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How much miombo is in each country

DATE: 2018-11-15

AUTHOR: John L. Godlee

For some quick and dirty statistics to quote in the introduction to a report, I wanted to know how much of the miombo biome (as defined by White's vegetation map) was in Angola. Afterwards, I decided to try and apply the same methods to all the countries in southern Africa. I used R to do the analyses.

First, load some packages:

library(rgdal)
library(rgeos)

Next, import data on White's veg map[1] and African countries[2].

1: http://omap.africanmarineatlas.org/BIOSPHERE/pages/3_terrestrial%20vegetation.htm

2: http://maplibrary.org/library/stacks/Africa/index.htm

white_veg <- readOGR(dsn = "whitesveg", layer = "Whites vegetation")

countries <- readOGR(dsn="africa", layer="Africa")

The Coordinate reference system (CRS) isn't explicitly defined in either of the spatial objects, but it's a good guess that they will be WGS84 long-lat, so let's add that.

proj4string(white_veg) <- CRS("+proj=longlat +ellps=WGS84 +datum=WGS84")
proj4string(countries) <- CRS("+proj=longlat +ellps=WGS84 +datum=WGS84")

I'm not interested in the other biomes defined in White's veg. map, only those that make up the "miombo". I use the miombo definition used in Ryan et al. (2016)[3].

3: http://rstb.royalsocietypublishing.org/content/royptb/371/1703/20150312.full.pdf

miombo <- white_veg[which(
    (white_veg$DESCRIPTIO == "Moist-infertile savanna") |
    (white_veg$DESCRIPTIO == "Mosaics of forest") |
    (white_veg$DESCRIPTIO == "Mopane savanna") |
    (white_veg$DESCRIPTIO == "Montane Forest") |
    (white_veg$DESCRIPTIO == "Hydropmorphic grassland") |
    (white_veg$DESCRIPTIO == "Arid-fertile savanna") |
    (white_veg$DESCRIPTIO == "Sedge and reed swamp")),]

Then, I only want to keep countries that contain miombo, which I can do using gIntersects() from rgeos.

country_list <- split(countries, countries$COUNTRY)

intersections <- lapply(country_list,
    function(x)
        as.vector(unlist(gIntersects(x, miombo))))

country_list_miombo <- country_list[intersections == TRUE]

Then, I define a function to find UTM zones, because countries vary in their UTM zone, so I can't just use a flat value. Converting from lat-long to UTM is necessary so I can get km^2 area estimates, rather than square degrees. The function takes any spatial object and uses the bounding box to estimate the UTM zone.

# Define a function to find the UTM zone
utm.zone <- function(x){
    num <- floor(((mean(x@bbox[1,]) + 180)) / 6) + 1
    let <- ifelse(mean(x@bbox[2,]) > 0, "N", "S")

    return(paste0(num, let))
}

Then, time for a big lengthy, possibly overly messy function to return a list of miombo area stats for each country.

miombo.country.perc <- function(country, miombo){
    country_fix <- gBuffer(
    	country,
    	byid = TRUE,
    	width = 0)

    miombo_country <- gIntersection(
    	country_fix,
    	miombo,
    	byid = TRUE,
    	drop_lower_td = TRUE)

    miombo_utm <- spTransform(
    	miombo,
    	CRS(paste0("+proj=utm +zone=", utm.zone(miombo), " ellps=WGS84")))

    miombo_country_utm <- spTransform(
    	miombo_country,
    	CRS(paste0("+proj=utm +zone=", utm.zone(miombo_country), " ellps=WGS84")))

    country_utm <- spTransform(
    	country_fix,
    	CRS(paste0("+proj=utm +zone=", utm.zone(country_fix), " ellps=WGS84")))

    area_miombo_km2 <- gArea(miombo_utm) / 1e6

    area_miombo_country_km2 <- gArea(miombo_country_utm) / 1e6

    area_country_km2 <- gArea(country_utm) / 1e6

    perc_miombo_country <- area_miombo_country_km2 / area_country_km2 * 100

    perc_miombo_all <- area_miombo_country_km2 / area_miombo_km2 * 100

    return(data.frame(area_miombo_country_km2,
    	area_country_km2, perc_miombo_country, perc_miombo_all))
}

Then I need to run the function on each country in the list of countries, using lapply().

country_miombo_stats <- lapply(country_list_miombo, miombo.country.perc, miombo = miombo)

And finally there is a bit of cleaning up to get a tidy data frame.

# Collapse the resulting list
country_miombo_stats <- do.call("rbind", country_miombo_stats)

# Add country as column
country_miombo_stats$country <- rownames(country_miombo_stats)

I could probably spend more time to just have the function and lapply call give me the tidy dataframe straight off, but I don't have the inclination.

I think the most useful thing to come out of this little exercise is actually the UTM zone function, I think it's pretty neat.