A Study of Land Cover Change Detection in Oddusuddan DS Division of Mullaitivu District in Sri Lanka Based on GIS and RS Technology

• Authors: V. Pathmanandakumar
• Languages of publication: EN

Abstracts

EN

Land cover change analysis between 1997 and 2016 was conducted in Oddusuddan Divisional Secretariat, Mullaitivu District, using remote sensing and geographic information system incorporated with field verifications. Various Satellite images and different digital maps have been used for extracting information. The overall objective of this study was to detect the magnitude of land cover change in Oddusuddan between 1997 and 2016. The methodology of this study was a change detection analysis of satellite imagery with Landsat ETM data. Two dates of Landsat image data of the 1997 and 2016 were used to produce a land cover map. The Maximum Likelihood algorithm was used for supervised classification to detect changes for twenty years. The result showed that during the last twenty years, the forest cover declined from 453.02 km2 in 1997 to 447.14 km2 in 2016. It was noticed that socio-economic factors were the major driving forces for the land cover change.

Keywords

EN

Change Matrix; Change detection; Geographical Information System & Remote Sensing; Land covers Mapping; Satellite Image Analysis

Discipline

• GEOPHYSICS: GEOPHYSICS

• Publisher: Scientific Publishing House „DARWIN”
• Journal: World News of Natural Sciences
• Year: 2020
• Volume: 29
• Issue: 3
• Pages: 198-211

Contributors

author

V. Pathmanandakumar

• Department of Geography, Faculty of Arts and Culture, Eastern University, Chenkaladi, Sri Lanka

