Abstract
The free access to satellite images since more than 40 years ago has provoked a rapid increase of multitemporal derived information of remote sensing data that should be summarized and analyzed for future inferences. In particular, the study of trends and trend changes is of crucial interest in many studies of phenology, climatology, agriculture, hydrology, geology or many other environmental disciplines. Overall, the normalized difference vegetation index (NDVI), as a satellite derived variable, plays a crucial role because of its usefulness for vegetation and landscape characterization, land use and land cover mapping, environmental monitoring, climate change or crop prediction models. Since the eighties, it can be retrieved all over the world from different satellites. In this work we propose to analyze its temporal evolution, looking for breakpoints or change-points in trends of the surfaces occupied by four NDVI classifications made in Spain from 1981 to 2015. The results show a decrease of bare soils and semi-bare soils starting in the middle nineties or before, and a slight increase of middle-vegetation and high-vegetation soils starting in 1990 and 2000 respectively.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ahmed M, Else B, Eklundh L, Ard J, Seaquist J (2017) Dynamic response of ndvi to soil moisture variations during different hydrological regimes in the sahel region. Int J Remote Sens 38(19):5408–5429
Antoch J, Hušková M, Prášková Z (1997) Effect of dependence on statistics for determination of change. J Stat Plan Inference 60(2):291–310
Atzberger C, Klisch A, Mattiuzzi M, Vuolo F (2013) Phenological metrics derived over the european continent from ndvi3g data and modis time series. Remote Sens 6(1):257–284
Auger IE, Lawrence CE (1989) Algorithms for the optimal identification of segment neighborhoods. Bull Math Biol 51(1):39–54
Bai J, Perron P (2003) Critical values for multiple structural change tests. Econ J 6(1):72–78
Bolton RJ, Hand DJ (2002) Statistical fraud detection: a review. Stat Sci 17(3):235–249
Chen J, Gupta AK (2011) Parametric statistical change point analysis: with applications to genetics, medicine, and finance. Springer, Heidelberg
Csörgö M, Horváth L (1997) Limit theorems in change-point analysis, vol 18. Wiley, New York
Detsch F (2016) Gimms: download and process GIMMS NDVI3g data. https://CRAN.R-project.org/package=gimms
Edwards AW, Cavalli-Sforza LL (1965) A method for cluster analysis. Biometrics 21(2):362–375
Hijmans RJ (2015) Raster: geographic data analysis and modeling. https://CRAN.R-project.org/package=raster
Holben BN (1986) Characteristics of maximum-value composite images from temporal avhrr data. Int J Remote Sens 7(11):1417–1434
James NA, Matteson DS (2014) ecp: an R package for nonparametric multiple change point analysis of multivariate data. J Stat Softw 62(7):1–25
de Jong R, de Bruin S, de Wit A, Schaepman ME, Dent DL (2011) Analysis of monotonic greening and browning trends from global ndvi time-series. Remote Sens Environ 115(2):692–702
Julien Y, Sobrino JA, Mattar C, Ruescas AB, Jiménez-Muñoz JC, Sòria G, Hidalgo V, Atitar M, Franch B, Cuenca J (2011) Temporal analysis of normalized difference vegetation index (ndvi) and land surface temperature (lst) parameters to detect changes in the iberian land cover between 1981 and 2001. Int J Remote Sens 32(7):2057–2068
Kern A, Marjanović H, Barcza Z (2016) Evaluation of the quality of ndvi3g dataset against collection 6 modis ndvi in central Europe between 2000 and 2013. Remote Sens 8(11):955
Killick R, Eckley IA (2014) Changepoint: an R package for changepoint analysis. J Stat Softw 58(3):1–19
Killick R, Fearnhead P, Eckley IA (2012) Optimal detection of changepoints with a linear computational cost. J Am Stat Assoc 107(500):1590–1598
Killick R, Haynes K, Eckley IA (2016) Changepoint: an R package for changepoint analysis. https://CRAN.R-project.org/package=changepoint
Li H, Wang C, Zhang L, Li X, Zang S (2017) Satellite monitoring of boreal forest phenology and its climatic responses in Eurasia. Int J Remote Sens 38(19):5446–5463
