Journal of Global Resources
Volume 6 (01)August 2019-January 2020
Page 238-246
UGC–CARE Listed Journal in Group D
ISSN: 2395-3160 (Print), 2455-2445 (Online)

38
DETERMINATION OF LAND SUITABILITY USING MULTI CRITERIA APPROACH:
A CASE STUDY OF BHILWARA DISTRICT RAJASTHAN
Naha Pandey and Chilka Sharma
School of Earth Science, Banasthali Vidyapith, Niwai, (Rajasthan) India
Email: pandeyneha2014@gmail.com, chilkasharma@gmail.com
Abstract: Land is one of the most important natural resource which facilitates the physicalbasis of various kinds of activities and natural process like habitations, agriculture, recreation
etc. Every type of land and every piece are suitable for some kind of utilization depending upon
their capabilities, potential and suitability for various uses. Methods and types of decisions forutilization must be on its suitability for different purposes. Now a days land resources are being
used without assessing/ determining capabilities of land and optimum method of utilization likemarginal land & unproductive land are being utilized for cropping, good fertile agricultural lands
are being engulfed by settlements, industries, construction of roads etc. As a result large areas
are losing their productivity and are converted into degraded wasteland. The purpose of thestudy is to determine the land suitability analysis through Remote Sensing & Geographical
Information Systems (GIS) technology (By assessing parameters like slope, geomorphology
and land-use/land-cover) in which decision rules are applied on the multi-criteria basis.
Decision rules are used for the categorization of lands for various purposes like intensive
agriculture, rainfed agriculture recreation, horticulture, pasture development, forest plantation,
Settlement etc. The present research is an attempt to analyse suitability of land resources of
Bhilwara district of Rajasthan state and suggests optimum utilization for various purposes
through integration of various thematic layers, present utilization carrying capacity of land inframework of multi-criteria decision rule and also suggested optimum purposes of utilization foreach piece of land in Bhilwara district.
Key words: Agriculture, Land Suitability, Remote Sensing & GIS, Multi-Criteria Decision Rule
Introduction
Land is considered to be the most important component of earth system and evaluation of land
plays major role for agriculture and other human activities of production and its classificationacts as an important tool for its better use. Proper use of land depends on the suitability orutilization of land. Land suitability analysis is needed for various purposes. It reduces thehuman influence on natural resources. Land resource inventories are used to determine land
suitability which becomes a standard part of planning process at different levels. In India
agriculture is one of the major economic activities in which two-third of Indian’s population is
engaged. Due to over exploitation and mismanagement land is being degraded specificallywhen land is utilized for the purpose it is not suitable. This problem of land degradation can be
managed through land suitability analysis and land should be utilized for the purpose, it isutilized. All the land is classified as per their suitability depending upon various parameterssuch as physical and chemical properties of soil, slope, present use etc. Our study focused on
proper utilization and management of land at field level which is very much essential for
farmers. Multi-criteria approaches have been applied for the land suitability analysis of Bhilwara
district. Remote sensing and GIS technology is being used to monitor agriculture and crop
growth status in term of both space and time (Jeyaseelan 2003; Seelan et al. 2003). It provides
facility to the user for detecting the changes on the Earth’s surface at different scales. Whereas
specific characteristics of remotely sensed data like; large area coverage, good spatial
resolution, accessibility to remote areas and faster interpretation with higher degree ofobjectivity and reproducibility made remote sensing becoming progressively important for
mapping land use and land cover (Ibrahim M.S, et.al. 2013). The use of land is not only
determined by the user but also by its suitability.

