232
Views
8
CrossRef citations to date
0
Altmetric
Original Articles

Wavelet and non-parametric statistical based approach for long term land cover trend analysis using time series EVI data

, , , , , , & show all
Pages 512-534 | Received 20 Apr 2018, Accepted 28 Aug 2018, Published online: 24 Oct 2018

References

  • Abera TA, Heiskanen J, Pellikka P, Maeda EE. 2018. Rainfall–vegetation interaction regulates temperature anomalies during extreme dry events in the Horn of Africa. Glob Planet Change. 167:35–45.
  • Boriah S, Kumar V, Steinbach M, Potter C, Klooster S. 2008. Land cover change detection: A case study. Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. p. 857–865.
  • Chakraborty K. 2009. Vegetation change detection in Barak Basin. Curr Sci. 96(9):1236.
  • Chakraborty A, Seshasai MVR, Rao KSVC, Dadhwal VKD. 2016. Geo-spatial analysis of temporal trends of temperature and its extremes over India using daily gridded (1°×1°) temperature data of 1969–2005. Theor Appl Climatol. 130(1–2):133–149.
  • Chakraborty S, Banerjee A, Gupta SKS, Christensen PR, Papandreou-Suppappola A. 2018. Time-varying modeling of land cover change dynamics due to forest fires. IEEE J Sel Top Appl Earth Obs Remote Sens. 99:1–8.
  • Chen J, Jönsson P, Tamura M, Gu Z, Matsushita B, Eklundh B. 2004. A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky–Golay filter. Remote Sens Environ. 91(3–4):332–344.
  • Crist EP. 1985. A TM tasseled cap equivalent transformation for relectance factor data. Remote Sens Environ. 17(3):301–306.
  • DeFries RS, Townshend JRG. 1994. NDVI-derived land cover classifications at a global scale. Int J Remote Sens. 15(17):3567–3586.
  • Deng JS, Wang K, Deng YH, Qi GJ. 2008. PCA-based land-use change detection and analysis using multitemporal and multisensory satellite data. Int J Remote Sens. 29(16):4823–4838.
  • Drápela K, Drápelová I. 2011. Application of Mann–Kendall test and the Sen’s slope estimates for trend detection in deposition data from Bílý Kříž (Beskydy Mts., the Czech Republic) 1997–2010. Beskydy. 4(2):133–146.
  • Eklundh L, Jonsson P. 2012. TIMESAT 3.1 software manual.
  • Erasmi S, Bothe M, Petta RA. 2006. Enhanced filtering of MODIS time-series data for the analysis of desertification processes in northeast Brazil. Int Arch Photogramm Remote Sens Spatial Inf Sci. 34:8–11.
  • Galford GL, Mustard JF, Melillo J, Gendrin A, Cerri CC, Cerri CEP. 2008. Wavelet analysis of MODIS time series to detect expansion and intensification of row-crop agriculture in Brazil. Remote Sens Environ. 112(2):576–587.
  • Gautam NC, Chennaiah GC. 1985. Land-use and land-cover mapping and change detection in Tripura using satellite Landsat data. Int J Remote Sens. 6(3–4):517–528.
  • Gu J, Li X, Huang C, Okin GS. 2009. A simplified data assimilation method for reconstructing time-series MODIS NDVI data. Adv Space Res. 44(4):501–509.
  • Huang X, Friedl MA. 2014. Distance metric-based forest cover change detection using MODIS time series. Int J Appl Earth Obs Geoinf. 29:78–92.
  • Huete AR, Justice C, van Leeuwen W. 1999. MODIS Vegetation Index (MOD13). Algorithm Theoretical Basis Document (ATBD) version 3.
  • Jeganathan C, Dash J, Atkinson PM. 2010. Mapping the phenology of natural vegetation in India using remote sensing derived chlorophyll index. Int J Remote Sens. 31(22):5777–5796.
  • Jonsson P, Eklundh L. 2002. Seasonality extraction by function fitting to time series of satellite sensor data. IEEE Trans Geosci Remote Sens. 40(8):1824–1832.
  • Jung M, Chang E. 2015. NDVI-based land-cover change detection using harmonic analysis. Int J Remote Sens. 36(4):1097–1113.
  • Kendall MG. 1975. Rank correlation methods. 4th ed. London (UK): Charles Griffin.
  • Kim S-R, Prasad AK, El-Askary H, Lee W-K, Kwak D-A, Lee S-H, Kafatos M. 2014. Application of the Savitzky–Golay filter to land cover classification using temporal MODIS vegetation indices. Photogramm Eng Remote Sens. 80(7):675–685.
  • Lambin EF, Strahler AH. 1994. Change vector analysis in multitemporal space: a tool to detect and categorize land cover change processes using high temporal resolution satellite data. Remote Sens Environ. 48(2):231–244.
