ArcHydro : DEM Processing for Watershed and Stream Network Delineation
Image Processing and DEM of Spatial Information · PDF fileImage Processing and DEM of Spatial...
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National Conference on Recent Trends in Computer Science and Technology (NCRTCST)-2013 All copyrights Reserved by NCRTCST-2013, Departments of CSE CMR College of Engineering and Technology, Kandlakoya(V), Medchal Road, Hyderabad, India. Published by IJECCE (www.ijecce.org) 129 International Journal of Electronics Communication and Computer Engineering Volume 4, Issue (6) NCRTCST-2013, ISSN 2249–071X Image Processing and DEM of Spatial Information Technology in Landslide Vulnerable Mapping Dr. M. Sunandana Reddy Associate Professor, Dept. of Civil Engineering, CMR Technical Campus, Kandlakoya, Medchal, Hyderabad, India Email: [email protected] Mr. K. Lakshmikanta Reddy, Research Scholar, Dept. of Geology Sri Venkateswara University, Tirupati, A.P. India Email: [email protected] Abstract –The growth in geospatial technologies has enabled communities to make maps of their lands, relief phases and resource uses, and to bolster the legitimacy of their customary claims to measure and to resources. This research paper on which it is based emerged out of common and yet distinct concerns among the authors that spatial information technologies at least in certain contexts and at certain scales alter the complexion and distribution of power. In order to test and refine our ideas about the Image processing and Digital Elevation Model of Geographic Information Technology in the mapping of Landslide areas, we carried a research case study of part of Nilgiri Dist, T.N. The landslide activity is related to the following causative factors like slope, geology, land use and rainfall etc,. Thematic maps were prepared for the factors land use, slope and soil, which were described and exchanged by using Extensible Markup Language (XML). The land use map was derived from the image processing technique called ‘supervised classification’ which is completely generated based on the spectral signature values of the satellite image; the slope map was derived from the digital elevation model (DEM) and the soil map was derived from the analysis of image interpretations elements. Vulnerable mapping was prepared by integrating the effect of various triggering factors. Digital image processing involves the manipulation and interpretation of digital images with the aid of computer. The digital image data became widely available for land remote sensing applications. The central idea behind digital image processing is quite simple. The image is fed into a computer one pixel at a time. The computer is programmed to insert these data into an equation, or series of equations, and then store the results of the computation for each pixel. These results from a new digital image that may be displayed or recorded in pictorial format or may itself be further manipulated by additional programs. The objective of image classification is to replace visual analysis of the image data with quantitative techniques for automating the identification of features in a scene. This normally involves the analysis of multispectral image data and the application of statistically based decision rules for determining the land cover identity of each pixel in an image. When these decision rules are based solely on the spectral radiances observed in the data, we refer to the classification process as spectral pattern recognition. An attempt has been made to suggest suitable remedial measure for the highly vulnerable zones by generating the factors from the algorithms of image processing. Keywords – Image processing, DEM, XML, Land Use, Slope, Soils, Landslides, Spatial Information Technology I. INTRODUCTION Landslides are frequent and annually recurring phenomenon in the Nilgiri district. Outward and downward movement of mass, consisting or rock and soils, due to natural or man-made causes is termed as landslide. High intensity rainfall triggered most of the landslides in the Nilgiri district. As long as landslides occur in remote, unpopulated regions, they are treated as just another denudation process sculpting the landscape, but when occur in populated regions, they become subjects of serious study. Most of the landslides occur due to exhaustive deforestation for the development of urbanization and plantation. In these areas rainwater directly penetrates into the soil and cause landslides. A. Classification of Landslides Based on the mass movement, landslides are divided into four major groups. 1. Slow Flowage: Rock Creep and Soil Creep 2. Rapid Flowage: Earth movements, Mudflows, Debris Avalanche 3. Sliding: Slumps, Rock Slides, Rock falls, and Landslips 4. Subsidence: Sinking of mass B. Causes of Landslides Internal factors: The steeping of the slope, water content of the stratum and mineralogical composition and structural features, which are tending to reduce the shearing strength of the rocks. External factors: A slight vibration or jerk to the mass would greatly add up against the frictional resistance and the mass would become unstable. The heavy traffic on hill roads is of great contributing factors towards causing the imbalance of the masses. II. OBJECTIVES To create spatial database in Geographic Information environment To demarcate landslide vulnerable mapping by image processing technique which is ‘supervised classification’ To visualize landslide prone areas in 3D view for hill are planning To describe and exchange the data from thematic layers by using Extensible Markup Language (XML)
Transcript of Image Processing and DEM of Spatial Information · PDF fileImage Processing and DEM of Spatial...