Li Z, Huffman T, McConkey B, Townley-Smith L (2013) Monitoring and modeling spatial and temporal patterns of grassland dynamics using time-series modis ndvi with climate and stocking data. Remote Sens Environ 138:232–244
Matteson DS, James NA (2014) A nonparametric approach for multiple change point analysis of multivariate data. J Am Stat Assoc 109(505):334–345
Militino AF, Ugarte MD, Pérez-Goya U (2017) Stochastic spatio-temporal models for analysing ndvi distribution of gimms ndvi3g images. Remote Sens 9(1):76
Neeti N, Eastman JR (2011) A contextual Mann–Kendall approach for the assessment of trend significance in image time series. Trans GIS 15(5):599–611
Pinzon JE, Tucker CJ (2014) A non-stationary 1981–2012 avhrr ndvi3g time series. Remote Sens 6(8):6929–6960
R Core Team (2017) R: a language and environment for statistical computing. R foundation for statistical computing, Vienna, Austria. https://www.R-project.org/
Rouse J Jr, Haas R, Schell J, Deering D (1974) Monitoring vegetation systems in the great plains with erts. NASA spec publ 351:309
Scott AJ, Knott M (1974) A cluster analysis method for grouping means in the analysis of variance. Biometrics 30(3):507–512
Sen A, Srivastava MS (1975) On tests for detecting change in mean. Ann Stat 3(1):98–108
Sobrino JA, Julien Y, Morales L (2011) Changes in vegetation spring dates in the second half of the twentieth century. Int J Remote Sens 32(18):5247–5265
Talih M, Hengartner N (2005) Structural learning with time-varying components: tracking the cross-section of financial time series. J R Stat Soc Ser B 67(3):321–341
Tucker CJ (1979) Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens Environ 8(2):127–150
Tucker CJ, Pinzon JE, Brown ME, Slayback DA, Pak EW, Mahoney R, Vermote EF, El Saleous N (2005) An extended avhrr 8-km ndvi dataset compatible with modis and spot vegetation ndvi data. Int J Remote Sens 26(20):4485–4498
Verbesselt J, Hyndman R, Newnham G, Culvenor D (2010a) Detecting trend and seasonal changes in satellite image time series. Remote Sens Environ 114(1):106–115
Verbesselt J, Hyndman R, Zeileis A, Culvenor D (2010b) Phenological change detection while accounting for abrupt and gradual trends in satellite image time series. Remote Sens Environ 114(12):2970–2980
Wang J, Dong J, Liu J, Huang M, Li G, Running SW, Smith WK, Harris W, Saigusa N, Kondo H, Liu Y, Hirano T, Xiao X (2014) Comparison of gross primary productivity derived from gimms ndvi3g, gimms, and modis in southeast Asia. Remote Sens 6(3):2108–2133
Yuan X, Li L, Chen X, Shi H (2015) Effects of precipitation intensity and temperature on ndvi-based grass change over northern china during the period from 1982 to 2011. Remote Sens 7(8):10164–10183
Zeileis A (2006) Implementing a class of structural change tests: an econometric computing approach. Comput Stat Data Anal 50:2987–3008
Zeileis A, Kleiber C, Krämer W, Hornik K (2003) Testing and dating of structural changes in practice. Comput Stat Data Anal 44:109–123
Zeileis A, Leisch F, Hornik K, Kleiber C (2002) Strucchange: an R package for testing for structural change in linear regression models. J Stat Softw 7(2):1–38
Acknowledgements
This research was supported by the Spanish Ministry of Economy, Industry, and Competitiveness (project MTM2017-82553-R) jointly financed with the European Regional Development Fund (FEDER), the Government of Navarre (PI015-2016 and PI043-2017 projects) and the Fundación CAN-Obra Social Caixa 2016.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this chapter
Cite this chapter
Militino, A.F., Ugarte, M.D., Pérez-Goya, U. (2018). Detecting Change-Points in the Time Series of Surfaces Occupied by Pre-defined NDVI Categories in Continental Spain from 1981 to 2015. In: Gil, E., Gil, E., Gil, J., Gil, M. (eds) The Mathematics of the Uncertain. Studies in Systems, Decision and Control, vol 142. Springer, Cham. https://doi.org/10.1007/978-3-319-73848-2_28
Download citation
DOI: https://doi.org/10.1007/978-3-319-73848-2_28
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-73847-5
Online ISBN: 978-3-319-73848-2
eBook Packages: EngineeringEngineering (R0)