Free Geomatics Resources for Terrain Evaluation and Land Resource Assessment: a Case Study in Eastern Ghats Province of Southwestern Odisha, India

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1 Free Geomatics Resources for Terrain Evaluation and Land Resource Assessment: a Case Study in Eastern Ghats Province of Southwestern Odisha, India Bijay Kumar Sahu Geological Survey of India Southern Region Hyderabad, India, bijay_sahu@hotmail.com Abstract - Assessment of land resources forms the base for any developmental planning of an area. Huge tracts of the Eastern Ghats Province (EGP) are under-developed and no serious attempt has been made to assess the land resources of the terrain. This work focuses on a quick and regional appraisal of the terrain morphometry and land resources using freely available geomatics resources. For terrain evaluation, the free source SRTM DEM with 90m resolution was processed to generate a seamless digital elevation model (DEM) of the study area. Morphometric characteristics (slope, stream network, catchment etc.) of the terrain were evaluated using the DEM. Freely available multispectral Landsat 7-ETM+ data was downloaded, processed and interpreted to extract information on major land forms (lineaments, geodynamic zones etc.) and delineation of land resources (forest/vegetation cover, agriculture, water resources, barren land, settlement areas etc.). Several filters and band rationing algorithms were used to extract geological features and vegetation characteristics. False Colour composites (FCC) using bands 4, 3 and 2 of ETM+ data proved to be quite adequate in delineating land resources for a regional assessment of the terrain. DEM and remote sensing data was integrated with geological data (lithology, structure) on a GIS platform for spatial correlation of terrain characteristics with the land resources. The entire work was done using Free and Open Software resources (ILWIS, OpenJump, PostGreSQL etc.) only. ILWIS was used to process and interpret SRTM (DEM) and remote sensing data. OpenJump along with PostgreSQL was used to create and manage vector data. This analysis has many areas of applications like planning for transport network (road and rail), water resource evaluation and management, natural hazards assessment and mitigation planning, environmental assessment and planning, geological studies for resource evaluation etc. The work also demonstrates that many such spatial analysis and modeling can be done without the use of expensive proprietary software and data products. Keywords- Geomatics, SRTM DEM, Landst ETM+, Morphometry, Eastern Ghats I. INTRODUCTION Use of free Geomatics resources (both software and data) provides a cost-effective and rapid analysis of terrain characteristics. Free and Open Source software (FOSS) like ILWIS, OpenJump and PostgreSQL have been used in this study to analyze SRTM DEM data, available freely in public domain, for parts of Eastern Ghats Province The study is aimed at evaluating the terrain characteristics and assess the terrain resources of the area. The study area falls in the central part of the Eastern Ghats Province (Figure 1) covering an area of approximately 22, 200 sq km spread over 6 degree sheets from to E and to N. Figure 1. Drainage derived from SRTM DEM is overlain on colour-shadow image of Digital Elevation Model. Location map of the area is given as inset. II. FREE GEOMATICS RESOURCES A. Spatial Data Huge datasets with moderate spatial resolution are available in public domain, which can effectively be used for geoscintific studies in regional scales. The following datasets have been used in this study. 1 P a g e