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National Conference on Recent Trends in Computer Science and Technology (NCRTCST)-2013
All copyrights Reserved by NCRTCST-2013, Departments of CSECMR College of Engineering and Technology, Kandlakoya(V), Medchal Road, Hyderabad, India.Published by IJECCE (www.ijecce.org) 129
International Journal of Electronics Communication and Computer EngineeringVolume 4, Issue (6) NCRTCST-2013, ISSN 2249–071X
Image Processing and DEM of Spatial InformationTechnology in Landslide Vulnerable Mapping
Dr. M. Sunandana ReddyAssociate Professor, Dept. of Civil Engineering,
CMR Technical Campus, Kandlakoya, Medchal, Hyderabad, IndiaEmail: [email protected]
Mr. K. Lakshmikanta Reddy,Research Scholar, Dept. of Geology
Sri Venkateswara University, Tirupati, A.P. IndiaEmail: [email protected]
Abstract –The growth in geospatial technologies hasenabled communities to make maps of their lands, reliefphases and resource uses, and to bolster the legitimacy oftheir customary claims to measure and to resources. Thisresearch paper on which it is based emerged out of commonand yet distinct concerns among the authors that spatialinformation technologies at least in certain contexts and atcertain scales alter the complexion and distribution of power.In order to test and refine our ideas about the Imageprocessing and Digital Elevation Model of GeographicInformation Technology in the mapping of Landslide areas,we carried a research case study of part of Nilgiri Dist, T.N.The landslide activity is related to the following causativefactors like slope, geology, land use and rainfall etc,.Thematic maps were prepared for the factors land use, slopeand soil, which were described and exchanged by usingExtensible Markup Language (XML). The land use map wasderived from the image processing technique called‘supervised classification’ which is completely generatedbased on the spectral signature values of the satellite image;the slope map was derived from the digital elevation model(DEM) and the soil map was derived from the analysis ofimage interpretations elements. Vulnerable mapping wasprepared by integrating the effect of various triggeringfactors. Digital image processing involves the manipulationand interpretation of digital images with the aid of computer.The digital image data became widely available for landremote sensing applications. The central idea behind digitalimage processing is quite simple. The image is fed into acomputer one pixel at a time. The computer is programmedto insert these data into an equation, or series of equations,and then store the results of the computation for each pixel.These results from a new digital image that may be displayedor recorded in pictorial format or may itself be furthermanipulated by additional programs. The objective of imageclassification is to replace visual analysis of the image datawith quantitative techniques for automating theidentification of features in a scene. This normally involvesthe analysis of multispectral image data and the applicationof statistically based decision rules for determining the landcover identity of each pixel in an image. When these decisionrules are based solely on the spectral radiances observed inthe data, we refer to the classification process as spectralpattern recognition.
An attempt has been made to suggest suitable remedialmeasure for the highly vulnerable zones by generating thefactors from the algorithms of image processing.
