scholarly journals WORLDDEM – A NOVEL GLOBAL FOUNDATION LAYER

2015 ◽  
Vol XL-3/W2 ◽  
pp. 183-187 ◽  
Author(s):  
G. Riegler ◽  
S. D. Hennig ◽  
M. Weber
Keyword(s):  
Digital Terrain Model ◽  
Current Status ◽  
Disruptive Technology ◽  
Surface Model ◽  
Terrain Model ◽  
Digital Terrain ◽  
Digital Elevation ◽  
Elevation Data ◽  
Elevation Model ◽  
German Aerospace

Airbus Defence and Space’s WorldDEM™ provides a global Digital Elevation Model of unprecedented quality, accuracy, and coverage. The product will feature a vertical accuracy of 2m (relative) and better than 6m (absolute) in a 12m x 12m raster. The accuracy will surpass that of any global satellite-based elevation model available. WorldDEM is a game-changing disruptive technology and will define a new standard in global elevation models. <br><br> The German radar satellites TerraSAR-X and TanDEM-X form a high-precision radar interferometer in space and acquire the data basis for the WorldDEM. This mission is performed jointly with the German Aerospace Center (DLR). Airbus DS refines the Digital Surface Model (e.g. editing of acquisition, processing artefacts and water surfaces) or generates a Digital Terrain Model. Three product levels are offered: WorldDEMcore (output of the processing, no editing is applied), WorldDEM™ (guarantees a void-free terrain description and hydrological consistency) and WorldDEM DTM (represents bare Earth elevation). <br><br> Precise elevation data is the initial foundation of any accurate geospatial product, particularly when the integration of multi-source imagery and data is performed based upon it. Fused data provides for improved reliability, increased confidence and reduced ambiguity. This paper will present the current status of product development activities including methodologies and tool to generate these, like terrain and water bodies editing and DTM generation. In addition, the studies on verification & validation of the WorldDEM products will be presented.

scholarly journals Konversi DSM Menjadi DTM Menggunakan Filter Berbasis Kelerengan Untuk Pemetaan Genangan Banjir Rob Di Kecamatan Tirto

2020 ◽  
Author(s):  
Trida Ridho Fariz ◽  
Nur Rokhayati
Keyword(s):  
Digital Terrain Model ◽  
Surface Model ◽  
Digital Surface Model ◽  
Model Data ◽  
Alos Palsar ◽  
Terrain Model ◽  
Digital Terrain ◽  
Digital Elevation ◽  
Elevation Model ◽  
Digital Elevation Model Data

Salah satu data penginderaan jauh yang penting adalah DEM (Digital Elevation Model). Data DEM memberikan informasi ketinggian suatu permukaan bumi dimana dikelompokkan menjadi 2 yaitu DSM (Digital Surface Model) yang menyajikan informasi ketinggian permukaan tutupan lahan dan DTM (Digital Terrain Model) yang menyajikan informasi ketinggian tanah. Pemetaan banjir rob secara umum menggunakan data DTM. Tetapi untuk mendapatkan data DTM sangatlah sulit. Salah satu data DEM yang tersedia secara gratis adalah data DEM terkoreksi hasil ekstraksi dari ALOS PALSAR yang memiliki resolusi spasial 12,5 meter, tidak terlalu bagus untuk digunakan sebagai data untuk pemetaan genangan banjir rob mengingat itu hanyalah DSM. Sedangkan menggunakan data titik ketinggian yang di interpolasi tidak terlalu merepresentatifkan kondisi ketinggian medan suatu wilayah kecuali jika jumlah titiknya banyak. Penelitian ini menggunakan metode slope based filtering untuk mengkonversi data DEM dari ALOS PALSAR menjadi DTM.Hasil dari metode ini dilakukan uji statistik berupa korelasi dengan data titik ketinggian dan mempunyai nilai korelasi yang sangat tinggi yaitu sebesar 0,80 dan nilai RMSE sebesar 1,402. Selanjutnya dibuat pemodalan spasial genangan banjir rob dari DTM. Hasil pemodelan spasial genanngan banjir rob kemudin diuji akurasi dengan uji statistik korelasi dan penghitungan RMSE dengan data hasil survey lapangan. Hasil pemodelan memiliki korelasi sebesar 0,78 dengan nilai RMSE tinggi genangan banjir rob sebesar 0,763. Yang berarti bahwa rata-rata selisih nilai ketinggian genangan banjir rob dari peta dan dilapangan adalah sebesar 0,763m. Wilayah genangan banjir rob meliputi Desa Jeruksari, Desa Tegaldowo, Desa Mulyorejo dan Desa Karangjompo.

scholarly journals Automatic LIDAR building segmentation based on DGCNN and euclidean clustering

