Timon
6efcc8cfec
- Updated all CI maps to use tm_scale_continuous() for proper tmap v4 compatibility - Added fixed color scale limits (1-8 for CI, -3 to +3 for differences) for consistent field comparison - Fixed YAML header formatting issues in CI_report_dashboard_planet.Rmd - Positioned RGB map before CI overview map as requested - Removed all obsolete use_breaks parameter references - Enhanced error handling and logging throughout the pipeline - Added new experimental analysis scripts and improvements to mosaic creation
92 lines
3.2 KiB
R
92 lines
3.2 KiB
R
# Required packages
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# library(ggplot2)
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# library(dplyr)
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raster_files_NEW <- list.files(merged_final,full.names = T, pattern = ".tif")
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# Extracting the dates from vrt_list (assuming the format "YYYY-MM-DD.vrt" at the end)
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no_cloud_dates <- as.Date(sapply(raster_files_NEW, function(x) {
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sub(".*/([0-9]{4}-[0-9]{2}-[0-9]{2})\\.tif", "\\1", x)
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}))
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# Generate a full sequence of dates in the range
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start_date <- min(no_cloud_dates)
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end_date <- max(no_cloud_dates)
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all_dates <- seq(start_date, end_date, by = "day")
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# Create a data frame marking no clouds (1) and clouds (0)
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cloud_data <- data.frame(
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date = all_dates,
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cloud_status = ifelse(all_dates %in% no_cloud_dates, 1, 0)
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)
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# Plot the data
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ggplot(cloud_data, aes(x = date, y = cloud_status)) +
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geom_point() +
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labs(x = "Date", y = "Cloud Status (1 = No Cloud, 0 = Cloud)") +
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scale_y_continuous(breaks = c(0, 1)) +
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theme_minimal()
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# Updated ggplot code to display months on the x-axis
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ggplot(cloud_data, aes(x = date, y = cloud_status)) +
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geom_point() +
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scale_x_date(date_labels = "%b", date_breaks = "1 month") +
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labs(x = "Month", y = "Cloud Status (1 = No Cloud, 0 = Cloud)") +
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scale_y_continuous(breaks = c(0, 1)) +
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theme_minimal()
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# Group data by year and week
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cloud_data <- cloud_data %>%
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mutate(week = isoweek(date), year = year(date)) %>%
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group_by(year, week) %>%
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summarise(no_cloud_days = sum(cloud_status == 1),
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cloud_days = sum(cloud_status == 0))
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# 1. Show how many weeks per year have no images (clouds for all 7 days)
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weeks_no_images <- cloud_data %>%
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filter(cloud_days == 7)
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# Plot weeks with no images
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ggplot(weeks_no_images, aes(x = week, y = year)) +
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geom_tile(fill = "red") +
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labs(x = "Week", y = "Year", title = "Weeks with No Images (Full Cloud Cover)") +
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theme_minimal()
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# 2. Determine when most clouds are present (cloud_days > no_cloud_days)
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weeks_most_clouds <- cloud_data %>%
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filter(cloud_days > no_cloud_days)
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# Plot when most clouds are present
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ggplot(weeks_most_clouds, aes(x = week, y = year)) +
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geom_tile(fill = "blue") +
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labs(x = "Week", y = "Year", title = "Weeks with Most Clouds") +
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theme_minimal()
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# Group weeks by number of cloud days and count how many weeks had 0-7 cloud days
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weeks_by_cloud_days <- cloud_data %>%
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group_by(cloud_days) %>%
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summarise(week_count = n())
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# Display the summary
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print(weeks_by_cloud_days)
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# Optional: Plot the results to visualise how many weeks had 0-7 cloud days
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ggplot(weeks_by_cloud_days, aes(x = cloud_days, y = week_count)) +
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geom_bar(stat = "identity", fill = "skyblue") +
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labs(x = "Number of Cloud Days (per week)", y = "Number of Weeks",
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title = "Distribution of Cloud Days per Week") +
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theme_minimal()
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weeks_by_no_cloud_days <- cloud_data %>%
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mutate(no_cloud_days = 7 - cloud_days) %>%
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group_by(no_cloud_days) %>%
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summarise(week_count = n())
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# Plot the results to visualise how many weeks had 0-7 cloud-free days
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ggplot(weeks_by_no_cloud_days, aes(x = no_cloud_days, y = week_count)) +
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geom_bar(stat = "identity", fill = "#00A799") +
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geom_text(aes(label = week_count), vjust = -0.5, size = 4) + # Add the count of weeks on top of bars
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labs(x = "Number of Cloud-Free Days (per week)", y = "Number of Weeks",
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title = "Distribution of Cloud-Free Days per Week") +
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theme_minimal()
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