Control plot selection for studies of post-fire dynamics: performance of non-parametric and autoregressive routines
Keywords:control plot selection, fire ecology, post-fire monitoring, NDVI MODIS, NDVI time series analysis
Natural fire regimes have been modified; therefore robust post-fire monitoring tools are needed to understand the post-fire recovery process. Satellites with high temporal resolution allow us to build time series of vegetation indices for monitoring post-fire vegetation recovery. One of the techniques used is to compare the time series of a burned plot with that of an unburned control plot. However, for its implementation it is necessary to select control plots in which the vegetation has the same structure and functioning than the plot burned before the fire. Previous study defined biological criteria to detect burned and unburned control plots with identical pre-fire vegetation functioning. Moreover, a non-parametric test routine of low statistical power was proposed to test them, this was based on the analysis of the QVI (Quotient Vegetation Index), calculated between NDVI (Normalized Difference Vegetation Index) time series of the burned and control site. However, currently there are autoregressive analysis techniques with greater statistical power. Therefore the aims were to propose six new statistical routines based on autoregressive test, and compare the performance of these with the non-parametric routine. We selected 13,700 forest plots and extracted the NDVI MODIS time series between 2002 and 2005. We randomly selected 43 reference plots, and through each routine, we compared each reference time series with the other 13,657 time series. We estimated the performance of the routines measuring the euclidian distance between the time series of the reference plot and the time series of the plots accepted for each routine. We also measured the quality and the amount of the QVI time series selected by each routine. Autoregressive routines showed better performance than the non-parametric routine, since they selected control plots with NDVI time series with greatest similarity with respect to the reference plots and QVI series with highest quality.
Alonso, A. M., Peña, D., Romo Urroz, J. 2002. Una revisión de los métodos de remuestreo en series temporales. Estadística Española, 150, 133-159.
Baret, F., Guyot, G. 1991. Potential and limits of vegetation indices for LAI and APAR assessment. Remote Sensing of Environment, 35, 161-173. https://doi.org/10.1016/0034-4257(91)90009-U
Box, G. E. P., Pierce, D. A. 1970. Distribution of Residual Autocorrelations in AutoregressiveIntegrated Moving Average Time Series Models. J. Am. Stat. Assoc., 65, 1509–1526. https://doi.org/10. 1080/01621459.1970.10481180
Brockwell, P. J., Davis, R. A. 2010. Introduction to time series and forecasting. New York: Springer.
Cabello, J., Fernández, N., Alcaraz-Segura, D., Oyonarte, C., Piñeiro, G., Altesor, A., Delibes, M., Paruelo, J. M. 2012. The ecosystem functioning dimension in conservation: insights from remote sensing. Biodiversity and Conservation, 21, 3287– 3305. https://doi.org/10.1007/s10531-012-0370-7
Casady, G. M., van Leeuwen, W. J. D., Marsh, S. E. 2010. Evaluating Post-wildfire Vegetation Regeneration as a Response to Multiple Environmental Determinants. Environmental Modeling & Assessment, 15, 295– 307. https://doi.org/10.1007/s10666-009-9210-x
Chatfield, C. 2000. Time-series forecasting. Boca Raton, Florida: Chapman & Hall/CRC. https://doi. org/10.1201/9781420036206
Cromwel, J. B., Labys, W. C., Teraza, M. 1994. Univariate test for time series models. California: Sage Publications. https://doi. org/10.4135/9781412986458
Di Bella, C. M., Fischer, M. A., Jobbágy, E. G. 2011. Fire patterns in north-eastern Argentina: influences of climate and land use/cover. International Journal of Remote Sensing, 32, 4961–4971. https://doi.org/1 0.1080/01431161.2010.494167
Di Mauro, B., Fava, F., Busetto, L., Crosta, G. F., Colombo, R. 2014. Post-fire resilience in the Alpine region estimated from MODIS satellite multispectral data. Int. J. Appl. Earth Obs. and Geoinformation, 32, 163-172. https://doi.org/10.1016/j.jag.2014.04.010
Díaz-Delgado, R., Lloret, F., Pons, X., Terradas, J. 2002. Satellite Evidence of Decreasing Resilience in Mediterranean Plant Communities after Recurrent Wildfires. Ecology, 83, 2293-2303. https://doi. org/10.1890/0012-9658(2002)083[2293:SEODRI]2 .0.CO;2
Flannigan, M., Cantin, A. S., de Groot, W. J., Wotton, M., Newbery, A., Gowman, L. M. 2013. Global wildland fire season severity in the 21st century. Forest Ecology and Management, 294, 54-61. https://doi.org/10.1016/j.foreco.2012.10.022
Fuller, W. A. 1996. Introduction to statistical time series. New York: Wiley.
Gao, X., Huete, A. R., Ni, W., Miura, T. 2000. Optical– biophysical relationships of vegetation spectra without background contamination. Remote Sensing of Environment, 74, 609–620. https://doi. org/10.1016/S0034-4257(00)00150-4
Giglio, L. 2010. MODIS collection 5 active fire product user’s guide version 2.4. University of Maryland.
