adjusted all functions and cleaned scripts
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Timon
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@ -1,195 +0,0 @@
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renv::activate()
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renv::restore()
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#download excel with planting dates
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library(googledrive)
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library(here)
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library(tidyverse)
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library(lubridate)
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library(readxl)
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#create directory
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storage_dir <- here("../laravel_app/storage/app")
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data_dir <- here(storage_dir, "Data")
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harvest_dir <- here(data_dir, "HarvestData")
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dir.create(file.path(data_dir))
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dir.create(file.path(harvest_dir))
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week = ymd(Sys.Date())
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file_name <- here(harvest_dir, paste("Data", week, ".xlsx"))
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# yield_file_path <- here("Data", "HarvestData", paste("Current - Pivots planting date and harvesting data.xlsx"))
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#Check its existence
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if (file.exists(file_name)) {
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#Delete file if it exists
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file.remove(file_name)
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print("File deleted")
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}
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#options(gargle_oauth_email = "henkpoldergraaf@gmail.com")
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#folder_url <- "https://docs.google.com/spreadsheets/d/1KPOPHvlzTBDvgFCYQetss0yoafeZeoNW/edit?rtpof=true#gid=1683410275"
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#folder <- drive_get(as_id(folder_url))
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#drive_download(folder, file_name, overwrite = T)
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excel_file_name = here(storage_dir,"harvesting_data", "Current - Pivots planting date and harevsting data.xlsx")
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# library(googlesheets4)
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# read_sheet(folder_url)
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#get dates in same column
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dates <- read_excel(excel_file_name,
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skip = 2,
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col_types = c("text", "numeric", "text", "date", "numeric", "numeric", "numeric",
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"date", "numeric", "skip", "skip", "numeric", "numeric", "numeric","skip", #2020 harvesting data
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"date", "numeric", "skip", "skip", "numeric", "numeric", "numeric","skip", #2021 harvesting data
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"date", "numeric", "skip", "skip", "numeric", "skip", "numeric","numeric", "skip", #2022 harvesting data
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"date", "numeric", "skip", "skip", "skip", "skip", "skip", "skip", "skip", "skip", "skip", "skip", "skip", "numeric", "numeric","numeric","skip", #2023 harvesting data
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"skip", "skip", "skip", "skip", "skip")) %>% #empty columns
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rename(pivot_quadrant = 1,
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area = 2,
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variety = 3,
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planting_date = 4,
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Age = 5,
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ratoon = 6,
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Year_replanted = 7,
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Harvesting_date_2020 = 8,
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Harvesting_age_2020 = 9,
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MT_weight_2020 = 10,
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Tcha_2020 = 11,
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Tchm_2020 = 12,
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Harvesting_date_2021 = 13,
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Harvesting_age_2021 = 14,
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MT_weight_2021 = 15,
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Tcha_2021 = 16,
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Tchm_2021 = 17,
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Harvesting_date_2022 = 18,
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Harvesting_age_2022 = 19,
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MT_weight_2022 = 20,
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Tcha_2022 = 21,
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Tchm_2022 = 22,
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Harvesting_date_2023 = 23,
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Harvesting_age_2023 = 24,
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MT_weight_2023 = 25,
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Tcha_2023 = 26,
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Tchm_2023 = 27,
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) %>%
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# slice(-1) %>% #select(-age) %>%
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# filter(pivot_quadrant != "Total") %>% #drop_na(pivot_quadrant) %>%
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mutate(planting_date = ymd(planting_date ),
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Harvesting_date_2020 = ymd(Harvesting_date_2020),
