scholarly journals Off-Road Trafficability for Military Operations Using Multi-Criteria Decision Analysis

2021 ◽  
Vol 10 (1) ◽  
pp. 3425-3437
Author(s):  
M. Nazish Khan ◽  
◽  
M. Kashif ◽  
A. Shah ◽  
◽  
...  
Keyword(s):  
Decision Analysis ◽  
Satellite Images ◽  
Military Operations ◽  
Topographic Wetness Index ◽  
Multi Criteria Decision Analysis ◽  
Digital Elevation ◽  
Roughness Index ◽  
Ground Conditions ◽  
Elevation Model ◽  
North Western

This study has been carried out in the Pathankot region, having strategic importance in terms of military operations. It explores the ability of remote sensing and GIS in assessing off-road trafficability which is integral part of terrain intelligence. Number of thematic layers has been prepared using Sentinal -2 satellite images and PALSAR Digital Elevation Model (DEM) viz. LULC, Slope, Topographic Wetness Index (TWI), Terrain Roughness Index (TRI) and ground conditions to assess the potential of off-road trafficability in the study area for military operations. Results clearly depict that most of the region is suitable for off-road movement. However, north western part is showing less suitability. Keywords PALSAR; Multi-criteria Decision Analysis; AHP; Trafficability

scholarly journals Environmental classification of administrative units of the Ukrainian Carpathians

2014 ◽  
pp. 245-253
Author(s):  
Ivan Kruhlov
Keyword(s):  
Cluster Analysis ◽  
Standard Deviation ◽  
Satellite Images ◽  
Environmental Classification ◽  
Digital Elevation ◽  
Elevation Data ◽  
Digital Elevation Data ◽  
Administrative Units ◽  
Elevation Model

Boundaries of 43 administrative units (raions and oblast towns) were digitized and manually rectified using official schemes and satellite images. SRTM digital elevation data were used to calculate mean relative elevation and its standard deviation for each unit, as well as to delineate altitudinal bioclimatic belts and their portions within the units. These parameters were used to classify the units via agglomerative cluster analysis into nine environmental classes. Key words: cluster analysis, digital elevation model, geoecosystem, geo-spatial analysis.

scholarly journals Fusion Analysis of Optical Satellite Images and Digital Elevation Model for Quantifying Volume in Debris Flow Disaster

2019 ◽  
Vol 11 (9) ◽  
pp. 1096 ◽  
Author(s):  
Hiroyuki Miura
Keyword(s):  
Debris Flow ◽  
Digital Elevation Model ◽  
Large Scale ◽  
Satellite Images ◽  
Erosion Depth ◽  
Digital Elevation ◽  
Elevation Model ◽  
Fusion Analysis ◽  
Optical Satellite Images ◽  
Debris Flow Disaster

Rapid identification of affected areas and volumes in a large-scale debris flow disaster is important for early-stage recovery and debris management planning. This study introduces a methodology for fusion analysis of optical satellite images and digital elevation model (DEM) for simplified quantification of volumes in a debris flow event. The LiDAR data, the pre- and post-event Sentinel-2 images and the pre-event DEM in Hiroshima, Japan affected by the debris flow disaster on July 2018 are analyzed in this study. Erosion depth by the debris flows is empirically modeled from the pre- and post-event LiDAR-derived DEMs. Erosion areas are detected from the change detection of the satellite images and the DEM-based debris flow propagation analysis by providing predefined sources. The volumes and their pattern are estimated from the detected erosion areas by multiplying the empirical erosion depth. The result of the volume estimations show good agreement with the LiDAR-derived volumes.

scholarly journals New discoveries on astronomical orientation of Inca site in Ollantaytambo, Peru

2015 ◽  
Vol 14 (2) ◽  
pp. 37-46
Author(s):  
Karolína Hanzalová ◽  
Jaroslav Klokočník ◽  
Jan Kostelecký
Keyword(s):  
Digital Elevation Model ◽  
Satellite Images ◽  
Observation Point ◽  
Google Earth ◽  
The Sun ◽  
Winter Solstice ◽  
Machu Picchu ◽  
Digital Elevation ◽  
Elevation Model ◽  
The Temple