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Ahmad Gamal ◽  
Ari Wibisono ◽  
Satrio Bagus Wicaksono ◽  
Muhammad Alvin Abyan ◽  
Nur Hamid ◽  
...  
Keyword(s):  
Digital Terrain Model ◽  
Primary Source ◽  
Surface Model ◽  
Lidar Data ◽  
Dynamic Graph ◽  
Urban And Regional Planning ◽  
Terrain Model ◽  
Digital Terrain ◽  
Digital Elevation ◽  
Elevation Model

AbstractThere has been growing demand for 3D modeling from earth observations, especially for purposes of urban and regional planning and management. The results of 3D observations has slowly become the primary source of data in terms of policy determination and infrastructure planning. In this research, we presented an automatic building segmentation method that directly uses LIDAR data. Previous works have utilized the CNN method to automatically segment buildings. However, the existing body of works have relied heavily on the conversion of LIDAR data into Digital Terrain Model (DTM), Digital Surface Model (DSM), or Digital Elevation Model (DEM) formats. Those formats required conversion of LIDAR data into raster images, which poses challenges to the evaluation of building volumes. In this paper, we collected LIDAR data with unmanned aerial vehicle and directly segmented buildings utilizing the said LIDAR data. We utilized a Dynamic Graph Convolutional Neural Network (DGCNN) algorithm to separate buildings and vegetation. We then utilized Euclidean Clustering to segment each building. We found that the combination of these methods are superior to prior works in the field, with accuracy up to 95.57% and an Intersection Over Union (IOU) score of 0.85.

Pembuatan Digital Terrain Model (DTM) dari Light Detection and Ranging (LiDAR) menggunakan Metode Morfologi Sederhana

Teknik ◽  
2019 ◽  
Vol 40 (1) ◽  
pp. 40
Author(s):  
Ayu Nur Safi'i ◽  
Prayudha Hartanto
Keyword(s):  
Digital Terrain Model ◽  
Light Detection And Ranging ◽  
Surface Model ◽  
Light Detection ◽  
Terrain Model ◽  
Digital Terrain ◽  
Digital Elevation ◽  
Morphological Filtering ◽  
Elevation Model ◽  
Data Surface

Pembuatan Peta RBI skala 1:5.000 membutuhkan waktu yang lama, khususnya untuk pembuatan layer kontur. Layer kontur bisa didapatkan dari data hasil ekstraksi foto udara dan data Light Detection and Ranging (LIDAR). Sekarang ini, teknologi LiDAR lebih diandalkan untuk membuat Data Surface Model (DSM). Dari DSM dilakukan proses ekstrasi data untuk mendapatkan data Digital Terrain Model (DTM) atau Digital Elevation Model (DEM) yang prosesnya lebih cepat dan membutuhkan biaya yang relatif rendah. Metode filtering yang digunakan adalah metode Simple Morphological Filtering (SMRF) dengan memasukkan nilai parameter cell size, slope, windows, elevation threshold dan scalling factor. Hasil Cohen’s kappa rata-rata menunjukkan indikator DTM dalam kondisi baik, yang artinya dengan menggunakan metode SMRF, nilai kappa berada diantara 0,4-0,7. Smoothing filter dilakukan untuk menghilangkan sel kosong/ sel tanpa data. DTM yang dihasilkan dibandingkan dengan data validasi lapangan. Root Mean Square Error (RMSE) yang diperoleh berkisar antara 0,621-0,930 dan nilai Linear Error 90% (LE90) yang diperoleh berkisar antara 1,025-1,605. Hasil penelitian ini menunjukkan nilai RMSE dan LE90 tersebut memenuhi ketelitian vertikal peta skala 1: 5.000 dan masuk dalam kelas 2 dan 3 sesuai Peraturan BIG No.6 Tahun 2018 mengenai perubahan atas Perka BIG No.15 Tahun 2014 tentang Pedoman Teknis Ketelitian Peta Dasar

The Coupling Relationship between Forest Vegetation and Terrain of Daliuta Mine

2014 ◽  
Vol 641-642 ◽  
pp. 1191-1194 ◽  
Author(s):  
Dong Wen Liu ◽  
Zhi Yong Qiao ◽  
Ting Ting Wei ◽  
Shu Jiang ◽  
Ya Kai Chen ◽  
...  
Keyword(s):  
Digital Terrain Model ◽  
Vegetation Coverage ◽  
Terrain Model ◽  
Digital Terrain ◽  
Digital Elevation ◽  
Object Use ◽  
Trend Data ◽  
Elevation Model ◽  
Digital Elevation Model Data ◽  
Evolution Trend

