50120140501011

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50120140501011

  1. 1. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING & 6367(Print), ISSN 0976 - 6375(Online), Volume 5, Issue 1, January (2014), © IAEME TECHNOLOGY (IJCET) ISSN 0976 – 6367(Print) ISSN 0976 – 6375(Online) Volume 5, Issue 1, January (2014), pp. 94-102 © IAEME: www.iaeme.com/ijcet.asp Journal Impact Factor (2013): 6.1302 (Calculated by GISI) www.jifactor.com IJCET ©IAEME ASSESSMENT OF RIVER BASIN FOR ENGINEERING RESTORATION IN GHATAPRABHA CATCHMENTS USING GEO-INFORMATICS APPLICATIONS Basavaraj Hutti1 and Basavarajappa. H. T2 1 Geoscience Domain Expert, L&T IES, SEZ-02, Industrial Area, Hootagalli, Mysore – 570018. Centre for Advanced Studies in Precambrian Geology, Dept. of Earth Sciences, University of Mysore, Mysore – 570006. INDIA 2 ABSTRACT Geographical design of river basin buffers with long-term vegetation cover for engineering restoration in Ghataprabha catchments needs to assess how much farmland is located in the buffers of a concerned catchment. Traditionally, this assessment was done by field surveying and manual mapping, which was a time-consuming and costly process for a large region. In this paper, geoinformatics which includes remote sensing (RS), geographical information system (GIS) and global positioning system (GPS) as cost-effective techniques were used to develop catchments based approach for identifying critical sites of Ghataprabha catchments buffer restoration. The method was explained through a case study of Ghataprabha catchments and results showed that only three of the sub-basin catchments were eligible in terms of higher priority for river basin buffer restoration. This research has methodological contributions to the spatial assessment of farming intensities in basin catchments buffers across a river basin and to the geographical designs of variable buffering scenarios within catchments. The former makes the river basin management strategy possible, and the latter provides alternative restoration scenarios to meet different management purposes, both of which have direct implementations to the engineering restoration of river basin buffers in the real world. This study, thus, highlights the great potential of geoinformatics applications to the planning and management of river basin buffer restoration in Ghataprabha catchments. Keywords: Basin, GIS, GPS, Remote Sensing, River. 94
  2. 2. International Journal of Computer Engineering and Technology (IJCET), ISSN 09766367(Print), ISSN 0976 - 6375(Online), Volume 5, Issue 1, January (2014), © IAEME 1. INTRODUCTION Growing awareness of the importance of river basin to eco-environmental functions has promoted river basin buffer restoration in semi-arid catchments [1]. This restoration is essentially an engineering project involving project planning, design, implementation, and management. Such a task requires scientific understanding and technical answers for many questions. For example, how much farmland is located in the river basin of a concerned catchment? Which basin should be targeted first in the basin if the available restoration budget is limited for a fiscal year? What kind of restoration buffering shape and size should be designed for the basin catchment? How these conceptual designs are technically implemented in the planning maps? These questions have been partially addressed in previous studies using geoinformatics. Geoinformatics have been increasingly used to the delineation and assessment of basin catchments buffers in environmental studies in recent years. For example, SRTM and digital line graph data were used to identify critical basin catchment strips for engineering restoration. Geoinformatics images were applied to monitor basin habitat conditions in terms of changes in land cover, catchment buffers, and wetlands [2]. The uncertainties of image classification were assessed for accurately characterizing land cover of catchment buffers [3]. New issues of applying RS images to mapping vegetation cover of river basin, locating restoration activities, and assessing previous management actions were recently examined [4]. Soil moisture maps were derived from satellite images to detect seasonal changes of river basin catchments [5]. Similarly, geoinformatics have been used in few studies on river basin buffer restoration. For example, geoinformatics was used to develop a new approach on generating variable buffers in basin buffer restoration planning [6]. Geoinformatics was also used to derive soil wetness from digital elevation model (DEM) for assessing the suitability of different sites in river basins for either preservation or restoration