ICTACT Journal on Image and Video Processing

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Land Use Land Cover Study of Sentinel-2A and Landsat-5 Images Using NDVI and Supervised Classification Techniques


Affiliations
1 Department of Computer Science, Karnataka State Akkamahadevi Women’s University, India
     

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Land Use Land Cover (LULC) change monitoring plays very significant role in planning, policy making, management programs required for development activities at regional levels of any country. This study is an attempt to monitor LULC change of Vijayapura taluk, Karnataka, India for the period of 25 years from 1995 to 2021 using Remote Sensing (RS) and Geographic Information System (GIS). Satellite Images from Sentinel-2A MSI (Multispectral Imager), Landsat-5TM (Thematic Mapper) are used to generate LULC maps. Vegetation Change in the study area is computed using Normalized Difference Vegetation Index (NDVI) and results show that vegetation rate is increased from 0.6% in 1995 to 27.5% in 2021. Supervised Classification is carried out by using Maximum Likelihood Classification (MLC). 5 major classes considered for classification are namely: Waterbodies, Cropland/Vegetation, Fallow Land, Built-up Area and Barren Land. ArcGIS software tool is used for implementing the proposed study. Google Earth Pro used for accuracy assessment which is done by taking ground truth values for corresponding Classifications. Results show that the proposed system is able to achieve 88.16% of overall accuracy.

Keywords

Land Use Land Cover, Maximum Likelihood Classification, Normalized Difference Vegetation Index, Remote Sensing, Supervised Classification
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  • Satpalda Geospatial Services, “Significance of Land Use/Land Cover (Lulu) Maps, Avaialble at https://www.satpalda.com/blogs/significance-of-land-useland-cover-lulc-maps, Accessed at 2021.

  • A. Barakat, Z. Ouargaf and R. Khellouk, “Land Use/Land Cover Change and Environmental Impact Assessment in Beni-Mellal District (Morocco) using Remote Sensing and GIS”, Earth Systems and Environment, Vol. 3, pp. 113-125, 2019.

  • E.O. Yilmaz, B. Varol, R.H. Topaloglu and E. Sertel, “Object-based Classification of Izmir Metropolitan City by using Sentinel-2 Images”, Proceedings of International Conference on Recent Advances in Space Technologies, pp. 407-412, 2019.

  • H. Saadat, J. Adamowski and R. Bonnell, “Land Use and Land Cover Classification over a Large Area in Iran based on Single Date Analysis of Satellite Imagery”, ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 66, pp. 608-619, 2011.

  • F. Yuan, K.E. Sawaya, B. Loeffelholz and M.E. Bauer, “Land Cover Classification and Change Analysis of the Twin Cities (Minnesota) Metropolitan Area by Multitemporal Landsat Remote Sensing”, Remote Sensing of Environment, Vol. 98, pp. 317-328, 2005.

  • S. Hathout, “The use of GIS for Monitoring and Predicting Urban Growth in East and West St Paul”, Journal of Environmental Management, Vol. 66, pp. 229-238, 2002.

  • M. Herold, M.E. Gardner and D.A. Robert, “Spectral Resolution Requirements for Mapping Urban Areas”, IEEE Transactions on Geoscience and Remote Sensing, Vol. 41, pp. 1907-1919, 2003.

  • P.K. Mishra, A. Rai and S.C. Rai, “Land Use and Land Cover Change Detection using Geospatial Techniques in the Sikkim Himalaya, India”, The Egyptian Journal of Remote Sensing and Space Science, Vol. 23, No. 2, pp. 133-143, 2020.

  • T. Wu, J. Luo, J. Fang and J. Ma, “Unsupervised ObjectBased Change Detection Via a Weibull Mixture ModelBased Binarization for High-Resolution Remote Sensing Images”, IEEE Geoscience and Remote Sensing Letters, Vol. 15, pp. 63-67, 2018.

  • Z. Lv, T. Liu, Y. Wan and X. Zhang, “Post-Processing Approach for Refining Raw Land Cover Change Detection of very High-Resolution Remote Sensing Images”, Remote Sensing, Vol. 10, pp. 472-483, 2018.

  • A. Sekertekin, A.M. Marangoz and H. Akcin, “Pixel based Classification Analysis of Land Use Land Cover using Sentinel-2 and Landsat-8 Data”, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. 4, No. 6, pp. 91-93, 2017.

  • H. Yu, P.K. Joshi, K.K. Das and D.D. Chauniyal, “Land Use/Cover Change and Environmental Vulnerability Analysis in Birahi Ganga Subwatershed of the Garhwal Himalaya, India”, Tropical Ecology, Vol. 48, No. 2, pp. 241256, 2007.

  • A. Salazar, G. Baldi and M. Hirota, “Land Use and Land Cover Change Impacts on the Regional Climate of NonAmazonian South America: A Review”, Global and Planetary Change, Vol. 128, pp. 103-119, 2015.

  • D. Phiri and J. Morgenroth, “Developments in Landsat Land Cover Classification Methods: A Review”, Remote Sensing, Vol. 9, pp. 1-13, 2017.

  • Anil Kumar Ratan, “Working Plan of Vijaypura (Bijapur) Forest Division for the Period from 2012-13 to 2021-22”, Available at https://aranya.gov.in/new/newdownloads/WP/vijaypura%2 0working%20plan.pdf, Accessed at 2021.