238

Study Area
Bhilwara district is located in south eastern part of Rajasthan state. The coordinates of this
district are 25⁰ 00' 38.87 to 25⁰ 57' 53.70 North latitude and 74⁰ 00' 31.67 to 75⁰ 27' 46.25 east
longitude. The total geographical area of the district is 10,455 sq km. According to the 2011
census Bhilwara district has a population of 2,408,523 of which male and female were1,220,736 and 1,187,787 respectively. It is bounded in the north by Ajmer district in the northwest, west and south-west by Rajsamand district; in the south and south-east by Chittaurgarh
district and in the east and north-east by Bundi and Tonk district. The mostly area of district iscovered by gently slope except in western & northwestern part where slope is high. Fig.1.
shows the geographical extent of study area.
Figure 01: Geographical Extent of Study Area

Many seasonal rivers such as Kothari, Banas, Menali, Bedach, Khari and Mansi meander their
way through Bhilwara district of Rajasthan. The district falls in the Banas (9157.2 sq km),
Chambal (1164.9 sq.km) & Luni basins (133.0 sq.km) (CGWB report- 2013). Banas is the majorriver of the district which flows towards east direction and its tributaries are Bedach, Kothari &Khari. The highest amount of water is received by the rainfall which is the most important factor
for agriculture, industry and domestic purpose. Figure 2 shows that study area has good
network of drainage, road and railway.
Figure 02: Base Map of Bhilwara district

Figure 03: Integrated LU/LC Map

239

Data Used
In the research, Landsat 8 satellite data for the period of 2017 for both Rabi season (23rd -30th
November, 2017) and kharif season (20th-27th September, 2017) data was downloaded fromhttps://www.usgs.gov./. Abstraction of slope has been done from DEM (30m). Administrative
map were collected from Survey of India. Geo-morphology map is collected from Geological
Survey of India.
Remote Sensing & GIS Analysis
Landsat-8 satellite image of the year 2017 is used to calculate the land-use/land-cover ofentire district. There are total 11 Lulc classes categorizes in which crops are categorized inthree classes i.e., double crop, Single rabi crop & single kharif crop and forest is alsoclassified in three classes i.e., dense forest, degraded forest & open forest and remainingclasses waterbody, built-up, scrubland, saline, barren rocky and mining/ dump areas which
are done through the unsupervised classification in ERDAS 2014. LULC map is presented in
Figure 3. By applying unsupervised classification on both Rabi & Kharif satellite data andfurther classified this information classify into two classes, crop & non-crop for both theseasons. Further these two classes were converted from raster to vector data and thenintersected both vector layers using ArcGIS 10.4. Through which 3 classes of crops arederived i.e, double crop, single Rabi & single Kharif crop. Thereafter we observed fourcombinations which are given below:
Kharif crop x Rabi crop = Double crop.
Kharif crop x Rabi-non-crop = Single Kharif.
Rabi crop x Kharif-non-crop = Single Rabi. Kharif-non-crop x Rabi-non-crop = Non-crop.
By applying Normalized Difference Vegetation Index (NDVI) forest are classified into three
categories i.e. dense forest, degraded forest & open forest.
Table 01: Area of Land use and Land Cover
#
1
2
3
4
5
6
7
8
9
10
11
12

Landuse/Landcover Classes
Water Body
Scrub Land
Settlements
MiningSaline Land
Barren rocky
Dense Forest
Degraded Forest
Open Forest
Double-cropSingle kharif cropSingle rabi crop

Area in Percent
01.15
28.33
1.23
0.66
2.71
0.62
3.24
1.16
0.88
26.48
23.30
10.25

Thematic Mapping and Database Generation
Various thematic maps such as lulc Geomorphology map & slope map were also prepared in
ArcGIS 10.4. Digitizing is the process of converting features on a paper map into digital format.
Slope map is generated using SRTM 90m DEM data. Slope was further classified in 3 classes
i.e. Gentle Slope, Moderate Slope & Steep Slope as presented in table 2.
Table 02: Percentage of Area under Each Slope Category#1
2
3

Slope Types
Gentle Slope
Moderate Slope
Steep Slope

Slope in Degree
0-3
3-5
5+

Area in Percent
92.11
4.5
3.39

The earth’s surface can be classfied into different geromorphic units/landforms basedon their physiographic expression, origin, material contant and climatic condition, etc. Geomorphology map is collected from Geological survey of India (GSI) and digitized in ArcGIS
10.4. Major geomorphic units of Bhilwara district are flood plain along rivers’-Khari, Masi,
Banas, and Kothari, Valley Fill occurs in Small scattered patches in east & west, Pediment240

Scattered in entire district, mainly in east & west parts. Denudation hills plateau and pediments
are found across the entire district (Fig.5).
Figure 04: Slope Map of Bhilwara District.

