greenSD (version 0.1.1)

compute_morphology: compute_morphology

Description

Compute greenspace morphology metrics at patch (Nowosad & Stepinski, 2019) or landscape level (see details), including average size (AREA_MN), fragmentation (PD), connectedness (COHESION), aggregation (AI), and complexity of the shape (SHAPE_AM), related to public health (Wang et al., 2024)

Usage

compute_morphology(r = NULL, directions = 4, grid_size = NULL, quiet = TRUE)

Value

A SpatVector object contains indivisual patches with metrics at patch level, when grid_size = NULL.

A SpatVector object contains landscape-level value of metrics, when grid_size is not NULL.

Arguments

r

SpatRaster. A single-band binary greenspace raster, where 0 or NA represents non-green areas and 1 represents green areas.

directions

numeric. The number of directions in which patches should be connected: 4 (default) or 8.

grid_size

numeric or sf polygons. (Optional) If specified, morphology metrics at grid level will be computed based on the size (in meters) of given grid cells or input (sf) polygons.

quiet

logical. Whether show progress bars for some process.

Details

To get information of metrics, please use landscapemetrics::list_lsm().

References

Nowosad J., TF Stepinski. 2019. Information theory as a consistent framework for quantification and classification of landscape patterns. https://doi.org/10.1007/s10980-019-00830-x

Wang, H., & Tassinary, L. G. (2024). Association between greenspace morphology and prevalence of non-communicable diseases mediated by air pollution and physical activity. Landscape and Urban Planning, 242, 104934.

Examples

green <- get_tile_green(
                        # bbox = c(-83.087174,42.333373,-83.042542,42.358748),
                        provider = "esri",
                        zoom = 16)
# p <- terra::ifel(green$green == 0, NA, 1)
m <- compute_morphology(
                       #r = p
                       directions = 8)