Time Series Analysis of Land Cover Change: Developing Statistical Tools to Determine Significance of Land Cover Changes in Persistence Analyses

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.

Assessment of Land Use-Cover Changes and Successional Stages of Vegetation in the Natural Protected Area Altas Cumbres, Northeastern Mexico, Using Landsat Satellite Imagery

“…However, although secondary succession is a central issue for the analysis of biological communities related to post-disturbance events, it is not always addressed in studies concerning land use-cover changes, whether these are conducted in NPAs or not [62,63]. This is of major importance, because any analysis of land cover transition or persistence implies different processes of recovery or modification in vegetation and, more precisely, time of regeneration, which is more accentuated for multitemporal analysis [3,8,9,[14][15][16][17][18][19]. Regardless of the method used to delimit the succession time or the presence of secondary vegetation [20,22,23], we must stress the necessity of the evaluation of succession patterns in future studies of land-use cover changes, as was done in this study.…”

“…In this sense, the use of Landsat satellite images for the analysis of LULCC is a widely used method [3,5,8,9,62], including multitemporal approaches which encompasses several stages [4,[14][15][16][17][18][19][20][21]64,81]. However, despite the use of five temporal stages (satellite images of 1973, 1986, 2000, 2005 and 2015), it is necessary to recognize the potential limitations of this study, since the disturbances presented between these stages in years not considered could influence the results.…”

Understanding Long-Term Savanna Vegetation Persistence across Three Drainage Basins in Southern Africa

“…Specifically, we utilize a measure called directional persistence, which indicates the overall direction of landscape change (increasing/decreasing) relative to a fixed benchmark condition (which could be tied to a climatic event, policy change, etc.) on a per pixel basis [45][46][47]. This metric is based on the principal of the random walk process, such that at any point in time, the likelihood of an increase versus a decrease in a value is identical (i.e., 0.5 probability) as this is a Bernouli random process.…”

“…Theoretically, as NDVI is a continuous variable bounded by −1 and +1, identical values of NDVI are impossible. Predetermined critical levels of statistical significance, based on a random walk statistic, highlight the nature and extent of changes across the landscape beyond which might be expected at random and which are possibly indicative of degradation or other changes (see [45][46][47] for more details). The directional persistence (D j ) in this case yields a maximum range of −23 to +23 with the critical value of (α = 0.025) ±11 [45].…”

“…NDVI Metrics: Persistence, Seasonal NDVI, and Seasonal Cumulative NDVI Using the NDVI time series a metric of vegetation persistence over time was developed [45][46][47]. Persistence, the increase or decrease in NDVI relative to a baseline status, was calculated on the time series to explore long-term spatial trends in vegetation.…”