is now a major research topic, and numerous studies have investigated the recent and longer-term evolution of climate, changes in glacier extent and volume, and glacier dynamics. These studies suggest that since the 1970s the extent and mass of glaciers in the central Karakoram have on average hardly changed (Bolch et al, 2017;Bajracharya et al, 2015;Bhambri et al, 2013), which also applies to the beginning of the 21st century (Lin et al, 2017;Brun et al, 2017;Gardelle et al, 2013;Gardner et al, 2013;Kääb et al, 2012), while glaciers in the mountain ranges of the Hindu Kush and Hindu Raj are mostly retreating (Sarıkaya et al, 2013;Haritashya et al, 2009). However, the patterns of climate-induced glacier change are not to be confounded with the strong geometric changes observed for the abundant surge-type glaciers in the region that might occur independent of climatic forcing (Bhambri et al, 2017;Paul, 2015;Quincey et al, 2015;Rankl et al, 2014;Copland et al, 2011).…”

Section: Glacier Changesmentioning

confidence: 87%

A consistent glacier inventory for Karakoram and Pamir derived from Landsat data: distribution of debris cover and mapping challenges

Mölg

Bolch

Rastner

et al. 2018

Earth Syst. Sci. Data

103

102

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Abstract. Knowledge about the coverage and characteristics of glaciers in High Mountain Asia (HMA) is still incomplete and heterogeneous. However, several applications, such as modelling of past or future glacier development, run-off, or glacier volume, rely on the existence and accessibility of complete datasets. In particular, precise outlines of glacier extent are required to spatially constrain glacier-specific calculations such as length, area, and volume changes or flow velocities. As a contribution to the Randolph Glacier Inventory (RGI) and the Global Land Ice Measurements from Space (GLIMS) glacier database, we have produced a homogeneous inventory of the Pamir and the Karakoram mountain ranges using 28 Landsat TM and ETM+ scenes acquired around the year 2000. We applied a standardized method of automated digital glacier mapping and manual correction using coherence images from the Advanced Land Observing Satellite 1 (ALOS-1) Phased Array type L-band Synthetic Aperture Radar 1 (PALSAR-1) as an additional source of information; we then (i) separated the glacier complexes into individual glaciers using drainage divides derived by watershed analysis from the ASTER global digital elevation model version 2 (GDEM2) and (ii) separately delineated all debris-covered areas. Assessment of uncertainties was performed for debris-covered and clean-ice glacier parts using the buffer method and independent multiple digitizing of three glaciers representing key challenges such as shadows and debris cover. Indeed, along with seasonal snow at high elevations, shadow and debris cover represent the largest uncertainties in our final dataset. In total, we mapped more than 27 800 glaciers >0.02 km2 covering an area of 35 520±1948 km2 and an elevation range from 2260 to 8600 m. Regional median glacier elevations vary from 4150 m (Pamir Alai) to almost 5400 m (Karakoram), which is largely due to differences in temperature and precipitation. Supraglacial debris covers an area of 3587±662 km2, i.e. 10 % of the total glacierized area. Larger glaciers have a higher share in debris-covered area (up to >20 %), making it an important factor to be considered in subsequent applications (https://doi.org/10.1594/PANGAEA.894707).

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Section: Glacier Changesmentioning

confidence: 87%

A consistent glacier inventory for Karakoram and Pamir derived from Landsat data: distribution of debris cover and mapping challenges

Mölg

Bolch

Rastner

et al. 2018

Earth Syst. Sci. Data

103

102

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“…Then, the gradient value of this point can be calculated using Equation (5). , then get the Perlin noise value as the sum of points P. The evaluation process is as follows: Order freq = 4, amp =1, octaves= 8; sum = 0; While octaves > 0 sum = sum + Lz freq = freq * 2 amp = amp * 1/2 octaves = octaves -1 End Figure 1 shows that the generated terrain through rendering data resulting from step (4) (100 * 100 mesh).…”

