REMOTE SENSING AND GIS BASED LAND … synoptic analyses of Earth - system function, patterning, ... gives the land use code, segment number of pixels in each land use classes, the

http://www.iaeme.com/IJMET/index.asp 866 [email protected]

International Journal of Mechanical Engineering and Technology (IJMET) Volume 8, Issue 11, November 2017, pp. 866–873, Article ID: IJMET_08_11_087

Available online at http://www.iaeme.com/IJMET/issues.asp?JType=IJMET&VType=8&IType=11

ISSN Print: 0976-6340 and ISSN Online: 0976-6359

© IAEME Publication Scopus Indexed

REMOTE SENSING AND GIS BASED LAND

UTILIZATION ANALYSIS: A MODEL STUDY

FROM VAMSADHARA RIVER BASIN

Kallakunta Ravi Kumar

Research Scholar, Department of Electronics & Communication Engineering,

K.L. University, Green Fields, Vaddeswaram-522502, Guntur (Dt), A.P, India.

SS. Asadi

Associate Dean Academics & Professor, Department of Civil Engineering,

K L University, Vaddeswaram, Guntur(D.t), A.P, India

Venkata Ratnam Kolluru

Associate Professor, Department of Electronics & Communication Engineering,

K.L. University, Green Fields, Vaddeswaram-522502, Guntur (D.t), A.P, India.

ABSTRACT:

This study deals with the use of GIS and Remote sensing in mapping for change

detection of Land Use/Land Cover (LU/LC) a model study of Vamsadhara River Basin

of 2011 and 2016. So as to detect the changes that has taken place in this status

between these periods. The result of the work shows in rapid growth of built-up land

between 2011 and 2016. LU/LC classification has been done using ERDAS imagine

9.2 software. The supervised classification technique was used to increase in built-up

area, open forest, plantation, and other lands. It is also noted that substantial amount

of agriculture land, water spread area, and dense forest area vanished during the

period of study which may be due to rapid urbanization of the study area.

Keywords: Land Use/Land Cover, Remote Sensing & GIS, Change detection

Cite this Article: Kallakunta Ravi Kumar, SS. Asadi and Venkata Ratnam Kolluru,

Remote Sensing and Gis Based Land Utilization Analysis: A Model Study from

Vamsadhara River Basin, International Journal of Mechanical Engineering and

Technology 8(11), 2017, pp. 866–873.

http://www.iaeme.com/IJMET/issues.asp?JType=IJMET&VType=8&IType=11

Remote Sensing and Gis Based Land Utilization Analysis: A Model Study From Vamsadhara River

Basin


1. INTRODUCTION:

The land use/land cover pattern of a region is an outcome of natural and socio – economic

factors and their utilization by man in time and space. Land is becoming a scarce resource due

to immense agricultural and demographic pressure. Hence, information on land use / land

cover and possibilities for their optimal use is essential for the selection, planning and

implementation of land use schemes to meet the increasing demands for basic human needs

and welfare. This information also assists in monitoring the dynamics of land use resulting

out of changing demands of increasing population.

Land use and land cover change has become a central component in current strategies for

managing natural resources and monitoring environmental changes. The advancement in the

concept of vegetation mapping has greatly increased research on land use land cover change

thus providing an accurate evaluation of the spread and health of the world’s forest, grassland,

and agricultural resources has become an important priority. Land use is a dynamic

phenomenon that modifies through time and space due to human-made pressure and

development. Appraising the present land use and its episodic change is convenient for urban

planners, policy makers and natural resource managers and remote sensing offers an

important Means of detecting and analyzing temporal changes. The considerate of the

progress dynamics of the urban cluster and land use changes is indispensable for ecologically

achievable developmental planning. Thus, there is an obvious need for ceaseless monitoring

of the phenomena of growth and mapping and scrutinizing LU/LC changes.

Remote Sensing (RS) and Geographic Information System (GIS) are now providing new

tools for advanced ecosystem management. The collection of remotely sensed data facilitates

the synoptic analyses of Earth - system function, patterning, and change at local, regional

and global scales over time; such data also provide an important link between intensive,

localized ecological research and regional, national and international conservation and

management of biological diversity.

The study is the Vamsadhara river basin having a spatial extent of 10,515 It is one of

the largest basin in southern India. The survey of India topographic maps that cover the entire

watershed are 65M/5-16, 65N/9, 65N/13-15, and 74/1-8 and B/1-3 and 74B/5. It is located in

between the and E longitude and and N latitude

(Fig:1) The basin forms part of Orissa and Andhra Pradesh States. The water flow is along the

south easterly direction. The area includes mineral soils of various textures as well as organic

soils.

To prepare the Land use / Land cover map& change detection analysis using satellite

images of years 2011 & 2016, of Vamsadhara River Basin in Srikakulam and Andhra Pradesh

districts. The goal of the work is also to map and monitor the land use / land cover and

identify the area of changes occurred during a year span of 15 years. The below figure shows

the location map of the basin.