<p>This paper deals with astronomical orientation of Incas objects in Ollantaytambo, which is located about 35 km southeast from Machu Picchu, about 40 km northwest from Cusco, and lies in the Urubamba valley. Everybody writing about Ollantaytambo, shoud read Protzen. (1)  He devoted his monograph to description and interpretation of that locality. Book of Salazar and Salazar (2) deals, among others, with the orientation of objects in Ollantaytambo with respect to the cardinal direction. Zawaski and Malville (3) documented astronomical context of major monuments of nine sites in Peru, including Ollantaytambo. We tested astronomical orientation in these places and confirm or disprove hypothesis about purpose of Incas objects. For assessment orientation of objects we used our measurements and also satellite images on Google Earth and digital elevation model from ASTER. The satellite images were used to estimate the astronomical-solar-solstice orientation, together with terrestrial images from Salazar and Salazar (2). The digital elevation model is useful in the mountains, where we need the actual horizon for a calculation of sunset and sunrise on specific days (solstices), which were for Incas people very important. We tested which astronomical phenomenon is connected with objects in Ollantaytambo. First, we focused on Temple of the Sun, also known the Wall of six monoliths.  We tested winter solstice sunrise and the rides of the Pleiades for the epochs 2000, 1500 and 1000 A.D. According with our results the Temple isn´t connected neither with winter solstice sunrise nor with the Pleiades. Then we tested also winter solstice sunset. We tried to use the line from an observation point near ruins of the Temple of Sun, to west-north, in direction to sunset. The astronomical azimuth from this point was about 5° less then we need. From this results we found, that is possible to find another observation point. By Salazar and Salazar (2) we found observation point at the corner (east rectangle) of the pyramid by <em>Pacaritanpu,</em> down by the riverside. There is a line connecting the east rectangular “platform” at the river, going along the Inca road up to vicinity of the Temple of the Sun and then in the direction to the Inca face. Using a digital elevation model we found the astronomical azimuth, which is needed for confirm astronomical orientation of the Temple. So, finally we are able to demonstrate a possibility of the solar-solstice orientation in Ollantaytambo.</p>

scholarly journals Digital soil mapping using multiple logistic regression on terrain parameters in southern Brazil

2006 ◽  
Vol 63 (3) ◽  
pp. 262-268 ◽  
Author(s):  
Elvio Giasson ◽  
Robin Thomas Clarke ◽  
Alberto Vasconcellos Inda Junior ◽  
Gustavo Henrique Merten ◽  
Carlos Gustavo Tornquist
Keyword(s):  
Land Use Planning ◽  
Soil Types ◽  
Sources Of Information ◽  
Topographic Wetness Index ◽  
Digital Elevation ◽  
Terrain Parameters ◽  
Logistic Regressions ◽  
Elevation Model ◽  
Soil Surveys ◽  
Soil Distribution

Soil surveys are necessary sources of information for land use planning, but they are not always available. This study proposes the use of multiple logistic regressions on the prediction of occurrence of soil types based on reference areas. From a digitalized soil map and terrain parameters derived from the digital elevation model in ArcView environment, several sets of multiple logistic regressions were defined using statistical software Minitab, establishing relationship between explanatory terrain variables and soil types, using either the original legend or a simplified legend, and using or not stratification of the study area by drainage classes. Terrain parameters, such as elevation, distance to stream, flow accumulation, and topographic wetness index, were the variables that best explained soil distribution. Stratification by drainage classes did not have significant effect. Simplification of the original legend increased the accuracy of the method on predicting soil distribution.

scholarly journals Monitoring Deformasi Gunung Merapi Menggunakan Citra Sentinel-1A Dengan Menggunakan Metode DInSAR (Studi Kasus: Gunung Merapi, Jawa Tengah)

2020 ◽  
Vol 4 (1) ◽  
pp. 14-23
Author(s):  
Rian Nurtyawan ◽  
Lady Suci Utami
Keyword(s):  
Digital Elevation Model ◽  
Volcanic Activity ◽  
Satellite Images ◽  
Disaster Mitigation ◽  
Deformation Method ◽  
Differential Interferometry ◽  
Digital Elevation ◽  
Image Pairs ◽  
Elevation Model ◽  
Deformation Map