of vegetative or farming catchment buffers [7]. In a recent review on river basin buffer studies, the role of geoinformatics in the parameterization and visualization of hydrological modeling on river basin buffer restoration was highlighted [8]. On the basis of previous studies, this research further applies geoinformatics technologies to develop a methodological framework for the assessment of catchment buffers in basin catchment. The developed framework could be used to evaluate spatial dynamics of farming intensities in river basin buffers by catchments and by buffer sizes, thus providing critical information for land restoration planning. Specific objectives achieved through a case study are: (1) to derive a land cover map from satellite images for an river basins; (2) to generate river basin buffering scenarios using DEM data; (3) to estimate farmland areas in the buffers by catchments; and (4) to rank the priority of catchments for their river basin buffer restoration and management. 2. DATA AND METHODS 2.1 Study site Ghataprabha basin catchment was selected as the study site, located in the Krishna river basin, India, is one of the most important agricultural areas in the north Karnataka. The predominantly rural population in the catchment is growing rapidly, which results in increasing demand for natural water resources. Basin catchment is predominantly semi-arid characterized by its natural water resources scarcity, low per capita water allocation and conflicting demands as well as shared water resources. The catchment of the basin lies approximately between the northern latitudes 15° 45’ and 16° 25’ and eastern longitudes 74° 00’ and 75° 55’. (Fig. 1) The river Ghataprabha rises from the western ghats in Maharastra at an altitude of 884m, flows eastward for 60 Km through the Sindhudurg and Kolhapur districts of Maharastra, forms the border between Maharastra and Karnataka for 8 Km and then enters Karnataka. In Karnataka, the 95
  3. 3. International Journal of Computer Engineering and Technology (IJCET), ISSN 09766367(Print), ISSN 0976 - 6375(Online), Volume 5, Issue 1, January (2014), © IAEME river flows for 216 Km through Belgaum district past Bagalkot. After a run of 283 Km the river joins the Krishna on the right bank at Kudalasangama at elevation of 500m, about 16 Km from Almatti. Total catchment area of the sub-basin is 5373 km2, out of which 77.2% lies in Karnataka and rest falls under Maharastra. The basin catchment was broadly divided into three sections: the upper, the middle, and the lower. The upper portion was mainly composed of wetlands and rural areas. The mid-portion was the most urbanized area in the catchment. Land use in the lower section was dominated by agriculture, a major contributor to the landscape fragmentation and surface water quality degradation. The basin catchment was selected as the study site for several reasons. First, it was experiencing water quality problems due to nonpoint source pollutions from intensive farming practices, road construction, and mining extraction. Thus, there was a high conservation need for water quality protection. Second, variety species had been officially designated at risk in the area, indicating a high wildlife conservation concern in the site. INDIA Administrative index map of Ghtataprabha sub-basin N KRISHNA BASIN KRISHNA BASIN 15km 0m 15km 30km GHATAPRABHA LEGEND FIG. 1: THE INDEX MAP OF THE GHATAPRABHA SUB-BASIN Third, some conservation programs including Gokak reserved forest and Rural Water Quality Program had been ongoing in a few sub-basins of the catchments, providing an applied background for this research. 2.2 Image classification and land cover map The Google Earth and SRTM image used in this study was acquired on May 24, 2008. It was orthorectified, radiometrically, and atmospherically corrected by the data provider. The image was 96
  4. 4. International Journal of Computer Engineering and Technology (IJCET), ISSN 09766367(Print), ISSN 0976 - 6375(Online), Volume 5, Issue 1, January (2014), © IAEME georeferenced to the UTM WGS1984 Zone 43N. It was then customized to the study area using the Ghataprabha basin catchment boundary to reduce the image processing time. All the image processing described below was conducted by using GIS software’s MapInfo, ERDAS and AutoCAD. Land use / cover are an important aspect for determining the various hydrological phenomena like infiltration, overland flow, evaporation and interception etc. Land use pattern has a significant influence on the quality and quantity of runoff available from it. The spatial distribution of land-use in Ghataprabha basin catchment is shown in Fig. 2, which reveals that there