Kallakunta Ravi Kumar, SS. Asadi and Venkata Ratnam Kolluru


Figure 1 Vamsadhara river basin Location Map

Figure 2 Satellite image of Vamsadhara river basin

2. OBJECTIVES

1. create spatial digital database consisting of land use/land cover of 2011 and 2016

using the Survey of India Toposheet and WIFS Satellite data

2. To study the Land use/Land cover (LU/LC) changes in study area for effective

management of Land Resources for future development.


Basin


3. METHODOLOGY

To work out the land use/cover classification, supervised classification method with

maximum likelihood algorithm was applied in the ERDAS Imagine 9.3 Software. Thematic

maps are vectorized from earlier generated raster thematic maps of the Vamsadhara river

basin. The satellite image has been used to compare the land use /land cover map of the study

area. The Arc Info and GIS softwares were used for digitizing the Land use/Land cover map.

The Arc-View softerware has been used for further processing of the data and taking the

outputs. Identification of objects and classification are visually based on image characteristics

commonly known as visual interpretation (Lillisand).There are certain fundamental

photo/image characteristics which help in interpretation of earth features . These are tone,

texture, pattern, size, shape and shadow and coupled with site /location and associated

features. In fact, the different objects reflect, emit and transmit different amount of radiation

in different wave length bands of satellite. These are recorded as tonal, colour or density

variations. The methodology is shown in Fig.2. For performing land use/cover change

detection, a post-classification detection method was employed. A pixel-based comparison

was used to produce change information on pixel basis and thus, interpret the changes more

efficiently taking the advantage of “-from, -to” information. Classified image pairs of two

different decade data were compared using cross-tabulation in order to determine qualitative

and quantitative aspects of the changes over the years 2011 and 2016.

Figure 3 Flowcharts for Preparing LU/LC Changes

4. RESULTS AND DISCUSSIONS:

4.1. Land use/Land cover

In the present study the visual interpretation of satellite imagery has been carried out. Land

use cover thematic map is generated by using generated thematic maps. Land use/Land cover

map is compared with Awifs satellite imagery. Finally Land use/Land cover map are

examined by using thematic maps and satellite imagery.

Thematic map is obtained from the earlier classified source for land use classification

using ERDAS 9.2 Imagine Software. It is a supervised classification model. The land use

classes are Dense/thick vegetation, Dry Crops, Gully erosion, Shifting cultivation, Sparse

vegetation, Wet crops. The generation of classification is by maximum likelihood classifier. It

gives the land use code, segment number of pixels in each land use classes, the threshold



value used, percentage of image occupied by each land use class etc.In the entire river basin

19% of the area is under thick vegetation. Dry crop cultivation amounts to 30% besides

shifting cultivation of 4% in total river basin area. Vegetation is spares in 33% of the entire

basin. Wet crops covers 12% and gully eroded lands are 3%.The study area based on the prior

knowledge of the study area for over 5 years and a brief reconnaissance survey with

additional information from previous research in the study area, a classification scheme was

developed for the study area the imaginary has been carried out and classified into different

classes, namely Dense/thick vegetation, Dry crops, Gully erosion, Shifting cultivation, Sparse

vegetation, Wet crops. After classification of satellite data land use/ land cover map is

digitized into vector form in polygon mode. Various land use pattern are given different

colors of easy identification and use/land cover classes are shown in Fig 3, and in

Table.1.Knowledge about land use/land cover has become important to overcome the problem

of biogeochemical cycles, loss of productive ecosystems, biodiversity, deterioration of

environmental quality, loss of agricultural lands, destruction of wetlands, and loss of fish and

wildlife habitat. The main reason behind the LU/LC changes includes rapid population

growth, rural-to-urban migration, reclassification of rural areas as urban areas, lack of

valuation of ecological services, poverty, ignorance of biophysical limitations, and use of

ecologically incompatible technologies. Due to involvement of multiple data sets, we used

latest technologies like remote sensing and GIS to quantify LU/LC. On the basis of

interpretation of remote sensing imagery, field surveys, and existing study area conditions, we

have classified the study area into six categories are Dense/thick Vegetation ,Dry crops/Wet

crops, Shifting cultivation, Sparse vegetation ,Gully erosion. The entire vegetation is grouped

under the regions dense/thick vegetation and sparse vegetation, as shown in the below table:1

for it affects the process of soil erosion Table.1: LU/LC Categories in the

Vamsadhara river basin.

Figure 4 LU/LC(2011) of Vamsadhara River Basin Map


Basin


Table 1 LU/LC(2011) Area wise statistical data

SI.NO Category Area in sq.km Area in %

1 Dense/thick Vegetation 1920.43 19%

2 Dry Crops 2990.32 30%

3 Gully Erosion 258.53 3%

4 Shifting Cultivation 379.8 4%

5 Sparse vegetation 3252.17 33%

6 Wet Crops 1220.23 12%

Figure 5 2011 areas wise of Percentage LU/LC Map

Figure 6 2016 LU/LC Coverage Map

The static land use land cover distribution for each study year as derived from the maps

are presented in the below table.