Keywords – Image processing, DEM, XML, Land Use,Slope, Soils, Landslides, Spatial Information Technology
I. INTRODUCTION
Landslides are frequent and annually recurringphenomenon in the Nilgiri district. Outward and downwardmovement of mass, consisting or rock and soils, due tonatural or man-made causes is termed as landslide. Highintensity rainfall triggered most of the landslides in theNilgiri district. As long as landslides occur in remote,unpopulated regions, they are treated as just anotherdenudation process sculpting the landscape, but when occurin populated regions, they become subjects of seriousstudy. Most of the landslides occur due to exhaustivedeforestation for the development of urbanization andplantation. In these areas rainwater directly penetrates intothe soil and cause landslides.A. Classification of Landslides
Based on the mass movement, landslides are divided intofour major groups.1. Slow Flowage: Rock Creep and Soil Creep2. Rapid Flowage: Earth movements, Mudflows, Debris
Avalanche3. Sliding: Slumps, Rock Slides, Rock falls, and Landslips4. Subsidence: Sinking of massB. Causes of Landslides
Internal factors: The steeping of the slope, water contentof the stratum and mineralogical composition and structuralfeatures, which are tending to reduce the shearing strengthof the rocks.
External factors: A slight vibration or jerk to the masswould greatly add up against the frictional resistance andthe mass would become unstable. The heavy traffic on hillroads is of great contributing factors towards causing theimbalance of the masses.
II. OBJECTIVES
To create spatial database in Geographic Informationenvironment
To demarcate landslide vulnerable mapping by imageprocessing technique which is ‘supervisedclassification’
To visualize landslide prone areas in 3D view for hillare planning
To describe and exchange the data from thematic layersby using Extensible Markup Language (XML)
A
National Conference on Recent Trends in Computer Science and Technology (NCRTCST)-2013
All copyrights Reserved by NCRTCST-2013, Departments of CSECMR College of Engineering and Technology, Kandlakoya(V), Medchal Road, Hyderabad, India.Published by IJECCE (www.ijecce.org) 130
International Journal of Electronics Communication and Computer EngineeringVolume 4, Issue (6) NCRTCST-2013, ISSN 2249–071X
III. STUDY AREA
Study area is located in Nilgiri district, which is amountainous terrain in the North West part of theTamilNadu, and the study area covers a part of theKothagiri Taluk. It is geographically located between 76°14' 00" and 77° 02' 00" E longitude and 11° 10' 00" and 11°42' 00" N latitude. The study area falls under the Survey OfIndia toposheet No: 58A/15 on scale 1:50,000 scale.
IV. METHODOLOGY
A. Landslide Hazard Zonation MappingLandsat – 7 of satellite image was processed from the
image processing techniques and then contour maps weregenerated from the SRTM data which is downloaded fromthe open web source. The SRTM data / contour maps aregiven as input into the GIS – 3D analyst. Here, the 3Dvisualization of the study area is created. Various thematiclayers (slope, land use, soil) are digitally interpreted fromthe image processing. The delineated thematic layers werevectorized. The landslide model is created and the ranksand weights are assigned to each category. Based onthemes and its impacts different zones were delineated.B. Database Generation for Landslide Vulnerable
Zonation MappingThe landslide investigation and hazard zonation mapping
study involves preparation of number of thematic databasessuch as slope, soil, rainfall, geomorphology, geology, andlanduse of the area. As the main purpose of the study is toprepare landslide hazard zonation map and collection ofthese information being carried out for several decadesthrough the traditional, conventional- ground basedsurveying and mapping methods, which had taken a lot oftime, more man power and cost. But nowadays theemerging satellite based remote-sensing techniques hasbecome more efficient tool for obtaining such informationwith less cost and time. In the present study, three thematicmaps have been interpreted digitally by the imageprocessing and by the Digital Elevation Model. Thedifferent thematic maps are slope map, soil map and landuse land cover map.1) Slope Map: Slope map is very important factor forlandslide analysis. If the slope is higher then there is apossibility of occurrence of landslide. Contour maps havebeen used for the preparation of slope map. Using the GIS– 3D analyst, the Elevation model for the study area iscreated from the derived contour map. From the elevationmodel, slope map was generated using “derive slope”command.2) Soil Map: By interpreting the Landsat – 7 image withhelp of photo interpretation keys like tone, texture,associated feature and with help of agriculturalengineering soil maps, soil types present in the study areahave been delineated and mapped. Eight soil series areidentified in the study area.
