EVALUASI HASIL INTEGRASI BERBAGAI KETELITIAN DATA MODEL ELEVASI DIGITAL

Data DEM yang dapat diakses dan digunakan dengan gratis antara lain adalah Shuttle Radar Topography Mission (SRTM) dan Advanced Spaceborne Thermal Emission and Reflection Radiometer Global DEM (ASTER GDEM). Kedua data tersebut mencakup seluruh wilayah di Indonesia, namun ketelitian dan resolusinya rendah, serta masih mengandung kesalahan tinggi. Selain data DEM global, data DEM dapat diperoleh dari hasil perekaman sensor Radio Detection and Ranging (RADAR), Light Detection and Ranging (LIDAR), maupun hasil stereoplotting foto udara dan citra satelit. Masing-masing data tersebut memiliki karakteristik seperti terdapatnya pit dan spire, diskontinuitas pada daerah sambungan dan ketelitian data yang bervariasi. Keberagaman karakteristik pada masing-masing sumber data tersebut dapat menyebabkan inkonsistensi nilai ketinggian antar sumber data. Pada penelitian ini dilakukan pembuatan DEM dengan data DTM Rupa Bumi Indonesia (RBI) skala 1:50.000 dan data DTM Interferometric Synthetic Aperture Radar (IFSAR) di Pulau Kalimantan yang dapat mengatasi inkonsistensi ketinggian tersebut. Metode yang digunakan adalah integrasi dan fusi DEM pada mozaik data-data ketinggian. Pada daerah yang bertampalan, dilakukan dua skenario mozaik yaitu mozaik tanpa bobot dan mozaik berbobot. Uji akurasi vertikal dilakukan dengan menggunakan standar Peraturan Kepala BIG Nomor 15 Tahun 2014 tentang Pedoman Teknis Ketelitian Peta Dasar. Penelitian ini menghasilkan mozaik data DTM yang seamless dan smooth menggunakan metode mozaik berbobot dengan akurasi vertikal sebesar 2,065 meter. Hasil mozaik tanpa bobot masih memiliki beberapa daerah yang tidak seamless dan smooth dengan akurasi vertikal sebesar 2,257 meter. Berdasarkan Tabel Ketelitian Geometri Peta RBI dalam PerKa BIG Nomer 15 Tahun 2014, kedua hasil mozaik tersebut masuk dalam skala 1:10.000.Kata kunci: model elevasi digital, mozaik, integrasi, fusi DEM

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Progress in operational flood mapping using satellite synthetic aperture radar (SAR) and airborne light detection and ranging (LiDAR) data

Progress in Physical Geography Earth and Environment ◽

10.1177/0309133316633570 ◽

2016 ◽

Vol 40 (2) ◽

pp. 196-214 ◽

Cited By ~ 18

Author(s):

Kyle M. Brown ◽

Crispin H. Hambidge ◽

Jonathan M. Brownett

Keyword(s):

Synthetic Aperture Radar ◽

Light Detection And Ranging ◽

Surface Elevation ◽

Synthetic Aperture ◽

Lidar Data ◽

Light Detection ◽

Elevation Data ◽

Sar Data ◽

Automated Methods ◽

Aperture Radar

During flooding, operational tools for mapping flood extent and depth of water in flood-prone areas are required by those planning emergency response, including UK statutory agencies such as the Environment Agency. Satellite data have become a source of information to map and monitor floods, but many of the methods developed to process the data are unsuitable for accurate, near real-time production of flood information products. This paper describes a new semi-automated methodology developed to provide operational mapping of flood extent and flood depth using satellite synthetic aperture radar (SAR) data combined with light detection and ranging (LiDAR) elevation data. In this method, an analyst uses the flood boundary derived from 8 m spatial resolution satellite SAR data to estimate the flood surface elevation at points around a flooded area using a digital terrain model derived from LiDAR data. This method is compared to a simple satellite ‘SAR-only’ method for generating flood extent and alternative, automated methods of generating flood extent and depth that also used SAR and LiDAR. TerraSAR-X and SPOT 5 data were used from an area including the UK Somerset Levels which suffered a major flood event in February 2014. The new semi-automated method produced similar overall accuracy to the SAR-only method ( Po = 95.8% and Po = 95.3%, respectively), but was more accurate at mapping flood extent where large vegetation or other objects appeared in the satellite SAR data. The automated methods were relatively inaccurate (overall accuracy ranged from Po = 83.4% to Po = 88.8%), but were used to identify where further work on improving the semi-automated-elevation method could be carried out. In addition to the flood extent information provided by the semi-automated-elevation method, flood surface elevation data were produced that could be used to estimated flood depths and flood volumes. The accuracy of the flood elevation surface was tested using LiDAR data acquired of the water surface during the flooding (root mean square error = 0.152 m). The paper discusses progress towards operational flood monitoring using SAR and LiDAR remote sensing products.

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Shear Plane Found in the Interior of Soil/Rock near a Tunnel Damaged in 2004 Niigata-Ken Chuetsu Earthquake

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.368-373.1621 ◽

2011 ◽

Vol 368-373 ◽

pp. 1621-1625 ◽

Cited By ~ 1

Author(s):

Yu Zhao ◽

Yuriko Tomiyasu ◽

Kazuo Konagai

Keyword(s):

Synthetic Aperture Radar ◽

Natural Disasters ◽

Spatial Data ◽

Shear Plane ◽

Cutting Edge ◽

Light Detection And Ranging ◽

Remote Detection ◽

Synthetic Aperture ◽

Interferometric Synthetic Aperture Radar ◽

Data Set

Landslides triggered by earthquakes are one of the most damaging natural disasters. Recently, cutting-edge technologies such as Light Detection and Ranging (LiDAR), Interferometric Synthetic Aperture Radar (InSAR) method have allowed us to combine a spatial data set of landslides with topographical information etc. However, remote detection of landslides may cause missing slightest signs of hidden landslides. Damages to Kizawa Tunnel and other facilities in its vicinity in the 2004 Niigata-ken Chuetsu Earthquakee are firstly introduced. Moreover, two wells at #1 and #2 respectively to the south of Kizawa tunnel were found dislocated in further survey. Dislocated wells together with other previous findings indicate that Kizawa helmet are all lying on about the same plane suggesting the presence of large shear plane and therefore the importance of thorough investigations.

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