Land capability and Crop Suitability using RS and GIS

Transcription

1 International Journal of Technical & Scientific Research Engineering Volume 1 Issue 2, July-August Anjan Kumar 1, Ravikumar A.S 2 1 (PG student, Water Resource Engg, Dept. of Civil Engg, UVCE /Bangalore University, India) 2 (Associate Professor, Dept. of Civil Engg, UVCE /Bangalore University, India) ABSTRACT: In the coming decades accelerating population growth, surface water pollution, and climate change together may produce a drastic decline in fresh water supply. Keeping the above factors in view, the quantification and conservation of surface water resource is needed for ensuring livelihood. The study area chosen is Kabini command area spread in Mysore and Chamrajnagar districts, Karnataka. The objective of the study is to assess the capability of land for agriculture in order to make the land more profitable and also to determine the best suitable areas for the crops viz,. Paddy, Sugarcane and Cereals. For LCC, slope, soil depth, texture and land use/ land cover conditions are assessed through remote sensing and GIS environment. The analysis reveals that 5 classes I, II, III, IV and V are present in the command area. The evaluation of land in terms of the suitability classes is based on the method as described in FAO guideline for land evaluation for rainfed agriculture. A land unit resulting from the overlay process of the selected theme layers has unique information of land qualities for which the suitability is based on. The selected theme layers includes soil texture, soil depth, land use/ land cover, rainfall, slope, aspect, ph, erosion, drainage density and temperature which are overlaiyed in ArcGIS using Analytical Heirchical Process (AHP). It reveals that 4 classes has been identified as highly suitable (S1), moderately suitable (S2), less suitable (S3) and not suitable (N). KEYWORDS Agriculture, AHP, GIS, land capability classification, land suitability, RS. I. INTRODUCTION The most important aspects of land are its role in providing anchorage space to all resources and the fact that most human activities take place on land. In the light of increasing population, the demand is also increasing, thus optimum use of land has become a necessity. Land is also unevenly distributed in terms of its qualities. Land can be improved for a particular use by certain measures, it can also be improved by a certain kind of land-use or at least sustained production can be assured. Land can deteriorate by its mismanagement, wrong land use or by certain cultivation practices, thus in order to avoid misuse and wastage of land. It should be used judiciously considering its capability and suitability for particular use. To reduce the human influence on natural resources and to identify an appropriate land use, it is essential to carry out scientific land evaluations. Such kind of analysis allows identifying the main limiting factors for the agricultural production and enables decision makers to develop crop managements able to increase the land productivity. The importance of land classifications are divided based on the quality and intensity of its use hardly needs any elaboration in land-use studies. Classifications of land mean assigning each tract, or piece of land within a specific area its proper class within a system of classes. The system relates to a quality or characteristics of land or distinctions of the quality or particular characteristics. In simple way land classification means dividing the land into different categories or classes according to a single factor or a particular interpretation. The capability classification provides three major categories of soil grouping viz., capability unit, capability subclass, and capability classes. According to this system the first category i.e., Capability Unit is a grouping of one or more individual soil-mapping units having similar potentials and continuing limitations or hazards. The soils in a capability unit are sufficiently uniform to produce similar kinds of cultivated crops and pasture plants with similar management practices, require similar conservation treatment and management under the same kind and condition of vegetative cover, and have comparable potential productivity. Anjan Kumar Page 9

