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A SWIR-based vegetation index for change detection in land cover using multi-temporal Landsat satellite dataset

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Abstract

Remotely sensed spectral vegetation indices (VIs) are widely used and have benefited numerous disciplines interested in the analysis of biomass, vegetation, plant stress, plant health, crop production, and change detection. This study’s objective is to propose a vegetation index based on short wave infrared (SWIR) for detecting the changes in geographical features using multi-temporal and multi-spectral satellite imagery. The proposed a vegetation index SWIR based normalized difference vegetation index (SNDVI) can play a significant role in monitoring vegetation plant’s health and growth and also increase the accuracy of classification and change detection. In this study, Landsat satellite imagery of different periods (1996, 2003, 2010 and 2017) has been used to develop the vegetation index. The proposed vegetation index is useful for increasing the accuracy of classification and change detection of geographical features. Using the Normalized Difference Vegetation Index (NDVI) method, the accuracy of classification of orchards, vegetation and rangelands has been obtained at 96.90%, 94.68% and 92.29% in 2017. While using the SNDVI method, higher accuracy of 98.09%, 95.50% and 92.02% has been obtained in the classification of orchards, vegetation and rangelands. The proposed SNDVI method can be significant in monitoring the health and growth of vegetation plants and detecting the changes in orchards biomass and other vegetation plants.

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References

  1. Costa L, Nunes L, Ampatzidis Y (2020) A new visible band index (vNDVI) for estimating NDVI values on RGB images utilizing genetic algorithms. Comput Electron Agric 172(November 2019):105334

    Article  Google Scholar 

  2. Kabiri P, Pandi MH, Kourkinejat S, Ghaderi H (2011) NDVI optimization using genetic algorithm. 2011 7th Iran. Conf. Mach. Vis. Image Process. MVIP 2011—Proc

  3. Arjasakusuma S, Yamaguchi Y, Nakaji T, Shamsuddin S, Lion M (2018) Assessment of values and trends in coarse spatial resolution NDVI datasets in Southeast Asia landscapes. Eur J Remote Sens 51(1):863–877

    Article  Google Scholar 

  4. Sellers PJ, Tucker CJ, Collatz GJ, Justice CO (1994) A global 1° by 1° NDVI data set for climate studies. Part 2: the generation of global fields of terrestrial biophysical parameters from the NDVI. Int J Remote Sens 15(17):3519–3545

    Article  Google Scholar 

  5. Wardlow BD, Egbert SL (2010) A comparison of MODIS 250-m EVI and NDVI data for crop mapping: a case study for southwest Kansas. Int J Remote Sens 31(3):805–830

    Article  Google Scholar 

  6. Morawitz DF, Blewett TM, Cohen A, Alberti M (2006) Using NDVI to assess vegetative land cover change in central Puget sound. Environ Monit Assess 114:85–106

    Article  Google Scholar 

  7. Xue J, Su B (2017) Significant remote sensing vegetation indices: a review of developments and applications. J Sensors 2017:1–17

    Article  Google Scholar 

  8. Gitelson AA, Viña A, Ciganda V, Rundquist DC, Arkebauer TJ (2005) Remote estimation of canopy chlorophyll content in crops. Geophys Res Lett 32(8):1–4

    Article  Google Scholar 

  9. She X, Zhang L, Cen Y, Wu T, Huang C (2015) Comparison of the continuity of vegetation indices derived from Landsat 8 OLI and Landsat 7 ETM+ data among different vegetation types. Remote Sens 7:13485–13506

    Article  Google Scholar 

  10. Tan Y, Sun J, Zhang B, Chen M, Liu Y, Liu X (2019) Sensitivity of a ratio vegetation index derived from hyperspectral remote sensing to the brown planthopper stress on rice plants. Sensors 19(375):1–12

    Google Scholar 

  11. Huete AR (1988) A soil-adjusted vegetation index (SAVI ). Remote Sens Environ 25:295–309

    Article  Google Scholar 

  12. Rokni K, Ahmad A, Selamat A, Hazini S (2014) Water feature extraction and change detection using multitemporal Landsat imagery. Remote Sens 6(5):4173–4189

    Article  Google Scholar 

  13. Wilson EH, Sader SA (2002) Detection of forest harvest type using multiple dates of Landsat TM imagery. Remote Sens Environ 80(3):385–396

    Article  Google Scholar 

  14. Banskota A, Kayastha N, Falkowski MJ, Wulder MA, Froese RE, White JC (2014) Forest monitoring using Landsat time series data: a review. Can J Remote Sens 40(5):362–384

    Article  Google Scholar 

  15. Siregar VP, Prabowo NW, Agus SB, Subarno T (2018) The effect of atmospheric correction on object based image classification using SPOT-7 imagery: a case study in the Harapan and Kelapa Islands. IOP Conf Ser Earth Environ Sci 176(1)

  16. Sims DA, Gamon JA (2002) Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Remote Sens Environ 81:337–354

    Article  Google Scholar 

  17. Tucker CJ (1979) Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens Environ

  18. Govaerts YM, Verstraete MM, Pinty B, Gobron N (1999) Designing optimal spectral indices: a feasibility and proof of concept study. Int J Remote Sens 20(9):1853–1873

    Article  Google Scholar 

  19. Olsen JL, Ceccato P, Proud SR, Fensholt R, Grippa M, Mougin E, Inge S (2013) Relation between seasonally detrended shortwave infrared reflectance data and land surface moisture in Semi-Arid Sahel. Remote Sens 5:2898–2927

    Article  Google Scholar 

  20. Aly AA, Al-omran AM, Sallam AS, Al-wabel MI, Al-shayaa MS (2016) Vegetation cover change detection and assessment in arid environment using multi-temporal remote sensing images and ecosystem management approach. Solid Earth 7:713–725

    Article  Google Scholar 

  21. Du P, Li X, Cao W, Luo Y, Zhang H (2010) Monitoring urban land cover and vegetation change by multi-temporal remote sensing information. Min Sci Technol 20(6):922–932

    Google Scholar 

  22. Liu J, Gong M, Qin K, Zhang P (2016) A deep convolutional coupling network for change detection based on heterogeneous optical and radar images. IEEE Trans Neural Netw Learn Syst 29(3):1–15

    MathSciNet  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science, Gurukula Kangri Vishwavidyalaya, Haridwar, Uttarakhand, India

    Saurabh Kumar & Shwetank Arya

  2. Department of Civil Engineering, IIT, Roorkee, India

    Kamal Jain

Authors
  1. Saurabh Kumar
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  2. Shwetank Arya
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  3. Kamal Jain
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Correspondence to Saurabh Kumar.

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Kumar, S., Arya, S. & Jain, K. A SWIR-based vegetation index for change detection in land cover using multi-temporal Landsat satellite dataset. Int. j. inf. tecnol. 14, 2035–2048 (2022). https://doi.org/10.1007/s41870-021-00797-6

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  • DOI: https://doi.org/10.1007/s41870-021-00797-6

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