This repository provides the implementation of the RWI (Rescaled Water Index), a modification of the MNDWI, designed to enhance the mapping of water surfaces (WSs) in urban areas using Sentinel-2 satellite imagery.
The RWI has shown promising results compared to widely used indices such as NDWI, MNDWI, AWEIsh, and AWEInsh. Tests conducted in six South American cities (São Paulo, Curitiba, Florianópolis, Porto Alegre, Buenos Aires, and Viña del Mar) revealed that RWI achieved better results in three locations and the best overall performance. Its primary contribution lies in improving the detection of water bodies in urban contexts and the delineation of coastal and riverine boundaries.
This R implementation is based on the paper: “RWI: A New Spectral Index for Mapping Aquatic Surfaces in Urban Contexts”. The original study, which proposed and evaluated the RWI, is available on EarthArXiv: https://eartharxiv.org/repository/view/7005/. See the original repository here.
The RWI is calculated as follows:
Where:
-
$Green$ : Green band (B03) from Sentinel-2 imagery -
$Swir1$ : Short-wave infrared band (B11) from Sentinel-2 imagery -
$m_{d}$ : Median value of the region of interest -
$e$ : Euler’s number
# compute the n value for the region of interest
n_by_region <- function(x) {
temp_df <- transform(x,
B3Pow = B3^(1/exp(1)))
temp_df <- apply(temp_df[, c("B3", "B3Pow")], 2, median, na.rm = TRUE)
temp_df[[2]]/temp_df[[1]]
}
# compute the RWI
rwi <- function(green, swir1, n) {
e <- exp(1)
rwi <- (green^(1/e) / n - swir1) / (green^(1/e) / n + swir1)
return(rwi)
}Data input from the original repository (availabe here)
df_SR <- read.csv("data/samplePointsCities_20240811_harmonized.csv")
dplyr::glimpse(df_SR)## Rows: 123,780
## Columns: 9
## $ B2 <dbl> 0.0199, 0.0129, 0.0137, 0.0143, 0.0105, 0.0098, 0.0089, 0.015…
## $ B3 <dbl> 0.0282, 0.0227, 0.0262, 0.0284, 0.0225, 0.0209, 0.0167, 0.026…
## $ B4 <dbl> 0.0209, 0.0195, 0.0188, 0.0211, 0.0149, 0.0154, 0.0160, 0.016…
## $ B8 <dbl> 0.0130, 0.0152, 0.0201, 0.0149, 0.0176, 0.0130, 0.0159, 0.017…
## $ B11 <dbl> 0.0168, 0.0186, 0.0269, 0.0439, 0.0243, 0.0162, 0.0193, 0.015…
## $ B12 <dbl> 0.0149, 0.0132, 0.0183, 0.0299, 0.0143, 0.0146, 0.0152, 0.009…
## $ city <chr> "Vina del Mar", "Vina del Mar", "Vina del Mar", "Vina del Mar…
## $ surface <chr> "water", "water", "water", "water", "water", "water", "water"…
## $ classWat <chr> "coastal wetland", "coastal wetland", "coastal wetland", "coa…
df_SR$city %>% janitor::tabyl()## . n percent
## Buenos Aires 18200 0.1470351
## Curitiba 15035 0.1214655
## Florianopolis 14452 0.1167555
## Porto Alegre 28887 0.2333737
## Sao Paulo 27881 0.2252464
## Vina del Mar 19325 0.1561238
df_SR_splited <- df_SR %>%
split(.$city)
sp_city <- df_SR_splited[[5]] # São Paulo
dplyr::as_tibble(sp_city) %>% head(20)## # A tibble: 20 × 9
## B2 B3 B4 B8 B11 B12 city surface classWat
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> <chr> <chr>
## 1 0.0287 0.0487 0.0253 0.0193 0.0233 0.0149 Sao Paulo water artificial pond
## 2 0.0255 0.0414 0.0179 0.0127 0.0065 0.0074 Sao Paulo water artificial pond
## 3 0.0236 0.0394 0.0178 0.0105 0.0085 0.0085 Sao Paulo water artificial pond
## 4 0.0211 0.0297 0.0172 0.0178 0.006 0.0068 Sao Paulo water artificial pond
## 5 0.0202 0.0234 0.0169 0.0179 0.0445 0.0346 Sao Paulo water artificial pond
## 6 0.0249 0.0406 0.0195 0.0164 0.011 0.0117 Sao Paulo water artificial pond
## 7 0.0197 0.0242 0.0136 0.0155 0.0116 0.0081 Sao Paulo water artificial pond
## 8 0.0225 0.0319 0.0187 0.0671 0.0282 0.018 Sao Paulo water artificial pond
## 9 0.0243 0.0405 0.0185 0.0134 0.0054 0.0072 Sao Paulo water artificial pond
## 10 0.0252 0.0394 0.0204 0.016 0.0096 0.0057 Sao Paulo water artificial pond
## 11 0.0257 0.0409 0.019 0.0117 0.0067 0.0041 