APPLICATION OF REMOTE SENSING AND GIS FOR IDENTIFICATION AND ASSESSMENT OF CROP GROWTH IN WAZIRABAD COMMAND AREA BY CALCULATION OF IRRIGATION LOSSES AND DEMAND TO IMPROVE THE FARMING
ABSTRACT Differentiating the land use and classification of crop precisely using Remote Sensing Satellite images and GIS analysis for calculating the irrigation demand and losses with respect to soil characteristics and topography is an added advantage in utilizing the optimum amount of water distribution to fields. Percolation losses are one of the main losses out of other losses in irrigating the crops. These losses accounts more in the case of course textured soils than finely textured. Integrating and analyzing the multi date satellite imagery and multiple themes using GPS based GIS helps to identify and estimate the crop acreage and assess the crop water requirements and optimum amount of water distribution through canals. Using geographical information systems helps the water resource engineers and managers for estimating the results both in the form of spatial and non-spatial. This paper is a part of research and highlights the advantages of GIS and Remote Sensing in identifying the crop and mapping the canal network in network model, delineating the Command Area in to Blocks/Sub-Blocks and calculating the percolation losses in the fields with a special emphasis on irrigation water management. In this study the parameters like canal network, detailed block boundaries under major to minor canals, crop classified satellite images and soil characteristics of the command area are taken as thematic inputs.
Application of Remote Sensing and GIS for Identification and Assessment… 79
APPLICATION OF REMOTE SENSING AND GIS FOR IDENTIFICATION
AND ASSESSMENT OF CROP GROWTH IN WAZIRABAD COMMAND
AREA BY CALCULATION OF IRRIGATION LOSSES AND DEMAND
TO IMPROVE THE FARMING
Ramesh Naidu1, M.V.S.S. Giridhar1 and Sanghamitra Ghosh2
1JNTU, Hyderabad, India
2Vidyasagar University, West Bengal, India
Differentiating the land use and classification of crop precisely using Remote Sensing Satellite images and GIS analysis
for calculating the irrigation demand and losses with respect to soil characteristics and topography is an added advantage in
utilizing the optimum amount of water distribution to fields. Percolation losses are one of the main losses out of other losses
in irrigating the crops. These losses accounts more in the case of course textured soils than finely textured. Integrating
and analyzing the multi date satellite imagery and multiple themes using GPS based GIS helps to identify and estimate
the crop acreage and assess the crop water requirements and optimum amount of water distribution through canals.
Using geographical information systems helps the water resource engineers and managers for estimating the results
both in the form of spatial and non-spatial. This paper is a part of research and highlights the advantages of GIS and
Remote Sensing in identifying the crop and mapping the canal network in network model, delineating the Command
Area in to Blocks/Sub-Blocks and calculating the percolation losses in the fields with a special emphasis on irrigation
water management. In this study the parameters like canal network, detailed block boundaries under major to minor
canals, crop classified satellite images and soil characteristics of the command area are taken as thematic inputs.
Keywords: Remote Sensing, Chak, Block, GIS and GPS, Command Area.
In irrigation fields, water losses largely result from Evapotranspiration, percolation and seepage. Evapotranspiration
is related to meteorological factors and to be calculated as separately for calculating the water requirement of crops.
Seepage and percolation, when compared with ET which is relatively stable in a given period within given agro-
ecological region with uniform climate, vary very much from place to place. Rice grown on sandy soils requires, on
the average, about three times more water than rice grown on clay soils (Fukuda and Tsutsui 1968). Because of
topography, crop type and soil characteristics, losses in irrigation system lead to overall in-efficiency in terms of water
productivity. Percolation losses are a function of the local soil, crop type and extent and topographic conditions. Therefore,
at any time the amount of rainfall or irrigation water entering a soil becomes greater than its water-holding capacity, loss
by the downward movement of free water (vertical percolation) will occur. Percolation is often defined as the
movement of moisture through saturated soils due to gravity, hydrostatic pressure or both.