Table 1Soil
ClassesSoil Texture
Soil Description Class1 Sandy loam – sandy clay loam NL1
2 Sandy clay loam NL2
3 Sandy clay NL3
4 Sandy clay – sandy clay loam NL4
5 Sandy clay loam – sandy loam NL5
6 Sandy loam NL6
3) Land use Map: Landuse refers to "man's activities andvarious use which carried on land". Land cover refers to"natural vegetation, water bodies, rock, soil, artificial coverand other resulted due to land transformation". In thepresent study, landuse / landcover map has been preparedon 1:50,000 scale using Landsat – 7 by image processing.C. Image Processing / Image clasification
Digital image processing involves the manipulation andinterpretation of digital images with the aid of computer.The digital image data became widely available for landremote sensing applications. The central idea behinddigital image processing is quite simple. The image is fedinto a computer one pixel at a time. The computer isprogrammed to insert these data into a equation, or seriesof equations, and then store the results of the computationfor each pixel. These results from a new digital image thatmay be displayed or recorded in pictorial format or mayitself be further manipulated by additional programs.The objective of image classification is to replace visualanalysis of the image data with quantitative techniques forautomating the identification of features in a scene. Thisnormally involves the analysis of multispectral image dataand the application of statistically based decision rules fordetermining the land cover identity of each pixel in animage. When these decision rules are based solely on thespectral radiances observed in the data, we refer to theclassification process as spectral pattern recognition.
In contrast, the decision rules may be based on thegeometric shapes, sizes, and patterns present in the imagedata.
Figure 1 summarizes the three basic steps involved in atypical supervised classification procedure. In the trainingstage (1), the analyst identifies representative trainingareas and develops a numerical description of the spectralattributes of each land cover type of interest in the scene.Next, in the classification stage (2), each pixel in theimage data set is categorized into the land cover class itmost closely resembles. If the pixel is insufficientlysimilar to any training data set, it is usually labeled“unknown.” The category label assigned to each pixel inthis process is then recorded in the corresponding cell ofan interpreted data set (an “output image”). Thus, themultidimensional image matrix is used to develop acorresponding matrix of interpreted land cover categorytypes. After the entire data set has been categorized, the
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National Conference on Recent Trends in Computer Science and Technology (NCRTCST)-2013
All copyrights Reserved by NCRTCST-2013, Departments of CSECMR College of Engineering and Technology, Kandlakoya(V), Medchal Road, Hyderabad, India.Published by IJECCE (www.ijecce.org) 131
International Journal of Electronics Communication and Computer EngineeringVolume 4, Issue (6) NCRTCST-2013, ISSN 2249–071X
results are presented in the output stage (3). Being digitalin character, the results may be used in a number ofdifferent ways that are amenable to inclusion in a spatialinformation technology.
Fig.1. Basic steps in supervised classification.
V. GIS ANALYSIS
In order to generate the landslide prone areas map forKothagiri area, a model has been developed in a GISenvironment. Data in the form of thematic maps such asslope, soil and land use were input in to GIS. The detailedmethod of assigning weights has been discussed below.Finally the landslide velnerable map has been prepared.1) Slope: Slope is a very important parameter in anylandslide hazard zonation mapping. In the study area slopevaries from 0 to greater than 54 deg. The entire slopecontour map was divided in to four categories as follows:
Table 2
SlopeSlope Categories
Slope Description Category1 Very steep slope >540
2 Steep slope 360 - 540
3 Moderate slope 180 - 360
4 Gentle slope 00 - 180
Thus, the slope contour map has got four categories andsuitable weights are assigned. The contour map (Figure 2)has been derived from the DEM data (Figure 3).
Fig.2. Contour Map derived from DEM.
Fig.3. 3D View of the study Area with Image Scene.