Figure 05: Geo-morphology of Bhilwara dist.

Decision Making for Land Suitability
Overlay analysis is used to identify the suitable area for the intensive agriculture, rainfed
agriculture, pasture development, Horticulture development, forest plantation and Settlement.
Land use, geo-morphology & slope layers are integrated and a decision rule for land suitability
is prepared for various land utilization by taken multi-criteria analysis, presented in table 3. This
study facilitates the farmer’s decision makers to identify the suitable area for various purposes.
Result Discussion
Land suitability classification is the first and most essential task to be taken for thesustainable use of land. In the study, multi-criteria decision rule is developed on basis of threelayers i.e., lulc, geo-morphology, slope and their different classes (table 3) by applying
intersect method using Spatial Analyst tools in ArcGIS 10.4. Multi-criteria decision rules areapplied for identification of land suitability for the various purposes which are intensive
agriculture, rainfed agriculture, recreation, horticulture, pasture development, forest plantation.
Table 03: Multi-Criteria Decision Rules for Suitability Analysis
Classes
Intensive Agriculture

Lulc
Double & Single rabi crop

Rainfed Agriculture

Single Rabi, Single Kharif &ScrublandSingle Kharif, Scrubland & saline
land
Scrubland & saline land

Horticulture
Development
Pasture Development
Settlement
Forest Plantation

Scrub land& settlement with 5 km
bufferDegraded forest

Recreation

Water bodies & Barren rocky

Geomorphology
Padiplain, Flood Plain & Valley
Fill
Pediment, Flood Plain &Padiplain
Flood Plain & Pediment
Denudational Hills, Pediment &Sandunesany
Geo-Morphology
any
Geo-Morphology
Structural Hills, Denotational
Hills & Waterbodies

Slope
0-3 degree
0-3 & 3-5
degree0-3 & 3-5
degree5+degree0-3 & 3-5
degreeany slope5+
degreeDecision rule have been developed for different land utilization which are as follows:a) Decision Rule for Intensive Agriculture.
b) Decision Rule for Horticulture Development.
c) Decision Rule for Rainfed Agriculture.
d) Decision Rule for forest-plantation.
e) Decision Rule for Pasture development. f) Decision Rule for settlement.
For preparing these rules we considered different lulc classes which were intersected withlayer of geomorphology and layer of slope. The Land suitability map for different parameters
discussed above is shown in Figure 6.

241

Area found suitable for intensive agriculture is already being used almost optimally
except few areas under single Rabi crop can be converted into double cropping with suitable
irrigation. This area is mainly confined to central and south-western part of district especially in
Sahara, Suwana and total portably of Bhilwara district.
Figure 06: Intensive Agriculture

Figure 07: Pasture Development

Decision rule for Intensive Agriculture = Double crop & Single Rabi crop ∩ Pedi plain, Flood Plain &Valley fill ∩ Plain area (slope 0-3 degree)
Pasture Development = Scrubland & saline land. ∩ Denudational hills, Pediment & sandunes ∩Steep slope (above 5 degree).

Low altitude of denudation hills and moderate to steep slope with small soil cover arefound for suitable for pasture development in north-western & north-eastern part of Bhilwara
district. 514.69 sq. km. (4.92 percent) of total area Bhilwara district is suitable for pasturedevelopment.
More than 50 percent of area suitability for rainfed agriculture which is kharif crop
specially concentration in northern blocks like Banera, Hurda, Shahpura and Asind block.
5273.78 sq. km. (50.44 percent) area of total area of Bhilwara district is suitable for rainfed
agriculture.
Figure 08: Rainfed Agriculture

Figure 09: Horticulture Development

Rainfed Agriculture = Single kharif, single Rabi crop & scrubland ∩ Pedi plain, Flood Plain & Pediment ∩Plain area & moderate slope (0-3 & 3-5 degree).
Horticulture Development = Single kharif crop, scrubland & saline land. ∩ Pediment and flood plain ∩Plain & moderate slope (0-3 & 3-5 degree).

