MULTI-TEMPORAL LAND USE/LAND COVER CHANGE ASSESSMENT IN KANNIYAKUMARI DISTRICT, INDIA, USING LANDSAT IMAGERY AND SAGA GIS

Author

R.Ramesh, M.Vaitheeswari, R.Krishna Sharma,S.Nagaveena


Abstract

 Land-use and land-cover (LULC) patterns in rapidly changing regions require repeated spatial assessment to identify the conversion of agricultural and natural resources into built-up surfaces. This study evaluates multi-temporal LULC change within a selected area of Kanniyakumari District, Tamil Nadu, using Landsat imagery and SAGA GIS. Three harmonized epochs—2015–2016, 2019–2020 and 2023–2024—were investigated using Landsat 8/9 imagery. Green, red and near-infrared bands were processed to generate false-colour composites and the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI) and Normalized Difference Built-up Index (NDBI). A supervised Maximum Likelihood classification was applied to identify major land-cover classes, followed by area estimation and accuracy assessment. The mapped built-up area increased from 111.40 to 142.00 km² between the first and third epochs, corresponding to a 27.47% increase. Over the same period, agricultural area decreased from 79.29 to 68.29 km² (13.87% decrease), while mapped water bodies declined from 68.78 to 42.47 km² (38.25% decrease). The classification assessment reported 55 correctly classified reference points out of 64, giving an overall accuracy of 85.93%. The results demonstrate substantial spatial transformation within the selected area and illustrate the value of freely available Landsat data and open-source GIS for local environmental monitoring and planning.


Keywords

Land use/land cover; Landsat 8; Landsat 9; remote sensing; SAGA GIS; NDVI; NDWI; NDBI; Kanniyakumari.



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References


  1. Muttitanon W, Tripathi NK. Land use/land cover changes in the coastal zone of Ban Don Bay, Thailand using Landsat 5 TM data. International Journal of Remote Sensing. 2005;26(11):2311–2323.
  2. Lambin EF, Turner BL, Geist HJ, et al. The causes of land-use and land-cover change: moving beyond the myths. Global Environmental Change. 2001;11(4):261–269.
  3. Rawat JS, Kumar M. Monitoring land use/cover change using remote sensing and GIS techniques: a case study of Hawalbagh block, district Almora, Uttarakhand, India. Egyptian Journal of Remote Sensing and Space Sciences. 2015;18(1):77–84.
  4. Prakasam C. 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. 2010;1(2):150–158.
  5. Lemenkova P. Evaluating land cover types from Landsat TM using SAGA GIS for vegetation mapping based on ISODATA and K-means clustering. Acta Agriculturae Serbica. 2021;26(52):159–165.
  6. SAGA GIS. Supervised Image Classification: SAGA-GIS Tool Library Documentation. SAGA GIS project documentation.
  7. Usha M, Anitha K, Iyappan LI. Land use change detection through image processing and remote sensing approach: a case study of Palladam Taluk, Tamil Nadu. International Journal of Engineering Research and Applications. 2012;2(4):289–294.
  8. Bagwan WAY. Dam triggered land use land cover change detection and comparison (transition matrix method) of Urmodi River watershed of Maharashtra, India: a remote sensing and GIS approach. Geology, Ecology and Landscape. 2023;7(3):189–197.
  9. Mousavinezhad M, Feizi A, Aalipour M. Performance evaluation of machine learning algorithm in change detection and change prediction of a watershed’s land use and land cover. International Journal of Environmental Research. 2023;17(2):29.
  10. United States Geological Survey. Landsat 8–9 band designations and spectral characteristics. USGS Landsat Mission resources.

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