spatial distribution of land use/ land cover analysis in...

Indian Journal of Geo Marine Sciences Vol. 48 (11), November 2019, pp. 1761-1768

Spatial Distribution of Land Use/ Land Cover Analysis in Hanamkonda Taluk, Telangana - A Case Study

Kunduru Rohin Reddy1, Suresh Devaraj2, Sandeep Biradar1, Kiran Yarrakula2* & K. Srinivas Kumar3

1School of Civil and Chemical Engineering, Vellore Institute of Technology, Vellore, India 2Centre for Disaster Mitigation and Management, Vellore Institute of Technology, Vellore, India

3Water Technology Centre, Acarya N.G. Ranga Agricultural University, Hyderabad, Telangana State *[E-mail: [email protected]]

Received 25 April 2018; revised 04 September 2018

Lack of opportunities in villages and towns, significant changes has been noted in the urban cities across the world which provides opportunities for the people to earn money. Being the second largest city in Telangana State, Warangal is well known for the monuments such as fortresses, lakes, temples, and stone gateways. To identify the expansion of build-up regions in Hanamkonda Taluk, a comparison study was made for the years 1992, 2001, 2011 and 2017. In the present study, series of Landsat images are used to perform the spatio temporal analysis. The supervised classification with maximum likelihood estimation was performed and the area of urban settlement coverage was found to be increased from 2.25% to 11.22% (1992 – 2017). Prediction of spatial distribution of urban settlement in Hanamkonda Taluk was performed and it is expected to increase to 32.33% in the year 2023.

[Keywords: Landsat; LULC; Maximum likelihood; Remote Sensing; Supervised Classification; Urbanisation]

Introduction Human activities on land are considered to be one

of most epoch-making factor. It leads to various disasters such as deforestation, global warming, biodiversity loss, etc.1. The impact of human activities on ecosystems has a long history and now the evidence to support the hypothesis was obtained with the help of Remote Sensing and GIS2. In the 21st Century, as India marches towards the goal of economic development, Land use/ Land Cover (LULC) transformations are said to play a major role3. Urbanisation leads to the increase in population which affects the water supply and also affect the ground water quantity. It also changes the watershed characteristics by increasing runoff and transferring pollutants4. The periodical assessments of land use patterns are essential for proper management of available resources.

Land is becoming a scarce resource due to the increase in population and migration of people towards towns and cities5. The advanced technology such as Remote Sensing and GIS can be used to reduce the effect on natural resources. The Land Use/ Land Cover changes has a direct or indirect impact on the ecology of the area6. Temporal analysis is only possible with the help of Remote Sensing satellites. View of earth from space at

regular intervals has become a vital tool in mapping land features7.

The relation between the urbanisation and change in LULC pattern has a large impact on environment and proper investigation is required for the management of resources. Remote Sensing and GIS is a tool that can be utilized for temporal analysis with higher accuracy at very low cost8. The multi-temporal analysis can be carried out with the help of Landsat images obtained at 30 m spatial resolution to analyse the historical effects of Land Use/ Land Cover properties4. The main aim of the present study is to investigate the temporal changes using Landsat images in ArcGIS platform for Hanamkonda Taluk, Telangana, India and to predict the LULC distribution for the year 2023. Study area

Hanamkonda taluk, a part of Warangal District covering an area of 2265 km2 lies between 79.1925° E and 79.7818°" E Longitude and 17.6137° N and 18.1423° N Latitude. Being the capital city for Kakathiya dynasty in 12th Century, the taluk is well known for Warangal Fort, Thousand Pillar Temple and Ramappa Temple. Bhadrakali Lake, Waddepally Lake, Dharmasagar Lake, Bhadrakali Temple, Padmakshi Temple, Kazipet Dargah are the notable

INDIAN J. MAR. SCI., VOL. 48, NO. 11, NOVEMBER 2019

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locations for tourism. Lying at an altitude of 297 meters above mean sea level, Hanamkonda receives an annual average rainfall of 994 mm. The location experiences hot summer with the mercury touching 45 °C and cold winter with a temperature of 13 °C. Figure 1 shows the geographical location of the study area. Methodology

The Base map for the study area is prepared with the help of Survey of India Toposheets (1:50000) and the Taluk boundary is obtained from the administrative layers provided by DivaGIS (http://www.diva-gis.org/gdata). The extent covers places such as Dharmasagar, Warangal, Gunpur, Hanamkonda, Wardhannapet, Parvathagiri of Warangal district. Landsat 5 and 8 images are downloaded from USGS earth explorer for 1992, 2001, 2011 and 2017 years respectively. The images downloaded are pre-processed and layer stacked in ArcGIS platform. The data is reprojected to WGS 84 projection (EPSG: 4326) to proceed with the analysis.