242

Area of foothill having slight and moderate slope up to 5 degree and having soil cover isfound suitable for horticulture plantation. Such area is found along the hills in Asind, Jahazpur,
Mandal, Raipur and portable blocks of Bhilwara district. 1286.23 sq. km. (12.30 percent) of
total area Bhilwara district is suitable for horticulture development.
Figure 10: Forest Plantation

Figure 11: Recreation Land

Forest Plantation = Degraded forest ∩ Any Geo-morphology ∩ Any slope.
Recreation = Water bodies & barren rocky ∩ denudation hills, structural hills & water bodies ∩Steep slope (above 5 degree).

All degraded forest areas a must be covered for forest plantation specially Mandalgarh& Jahazpur blocks. 119.42 sq. km. (1.14 percent) of total area Bhilwara district is suitable for
pasture development. 716.81 sq. km. (6.86 percent) area of total area of Bhilwara district issuitable for rainfed agriculture.
Area surrounding to the settlement with little slope and under any land-use categories
are suitable for expansion of habitation. 402.88 sq. km. (3.85 percent) of total area Bhilwara
district is suitable for pasture development.
Figure 12: Settlement

Settlement = Scrub land & settlement 5km buffer ∩ Any Geo-morphology ∩ Plain & moderate slope (0-3
& 3-5 degree).

243

Table 03: Area Suitable for Optimum Utilization of Land
Block Name

Area
SuitableforIntensive
Agriculture

Total
GeographicalArea Of Blockin(sq. km.)

Asind
BaneraHurda
Jahazpur
Kotri
MandalMandalgarh
Raipur(sq. km.)184.03205.52140.80141.92315.39320.40175.35126.761124.00
669.00609.001039.00
927.001214.00
1545.60
520.00Total Areaof eachBlock in %forIntensive
Agriculture
16.37
30.72
23.12
13.66
34.02
26.39
11.34
24.38