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Harvesting_date_2021 = ymd(Harvesting_date_2021),
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Harvesting_date_2022= ymd(Harvesting_date_2022),
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Age = round(Age,0)) %>% filter(pivot_quadrant != "Total/Average")%>%
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filter(pivot_quadrant != "Total")
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#copy each row and add ABCD
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quadrants <- dates %>% slice(rep(1:n(), each=4)) %>%
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group_by(pivot_quadrant) %>%
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mutate(pivot_quadrant = paste0(pivot_quadrant, c("A", "B", "C", "D"))) %>%
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filter(pivot_quadrant != "P1.8D" & pivot_quadrant != "P1.8 Q.DA"& pivot_quadrant != "P1.8 Q.DB"& pivot_quadrant != "P1.8 Q.DC") %>%
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mutate(pivot_quadrant = case_when(pivot_quadrant == "P1.3 ABA" ~ "P1.3A",
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pivot_quadrant == "P1.3 ABB" ~ "1.3B",
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pivot_quadrant == "P1.3 ABC" ~ "1.3A",
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pivot_quadrant == "P1.3 ABD" ~ "1.3A",
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pivot_quadrant == "P1.3 CDA" ~ "1.3C",
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pivot_quadrant == "P1.3 CDB" ~ "1.3D",
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pivot_quadrant == "P1.3 CDC" ~ "1.3C",
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pivot_quadrant == "P1.3 CDD" ~ "1.3C",
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pivot_quadrant == "P1.8 Q.DD" ~ "1.8D",
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pivot_quadrant == "P1.9.ABA" ~ "1.9A",
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pivot_quadrant == "P1.9.ABB" ~ "1.9B",
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pivot_quadrant == "P1.9.ABC" ~ "1.9A",
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pivot_quadrant == "P1.9.ABD" ~ "1.9A",
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pivot_quadrant == "P1.9.CDA" ~ "1.9C",
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pivot_quadrant == "P1.9.CDB" ~ "1.9D",
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pivot_quadrant == "P1.9.CDC" ~ "1.9C",
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pivot_quadrant == "P1.9.CDD" ~ "1.9C",
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pivot_quadrant == "P2.3AA" ~ "2.3A",
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pivot_quadrant == "P2.3AB" ~ "2.3A",
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pivot_quadrant == "P2.3AC" ~ "2.3A",
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pivot_quadrant == "P2.3AD" ~ "2.3A",
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pivot_quadrant == "P2.3DA" ~ "2.3D",
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pivot_quadrant == "P2.3DB" ~ "2.3D",
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pivot_quadrant == "P2.3DC" ~ "2.3D",
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pivot_quadrant == "P2.3DD" ~ "2.3D",
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pivot_quadrant == "P2.3BCA" ~ "2.3B",
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pivot_quadrant == "P2.3BCB" ~ "2.3B",
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pivot_quadrant == "P2.3BCC" ~ "2.3C",
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pivot_quadrant == "P2.3BCD" ~ "2.3C",
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pivot_quadrant == "P2.5 ABA" ~ "2.5A",
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pivot_quadrant == "P2.5 ABB" ~ "2.5B",
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pivot_quadrant == "P2.5 ABC" ~ "2.5A",
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pivot_quadrant == "P2.5 ABD" ~ "2.5A",
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pivot_quadrant == "P2.5 CDA" ~ "2.5C",
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pivot_quadrant == "P2.5 CDB" ~ "2.5D",
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pivot_quadrant == "P2.5 CDC" ~ "2.5C",
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pivot_quadrant == "P2.5 CDD" ~ "2.5C",
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pivot_quadrant == "P3.1ABA" ~ "3.1A",
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pivot_quadrant == "P3.1ABB" ~ "3.1B",
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pivot_quadrant == "P3.1ABC" ~ "3.1A",
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pivot_quadrant == "P3.1ABD" ~ "3.1A",
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pivot_quadrant == "P3.1CDA" ~ "3.1C",
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pivot_quadrant == "P3.1CDB" ~ "3.1D",
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pivot_quadrant == "P3.1CDC" ~ "3.1C",
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pivot_quadrant == "P3.1CDD" ~ "3.1C",
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pivot_quadrant == "P3.2 ABA" ~ "3.2A",
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pivot_quadrant == "P3.2 ABB" ~ "3.2B",
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pivot_quadrant == "P3.2 ABC" ~ "3.2A",
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pivot_quadrant == "P3.2 ABD" ~ "3.2A",
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pivot_quadrant == "P3.2 CDA" ~ "3.2C",
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pivot_quadrant == "P3.2 CDB" ~ "3.2D",
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pivot_quadrant == "P3.2 CDC" ~ "3.2C",
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pivot_quadrant == "P3.2 CDD" ~ "3.2C",
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pivot_quadrant == "DL 1.3A" ~ "DL1.3",
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pivot_quadrant == "DL 1.3B" ~ "DL1.3",
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pivot_quadrant == "DL 1.3C" ~ "DL1.3",
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pivot_quadrant == "DL 1.3D" ~ "DL1.3",
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pivot_quadrant == "DL 1.1A" ~ "DL1.1",
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pivot_quadrant == "DL 1.1B" ~ "DL1.1",
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pivot_quadrant == "DL 1.1C" ~ "DL1.1",
