Applications of Digital Elevation Models and Geographic Information Systems to Coastal Flood Studies along the Shoreline of Raritan Bay, New Jersey

2001 ◽  
Vol 8 (1) ◽  
pp. 11-20 ◽  
Author(s):  
John Dobosiewicz
Keyword(s):  
New Jersey ◽  
Information Systems ◽  
Geographic Information Systems ◽  
Digital Elevation Models ◽  
Geographic Information ◽  
Coastal Flood ◽  
Digital Elevation

Pemetaan Bahaya Tsunami Wilayah Kabupaten Serang Bagian Barat Menggunakan Sistem Informasi Geografis

2021 ◽  
Vol 10 (3) ◽  
pp. 233-241
Author(s):  
Padma Paramita ◽  
Sesa Wiguna ◽  
Fathia Zulfati Shabrina ◽  
Aida Sartimbul
Keyword(s):  
Information Systems ◽  
Geographic Information Systems ◽  
Digital Elevation Model ◽  
Accurate Method ◽  
Geographic Information ◽  
Tsunami Hazard ◽  
Disaster Mitigation ◽  
Hazard Map ◽  
Digital Elevation ◽  
Elevation Model

Indonesia merupakan negara yang memiliki potensi tinggi akan kejadian tsunami. Salah satu wilayah tersebut adalah Kabupaten Serang bagian barat. Saat ini evolusi teknologi penginderaan jauh dan Sistem Informasi Geografis (SIG) dapat dimanfaatkan untuk membantu upaya mitigasi. Tujuan penelitian ini adalah untuk menganalisis potensi tsunami dan menyediakan peta bahaya tsunami sebagai salah satu upaya mitigasi bencana berbasis Sistem Informasi Geografis (SIG) berdasarkan panduan dari Badan Nasional Penanggulangan Bencana (BNPB). Metode yang digunakan dalam penelitian ini adalah metode matematis yang dikembangkan oleh Berryman-2006. Metode ini merupakan metode yang sederhana namun cukup akurat dalam memperkirakan daerah yang berpotensi terdampak tsunami. Data Digital Elevation Model (DEM) dan shapefile rupa bumi yang bersumber dari Badan Informasi Geospasial (BIG) Indonesia merupakan data utama yang digunakan. Hasil analisis menunjukkan bahwa potensi bahaya tsunami di Kabupaten Serang bagian barat terdiri dari 3 kelas yaitu kelas rendah, sedang, dan tinggi yang didominasi oleh kelas bahaya tinggi dengan total luas area terdampak sebesar 377,64 ha. Peta bahaya tsunami ini selanjutnya dapat dijadikan sebagai salah satu basis informasi dalam perencanaan mitigasi bencana di Kabupaten Serang.  Indonesia is a country that has a high potential for tsunami events. One of these areas is the western part of Serang Regency. Currently, the evolution of remote sensing technology and Geographic Information Systems (GIS) can be utilized to assist mitigation efforts. The purpose of this study is to analyze the potential for tsunamis and provide a tsunami hazard map as one of the efforts to mitigate disasters based on Geographic Information Systems (GIS) based on guidelines from the National Disaster Management Agency (BNPB). The method used in this research is a mathematical method developed by Berryman-2006. This method is a simple but fairly accurate method for estimating areas potentially affected by a tsunami. Digital Elevation Model (DEM) data and the shapefile of the earth's appearance sourced from the Indonesian Geospatial Information Agency (BIG) are the main data used. The results of the analysis show that the potential tsunami hazard in the western part of Serang Regency consists of 3 classes, namely low, medium, and high classes which are dominated by high hazard classes with a total area of 377.64 ha affected. This tsunami hazard map can then be used as one of the information bases in disaster mitigation planning in Serang Regency.

The aspects of Aspect: Understanding land exposure and its part in geographic information systems analysis

2018 ◽  
Vol 29 (6) ◽  
pp. 1022-1037 ◽  
Author(s):  
George D Malaperdas ◽  
Vayia V Panagiotidis
Keyword(s):  
Information Systems ◽  
Spatial Analysis ◽  
Geographic Information Systems ◽  
Learning Process ◽  
Systems Analysis ◽  
Geographic Information ◽  
Digital Elevation ◽  
New Students ◽  
Using Data ◽  
Elevation Model

One of the hardest terms for students new to geographic information systems to understand is the meaning and application of Aspect. When taking one’s first steps in spatial analysis using data in the form of rasters, the first three things new users are called to learn and use are exporting a digital elevation model, the Slope (land incline) and finally the Aspect (orientation of a slope). While the first two are quite straightforward and easily comprehended even from newcomers to spatial analysis in geographic information systems, Aspect continues throughout the learning process to be difficult as a function with one out of three new students not able to decipher it. This paper attempts to give a simpler definition to Aspect including its analytical significance in geographic information systems.

scholarly journals Comparison of different approaches to LS factor calculations based on a measured soil loss under simulated rainfall.

2017 ◽  
Vol 12 (No. 2) ◽  
pp. 69-77 ◽  
Author(s):  
M. Hrabalíková ◽  
M. Janeček
Keyword(s):  
Information Systems ◽  
Geographic Information Systems ◽  
Soil Erosion ◽  
Soil Loss ◽  
Geographic Information ◽  
Experimental Plot ◽  
Simulated Rainfall ◽  
Slope Length ◽  
Digital Elevation ◽  
Elevation Model

Geographic Information Systems (GIS) in combination with soil loss models can enhance evaluation of soil erosion estimation. SAGA and ARC/INFO geographic information systems were used to estimate the topographic (LS) factor of the Universal Soil Loss Equation (USLE) that in turn was used to calculate the soil erosion on a long-term experimental plot near Prague in the Czech Republic. To determine the influence of a chosen algorithm on the soil erosion estimates a digital elevation model with high accuracy (1 × 1 m) and a measured soil loss under simulated rainfall were used. These then provided input for five GIS-based and two manual procedures of computing the combined slope length and steepness factor in the (R)USLE. The results of GIS-based (R)USLE erosion estimates from the seven procedures were compared to the measured soil loss from the 11 m long experimental plot and from 38 rainfall simulations performed here during 15 years. The results indicate that the GIS-based (R)USLE soil loss estimates from five different approaches to calculation of LS factor are lower than the measured average annual soil loss. The two remaining approaches over-predicted the measured soil loss. The best method for LS factor estimation on field scale is the original manual method of the USLE, which predicted the average soil loss with 6% difference from the measured soil loss. The second method is the GIS-based method that concluded a difference of 8%. The results of this study show the need for further work in the area of soil erosion estimation (with particular focus on the rill/interrill ratio) using the GIS and USLE. The study also revealed the need for an application of the same approach to catchment area as it might bring different outcomes.

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