Change Detection Analysis using Landsat Multi-Temporal Imagery and GIS Techniques: A Case Study for Tirupati, South India

V. Raja Rajeswari*, S. Narayana Reddy**
*_**Department of Electronics and Communication Engineering, SVUCE, S. V. University, Tirupati, Andhra Pradesh, India.
Periodicity:April - June'2019
DOI : https://doi.org/10.26634/jip.6.2.16166

Abstract

Land use/land cover (LULC) information in the form of maps is essential for the planning, supervising natural resources, utilisation of land to meet the increasing human demands and monitoring changes in the ecosystem. In this study, remote sensing data and geographic information system applications were used to analyse the LULC and its changes in Tirupati, which is located in the Seshachalam hill range in the Chittoor district of Andhra Pradesh (AP) state, South India. The study area is a world-famous pilgrimage centre and fast-developing town. Therefore, updated LULC maps must be created for various departments. The aim of this study was to classify and determine changes in the LULC over the 40-year period 1978-2018 by using multi-temporal Landsat satellite images and Survey of India toposheet map. The 1978 and 2018 Landsat images and field survey data were selected to classify the data. The ERDAS Imagine v16 and ArcGIS v10.1 were used to process images and assess the changes in land use of this study area. Classification was performed using the maximum likelihood classifier algorithm of supervised classification. Images were classified into five major classes: forest, water bodies, agricultural land, barren land, and built-up land. A post-classification change detection technique was used here to find changes in LULC. Changes were mainly observed in the built-up areas. The results demonstrate that during the forty years period, built-up area and barren land/other land increased 454.33%, and 104.7%, and area under waterbodies, agriculture, and forest decreased to 73.07%, 61.84%, and 31%, respectively. In future, these changes may have a significant influence on the ecosystem.

Keywords

Remote Sensing, Land Use/Land Cover (LULC), Image Classification, Change Detection.

How to Cite this Article?

Rajeswari, V. R., & Reddy, S. N. (2019). Change Detection Analysis using Landsat Multi-Temporal Imagery and GIS Techniques: A Case Study for Tirupati, South India. i-manager's Journal on Image Processing, 6(2), 34-39. https://doi.org/10.26634/jip.6.2.16166

References

[1]. Arveti, N., Etikala, B., & Dash, P. (2016). Land use/land cover analysis based on various comprehensive geospatial datasets: A case study from Tirupati area, South India. Advances in Remote Sensing, 5(02), 73-82.
[2]. Ballany, S., & Nair, B. (2002). Application of satellite imagery and GIS in the preparation of development plans: A case study for Tirupati region. Indian Cartographer, 245-253.
[3]. ERDAS (n.d). ERDAS Imagine Tour Guides. ERDAS Imagine Version 14. Erdas, Atlanta, Georgia. Retrieved from http://web.pdx.edu/~nauna/TourGuide9_1.pdf
[4]. Foody, G. M. (2002). Status of land cover classification accuracy assessment. Remote Sensing of Environment, 80(1), 185-201.
[5]. Gadrani, L., Lominadze, G., & Tsitsagi, M. (2018). F assessment of landuse/ landcover (LULC) change of Tbilisi and surrounding area using remote sensing (RS) and GIS. Annals of Agrarian Science, 16(2), 163-169.
[6]. Islam, K., Jashimuddin, M., Nath, B., & Nath, T. K. (2018). Land use classification and change detection by using multi-temporal remotely sensed imagery: The case of Chunati wildlife sanctuary, Bangladesh. The Egyptian Journal of Remote Sensing and Space Science, 21(1), 37-47.
[7]. Kafi, K. M., Shafri, H. Z. M., & Shariff, A. B. M. (2014). An analysis of LULC change detection using remotely sensed data; A case study of Bauchi City. In IOP conference series: Earth and environmental science (Vol. 20, No. 1, p. 012056). IOP Publishing.
[8]. Kaliraj, S., Chandrasekar, N., Ramachandran, K. K., Srinivas, Y., & Saravanan, S. (2017). Coastal landuse and land cover change and transformations of Kanyakumari coast, India using remote sensing and GIS. The Egyptian Journal of Remote Sensing and Space Science, 20(2), 169-185.
[9]. Kar, R., Reddy, G. O., Kumar, N., & Singh, S. K. (2018). Monitoring spatio-temporal dynamics of urban and periurban landscape using remote sensing and GIS–A case study from Central India. The Egyptian Journal of Remote Sensing and Space Science, 21(3), 401-411. https://doi.org/10.1016/j.ejrs.2017.12.006.
[10]. Mas, J. F. (1999). Monitoring land-cover changes: a comparison of change detection techniques. International Journal of Remote Sensing, 20(1), 139-152.
[11]. Mubako, S., Belhaj, O., Heyman, J., Hargrove, W., & Reyes, C. (2018). Monitoring of land use/land-cover changes in the arid transboundary middle Rio grande basin using remote sensing. Remote Sensing, 10(12), 1- 17.
[12]. Prakasam, C. (2010). Land use and land cover change detection through remote sensing approach: A case study of Kodaikanal taluk, Tamil Nadu. International Journal of Geomatics and Geosciences, 1(2), 150.-158.
[13]. Reddy, M. A. (2008). Remote Sensing and Geographical Information Systems (3rd Ed.). BS Publications.
[14]. Singh, A. (1989). Review Article Digital change detection techniques using remotely-sensed data. International Journal of Remote Sensing, 10(6), 989- 1003. DOI: 10.1080/01431168908903939
[15]. Sundarakumar, K., Harika, M., Begum, S. A., Yamini, S., & Balakrishna, K. (2012). Land use and land cover change detection and urban sprawl analysis of Vijayawada city using multitemporal landsat data. International Journal of Engineering Science and Technology, 4(1), 170-178.
[16]. Toure, S. I., Stow, D. A., Shih, H. C., Weeks, J., & Lopez- Carr, D. (2018). Land cover and land use change analysis using multi-spatial resolution data and object-based image analysis. Remote Sensing of Environment, 210, 259-268.
[17]. US Geological Survey (2016). Landsat - Earth observation satellites (ver. 1.1, August 2016) U.S. Geological Survey Fact Sheet 2015-3081, 4 p. http://dx.doi.org/10.3133/ fs20153081.
[18]. Vishwakarma, C. A., , Somanath, T, Rai, P. K., Kamal, V., & Mukherjee, S. ( 2016).Changing Land Trajectories: A case study from India using a Remote Sensing based approach. European Journal of Geography, 7(2), 61-71.
[19]. Yuan, D., Cui, X., Qiu, Y., Gu, X., & Zhang, L. (2013). Accuracy analysis on the Automatic Registration of Multi- Source Remote Sensing Images based on the Software of ERDAS Imagine. Advances in Remote Sensing, 2(2), 140- 148.
If you have access to this article please login to view the article or kindly login to purchase the article

Purchase Instant Access

Single Article

North Americas,UK,
Middle East,Europe
India Rest of world
USD EUR INR USD-ROW
Pdf 35 35 200 20
Online 35 35 200 15
Pdf & Online 35 35 400 25

Options for accessing this content:
  • If you would like institutional access to this content, please recommend the title to your librarian.
    Library Recommendation Form
  • If you already have i-manager's user account: Login above and proceed to purchase the article.
  • New Users: Please register, then proceed to purchase the article.