Total Areaof eachblock in %for Rainfed
Agriculture

Total Areaof eachBlock in %forHorticulture

Total area of
each Blockin % for
PastureLand

Total areaof eachBlock in %for ForestPlantation

Total Area of
each Block in% for
Recreation

Total Areaof eachBlock in %forSettlement

58
64
72
53
58
50
20
49

25.29
2.73
9.22
17.59
12.13
14.07
8.72
18.17

15.82
0.86
1.12
10.02
3.53
4.90
2.83
6.02

0.25
0.12
1.86
0.37
0.04
5.78
0.01
0.14
not
available
notavailable
notavailable

19
2
1
16
4
6
7
8

5.23
0.64
0.78
4.38
0.54
8.28
0.52
3.86

Sahara258.84636.00

40.70

54

11.96

4.25

5

6.52

Shahpura

305.591129.00

27.07

68

8.65

1.47

1

5.07

Suwana419.75932.46

45.02

42

6.27

1.01

2

6.14

244

Conclusion
Decision rule prepared for land suitability analysis for various purposes are working very welland the system is able to identify appropriate areas suitable for intensive agriculture, rainfed
agriculture, horticulture development, pasture development, recreation, forest plantation etc.
The result shows that every piece of land can be utilized optimally with science and technology
interventions through well designed decision rules.
Acknowledgement
The authors are thankful to Birla institute of Scientific Research (BISR) Jaipur for providing theresources that enabled us to carry out this study. The assistance provided by School of Earth
Sciences, Banasthali Vidyapith. The authors are also thankful to USGS for providing all thesatellite images.
References
1. Panhalkar, S. (2011) Land capability classification for integrated watershed development byapplying remote sensing and GIS techniques. Journal of Agricultural and Biological
Science, 6(4), 46-55.
2. Raut, S., Sarma, K. S. S., & Das, D. K. (2010) Study of irrigation and crop waterrequirements and growth of two Rabi crops grown in a semi arid region using agrometeorology and remote sensing. Journal of the Indian Society of Remote
Sensing, 38(2), 321-331.
3. Abdel Rahman, M. A., Natarajan, A., & Hegde, R. (2016) Assessment of land suitability andcapability by integrating remote sensing and GIS for agriculture in Chamarajanagar district,
Karnataka, India. The Egyptian Journal of Remote Sensing and Space Science, 19(1), 125.
4. Ambarwulan, W., Santoso, P. B., Sabiham, S., & Hikmat, M. (2016) Remote sensing andland suitability analysis to establish local specific inputs for paddy fields in Subang, West
Java. Procedia Environmental Sciences, 33, 94-107.
5. Barham, P., Dragovic, B., Fraser, K., Hand, S., Harris, T., Ho, A., & Warfield, A. (2003,
October). Xen and the art of virtualization. In ACM SIGOPS operating systems review (Vol.
37, No. 5, pp. 164-177). ACM.
6. Bhavani, P., Chakravarthi, V., Roy, P. S., Joshi, P. K., & Chandrasekar, K. (2017) Longterm agricultural performance and climate variability for drought assessment: a regional
study from Telangana and Andhra Pradesh states, India. Geomatics, Natural Hazards andRisk, 8(2), 822-840.
7. Borana, S. L., Yadav, S. K., Parihar, S. K., & Paturkar, R. T. (2013, September). Integrationof Remote Sensing & GIS for Urban Land Use/Cover Change Analysis of the Jodhpur city.
In 33rd INCA International Congress (pp. 19-21).
8. Census Report-2011, series-09 district Census Handbook Bhilwara.
9. El Baroudy, A. A., & Moghanm, F. S. (2014). Combined use of remote sensing and GIS for
degradation risk assessment in some soils of the Northern Nile Delta, Egypt. The Egyptian
Journal of Remote Sensing and Space Science, 17(1), 77-85.
10. El Garouani, A., Mulla, D. J., El Garouani, S., & Knight, J. (2017) Analysis of urban growthand sprawl from remote sensing data: Case of Fez, Morocco. International Journal of
Sustainable Built Environment, 6(1), 160-169.
11. Eldrandaly, K. (2007) GIS software selection: a multicriteria decision making
approach. Applied GIS, 3(5), 1-17.
12. Government of India ministry of water resources central ground water board, Bhilwara
district 2013, Rajasthan.
13. Hasmadi, M., Pakhriazad, H. Z., & Shahrin, M. F. (2017) Evaluating supervised andunsupervised techniques for land cover mapping using remote sensing data. GeografiaMalaysian Journal of Society and Space, 5(1).
14. Ibrahim, M. M. M., Dafalla, M. S., Elhag, A. M. H., & Ibrahim, I. S. (2013) Monitoring,predicting and quantifying soil salinity, sodicity and alkalinity in Sudan, using soil
techniques, remote sensing and GIS Analysis, Case study: University of Khartoum top
farm. International Journal of Scientific and Research Publications, 3(3), 1-7.
15. Jamil, M., & Sajjad, H. (2016) Deriving cropping system efficiency pattern using remotesensing and GIS: A case study of Bijnor district, India. International Journal ofAdvancement in Remote Sensing, GIS and Geograph, 4(2), 27-40.
245