2 Landsat 7 ETM+ Imagery: Imagery from the Landsat satellites has been of great importance to the development of land cover science, and Earth science in general. Three major sensors have been used on the six successful vehicles: the Multi-Spectral Scanner (MSS) on Landsats 1-4, the Thematic Mapper (TM) on Landsats 4-5, and the Enhanced Thematic Mapper Plus (ETM+) on Landsat 7. The high resolution of these sensors, especially TM and ETM+ (30m) is well suited for determining land cover, vegetation type and health, and geologic characteristics. Landsat 7 ETM+ images (Bands 1, 2, 3 & 4) were downloaded from the same site as that of SRTM data. The study area consists parts of 5 scenes (each measuring (170 kmx183 km). The downloaded data was imported to ILWIS and georeferenced. The corrected imageries were glued together and the data for the study area was extracted. Digital Elevation Model (SRTM): The Shuttle Radar Topography Mission (SRTM) is an international research effort that obtained digital elevation models on a near-global scale from 56 S to 60 N to generate the most complete highresolution digital topographic database of Earth prior to the release of the ASTER GDEM in The resolution of the cells of the source data is one arc second. But 1" (approx. 30 meter) data have only been released for United States territory; whereas only three-arc-second (approx. 90-meter) data are available for the rest of the world. SRTM data (8 degree tiles) for the entire study area was downloaded from GLCF site [2]. Lithology: The lithological data was obtained from the interactive 1: 2,000,000 geological map available in Geological Survey of India portal [3]. The image thus obtained was digitized using OpenJump to create vector layer for superimposing on other layers. Figure 2 shows the spatial association of litho-units with the elevation. Figure 2. Lithological units draped over colour shadow map of Digital Elevation Model B. Software Resources ILWIS: ILWIS [4] is a GIS / Remote sensing software for both vector and raster processing. ILWIS features include digitizing, editing, analysis and display of data as well as production of quality maps. It was initially developed and distributed by ITC (International Institute for Geo-Information Science and Earth Observation) in the Netherlands for use by its researchers and students, but since 1 July 2007 it has been distributed under the terms of the GNU (General Public License) and is thus a free software. The current version is ILWIS 3.7 and can be downloaded from website. 3DEM: 3DEM [1] is a free 3D visualization of elevation data. It is capable of handling any topographic data file organized by rows and columns of elevation data like SRTM and many other formats. 3DEM will export terrain data as USGS DEM or GeoTiff for use in other Geographic Information System (GIS) programs. OpenJump: OpenJUMP [5] is a Java based vector GIS and programming framework, which is platform independent (Windows, Linux, Unix, Macintosh). It reads and writes ESRI Shape file, GML files, DXF and PostGIS files and reads raster files like TIFF, JPEG, PNG and ECW. It has the capability of full geometry and attribute editing. Moreover, it is free for download and use. OpenJump has been used to manage and analyze vector data in the present study. III. DATA PROCESSING The downloaded SRTM data contains no-data holes, where water or heavy shadow prevented quantification of elevation values. These holes, some times, give undesired results in morphometric analysis and hydrological modeling. The holes in the data were filled using 3DEM software with patch missing data option, which apples a simple interpolation algorithm. A seamless DEM dataset was created by combining all the tiles using Glue Maps operation in ILWIS. The DEM data for the study area was extracted from the seamless DEM. A. Morhometric Analysis The SRTM data used to derive morphometric parameters like terrain characteristics and slope [7]. The elevation of the area (Figure 3) ranges between 32m and 1507m. The lowest elevation area (<100m) is recorded along the river course in the northern part covering about 1,000 sq km (~3.5%). About ~34% of the total area with elevation ranging from 100 to 300m occupies the western, northern and eastern parts. The elevation in the sloping areas in the central and western parts ranges from 300 to 700m (~48%). The flat topped plateau areas in the central and western parts (~15.5%) are represented by elevation values ranging from 700 to 1000m. The highest peaks of >1000m elevation (~1%) are restricted to central and western parts. Slope analysis of the data reveals that the slope in the area varies from 0 to 55. Figure 4 shows the classified slope map of the area. Most parts of the area (~48%) have slope <5, whereas only 0.1% area is having slope >45. B. Hydrologic Modeling The SRTM data proves to be very useful in modeling hydrologic characteristics of the terrain. DEM hydroprocessing module of ILWIS is very powerful for the drainage network extraction, drainage network ordering and catchment 2 P a g e

3 extraction (Figures 1 and 5). The local depressions (of single pixels and of multiple pixels) in the DEM were filled with Fill sinks operation. The flow direction and flow accumulation maps were prepared from the filled DEM. Extraction of drainage network and stream network ordering were done using the filled DEM, Flow direction and Flow accumulation maps. The drainage network and Flow direction maps were used to delineation of catchments for each of the drainage basin. A. NDVI Normalized Derived Vegetation Index (NDVI) is one of the rationing algorithms for evaluating vegetation characteristic and is calculated as: NDVI = (NIR R) / (NIR + R) where, NIR = Near Infrared Band value, R = Red Band value, recorded by the satellite sensor. This technique not only highlights the vegetated areas of an image but also gives an idea regarding as to how healthy the vegetation is [6]. In the present study Band 4 (Near Infra Red) and Band 3 (Red) ETM+ bands are used to calculate NDVI. The NDVI varies from to 0.67 (Figure 7). Figure 3. Classified image of the Digital Elevation Model Figure 5. Catchment map of the area delineated from SRTM DEM Figure 4. Classified slope map of the area derived from SRTM DEM IV. IMAGE ANALYSIS Several stretching and filtering techniques were used to enhance the interpretability of the image (Gupta 1991). Histogram for each band was studied before applying stretching. Spatial filters (majority and average and directional) were applied to make the image more interpretable. The ETM+ image was subjected to edge enhancement filter to highlight the linear features like faults, lineaments and shear zones (Figure 6). Figure 6. Enhanced False Colour Composite image (ETM+ 4, 3, 2 bands) with interpreted lineaments The image shows relatively high vegetative index (>0.5) in higher reaches indicating healthy vegetation that has relatively high near-infrared and low visible reflectance. The sloping areas in the middle part are represented by indices (>0.25 and <0.5) indicating moderate vegetation. The low-lying areas with slope <5 ) in the western and northwestern parts shows low 3 P a g e