2 The second category, i.e. the capability sub-class is a grouping of capability units having similar kinds of limitations and the symbol given for each is used as suffix to the concerned land capability class: a) Erosion and runoff (including risk of erosion and post erosion damage) b) Excess of water (wetness, high water-table, problem of drainage, overflow) c) Root zone limitations (Shallow depth, low water holding capacity, salinity or alkalinity) d) Climatic limitations. Land capability sub-classes have been used widely in many parts of the world with slight modifications mostly incorporating local hazards. The third category and broadest category in the capability classification is the Capability Classes. Capability classes are groups of capability sub-classes or capability units that have the same relative degree of hazard or limitation. The risks of soil damage limitation in use become progressively grater from class I to class VIII. The capability classes are useful as a means of introducing the map user to obtain the more detailed information on the soil map. Class I: Soils in land class I have either no or only slight permanent limitations or risks of damage. The soils are deep, productive, easily worked, and nearly level and can be cultivated safely with ordinary good farming methods. Class II: Soils in this class, subjected to moderate limitations and moderate risks of damage. They can be cultivated with easily applied practices. Soils in this class have gentle slopes, moderate depths which are subject to occasional overflows and are in need of drainage. Class III: Soils in class III are subject to severe limitations in use of cropland. They are subject to severe risks or damages. They are moderately good soils and they can be used regularly for crops, provided they are planted according to good rotations and given the proper treatment. Soils in this class have moderately steep, slopes and are subject to more severe erosion. Class IV: Class IV land is composed of soils, which are very severe permanent limitations or hazards if used for cropland. The soils are fairly good and are frequently on steeps slopes and subject to severe erosion. They should usually be kept in hay or pasture, although a grain crops may be grown once in five or six years. Class V: Soils in class V should be kept in permanent vegetation. They should be used for pasture or forestry. They have few or no permanent limitations and not more than slight hazards. Cultivation is not feasible, however, because of wetness or other limitations. Class VI: Class VI soil should be used for grazing and forestry and may have moderate hazards when in use. They are subject to moderate permanent limitations and are unsuited for cultivation and are steep, or shallow. Class VII: Soils in class VII are subject to severe permanent limitations or hazards when used for grazing or forestry. They are steep, eroded rough, shallow, droughty or swampy. They are either fair or poor for grazing or forestry and must be handled with care. Class VIII: Soils in class VIII are rough even for woodland or grazing. They should be used for wildlife, recreation or watershed uses. Agricultural land suitability is an interdisciplinary approach. Determination of optimum land use type for an area involves integration of data from various domains and sources like soil science to social science, meteorology to management science. All these major streams can be considered as separate groups, further each group can have various parameters (criteria). However, all the criteria are not equally important, every criteria will contribute towards the suitability at different degrees. The relative degree of contribution of various criteria can be addressed well when they are grouped into various groups and organized at various hierarchies. Agricultural land suitability also involves major decisions at various levels starting from choosing a major land use types (LUT), selection of criteria, organization of the criteria, deciding suitability limits for each class of the criteria, deciding the preferences (qualitative and quantitative). Relative importance of these parameters can be well evaluated to determine the suitability by multi-criteria evaluation techniques (Ceballos - Silva and Lopez- Blanco 2003). The present popular methods that are followed for land suitability analysis includes ranking and ratings, weighted summation, requirement matching etc. The weights are arbitrarily chosen, and are aggregated using simple Boolean overlay methods. Although these methods are simple and straight forward and lack solid mathematical foundations. Anjan Kumar Page 10

3 II. STUDY AREA The area chosen is Kabini command area which spreads in Chamrajnagar and Mysore districts. The study area geographically lies between E and E longitude and N and N latitude with an area of sq.km which is covered on Survey of India (SOI) Toposheet numbers 57H04, 57H03, 57D16, 58E01, 58A13, 57D12, 57D08, 58A05 and 58A01 on 1:50000 scale. The maximum length and width of the command is km and km respectively. Kabini river is one of the major rivers in Cauvery basin and constitutes C-2 subbasin. The river originates in Western Ghats at an altitude of 2134 m in Wynad tauk, Kerala state and flows for a length of 212 km before joining the river Cauvery at Tirumakudalu Narasipura, Karnataka state. Fig.1 shows the location map of Kabini command area. Fig. 1. Location map of the study area Data products The following data products are used in the present study: i. Survey of India (SOI) Topomaps on 1:50,000 scale ii. Digital Elevation Model (DEM ) data iii. Rainfall Data iv. Hydrometeorological data v. Soil data vi. LANDSAT7 imagery and LISS III imagery (Table 1) Table 1: List of data used Type of data Description of data Sources of data Soil data Soil PH, soil depth, soil texture NBSS & LUP / KSRSAC, bengaluru Climate data Rainfall and temperature data IMD, Bengaluru Digital Elevation Model ASTER DEM (resolution: 90 m) USGS (Earth Explorer) Satellite image Land use/land cover LISS III Imagery III. METHODOLOGY 1. Data collection and preparation of Thematic layers: A common base map is prepared from SOI topomaps on 1:50000 scale before thematic mapping. The base map provides the basic details such as watershed boundary, latitude, longitude, major roadway, railway, rivers/ streams, water bodies, taluk boundaries, location of important settlements, etc. which serves as control points during interpretation of remotely sensed data Land use/ land cover and soil data (soil ph, soil texture, and soil depth) are collected from Karnataka State Remote Sensing Application Centre (KSRSAC and NBSS and LUP, Bengaluru respectively. Climate data (rainfall and temperature) is obtained from Indian Meteorological Dept. Anjan Kumar Page 11