Percolation occurs in a vertical direction. Water is lost through seepage by its horizontal movement through a levee.
In practice, percolation and seepage are combined and taken as a measure of the water-retaining capacity of a field.
Percolation is largely affected by topography, soil characteristics, and the depth of the water table. In extreme cases,
percolation plus seepage ranges from almost nil on heavy soils to greater than 100 mm on sandy soils. Seepage and
percolation rates are mainly governed by the profile characteristics and topography and are much greater in sandy
than clay soils. It also increases with increase of depth of standing water. The rate of seepage and percolation are
about 6 mm/day in well drained soils and 3mm/day in poorly drained soils. Where the soil is heavy and the water
table is close to the soil surface, percolation losses are low- about 1 mm/day or less. Where soil is light and water table is
deep, percolation losses may be high-10mm/day or more. Other factors that affect percolation losses are presence of
a crop, the amount and distribution of rainfall, soil shrinkage and cracking, soil contraction, flooding and depth of water
and soil puddling. Researches have indicated that a percolation rate of 10 to 15 mm/day was favorable for supply of
dissolved oxygen, the removal of harmful substances and the maintenance of root activity. However there is little
benefit on yield under good soil conditions. Infect with some situations, the loss of plant nutrients are serious if the
percolation rate is high. Various studies suggest that the range of percolation varies between wide limits from less
than 1mm/day in compact soil up to several 100 mms/day in loose soil.
Proceedings of AIPA 2012, INDIA
80 Agro-Informatics and Precision Agriculture 2012 (AIPA 2012)
Using GIS and Remote Sensing Technology and its powerful tools helps to calculate the losses effectively by taking
several parameters at one attempt.
2. STUDY AREA
The selected Wazirabad Command Area is in Zone1 under block No. 5 of Left Main Canal in Nagarjunasagar Project
(NSP). It is located between 16°39′ 2.84″ and 16°56′ 40.81″ N latitude and 79°25′ 16.01″ and 79°40′ 52.90″ E longitude.
Mirialguda is the nearest town connected by ground road network. The total extent of command area is 26700
hectares. The study area is located flat and medium undulating terrain with maximum and minimum elevation ranging
between 187 and 44 m above MSL. The area experiences semi-arid climatic conditions and receives an average
annual rainfall of about 750 mm. In general, the slope of the command area is towards east.
As described in the introduction the percolation and seepage mainly depend on crop, soil, ground water and topography. In
the Wazirabad command area the groundwater levels and fluctuations and the topography of the command area that
serves by canals are almost uniform and there is no impact in calculating the losses. Hence Crop and Soil is taken as
main parameter for calculating the percolation losses since the soils in command area are distributed with several different
categories. GIS is used as a tool since the variability of losses can be correctly defined and calculated spatially and
can be represented the calculated losses in a map format by overlaying all block/sub-block boundaries and canal
network concerned to command areas for taking effective decisions during the release of water to canals.
Using the canal network as one theme, crop and soil as supporting themes and block/sub-block are the reference
theme the overlay analysis has been used for calculation of losses.
Percolation losses have been calculated by GIS Overlay analysis by using union operation with Crop, Soil theme
and sub-block theme. The criteria are based on the amount of percolation losses defined for each soil category as
defined by International Institute for Aerospace Survey and Earth Sciences (ITC), The Netherlands.
The flow chart of the detailed methodology of Crop identification and Classification is represented in Figure 1.
The following layers were generated in GIS Platform:
• Canal line
• Canal Node
• Crop Map
• Soil Map
• Contour and Digital Elevation Model
• Command Area Boundary including Block and chak boundary
• Water User Association Boundary.
