CODEWITHSUNDEEP
rr-sp
Manuel Popp
Manuel Popp

Reputation: 1175

How to subset a large SpatialPolygonsDataFrame

I want to calculate the area of a wildfire. I tried this by substracting the NDVI calculated on a Landsat image before and another image after the fire and see where the NDVI was reduced. However, not only in the burning areas the NDVI has changed, but there are also many random differences. I used rasterToPolygons to create a large SpatialPolygonsDataFrame containing all areas where NDVI after - NDVI before < 0. Now I want to remove all the polygons with an area below a certain threshold value. However, I cannot find a way to subset the large SpatialPolygonsDataFrame.

I found an example on how to get a list of the polygons with an area above the threshold (where burned_poly is the large SpatialPolygonsDataFrame):

pols <- lapply(burned_poly@polygons , slot , "Polygons")
pols_areas <- lapply(pols[[2]], function(x) slot(x, "area"))

However, accessing the large SpatialPolygonsDataFrame like this

bp <- burned_poly@polygons[[1]]@Polygons[pols_areas >= 9000]

gives me a list which I am currently unable to coerce into a SpatialPolygonsDataFrame.

Can someone tell me how to do this last step (I have trouble with the Sf argument of which I don't know what it is in the SpatialPolygonsDataFrame function), or maybe there is a different and better approach to extract the fire extent as a polygon?

Upvotes: 0

Views: 2322

Answers (2)

Manuel Popp
Manuel Popp

Reputation: 1175

Alright, I think I have found a way thanks to Orlandos suggestion to use sf. I transformed my large SpatialPolygonsDataFrame object to a sf object via st_as_sf() which gave me a multipolygon. This stf_MULTIPOLYGON object can be subdivided into single polygons using st_cast() and the resulting object is subsettable like a data.frame.

bp_sf <- st_as_sf(burned_poly)
bps_sf <- st_cast(bp_sf, "POLYGON")
BpSf <- bps_sf[as.numeric(st_area(bps_sf))>=10000,]

Upvotes: 2

Orlando Sabogal
Orlando Sabogal

Reputation: 1630

If you are using the simple features sf library you can use functions from the tidyverse. Filtering data is a matter of using the filter() function. Notice that you can convert your objects to sf using st_as_sf(). See: https://r-spatial.github.io/sf/reference/st_as_sf.html and How to filter an R simple features collection using sf methods like st_intersects()?

Upvotes: 0

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