References

• [1] Boyd, D. S., Foody, G. M., & Ripple, W. J. (2002). Evaluation of approaches for forest cover estimation in the Pacific Northwest, USA, using remote sensing. Applied Geography, 4(22), 375–392
• [2] Broich, M., Hansen, M. C., Potapov, P., Adusei, B., Lindquist, E., & Stehman, S. V. (2011). Time-series analysis of multi-resolution optical imagery for quantifying forest cover loss in Sumatra and Kalimantan, Indonesia. International Journal of Applied Earth Observation and Geoinformation, 13(2), 277–291. https://doi.org/10.1016/j.jag.2010.11.004
• [3] Coops, N. C., & Catling, P. C. (1997). Predicting the complexity of habitat in forests from airborne videography for wildlife management. International Journal of Remote Sensing, 18(12), 2677–2682. https://doi.org/10.1080/014311697217530
• [4] Department of Census and Statistics. (2012). Census of Population and Housing 2012. Ministry of Finance and Planning.
• [5] Dorren, L. K. A., Maier, B., & Seijmonsbergen, A. C. (2003). Improved Landsat-based forest mapping in steep mountainous terrain using object-based classification. Forest Ecology and Management, 183(1), 31–46. https://doi.org/10.1016/S0378-1127(03)00113-0
• [6] Forkuo, E. K., & Frimpong, A. (2012). Analysis of Forest Cover Change Detection. International Journal of Remote Sensing Application, 2(4), 82–92.
• [7] Grainger, A. (2009). Controlling Tropical Deforestation. Earthscan. ISBN 1849710252, 9781849710251
• [8] Imbernon, J., & Branthomme, A. (2001). Characterization of landscape patterns of deforestation in tropical rain forests. International Journal of Remote Sensing, 22(9), 1753–1765. https://doi.org/10.1080/01431160118426
• [9] Karia, J. P., Porwal, M. C., Roy, P. S., & Sandhya, G. (2001). Forest change detection in Kalarani round, Vadodara, Gujarat— a Remote Sensing and GIS approach. Journal of the Indian Society of Remote Sensing, 29(3), 129. https://doi.org/10.1007/BF02989924
• [10] Kayet, N., & Pathak. (2015). Remote Sensing and GIS-Based Land use/Land cover Change Detection Mapping in Saranda Forest, Jharkhand, India. International Research Journal of Earth Sciences, 3(2321–2527), 1–6
• [11] Larsson, H. (2002). Acacia canopy cover changes in Rawashda forest reserve, Kassala Province, Eastern Sudan, using linear regression NDVI models. International Journal of Remote Sensing, 23(2), 335–339. https://doi.org/10.1080/01431160010014279
• [12] Luque, S. S. (2000). Evaluating temporal changes using Multi-Spectral Scanner and Thematic Mapper data on the landscape of a natural reserve: The New Jersey Pine Barrens, a case study. International Journal of Remote Sensing, 21(13–14), 2589–2610. https://doi.org/10.1080/01431160050110197
• [13] Mcleod, R. ., & Conglton, R. G. . (1998). A Quantitative Comparison of Change-Detection Algorithms for Monitoring Eelgrass from Remotely Sensed Data 172. Engineering and Remote Sensing, 64(3), 207–216
• [14] Mihai, B., Săvulescu, I., Rujoiu-Mare, M., & Nistor, C. (2017). Recent forest cover changes (2002–2015) in the Southern Carpathians: A case study of the Iezer Mountains, Romania. Science of The Total Environment, 599–600, 2166–2174. https://doi.org/10.1016/j.scitotenv.2017.04.226
• [15] O’Callaghan, J. (2012). Remote sensing: principles and interpretation. Journal Cartography, 11(4), 251–252. https://doi.org/10.1080/00690805.1980.10438124
• [16] Panigrahy, R. K., Kale, M. P., Dutta, U., Mishra, A., & Banerjee, B. (2010). Forest Cover Change Detection of Western Ghats of Maharashtra using Satellite Remote Sensing based Visual Interpretation Technique. Current Science, 98(5), 657–664
• [17] Reis, S., & Yomralioglu, T. (2006). Detection of current and potential hazelnut plantation areas in Tabzon, North East Turkey using GIS and RS. Journal of Environmental Biology, 27(4), 653–659
• [18] Roy, P. S., & Joshi, P. . (2010). Forest cover assessment in north-east India--the potential of temporal wide swath satellite sensor data (IRS-1C WiFS): International Journal of Remote Sensing. International Journal of Remote Sensing, 23(23), 4881–4896. https://doi.org/10.1080/01431160110114475
• [19] Sadeghi, M., Malekian, M., & Khodakarami, L. (2017). Forest losses and gains in Kurdistan province, western Iran: Where do we stand? The Egyptian Journal of Remote Sensing and Space Science, 20(1), 51–59. https://doi.org/10.1016/j.ejrs.2016.07.001
• [20] Sajjad, A., Hussain, A., Wahab, U., Adnan, S., Ali, S., Ahmad, Z., & Ali, A. (2015). Application of Remote Sensing and GIS in Forest Cover Change in Tehsil Barawal, District Dir, Pakistan. American Journal of Plant Sciences, 06, 1501. https://doi.org/10.4236/ajps.2015.69149
• [21] Sakthivel, R., Manivel, M., Raj, N. J., Pugalanthi, V., Ravichandran, N., & Anand, V. D. (2010). Remote sensing and GIS based forest cover change detection study in Kalrayan hills, Tamil Nadu. Journal of Environmental Biology, 31(5), 737–747
• [22] Singh, A. (1989). Digital change detection techniques using remotely sensed data. International Journal of Remote Sensing, 10, 989–1003
• [23] Unni, M. N. V, Roy, P. ., & Parthtasarathy, V. (1985). Evaluation of LANDSAT and airborne multispectral data and aerial photographs for mapping forest features and phenomena in a part of the Godavari basin: International Journal of Remote Sensing: Vol 6, No 3-4. International Journal of Remote Sensing, 6(3–4), 419–431. https://doi.org/10.1080/01431168508948464
• [24] Yismaw, A. (2014). Forest Cover Change Detection Using Remote Sensing and GIS in Banja District, Amhara Region, Ethiopia. International Journal of Environmental Monitoring and Analysis, 2(6), 354. https://doi.org/10.11648/j.ijema.20140206.19
• [25] Yismaw, A., Gedif, B., Addisu, S., & Zewudu, F. (2014). Forest Cover Change Detection Using Remote Sensing and GIS in Banja District, Amhara Region, Ethiopia. International Journal of Environmental Monitoring and Analysis, 2(6), 354. https://doi.org/10.11648/j.ijema.20140206.19
• [26] Young, S. S., & Wang, C. Y. (2001). Land-cover change analysis of China using global-scale Pathfinder AVHRR Landcover (PAL) data, 1982?92. International Journal of Remote Sensing, 22(8), 1457–1477. https://doi.org/10.1080/01431160116787
• [27] Yu, X. (2007). Methods and techniques for forest change detection and growth estimation using airborne laser scanning data. Geodeettinen Laitos, Kirkkonummi.
• [28] Zimble, D. A., Evans, D. L., Carlson, G. C., Parker, R. C., Grado, S. C., & Gerard, P. D. (2003). Characterizing vertical forest structure using small-footprint airborne LiDAR. Remote Sensing of Environment, 87(2), 171–182. https://doi.org/10.1016/S0034-4257(03)00139-1

• Document Type: article