In MarconiS/hyperspectral_meteR:

get the vegetation structure data filtered for those trees for which we have a subset of traits information. Result in 813 individual trees at continental scale

setwd("..")
inpath = "~/Documents/Data/Chapter3"
dat = sf::read_sf(paste(inpath, "field_data/cfc_field_data.shp", sep="/"))

field_data <- neonUtilities::loadByProduct("DP1.10026.001", check.size = F)

now, clip the information for the stem locations. Not ideal, for we ultimately want to get the itcs rather than the stem points, but a decent way to start it over. in this case we are extracting topography and spectra.

#extract just a single pixel per individual crown, assuming the coordinates hit the crown
inpath="~/Documents/Data/Chapter3"
inpath = "/Volumes/Athena/Chapter3_Data_clips/plots/"
prdr = c("_CHM", "_DSM", "_DTM", "lope", "pect")
get_list_augmented_matrix = pbapply::pblapply(1:dim(dat)[1], 
            function(x, prdr, inpath) get_dataset_from_point(dat[x,]),  prdr = prdr, inpath= inpath)

Quickly recompose dataset

#extract just a single pixel per individual crown, assuming the coordinates hit the crown
neon_data = do.call(rbind.data.frame, get_list_augmented_matrix)
readr::write_csv(neon_data, "./outdir/traits_16p_refl.csv")

And extract climate data from netcdf

#extract just a single pixel per individual crown, assuming the coordinates hit the crown
dat = dplyr::filter(data, !is.na(individualID))
climate_data =  get_climate(field_data = dat
                            , tmppath = "./tmp/climate/"
                            , startdate =1998
                            , enddate = 2018
                            , provider = "daymet")
climate_data = do.call(rbind.data.frame, climate_data)

Now, let's get to soil stuff.

soil_data <- list()
for(st in unique(field_data$siteID)){
  soil_data[[st]] <- field_data %>% filter(siteID == st) %>%  get_soil_features
}

s_data <- do.call(rbind.data.frame, soil_data) %>%
  dplyr::select(individualID, areasymbol, spatialversion, 
                musym, nationalmusym, mukey, muareaacres, mupolygonkey, geometry)

in.statement <- soilDB::format_SQL_in_statement((s_data$mukey))
q <- paste("SELECT mukey, muname FROM mapunit WHERE mukey IN ", in.statement, sep="")

q <- paste("SELECT 
mukey, cokey, comppct_r, compname, taxclname
FROM component
WHERE mukey IN ", in.statement, sep="")

q <- paste("SELECT 
component.mukey, compname, taxclname, 
taxorder, taxsuborder, taxgrtgroup, taxsubgrp
FROM legend
INNER JOIN mapunit ON mapunit.lkey = legend.lkey
INNER JOIN component ON component.mukey = mapunit.mukey
WHERE mapunit.mukey IN ", in.statement, sep="")

res <- soilDB::SDA_query(q)
res

Finally, get NEON data phylogenetic tree

species_list <- field_ %>% dplyr::select(taxonID, scientificName)
phylogeny_data <- get_cophenetic_distance(species_list)

MarconiS/hyperspectral_meteR documentation built on April 25, 2020, 12:59 a.m.