The Annals of Applied Statistics

Applying multiple testing procedures to detect change in East African vegetation

Nicolle Clements, Sanat K. Sarkar, Zhigen Zhao, and Dong-Yun Kim

Full-text: Open access

Abstract

The study of vegetation fluctuations gives valuable information toward effective land use and development. We consider this problem for the East African region based on the Normalized Difference Vegetation Index (NDVI) series from satellite remote sensing data collected between 1982 and 2006 over 8-kilometer grid points. We detect areas with significant increasing or decreasing monotonic vegetation changes using a multiple testing procedure controlling the mixed directional false discovery rate (mdFDR). Specifically, we use a three-stage directional Benjamini–Hochberg (BH) procedure with proven mdFDR control under independence and a suitable adaptive version of it. The performance of these procedures is studied through simulations before applying them to the vegetation data. Our analysis shows increasing vegetation in the Northern hemisphere as well as coastal Tanzania and generally decreasing Southern hemisphere vegetation trends, which are consistent with historical evidence.

Article information

Source
Ann. Appl. Stat., Volume 8, Number 1 (2014), 286-308.

Dates
First available in Project Euclid: 8 April 2014

Permanent link to this document
https://projecteuclid.org/euclid.aoas/1396966287

Digital Object Identifier
doi:10.1214/13-AOAS686

Mathematical Reviews number (MathSciNet)
MR3191991

Zentralblatt MATH identifier
06302236

Keywords
False discovery rate directional false discovery rate NDVI East Africa vegetation

Citation

Clements, Nicolle; Sarkar, Sanat K.; Zhao, Zhigen; Kim, Dong-Yun. Applying multiple testing procedures to detect change in East African vegetation. Ann. Appl. Stat. 8 (2014), no. 1, 286--308. doi:10.1214/13-AOAS686. https://projecteuclid.org/euclid.aoas/1396966287


Export citation

References

  • Abelson, R. P. and Tukey, J. W. (1963). Efficient utilization of non-numerical information in quantitative analysis: General theory and the case of simple order. Ann. Math. Statist. 34 1347–1369.
  • Benjamini, Y. and Heller, R. (2007). False discovery rates for spatial signals. J. Amer. Statist. Assoc. 102 1272–1281.
  • Benjamini, Y. and Hochberg, Y. (1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. Roy. Statist. Soc. Ser. B 57 289–300.
  • Benjamini, Y. and Yekutieli, D. (2001). The control of the false discovery rate in multiple testing under dependency. Ann. Statist. 29 1165–1188.
  • Benjamini, Y. and Yekutieli, D. (2005). False discovery rate-adjusted multiple confidence intervals for selected parameters. J. Amer. Statist. Assoc. 100 71–93.
  • Brillinger, D. R. (1989). Consistent detection of a monotonic trend superposed on a stationary time series. Biometrika 76 23–30.
  • Chen, J., Jonsson, P. and Tamura, M. (2004). A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky–Golay filter. Remote Sensing of Environment 91 332–344.
  • Clements, N., Sarkar, S. K. and Guo, W. (2011). Astronomical transient detection controlling the false discovery rate. In Statistical Challenges in Modern Astronomy V (E. D. Feigelson and G. J. Babu, eds.) 383–396. Springer, New York.
  • Cole, J. E., Dunbar, R. B., McClanahan, T. R. and Muthiga, N. A. (2000). Tropical pacific forcing of decadal SST variability in the western Indian Ocean over the past two centuries. Science 287 617–619.
  • Cressie, N. and Wikle, C. K. (2011). Statistics for Spatio-Temporal Data. Wiley, Hoboken, NJ.
  • Curran, P. J. (1980). Multispectral remote sensing of vegetation amount. Progress in Physical Geography 4 315–341.
  • Duveiller, G., Defourny, P., Desclee, B. and Mayaux, P. (2007). Deforestation in Central Africa: Estimates at regional, national and landscape levels by advanced processing of systematically-disturbed Landsat extracts. Remote Sensing of Environment 112 1969–1981.
  • Foody, G. M. (2003). Geographical weighting as a further refinement to regression modeling: An example focused on the NDVI–rainfall relationship. Remote Sensing of Environment 88 283–293.
  • Guo, W. and Sarkar, S. (2012). Adaptive controls of the FWER and FDR under block dependence. Unpublished manuscript. Available at http://web.njit.edu/~wguo/research.html.
  • Hayes, D. J. and Sader, S. A. (2001). Comparison of change-detection techniques for monitoring tropical forest clearing and vegetation regrowth in a time series. Photogrammetric Engineering and Remote Sensing 67 1067–1075.
  • Jackson, R. D., Slater, P. N. and Pinter, P. J. (1983). Discrimination of growth and water stress in wheat by various vegetation indices through clear and turbid atmospheres. Remote Sensing of Environment 13 187–208.
  • OCHA (2011). Eastern Africa drought humanitarian report No. 3. OCHA, UN Office for the Coordination of Humanitarian Affairs reliefweb.int.
  • Pacifico, M. P., Genovese, C., Verdinelli, I. and Wasserman, L. (2004). False discovery control for random fields. J. Amer. Statist. Assoc. 99 1002–1014.
  • Sarkar, S. K. (2002). Some results on false discovery rate in stepwise multiple testing procedures. Ann. Statist. 30 239–257.
  • Storey, J. D., Taylor, J. E. and Siegmund, D. (2004). Strong control, conservative point estimation and simultaneous conservative consistency of false discovery rates: A unified approach. J. R. Stat. Soc. Ser. B Stat. Methodol. 66 187–205.
  • Tobler, W. (1970). A computer movie simulating urban growth in the Detroit region. Economic Geography 46 234–240.
  • Tucker, C., Pinzon, J., Brown, M., Slayback, D., Pak, E., Mahoney, R., Vermote, E. and Saleous, N. (2005). An extended AVHRR 8-km NDVI data set compatible with MODIS and SPOT vegetation NDVI data. International Journal of Remote Sensing 26 4485–4498.
  • Usongo, L. and Nagahuedi, J. (2008). Participatory land-use planning for priority landscapes of the Congo Basin. Unasylva 230 17–24.
  • Vrieling, A., de Beurs, K. M. and Brown, M. E. (2008). Recent trends in agricultural production of Africa based on AVHRR NDVI time series. Proceedings of the SPIE Conference: Remote Sensing for Agriculture, Ecosystems and Hydrology X.