3. validation of mismatching locations

packages

# library(plyr)

data

Dataset created to validate used locations by roe and red deer. For each of the five roe and red deer population 100 points were compared with a satellite layer (i.e., ground-truth). Hence, in total we did a validation of 500 points per species. For some locations we were not able to determine the ground-truth due to inclarity of the satellite layer. These locations have a missing value (NA) in the column ‘satellite’.

• population: unique identifier study area as defined in the eurodeer database
• animals_id: unique identifier of animal as defined in the eurodeer database
• latitude: latitude of mismatching location (in WGS84)
• longitude: latitude of mismatching location (in WGS84)
• yearx: the year of the observation point
• tcd: 0/1, 1 if the location is identified as forest by tcd, else 0
• clc: 0/1, 1 if the location is identified as forest by clc, else 0
• satellite: 0/1, 1 if the location is identified as forest by the ground-truth layer, else 0
• correct: tcd, if tcd is corresponding to the ground-truth, else clc

# # load data 
# roe <- read.csv("../data/3_validation_gps/roe.csv",header=T, sep=',') 
# red <- read.csv("../data/3_validation_gps/red.csv",header=T, sep=',')
# 
# # change 0 and 1 to open and forest, respectively  
# roe[which(roe$tcd==1),]$tcd <- 'forest'
# roe[which(roe$tcd==0),]$tcd <- 'open'
# red[which(red$tcd==1),]$tcd <- 'forest'
# red[which(red$tcd==0),]$tcd <- 'open'
# roe[which(roe$satellite==1),]$satellite <- 'forest'
# roe[which(roe$satellite==0),]$satellite <- 'open'
# red[which(red$satellite==1),]$satellite <- 'forest'
# red[which(red$satellite==0),]$satellite <- 'open'
# roe$punto <- NULL
# red$punto <- NULL
# 
# head(roe)
# nrow(roe)
# 
# head(red)
# nrow(red)

table 3b: validation of mismatching points

# ## confusion matrices 
# 
# #### roe ####
# 
# ### absolute 
# (roe_t <- table(roe[,c('tcd','satellite')]))
# ### proportion
# (roe_p <- prop.table(roe_t))
# 
# #### red ####
# 
# #### absolute 
# (red_t <- table(red[,c('tcd','satellite')]))
# #### proportion 
# (red_p <- prop.table(red_t))