Characterization of Land Transitions Patterns from Multivariate Time Series Using Seasonal Trend Analysis and Principal Component Analysis
by
Benoit Parmentier 1,2,3,4
Benoit Parmentier 1,2,3,4
1
Clark Labs, Clark University, 950 Main Street, Worcester, MA 01610, USA
2
Graduate School of Geography, Clark University, 950 Main Street, Worcester, MA 01610, USA
3
National Center for Ecological Analysis and Synthesis, University of California Santa Barbara, 735 State Street, Suite 300, Santa Barbara, CA 93101, USA
4
Sustainability Solutions Initiative, University of Maine, Deering Hall Room 302, Orono, ME 04469, USA
Remote Sens. 2014, 6(12), 12639-12665; https://doi.org/10.3390/rs61212639
Received: 5 October 2014 / Revised: 5 December 2014 / Accepted: 8 December 2014 / Published: 16 December 2014
Characterizing biophysical changes in land change areas over large regions with short and noisy multivariate time series and multiple temporal parameters remains a challenging task. Most studies focus on detection rather than the characterization, i.e., the manner by which surface state variables are altered by the process of changes. In this study, a procedure is presented to extract and characterize simultaneous temporal changes in MODIS multivariate times series from three surface state variables the Normalized Difference Vegetation Index (NDVI), land surface temperature (LST) and albedo (ALB). The analysis involves conducting a seasonal trend analysis (STA) to extract three seasonal shape parameters (Amplitude 0, Amplitude 1 and Amplitude 2) and using principal component analysis (PCA) to contrast trends in change and no-change areas. We illustrate the method by characterizing trends in burned and unburned pixels in Alaska over the 2001–2009 time period. Findings show consistent and meaningful extraction of temporal patterns related to fire disturbances. The first principal component (PC1) is characterized by a decrease in mean NDVI (Amplitude 0) with a concurrent increase in albedo (the mean and the annual amplitude) and an increase in LST annual variability (Amplitude 1). These results provide systematic empirical evidence of surface changes associated with one type of land change, fire disturbances, and suggest that STA with PCA may be used to characterize many other types of land transitions over large landscape areas using multivariate Earth observation time series.
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Keywords:
temporal patterns; windowed Fourier transform; Mann–Kendall; burned areas; fire; albedo; NDVI; land cover change; Alaska
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
MDPI and ACS Style
Parmentier, B. Characterization of Land Transitions Patterns from Multivariate Time Series Using Seasonal Trend Analysis and Principal Component Analysis. Remote Sens. 2014, 6, 12639-12665.
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