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pivot_quadrant == "DL 1.1D" ~ "DL1.1",
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TRUE ~ pivot_quadrant) ) %>% unique() %>%
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mutate_at("pivot_quadrant", str_replace, "P", "") %>%
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mutate(pivot = pivot_quadrant) %>%
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mutate_at("pivot",str_replace, "[ABCD]", "") %>%
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mutate(pivot = case_when(pivot == "L1.1"~"DL1.1",
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pivot == "L1.3" ~"DL1.3",
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TRUE ~ pivot))
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quadrants2 <- quadrants %>%
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mutate(
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season_start_2021 = case_when(!is.na(Harvesting_date_2021) ~ Harvesting_date_2021 - (Harvesting_age_2021 * 30)) ,
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season_end_2021 = case_when(!is.na(Harvesting_date_2021) ~ Harvesting_date_2021),
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season_start_2022 = case_when(is.na(Harvesting_date_2021) & !is.na(Harvesting_date_2022) ~ Harvesting_date_2022 - (Harvesting_age_2022 * 30),
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!is.na(Harvesting_date_2021) & !is.na(Harvesting_date_2022) ~ Harvesting_date_2021
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),
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season_end_2022 = case_when(!is.na(Harvesting_date_2022) & !is.na(season_start_2022) ~ Harvesting_date_2022),
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season_start_2023 = case_when(ratoon == 0 ~ planting_date,
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TRUE ~ Harvesting_date_2022),
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season_start_2023 = case_when(is.na(Harvesting_date_2022) ~ Harvesting_date_2021,
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TRUE ~ season_start_2023),
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season_end_2023 = case_when(!is.na(Harvesting_date_2023) ~ Harvesting_date_2023,
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TRUE ~ ymd(Sys.Date())),
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season_start_2024 = case_when(!is.na(Harvesting_date_2023) ~ Harvesting_date_2023,
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TRUE ~ NA),
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season_end_2024 = case_when(!is.na(season_start_2024) ~ ymd(Sys.Date())),
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)
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saveRDS(quadrants2, here(harvest_dir, "harvest_data_new"))
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@ -7,7 +7,6 @@ library(lubridate)
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library(exactextractr)
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library(readxl)
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# Vang alle command line argumenten op
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args <- commandArgs(trailingOnly = TRUE)
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@ -54,38 +53,31 @@ weekly_CI_mosaic <- here(laravel_storage_dir, "weekly_mosaic")
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daily_vrt <- here(data_dir, "vrt")
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harvest_dir <- here(data_dir, "HarvestData")
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source(here("r_app/parameters_project.R"))
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source(here("r_app/ci_extraction_utils.R"))
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# source(here("r_app/mosaic_creation_utils.R"))
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source("parameters_project.R")
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source("ci_extraction_utils.R")
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source("mosaic_creation_utils.R")
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# source("mosaic_creation_utils.R")
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dir.create(here(laravel_storage_dir))
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dir.create(here(data_dir))
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dir.create(here(extracted_CI_dir))
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dir.create(here(daily_CI_vals_dir))
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dir.create(here(cumulative_CI_vals_dir))
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dir.create(here(weekly_CI_mosaic))
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dir.create(here(daily_vrt))
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dir.create(merged_final)
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dir.create(harvest_dir)
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# dir.create(here(laravel_storage_dir))
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# dir.create(here(data_dir))
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# dir.create(here(extracted_CI_dir))
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# dir.create(here(daily_CI_vals_dir))
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# dir.create(here(cumulative_CI_vals_dir))
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# dir.create(here(weekly_CI_mosaic))
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# dir.create(here(daily_vrt))
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# dir.create(merged_final)
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# dir.create(harvest_dir)
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# end_date <- lubridate::dmy("20-6-2024")
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end_date <- lubridate::dmy("28-08-2024")
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week <- week(end_date)
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#weeks_ago = 0
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# Creating weekly mosaic
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#dates <- date_list(weeks_ago)
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dates <- date_list(end_date, offset)
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print(dates)
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#load pivot geojson
