Abstract
Despite the existence of long term remotely sensed datasets, change detection methods are limited and often remain an obstacle to the effective use of time series approaches in remote sensing applications to Land Change Science. This paper establishes some simple statistical tests to be applied to NDVI-derived time series of remotely sensed data products. Specifically, the methods determine the statistical significance of three separate metrics of the persistence of vegetation cover or changes within a landscape by comparison to various forms of “benchmarks”; directional persistence (changes in sign relative to some fixed reference value), relative directional persistence (changes in sign relative to the preceding value), and massive persistence (changes in magnitude relative to the preceding value). Null hypotheses are developed on the basis of serially independent, normally distributed random variables. Critical values are established theoretically through consideration of the numeric properties of those variables, application of extensive Monte Carlo simulations, and parallels to random walk processes. Monthly pixel-level NDVI values for the state of Florida are analyzed over 25 years, illustrating the techniques’ abilities to identify areas and/or times of significant change, and facilitate a more detailed understanding of this landscape. The potential power and utility of such techniques is diverse within the area of remote sensing studies and Land Change Science, especially in the context of global change.
Highlights
Interest in human impact on the natural environment has increased dramatically over the last quarter century [1,2], as evidenced by such multi-disciplinary, multi-national programs as the International Geosphere Biosphere Programme [3]
Critical values of the persistence metrics provide the null circumstances against which observed values may be compared temporally and spatially, in an objective and repeatable fashion
These differences might arise from climatically induced trends in NDVI, or step functions resulting from rapid changes in Land use and land cover (LULC) [39], but the methodology highlights those areas and times where and when values differ significantly from random, which are worthy of further investigation
Summary
Interest in human impact on the natural environment has increased dramatically over the last quarter century [1,2], as evidenced by such multi-disciplinary, multi-national programs as the International Geosphere Biosphere Programme [3]. Over roughly the same period, repeat digital, synoptic measures of the Earth‘s surface have stimulated many Land Change Science (LCS) research questions designed to improve understanding of human-environment interactions. Satellite remote sensing is ideal for this task, providing consistent, repeatable measurements across a suite of spatial scales, and is well-suited to capture processes of land surface change, such as human activity, fire, floods, etc. Implicit in the use of remote sensing in LCS is the assumption that environmental parameters which define human-environment interactions can be detected in this way [6]. Extended time series measured over short time increments are ideal [1], yet, despite the existence of longer term remotely sensed datasets, change detection methods are limited [5] and remain a major stumbling block in the effective use of time series approaches to remotely sensed data in research
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