4 NDVI values (0 to 0.25) indicating poor vegetation. The water bodies show negative indices due to larger visible reflectance than near-infrared reflectance. Figure 7. Normalized Derived Vegetation Index (NDVI) image of the area B. Image Classification For example the slope and aspect analysis of SRTM DEM using ILWIS can effectively used for landslide studies. Similarly, the drainage network extraction and ordering can play a vital role in executing sample collection under National Geochemical Mapping Programme. In low-lying areas, where streams are rarely seen in the filed, the drainage map prepared from SRTM DEM can be used as a guide for locating sample points. Though, the spatial analytical capabilities of both ILWIS (both raster and vector) and OpenJump (vector) have not been utilized fully in this study, both the software are quite strong in carrying out such studies. The image was classified into 5 landuse classes (Figure 8) based on the clustering of pixel values of the image. An unsupervised method of classification was used to classify the image. But the exact nature of the landuse classes must be ascertained with limited field check. Figure 8. Landuse map of the area derived by unsupervised classification of the ETM+ image V. DISCUSSION With the availability of huge amount of geomatics resources in public domain, more meaningful geoscintific studies can be carried out, at least in a regional scale for over all societal benefits. The present study demonstrates that the high-cost proprietary software and data sources are not the only solutions for dealing with spatial data for geoscintific studies. REFERENCES [1] 3DEM ( [2] GLCF ( [3] GSI ( Interactive map showing generalized geology of India Gupta, R.P., Remote Sensing Geology, 1st Ed. Springer-Verlag, NY [4] ILWIS ( [5] OpenJump. [6] Su. Z., Remote Sensing of land use and Vegetation for mesoscale hydrological studies. Int. J. Remote Sensing, 21(2), [7] Wilson, J.P. and Gallant, J.C., Terrain analysis: principles and applications. John Wiley and Sons, 479 pages Authors Profile AUTHOR S PROFILE 1. Name: Dr BIJAY KUMAR SAHU 2. Address: Geodata Division, GSI, Southern Region, Hyderabad b.sahu@gsi.gov.in 3. Areas of Specialization: Processing, interpretation and modeling of geophysical (magnetic, gravity & radiometric) data, Digital Image Processing, Geoinformatics: creation, manipulation, analysis and modeling of geospatial data using GIS, Geological mapping, map making and mineral investigation, Paleo-map making with geological and geophysical constraints 4. Current areas of Research: Application of Geoinformatics in Mineral Prognostication and Disaster Management, Reassembly of supercontinents using geophysical and geological constraints, Processing and interpretation of aeromagnetic data of different parts of India, Implications of continental-scale compilation of geophysical and geological data 5. Academic record : M. Sc. Tech. (App. Geol) from Indian School of Mines, Dhanbad, India, M.Tech. (App. Geol.) from IIT, Kharagpur; Ph.D. from International Institute for Geoinformation Science and Earth Observation (ITC), The Netherlands & University of Cape Town, South Africa 6. Research Experience: Research on Reassembly of supercontinents at ITC, the Netherlands and Cape Town University of South Africa for 4 years; continuing in service research on various aspects of Earthsciences 7. Professional Experience: Geological Mapping (8 years); Mineral Investigation (2 years); Geoinformatics (6 years); Teaching in GSI Training Institute (CGMT), Hyderabad as a faculty ( 5 years) 8. Important Conferences/ Seminars attended: Papaers/presented in More than 20 National/International Seminars/ Symosiums including Gondwana Speakeasy, Delft, The Netherlands (1996), International Symposium on Gondwana Gondwana 10: Cape Town, South Africa (1998), Geol Soc of Africa: GSA- 11, Cape Town, South Africa (1999), International Symposium (IGCP- 418/419), Kitwe, Zambia (1999), International Seminar on Exploration 4 P a g e

5 Geophysics organized by AEG of India, Hyderabad (2006, XXX International INCA Congress at Dehradun (2010). 9. Publications: Over 20 technical papers published in National/International Journals/ Symposium Proceedings 10. Other Relevant Information: Widely travelled in the continents of Europe and Africa besides most parts of India; Professional contacts with many Universities, Institutes and Geological Surveys worldwide; Reviewed a number of technical papers for Geological Society of India, Current Science and GSI publications 5 P a g e