The study area is clipped and the training dataset is provided as input for maximum likelihood supervised classification. The classified output is obtained which is the LULC map for the corresponding year. The process is repeated to obtain the LULC map for the year 1992, 2001, 2011 and 2017, respectively. The comparison is made to identify the changes between the years in comparison with the population data. The workflow adopted in the present study is shown in Figure 2. The datasets used in the present study are listed in Table 1. Prediction of LULC for the year 2023 is carried out with 2011 and 2017 LULC data as input in QGIS using Modules for Land Use Change Evaluation (MOLUSCE) plugin. Results and Discussion

The spatial distribution of the earth surface features over a given area is shown in a thematic map rather than a data description. Classification of images is the process adopted widely to produce thematic maps. Researchers have explored many methods for the classification of satellite images over years. Several

Fig. 1 — Study area – Hanamkonda Taluk

REDDY et al.: LAND USE/ LAND COVER ANALYSIS IN HANAMKONDA TALUK, TELANGANA

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satellite missions have also been launched by various space agencies for capturing the information on the surface of the earth at regular intervals. Land use/ Land cover classification is done effectively using the optical images. In order to classify an image into categories of interest, the classification algorithm needs to be trained to distinguish those categories from each other. The training of classification algorithm can be either supervised or un-supervised. In supervised training, the prototype pixel samples are already labelled by virtue of ground truth, existing maps, or photo interpretation.

In un-supervised training, the prototype pixels are not labelled, but are determined to have distinguishing intrinsic data characteristics.With various algorithms available in market, maximum likelihood supervised classifier is one of the method which provides promising results and is widely used in variety of applications. The classification is carried out based on the probability of belonging to a particular class whose mean and covariance are modelled as forming a normal distribution9. In the analysis, Landsat images are layer stacked and maximum likelihood classification is adopted with the training datasets in ArcGIS platform. Shortwave Infrared, Infrared, Red and Green bands are used to create a false colour composite (FCC) image at 30 m resolution. The generated FCC image is classified into five classes namely vegetation, water, settlement, uncultivated land and wasteland. For validating these results, ground truth verification is also conducted and

Fig. 2 — Methodology adopted in the present study

Table 1 — Dataset used in the present study Year Landsat

Satellite Sensor Resolution Path Row Date

2017 Landsat 8 OLI/TIRS 30 143 48 28.02.2017 2011 Landsat 5 TM 30 143 48 12.02.2011 2001 Landsat 7 ETM+ 30 143 48 22.10.2001 1992 Landsat 5 TM 30 143 48 06.11.1992


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classification accuracy is determined. The LULCimages obtained from Landsat images for the year 1992, 2001, 2011 and 2017 are shown in Figure 3, 4, 5 and 6, respectively.

Based on the LULC maps obtained for the study area, the area coverage of each class from 1992 to 2017 are listed in Table 2 and the percentage of change of each class from 1992 to 2017 are listed in Table 3. The changes between the years 1992, 2001, 2011 and 2017 are identified by using raster calculator in ArcGIS environment. The output obtained are in the form of pixels at 30 m X 30 m resolution. To obtain the area of each class, pixel count of corresponding classes are identified and multiplied with 900 m2 to obtain the area. The statistics of the spatio temporal analysis of Hanamkonda Taluk are listed in Table 3. Figure 7 shows the change detection analysis for the years 1992, 2001, 2011 and 2017.

The accuracy of the classification is expressed using an error matrix or a confusion matrix, comparing the

relationship between the ground truth data and corresponding classified results10. The overall accuracy of the classified result is calculated by dividing the total number of correctly classified pixels with the total number of reference pixels11. Evaluation of the accuracy is performed by considering the field data and the error matrix subjected to detailed interpretation and statistical analysis. For the accuracy analysis, KHAT statistics is introduced which is the measure of difference between the actual agreement between reference data and automated classifier and the chance agreement between the reference data and a random classifier12.The KHAT statistic is computed by using the formula,

1 1

2

1

( )ˆ

( )

r r

ii i ii i

r

i ii

N x x xk

N x x

(1)

Fig. 3 — Land Use/ Land Cover Map for the year 1992 Fig. 5 — Land Use/ Land Cover Map for the year 2011

Fig. 4 — Land Use/ Land Cover Map for the year 2001 Fig. 6 — Land Use/ Land Cover Map for the year 2017


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Where, r is the number of rows in the error matrix, xii is the number of observations in row i and column i (on the major diagonal), xi+is the total number of observations in row i (shown as marginal total to right of the matrix), x+i = total number of observations in column i (shown as marginal total at bottom of the matrix) and N is the total number of observations included in matrix. The value of KHAT statistics ranges from 0 to 1. For the evaluation of accuracy assessment, it is desirable to calculate both the overall accuracy and KHAT statistic. The overall accuracy and kappa coefficient obtained for the classified images are listed on Table 2.

Logistic Regression (LR) is one of the widely used technique that establishes the relation between the input layers. With the availability of field statistics in the form of LULC map, multivariate analysis can be performed in QGIS platform using MOLUSCE Plugin. In LR technique the prediction is carried out based on the probability of different variables in the location13. 2011 and 2017 LULC layers are used as input to LR for the prediction of 2023 LULC distribution. Figure 8 shows the predicted LULC map for 2023. Table 4 shows the predicted area of different land use land cover for the year 2023.