Sao Paulo water artificial pond
## 12 0.0242 0.041 0.0195 0.0102 0.01 0.0075 Sao Paulo water artificial pond
## 13 0.0192 0.0274 0.0145 0.0154 0.0108 0.0042 Sao Paulo water artificial pond
## 14 0.0217 0.0278 0.0155 0.0328 0.0457 0.0282 Sao Paulo water artificial pond
## 15 0.0248 0.0436 0.0196 0.0152 0.0086 0.0065 Sao Paulo water artificial pond
## 16 0.0245 0.0415 0.017 0.0123 0.0068 0.0059 Sao Paulo water artificial pond
## 17 0.023 0.0296 0.0146 0.0135 0.0092 0.0059 Sao Paulo water artificial pond
## 18 0.0243 0.043 0.0172 0.0198 0.0134 0.0062 Sao Paulo water artificial pond
## 19 0.0189 0.0369 0.016 0.0233 0.0123 0.0093 Sao Paulo water artificial pond
## 20 0.0234 0.0413 0.0199 0.0146 0.01 0.0085 Sao Paulo water artificial pond
sp_city_rwi <- transform(sp_city,
RWI = rwi(B3, B11,
n_by_region(sp_city))
)
# Compute the range of RWI values for each surface type
sp_city_rwi %>% split(.$surface) %>%
lapply(function(x) {
range(x$RWI)
}) %>% as.data.frame() %>%
t() %>% as.data.frame() %>%
dplyr::rename(RWI_min = V1, RWI_max = V2)## RWI_min RWI_max
## non.water -0.6921607 0.5658038
## water -0.3412029 0.8915109
sp_city_rwi %>%
dplyr::mutate(surface = as.factor(surface)) %>%
ggplot2::ggplot(aes(x = RWI, fill = surface)) +
ggplot2::geom_density(alpha = 0.6) +
labs(title = " ",
x = "RWI Value",
y = "Density") +
scale_fill_manual(values = c("water" = "blue", "non-water" = "red")) +
labs(fill = "Surface") +
theme_minimal()
df_SR_rwi <- do.call(rbind,
lapply(df_SR_splited, \(x) {
transform(x,
RWI = rwi(B3, B11, n_by_region(x)))
}))
df_SR_rwi %>% split(.$surface) %>%
lapply(function(x) {
range(x$RWI)
}) %>% as.data.frame() %>%
t() %>% as.data.frame() %>%
dplyr::rename(RWI_min = V1, RWI_max = V2)## RWI_min RWI_max
## non.water -0.9422513 0.5658038
## water -0.5333735 0.9970163
df_SR_rwi %>%
mutate(surface = as.factor(surface),
city = as.factor(city)) %>%
ggplot2::ggplot(aes(x = RWI, fill = surface)) +
ggplot2::geom_density(alpha = 0.6) +
ggplot2::facet_wrap(~city, scales = c("fixed", "free")[2]) +
labs(title = "",
x = "RWI Value",
y = "Density") +
scale_fill_manual(values = c("water" = "blue", "non-water" = "red")) +
labs(fill = "Surface") +
theme_minimal()
df_SR_all <- df_SR_rwi %>%
dplyr::mutate(NDWI = ndwi(B3, B8),
MNDWI = mndwi(B3, B11),
AWEIsh = AWEIsh(B2, B3, B8, B11, B12),
AWEInsh = AWEInsh(B2, B3, B8, B11, B12))
# get the range of the indices
df_SR_all %>% split(.$surface) %>%
lapply(function(x) {
x[10:13] %>%
apply(2, function(x) range(x))
}) %>% as.data.frame() %>%
t() %>% as.data.frame() %>%
dplyr::rename(Index_min = V1, Index_max = V2)## Index_min Index_max
## non.water.RWI -0.9422513 0.5658038
## non.water.NDWI -0.9967897 0.5400000
## non.water.MNDWI -0.9990015 0.5236884
## non.water.AWEIsh -1.0052500 1.9856000
## water.RWI -0.5333735 0.9970163
## water.NDWI -0.7377239 0.9967949
## water.MNDWI -0.8246753 0.9972222
## water.AWEIsh -0.4876750 0.2897250
df_SR_all %>% dplyr::select(RWI, NDWI, MNDWI, AWEIsh, AWEInsh, surface) %>%
tidyr::pivot_longer(cols = -surface, names_to = "index", values_to = "value") %>%
dplyr::mutate(surface = as.factor(surface),
index = factor(index, levels = c('RWI', 'MNDWI', 'NDWI', 'AWEInsh', 'AWEIsh'))) %>%
ggplot2::ggplot(aes(x = value, fill = surface)) +
ggplot2::geom_density(alpha = 0.6) +
ggplot2::facet_wrap(~index, scales = c("fixed", "free")[2]) +
labs(title = " ",
x = "Value",
y = "Density") +
scale_fill_manual(values = c("water" = "blue", "non-water" = "red")) +
labs(fill = "Surface") +
theme_minimal()
This work is based on the following paper: Eduardo Justiniano, Fernando kawakubo, Edimilson dos Santos Júnior, Breno de Melo, Gustavo Menezes, Marcel Fantin, Julio Pedrassoli, Marcos Martines, Rúbia Morato, August 20, 2024, “RWI”, IEEE Dataport, doi: https://dx.doi.org/10.21227/1ybz-1y91.