ABSTRAKIndonesia mempunyai 127 gunung api aktif yang tersebar dari Sabang sampai Merauke. Oleh karena itu, perlu adanya pemantauan aktivitas gunung api yang dapat digunakan untuk acuan mitigasi bencana. Pada penelitian ini menggunakan metode deformasi, metode deformasi merupakan perubahan bentuk, posisi, dan dimensi dari suatu benda. Tujuan dari pemantauan deformasi ini untuk mengetahui perubahan gunung api yang disebabkan oleh aktivitas gunung api. Pemantauan aktivitas gunung api metode deformasi dilakukan dengan menggunakan citra Sentinel-1A yang diolah dengan teknologi Differential Interferometry SAR (DInSAR). Dalam penelitian ini dilakukan pengolahan dengan teknologi DInSAR metode two-pass dari empat buah citra satelit sentinel-1A 10 Januari 2018, 27 Februari 2018, 10 Mei 2018 dan 22 Januari 2019 serta data Digital Elevation Model (DEM) SRTM dengan ketelitian 30 meter .Hasil dari penelitian ini yaitu peta deformasi pra 1 erupsi yang diolah dari pasangan citra 10 Januari 2018 dengan citra 27 Februari 2018 yang menghasilkan deflasi sebesar -0,12 meter, dan peta deformasi pra 2 erupsi yang diolah dari pasangan citra 27 Februari 2018 dan 10 Mei 2018 menghasilkan deflasi sebesar -0,27 meter serta peta pasca erupsi yang diolah dari pasangan citra 10 Mei 3018 dan 22 Januari 2019 menghasilkan deflasi sebesar -0,194 meter.Kata kunci: Deformasi, Gunung Merapi, Sentinel-1A, DInSAR. ABSTRACT Indonesia has 127 active volcanoes spread over from Sabang to Merauke. Therefore, it is necessary to monitor volcanic activity that can be used as a reference for disaster mitigation. In this study, deformation method was used to reflect a change in the shape, position, and dimensions of an object. The purpose of monitoring this deformation is to find out volcanic changes caused by volcanic activity. Monitoring the volcanic activity of the deformation method is carried out using Sentinel-1A images processed with Differential Interferometry SAR (DInSAR) technology. In this research, two-pass method of DInSAR technology was processed using four sentinel-1A satellite images on January 10, 2018, February 27, 2018, May 10, 2018 and January 22, 2019 and SRTM Digital Elevation Model (DEM) data with 30 meters accuracy. This research processed pre-eruption deformation map from the 10 January 2018 imagery pair with the 27 February 2018 image which resulted in a deflation of 0.12 meters. Pre- eruption 2 deformation map was processed from the 27 February 2018 and 10 May 2018 image pairs and resulted in a deflation of 0.27 meters while post-eruption map processed from the 10 May 3018 and 22 January 2019 image pairs resulted in deflation of 0.194 meters.Keywords: Deformation, Merapi Mountain, Sentinel-1A, DinSAR.

Geoinformation mapping of landscapes on the example of the Primorskiy Ridge (Baikal region)

2020 ◽  
Vol 954 (12) ◽  
pp. 20-30
Author(s):  
Yu.V. Vanteeva ◽  
Е.А. Rasputina ◽  
S.V. Solodyankina
Keyword(s):  
Digital Elevation Model ◽  
Remote Sensing Data ◽  
Landsat 8 ◽  
Topographic Wetness Index ◽  
Space Images ◽  
Topographic Position Index ◽  
Digital Elevation ◽  
Different Seasons ◽  
Position Index ◽  
Elevation Model