Field survey is carried out along the city and the surrounding areas to monitor the changes in the location. “Locus Map Free - Hiking GPS navigation and maps” mobile application is used to carry out the field survey. The locations of ground truth points are shown in Figure 9 and images captured during the

field survey are shown in Figure 10. Based on the field survey it is found that the regions around the Hanamkonda and Warangal are under construction of

Table 2 — Area of Coverage in Hanamkonda Taluk from 1992 to 2017 in km2

Classes Year / Area in km2. 1992 2001 2011 2017

Built-up land 51.04 110.46 207.67 254.22 Water Bodies 26.01 49.49 51.03 62.03 Agriculture 809.28 903.98 406.96 541.93 Fallow land 914.53 1072.15 1385.23 1335.32 Wastelands 464.85 129.64 214.83 72.21 Accuracy 98.48 % 92.90 % 90.41 % 93.84 % Kappa Coefficient

0.98 0.90 0.88 0.92

Table 3 — Area of Coverage in Hanamkonda Taluk from 1992 to 2017 in Percentage

Classes Year / Area in Percentage 1992 2001 2011 2017

Built-up land 2.25 4.88 9.17 11.22 Water Bodies 1.15 2.18 2.25 2.74 Agriculture 35.72 39.90 17.96 23.92 Fallow land 40.36 47.32 61.14 58.94 Wastelands 20.52 5.72 9.48 3.19

Table 4 — Predicted area of coverage in Hanamkonda Taluk for the year 2023

Classes Year / Area in km2. Area in Percentage 2023 2023

Built-up land 730.83 32.33 Water Bodies 167.38 7.40 Agriculture 834.44 36.92 Fallow land 232.57 10.29 Wastelands 295.15 13.06

Fig. 7 — Changes in Hanamkonda Taluk from 1992 to 2017


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apartments and agricultural regions are converted to plots and are open for sale to the public.

From the analysis it is found that the area of settlement in the year 1992 has increased from 2.25 % of the total area to 4.88 % in 2001, 9.17 % in 2011 and 11.22 % in 2017. Similarly, the water body also increased from 1.15 % in 1992 to 2.18 % in 2001, 2.25 % in 2011 and 2.74 % in 2017. The images covering the taluk are obtained at different seasons due to the non-availability of cloud free

images. The water body in the locality was found to be increasing due to the implementation of three water tank namely, Mylaram balancing reservoir and Station Gunpoor reservoir which serves the locality in summer season. The storage capacity of Dharmasagar, Wadepally and Bhadrakali tanks are also increased to serve the locality.Based on the prediction, it is found that the settlement is expected to reach 32.33 % by the year 2023 and the water is also expected to reach 7 % of the total area. The

Fig. 8 — Predicted Land Use/ Land Cover Map for the year 2023

Fig. 9 — Ground truth locations – Hanamkonda Talk


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increase of water bodies may be due establishment of Mission Bhageeradha in Telangana State. Conclusion

LULC changes are governed by carious geographical, environmental and socio-economic

factors. Remote Sensing and GIS based LULC study are effectively used by authorities for proper management of resources. From the study it is clear that the area of urban settlement has increased from 51.04 km2 in 1992 to 254.22 km2 in 2017 and is expected to reach 730.83 km2 by 2023. Hanamkonda

Fig. 10 — Ground truth sample images in and around Hanamakonda Taluk


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Taluk acts as a centre of attraction for the people in the rural regions as Warangal was one of the well-established city in the Telangana state and several industries started establishing in Warangal since 1991. As the city changed to Municipal Corporation in 1994, it received various schemes from the state and central government for development activities which created opportunities for the people. The city is also addressed for education, medical, trading and cultural needs which force the people from various district to migrate towards Warangal. In 2015, the Telangana government accorded the status of the municipality to “Greater Warangal Municipal Corporation” and is also listed as one of the city under the Smart Cities Mission. With the upgrade in the city status, the city is enabled to secure funds from central government in strengthening the infrastructure and services which also attracts the people towards migration. Huge changes in urban settlements and implementation of Smart Cities Mission in Warangal would help the city to develop further in both industrial and infrastructural prospects, and will contribute to aesthetic value with an expected 471.75 km2 areal extension. Playing a major role in the Telangana, the city of Warangal and Hanamkonda is growing as a commercial place that provides opportunities to people from various location of the state and country. With all the major facilities located within the city, and the focus on the management of the water resources to serve the future needs attracts people from various location to migrate in search of employment and to serve their daily needs without any difficulty. The results from the analysis proves that the local authorities are focusing on the development of the city and also the proper management of the available resources. Acknowledgement

We gratefully acknowledge the Centre for Disaster Mitigation and Management (CDMM), Vellore Institute of Technology (VIT), Vellore for their support and lab facility.

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