4 (IMD), Bengaluru and Hydrometerological (HM) stations. There are four H.M stations are present in the study area viz,. Gundal, Kabini, Suvarnavathy and Nanjangudu. Digital Elevation Model (DEM) is obtained from USGS (Earth Explorer, SRTM 90m resolution). The mosaic has been processed and rectified to WGS84 coordinate system. Slope information is obtained from SOI Toposheets. Thematic maps for each of the soil parameters and slope are prepared using ArcGIS10.1 software. Annual rainfall and mean temperature, thematic maps are generated using Inverse Distance Weighted (IDW) interpolation. IDW interpolation determines cell values using a linearly weighted combination of a set of sample points. Fig.2 shows the methodology adopted for preparation of thematic maps as per the IMSD (NRSA, 1995) Technical Guidelines, NRSA, Hyderabad. Fig 3-13 shows the various thematic maps of the study area. Fig. 2 Methodology for preparation of thematic maps Fig.3 Land use/ land cover map of Kabini command Fig.4 Land use/ land cover map of Kabini command Fig.5. (DEM) of Kabini command Anjan Kumar Page 12

5 Fig.6 Slope map of Kabini command Fig. 7 Soil depth map of Kabini command Fig.8 Erosion map of Kabini command Fig. 9 Rainfall map of Kabini command Fig. 10 Temperature map of Kabini command Fig. 11 PH map of Kabini command Anjan Kumar Page 13

6 Fig.12 Aspect map of Kabini command Fig.13 Drainage density map of Kabini command 2. Land Capability classification The generalized methodology for Land Capability Classification (LCC) using GIS is depicted in the form of a flow chart (Fig. 14). The geospatial technique helps in generation of a reliable spatial and non-spatial information database. Such a database helps immensely in the efficient and scientific decision-making. The procedure essentially consists of 2 stages; i. To map the controlling and indicative parameters with the existing information and ii. Integration of the controlling and indicative parameter layers digitally through GIS. The land capability classification has been carried out by applying parameters like soil depth, soil texture, land use / land cover, erosion and slope of land. This information is used as a basis for placing lands in capability classes and subclasses. These informations are related with soil depth and soil texture about study region has been used from National bureau and soil survey and land use planning (NBSS and LUP). Shuttle Radar Topographic Mission (SRTM) data of the study region is used to assess the terrain conditions. 90 m data is resampled to 30m by using 3D surfacing utility of Erdas 9.0 software to get better accuracy. Landsat ETM image is used to assess the land use/land cover categories by applying Supervised Classification technique. Here, the advance navigation technique like GPS has been used to collect training site data and to field check classified datasets. These parameters are integrated in GIS environment by using intersect overlay technique. Moreover the surface and overlay analysis capabilities in GIS can effectively facilitate in handling vast amount of spatial information. The Intersect tool calculates the geometric intersection of input feature classes. The features or portion of features that are common to (intersect) all inputs will be written to the output feature class (Final LCC map). Anjan Kumar Page 14

7 La nd Cover typ e Fig. 14 Methodology used for Land Capability Classification Table 2 shows land cover classification into land capability groups. Table 3 shows land capability classes, characteristic features and recommendations. Table 4 shows Slope Classification. Table 2 Land cover classification into land capability groups Land Capability Group I II III IV V Kharif crop Tank (dry) Wasteland Wasteland with with/without stony waste/ Settlement Double crop scrub rockout crops Tank Agricultural plantation Forest Stream/river The depth of soil determines the effective rooting depth for plants, in accordance with texture, mineralogy and gravel content the capacity of the soil column to hold water. Seven depth classes given by Sehgal et al. (1987) ware used to classify the soil map in to depth class association (Table 5). 3. Crop Suitability Analysis 3.1 Selection of evaluation criteria: The study of Kabini has shown that it is important to consider other terrain features in addition to soil characteristics in order to arrive with a more informed decision on optimal crop growing areas. Opinions of agronomist and literature review of various references helped in identifying criterias viz,. soil P H, soil depth, drainage density, rainfall, slope, temperature, aspect, erosion, soil texture and land use/land cover necessary to determine suitable for growing paddy, sugarcane and cereals. Fig.15 shows the step by step procedure for Hierarchical process. Anjan Kumar Page 15

