OpenLand

OpenLand is an open-source R package for the analysis of land use and cover (LUC) time series. It includes support for consistency check and loading spatiotemporal raster data and synthesized spatial plotting. Several LUC change (LUCC) metrics in regular or irregular time intervals can be extracted and visualized through one- and multistep sankey and chord diagrams. A complete intensity analysis according to Aldwaik and Pontius (2012) doi:10.1016/j.landurbplan.2012.02.010 is implemented, including tools for the generation of standardized multilevel output graphics.

Installation

Install the released version of OpenLand from CRAN:

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install.packages("OpenLand")

Or install the development version from GitHub with:

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# install.packages("devtools")
devtools::install_github("reginalexavier/OpenLand")

What is Intensity Analysis?

Intensity Analysis (IA) is a quantitative method to analyze LUC maps at several time steps, using cross-tabulation matrices, where each matrix summarizes the LUC change at each time interval. IA evaluates in three levels the deviation between observed change intensity and hypothesized uniform change intensity. Hereby, each level details information given by the previous analysis level. First, the interval level indicates how size and rate of change varies across time intervals. Second, the category level examines for each time interval how the size and intensity of gross losses and gross gains in each category vary across categories for each time interval. Third, the transition level determines for each category how the size and intensity of a category’s transitions vary across the other categories that are available for that transition. At each level, the method tests for stationarity of patterns across time intervals (Aldwaik and Pontius 2012).

Outcomes of intensity analysis

The data is extracted from the rasters with the contingencyTable() function which returns a multiple grid information in tables for the next processing steps. Within the OpenLand package, the intensityAnalysis() function computes the three levels of analysis. It requires the object returned by the contingenceTable() function and that the user predefines two LUC categories n and m. Generally, n is a target category which experienced relevant gains and m a category with important losses.