are four different types of land uses in Ghataprabha basin catchment. These are 1) Agriculture land; 2) Barren / Fallow land; 3) Shrubs; and 4) Forests. They were chosen on the ability to be separated within the image from other land uses as well as their hydrological properties that would be of interest in basin management. Five hundred GPS-based ground-truth points were collected for surveying land classes and three hundred of them were used as image classification sites. About 50 pixels per class were gathered for each site. The sites selected were distributed as evenly as possible throughout the image to account for regional variability within the same information class. Maximum likelihood classifier (MLC) was used as the classification algorithm to classify the pixels. The MLC assumed that the selected data were normally distributed. Each pixel was classified according to its probability of membership to each information class. The limitation of this classification method was that pixels lying outside the probability of the information classes were set as “null class.” The classification results yielded an overall accuracy of 81.75% with the Kappa value of 0.7761. For the convenience in later analyses, the four classes are agriculture, open water, forest and other land use/cover types, which were relevant to this study. 2.3 Water body and buffer generation SRTM and Google Earth image sensors could only detect such surface water bodies as wider rivers, reservoirs, ponds, and lakes, whose sizes were larger than one-pixel area (30 m×30 m). An additional DEM data layer with 10 m resolution was thus used to generate surface water networks of the basin catchment in MapInfo and AutoCAD. The hydrograph included linear stream/river systems and polygon water bodies (reservoirs, ponds, and lakes). For simplicity, no buffer was generated for the polygon water bodies. The buffers to be considered for restoration were generated only along the linear streams / rivers using a proximity analysis. Based on previous studies, buffer widths from 3 to 15 m were recommended for stabilizing eroding banks, from 3 to 60 m for filtering chemicals and from 20 to 200 m for establishing agriculture dispersal corridors and habitats. (10 – 12) Thus, eight buffering widths of 25, 50, 75, 100, 125, 150, 175, and 200 m from both stream sides were designed as potential restoration scenarios to improve water resources and agriculture habitats. These buffers were converted into raster grids with 5 m resolution for farmland inventory within the buffer zones. 2.4 Farmland inventory To refine the statistical unit for estimating the farmland area, the land cover data with 30 m resolution was re-sampled to a raster grid with a cell size of 5 m using the nearest neighbor assignment rule. This re-sampling process would not increase the accuracy of the land cover data but is necessary for counting the cropland area in the 25 m buffers. The re-sampled land cover map was then overlaid with the eight raster buffer grids to get new buffer grids with updated land use information. According to local basin management plans, the Ghataprabha basin catchment was divided into three sub-basins using the DEM data. In each sub-basin, there were five buffers as candidates for engineering restoration. The sub-basin data layer was overlaid with each buffer grid to summarize agricultural land areas in different buffers by sub-basin. 97
  5. 5. International Journal of Computer Engineering and Technology (IJCET), ISSN 09766367(Print), ISSN 0976 - 6375(Online), Volume 5, Issue 1, January (2014), © IAEME 3. RESULTS The classified land cover and DEM-derived water bodies for the Ghataprabha basin are shown in Fig. 3 and 4, respectively. The DEM-derived vector water systems are in agreement with the visible surface water in the raster land cover map. The inventory of cropland in different buffering zones by sub-basin is presented in Fig. 5. There is an approximately linear relationship between the cropland area in a buffer and its width. The Ghataprabha River has the largest cropland area in buffers among the three sub-basins, whereas the Markenday has the smallest cropland area in buffers. The spatial dynamics of cropland in different buffers by sub-basin across the catchment are shown in Fig. 6. The Ghataprabha River with the largest cropland area in buffers is also the largest one among all sub-basins; however, the Markenday with the smallest cropland area in buffers is not the smallest sub-basin. To make spatial comparison valid in selecting a potential site for engineering restoration, a relative cropland area as the percentage of cropland in a buffer over its total area is estimated. Only