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# pivot_sf_q <- st_read(here(data_dir, "pivot.geojson")) %>% dplyr::select(pivot, pivot_quadrant) %>% vect()
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# raster_files <- list.files(planet_tif_folder,full.names = T, pattern = ".tif")
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# filtered_files <- map(dates$days_filter, ~ raster_files[grepl(pattern = .x, x = raster_files)]) %>%
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# compact() %>%
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# flatten_chr()
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# head(filtered_files)
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raster_files <- list.files(planet_tif_folder,full.names = T, pattern = ".tif")
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@ -93,39 +85,13 @@ filtered_files <- map(dates$days_filter, ~ raster_files[grepl(pattern = .x, x =
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compact() %>%
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flatten_chr()
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head(filtered_files)
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# filtered_files <- raster_files #for first CI extraction
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# create_mask_and_crop <- function(file, field_boundaries) {
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# message("starting ", file)
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# CI <- rast(file)
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# # names(CI) <- c("green","nir")
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# message("raster loaded")
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#
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# CI <- CI[[2]]/CI[[1]]-1
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# # CI <- CI$nir/CI$green-1
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# message("CI calculated")
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# CI <- terra::crop(CI, field_boundaries, mask = TRUE) #%>% CI_func()
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#
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# new_file <- here(merged_final, paste0(tools::file_path_sans_ext(basename(file)), ".tif"))
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# writeRaster(CI, new_file, overwrite = TRUE)
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#
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# vrt_file <- here(daily_vrt, paste0(tools::file_path_sans_ext(basename(file)), ".vrt"))
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# terra::vrt(new_file, vrt_file, overwrite = TRUE)
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#
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# return(CI)
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# }
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vrt_list <- list()
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# for (file in raster_files) {
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# v_crop <- create_mask_and_crop(file, field_boundaries)
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# message(file, " processed")
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# gc()
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# }
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for (file in filtered_files) {
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v_crop <- create_mask_and_crop(file, field_boundaries)
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v_crop <- create_mask_and_crop(file, field_boundaries, merged_final)
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emtpy_or_full <- global(v_crop, "notNA")
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vrt_file <- here(daily_vrt, paste0(tools::file_path_sans_ext(basename(file)), ".vrt"))
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@ -140,63 +106,59 @@ for (file in filtered_files) {
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gc()
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}
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# Extracting CI
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# pivot_sf_q <- st_read(here("..", "Data", "pivot_20210625.geojson")) %>% dplyr::select(pivot, pivot_quadrant) %>% vect()
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# pivot_sf <- st_read(here(data_dir, "pivot.geojson")) %>% dplyr::select(pivot, pivot_quadrant) %>% group_by(pivot) %>% summarise() %>% vect()
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# message("pivot loaded")
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raster_files_NEW <- list.files(merged_final,full.names = T, pattern = ".tif")
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# pivots_dates0 <- readRDS(here(harvest_dir, "harvest_data_new")) %>% filter(
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# pivot %in% c("1.1", "1.2", "1.3", "1.4", "1.6", "1.7", "1.8", "1.9", "1.10", "1.11", "1.12", "1.13",
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# "1.14" , "1.16" , "1.17" , "1.18" ,"2.1", "2.2", "2.3" , "2.4", "2.5", "3.1", "3.2", "3.3",
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# "4.1", "4.2", "4.3", "4.4", "4.5", "4.6", "5.1" ,"5.2", "5.3", "5.4", "6.1", "6.2", "DL1.1", "DL1.3")
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# )
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# harvesting_data <- pivots_dates0 %>%
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# select(c("pivot_quadrant", "season_start_2021", "season_end_2021", "season_start_2022", "season_end_2022", "season_start_2023", "season_end_2023", "season_start_2024", "season_end_2024")) %>%
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# pivot_longer(cols = starts_with("season"), names_to = "Year", values_to = "value") %>%
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# separate(Year, into = c("name", "Year"), sep = "(?<=season_start|season_end)\\_", remove = FALSE) %>%
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# mutate(name = str_to_title(name)) %>%
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# pivot_wider(names_from = name, values_from = value) %>%
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# rename(field = pivot_quadrant)
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#If run for the firsttime, it will extract all data since the start and put into a table.rds. otherwise it will only add on to the existing table.