The authors present the results of geoinformation mapping the Primorskiy Ridge landscapes using Landsat 8 satellite images, the digital elevation model SRTM and the factor-dynamic classification of geosystems. At the first stage, the remote sensing data for different seasons were classified using the ISODATA method. Then, using the digital elevation model, the landforms were classified basing upon the topographic position index. According to combining the classification parameters of one of the space images and digital elevation model, each polygon is automatically assigned to a certain preliminary type of landscapes using boolean expressions. Legend adjustments were made basing upon the fieldwork materials. As a result, a digital landscape map of the southern part of the Primorsky Ridge was created; it reflects the landscape structure at the level of facies groups and contains attributive information about the landform, altitude, slope and aspect, topographic wetness index. The analysis of the landscape pattern showed a high fragmentation of landscape polygons, formed due to overlay operations, which indicates the need for generalization of landscape contours.

scholarly journals Soil loss estimation using geographic information system in enfraz watershed for soil conservation planning in highlands of Ethiopia

2016 ◽  
Vol 5 (2) ◽  
pp. 21-30 ◽  
Author(s):  
Gizachew Tiruneh ◽  
Mersha Ayalew
Keyword(s):  
Information System ◽  
Geographic Information System ◽  
Soil Loss ◽  
Satellite Images ◽  
Geographic Information ◽  
Rainfall Erosivity ◽  
Gis And Remote Sensing ◽  
Digital Elevation ◽  
Elevation Model ◽  
Soil Loss Rate

Accelerated soil erosion is a worldwide problem because of its economic and environmental impacts. Enfraz watershed is one of the most erosion-prone watersheds in the highlands of Ethiopia, which received little attention. This study was, therefore, carried out to spatially predict the soil loss rate of the watershed with a Geographic Information System (GIS) and Remote Sensing (RS). Revised Universal Soil Loss Equation (RUSLE) adapted to Ethiopian conditions was used to estimate potential soil losses by utilizing information on rainfall erosivity (R) using interpolation of rainfall data, soil erodibility (K) using soil map, vegetation cover (C) using satellite images, topography (LS) using Digital Elevation Model (DEM) and conservation practices (P ) using satellite images. Based on the analysis, about 92.31% (5914.34 ha) of the watershed was categorized none to slight class which under soil loss tolerance (SLT) values ranging from 5 to 11 tons ha-1 year-1. The remaining 7.68% (492.21 ha) of land was classified under moderate to high class about several times the maximum tolerable soil loss. The total and an average amount of soil loss estimated by RUSLE from the watershed was 30,836.41 ton year-1 and 4.81 tons ha-1year-1, respectively.Int. J. Agril. Res. Innov. & Tech. 5 (2): 21-30, December, 2015

scholarly journals Evaluating digital terrain indices for soil wetness mapping – a Swedish case study

2014 ◽  
Vol 18 (9) ◽  
pp. 3623-3634 ◽  
Author(s):  
A. M. Ågren ◽  
W. Lidberg ◽  
M. Strömgren ◽  
J. Ogilvie ◽  
P. A. Arp
Keyword(s):  
Surface Flow ◽  
Topographic Wetness Index ◽  
Water Index ◽  
Digital Terrain ◽  
Digital Elevation ◽  
Soil Disturbances ◽  
Soil Wetness ◽  
Elevation Model ◽  
Terrain Indices

Abstract. Trafficking wet soils within and near stream and lake buffers can cause soil disturbances, i.e. rutting and compaction. This – in turn – can lead to increased surface flow, thereby facilitating the leaking of unwanted substances into downstream environments. Wet soils in mires, near streams and lakes have particularly low bearing capacity and are therefore more susceptible to rutting. It is therefore important to model and map the extent of these areas and associated wetness variations. This can now be done with adequate reliability using a high-resolution digital elevation model (DEM). In this article, we report on several digital terrain indices to predict soil wetness by wet-area locations. We varied the resolution of these indices to test what scale produces the best possible wet-areas mapping conformance. We found that topographic wetness index (TWI) and the newly developed cartographic depth-to-water index (DTW) were the best soil wetness predictors. While the TWI derivations were sensitive to scale, the DTW derivations were not and were therefore numerically robust. Since the DTW derivations vary by the area threshold for setting stream flow initiation, we found that the optimal threshold values for permanently wet areas varied by landform within the Krycklan watershed, e.g. 1–2 ha for till-derived landforms versus 8–16 ha for a coarse-textured alluvial floodplain.

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