- Introduction
- Methodology
- Prepare and Download sentinel data from GEE.
- Compute the dNBR and other indexes.
- Workflow
- Burn Severity
- Burn severity (Autumn Fire)
- Mapping
- Burn severity (Spring Fire)
- Mapping
- Burn Severity (all areas)
- Burn Severity by PLOTS
fire_severity_sentinel
Antonio J. Pérez-Luque (@ajpelu)
2021-09-20
Last updated: 2021-09-22
Checks: 7 0
Knit directory: fire_alcontar/
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library(tidyverse)
library(sf)
library(raster)
library(here)
library(DiagrammeR)
library(exactextractr)
library(rasterVis)
library(ggpubr)
library(patchwork)
library(DT)Introduction
Our aim is to compute the Burn Severity for the two Fires (atumn and spring fires) in our study area.
There are several indexes to compute the burn severity, all of them derived from the Normalized Burn Ratio (NBR). The NBR is defined as: NBR=ρNIR−ρSWIRρNIR+ρSWIR
with ρNIR as the reflectance in the near-infrared, and ρSWIR as the reflectande in the shortwave infrared.This index is based on the observation that the healthy vegetation shows a very high reflectance in the NIR and a low reflectance in the SWIR portion of the spectrum. On the other hand, recently burnt areas show low reflectances in the NIR and high reflectances in the SWIR. Therefore, high values of NBR indicate healthy vegetation, and low values indicate bare ground and recently burnt areas.
The difference between pre-fire and post-fire NBR images is used to compute the ΔNBR (or dNBR). Higher value of dNBR indicates more severe damage.
There are several formulas to compute dNBR:
ΔNBR=prefireNBR−postfireNBR o dNBR
RelativeΔNBR=ΔNBRprefireNBR o RdNBR
RelativizedBR=ΔNBR(prefireNBR+1.001)
Methodology
Prepare and Download sentinel data from GEE.
We compute two pre-fire images and two post-fire images for each of the fires analyzed. The images’ date depend on the availability of Sentinel images. Specifically we processes the following dates:
- Autumn fire (2018-18-12): pre-fire: 20181212, 20181217; post-fire: 20181222, 20181227;
- Spring fire (2019-05-07): pre-fire: 20190426, 20190506; post-fire: 20190511, 20190514;
For each date we computed the NBR index as the above mentioned equation (see this GEE code)
# Ojo ver en este trabajo el codigo GEE https://www.mdpi.com/2072-4292/10/6/879 - Images were downloaded and processes in R.
Compute the dNBR and other indexes.
In R, we computed the dNBR and the RBR (see Parks et al. 2014). The RBR is a modification of the RdNBR, that is less correlated with the previous fire condition
We explored the correlation of both indexes with the prefire conditions (prefireNBR). See Parks et al. 2014.
Then we generated a raster with the nbr (dNBR) using only the previous and post fire images.
