Extract Habitat Classification from the Allen Coral Atlas

Introduction

Warning

Note

Dataset description

Geomorphic

Benthic

Further information

Access the data

Connect to the ACA Server

The code below allows you to create a connection to the server and explore the available data layers.

Code

library(sf) # simple features packages for handling vector GIS data
library(httr) # generic webservice package
library(tidyverse) # a suite of packages for data wrangling, transformation, plotting, ...
library(ows4R) # interface for OGC webservices
library(rnaturalearth) #World Map data from Natural Earth
library(leaflet)
library(leafem)
library(knitr)


#Base Layer for ACA GeoServer
aca_geo<-"https://allencoralatlas.org/geoserver/ows"

# Establish a connection
aca_client <- WFSClient$new(aca_geo, 
                           serviceVersion = "1.0.0")
#List of features
aca_lyrs<-aca_client$getFeatureTypes(pretty = TRUE)
aca_lyrs

                                 name                             title
1    coral-atlas:benthic_data_verbose    Allen Coral Atlas benthic data
2 coral-atlas:geomorphic_data_verbose Allen Coral Atlas geomorphic data

Request data for your monitoring region

Let’s assume I have two monitoring sites (i.e., locations) in Palau, and I want to extract the geomorphic habitat classification for these sites. The code below allows you to generate your sites and create a bounding box to delineate the spatial extent of your monitoring and query the GeoServer.

Code

url <- parse_url(aca_geo)

#Generate sites 
sites<- data.frame(x=c(134.35, 134.43), y=c(7.42,7.28))%>%
  as.matrix() %>%
  st_multipoint() %>% 
  st_sfc(crs=4326) %>% 
  st_cast('POINT') %>%
  st_sf(name=c("Site1", "Site2"))

# create a bounding box for a spatial query.
my.bbox<-st_bbox(st_buffer(sites,dist = 0.1))

leaflet()%>%addExtent(data=my.bbox,
                        color = "red", 
                          stroke=T,
                      weight = 1, 
                      smoothFactor = 0.5,
                      opacity = 1.0, 
                      fillOpacity = 0.5,
                      fillColor = NULL,
                      highlightOptions = highlightOptions(color = "white", weight = 2,bringToFront = TRUE))%>%
  addMarkers(data=sites, label = ~name)%>%
  addProviderTiles(providers$Esri.WorldImagery)

Using this bounding box, we can query the server to extract the geomorphic data within this region. The table below is a simple feature data frame (spatial object) containing all the data stored in the GeoServer for geopmorphic habitat classification within the bounding box.

Code

#convert bounding box into string for the query
my.bbox<- my.bbox %>%
  as.character()%>%
  paste(.,collapse = ',')

#set up your query
url$query <- list(service = "WFS",
                  version = "1.0.0",
                  request = "GetFeature",
                  typename = aca_lyrs$name[2], # I am selecting this layer:"reefcloud:storm4m_exposure_year_tier",
                  bbox = my.bbox,
                  width=768,
                  height=330,
                  srs="EPSG%3A4326",
                  styles='',
                  format="application/openlayers")
request <- build_url(url)

#request the data and set up coordinate reference
palau<- read_sf(request)%>%st_set_crs(4326)


pal <- colorFactor(
  palette = "YlOrRd",
  domain = palau$class_name
)

leaflet(palau)%>%addPolygons(color = "#444444", 
                          stroke=F,
                      weight = 1, 
                      smoothFactor = 0.5,
                      opacity = 1.0, 
                      fillOpacity = 1,
                      fillColor = ~pal(as.factor(palau$class_name)),
                      highlightOptions = highlightOptions(color = "white", weight = 2,bringToFront = TRUE))%>%
  addLegend("bottomright", pal = pal, values = ~as.factor(palau$class_name),
    title = "Geomorphic classification </br> Allen Coral Atlas",
    labFormat = labelFormat(),
    opacity = 1
  )%>%
  addMarkers(data=sites, label = ~name)%>%
  addProviderTiles(providers$Esri.WorldImagery)

Extract data for monitoring sites

The steps above allowed us to download the habitat data within the bounding box. Now the data are loaded in the memory, we want to extract the specific habitat classification for each site. This code intercepts your sites with the habitat layer to produce tabulated data of habitat classification at each site.

Code

data = st_intersection(st_buffer(sites, 0.0002), palau)%>%st_drop_geometry()%>%
  dplyr::select(name,class_name)%>%
  rename(Geomorphic=class_name)
  
kable(data,format="html",
             caption="Table 1. Geomophic habitat classification for monitoring sites. Source: Allen Coral Atlas.",
            ) 

Table 1. Geomophic habitat classification for monitoring sites. Source: Allen Coral Atlas.

	name	Geomorphic
2	Site2	Deep Lagoon
1	Site1	Inner Reef Flat

Now, you are ready to start your analyses. Enjoy!

Notes