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)
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.
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.
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)