  • Li J, Wang Z, Lai C, Wu X, Zeng Z, Chen X, Lian Y. 2018. Response of net primary production to land use and land cover change in mainland China since the late 1980s. Sci Total Environ. 639:237–247.
  • Lu D, Mausel P, Brondizio E, Moran E. 2004. Change detection techniques. Int J Remote Sens. 25(12):2365–2401.
  • Lu X, Liu R, Liu J, Liang S. 2007. Removal of noise by wavelet method to generate high quality temporal data of terrestrial MODIS products. Photogramm Eng Remote Sens. 73(10):1129–1139.
  • Lund HG. 1983. Change: now you see it—now you don’t!. Proceedings of the International Conference on Renewable Resource Inventories for Monitoring Changes and Trends; Oregon State University, Corvallis, OR, USA. p. 211–213.
  • Lunetta RS, Joseph FK, Ediriwickrema J, Lyon JG, Worthy LD. 2006. Land cover change detection using multi-temporal MODIS NDVI data. Remote Sens Environ. 105(2):142–154.
  • Mann HB. 1945. Non-parametric tests against trend. Econometrica. 13(3):245–259.
  • Martinez B, Gilabert MA. 2009. Vegetation dynamics from NDVI time series analysis using the wavelet transform. Remote Sens Environ. 113(9):1823–1842.
  • Milne AK. 1988. Change direction analysis using Landsat imagery: a review of methodology. Proceedings of the IGARSS’88 Symposium Edinburgh, Scotland, ESASP-284. Noordwijk, Netherlands: ESA. p. 541–544.
  • Nalley D, Adamowski J, Khalil B. 2012. Using discrete wavelet transforms to analyze trends in streamflow and precipitation in Quebec and Ontario (1954–2008). J Hydrol. 475:204–228.
  • Jayaram C, Priyadarshi N, Kumar JP, Bhaskar TVSU, Raju D, Kochuparampil AJ. 2018. Analysis of gap-free chlorophyll-a data from MODIS in Arabian Sea, reconstructed using DINEOF. Int J Remote Sens. doi:10.1080/01431161.2018.1471540.
  • Pan N, Feng X, Fu B, Wang S, Ji F, Pan S. 2018. Increasing global vegetation browning hidden in overall vegetation greening: Insights from time-varying trends. Remote Sens Environ. 214:59–72.
  • Pandey BR, Tiwari H, Khare D. 2017. Trend analysis using discrete wavelet transform (DWT) for long-term precipitation (1851–2006) over India. Hydrol Sci J. 62(13):2187–2208.
  • Priyadarshi N, Chowdary VM, Srivastava YK, Rao GS, Raj U. 2017a. Land cover change analysis and trend identification in time series MODIS EVI using Wavelet Transform. Asian Conference on Remote Sensing (ACRS); Oct 2017; New Delhi.
  • Priyadarshi N, Chowdary VM, Srivastava YK, Das IC, Jha CS. 2017b. Reconstruction of time series MODIS EVI data using de-noising algorithms. Geocarto Int. 33(10):1095–1113.
  • Savitzky A, Golay MJE. 1964. Smoothing and differentiation of data by simplified least squares procedures. Anal Chem. 36(8):1627–1639.
  • Sen PK. 1968. Estimation of regression coefficients based on Kendall’s tau. J Am Stat Assoc. 63(324):1379–1389.
  • Setiawan Y, Yoshino K. 2010. Temporal pattern analysis of wavelet-filtered MODIS EVI to detect land use change in JAVA Island, Indonesia. Int Arch Photogramm Remote Sens Spatial Inf Sci. 38:820–825.
  • Sifuzzaman M, Islam MR, Ali MZ. 2009. Application of wavelet transform and its advantages compared to Fourier transform. J Phys Sci. 13:121–134.
  • Singh A. 1989. Digital change detection techniques using remotely-sensed data. Int J Remote Sens. 10(6):989–1003.
  • Singh S, Talwar R. 2014. A comparative study on change vector analysis based change detection techniques. Indian Acad Sci. 39(6):1311–1331.
  • Solano R, Didan K, Jacobson A, Huete A. 2010. MODIS Vegetation Index user’s guide (MOD13 Series) Version 2.00 (Collection 5).
  • Srivastava DS, Easa PS, Jauher JB. 2013. Integrated Wildlife Management Plan for West Singhbhum, Jharkhand. Department of Forest and Environment, Government of Jharkhand.
  • Stuligross D. 2008. Resources, representation, and authority in Jharkhand, India. Asia Pac Viewp. 49(1):83–97.
  • Sundar N, editor. 2009. Legal grounds: natural resources, identity, and the law in Jharkhand. Oxford: OUP.
  • Swets DL, Reed BC, Rowland JD, Marko SE. 1999. A weighted least squares approach to temporal NDVI smoothing. Proceedings of the ASPRS Annual Conference; May 17–21; Washington, DC. p. 526–536.
  • Xing J, Sieber R, Caelli T. 2018. A scale-invariant change detection method for land use/cover change research. ISPRS J Photogramm Remote Sens. 141:252–264.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.