temporal_evolution_ndvi
Antonio J Perez-Luque
2021-07-13
Last updated: 2021-07-19
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Knit directory: ndvi_alcontar/
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library(tidyverse)
library(stringr)
library(here)
library(DiagrammeR)
library(plotrix)
library(dygraphs)
library(xts)
library(lubridate)
library(ISOweek)Perpare data
Prepare data. Split dataset into metadata (
parcela_metadata) (parcelas’s name, coordinates, cell identifier), and data (ndvi) (value of ndvi by date and parcela; long format)Remove ndvi values < 0
Create new tags for the treatment:
- PR ~ Grazing+Spring
- P ~ Grazing+Autumn
- NP ~ NoGrazing+Autumn
- CON ~ Control
df <- read_csv(here::here("data/s2ndvi.csv")) %>%
mutate(treatment = case_when(
str_detect(NOMBRE, "AL_PR") ~ "Grazing+Spring",
str_detect(NOMBRE, "AL_P_") ~ "Grazing+Autumn",
str_detect(NOMBRE, "AL_NP_") ~ "NoGrazing+Autumn",
str_detect(NOMBRE, "AL_CON_NP") ~ "Control",
str_detect(NOMBRE, "AL_CON_P") ~ "Control"))
ndvi <- df %>% filter(value >= 0)- Average values by date, parcela and treatment
ndvi_avg <- ndvi %>%
mutate(datew = paste(
year(date),
paste0("W", sprintf("%02d", week(date))),
sep = "-")) %>%
group_by(NOMBRE, treatment, datew) %>%
summarise(ndvi_mean = mean(value, na.rm=TRUE),
ndvi_sd = sd(value, na.rm=TRUE),
ndvi_se = plotrix::std.error(value, na.rm=TRUE),
n = length(value)) %>%
ungroup() %>%
mutate(date = ISOweek2date(paste(datew, "1", sep="-"))) - Prepare control values
control_np <- ndvi_avg %>%
filter(str_detect(NOMBRE, "AL_CON_NP")) %>%
group_by(date) %>%
summarise(
CONTROL = mean(ndvi_mean, na.rm = TRUE),
CONTROL_sd = sd(ndvi_mean, na.rm = TRUE),
CONTROL_se = plotrix::std.error(ndvi_mean, na.rm = TRUE),
n = length(ndvi_mean)
) %>%
ungroup()
control_p <- ndvi_avg %>%
filter(str_detect(NOMBRE, "AL_CON_P")) %>%
group_by(date) %>%
summarise(
CONTROL = mean(ndvi_mean, na.rm = TRUE),
CONTROL_sd = sd(ndvi_mean, na.rm = TRUE),
CONTROL_se = plotrix::std.error(ndvi_mean, na.rm = TRUE),
n = length(ndvi_mean)
) %>%
ungroup()
control_all <- ndvi_avg %>%
filter(str_detect(NOMBRE, "AL_CON_")) %>%
group_by(date) %>%
summarise(
CONTROL = mean(ndvi_mean, na.rm = TRUE),
CONTROL_sd = sd(ndvi_mean, na.rm = TRUE),
CONTROL_se = plotrix::std.error(ndvi_mean, na.rm = TRUE),
n = length(ndvi_mean)
) %>%
ungroup()Compute QNDVI
QNDVI represents a way of monitoring vegetation recovery after burning (see Díaz-Delgado et al. 2002, and references therein). It is the quotient between the NDVI values of the burned area and the unburned control areas of similar phenological variation and specific composition before the fire.
QNDVI provides revegetation monitoring that is more independent of year and period of year.
Wildfire produce a clear drop in NDVI.
Regeneration could be evaluated simply monitoring NDVI recovery after fire. However, results would be affected by seasonal and interannual variations in phenology (note that images are not always available at the same period because of cloud coverage) and by the particular climatic conditions of each year. In order to minimize these effects, the quotient between the average NDVI measurements of burned areas and the average NDVI measurements of unburned, neighboring areas are computed.