Taking Daliuta mine as research object, use its 2002, 2011 two same period Landsat TM/ ETM and remote sensing image as the data source, use pixel dichotomy to get its vegetation coverage evolution trend data; Use DEM digital elevation model data in the region to generate digital terrain model based on ArcGIS, and make overlay analysis with the vegetation coverage evolution trend data to study the relationship between the vegetation coverage and terrain factor of the mine area. The results showed that: From 2002 to 2011, the vegetation coverage evolution trend of Daliuta mining mainly moderate improvement and significantly improvement, and concentrated in middle altitude, low slope, sunny area.

scholarly journals Comparison of Digital Elevation Models by Visibility Analysis in Landscape

2016 ◽  
Vol 19 (2) ◽  
pp. 28-31
Author(s):  
Jozef Sedláček ◽  
Ondřej Šesták ◽  
Miroslava Sliacka
Keyword(s):  
Field Survey ◽  
Reference Model ◽  
Digital Terrain Model ◽  
Surface Model ◽  
Digital Surface Model ◽  
Visibility Analysis ◽  
Terrain Model ◽  
Digital Terrain ◽  
Model Average ◽  
Digital Elevation

Abstract The paper investigates suitability of digital surface model for visibility analysis in GIS. In experiment there were analysed viewsheds from 14 observer points calculated on digital surface model, digital terrain model and its comparison to field survey. Data sources for the investigated models were LiDAR digital terrain model and LiDAR digital surface model with vegetation distributed by the Czech Administration for Land Surveying and Cadastre. The overlay method was used for comparing accuracy of models and the reference model was LiDAR digital surface model. Average equalities in comparison with LiDAR digital terrain model, ZABAGED model and field survey were 15.5 %, 17.3% and 20.9%, respectively.

scholarly journals Application of the global SRTM and AW3D30 digital elevation models to mapping folds at cave sites

2021 ◽  
Vol 50 (1) ◽  
pp. 75-89
Author(s):  
Mark Abolins ◽  
Albert Ogden
Keyword(s):  
Cell Size ◽  
Forest Canopy ◽  
Digital Elevation Models ◽  
Digital Terrain Model ◽  
Surface Model ◽  
M Cell ◽  
Terrain Model ◽  
Digital Terrain ◽  
Digital Elevation ◽  
The Mean

A novel method to map and quantitatively describe very gentle folds (limb dip <5°) at cratonic cave sites was evaluated at Snail Shell and Nanna caves, central Tennessee, USA. Elevations from the global SRTM digital terrain model (DTM) were assigned to points on late Ordovician geologic contacts, and the elevations of the points were used to interpolate 28 m cell size natural neighbor digital elevation models (DEM’s) of the contacts. The global Forest Canopy Height Dataset was subtracted from the global 28 m cell size AW3D30 digital surface model (DSM) to create a DTM, and that DTM was applied in the same way. Comparison of mean and modal strikes of the interpolated surfaces with mean and modal cave passage trend shows that many passages are sub-parallel to the trend of an anticline. WithiSn 500 m of the caves, the SRTM- and AW3D30-based interpolated surfaces have mean strikes within 8° of the mean strike of an interpolated reference surface created with a high resolution (~0.76 m cell size and 10 cm RMSE) Tennessee, USA LiDAR DTM. This evaluation shows that the SRTM- and AW3D30-based method has the potential to reveal a relationship between the trend of a fold, on one hand, and cave passages, on the other, at sites where a geologic contact varies in elevation by >35 m within an area of <12.4 km2 and the mean dip of bedding is >0.9°.

scholarly journals EVALUATING ERROR OF LIDAR DERIVED DEM INTERPOLATION FOR VEGETATION AREA

2016 ◽  
Vol XLII-4/W1 ◽  
pp. 141-150 ◽  
Author(s):  
Z. Ismail ◽  
M. F. Abdul Khanan ◽  
F. Z. Omar ◽  
M. Z. Abdul Rahman ◽  
M. R. Mohd Salleh
Keyword(s):  
Spatial Resolution ◽  
Accuracy Assessment ◽  
Digital Terrain Model ◽  
Geographical Information ◽  
Interpolation Methods ◽  
Terrain Model ◽  
Digital Terrain ◽  
Digital Elevation ◽  
Elevation Model ◽  
Slope Class

