scholarly journals Analysis of Digital Elevation Model and LNDSAT Data Using Geographic Information System for Soil Mapping in Urban Areas

2017 ◽  
Vol 08 (12) ◽  
pp. 767-787 ◽  
Author(s):  
Mohamed Ali Mohamed
Keyword(s):  
Information System ◽  
Geographic Information System ◽  
Digital Elevation Model ◽  
Urban Areas ◽  
Geographic Information ◽  
Soil Mapping ◽  
Digital Elevation ◽  
Elevation Model

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

DISASTER RECOVERY THROUGH PREDICTION OF SAFE ROUTE USING DEM LEVELS

2017 ◽  
Vol 10 (13) ◽  
pp. 20
Author(s):  
Sangavi Vp ◽  
N Mounika ◽  
S Graceline Jasmine
Keyword(s):  
Information System ◽  
Geographic Information System ◽  
Digital Elevation Model ◽  
Tamil Nadu ◽  
Java Application ◽  
Digital Elevation ◽  
Safe Area ◽  
Elevation Model ◽  
Evacuation Route ◽  
Model Layer

When a disaster occurs, the normal commutation routes are disrupted. People get stuck at these disaster points and would be in trouble, hence people in those areas find it difficult to communicate and evacuation route to safe area is unknown. The aim of the paper is to predict safe routes to reach the refuge point from the disaster point. The prototype was developed using Arc geographic information system runtime SDK for Java Application and APIs in Eclipse. The system was developed with digital elevation model layer, and route layer for India basemap focused to Tamil Nadu. The safe route is found based on the elevation values of the area from the disaster point to a safe point. The developed system could be used by the relief providers to reach the disaster point and rescue victims.

Delineating soil landscape facets from digital elevation models using compound topographic index in a geographic information system

Soil Research ◽  
2007 ◽  
Vol 45 (8) ◽  
pp. 569 ◽  
Author(s):  
X. Yang ◽  
G. A. Chapman ◽  
J. M. Gray ◽  
M. A. Young
Keyword(s):  
Information System ◽  
Geographic Information System ◽  
Land Use Planning ◽  
Digital Elevation Models ◽  
Geographic Information ◽  
Coastal Areas ◽  
Topographic Index ◽  
Digital Elevation ◽  
Soil Landscape ◽  
Compound Topographic Index

Soil landscapes and their component facets (or sub-units) are fundamental information for land capability assessment and land use planning. The aim of the study was to delineate soil landscape facets from readily available digital elevation models (DEM) to assist soil constraint assessment for urban and regional planning in the coastal areas of New South Wales (NSW), Australia. The Compound Topographic Index (CTI) surfaces were computed from 25 m DEM using a D-infinity algorithm. The cumulative frequency distribution of CTI values within each soil landscape was examined to identify the values corresponding to the area specified for each unmapped facet within the soil landscape map unit. Then these threshold values and CTI surfaces were used to generate soil landscape facet maps for the entire coastal areas of NSW. Specific programs were developed for the above processes in a geographic information system so that they are automated, fast, and repeatable. The modelled facets were assessed by field validation and the overall accuracy reached 93%. The methodology developed in this study has been proven to be efficient in delineating soil landscape facets, and allowing for the identification of land constraints at levels of unprecedented detail for the coast of NSW.

scholarly journals Spatial Analysis Using Temporal Point Clouds in Advanced GIS: Methods for Ground Elevation Extraction in Slant Areas and Building Classifications

2019 ◽  
Vol 8 (3) ◽  
pp. 120 ◽  
Author(s):  
Sara Shirowzhan ◽  
Samad Sepasgozar
Keyword(s):  
Digital Elevation Model ◽  
Urban Growth ◽  
Urban Areas ◽  
Future Directions ◽  
3D Data ◽  
Contour Lines ◽  
Digital Elevation ◽  
The Future ◽  
Elevation Model ◽  
Skewness And Kurtosis

Deriving 3D urban development patterns is necessary for urban planners to control the future directions of 3D urban growth considering the availability of infrastructure or being prepared for fundamental infrastructure. Urban metrics have been used so far for quantification of landscape and land-use change. However, these studies focus on the horizontal development of urban form. Therefore, questions remain about 3D growth patterns. Both 3D data and appropriate 3D metrics are fundamentally required for vertical development pattern extraction. Airborne light detection and ranging (Lidar) as an advanced remote-sensing technology provides 3D data required for such studies. Processing of airborne lidar to extract buildings’ heights above a footprint is a major task and current automatic algorithms fail to extract such information on vast urban areas especially in hilly sites. This research focuses on proposing new methods of extraction of ground points in hilly urban areas using autocorrelation-based algorithms. The ground points then would be used for digital elevation model generation and elimination of ground elevation from classified buildings points elevation. Technical novelties in our experimentation lie in choosing a different window direction and also contour lines for the slant area, and applying moving windows and iterating non-ground extraction. The results are validated through calculation of skewness and kurtosis values. The results show that changing the shape of windows and their direction to be narrow long squares parallel to the ground contour lines, respectively, improves the results of classification in slant areas. Four parameters, namely window size, window shape, window direction and cell size are empirically chosen in order to improve initial digital elevation model (DEM) creation, enhancement of the initial DEM, classification of non-ground points and final creation of a normalised digital surface model (NDSM). The results of these enhanced algorithms are robust for generating reliable DEMs and separation of ground and non-ground points in slant urban scenes as evidenced by the results of skewness and kurtosis. Offering the possibility of monitoring urban growth over time with higher accuracy and more reliable information, this work could contribute in drawing the future directions of 3D urban growth for a smarter urban growth in the Smart Cities paradigm.

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