Fig.4. Slope Map derived from the Elevation Data
2) Soil: The occurrence of landslide is mainly due to thepresence of huge thickness of loose soils when mixed withwater, it triggers the landslide. In the study area, based onthe soils erodable nature, it is divided in to four categoriesas follows: Very highly erodable Highly erodable Moderately erodable Poorly erodable.
Thus, the Soil map (Figure 5) has got four categoriesand suitable weights are assigned.
Fig.5. Soil Map.
3) Land Use: Landuse / land cover map of an area underinvestigation has got direct or indirect influence intriggering the landslides. Different types of landuse /landcover features are identified in the study area are such astea, tea+tree, agriculture, grassland, others, settlement andforest. Suitable weights are assigned. Figure 6 summarizesthe input and output of the image scene.
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National Conference on Recent Trends in Computer Science and Technology (NCRTCST)-2013
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International Journal of Electronics Communication and Computer EngineeringVolume 4, Issue (6) NCRTCST-2013, ISSN 2249–071X
Fig.6. Input of the satellite image and Output of the imagescene.
4) Criterion Table:Table 3
ThemeRank 1 Rank 2 Rank 3 Rank 4
Weight-4 Weight-3 Weight-2 Weight-1
LandUse
Grass land,Agriculture
Tea,Tea+Silve
roakForest Settlement
Soil NL1 NL3,NL2NL2,NL4,
NL5NL1,NL6
Slope 360 - 540 180 - 360 >540 00 - 180
5) Landslide Hazard Zonation Mapping:Using union command, Landslide hazard zonation map
was prepared by integrating the effect of various triggeringfactors. The zonation map divides the study area into fourzones of landslide vulnerability viz., very high, high,moderate and poor. Thus, the landslide prone areas havingfour zones were obtained.
The Extensible Markup Language (XML) has beendeveloped to compliment HTML, taking over from simplydefining what data looks like to describe what is actual is.An XML element can declare its associated data to be anaddress, a point value with associated attribute data or anyother desired data element.
VI. CONCLUSION
Geospatial technologies play a significant role inlandslide zonation mapping. Landslide identification,which is a crucial parameter for any regional landslidehazard assessment, can be very well done particularly withimage processing. Coupled with satellite image,Geographic Information Technology is an excellent tool todisplay the spatial distribution of landslides along withtheir attributes.
REFERENCES
[1] Mohanadoss, Sunitha, "Landslide Hazard Zonation Map ofMarappalam using Remote sensing technique and GIS",AnnaUniversity, Chennai-25.
[2] Quido Zaruba, and Vojtech Mencl, "Landslides and theircontrol" Czechoslovakia, 1982.
[3] S. S. Ramakrishnan, M. G. S. M. Zaffar Sadiq, V. SanjeeviKumar, and K. Venugopal, “Landslide Zonation for hill areadevelopment” in Geomatics 2002, pp. 100-104, September 2002.
[4] P.A.Burrough, " Principles of Geographical Information Systemsfor Land Resources Assessment," Oxford Science publications.
[5] Geological Survey of India, "The Nilgiri Landslides," -Publication no: 57, 1982.
[6] Thomas M. Lillesand, and Ralph W. Kiefer, "Remote Sensingand Image Interpretation," 3rd ed., Pub: John Wiley & Sons.
AUTHORS PROFILE
Dr. M. Sunandana Reddyobtained M.Tech (Remote Sensing & GIS) fromBharathidasan University, Trichy, T.N. in 2004 andawarded Ph.D in Water Resources Development byRemote Sensing and GIS Applications from SriVenkateswara University, Tirupati, A.P. in 2011. Hehas vast experience in the filed of Remote Sensing
and GIS applications in various natural resources and worked undervarious organizations for defferent research projects. Presently workingas Associate Professor in Civil Engineering, CMR Technical Campus,Hyderabad.
Mr. K. Lakshmikanta Reddyreceived his M.Tech. from Bharathidasan Univeristy, T.N. and presentlypursuing his Ph.D. from Sri Venkateswara University, Tirupati.