8 Fig. 15 Hierarchical organisation of the criteria considered for the study. The suitability levels are based on the Food and Agriculture Organization (FAO) land suitability classification and ranked as highly suitable (S1), moderately suitable (S2), marginally suitable (S3) and not suitable (S4). Suitability levels for each of the criteria are defined according to the FAO guideline for rainfed agriculture, literature review and agronomist opinions (Table 6) Sl No. Table 6 Standard criteria for Major crops (NBSS & LUP) 1. Paddy crop Suitability class Criteria Highly suitable, Moderately Marginaly S1 suitable, S2 suitable, S3 1 Temperature ( o C) Not suitable, N > < 15 2 Rainfall (mm) < Texture c, sic, cl, sicl, sl scl, sil, l sl, ls s 4 ph > < Soil depth (cm) > < 25 6 Slope > 5 2. Cereals/Pulses 1 Temperature ( o C) < 20 2 Rainfall (mm) < 400 Anjan Kumar Page 16

9 3 Texture sl, sclsil, cl, sic, sicl, c ls 4 ph > Soil depth (cm) > < 40 6 Slope < Temperature ( o C) 3. Sugarcane crop > < 20 2 Rainfall (mm) < Texture c, sic, cl, sicl, sl scl, sil, l sl, ls s 4 ph < < Soil depth (cm) > < 50 6 Slope < > 8 ( s- sand, ls loamy sand, sl sandy loam, scl sandy clay loam, cl clay loam, sil silt loam, l loam, sic silty loam, sc sandy clay, c- clay) Suitability levels S1, S2, S3 and N are assigned score 9, 7, 5 and 3 respectively. Classes with higher scores are most suitable for suitability evaluation. Using these scores and the defined suitability levels, all thematic maps area reclassified. 3.2 Applying MCE and assigning weights : To determine the relative importance/weights of criteria, AHP method of MCE is used. In order to compute the weights for the criteria, a pairwise comparison matrix (PWCM) is constructed using the information obtained through interviews, each factor has compared with the other factors, relative to its importance, on a scale from 1/9 to 9 introduced by Saaty (2008). Table 7 shows the scale of comparison used for the present study. Equal importance Table 7 Scale of comparison Degree of preference Moderate importance of one thematic layer over another Strong importance Very importance Extreme importance Values for inverse comparison The diagonal elements of PWCM are assigned the value of unity (i.e., when a factor is compared with itself). Since the matrix is symmetrical, only the lower triangular half actually needs to be filled in. The remaining cells are then reciprocals of the lower triangular half (Kihoro, Njore & Murage 2013). The normalized matrix is derived from pairwise comparison by adding the entries in each column of comparison matrix and then dividing each entry a jk with the sum of the entries the corresponding column a jk of the comparison matrix. The sum of normalized entries in each column will be equal to 1. Therefore, each entry in the normalized matrix = ajk When performing pairwise comparison, some incosnistencies may typically arise. The AHP incorporates an effective technique for checking the consistency of the evaluations made by the decision maker. In the AHP, the pairwise comparison in a judgment matrix are considered to be adequately consistent if the corresponding consistency ratio (CR) is less than 10% (Triantaphyllou & Mann 1995). Anjan Kumar Page 17

10 To calculate CR, the consistency index (CI) is estimated by multiplying judgement matrix by the approximated eigen vector. Each component of the resulting matrix is then divided by the corresponding approximated eigen vector. This yields an approximation of the maximum eigen value ( ). Then, the CI value is calculated by using the formula: CI = ( max )/( 1). Finally, the CR is obtained by dividing the CI value by the Random Consistency Index (RCI) as recommended by Saaty, (1980). Table 8 shows the random index (RI) values, Table 9 shows the pairwise comparison matrix and Table 10 shows the normalized matrix for the thematic layers. Table 8 Values of Random Index (RI) for number of thematic layers (n) n RI Table 9 Pairwise comparison matrix of thematic layers Table 10 Normalized matrix / priority matrix 3.3 Overlaying map layers: The reclassified thematic maps / layers of each variable are weighted using the weights derived from the AHP process. The weighted maps / layers are combined by performing the weighted overlay using spatial analyst tools. Finally, the suitability map is prepared. Anjan Kumar Page 18

