In Nowosad/lopata: Local Pattern Analysis

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The fundamental element of the pattern-based spatial analysis is a numerical description of spatial patterns (so-called signature). This vignette shows how to derive spatial signatures using the lsp_signature() function on example datasets. Let's start by attaching necessary packages:

library(motif)
library(stars)
library(sf)

For this vignette, we use several spatial objects. The first one is a "raster/landcover2015.tif" file.

landcover = read_stars(system.file("raster/landcover2015.tif", package = "motif"))

This file contains a land cover data for New Guinea, with seven possible categories: (1) agriculture, (2) forest, (3) grassland, (5) settlement, (6) shrubland, (7) sparse vegetation, and (9) water.

landcover = droplevels(landcover)
plot(landcover, key.pos = 4, key.width = lcm(5), main = NULL)

The second one is a "raster/landform.tif" file.

landform = read_stars(system.file("raster/landform.tif", package = "motif"))

It has fourteen landform categories (plus surface water) for the same area.

landform = droplevels(landform)
plot(landform, key.pos = 4, key.width = lcm(8), main = NULL)

The third example object is random_ndvi.

set.seed(222)
random_ndvi = landcover
random_ndvi$ndvi = runif(length(random_ndvi[[1]]), min = 1, max = 10)
random_ndvi$ndvi[is.na(random_ndvi$landcover2015.tif)] = NA
random_ndvi$landcover2015.tif = NULL

It is an artificial dataset representing numerical weights for each cell for the same area as the datasets above.

plot(random_ndvi)

A co-occurrence matrix (`"coma"`) representation - one layer

The first type of signature is a co-occurrence matrix ("coma"). It requires just one layer (one attribute in a stars object).

coma_output = lsp_signature(landcover, type = "coma", window = 100)
coma_output

The output is an object of class lsp with three columns:

id - an id of each window (area)
na_prop - share (0-1) of NA cells for each window
signature - a list-column containing with calculated signatures

We can see a signature for selected local landscape extracting it from the signature column:

coma_output$signature[[1]]

Co-occurrence vector ("cove") is rearrangement of a co-occurrence matrix into one-dimensional object:

cove_output = lsp_signature(landcover, type = "cove", window = 100)
cove_output

This representation can be used to compare different local landscapes.

Learn more about these representations at https://nowosad.github.io/comat/articles/coma.html.

A weighted co-occurrence matrix (`wecoma`) representation - two layers

The next type of signature is a weighted co-occurrence matrix ("wecoma"). It requires two layers - a stars object with two attributes. The first one is a categorical raster data, while the second one is a continuous raster data containing weights.

wecoma_output = lsp_signature(c(landcover, random_ndvi), 
                               type = "wecoma", window = 100)
wecoma_output

Weighted co-occurrence vector ("wecove") is rearrangement of a weighted co-occurrence matrix into one-dimensional object:

wecove_output = lsp_signature(c(landcover, random_ndvi),
                               type = "wecove", window = 100)
wecove_output

Learn more about these representations at https://nowosad.github.io/comat/articles/wecoma.html.

An integrated co-occurrence matrix (`incoma`) representation - two or more layers

The next type of signature is an integrated co-occurrence matrix (incoma). It requires two or more layers - a stars object with two or more attributes. All layers must be categorical raster data.

incoma_output = lsp_signature(c(landcover, landform),
                               type = "incoma", window = 100)

Integrated co-occurrence vector ("incove") is rearrangement of an integrated co-occurrence matrix into one-dimensional object:

incove_output = lsp_signature(c(landcover, landform),
                               type = "incove", window = 100)

Learn more about these representations at https://nowosad.github.io/comat/articles/incoma.html

A composition representation (`"composition"`) - one layer

A composition signature describes proportions of categories in a local landscape. It requires one layer (a stars object with one attribute).

composition_output = lsp_signature(landcover, 
                                    type = "composition", window = 100)
composition_output

By default, it is normalized to sum to 1:

composition_output$signature[[1]]

To get an actual number of cells of each category, the normalization should be set to "none":

composition_output2 = lsp_signature(landcover, 
                                    type = "composition", window = 100,
                                    normalization = "none")
composition_output2$signature[[1]]

User-defined functions - one or more layers

The motif package also allows calculating signature based on a user-defined function. This function should accept only one argument, which is a list containing one or more matrices. For example my_fun() below counts how many non-NA cells exist in each local landscape.

my_fun = function(x){
  sum(!is.na(c(x[[1]])))
}

Importantly, we need to set normalization = "none" to prevent the normalization of the output.

my_fun_output = lsp_signature(landcover, 
                               type = my_fun, window = 100,
                               normalization = "none")
my_fun_output

We can see that in the first local landscape we have r my_fun_output$signature[[1]] non-NA cells.