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2017 | 26 | 6 |

Tytuł artykułu

Landsat-5 time series analysis for land use/land cover change detection using NDVI and semi-supervised classification techniques

Warianty tytułu

Języki publikacji

EN

Abstrakty

EN
Rapid urbanization and the risk of climatic variations, including a rise in temperature and increased rainfall, have urged research in the development of methods and techniques to monitor the modification of land use/land cover (LULC). This study employed the normalized differencing vegetative index (NDVI) and semi-supervised image classification (SSIC) integrated with high-resolution Google Earth images of the Kuantan River Basin (KRB) in Malaysia. The Landsat-5 (TM) images for the years 1993, 1999, and 2010 were selected. The results from both classifications provided a consistent accuracy of assessment with a reasonable level of agreement. However, SSIC was found to be more precise than NDVI. Overall accuracy was 82% for 1993 and 1999, and 80% for 2010, with the kappa values ranging from 0.789 to 0.761. Meanwhile, NDVI accuracy was attained at 64% with kappa value at 0.527 for 1999. In addition, 70% and 72% accuracy were obtained for 1993 and 2010 with estimated kappa values of 0.651 and 0.672, respectively. The study is anticipated to assist decision makers for better emergency response and sustainable land development action plans, thus mitigating the challenges of rapid urban growth.

Słowa kluczowe

Wydawca

-

Rocznik

Tom

26

Numer

6

Opis fizyczny

p.2833-2840,fig.,ref.

Twórcy

autor
  • Faculty of Civil Engineering & Earth Resources, University Malaysia Pahang (UMP), Malaysia
autor
  • Faculty of Civil Engineering & Earth Resources, University Malaysia Pahang (UMP), Malaysia
autor
  • Institute of Ocean and Earth Sciences (IOES) University of Malaya (UM), Malaysia
autor
  • Faculty of Civil Engineering & Earth Resources, University Malaysia Pahang (UMP), Malaysia
  • Faculty of Civil Engineering & Earth Resources, University Malaysia Pahang (UMP), Malaysia
  • Centre for Earth Resources and Research Management (CERRM), UMP, Malaysia

Bibliografia

  • 1. Yang, Z.-L., Modeling land surface processes in shortterm weather and climate studies. 3, 2004, World Scientific Series on Meteorology of East Asia.
  • 2. Arkema K.K., et al., Coastal habitats shield people and property from sea-level rise and storms. Nature Climate Change, 3 (10), 913, 2013.
  • 3. Liu D., et al., Effects of land use classification on landscape metrics based on remote sensing and GIS. Environmental earth sciences, 68 (8), 2229, 2013.
  • 4. Gilani H., et al., Decadal land cover change dynamics in Bhutan. Journal of environmental management, 148, 91, 2015.
  • 5. Tan K.C., et al., Landsat data to evaluate urban expansion and determine land use/land cover changes in Penang Island, Malaysia. Environmental Earth Sciences, 60 (7), 1509, 2010.
  • 6. McColl C., Aggett G. Land-use forecasting and hydrologic model integration for improved land-use decision support. Journal of environmental management, 84 (4), 494, 2007.
  • 7. Akbari A., et al., The application of the Water Erosion Prediction Project (WEPP) model for the estimation of runoff and sediment on a monthly time resolution. Environmental Earth Sciences, 74 (7), 5827, 2015.
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  • 9. Yeganeh H., et al., Monitoring rangeland ground cover vegetation using multitemporal MODIS data. Arabian Journal of Geosciences, 7 (1), 287, 2014.
  • 10. Singh A.Review article digital change detection techniques using remotely-sensed data. International journal of remote sensing, 10 (6), 989, 1989.
  • 11. Sun J., Qin X. Precipitation and temperature regulate the seasonal changes of NDVI across the Tibetan Plateau. Environmental Earth Sciences, 75 (4), 1, 2016.
  • 12. Niraula R.R., et al., Measuring impacts of community forestry program through repeat photography and satellite remote sensing in the Dolakha district of Nepal. Journal of environmental management, 26, 20, 2013.
  • 13. Vogelmann J.E., et al., Monitoring gradual ecosystem change using Landsat time series analyses: Case studies in selected forest and rangeland ecosystems. Remote Sensing of Environment, 122, 92, 2012.
  • 14. Jia K., et al., Land cover classification of finer resolution remote sensing data integrating temporal features from time series coarser resolution data. ISPRS Journal of Photogrammetry and Remote Sensing, 93, 49, 2014.
  • 15. Tokola T., Löfman S., Erkkilä A. Relative calibration of multitemporal Landsat data for forest cover change detection. Remote Sensing of Environment, 68 (1), 1, 1999.
  • 16. Janzen D.T., Fredeen A.L., Wheate R.D. Radiometric correction techniques and accuracy assessment for Landsat TM data in remote forested regions. Canadian Journal of Remote Sensing, 32 (5), 330, 2006.
  • 17. Chander G., Markham B.L., Barsi J.A. Revised Landsat-5 thematic mapper radiometric calibration. IEEE Geoscience and remote sensing letters, 4 (3), 490, 2007.
  • 18. Chander G., Markham B.L., Helder D.L. Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote sensing of environment, 113 (5), 893, 2009.
  • 19. Chander G., Markham B. Revised Landsat-5 TM radiometric calibration procedures and postcalibration dynamic ranges. IEEE Transactions on geoscience and remote sensing, 41 (11), 2674, 2003.
  • 20. Foody G.M. Status of land cover classification accuracy assessment. Remote sensing of environment, 80 (1), 185, 2002.
  • 21. Foody G.M. Classification accuracy comparison: hypothesis tests and the use of confidence intervals in evaluations of difference, equivalence and non-inferiority. Remote Sensing of Environment, 113 (8), 1658, 2009.
  • 22. Rouse Jr J.W., et al., Monitoring vegetation systems in the Great Plains with ERTS. NASA special publication, 351, 309, 1974.
  • 23. Pantanahiran W. Land use change on sloping areas in Phuket Province, Thailand. in Agro-geoinformatics (Agrogeoinformatics 2014), Third International Conference on. 2014. IEEE.
  • 24. Ghobadi Y., et al., Assessment of spatial relationship between land surface temperature and landuse/cover retrieval from multi-temporal remote sensing data in South Karkheh Sub-basin, Iran. Arabian Journal of Geosciences, 8 (1), 525, 2015.
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Typ dokumentu

Bibliografia

Identyfikatory

Identyfikator YADDA

bwmeta1.element.agro-d7489781-1722-42e0-a107-ebaeae3326a3
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