11 IV. RESULTS AND DISCUSSIONS For LCC, slope, soil depth/ texture and land use/ land cover conditions are assessed in remote sensing and GIS environment. As these parameters have greater influence on capability of land. Five different classes (Fig.16) are observed in the command and tabulated in Table 11. Table 11. Agriculture land capability classification for Kabini command area Sl Land Capability Area % no. Class (sq.km) Area 1 Class I Class II Class III Class IV Class V TOTAL Fig. 16 Agriculture Land Capability Classification map for Kabini command area The weighted overlay analysis carried out using the criteria layers with their respective weights generated a combined suitability map (Fig. 17, 18 & 19). From the Table 12, reveals that % as highly suitable, sq.km (36.92%) as moderately suitable and sq.km (43.41%) as marginally suitable and sq.km (2.6%) not suitable for Paddy in Kabini command area. About sq.km (15.57%) as highly suitable, sq.km (36.92%) as moderately suitable and sq.km (4.82%) & sq.km (5.09%) not suitable for Sugarcane crop in Kabini command. About sq.km (133.53%) as highly suitable, sq.km (54%) as moderately suitable and sq.km (3.81%) not suitable for cereals crops in Kabini command. Fig. 17 Paddy suitability map for Kabini command area command area Fig. 18 Sugarcane suitability map for Kabini Anjan Kumar Page 19

12 Fig. 19 Cereals suitability map for Kabini command area Table 12 Crop Suitability for Kabini command area Paddy Sugarcane Cereals Sl no. Class Area % Area % Area % Description (sq.km) Area (sq.km) Area (sq.km) Area 1 S Highly suitable 2 S Moderately suitable 3 S Marginally suitable 4 N Not suitable TOTAL V. CONCLUSIONS The methodology adopted here to indicate land capability classes for decision-making intervention. The analysis reveals that 5 classes I, II, III, IV and V are present in the command area. Out of that Class V which is not suitable for agriculture accounts 0.18 %. Class I is a dominating class as far as the areal extent is concerned with %. Class III & IV are most susceptible to land degradation which accounts for 10 per cent. The FAO (1976) has given a framework for land suitability analysis for crops in terms of suitability classes from highlysuitable to not suitable based on the crop specific soil, climatic and topographic data. The same framework has been incorporated in the study with some modification in order to make the situation more compatible to Indian cases. Land suitability evaluation for crop suitability also highly dependent on specific crop requirement. Parameters used for analysis of crop suitability are slope, drainage, depth, texture, rainfall, temperature, etc. Suitability maps for all the crops are also developed. The suitability map for each crop is classified I to IV suitability classes. Among these crops Paddy & Sugarcane has the more suitability in the present study area. REFERENCES [1] Ashwini R., (2007) Analysis of Land use Capability Classification using Remote Sensing and Geographical Information System, M.E. Dissertation, Dept. of Civil Engineering, UVCE, Bangalore University, Bangalore. [2] Bhaskar J. Das, Singal S. K., Ranvir Singh., (2008) Estimation of erosion in an ungauged Wetland Catchment, Hydrology Journal, 31 (1-2), pp Anjan Kumar Page 20

13 [3] Chow V.T., Maidment, D.R., and Mays W., (1988). Applied Hydrology, Mc Graw Hill, New York. [4] Clarke J.I., (1966) Morphometry from Maps, Essays in Geomorphology, Elsevier Publ. Co., New York, pp [5] National Bureau of Soil Survey and Land Use Planning (NBSS and LUP, 1998), Soils of Karnataka for optimizing land use Executive summary in co-operation with Government of Karnataka, Bangalore. [6] Singh A.K., Bhattacharya A.K., (2006) Water management and crop production, Handbook of Agriculture, Directorate of information and publication of agriculture. ICAR, New Delhi, pp [7] Feizizadeh, B & Blaschke, T (2012), Land suitability analysis for Tabriz country, Iran: a multi-criteria evaluation approach using GIS, Journal of Environmental Planning and Management, vol. 1, pp [8] Saaty, T, L 2008, Decision making with the analytic hierarchy process, Int. J. Services Sciences, vol. 1, no. 1, pp [9] Triantaphyllou, E & Mann, S,H (1995), using the analytic hierarchy process for decision making in engineering applications: some challeges,inter l Journal of Industrial Engineering: Applications and Practices, vol.2, no.1, pp [10] FAO (1976). A framework for land evaluation, FAO Soil Bulletin, 32, Rome, Italy. [11] Cellabos-Silva, A.and J. Lopez-Blanco (2003). Delieation of suitable areas for crops using Multi-Criteria Evaluation approach and lan use/cover mapping: a case study in Central Mexico. Agriculture systems 77(2): [12] Sehgal J. (1996). Pedology concepts and applications, New Dehli, Kalyani Publishers. Anjan Kumar Page 21