11/10/2023 0 Comments Save h5 file to raster in r![]() This data comes in units of hours of apparent fishing effort associated with the coordinate, geartype, and year of each observation. In 2022, OHI switched the data source from fisheries catch to apparent fishing effort for trawling and dredging from Global Fishing Watch using their new user-friendly API. The fisheries catch data was rasterized and overlaid onto polygons of exclusive economic zones for all OHI regions, then spatially standardized by summing each fishing coordinate within the exclusive economic zone and dividing that sum by the square kilometers of softbottom habitat. The way OHI historically calculated soft bottom habitat destruction was by using annual fisheries catch data as a proxy for trawling and dredging activity, because these types of fishing severely disturb benthic habitat. The Ocean Health Index (OHI) uses various spatial datasets to monitor the relationship between the health of marine systems and human well-being for 220 regions around the world. Let’s take a look at how we converted our workflow to calculate global soft bottom habitat destruction through the years 2012-2020. This is also know as down-sampling.Ĭreate a frequency table of the values of a raster. The value for the resulting cells is computed with a user-specified function. Aggregation groups rectangular areas to create larger cells. For example, the spatial object from which we are extracting is a geometry columns of polygons, the user cannot input the entire spatial dataframe, but rather needs to vectorize the geometry column of the polygons using terra::vect() then input that object into terra::extract().Ĭombine cells of a raster to create a new raster with a lower resolution. For terra, the second spatial object must be a vector or matrix/dataframe of coordinates. For raster(), the spatial objects can be points, lines, and polygons. Pull values from a raster object where they intersect the locations of another spatial object, such as points that fall within polygons. ![]() For example, you might want to add two rasters, but need to convert the first raster from a resolution of 0.5 degrees to 0.01 degrees to match the higher resolution of the second raster. terra has multiple functions with varying degrees of flexibility depending on if the function is applied across layers, and if the same function is applied to each layer.Ĭonvert the origin and/or resolution of a raster to that of another. Alternatively, if the files have already been read in as spatRasters, use c() to stack them and assign the stack to a new object name.Įxecute a function across a raster or raster stack. tif files in a directory, then automatically stacks them. terra::rast() is a more broadly applicable function since it can detect the quantity of spatRasters present as. tif or a spatial dataframe) into a rasterLayer (for the raster package) or spatRaster (for the terra package)Ĭreate raster stack to execute calculations across layers. Some examples of similar functions between raster and terra are as follows: raster The terra package is essentially the modern version of raster, but with faster processing speeds and more flexible functions. ![]() In pursuit of improving methodology and keeping up with the hip trends in environmental science, many scientists are motivated to make the spatial switch from raster to terra. Most data scientists have historically utilized the raster package to calculate cell values across stacked layers and recognize patterns over space and time. tif to name a few) with various resolutions and coordinate reference systems, it can be challenging to produce accurate maps. With so many different spatial file types (. Spatial data in R has a reputation for being tedious and time consuming. Switching from the raster package to terra for spatial analysis
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