Light Detection and Ranging or LiDAR data is a data source for deriving digital terrain model while Digital Elevation Model or DEM is usable within Geographical Information System or GIS. The aim of this study is to evaluate the accuracy of LiDAR derived DEM generated based on different interpolation methods and slope classes. Initially, the study area is divided into three slope classes: (a) slope class one (0° – 5°), (b) slope class two (6° – 10°) and (c) slope class three (11° – 15°). Secondly, each slope class is tested using three distinctive interpolation methods: (a) Kriging, (b) Inverse Distance Weighting (IDW) and (c) Spline. Next, accuracy assessment is done based on field survey tachymetry data. The finding reveals that the overall Root Mean Square Error or RMSE for Kriging provided the lowest value of 0.727 m for both 0.5 m and 1 m spatial resolutions of oil palm area, followed by Spline with values of 0.734 m for 0.5 m spatial resolution and 0.747 m for spatial resolution of 1 m. Concurrently, IDW provided the highest RMSE value of 0.784 m for both spatial resolutions of 0.5 and 1 m. For rubber area, Spline provided the lowest RMSE value of 0.746 m for 0.5 m spatial resolution and 0.760 m for 1 m spatial resolution. The highest value of RMSE for rubber area is IDW with the value of 1.061 m for both spatial resolutions. Finally, Kriging gave the RMSE value of 0.790m for both spatial resolutions.

scholarly journals Horizon v0.11b

2018 ◽  
Vol 3 (2) ◽  
pp. 269-270 ◽  
Author(s):  
Andrew G. K. Smith
Keyword(s):  
Digital Terrain Model ◽  
Landscape Archaeology ◽  
The Sun ◽  
Mapping Data ◽  
Terrain Model ◽  
Digital Terrain ◽  
Digital Elevation ◽  
Model Mapping ◽  
Elevation Model ◽  
Rendering Techniques

Horizon is a Geographic Information System (GIS) tool designed for archaeoastronomers investigating alignments of prehistoric monuments with astronomical phenomena (for example, rising and setting of the Sun, Moon and stars). It gets its name from its primary function, calculating accurate horizon profiles using Digital Terrain Model/Digital Elevation Model mapping data. More generally, it is a landscape visualisation tool which can generate full 360° panoramic scenes using 3D rendering techniques which may have some applications in the field of landscape archaeology.

scholarly journals Application of 30m Resolution SRTM DEM in Nepal

2018 ◽  
Vol 14 (1) ◽  
pp. 235-240 ◽  
Author(s):  
Raghunath Jha
Keyword(s):  
High Resolution ◽  
Digital Terrain Model ◽  
Topographic Map ◽  
Srtm Dem ◽  
Terrain Model ◽  
Digital Terrain ◽  
Digital Elevation ◽  
Raster Analysis ◽  
Elevation Model ◽  
Almost All

 Digital Terrain Model (DTM) or Digital Elevation Model (DEM) is an important data for Raster Analysis in modern GIS. Its use is extremely important for almost all fields of engineering, especially Water Resources Engineering. In Nepal, high-resolution DTM is not available, and often funds are limited to generate high-resolution DTM using modern day technology such as LiDAR or Aerial Photography. As a result most of the works are based on SRTM DEM which is available free of cost. Presently, 1arc second DTM is available in SRTM for Nepalese Territories. In this study, the applicability of 1arc second or 30m resolution SRTM is checked in comparison with the Department of Survey Digital Topographic Map. It is found that SRTM DEM performs better than DEM generated from Data available with Survey Department.Journal of the Institute of Engineering, 2018, 14(1): 235-240

scholarly journals PREPROCESSING OF SATELLITE DATA FOR URBAN OBJECT EXTRACTION

2015 ◽  
Vol XL-3/W2 ◽  
pp. 115-120 ◽  
Author(s):  
T. Krauß
Keyword(s):  
Digital Terrain Model ◽  
Stereo Pair ◽  
Spectral Classification ◽  
Terrain Model ◽  
Digital Terrain ◽  
Digital Elevation ◽  
Elevation Model ◽  
Processing Steps

Very high resolution (VHR) DSMs (digital surface models) derived from stereo- or multi-stereo images from current VHR satellites like WorldView-2 or Pléiades can be produced up to the ground sampling distance (GSD) of the sensors in the range of 50 cm to 1 m. From such DSMs the digital terrain model (DTM) representing the ground and also a so called nDEM (normalized digital elevation model) describing the height of objects above the ground can be derived. In parallel these sensors deliver multispectral imagery which can be used for a spectral classification of the imagery. Fusion of the multispectral classification and the nDEM allows a simple classification and detection of urban objects. In further processing steps these detected urban objects can be modeled and exported in a suitable description language like CityGML. In this work we present the pre-processing steps up to the classification and detection of the urban objects. The modeling is not part of this work. The pre-processing steps described here cover briefly the coregistration of the input images and the generation of the DSM. In more detail the improvement of the DSM, the extraction of the DTM and nDEM, the multispectral classification and the object detection and extraction are explained. The methods described are applied to two test regions from two satellites: First the center of Munich acquired by WorldView-2 and second the center of Melbourne acquired by Pl´eiades. From both acquisitions a stereo-pair from the panchromatic bands is used for creation of the DSM and the pan-sharpened multispectral images are used for spectral classification. Finally the quality of the detected urban objects is discussed.

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