50120140501011

International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-

6367(Print), ISSN 0976 - 6375(Online), Volume 5, Issue 1, January (2014), © IAEME

94

ASSESSMENT OF RIVER BASIN FOR ENGINEERING RESTORATION IN

GHATAPRABHA CATCHMENTS USING GEO-INFORMATICS

APPLICATIONS

Basavaraj Hutti1 and Basavarajappa. H. T

2

1Geoscience Domain Expert, L&T IES, SEZ-02, Industrial Area, Hootagalli, Mysore – 570018. 2Centre for Advanced Studies in Precambrian Geology, Dept. of Earth Sciences, University of

Mysore, Mysore – 570006. INDIA

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.

INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING &

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)

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IJCET

© I A E M E



95

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



96

N

GHATAPRABHA

KRISHNA BASIN

Administrative index map of Ghtataprabha sub-basin

LEGEND

30km15km15km 0m

INDIA

KRISHNA BASIN

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.

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



97

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.



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BAGALKOT

BADAMIRAMDURGA

HUKKERIGOKAK

CHIKKODO RAIBAGH

BILGIMUDHOL

BELGAUM BAILHONGAL SOUDATTI

SINDHUDURG

Land use of Ghataprabha basin

N

30km15km15km 0m

LEGEND

Agriculture

Fallow Lands

Forest

Shrubs

Basin Boundary

Taluka Boundary

Geo Grids

KOLHAPUR

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.

FIG. 2: LAND USE MAP OF GHATAPRABHA BASIN



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30km15km15km 0m

BAGALKOT

BADAMIRAMDURGA

HUKKERIGOKAK

CHIKKODI RAIBAGH

BILGIMUDHOL

BELGAUM

BAILHONGAL

SOUDATTISINDHUDURG

Drainage map of Ghataprabha basin

N

KOLHAPUR

GRID NORTH

Basin Boundary

Taluka Boundary

River / Stream - Perennial

River / Stream - non Perennial

Reservoir / Tanks

Geo Grids

LEGEND

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

FIG. 4: DRAINAGE DENSITY MAP OF GHATAPRABHA BASIN



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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 sub-

basin 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.



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Basin Boundary

Sub catchment area

Ghataprabha sub catchment

Hiranyakeshi sub catchment

Markandeya sub catchment

Geo Grids

LEGEND

30km15km15km 0m

Sub catchment area of Ghataprabha basin

N

GRID NORTH

MARKANDEYA

HIRANYAKESHI

GHATAPRABHA

1432 km²

855 km²

3086 km²

HIDKALRESERVOIR

ALMATTIRESERVOIR

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.



102

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50120140501011

Technology

importance of river

iaemeassessment of river

river basin buffer studies

subbasin catchments

river basins

restoration activities

restoration of vegetative

farming catchment buffers