GSIF (version 0.5-5.1)

as.geosamples: Converts an object to geosamples class

Description

Converts an object of class "SoilProfileCollection" or "SpatialPointsDataFrame" to an object of class "geosamples" with all measurements broken into individual records. Geosamples are standardized spatially and temporally referenced samples from the Earth's surface.

Usage

# S4 method for SoilProfileCollection
as.geosamples(obj, 
    registry = as.character(NA), sample.area = 1, mxd = 2, TimeSpan.begin, TimeSpan.end)
# S4 method for SpatialPointsDataFrame
as.geosamples(obj, 
    registry = as.character(NA), sample.area = 1, mxd = 2, TimeSpan.begin, TimeSpan.end)

Arguments

obj

object of class "SoilProfileCollection"

registry

URI specifying the metadata registry (web-service that carries all metadata connected to the certain method ID and/or sample ID)

sample.area

standard sample area in square meters (assumed to be 1 by 1 m)

mxd

maximum depth of interest in meters

TimeSpan.begin

vector of class "POSIXct"; begin of the measurement period

TimeSpan.end

vector of class "POSIXct"; end of the measurement period

Value

Returns an object of type "geosamples". Many columns required by the "geosamples" class might be not available and will result in NA values. To ensure compatibility, when building an object of type "SoilProfilesCollection", use some standard naming convention to attach attributes to each measurement (horizons and sites slots in the "SoilProfileCollection-class"):

"locationError"

can be used to attach location errors in meters to each spatial location

"sampleArea"

can be used to attach spatial support to each measurement (usually 1 by 1 meter)

"measurementError"

can be used to attach specific measurement errors to each measurement in both site and horizons table

"IGSN"

can be used to attach the unique identifier (International Geo Sample Number) to each specific observation (corresponds to the "observationid" column)

See Also

geosamples-class, as.data.frame, aqp::SoilProfileCollection

Examples

# NOT RUN {
library(aqp)
library(plyr)
library(rgdal)
library(sp)
# sample profile from Nigeria:
lon = 3.90; lat = 7.50; time = as.POSIXct("1978", format="%Y") 
id = "ISRIC:NG0017"; TAXNFAO8 = "LXp" 
top = c(0, 18, 36, 65, 87, 127) 
bottom = c(18, 36, 65, 87, 127, 181)
ORCDRC = c(18.4, 4.4, 3.6, 3.6, 3.2, 1.2)
methodid = c("TAXNFAO8", "ORCDRC")
description = c("FAO 1988 classification system group", 
    "Method of Walkley-Black (Org. matter = Org. C x 1.72)")
units = c("FAO 1988 classes", "permille")
detectionLimit = c(as.character(NA), "0.1")
# prepare a SoilProfileCollection:
prof1 <- join(data.frame(id, top, bottom, ORCDRC), 
    data.frame(id, lon, lat, time, TAXNFAO8), type='inner')
depths(prof1) <- id ~ top + bottom
site(prof1) <- ~ lon + lat + time + TAXNFAO8 
coordinates(prof1) <- ~ lon + lat + time
proj4string(prof1) <- CRS("+proj=longlat +datum=WGS84")
# add measurement errors:
attr(prof1@horizons$ORCDRC, "measurementError") <- c(1.5, 0.5, 0.5, 0.5, 0.5, 0.5)
attr(prof1@sp@coords, "locationError") <- 1500
# add the metadata:
prof1@metadata <- data.frame(methodid, description, units, detectionLimit)
# convert to geosamples:
x <- as.geosamples(prof1)
x
# print only the sampled values of ORCDRC:
ORCDRC <- subset(x, "ORCDRC")
ORCDRC[,c("sampleid", "altitude", "observedValue")]

# convert object of type SpatialPointsDataFrame:
data(meuse)
# prepare columns:
names(meuse)[which(names(meuse)=="x")] = "longitude"
names(meuse)[which(names(meuse)=="y")] = "latitude"
meuse$altitude = -.15
meuse$time = unclass(as.POSIXct("1992-01-01"))
coordinates(meuse) <- ~ longitude + latitude + altitude + time
proj4string(meuse) <- CRS("+init=epsg:28992")
library(plotKML)
hm <- reproject(meuse[,c("zinc", "copper")])
hm.geo <- as.geosamples(hm)
hm.geo
# }