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# Define the path to the file
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file_path <- here(cumulative_CI_vals_dir,"combined_CI_data.rds")
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file_path <- here(cumulative_CI_vals_dir, "combined_CI_data.rds")
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# Check if the file exists
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if (!file.exists(file_path)) {
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# Create the file with columns "column1" and "column2"
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data <- data.frame(sub_field=NA, field=NA)
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saveRDS(data, file_path)
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# File does not exist, create it with all available data
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print("combined_CI_data.rds does not exist. Preparing combined_CI_data.rds file for all available images.")
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# Extract data from all raster files
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walk(raster_files_NEW, extract_rasters_daily, field_geojson = field_boundaries, quadrants = FALSE, daily_CI_vals_dir)
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# Combine all extracted data
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extracted_values <- list.files(here(daily_CI_vals_dir), full.names = TRUE)
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pivot_stats <- extracted_values %>%
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map(readRDS) %>% list_rbind() %>%
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group_by(sub_field)
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# Save the combined data to the file
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saveRDS(pivot_stats, file_path)
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print("All CI values extracted from all historic images and saved to combined_CI_data.rds.")
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} else {
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# File exists, add new data
|
||||
print("combined_CI_data.rds exists, adding the latest image data to the table.")
|
||||
|
||||
# Filter and process the latest data
|
||||
filtered_files <- map(dates$days_filter, ~ raster_files_NEW[grepl(pattern = .x, x = raster_files_NEW)]) %>%
|
||||
compact() %>%
|
||||
flatten_chr()
|
||||
|
||||
walk(filtered_files, extract_rasters_daily, field_geojson = field_boundaries, quadrants = TRUE, daily_CI_vals_dir)
|
||||
|
||||
# Extract new values
|
||||
extracted_values <- list.files(daily_CI_vals_dir, full.names = TRUE)
|
||||
extracted_values <- map(dates$days_filter, ~ extracted_values[grepl(pattern = .x, x = extracted_values)]) %>%
|
||||
compact() %>%
|
||||
flatten_chr()
|
||||
|
||||
pivot_stats <- extracted_values %>%
|
||||
map(readRDS) %>% list_rbind() %>%
|
||||
group_by(sub_field)
|
||||
|
||||
# Load existing data and append new data
|
||||
combined_CI_data <- readRDS(file_path)
|
||||
pivot_stats2 <- bind_rows(pivot_stats, combined_CI_data)
|
||||
|
||||
# Save the updated combined data
|
||||
saveRDS(pivot_stats2, file_path)
|
||||
|
||||
print("All CI values extracted from the latest images and added to combined_CI_data.rds.")
|
||||
}
|
||||
|
||||
print("combined_CI_data.rds exists, adding the latest image data to the table.")
|
||||
|
||||
filtered_files <- map(dates$days_filter, ~ raster_files_NEW[grepl(pattern = .x, x = raster_files_NEW)]) %>%
|
||||
compact() %>%
|
||||
flatten_chr()
|
||||
walk(filtered_files, extract_rasters_daily, field_geojson= field_boundaries, quadrants = TRUE, daily_CI_vals_dir)
|
||||
|
||||
extracted_values <- list.files(daily_CI_vals_dir, full.names = TRUE)
|
||||
|
||||
extracted_values <- map(dates$days_filter, ~ extracted_values[grepl(pattern = .x, x = extracted_values)]) %>%
|
||||
compact() %>%
|
||||
flatten_chr()
|
||||
|
||||
pivot_stats <- extracted_values %>%
|
||||
map(readRDS) %>% list_rbind() %>%
|
||||
group_by(sub_field)
|
||||
|
||||
|
||||
combined_CI_data <- readRDS(here(cumulative_CI_vals_dir,"combined_CI_data.rds")) #%>% drop_na(pivot_quadrant)
|
||||
head(combined_CI_data)
|
||||
pivot_stats2 <- bind_rows(pivot_stats, combined_CI_data)
|
||||
# pivot_stats2 <- combined_CI_data
|
||||
print("All CI values extracted from latest image.")
