Characterization of Land Transitions Patterns from Multivariate Time Series Using Seasonal Trend Analysis and Principal Component Analysis

Benoit Parmentier

doi:10.3390/rs61212639

Abstract

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.

Highlights

Characterizing change over large areas remains an important challenge in remote sensing and land change science [1,2,3,4]
The National Land Cover Database (NLCD) map was reclassified into six categories: Non-woody vegetation (NWV), low shrub (LSH), high shrub (HSH), mixed forest
The proportion of evergreen was derived from the NLCD data, because the majority of fires occurs in the evergreen forest (EGF) category, and studies indicate that black spruce shows a stronger correlation with severity and is associated with higher severity levels [91]

Summary

Introduction

Characterizing change over large areas remains an important challenge in remote sensing and land change science [1,2,3,4]. Despite multiple research efforts under way, there is a need to improve existing methods to characterize land cover change over large areas [4,12,13,14,15] by exploiting temporal patterns from time series. Land cover types are characterized by a set of biophysical properties related to their structure and constitutive materials [19,20,21] These properties regulate land surface atmosphere exchanges and affect the local climate, as well as fluxes in water, carbon and energy [20,22]. Land transitions affect multiple surface properties simultaneously, which are part of the biophysical consequences of the land cover change process [25,26]. Documenting and tracking such characteristic changes may help to infer the land change processes at work and benefits a host of studies [27,28,29]

Objectives

Methods

Results

Conclusion