Hirankeshi catchment have relatively small cropland ratios of the total land in buffers. Buffers of the remaining sub-basins have more than 50% cropland, indicating high needs for restoration action in those sub-basins. In addition, since most headwater streams are located in the Ghataprabha basin farming activities there would impact downstream water quality largely, thus making them into the first priority for land restoration. Land use of Ghataprabha basin CHIKK ODO N RAIBAGH MUDHOL HUKKERI GOKAK BILG I BAGALKOT KOLHAPUR RAMD SIN 15km DH 0m UD UR G BELG 15km AUM AL ONG B AILH SO A UD I TT URGA MI DA BA LEGEND Agriculture 30km Fallow Lands Forest Shrubs Basin Boundary Taluka Boundary Geo Grids FIG. 2: LAND USE MAP OF GHATAPRABHA BASIN 98
  6. 6. International Journal of Computer Engineering and Technology (IJCET), ISSN 09766367(Print), ISSN 0976 - 6375(Online), Volume 5, Issue 1, January (2014), © IAEME 4. DISCUSSION The results reported above show the current cropland status of river basin buffers in the catchment. Such information is essential for the restoration planning and management of river basin buffers to achieve water quality improvement and refine agriculture habitats. The linear relationship between the cropland area in a buffer and its width is reasonable because if the basin buffer within 15 m from the stream had been cleaned up for farming, then the land beyond the 20 m buffer from the stream was most likely be farmed. This inference could be verified from the classified SRTM and Google Earth image. FIG. 3: DEM OF GHATAPRABHA BASIN Drainage map of Ghataprabha basin C HIKK N RAIBAGH ODI GRID NORTH MUDHOL HUKKERI GOKAK B ILG I BAGALKOT KOLHAPUR RAMD SIN DH U DU RG UD SO URGA BA MI DA TI AT BAILHONGAL B ELG AUM LEGEND 15km 0m 15km 30km Basin Boundary Taluka Boundary River / Stream - Perennial River / Stream - non Perennial Reservoir / Tanks Geo Grids FIG. 4: DRAINAGE DENSITY MAP OF GHATAPRABHA BASIN 99
  7. 7. International Journal of Computer Engineering and Technology (IJCET), ISSN 09766367(Print), ISSN 0976 - 6375(Online), Volume 5, Issue 1, January (2014), © IAEME The Ghataprabha basin has the largest cropland in buffers among all sub-basins because it has the largest basin area with intensive farming activities (Fig.2 and 6). The Markenday has the smallest cropland area because it is largely occupied by urban buildings. The Hiranyakeshi have relative small cropland ratios of the total land in buffers because most of the catchment land in the Markenday basin remain naturally untouched, whereas land in catchment buffers of the other subbasin have been cleaned for urban development. This observation suggests that the Markenday basin should not be prioritized for catchment buffer restoration. FIG. 5: CROPLAND IN DIFFERENT BUFFERS BY SUBBASIN This research contributes two innovative points on the spatial assessment of farming intensities in catchment buffers of agricultural basin. One is the focus on the dynamics of cropland in buffers by sub-basin across the catchment and the other is the designs of buffering scenarios with varying buffer widths. These contributions differ from previous studies. For example, similar studies were conducted in two previous studies [9]. However, the critical sites indentified here were distributed across the entire study basin catchment, thus making the sub-basin management strategy impossible. Another closely related study focused on accuracy assessment of image classification and proposed bio-minimum mapping units to characterize land cover in catchment buffer zones and non-catchment buffer areas of the basin [10]. Classified catchment buffers into 5 classes based on the density of tree cover in catchment buffers with a 20 m width, in which buffer areas with less than 50% of tree cover were considered as critical sites for engineering restoration [11]. [12] used both vegetation indices and soil moisture maps to accurately delineate catchment buffers from other land areas. However, none of those studies reported the ideas proposed here, which involved spatial assessment of catchment buffers by buffer size across sub-basins. 100