Section: Perlin Noise Terrain Generation Methodsmentioning

confidence: 99%

“…At present, a simulation method based on the digital elevation model (DEM) and terrain generation algorithms based on fractal theory is commonly used to generate terrain [5,6], such as the random midpoint displacement method [7,8], Perlin noise superposition method [9,10], and so on. The Perlin noise function was proposed by Ken Perlin in 1985 [11] and was improved in 2002 [12].…”

Section: Introductionmentioning

confidence: 99%

A Parallel Algorithm Using Perlin Noise Superposition Method for Terrain Generation Based on CUDA architecture

Li¹,

Tuo²,

Liu³

et al. 2015

Proceedings of the 2015 International Conference on Materials Engineering and Information Technology Applications

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Abstract.A parallel algorithm for terrain generation based on CUDA architecture is proposed in this paper, which aims to address the problems of high computational load and low efficiency when generating large scale terrains using the Perlin noise superposition method. The Perlin noise superposition method is combined with independent calculation of each point based on the characteristics of all adjacent points. The Perlin noise value of each terrain grid point is transferred to a GPU thread for calculation, so that the terrain generation process is executed in completely parallel in the GPU. Experimental results show that The GPU algorithm generates a grid of size 25000000 (25 million grid points) needs only 0.6355 s, while the original CPU algorithm takes 23.3723 s, so, the parallel processing algorithm can improve the efficiency of the terrain generation and meet the requirements for large-scale terrain generation compared with the original algorithm.

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“…In 2000, the National Aeronautics and Space Administration (NASA) and the National Geospatial Intelligence Agency (NGA) operated the Shuttle Radar Topographic Mission (SRTM), which acquired SRTM data (elevation data) covering more than 80% of the earth's surface between the north latitude 60 degree and south latitude 56 degree (Rodriguez et al, 2005(Rodriguez et al, , 2006Farr et al, 2007;Wendleder et al, 2016). The SRTM data have been widely used in the field of remote sensing, such as natural disaster monitoring (Jafarzadegan & Merwade, 2017), meteorological forecast (Yue et al, 2015), and glacial evolution detection (Lee et al, 2015;Yang et al, 2015). In addition, digital elevation models (DEMs) were created based on the SRTM data.…”

Section: Introductionmentioning

confidence: 99%

A shadow constrained conditional generative adversarial net for SRTM data restoration

Dong

Huang

Smith

et al. 2020

Remote Sensing of Environment

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The original data produced by the Shuttle Radar Topography Mission (SRTM) tend to have an abundance of voids in mountainous areas where the elevation measurements are missing. In this paper, deep learning models are investigated for restoring SRTM data. To this end, we explore generative adversarial nets, which represent one state of the art family of deep learning models. A conditional generative adversarial network (CGAN) is introduced as the baseline method for filling voids in incomplete SRTM data. The problem regarding shadow violation that possibly arises from the CGAN restored data is investigated. To address this deficiency, shadow geometric constraints based on shadow maps of satellite images are devised. In addition, a shadow constrained conditional generative adversarial network (SCGAN), which incorporates the shadow geometric constraints into the CGAN, is developed. Training the SCGAN model requires both the remote sensing observations (i.e., the original incomplete SRTM data and satellite images) and the ground truth data (i.e., the complete SRTM data, which are manually refined from the incomplete SRTM data with the reference of in-situ measurements). The integration of the multi-source training data enables the SCGAN model to be characterized by comprehensive information including both mountain shape variation and mountain shadow geometry. Experimental results validate the superiority of the SCGAN over the comparison methods, i.e., the interpolation, the convolutional neural network (CNN) and the baseline CGAN, in SRTM data restoration.

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Automated Generation of a Digital Elevation Model Over Steep Terrain in Antarctica From High-Resolution Satellite Imagery

Cited by 18 publications

References 18 publications

A consistent glacier inventory for Karakoram and Pamir derived from Landsat data: distribution of debris cover and mapping challenges

A consistent glacier inventory for Karakoram and Pamir derived from Landsat data: distribution of debris cover and mapping challenges

A Parallel Algorithm Using Perlin Noise Superposition Method for Terrain Generation Based on CUDA architecture

A shadow constrained conditional generative adversarial net for SRTM data restoration

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