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 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 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

scholarly journals Digital Elevation Models: Terminology and Definitions

2021 ◽  
Vol 13 (18) ◽  
pp. 3581
Author(s):  
Peter L. Guth ◽  
Adriaan Van Niekerk ◽  
Carlos H. Grohmann ◽  
Jan-Peter Muller ◽  
Laurence Hawker ◽  
...  
Keyword(s):  
Near Infrared ◽  
Digital Elevation Models ◽  
Three Dimensional ◽  
Digital Terrain Model ◽  
General Term ◽  
Surface Model ◽  
Infrared Sensors ◽  
Terrain Model ◽  
Digital Elevation ◽  
Wide Range

Digital elevation models (DEMs) provide fundamental depictions of the three-dimensional shape of the Earth’s surface and are useful to a wide range of disciplines. Ideally, DEMs record the interface between the atmosphere and the lithosphere using a discrete two-dimensional grid, with complexities introduced by the intervening hydrosphere, cryosphere, biosphere, and anthroposphere. The treatment of DEM surfaces, affected by these intervening spheres, depends on their intended use, and the characteristics of the sensors that were used to create them. DEM is a general term, and more specific terms such as digital surface model (DSM) or digital terrain model (DTM) record the treatment of the intermediate surfaces. Several global DEMs generated with optical (visible and near-infrared) sensors and synthetic aperture radar (SAR), as well as single/multi-beam sonars and products of satellite altimetry, share the common characteristic of a georectified, gridded storage structure. Nevertheless, not all DEMs share the same vertical datum, not all use the same convention for the area on the ground represented by each pixel in the DEM, and some of them have variable data spacings depending on the latitude. This paper highlights the importance of knowing, understanding and reflecting on the sensor and DEM characteristics and consolidates terminology and definitions of key concepts to facilitate a common understanding among the growing community of DEM users, who do not necessarily share the same background.

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 Photogrammetric surveying forests and woodlands with UAVs: techniques for automatic removal of vegetation and digital terrain model production for hydrological applications

2019 ◽  
Vol 7 (1) ◽  
pp. 1-20
Author(s):  
Fotis Giagkas ◽  
Petros Patias ◽  
Charalampos Georgiadis
Keyword(s):  
Vegetation Type ◽  
Digital Terrain Model ◽  
Point Clouds ◽  
Aerial Images ◽  
Surface Model ◽  
Terrain Model ◽  
Vegetation Height ◽  
Digital Terrain ◽  
Dense Point ◽  
Height Model

The purpose of this study is the photogrammetric survey of a forested area using unmanned aerial vehicles (UAV), and the estimation of the digital terrain model (DTM) of the area, based on the photogrammetrically produced digital surface model (DSM). Furthermore, through the classification of the height difference between a DSM and a DTM, a vegetation height model is estimated, and a vegetation type map is produced. Finally, the generated DTM was used in a hydrological analysis study to determine its suitability compared to the usage of the DSM. The selected study area was the forest of Seih-Sou (Thessaloniki). The DTM extraction methodology applies classification and filtering of point clouds, and aims to produce a surface model including only terrain points (DTM). The method yielded a DTM that functioned satisfactorily as a basis for the hydrological analysis. Also, by classifying the DSM–DTM difference, a vegetation height model was generated. For the photogrammetric survey, 495 aerial images were used, taken by a UAV from a height of ∼200 m. A total of 44 ground control points were measured with an accuracy of 5 cm. The accuracy of the aerial triangulation was approximately 13 cm. The produced dense point cloud, counted 146 593 725 points.

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 METHODS FOR THE UPDATE AND VERIFICATION OF FOREST SURFACE MODEL

2016 ◽  
Vol XLI-B4 ◽  
pp. 51-54
Author(s):  
M. Rybansky ◽  
M. Brenova ◽  
P. Zerzan ◽  
J. Simon ◽  
T. Mikita
Keyword(s):  
Laser Scanning ◽  
Digital Terrain Model ◽  
Geographic Location ◽  
Canopy Height ◽  
Aerial Photographs ◽  
Surface Model ◽  
Vegetation Growth ◽  
Terrain Model ◽  
Digital Terrain ◽  
Pulse Echo

The digital terrain model (DTM) represents the bare ground earth's surface without any objects like vegetation and buildings. In contrast to a DTM, Digital surface model (DSM) represents the earth's surface including all objects on it. The DTM mostly does not change as frequently as the DSM. The most important changes of the DSM are in the forest areas due to the vegetation growth. Using the LIDAR technology the canopy height model (CHM) is obtained by subtracting the DTM and the corresponding DSM. The DSM is calculated from the first pulse echo and DTM from the last pulse echo data. The main problem of the DSM and CHM data using is the actuality of the airborne laser scanning. <br><br> This paper describes the method of calculating the CHM and DSM data changes using the relations between the canopy height and age of trees. To get a present basic reference data model of the canopy height, the photogrammetric and trigonometric measurements of single trees were used. Comparing the heights of corresponding trees on the aerial photographs of various ages, the statistical sets of the tree growth rate were obtained. These statistical data and LIDAR data were compared with the growth curve of the spruce forest, which corresponds to a similar natural environment (soil quality, climate characteristics, geographic location, etc.) to get the updating characteristics.

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