lacunaritycovariance (version 1.1-2)

lacunaritycovariance-package: lacunaritycovariance

Description

lacunaritycovariance

Details

Random closed sets (RACS) (Chiu et al., 2013; Molchanov, 2005) are a well known tool for modelling binary coverage maps. The package author recently developed new, improved estimators of gliding box lacunarity (GBL) for RACS (Hingee et al., 2017) and described contagion-like properties for RACS (Hingee, 2016). A forthcoming PhD thesis (Hingee, 2019) will provide additional background for GBL, and for RACS in landscape metrics (which includes contagion).

This package expects RACS observations to be in the form of binary maps either in raster format, or as a set representing foreground with a second set giving the observation window. If in raster format, the binary map is expected to be a spatstat im object with pixel values that are only 1 and 0, or are logically valued (i.e. TRUE or FALSE). In both cases the observation window is taken to be the set of pixels with values that are not NA (i.e. NA values are considered outside the observation window). The foreground of the binary map, corresponding to locations within the realisation of the RACS, is taken to be pixels that have value 1 or TRUE. If the binary map is in set format then a spatstat owin object is used to represent foreground and a second owin object is used to represent the observation window.

We will usually denote a RACS as \(\Xi\) ('Xi') and a realisation of \(\Xi\) observed as a binary map as \(xi\). We will usually denote the observation window as obswin.

A demonstration converting remotely sensed data into a binary map in im format can be accessed by typing demo("import_remote_sense_data", package = "lacunaritycovariance"). A short example of estimating RACS properties can be found in the vignette estimate_RACS_properties, which can be accessed with vignette("estimate_RACS_properties").

The key functions within this package for estimating properties of RACS are:

• coverageprob estimates the coverage probability of a stationary RACS

• racscovariance estimates the covariance of a stationary RACS

• gbl estimates the GBL of a stationary RACS

• cencovariance estimates the centred covariance of a stationary RACS

• paircorr estimates the pair-correlation of a stationary RACS

• secondorderprops estimates GBL, covariance and other second order properties of stationary RACS

• contagdiscstate estimates the disc-state contagion of a stationary RACS

Key functions for simulating RACS are:

• rbdd simulates a Boolean model with grains that are discs with fixed radius (deterministic discs).

• rbdr simulates a Boolean model with grains that are rectangles of fixed size and orientation.

• rbpto simulates a Boolean model with grains that of fixed shape and random scale distributed according to a truncated Pareto distribution.

• placegrainsfromlib randomly places grains on a set of points (used to simulate Boolean models and other germ-grain models).

References

Chiu, S.N., Stoyan, D., Kendall, W.S. and Mecke, J. (2013) Stochastic Geometry and Its Applications, 3rd ed. Chichester, United Kingdom: John Wiley & Sons.

Hingee, K.L. (2016) Statistics for patch observations. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences pp. 235-242. Prague: ISPRS.

Hingee, K.L. (2019) Spatial Statistics of Random Closed Sets for Earth Observations. PhD: Perth, Western Australia: University of Western Australia. Submitted.

Hingee K, Baddeley A, Caccetta P, Nair G (2019). Computation of lacunarity from covariance of spatial binary maps. Journal of Agricultural, Biological and Environmental Statistics, 24, 264-288. DOI: 10.1007/s13253-019-00351-9.

Molchanov, I.S. (2005) Theory of Random Sets. USA: Springer.

Examples

# NOT RUN {
# Estimates from the heather data in spatstat
xi_owin <- heather$coarse
xi_owin_obswin <- Frame(heather$coarse)

# Convert binary map to an im object (optional)
xi <- as.im(xi_owin, value = TRUE, na.replace = FALSE)

# Estimate coverage probability, covariance, GBL, and disc-state contagion
cphat <- coverageprob(xi)
cvchat <- racscovariance(xi, estimator = "pickaH")
# }
# NOT RUN {
  gblhat <- gbl(xi, seq(0.1, 5, by = 1), estimators = "GBLcc.pickaH")
  contagds <- contagdiscstate(Hest(xi), Hest(!xi), p = cphat)
# }
# NOT RUN {
# Simulate a Boolean model with grains that are discs of fixed radius:
# }
# NOT RUN {
  xi_sim <- rbdd(10, 0.1, owin())
# }