  8. 8. International Journal of Computer Engineering and Technology (IJCET), ISSN 09766367(Print), ISSN 0976 - 6375(Online), Volume 5, Issue 1, January (2014), © IAEME Sub catchment area of Ghataprabha basin N GRID NORTH GHATAPRABHA 3086 km² ALMATTI RESERVOIR HIRANYAKESHI 1432 km² HIDKAL RESERVOIR MARKANDEYA 855 km² LEGEND 15km 0m 15km 30km Basin Boundary Sub catchment area Ghataprabha sub catchment Hiranyakeshi sub catchment Markandeya sub catchment Geo Grids FIG. 6: SPATIAL DISTRIBUTION OF CROPLANDS IN DIFFERENT BUFFERS BY SUB-BASIN 5. CONCLUSION This study contributes a simple method for assessing the spatial dynamics of farming intensities in catchment buffers by buffer size across sub-basins, which are of interest to environmental planners and practitioners. This study also adds further evidences for the potential applications of geoinformatics technologies to the planning and management of catchment buffer restoration in agricultural watersheds. The geoinformatics images provide fundamental data sources for land cover and land use mapping. Geoinformatics could be used to estimate, analyze, and visualize the spatial distribution of cropland in designed restoration buffers. Such information is essential for engineering restoration planning and management. For example, the maps and charts in Figs.3-5 show where the restoration should be prioritized if the public fund is limited. The results suggest that the Ghataprabha basin in the catchment should be placed with a higher priority for buffer restoration. 101
  9. 9. International Journal of Computer Engineering and Technology (IJCET), ISSN 09766367(Print), ISSN 0976 - 6375(Online), Volume 5, Issue 1, January (2014), © IAEME REFERENCES [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] Basavaraj Hutti and Nijagunappa, R., Applications of geoinformatics in water resources management of semi arid region, north Karnataka, India, International Journal of Geomatics and Geosciences, v.2 Issue. 2, pp.373-382, (2011). Klemas. V. V., Remote sensing of landscape-level coastal environmental indicators. Environmental Management, v.27 (1), pp. 47-57, (2001). Lunetta, R. S., Ediriwickrema, J., Iiames, J., A quantitative assessment of a combined spectral and GIS rule-based land-cover classification in the Neuse River Basin of North Carolina. Photogrammetric Engineering & Remote Sensing, v.69 (3), pp. 299-310, (2003). Basavaraj Hutti and Nijagunappa, R., Geoinformatics technology application in north Karnataka for water resources management, Universal Journal of Environmental Research and Technology, v.1, Issue. 3, pp. 222-232, (2011). Israil, M., Al-Hadithi, M and Singhal, D. C., Application of resistivity survey and geographical information system (GIS) analysis for hydrogeological zoning of a piedmont area, Himalayan foothill region, India. Hydrogeology Journal, 14, pp. 753–759, (2006). Basavaraj Hutti. and Nijagunappa, R., Geoinformatics based decision support system tools for water resources management in north Karnataka, India., Indian Journal of Natural Sciences International Bimonthly, v.1I Issue. 10, pp.779-791, (2012). Basavaraj Hutti., Development of water resource management strategies in north Karnataka semi-arid region using geoinformatics., Environmental science, Gulbarga university, Ph.D. Thesis, p.225. (2012). Sikdar, P.K., Chakraborty, S., Adhya, E. and Paul, P.K., Land use/land cover changes and groundwater potential zoning in and around Raniganj coal mining area, Bardhaman District, West Bengal: A GIS and remote sensing approach. Journal of Spatial Hydrology, v.4, pp. 1– 24. (2004). Basavaraj Hutti. and Nijagunappa, R., Identification of groundwater potential zone using geoinformatics in ghataprabha basin, north Karnataka, India, International Journal of Geomatics and Geosciences, v.2 Issue. 1, pp.91-109. (2011). Goetz, S. J., Remote sensing of riparian buffers: past progress and future prospects. Journal of the American Water Resources Association, v.42 (1), pp. 133-143. (2006). Basavaraj Hutti. and Nijagunappa, R., Development of groundwater potential zone in northKarnataka semi-arid region using geoinformatics technology. Universal Journal of Environmental Research and Technology, v.1, Issue. 4, pp.500-514. (2011) Makkeasorn, A., Chang, N. B., Li, J. H., Seasonal change detection of riparian zones with remote sensing images and genetic programming in a semi-arid watershed. Journal of Environmental Management, v.90 (2), pp.1069-1080. (2009). Mohammed Hashim Ameen and Dr. R. K. Pandey, “Delineation of Irrigation Infrastructural, Potential and Land Use/ Land Cover of Muzaffarnagar by using Remote Sensing and GIS”, International Journal of Civil Engineering & Technology (IJCIET), Volume 4, Issue 3, 2013, pp. 1 - 11, ISSN Print: 0976 – 6308, ISSN Online: 0976 – 6316. Basavarajappa H.T, Parviz Tazdari and Manjunatha M.C, “Integration of Soil and Lineament on Suitable Landfill Sites Selection and Environmental Appraisal Around Mysore City, Karnataka, India Using Remote Sensing & Gis Techniques”, International Journal of Civil Engineering & Technology (IJCIET), Volume 4, Issue 6, 2013, pp. 177 - 185, ISSN Print: 0976 – 6308, ISSN Online: 0976 – 6316. 102

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