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

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.

2018 ◽  
Vol 2 ◽  
pp. 535
Author(s):  
Maundri Prihanggo

<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>


2021 ◽  
Vol 13 (12) ◽  
pp. 2417
Author(s):  
Savvas Karatsiolis ◽  
Andreas Kamilaris ◽  
Ian Cole

Estimating the height of buildings and vegetation in single aerial images is a challenging problem. A task-focused Deep Learning (DL) model that combines architectural features from successful DL models (U-NET and Residual Networks) and learns the mapping from a single aerial imagery to a normalized Digital Surface Model (nDSM) was proposed. The model was trained on aerial images whose corresponding DSM and Digital Terrain Models (DTM) were available and was then used to infer the nDSM of images with no elevation information. The model was evaluated with a dataset covering a large area of Manchester, UK, as well as the 2018 IEEE GRSS Data Fusion Contest LiDAR dataset. The results suggest that the proposed DL architecture is suitable for the task and surpasses other state-of-the-art DL approaches by a large margin.


Water ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1142
Author(s):  
Juliano Calil ◽  
Geraldine Fauville ◽  
Anna Carolina Muller Queiroz ◽  
Kelly L. Leo ◽  
Alyssa G. Newton Mann ◽  
...  

As coastal communities around the globe contend with the impacts of climate change including coastal hazards such as sea level rise and more frequent coastal storms, educating stakeholders and the general public has become essential in order to adapt to and mitigate these risks. Communicating SLR and other coastal risks is not a simple task. First, SLR is a phenomenon that is abstract as it is physically distant from many people; second, the rise of the sea is a slow and temporally distant process which makes this issue psychologically distant from our everyday life. Virtual reality (VR) simulations may offer a way to overcome some of these challenges, enabling users to learn key principles related to climate change and coastal risks in an immersive, interactive, and safe learning environment. This article first presents the literature on environmental issues communication and engagement; second, it introduces VR technology evolution and expands the discussion on VR application for environmental literacy. We then provide an account of how three coastal communities have used VR experiences developed by multidisciplinary teams—including residents—to support communication and community outreach focused on SLR and discuss their implications.


Author(s):  
Lesley C. Ewing

Coastal areas are important residential, commercial and industrial areas; but coastal hazards can pose significant threats to these areas. Shoreline/coastal protection elements, both built structures such as breakwaters, seawalls and revetments, as well as natural features such as beaches, reefs and wetlands, are regular features of a coastal community and are important for community safety and development. These protection structures provide a range of resilience to coastal communities. During and after disasters, they help to minimize damages and support recovery; during non-disaster times, the values from shoreline elements shift from the narrow focus on protection. Most coastal communities have limited land and resources and few can dedicate scarce resources solely for protection. Values from shore protection can and should expand to include environmental, economic and social/cultural values. This paper discusses the key aspects of shoreline protection that influence effective community resilience and protection from disasters. This paper also presents ways that the economic, environmental and social/cultural values of shore protection can be evaluated and quantified. It presents the Coastal Community Hazard Protection Resilience (CCHPR) Index for evaluating the resilience capacity to coastal communities from various protection schemes and demonstrates the use of this Index for an urban beach in San Francisco, CA, USA.


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