Application of Remote Sensing and GIS for Identification and Assessment… 81
3.1 Canal Network Database Creation
Canal network is digitized from the Survey of India Topo maps and later converted to GIS database using ArcInfo
were Software. The data was prepared in a network module which constitutes nodes and lines. The node data refers to
Sluice OT and line refers to canal. The flow direction and continuity errors were taken care while digitization and
later checked for continuity in the ArcGIS Network Module. The canal ID is created with a unique multi digit
number constitutes a combination of alphabets and numbers. All canal reaches have only one upstream reach but have
more than one downstream reaches. Only the upstream reach ID is kept for each section in the developed data
system. The hierarchic relationship can be retrieved through the use of Upper id.
3.2 Crop Classification
The crop acreage for Wazirabad Command Area was estimated based on the unsupervised classification using Erdas
imagine software. The crop acreage reports were generated blockwise, chakwise and WUAwise using the GIS
Analysis to find out the water demands for each canal and command area unit. In Rabi season the command area is
covered with paddy and some small patches of non-paddy. There was difficulty in identifying crop and cultivated
lands because of the coexistence of early and late stages of crop. However these growth stages were successfully identified
or distinguished from taking the two images—one in February and the second one in March. Uncultivated fields and
fallow lands were identified using infrared band values due to surface reflectance of soil, while more advanced
growth stage fields were identified with the brightness values. To improve accuracy the non-agricultural areas like
settlements, water bodies, rocky, scrub and dense forest etc. were masked out by using non-agricultural mask.
3.3 Creation of Contour Map, Digital Elevation Model (DEM) and Aspect Map
Digital representations of the terrain often form one of the main elements of the mapping process. Digital Elevation
Model (DEM) represents continuous variation of topography over space that helps in assessing landscape characteristics
and has a wide application in surface hydrology modeling. These characteristics help to determine slope, flow directions,
areas, boundaries and outlets of drainage basins and ultimately in delineating the Block and Chak boundaries for this
study. Using GRASS GIS the DEM is generated. Contours are digitized from the Block maps collected from the Irrigation
Department. These contours used as an elevation data for creating the Digital Elevation Model. The DEM is used as an
input for creating the Aspect Map.
3.4 Delineation of Command Area, block and Chak boundaries
The delineation is based on surface modeling techniques available in many GIS and Remote Sensing software.
GRASS Software is used for doing the surface modeling. The Chaks and Block Boundaries under each canal are
delineated reference to canal network, DEM, Aspect and drainage network extracted from surface modeling and SOI
Topo maps. The Chaks are mapped as per the type of canal and its flow direction. If the canal is a ridge canal Chaks
are identified on both sides of it and if it is a contour canal Chaks are on one side only. Spread of a Chak is between the
canal and the drainage line.
3.5 Calculation of Seepage/Percolation Losses
According to US Bureau of Reclamation Data for unlined canals the seepage rate in various types of soils are given in
Table 1: Soilwise Percolation/Seepage Losses
Texture Qs-Percolation/Seepage Losses(mm/Day)
A simple linear equation has been used to calculate the seepage and percolation losses. The equation is: P = Qs * A
where P is Percolation loss in lit/sec, Qs is in mm/day, A is the crop acreage in Hectares. Using the above criteria the
canal network theme, soil theme and chak themes are overlaid for chakwise losses.
82 Agro-Informatics and Precision Agriculture 2012 (AIPA 2012)
4. RESULTS AND DISCUSSIONS
The canal network and the types of soil in command area are shown in Figure 2 and Figure 5, respectively. The type
of satellite imagery used and the classified crop map generated is shown in Figures 3 and 4, respectively. Delineation of
Sub-block and Chak boundaries are shown in Figure 6. There are 13 canals mapped in network model and including
major canal and under each canal the sub-blocks and chaks are identified with reference to DEM and identified
topographic features. Due to the hierarchy and the unique identity of block and chak under each canal the spatial queries
and retrieval of data and results are more convenient for decision making and planning of water releases. Clay soils
are occupied around 13050 hectares of area, loamy soils distributed around 6035 hectares and sandy soils are
occupied around 7636 hectares. The chaks under sandy soils influence high percolation losses and the chaks under clay
soils influence low percolation losses. Within a sub-block under a minor canal there are different types of soils and
influence varied amount of losses though the chak areas are equal in size.
