mct/map_biomes.Rmd at master · geco-bern/mct · GitHub

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---
title: "Mapping to biomes"
author: "Beni Stocker"
date: "5/14/2020"
output: html_document
---

```{r setup, include=FALSE}
library(sf)

library(rgdal)
library(sp)
library(raster)
library(rgeos)
library(XML)

source("R/get_ecoregions.R")
```

## Read Ecoregions

Following https://stackoverflow.com/questions/34272309/extract-shapefile-value-to-point-with-r
```{r}
shape <- shapefile("~/data/biomes/wwf_ecoregions/data/commondata/data0/wwf_terr_ecos.shp")
head(shape)
plot(shape[shape$BIOME == 10, ], axes = TRUE)

pts <- SpatialPoints(cbind(7, 46),
                     proj4string = CRS(proj4string(shape)))
over(pts, shape)
```

Using nice function found on internet ;-)
```{r}
out <- get_ecoregions(8.561630, 47.394666, var.extract = 'eco_code')
```

Or some other code I found: (https://github.com/azizka/speciesgeocodeR/blob/master/R/WwfLoad.R)
```{r}
x <- "~/data/biomes/"

download.file("http://assets.worldwildlife.org/publications/15/files/original/official_teow.zip",
        destfile = file.path(x, "wwf_ecoregions.zip"))

unzip(file.path(x, "wwf_ecoregions.zip"), exdir = file.path(x, "WWF_ecoregions"))
file.remove(file.path(x, "wwf_ecoregions.zip"))

## this works too
wwf <- readOGR(dsn = "~/data/biomes/wwf_ecoregions/official/",
               layer = "wwf_terr_ecos")

geo.proj <- proj4string(wwf)

# create SpatialPoints object for plots
pts <- sp::SpatialPoints(cbind(c(8.561630, 9.561630), c(47.394666, 47.394666)), proj4string = CRS(geo.proj))

# creates object that assigns each plot index to an ecoregion
out_ecoregion <- over(pts, wwf)
print(out_ecoregion)

## get meta info (can't make sense of it)
result <- xmlParse(file = "~/data/biomes/wwf_ecoregions/metadata/metadata1.xml")
result  # For a description of each Global 200 biome (1-14), see BIOME.</attrdef>
xmldataframe <- xmlToDataFrame("~/data/biomes/wwf_ecoregions/metadata/metadata1.xml")

## biome description is given here: http://omap.africanmarineatlas.org/BIOSPHERE/data/note_areas_sp/Ecoregions_Ecosystems/WWF_Ecoregions/WWFecoregions.htm

# BIOME (Formally known as Major Habitat Types or MHTs) - Broad kinds of ecoregions that:
# a) Experience comparable climatic regimes;
# b) Have similar vegetation structure;
# c) Display similar spatial patterns of biodiversity; and
# d) Contain flora and fauna with similar guild structures and life histories.
# e) Similar minimum requirements and thresholds for maintaining certain biodiversity features.
# f) Have similar sensitivities to human disturbance.
#
# There are 14 terrestrial biomes. Each biome is given a number (1-14) shown below:
# 1 = Tropical & Subtropical Moist Broadleaf Forests
# 2 = Tropical & Subtropical Dry Broadleaf Forests
# 3 = Tropical & Subtropical Coniferous Forests
# 4 = Temperate Broadleaf & Mixed Forests
# 5 = Temperate Conifer Forests
# 6 = Boreal Forests/Taiga
# 7 = Tropical & Subtropical Grasslands, Savannas & Shrublands
# 8 = Temperate Grasslands, Savannas & Shrublands
# 9 = Flooded Grasslands & Savannas
# 10 = Montane Grasslands & Shrublands
# 11 = Tundra
# 12 = Mediterranean Forests, Woodlands & Scrub
# 13 = Deserts & Xeric Shrublands
# 14 = Mangroves
```


Using sf library:
```{r}
shp_biome_wwf <- st_read('~/data/biomes/wwf_ecoregions/data/commondata/data0/')
```

## Including Plots

You can also embed plots, for example:

```{r pressure, echo=FALSE}
plot(pressure)
```

Note that the `echo = FALSE` parameter was added to the code chunk to prevent printing of the R code that generated the plot.