  • After 50 years, Begum Talab Brims with Life, Available at https://www.thehindu.com/news/national/karnataka/After50-years-Begum-Talab-brims-withlife/article15478911.ece, Accessed at 2016.

  • Climate Vijayapura, Accessed at https://en.climatedata.org/asia/india/karnataka/vijayapura2796/, Accessed at 2019.

  • A. Anudeep and O. Dikshit, “Potential of Landsat-8 and Sentinel-2A Composite for Land Use Land Cover Analysis”, Geocarto International, Vol. 34, pp. 1-32, 2018.

  • Aziz Makandar and Shilpa Kaman, “Remote Sensing of Satellite Images using Digital Image Processing Techniques A Survey”, Proceedings of International Conference on Artificial Intelligence and Soft Computing, pp. 1-13, 2019.

  • K. Liu, W. Shi and H. Zhang, “A Fuzzy Topology-based Maximum Likelihood Classification”, ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 66, No. 11, pp. 103-114, 2011.

  • S. Shlien and A. Smith, “A Rapid Method to Generate Spectral Theme Classification of Landsat Imagery”, Remote Sensing of Environment, Vol. 4, pp. 67-77, 1976.

  • P.V. Bolstad and T.M. Lillesand, “Rapid Maximum Likelihood Classification”, Photogrammetric Engineering and Remote Sensing, Vol. 57, No. 1, pp. 67-74, 1991.

  • F. Maselli, C. Conese and L. Petkov, “Inclusion of Prior Probabilities Derived from a Nonparametric Process into the Maximum-Likelihood Classifier”, Photogrammetric Engineering and Remote Sensing, Vol. 58, No. 2, pp. 201207, 1992.

  • Jiabo Sun, Jianyu Yang, Chao Zhang, Wenju Yun and Jieqing Qu, “Automatic Remotely Sensed Image Classification in a Grid Environment based on the Maximum Likelihood Method”, Mathematical and Computer Modelling, Vol. 58, No. 3-4, pp. 573-581, 2013.

  • J.N. Nilsson, “Learning Machines: Foundations of Trainable Pattern-Classifying Systems”, McGraw-Hill, 1965.

  • O. Hagner and H. Reese, “A method for Calibrated Maximum Likelihood Classification of Forest Types”, Remote Sensing of Environment, Vol. 110, No. 4, pp. 438-444, 2007.

  • D.K. Jain, G. Saxena and V.K. Singh, “Image Mosaicking using Corner Techniques”, Proceedings of International Conference on Communication Systems and Network Technologies, pp. 79-84, 2012.

  • ArcMap, “Mosaicking”, Available at

  • https://desktop.arcgis.com/en/arcmap/10.3/managedata/raster-and-images/what-is-a-mosaic.htm, Accessed at 2021.

  • Saga GIS Tutorials, Available at https://sagatutorials.wordpress.com/band-composite-rgbdisplay/, Accessed at 2021.

  • J. Sun, “Precipitation and Temperature Regulate the Seasonal Changes of NDVI across the Tibetan Plateau”, Environmental Earth Sciences, Vol. 75, pp. 291-309, 2016.

  • Nikita Marwaha Kraetzig, “5 Things to Know About NDVI (Normalized Difference Vegetation Index)”, Available at https://up42.com/blog/tech/5-things-to-know-about-ndvi, Accessed at 2020.

  • Nagi Zomrawi and Ghazi, “Positional Accuracy Testing of Google Earth”, International Journal of Multidisciplinary Sciences and Engineering, Vol. 4, No. 6, pp. 2045-7057, 2013.


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  • Land Use Land Cover Study of Sentinel-2A and Landsat-5 Images Using NDVI and Supervised Classification Techniques

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Authors

Aziz Makandar
Department of Computer Science, Karnataka State Akkamahadevi Women’s University, India
Shilpa Kaman
Department of Computer Science, Karnataka State Akkamahadevi Women’s University, India

Abstract


Land Use Land Cover (LULC) change monitoring plays very significant role in planning, policy making, management programs required for development activities at regional levels of any country. This study is an attempt to monitor LULC change of Vijayapura taluk, Karnataka, India for the period of 25 years from 1995 to 2021 using Remote Sensing (RS) and Geographic Information System (GIS). Satellite Images from Sentinel-2A MSI (Multispectral Imager), Landsat-5TM (Thematic Mapper) are used to generate LULC maps. Vegetation Change in the study area is computed using Normalized Difference Vegetation Index (NDVI) and results show that vegetation rate is increased from 0.6% in 1995 to 27.5% in 2021. Supervised Classification is carried out by using Maximum Likelihood Classification (MLC). 5 major classes considered for classification are namely: Waterbodies, Cropland/Vegetation, Fallow Land, Built-up Area and Barren Land. ArcGIS software tool is used for implementing the proposed study. Google Earth Pro used for accuracy assessment which is done by taking ground truth values for corresponding Classifications. Results show that the proposed system is able to achieve 88.16% of overall accuracy.

Keywords


Land Use Land Cover, Maximum Likelihood Classification, Normalized Difference Vegetation Index, Remote Sensing, Supervised Classification

References