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01_Extract_elevation.Rmd
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01_Extract_elevation.Rmd 3.25 KiB
title: "sPlot 3.0 - Construction"
subtitle: "Extract elevation"
author: "Francesco Maria Sabatini"
date: "6/21/2019"
output: html_document
Timestamp: r date()
Drafted: Francesco Maria Sabatini
Version: 1.0
This report describes how elevation data was matched to plot locations.
knitr::opts_chunk$set(echo = TRUE)
library(reshape2)
library(tidyverse)
library(readr)
library(dplyr)
library(data.table)
library(elevatr)
library(sp)
library(raster)
library(knitr)
library(kableExtra)
library(viridis)
library(grid)
library(gridExtra)
library(ggforce)
Import header data
header <- readr::read_csv("../sPlot_data_export/sPlot_data_header_fix1.csv",
col_types=cols(
PlotObservationID = col_double(),
PlotID = col_double(),
`TV2 relevé number` = col_double(),
Country = col_factor(),
`Cover abundance scale` = col_factor(),
Author = col_character(),
`Date of recording` = col_date(format="%Y"),
`Relevé area (m²)` = col_double(),
`Altitude (m)` = col_double(),
`Aspect (°)` = col_double(),
`Slope (°)` = col_double(),
`Cover total (%)` = col_double(),
`Cover tree layer (%)` = col_double(),
`Cover shrub layer (%)` = col_double(),
`Cover herb layer (%)` = col_double(),
`Cover moss layer (%)` = col_double(),
`Cover lichen layer (%)` = col_double(),
`Cover algae layer (%)` = col_double(),
`Cover litter layer (%)` = col_double(),
`Cover open water (%)` = col_double(),
`Cover bare rock (%)` = col_double(),
`Height (highest) trees (m)` = col_double(),
`Height lowest trees (m)` = col_double(),
`Height (highest) shrubs (m)` = col_double(),
`Height lowest shrubs (m)` = col_double(),
`Aver. height (high) herbs (cm)` = col_double(),
`Aver. height lowest herbs (cm)` = col_double(),
`Maximum height herbs (cm)` = col_double(),
`Maximum height cryptogams (mm)` = col_double(),
`Mosses identified (y/n)` = col_logical(),
`Lichens identified (y/n)` = col_logical(),
Locality = col_character(),
Naturalness = col_factor(),
Forest = col_logical(),
Shrubland = col_logical(),
Wetland = col_logical(),
Grassland = col_logical(),
`Sparse vegetation` = col_logical(),
EUNIS = col_factor(),
Longitude = col_double(),
Latitude = col_double(),
`Location uncertainty (m)` = col_double(),
Dataset = col_factor()
))
Split data into tiles of 5 x 5 degrees, and create sp files
header.sp <- header %>%
dplyr::select(PlotObservationID, Longitude, Latitude) %>%
mutate_at(.vars=vars(Longitude, Latitude),
.funs=list(tile=~cut(., breaks = seq(-180,180, by=5)))) %>%
filter(!is.na(Longitude_tile) & !is.na(Latitude_tile) ) %>%
mutate(tilenam=factor(paste(Longitude_tile, Latitude_tile)))
#group_by(Longitude_tile, Latitude_tile) %>%
#summarize(n())
#create sp
sp.tile <- SpatialPoints(coords=header.sp %>%
filter(tilenam %in% levels(header.sp$tilenam)[1]) %>%
dplyr::select(Longitude, Latitude),
proj4string = CRS("+init=epsg:4326"))
raster.tile <- get_elev_point(sp.tile )