@Article{LuPebeSancVerb:2016:DeDeMO,
author = "Lu, Meng and Pebesma, Edzer and Sanchez Ipia, Alber Hamersson and
Verbesselt, Jan",
affiliation = "{Westf{\"a}lische Wilhelms-Universitt M{\"u}nster} and
{Westf{\"a}lische Wilhelms-Universitt M{\"u}nster} and
{Instituto Nacional de Pesquisas Espaciais (INPE)} and Wageningen
University, Droevendaalsesteeg",
title = "Spatio-temporal change detection from multidimensional arrays:
Detecting deforestation from MODIS time series",
journal = "ISPRS Journal of Photogrammetry and Remote Sensing",
year = "2016",
volume = "117",
pages = "227--236",
month = "July",
keywords = "BFAST, Time series analysis, Spatial correlation, Temporal
correlation, Array database, Spatio-temporal change modeling.",
abstract = "Growing availability of long-term satellite imagery enables change
modeling with advanced spatio-temporal statistical methods.
Multidimensional arrays naturally match the structure of
spatio-temporal satellite data and can provide a clean modeling
process for complex spatio-temporal analysis over large datasets.
Our study case illustrates the detection of breakpoints in MODIS
imagery time series for land cover change in the Brazilian Amazon
using the BFAST (Breaks For Additive Season and Trend) change
detection framework. BFAST includes an Empirical Fluctuation
Process (EFP) to alarm the change and a change point time locating
process. We extend the EFP to account for the spatial
autocorrelation between spatial neighbors and assess the effects
of spatial correlation when applying BFAST on satellite image time
series. In addition, we evaluate how sensitive EFP is to the
assumption that its time series residuals are temporally
uncorrelated, by modeling it as an autoregressive process. We use
arrays as a unified data structure for the modeling process, R to
execute the analysis, and an array database management system to
scale computation. Our results point to BFAST as a robust approach
against mild temporal and spatial correlation, to the use of
arrays to ease the modeling process of spatio-temporal change, and
towards communicable and scalable analysis.",
doi = "10.1016/j.isprsjprs.2016.03.007",
url = "http://dx.doi.org/10.1016/j.isprsjprs.2016.03.007",
issn = "0924-2716",
language = "en",
targetfile = "lu_spatio.pdf",
urlaccessdate = "28 abr. 2024"
}