Workflow
l <- list.files(here::here("data/sentinel_nbr"),
pattern=".tif",
full.names = TRUE)
r <- raster::stack(l)
# Read spatial data
# Add fire treatment: if NP or P --> AutumnFire /else/ SpringFire
ep <- st_read(dsn = here::here("data/spatial/EP_Andalucía.shp"),
quiet = TRUE) %>%
mutate(fireEpoch = case_when(ABREVIA == "PR" ~ "Spring",
TRUE ~ "Autumn"))
ep_autumn <- subset(ep, fireEpoch == "Autumn")
ep_spring <- subset(ep, fireEpoch == "Spring")my.at <- c(-.25, -.1, .1, .27, .44, .66)
cols <- c("#1a9850", # Regrowth-High
"#91cf60", # Regrowth-Low
"#d9ef8b", # Unburned
"#ffffbf", # Low Severity
"#fee08b", # Moderate-Low Severity
"#fc8d59", # Moderate-High Severity
"#d73027") # High Severity
# cols <- colorRampPalette(c("lightgreen", "yellow","red" ))(length(my.at))Burn Severity
Burn severity (Autumn Fire)
nbr_autumn <- raster::overlay(
r[['s2nbr_2018.12.17']], r[['s2nbr_2018.12.22']],
fun = function(pre,post){return(pre-post)})
names(nbr_autumn) <- 'nbr'
rbr_autumn <- raster::overlay(
r[['s2nbr_2018.12.17']], r[['s2nbr_2018.12.22']],
fun = function(pre,post){return((pre-post)/(pre + 1.001))})
names(rbr_autumn) <- 'rbr'- Explore the correlation of both indices
rbr_autumn.df <- as.data.frame(rbr_autumn, xy=TRUE)
nbr_autumn.df <- as.data.frame(nbr_autumn, xy=TRUE)
prefire_autumn.df <- as.data.frame(r[['s2nbr_2018.12.17']], xy= TRUE)
y <- inner_join(rbr_autumn.df,
nbr_autumn.df) %>%
inner_join(prefire_autumn.df) %>%
mutate(severity =
cut(nbr, breaks = c(-Inf, -.251, -.101, .099,
.269, .439,.659, Inf),
labels = c(
"Regrowth High", "Regrowth Low", "Unburned",
"Low Severity", "Moderate Low Severity",
"Moderate High Severity", "High Severity")
))
## Correlation of NBR or RBR with previous prefire values.
## See Parks et al. 2014 doi:10.3390/rs6031827
p.nbr <- ggscatter(y,
x = "s2nbr_2018.12.17",
y = "nbr", alpha=.5,
color = "severity") +
stat_cor(p.accuracy = 0.001, r.accuracy = 0.01,
color = "red") +
ylab("NBR") +
xlab("Prefire-NBR")
p.rbr <- ggscatter(y,
x = "s2nbr_2018.12.17",
y = "rbr",
color = "severity",
alpha=.5) +
stat_cor(p.accuracy = 0.001, r.accuracy = 0.01,
color = "red") +
ylab("RBR") +
xlab("Prefire-NBR")
p.nbr + p.rbr
Mapping
nbr_autumn_crop <- crop(nbr_autumn, extent(ep_autumn))
nbr_autumn_mask <- mask(nbr_autumn_crop, ep_autumn)
levelplot(nbr_autumn_mask, margin = FALSE,
at=my.at,
col.regions = cols,
color=list(
labels = list(at=my.at -0.1,
labels =
c("regrowth",
"unburned",
"low",
"moderate-low",
"moderate-high",
"high")))) +
layer(sp::sp.polygons(as_Spatial(ep_autumn)))
Burn severity (Spring Fire)
nbr_spring <- raster::overlay(
r[['s2nbr_2019.05.06']], r[['s2nbr_2019.05.11']],
fun = function(pre,post){return(pre-post)})
names(nbr_spring) <- 'nbr'
rbr_spring <- raster::overlay(
r[['s2nbr_2019.05.06']], r[['s2nbr_2019.05.11']],
fun = function(pre,post){return((pre-post)/(pre + 1.001))})
names(rbr_spring) <- 'rbr'- Explore the correlation of both indices
rbr_spring.df <- as.data.frame(rbr_spring, xy=TRUE)
nbr_spring.df <- as.data.frame(nbr_spring, xy=TRUE)
prefire_spring.df <- as.data.frame(r[['s2nbr_2019.05.06']], xy= TRUE)
y <- inner_join(rbr_spring.df,
nbr_spring.df) %>%
inner_join(prefire_spring.df) %>%
mutate(severity =
cut(nbr, breaks = c(-Inf, -.251, -.101, .099,
.269, .439,.659, Inf),
labels = c(
"Regrowth High", "Regrowth Low", "Unburned",
"Low Severity", "Moderate Low Severity",
"Moderate High Severity", "High Severity")
))