Table 2 LU/LC(2016) Area wise statistical data

SI.NO Category Area in sq.km Area in %

1 Shifting cultivation 213.13 2%

2 Degrade Forest 1281.53 12%

3 Gullies 819.68 8%

4 Dense/Thick Vegetation 2368.38 23%

5 Sparse vegetation 2066.23 20%

6 Moisture area 3563.6 35%

Figure 7 Percentages of vegetative coverage from Awifs data

REFERENCES:

[1] Adeniyi P.O and Omojola A. (1999) Landuse landcover change evaluation in Sokoto –

Rima Basin of North Western Nigeria based on Archival of the Environment (AARSE)

on Geoinformation Technology Applications for Resource and Environmental

Management in Africa. Pp 143-172.

[2] M. K. Jat, P. K. Garg, and D. Khare, “Monitoring and modelling of urban sprawl using

remote sensing and GIS techniques,” International Journal of Applied Earth Observation

and Geoinformation, vol. 10, no. 1, pp. 26–43, 2008. View at Publisher

[3] Alphan H, Doygun H, Unlukaplan YI (2009) Post-classification comparison of land cover

using multitemporal Landsat and ASTER imagery: the case of Kahramanmaraş, Turkey.

Environ Monit Assess 151:327-336.

[4] Tahir M, Imam E, Hussain T (2013) Evaluation of land use/land cover changes in Mekelle

City, Ethiopia using Remote Sensing and GIS. Computational Ecology and Software 3: 9-

16.

[5] C. A. Berlanga-Robles and A. Ruiz-Luna, “Land use mapping and change detection in the

coastal zone of northwest Mexico using remote sensing techniques,” Journal of Coastal

Research, vol. 18, no. 3, pp. 514–522, 2002.

[6] J. G. M. Tziztiki, F. M. Jean, and A. H. Everett, “Land cover mapping applications with

MODIS: a literature review,” International Journal of Digital Earth, vol. 5, no. 1, pp. 63–

87, 2012.

[7] H. Long, X. Wu, W. Wang, and G. Dong, Analysis of urban-rural land-use change during

1995-2006 and its policy dimensional driving forces in Chongqing, China, Sensors, vol. 8,

no. 2, pp. 681–699, 2008.

[8] S. Martinuzzi, W. A. Gould, and O. M. R. González, Land development, land use, and

urban sprawl in Puerto Rico integrating remote sensing and population census data,

Landscape and Urban Planning, vol. 79, no. 3-4, pp. 288–297, 2007.


Basin


[9] Landuse/ Landcover Change Detection Study for Effective Marine Environmental

Management Using High Resolution Satellite Data SS. Asadi, P.J. Ratnakar,

B.V.T. Vasantha Rao and A.K. Vuppaladadiyam Published in the International

Journal of Applied Engineering Research, Volume 9, Number 19 (2014) pp. 6267-

6276,

[10] Preparation of Village Wise Land Use/Land CoverInformation of Pakala Watershed using

Remote Sensing and GIS. M. Satish Kumar, SS. Asadi and S. S. VUTUKURU Published

in the International Journal of Applied Environmental Sciences Volume 9, Number 4,

(2014), pp. 1841-1848.

[11] Evaluation of Land use Land Cover Impacts on Environmental Parameters in municipal

corporation of Vijayawada, A.P SS.Asadi, U.Rakesh, M.Vinod,,Published in International

Journal of Advanced Scientific and Technical Research Issue 6 volume 3, May –June

2016 , ,Page 82-100

[12] Dr. Sanjaykumar Jagannath Patil. Land Utilization and Seed Selection of Cotton Crop by

Cotton Growing Farmers of Khandesh Region, International Journal of Management,

6(10), 2015, pp. 27-34.

[13] G.S.Sarma, SS. Asadi and S.Lakshmi Narayana, Creation of Remote Sensing & GIS

Based Land Resources Information System For Land Resources Management.

International Journal of Civil Engineering and Technology, 8(8), 2017, pp. 1738–1746.

[14] N. Vaani and P. Porchelvan. GIS Based Agricultural Drought Assessment for the State of

Tamilnadu, India Using Vegetation Condition Index (VCI). International Journal of Civil

Engineering and Technology, 8(5), 2017, pp. 1185–1194.

[15] Shabir Hussain Khahro and Zubair Ahmed Memon, Gis Based Land Suitability Analysis

for Petrol Stations, International Journal of Civil Engineering and Technology, 8(10),

2017, pp. 01–06.

[16] R. Chandramohan, Dr. T.E. Kanchanabhan, N. Siva Vignesh and Radha Krishnamoorthy,

Remote Sensing and GIS Based Approach for Delineation of Artificial Recharge Sites in

Palani Taluk, Dindigul District, Tamilnadu, India. International Journal of Civil

Engineering and Technology, 8(8), 2017, pp. 698–706.

Documents

REMOTE SENSING AND GIS BASED LAND … synoptic analyses of Earth - system function, patterning, ... gives the land use code, segment number of pixels in each land use classes, the