scholarly journals Photogrammetry-derived digital surface model and orthoimagery of Valdez Glacier ice-dammed lake, Valdez, Alaska, October 15, 2014

2019 ◽  
Author(s):  
Katreen Wikstrom Jones ◽  
G.J. Wolken ◽  
E.N. Whorton ◽  
M.D. Hendricks
Keyword(s):  
Surface Model ◽  
Digital Surface Model ◽  
Glacier Ice ◽  
Dammed Lake

scholarly journals Thickness estimation of supraglacial debris above ice cliff exposures using a high-resolution digital surface model derived from terrestrial photography

2017 ◽  
Vol 63 (242) ◽  
pp. 989-998 ◽  
Author(s):  
L. NICHOLSON ◽  
J. MERTES
Keyword(s):  
High Resolution ◽  
Cost Effective ◽  
Ablation Rate ◽  
Surface Model ◽  
Digital Surface Model ◽  
High Frequency Radar ◽  
Ice Surface ◽  
Cost Effective Method ◽  
Debris Cover ◽  
Glacier Ice

ABSTRACTThe thickness of supraglacial debris cover controls how it impacts the ablation rate of underlying glacier ice, yet this quantity remains challenging to measure, particularly at glacier scales. We present a relatively straightforward, and cost-effective method to estimate debris thickness exposed above ice cliffs using simplified geometrical measurements from a high-resolution digital surface model (DSM), derived from a terrestrial photographic survey and a Structure from Motion with Multi-View Stereo workflow (SfM-MVS). As the ice surface relief beneath the debris cover is unknown, we assume it to be horizontal and provide error bounds based on characteristic ice-surface slope at the visible debris/ice interface. Debris thickness around the three sampled ice cliffs was highly variable (interquartile range of 0.80–2.85 m) and negatively skewed with a mean thickness of 2.08 ± 0.68 m. Manual, and high-frequency radar, determinations of debris thickness in the same area show similar thickness distributions, but statistically different mean debris thickness, due to local heterogeneity. Debris thickness values derived in this study all exceed estimates from satellite surface temperature inversions. Wider application of the method presented here would provide useful data for improving debris thickness approximations from satellite imagery.

EVALUASI KETELITIAN HORIZONTAL DARI CITRA TEGAK SATELIT RESOLUSI SANGAT TINGGI MENGGUNAKAN SRTM DAN TERRASAR-X UNTUK PEMETAAN SKALA BESAR

2018 ◽  
Vol 2 ◽  
pp. 535
Author(s):  
Maundri Prihanggo
Keyword(s):  
Control Point ◽  
Ground Control ◽  
Surface Model ◽  
Digital Surface Model ◽  
Check Point ◽  
Ground Control Point

<p>Saat ini, citra satelit resolusi sangat tinggi digunakan dalam berbagai macam aplikasi, terutama pemetaan skala besar. Sebelum dapat digunakan, citra satelit tersebut harus diorthorektifikasi terlebih dahulu. Data <em>Digital Surface Model </em>(DSM) dan <em>Ground Control Point</em> (GCP) adalah dua data utama yang diperlukan saat melakukan orthorektifikasi. Perbedaan data DSM yang digunakan akan menghasilkan perbedaan nilai ketelitian horizontal pada kedua citra tegak hasil orthorektifikasi. Pada penelitian ini digunakan dua jenis DSM yaitu SRTM dan Terrasar-X. Ketelitian vertikal dari SRTM adalah 90 m sedangkan ketelitian vertikal dari Terrasar-X adalah 12,5 m. Penelitian ini berlokasi di Wilayah Buli, Kabupaten Halmahera Timur, Provinsi Maluku. Terdapat tiga sensor citra satelit yang digunakan yaitu Pleiades, Quickbird dan Worldview-2 yang digunakan pada lokasi penelitian. Total GCP yang digunakan adalah 33 titik, tiap titiknya diukur dengan melakukan pengamatan geodetik dan memiliki ketelitian horizontal ≤15 cm dan ketelitian vertikal ≤30 cm. Ketelitian horizontal dari citra tegak satelit resolusi sangat tinggi diperoleh dengan melakukan uji terhadap Independent Check Point (ICP). Total ICP yang digunakan adalah 12 titik, tiap titik ICP diukur dengan metode dan standar yang sama dengan titik GCP. Ketelitian horizontal dengan Circular Error (CE 90) dari citra tegak satelit menggunakan data SRTM adalah 18,856 m sedangkan ketelitian horizontal dengan Circular Error (CE 90) dari citra tegak satelit menggunakan data Terrasar-X adalah 2.168 m . Hasil dari penelitian ini membuktikan bahwa ketelitian vertikal data DSM yang digunakan memberikan pengaruh pada citra tegak satelit hasil orthorektifikasi tersebut. Mengacu pada Peraturan Kepala BIG nomor 15 tahun 2014, citra tegak satelit hasil orthorektifikasi menggunakan data Terrasar-X sebagai DSM memenuhi ketelitian horizontal peta dasar kelas 3 skala 1:5.000 sedangkan citra tegak satelit hasil orthorektifikasi menggunakan data SRTM sebagai DSM tidak dapat memenuhi ketelitian horizontal peta dasar skala besar.</p><p><strong>Kata kunci:</strong> orthorektifikasi, DSM, ketelitian horizontal</p>

Measuring historical flooding and erosion in Goodnews Bay using datasets commonly available to Alaska communities

Shore & Beach ◽  
2020 ◽  
pp. 3-13
Author(s):  
Richard Buzard ◽  
Christopher Maio ◽  
David Verbyla ◽  
Nicole Kinsman ◽  
Jacquelyn Overbeck
Keyword(s):  
Rural Communities ◽  
Coastal Communities ◽  
Aerial Imagery ◽  
Coastal Hazards ◽  
Surface Model ◽  
Digital Surface Model ◽  
Wave Impact ◽  
Information Gap ◽  
Quantitative Analyses ◽  
Gnss Measurements

Coastal hazards are of increasing concern to many of Alaska’s rural communities, yet quantitative assessments remain absent over much of the coast. To demonstrate how to fill this critical information gap, an erosion and flood analysis was conducted for Goodnews Bay using an assortment of datasets that are commonly available to Alaska coastal communities. Measurements made from orthorectified aerial imagery from 1957 to 2016 show the shoreline eroded 0 to 15.6 m at a rate that posed no immediate risk to current infrastructure. Storm surge flood risk was assessed using a combination of written accounts, photographs of storm impacts, GNSS measurements, hindcast weather models, and a digital surface model. Eight past storms caused minor to major flooding. Wave impact hour calculations showed that the record storm in 2011 doubled the typical annual wave impact hours. Areas at risk of erosion and flooding in Goodnews Bay were identified using publicly available datasets common to Alaska coastal communities; this work demonstrates that the data and tools exist to perform quantitative analyses of coastal hazards across Alaska.

scholarly journals Automated Generation of Digital Terrain Model using Point Clouds of Digital Surface Model in Forest Area

2011 ◽  
Vol 3 (5) ◽  
pp. 845-858 ◽  
Author(s):  
Kande R.M.U. Bandara ◽  
Lal Samarakoon ◽  
Rajendra P. Shrestha ◽  
Yoshikazu Kamiya
Keyword(s):  
Digital Terrain Model ◽  
Point Clouds ◽  
Forest Area ◽  
Surface Model ◽  
Digital Surface Model ◽  
Automated Generation ◽  
Terrain Model ◽  
Digital Terrain
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