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Organisation of the data and README information file.
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ESA99 committed Aug 18, 2022
1 parent beaa95f commit 229b691
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35 changes: 23 additions & 12 deletions LSat_dataCD.R → TS_bfast_Amtsvenn_esanchez/LSat_dataCD.R
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Expand Up @@ -15,11 +15,11 @@ setwd("~/R/Projects/Landsat_AV/data")

#________Load and prepare data_______________

# A <- read.csv("AV_L_clip.csv")
# B <- read.csv("L_AV_Bog_new.csv")
# C <- read.csv("L_AV_bog_plotR_new.csv")
#
# A <- subset (A, select = -c(3,5,6))
A <- read.csv("AV_L_clip.csv")
B <- read.csv("L_AV_Bog_new.csv")
C <- read.csv("L_AV_bog_plotR_new.csv")

A <- subset (A, select = -c(3,5,6))

data.prep <- function(a){

Expand Down Expand Up @@ -49,9 +49,9 @@ data.prep <- function(a){
return(a)
}

# A <- data.prep(A)
# B <- data.prep(B)
# C <- data.prep(C)
A <- data.prep(A)
B <- data.prep(B)
C <- data.prep(C)


# Dates and coordinates
Expand All @@ -64,10 +64,21 @@ Bcoordinates <- unique(B$coordinates)
Cdates <- unique(C$date)
Ccoordinates <- unique(C$coordinates)

#
A_agg <- read.table("/A_agg.txt", sep = ";", dec = ".", header = T)
B_agg <- read.table("/B_agg.txt", sep = ";", dec = ".", header = T)
C_agg <- read.table("/C_agg.txt", sep = ";", dec = ".", header = T)

#'*If loading the aggregated subsets: *
# A_agg <- read.table("/A_agg.txt", sep = ";", dec = ".", header = T)
# B_agg <- read.table("/B_agg.txt", sep = ";", dec = ".", header = T)
# C_agg <- read.table("/C_agg.txt", sep = ";", dec = ".", header = T)
#
# Adates <- unique(A_agg$date)
# Acoordinates <- unique(A_agg$coordinates)
#
# Bdates <- unique(B_agg$date)
# Bcoordinates <- unique(B_agg$coordinates)
#
# Cdates <- unique(C_agg$date)
# Ccoordinates <- unique(C_agg$coordinates)


# Aggregate
A_agg <- aggregate.data.frame(A, by = list(A$date), FUN = median)
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17 changes: 17 additions & 0 deletions TS_bfast_Amtsvenn_esanchez/README.txt
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@@ -0,0 +1,17 @@
L_Sat_dataCD:
Loading of all the required packages as well as preparing the data for the analysis.

Time_SeriesCD:
Quarterly and Monthly time series analysis as well as for one month yearly.

MonthRegCD:
Calculation and presentation of change intensities by month.


To get the full data as well as the hyperspectral measurements vistit:
https://uni-muenster.sciebo.de/s/bW0QtZmXvgSJdYo



Landsat data was retrieved via Google Earth Engine and hyperspectral field measurements performed by Emilio Sanchez.

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