## Correlation of NBR or RBR with previous prefire values.
## See Parks et al. 2014 doi:10.3390/rs6031827
p.nbr <- ggscatter(y,
x = "s2nbr_2019.05.06",
y = "nbr", alpha=.5,
color = "severity") +
stat_cor(p.accuracy = 0.001, r.accuracy = 0.01,
color = "red") +
ylab("NBR") +
xlab("Prefire-NBR")
p.rbr <- ggscatter(y,
x = "s2nbr_2019.05.06",
y = "rbr", alpha=.5,
color = "severity") +
stat_cor(p.accuracy = 0.001, r.accuracy = 0.01,
color = "red") +
ylab("RBR") +
xlab("Prefire-NBR")
p.nbr + p.rbr
Mapping
nbr_spring_crop <- crop(nbr_spring, extent(ep_spring))
nbr_spring_mask <- mask(nbr_spring_crop, ep_spring)
levelplot(nbr_spring_mask, margin = FALSE,
at=my.at,
col.regions = cols,
color=list(
labels = list(at=my.at -0.1,
labels =
c("regrowth",
"unburned",
"low",
"moderate-low",
"moderate-high",
"high")))) +
layer(sp::sp.polygons(as_Spatial(ep_spring)))
Burn Severity (all areas)
nbr_alcontar <- raster::merge(nbr_spring_mask, nbr_autumn_mask)
levelplot(nbr_alcontar, margin = FALSE,
at=my.at,
col.regions = cols,
color=list(
labels = list(at=my.at -0.1,
labels =
c("regrowth",
"unburned",
"low",
"moderate-low",
"moderate-high",
"high")))) +
layer(sp::sp.polygons(as_Spatial(ep_spring))) +
layer(sp::sp.polygons(as_Spatial(ep_autumn))) 
writeRaster(nbr_alcontar, here::here("data/spatial/computed_nbr.tiff"), overwrite=TRUE)rbr_spring_crop <- crop(rbr_spring, extent(ep_spring))
rbr_spring_mask <- mask(rbr_spring_crop, ep_spring)
rbr_autumn_crop <- crop(rbr_autumn, extent(ep_autumn))
rbr_autumn_mask <- mask(rbr_autumn_crop, ep_autumn)
rbr_alcontar <- raster::merge(rbr_spring_mask, rbr_autumn_mask)
writeRaster(rbr_alcontar, here::here("data/spatial/computed_rbr.tiff"), overwrite = TRUE)Burn Severity by PLOTS
- Now we explore the burn severity of each plot
parcelas <- st_read(dsn = here::here("data/spatial/GEO_PARCELAS.shp"),
quiet = TRUE)
p <- subset(parcelas, TIPO=="QUEMA")nbr_parcelas <- exact_extract(nbr_alcontar, p,
include_cols = "NOMBRE",
force_df = TRUE,
progress = FALSE) %>%
bind_rows() %>%
mutate(fract = round(coverage_fraction,2)) %>%
mutate(nbr_class =
cut(value,
breaks = c(-Inf, -.251, -.101, .099,
.269, .439,.659, Inf),
labels = c(
"Regrowth High", "Regrowth Low", "Unburned",
"Low Severity", "Moderate Low Severity",
"Moderate High Severity", "High Severity")
))
df <- nbr_parcelas %>%
group_by(NOMBRE, nbr_class) %>%
summarise(n = sum(fract)) %>% # ponderamos por frac
pivot_wider(names_from = nbr_class, values_from = n) %>%
rowwise() %>%
mutate(n = sum(Unburned,`Low Severity`,`Moderate Low Severity`, na.rm=TRUE)) %>%
mutate(unburned.pct = Unburned / n,
