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 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 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

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 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 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.

scholarly journals A New Simplified DSM-to-DTM Algorithm – dsm-to-dtm-step

2018 ◽  
Author(s):  
Thomas Krauß
Keyword(s):  
Opposite Direction ◽  
Ground Surface ◽  
Digital Terrain Model ◽  
Ground Level ◽  
Test Area ◽  
Surface Model ◽  
Digital Surface Model ◽  
Simplified Approach ◽  
Terrain Model ◽  
Digital Terrain

In this paper we will present a simplified approach for extracting the ground level &ndash; a digital terrain model (DTM) &ndash; from the surface provided in a digital surface model (DSM). Most existing algorithms try to find the ground values in a digital surface model. Our approach works the opposite direction by detecting probable above ground areas. The main advantage of our approach is the possibility to use it with incomplete DSMs containing much no data values which can be e.g. occlusions in the calculated DSM. A smoothing or filling of such original derived DSMs will destroy much information which is very useful for deriving a ground surface from the DSM. Since the presented approach needs steep edges to detect potential high objects it will fail on smoothed and filled DSMs. After presenting the algorithm it will be applied to a test area in Salzburg and compared to a terrain model freely available from the Austrian government.

scholarly journals Development of a digital surface model and a digital terrain model based on ERS data

2020 ◽  
Vol 905 ◽  
pp. 012025
Author(s):  
I G Gairabekov ◽  
A I Hamzatov ◽  
A T Mishieva ◽  
E I Ibragimova ◽  
M-B I Gairabekov ◽  
...  
Keyword(s):  
Digital Terrain Model ◽  
Surface Model ◽  
Digital Surface Model ◽  
Terrain Model ◽  
Model Based ◽  
Digital Terrain

scholarly journals Mapping with Pléiades—End-to-End Workflow

2019 ◽  
Vol 11 (17) ◽  
pp. 2052 ◽  
Author(s):  
Roland Perko ◽  
Hannes Raggam ◽  
Peter M. Roth
Keyword(s):  
Digital Terrain Model ◽  
Building Blocks ◽  
Surface Model ◽  
Data Sets ◽  
3D Mapping ◽  
Digital Surface Model ◽  
Terrain Model ◽  
Digital Terrain ◽  
Surface Models ◽  
End To End

In this work, we introduce an end-to-end workflow for very high-resolution satellite-based mapping, building the basis for important 3D mapping products: (1) digital surface model, (2) digital terrain model, (3) normalized digital surface model and (4) ortho-rectified image mosaic. In particular, we describe all underlying principles for satellite-based 3D mapping and propose methods that extract these products from multi-view stereo satellite imagery. Our workflow is demonstrated for the Pléiades satellite constellation, however, the applied building blocks are more general and thus also applicable for different setups. Besides introducing the overall end-to-end workflow, we need also to tackle single building blocks: optimization of sensor models represented by rational polynomials, epipolar rectification, image matching, spatial point intersection, data fusion, digital terrain model derivation, ortho rectification and ortho mosaicing. For each of these steps, extensions to the state-of-the-art are proposed and discussed in detail. In addition, a novel approach for terrain model generation is introduced. The second aim of the study is a detailed assessment of the resulting output products. Thus, a variety of data sets showing different acquisition scenarios are gathered, allover comprising 24 Pléiades images. First, the accuracies of the 2D and 3D geo-location are analyzed. Second, surface and terrain models are evaluated, including a critical look on the underlying error metrics and discussing the differences of single stereo, tri-stereo and multi-view data sets. Overall, 3D accuracies in the range of 0 . 2 to 0 . 3 m in planimetry and 0 . 2 to 0 . 4 m in height are achieved w.r.t. ground control points. Retrieved surface models show normalized median absolute deviations around 0 . 9 m in comparison to reference LiDAR data. Multi-view stereo outperforms single stereo in terms of accuracy and completeness of the resulting surface models.

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

scholarly journals ANALYSIS OF SOLAR POTENTIAL OF ROOFS BASED ON DIGITAL TERRAIN MODEL

2017 ◽  
Vol XLII-4/W3 ◽  
pp. 37-41 ◽  
Author(s):  
M. Gorički ◽  
V. Poslončec-Petrić ◽  
S. Frangeš ◽  
Ž. Bačić
Keyword(s):  
Detailed Analysis ◽  
Smart City ◽  
Digital Terrain Model ◽  
Surface Model ◽  
Digital Surface Model ◽  
Solar Panels ◽  
High Quality ◽  
Terrain Model ◽  
Digital Terrain ◽  
The Republic

One of the basic goals of the smart city concept is to create a high-quality environment that is long sustainable and economically justifiable. The priority and concrete goal today is to promote and provide sustainable sources of energy (SSE). Croatia is rich with sun energy and as one of the sunniest European countries, it has a huge insufficiently used solar potential at its disposal. The paper describes the procedure of analysing the solar potential of a pilot area Sveti Križ Začretje by means of digital surface model (DSM) and based on the data available in the Meteorological and Hydrological Service of the Republic of Croatia. Although a more detailed analysis would require some additional factors, it is clear that the installation of 19,6m2 of solar panels in each household could cover annual requirements of the household in the analysed area, the locality Sveti Križ Začretje.

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