qndvi <- ndvi_avg %>%
filter(!(str_detect(NOMBRE, "AL_CON"))) %>%
dplyr::select(NOMBRE, ndvi_mean, date) %>%
pivot_wider(values_from = ndvi_mean, names_from = NOMBRE)
qndvi <- inner_join(
qndvi,
control_all %>%
dplyr::select(date, CONTROL)
) %>%
mutate_at(vars(starts_with("AL_")), function(i) i / .$CONTROL)Explore QNDVI at plot level
qndvi_long <- qndvi %>%
pivot_longer(-date, names_to = "NOMBRE", values_to = "Qndvi") %>%
filter(NOMBRE != "CONTROL") %>%
mutate(treatment = case_when(
str_detect(NOMBRE, "AL_PR") ~ "Grazing+Spring",
str_detect(NOMBRE, "AL_P_") ~ "Grazing+Autumn",
str_detect(NOMBRE, "AL_NP_") ~ "NoGrazing+Autumn")) %>%
filter(Qndvi < 2)
qndvi_long %>%
ggplot(aes(x = date, y = Qndvi, group = NOMBRE, colour = treatment)) +
geom_line() +
facet_wrap(~NOMBRE, scales = "free_y") +
geom_hline(yintercept = 1) +
theme_bw() +
theme(panel.grid.minor = element_blank(),
legend.position = "bottom")
qndvi_avg <- qndvi_long %>%
group_by(date, treatment) %>%
summarise(mean = mean(Qndvi, na.rm=TRUE),
sd = sd(Qndvi, na.rm=TRUE),
se = plotrix::std.error(Qndvi, na.rm=TRUE)) qndvi_avg %>%
filter(treatment != "Grazing+Spring") %>%
ggplot(aes(x=date, y=mean, colour=treatment, fill=treatment)) +
geom_ribbon(aes(ymin = mean - se, ymax=mean+se), alpha = .3) +
geom_line(aes(y=mean)) +
theme_bw() +
theme(panel.grid.minor = element_blank()) +
geom_hline(yintercept = 1) +
scale_colour_manual(values=c("blue", "orange")) +
scale_fill_manual(values=c("blue", "orange")) library(dygraphs)
library(xts)
np <- qndvi_avg %>%
filter(treatment == "NoGrazing+Autumn") %>%
rowwise() %>%
mutate(lwr.np = mean - se,
upr.np = mean + se) %>%
dplyr::select(date, mean.np = mean, lwr.np, upr.np)
p <- qndvi_avg %>%
filter(treatment == "Grazing+Autumn") %>%
rowwise() %>%
mutate(lwr.p = mean - se,
upr.p = mean + se) %>%
dplyr::select(date, mean.p = mean, lwr.p, upr.p)
pr <- qndvi_avg %>%
filter(treatment == "Grazing+Spring") %>%
rowwise() %>%
mutate(lwr.pr = mean - se,
upr.pr = mean + se) %>%
dplyr::select(date, mean.pr = mean, lwr.pr, upr.pr)
df.ts <- inner_join(np, p) %>%
inner_join(pr)
ts <- xts(x= df.ts[,-1], order.by =df.ts$date)Grazing+Autumn vs. NoGrazing+Autumn
QNDVI
### NDVI
All series
QNDVI
NDVI
Appendix
- Explore NDVI evolution of control plots
control_np <- ndvi_avg %>%
filter(str_detect(NOMBRE, "AL_CON_NP")) %>%
group_by(date) %>%
summarise(
CONTROL = mean(ndvi_mean, na.rm = TRUE),
CONTROL_sd = sd(ndvi_mean, na.rm = TRUE),
CONTROL_se = plotrix::std.error(ndvi_mean, na.rm = TRUE),
n = length(ndvi_mean)
) %>%
ungroup()
control_p <- ndvi_avg %>%
filter(str_detect(NOMBRE, "AL_CON_P")) %>%
group_by(date) %>%
summarise(
CONTROL = mean(ndvi_mean, na.rm = TRUE),
CONTROL_sd = sd(ndvi_mean, na.rm = TRUE),
CONTROL_se = plotrix::std.error(ndvi_mean, na.rm = TRUE),
n = length(ndvi_mean)
) %>%
ungroup()
control_all <- ndvi_avg %>%
filter(str_detect(NOMBRE, "AL_CON_")) %>%
group_by(date) %>%
summarise(
CONTROL = mean(ndvi_mean, na.rm = TRUE),
CONTROL_sd = sd(ndvi_mean, na.rm = TRUE),
CONTROL_se = plotrix::std.error(ndvi_mean, na.rm = TRUE),
n = length(ndvi_mean)
) %>%
ungroup()ggplot(control_all, aes(x=date, y=CONTROL))+
geom_line() +
geom_line(data=control_np, aes(x=date, y=CONTROL), colour = "blue") +
geom_line(data=control_p, aes(x=date, y=CONTROL), colour = "red") +
theme_bw() 
sessionInfo()R version 4.0.2 (2020-06-22)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Catalina 10.15.3
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] ISOweek_0.6-2 lubridate_1.7.10 xts_0.12.1 zoo_1.8-8
[5] dygraphs_1.1.1.6-1 plotrix_3.8-1 DiagrammeR_1.0.6.1 here_1.0.1
[9] forcats_0.5.1 stringr_1.4.0 dplyr_1.0.6 purrr_0.3.4
[13] readr_1.4.0 tidyr_1.1.3 tibble_3.1.2 ggplot2_3.3.3
[17] tidyverse_1.3.1 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] Rcpp_1.0.6 lattice_0.20-41 visNetwork_2.0.9 assertthat_0.2.1
[5] rprojroot_2.0.2 digest_0.6.27 utf8_1.1.4 R6_2.5.0
[9] cellranger_1.1.0 backports_1.2.1 reprex_2.0.0 evaluate_0.14
[13] highr_0.8 httr_1.4.2 pillar_1.6.1 rlang_0.4.10
[17] readxl_1.3.1 rstudioapi_0.13 whisker_0.4 jquerylib_0.1.3
[21] rmarkdown_2.8 labeling_0.4.2 htmlwidgets_1.5.3 munsell_0.5.0
[25] broom_0.7.6 compiler_4.0.2 httpuv_1.5.5 modelr_0.1.8
[29] xfun_0.23 pkgconfig_2.0.3 htmltools_0.5.1.1 tidyselect_1.1.0
[33] fansi_0.4.2 crayon_1.4.1 dbplyr_2.1.1 withr_2.4.1
[37] later_1.1.0.1 grid_4.0.2 jsonlite_1.7.2 gtable_0.3.0
[41] lifecycle_1.0.0 DBI_1.1.1 git2r_0.28.0 magrittr_2.0.1
[45] scales_1.1.1 cli_2.5.0 stringi_1.5.3 farver_2.0.3
[49] fs_1.5.0 promises_1.2.0.1 xml2_1.3.2 bslib_0.2.4
[53] ellipsis_0.3.2 generics_0.1.0 vctrs_0.3.8 RColorBrewer_1.1-2
[57] tools_4.0.2 glue_1.4.2 hms_1.0.0 yaml_2.2.1
[61] colorspace_2.0-0 rvest_1.0.0 knitr_1.31 haven_2.3.1
[65] sass_0.3.1