16. Jeyaseelan, A. T. (2003) Droughts & floods assessment and monitoring using remotesensing and GIS. In Satellite remote sensing and GIS applications in agriculturalmeteorology (Vol. 291). Dehra Dun, India. Geneva, Switz: World Meteorol. Org.
17. Kumar, A., Sheoran, H. S., Yadav, M., & Hooda, R. S. Geospatial Approach for Block Wise
Area Estimation of Kharif Season Crops in Fatehabad district, Haryana (India).
18. Kumar, K. N., Rajeevan, M., Pai, D. S., Srivastava, A. K., & Preethi, B. (2013) On theobserved variability of monsoon droughts over India. Weather and Climate Extremes, 1, 42.
19. Lambin, E. F. (1997). Modelling and monitoring land-cover change processes in tropicalregions. Progress in physical geography, 21(3), 375-393.
20. LIU, L., LI, D., & SHAO, Z. Research on geospatial information sharing platform based on
ArcGIS Server (Vol. 37, pp. 791-795) The International Archives of the Photogrammetry,
Remote Sensing and Spa-tial Information Sciences.
21. Ministry of “Environment, Forest and Climate Change” Bhilwara district survey report inGazette Notification Dated 15th January, 2016.
22. Mohapatra, S. N., Pani, P., & Sharma, M. (2014) Rapid urban expansion and its
implications on geom.: A remote sensing and GIS based study. Geography Journal, 2014.
23. Mustafa, A. A., Singh, M., Sahoo, R. N., Ahmed, N., Khanna, M., Sarangi, A., & Mishra, A.
K. (2011) Land suitability analysis for different crops: a multi criteria decision making
approach using remote sensing and GIS. Researcher, 3 (12), 61-84.
24. Prasad, H. C., Bhalla, P., & Palria, S. (2014) Site Suitability Analysis of Water Harvesting
Structures Using Remote Sensing and GIS-A Case Study of Pisangan Watershed, Ajmer
district, Rajasthan. The International Archives of Photogrammetry, Remote Sensing andSpatial Information Sciences, 40(8), 1471.
25. Preethi, B., & Revadekar, J. V. (2013). Kharif foodgrain yield and daily summer monsoon
precipitation over India. International Journal of Climatology, 33(8), 1978-1986.
26. Rana, R. S., & Prakash, C. (1992). Soil quality of Bhilwara district (Rajasthan) in relation topisciculture. Journal of the Indian Fisheries Association, 22, 83-87.
27. Rogan, J., & Chen, D. (2004) Remote sensing technology for mapping and monitoring landcover and land-use change. Progress in planning, 61(4), 301-325.
28. Sarajlic, S. (2012) Land Cover Change and Mineral Composite Assessment of Tushka
Depression, in Egypt, Using Remote Sensing and GIS.
29. Seelan, S. K., Laguette, S., Casady, G. M., & Seielstad, G. A. (2003) Remote sensing
applications for precision agriculture: A learning community approach. Remote Sensing ofEnvironment, 88(1-2), 157-169.
30. Sehgal, K. K. (1960) Raj. District Gazetteers Bhilwara, Govt. of Rajasthan, Jaipur, 2013.
31. Singh, A., & Sharma, A. L. (2014) Agriculturally important insect’s diversity in Kharif andRabi crops of Talwandi sabo, Punjab. International Journal of Scientific and Research
Publications, 4(8), 1-5.
32. Singha, C., & Swain, K. C. (2016) Land suitability evaluation criteria for agricultural crop
selection: A review. Agricultural Reviews, 37(2).
33. Thenkabail, P. S., Enclona, E. A., Ashton, M. S., & Van Der Meer, B. (2004) Accuracy
assessments of hyperspectral waveband performance for vegetation analysis
applications. Remote sensing of environment, 91(3-4), 354-376.
34. Waikar, M. L., & Nilawar, A. P. (2014) Identification of groundwater potential zone using
remote sensing and GIS technique. Int J Innov Res Sci Eng Technol, 3(5), 12163-12174.
35. Yalew, S. G., van Griensven, A., Mul, M. L., & van der Zaag, P. (2016) Land suitability
analysis for agriculture in the Abbay basin using remote sensing, GIS and AHP
techniques. Modeling Earth Systems and Environment, 2(2), 101.
36. Zaitunah, A., Ahmad, A. G., & Safitri, R. A. (2018, March). Normalized difference vegetationindex (ndvi) analysis for land cover types using landsat 8 oli in besitang watershed,
Indonesia. In IOP Conference Series: Earth and Environmental Science (Vol. 126, No. 1, p.
012112). IOP Publishing.
Links:
1. http://bhuvan.nrsc.gov.in/bhuvan_links.php
2. https://www.rajras.in/index.php/soils-of-rajasthan-2/
3. http://plan.rajasthan.gov.in/statistics
4. https://www.nrsc.gov.in/Careers
5. http://www.censusindia.gov.in/2011census/dchb/0824_PART_B_DCHBpercent20_BHILWARA.
pdf

246