For canals of WX and WL2 are almost same lengths and the corresponding command areas of two canals are almost equal
in area but the losses in canal WL2 are double than the canal WX. These results are tabulated in Tables 2 and 3.
Fig. 2: Canal Network in GIS Platform
Fig. 3: Satellite Imagery used for Crop Classification Fig. 4: Classified Crop Map. Pin Colour shown as Crop
Application of Remote Sensing and GIS for Identification and Assessment… 83
Fig. 5: Soil Distribution in Command Area Fig. 6: Block and Chak Boundaries from GIS Analysis
Table 2: Canal or Sub-blockwise Crop Acreage
S. No. Block Area in Hectares Canal Canal-Length Late Paddy Paddy Fallow Total Paddy in Hectares
1. 213.098 WL1 1496.440 30.921 133.651 11.215 164.572
2. 3434.9674 WL2 26457.494 705.798 2491.292 66.18 3197.09
3. 209.4619 WL3 1492.796 28.017 167.037 0.074 195.054
4. 294.3726 WL4 1581.590 49.563 230.831 2.722 280.394
5. 7721.4262 WL5 57092.800 956.04 2168.691 86.629 3124.731
6. 1073.667 WL6 4049.046 119.129 449.747 0.147 568.876
7. 1185.734 WR1 11427.452 89.971 1032.19 5.81 1122.161
8. 383.5992 WR2 1897.032 88.832 259.068 18.973 347.9
9. 711.869 WR3 3650.757 45.592 630.352 2.867 675.944
10. 712.8629 WR4 4473.000 27.538 672.158 1.545 699.696
11. 803.6684 WR5 3486.690 72.948 686.607 14.67 759.555
12. 2141.2722 WR6 13784.440 419.12 510.635 25.774 929.755
13. 7839.4408 WX 24956.594 219.983 1036.381 21.252 1256.364
Total 26725.4396 155846.131 2853.452 10468.64 257.858 13322.092
Table 3: Canal or Sub-blockwise Percolation/Seepage Losses
Sl. No. Canal-Id Sub-block Length (M) Command Area in Sq. Metres
Percolation Losses in
1. WX WX 24956.76 32421489.156 2.821
2. WL1 WL1 1496.46 2888445.532 0.467
3. WL2 WL2 26457.68 34349697.839 5.025
4. WL3 WL3 1492.82 2094636.229 0.339
5. WL4 WL4 1581.59 2943784.722 0.476
6. WL5 WL5 57093.04 77214212.748 6.584
7. WL6 WL6 4049.10 10736690.313 0.604
8. WR1 WR1 11427.41 11857393.828 1.920
9. WR2 WR2 1897.11 3835936.438 0.621
10. WR3 WR3 3650.75 7118706.768 1.153
11. WR4 WR4 4473.04 7128574.473 1.154
12. WR5 WR5 3486.65 8036570.211 0.998
13. WR6 WR6 13784.47 21412738.698 1.013
84 Agro-Informatics and Precision Agriculture 2012 (AIPA 2012)
Integration of Crop classified data and Geospatial Database with Water Resources and Land Management domain has
definitely an edge over conventional way of Planning and Management. Applying LISS-III images to paddy crop
classification gives acceptable results. Most successful identification of paddy crops would have required multi-temporal
images. Detailed Level of Chak/Block Statistics in calculating the conveyance losses increases Water Use Efficiency.
Chakwise Percolation Losses are used to change the water allocation strategies for optimum utilization of water. Effective
maintenance of irrigation projects is possible through Geospatial Data Integration and can be integrated with SCADA.
Through this Modernization, maintenance and efficient operation of the irrigation system up to the Chak level is possible.
Water allocation in an irrigation system is possible with due regard to equity and social justice. Disparities in the
availability of water between head-reach and tail end farms and between large and small farms should be obviated by
referring the actual need of irrigation with respect to losses calculated and supply on a volumetric basis subject to
certain ceilings and rational pricing.
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