low.pct = `Low Severity` / n,
moderatelow.pct = `Moderate Low Severity` / n) %>%
mutate(treatment = case_when(
str_detect(NOMBRE, "AL_NP") ~ "NonGrazing",
TRUE ~ "Grazing"
))df %>% dplyr::select(NOMBRE,
unburned.pct,
low.pct,
moderatelow.pct) %>%
DT::datatable() %>%
formatRound(columns=c("unburned.pct", "low.pct", "moderatelow.pct"), digits=2)df %>% dplyr::select(NOMBRE, treatment,
unburned.pct,
low.pct,
moderatelow.pct) %>%
pivot_longer(cols=unburned.pct:moderatelow.pct) %>%
ggplot(aes(x=name, y=value, fill=name)) + geom_bar(stat="identity") +
facet_wrap(~treatment+NOMBRE, ncol=4) +
scale_fill_manual(breaks = c("unburned.pct",
"low.pct",
"moderatelow.pct"),
values = c("lightgreen",
"lightyellow",
"orange")) +
theme_minimal() +
ylab("% pixels") + xlab("") +
theme(axis.text.x = element_blank(),
legend.position = "bottom")
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] DT_0.17 patchwork_1.1.1 ggpubr_0.4.0
[4] rasterVis_0.49 latticeExtra_0.6-29 lattice_0.20-41
[7] exactextractr_0.5.1 DiagrammeR_1.0.6.1 here_1.0.1
[10] raster_3.4-5 sp_1.4-5 sf_1.0-2
[13] forcats_0.5.1 stringr_1.4.0 dplyr_1.0.6
[16] purrr_0.3.4 readr_1.4.0 tidyr_1.1.3
[19] tibble_3.1.2 ggplot2_3.3.5 tidyverse_1.3.1
[22] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] colorspace_2.0-0 ggsignif_0.6.0 ellipsis_0.3.2 class_7.3-18
[5] rio_0.5.16 rgdal_1.5-23 rprojroot_2.0.2 fs_1.5.0
[9] rstudioapi_0.13 proxy_0.4-26 farver_2.0.3 hexbin_1.28.2
[13] fansi_0.4.2 lubridate_1.7.10 xml2_1.3.2 codetools_0.2-18
[17] knitr_1.31 jsonlite_1.7.2 broom_0.7.9 dbplyr_2.1.1
[21] png_0.1-7 compiler_4.0.2 httr_1.4.2 backports_1.2.1
[25] assertthat_0.2.1 fastmap_1.1.0 cli_2.5.0 later_1.1.0.1
[29] visNetwork_2.0.9 htmltools_0.5.2 tools_4.0.2 gtable_0.3.0
[33] glue_1.4.2 Rcpp_1.0.7 carData_3.0-4 cellranger_1.1.0
[37] jquerylib_0.1.3 vctrs_0.3.8 crosstalk_1.1.1 xfun_0.23
[41] openxlsx_4.2.3 rvest_1.0.0 lifecycle_1.0.0 rstatix_0.6.0
[45] zoo_1.8-8 scales_1.1.1 hms_1.0.0 promises_1.2.0.1
[49] parallel_4.0.2 RColorBrewer_1.1-2 yaml_2.2.1 curl_4.3
[53] sass_0.3.1 stringi_1.7.4 highr_0.8 e1071_1.7-8
[57] zip_2.1.1 rlang_0.4.10 pkgconfig_2.0.3 evaluate_0.14
[61] labeling_0.4.2 htmlwidgets_1.5.3 tidyselect_1.1.1 magrittr_2.0.1
[65] R6_2.5.0 generics_0.1.0 DBI_1.1.1 pillar_1.6.1
[69] haven_2.3.1 whisker_0.4 foreign_0.8-81 withr_2.4.1
[73] units_0.7-2 abind_1.4-5 modelr_0.1.8 crayon_1.4.1
[77] car_3.0-10 KernSmooth_2.23-18 utf8_1.1.4 rmarkdown_2.8
[81] jpeg_0.1-8.1 grid_4.0.2 readxl_1.3.1 data.table_1.14.0
[85] git2r_0.28.0 reprex_2.0.0 digest_0.6.27 classInt_0.4-3
[89] httpuv_1.5.5 munsell_0.5.0 viridisLite_0.3.0 bslib_0.2.4