50120140501011

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)
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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.

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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

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

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

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
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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

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


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

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