Gitas, I., Mitri, G., Veraverbeke, S., Polychronaki, A. 2012. Advances in Remote Sensing of Post-Fire Vegetation Recovery Monitoring -. In T. Fatoyinbo (Ed.), Remote Sensing of Biomass - Principles and Applications, Shangai: INTECH Open Access Publisher, 143-176. https://doi.org/10.5772/20571
Huete, A., Didan, K., Miura, T., Rodriguez, E. P., Gao, X., Ferreira, L. G. 2002. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83, 195–213. https://doi.org/10.1016/ S0034-4257(02)00096-2
Key, C. H. 2006. Ecological and sampling constraints on defining landscape fire severity. Fire Ecology, 2, 34–59. https://doi.org/10.4996/fireecology.0202034
Kuenzer, C., Dech, S., Wagner, W. 2015. Remote Sensing Time Series. New York: Springer. https:// doi.org/10.1007/978-3-319-15967-6
Landi, M. A., Di Bella, C. M., Ojeda, S., Salvatierra, P., Argañaraz, J. P., Bellis, L. M. 2017. Selecting control sites for post-fire ecological studies using biological criteria and MODIS time series data. Fire Ecology, 13(2), 1–17. https://doi.org/10.4996/ fireecology.130274623
Lhermitte, S., Verbesselt, J., Verstraeten, W. W., Coppin, P. 2010. A pixel based regeneration index using time series similarity and spatial context. Photogramm. Eng. Remote Sens., 76, 673–682. https://doi. org/10.14358/PERS.76.6.673
Lhermitte, S., Verbesselt, J., Verstraeten, W. W., Veraverbeke, S., Coppin, P. 2011. Assessing intraannual vegetation regrowth after fire using the pixel based regeneration index. ISPRS J. Photogramm. Remote Sens., 66, 17–27. https://doi.org/10.1016/j. isprsjprs.2010.08.004
McKenzie, D., Miller, C., Falk, D. A. 2011. The landscape ecology of fire. New York: Springer. https://doi.org/10.1007/978-94-007-0301-8
McLeod, A. I., Hipel, K. W., Bodo, B. A. 1991. Trend analysis methodology for water quality time series. Environmetrics, 2, 169–200. https://doi.org/10.1002/ env.3770020205
Moretin, P. A., Castro, T. 1987. Previsao de Séries Temporais. Sao Pablo: Atual.
Pérez-Cabello, F., Echeverría, M. T., Ibarra, P., de la Riva, J. 2009. Effects of fire on vegetation, soil and hydrogemorphological behavior in Mediterranean ecosystems. In: E. Chuvieco (Ed.), Earth Observations of Wildland Fires in Mediterranean Ecosystems, Berlin: Springer, 111–128. https://doi. org/10.1007/978-3-642-01754-4_9
Poling, A., Fuqua, R. W. 1986. Research Methods in Applied Behavior Analysis. Boston: Springer. https://doi.org/10.1007/978-1-4684-8786-2
Quinn, G. P., Keough, M. J. 2002. Experimental design and data analysis for biologists. Cambridge: Cambridge University Press. https://doi.org/10.1017/ CBO9780511806384
San-Miguel-Ayanz, J., Moreno, J. M., Camia, A. 2013. Analysis of large fires in European Mediterranean landscapes: Lessons learned and perspectives. Forest Ecology and Management, 294, 11-22. https://doi. org/10.1016/j.foreco.2012.10.050
Sutradhar, B. C., MacNeil, I. B., Dagum, E. B. 1995. A simple test for stable seasonality. Journal of Statistical Planning and Inference, 43, 157-167. https://doi.org/10.1016/0378-3758(94)00016-O
Van Leeuwen, W. J. D., Casady, G. M., Neary, D. G., Bautista, S., Alloza, J. A., Carmel, Y., Wittenberg, L., Malkinson, D., Orr, B. J. 2010. Monitoring post-wildfire vegetation response with remotely sensed time-series data in Spain, USA and Israel. International Journal of Wildland Fire, 19, 75-93. https://doi.org/10.1071/WF08078
Wenze,Y., Huang, D., Tan, B., Stroeve, J. C., Shabanov, N. V., Knyazikhin, Y., Nemani, R. R., Myneni, R. B. 2006. Analysis of leaf area index and fraction of PAR absorbed by vegetation products from the terra MODIS sensor: 2000-2005. IEEE Trans. Geosci. Remote Sens., 44, 1829–1842. https://doi. org/10.1109/TGRS.2006.871214
Yaffee, R. A., McGee, M. 2000. An introduction to time series analysis and forecasting: with applications of SAS and SPSS. New York: Academic Press.
Yue, S., Pilon, P., Cavadias, G. 2002. Power of the Mann–Kendall and Spearman’s rho tests for detecting monotonic trends in hydrological series. Journal of Hydrology, 259, 254–271. https://doi. org/10.1016/S0022-1694(01)00594-7
This journal is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International