|
||||
saveRDS(pivot_stats2, here(cumulative_CI_vals_dir,"combined_CI_data.rds")) #used to save the rest of the data into one file
|
||||
|
|
|
|||
|
|
@ -1,6 +1,5 @@
|
|||
# Utils for ci extraction
|
||||
|
||||
date_list <- function(end_date, offset, week){
|
||||
date_list <- function(end_date, offset){
|
||||
offset <- as.numeric(offset) - 1
|
||||
end_date <- as.Date(end_date)
|
||||
start_date <- end_date - lubridate::days(offset)
|
||||
|
|
@ -12,44 +11,26 @@ date_list <- function(end_date, offset, week){
|
|||
return(list("week" = week, "year" = year, "days_filter" = days_filter))
|
||||
}
|
||||
|
||||
# date_list <- function(weeks_in_the_paste){
|
||||
# week <- week(Sys.Date()- weeks(weeks_in_the_paste) )
|
||||
# year <- year(Sys.Date()- weeks(weeks_in_the_paste) )
|
||||
# days_filter <- Sys.Date() - weeks(weeks_in_the_paste) - days(0:6)
|
||||
#
|
||||
# return(c("week" = week,
|
||||
# "year" = year,
|
||||
# "days_filter" = list(days_filter)))
|
||||
#
|
||||
# }
|
||||
|
||||
CI_func <- function(x, drop_layers = FALSE){
|
||||
CI <- x[[4]]/x[[2]]-1
|
||||
add(x) <- CI
|
||||
names(x) <- c("red", "green", "blue","nir", "cloud" ,"CI")
|
||||
if(drop_layers == FALSE){
|
||||
return(x)
|
||||
}else{
|
||||
return(x$CI)
|
||||
}
|
||||
}
|
||||
|
||||
mask_raster <- function(raster, fields){
|
||||
# x <- rast(filtered_files[1])
|
||||
x <- rast(raster)
|
||||
emtpy_or_full <- global(x, sum)
|
||||
create_mask_and_crop <- function(file, field_boundaries, merged_final_dir) {
|
||||
message("starting ", file)
|
||||
loaded_raster <- rast(file)
|
||||
names(loaded_raster) <- c("Red", "Green", "Blue", "NIR")
|
||||
message("raster loaded")
|
||||
|
||||
if(emtpy_or_full[1,] >= 2000000){
|
||||
names(x) <- c("red", "green", "blue","nir", "cloud")
|
||||
cloud <- x$cloud
|
||||
|
||||
cloud[cloud == 0 ] <- NA
|
||||
x_masked <- mask(x, cloud, inverse = T) %>% crop(.,fields, mask = TRUE )
|
||||
x_masked <- x_masked %>% CI_func()
|
||||
|
||||
message(raster, " masked")
|
||||
return(x_masked)
|
||||
}
|
||||
CI <- loaded_raster$NIR / loaded_raster$Green - 1
|
||||
|
||||
loaded_raster$CI <- CI
|
||||
message("CI calculated")
|
||||
loaded_raster <- terra::crop(loaded_raster, field_boundaries, mask = TRUE) #%>% CI_func()
|
||||
|
||||
loaded_raster[loaded_raster == 0] <- NA
|
||||
new_file <- here(merged_final_dir, paste0(tools::file_path_sans_ext(basename(file)), ".tif"))
|
||||
writeRaster(loaded_raster, new_file, overwrite = TRUE)
|
||||
|
||||
vrt_file <- here(daily_vrt, paste0(tools::file_path_sans_ext(basename(file)), ".vrt"))
|
||||
terra::vrt(new_file, vrt_file, overwrite = TRUE)
|
||||
|
||||
return(loaded_raster)
|
||||
}
|
||||
|
||||
date_extract <- function(file_path) {
|
||||
|
|
@ -57,228 +38,16 @@ date_extract <- function(file_path) {
|
|||
}
|
||||
|
||||
extract_rasters_daily <- function(file, field_geojson, quadrants = TRUE, save_dir) {
|
||||
# x <- rast(filtered_files[1])%>% CI_func(drop_layers = TRUE)
|
||||
# date <- date_extract(filtered_files[1])
|
||||
# field_geojson <- sf::st_as_sf(pivot_sf_q)
|
||||
field_geojson <- sf::st_as_sf(field_geojson)
|
||||
x <- rast(file[1])
|
||||
date <- date_extract(file)
|
||||
|
||||
pivot_stats <- cbind(field_geojson, mean_CI = round(exactextractr::exact_extract(x$CI, field_geojson, fun = "mean"), 2)) %>%
|
||||
st_drop_geometry() %>% rename("{date}" := mean_CI)
|
||||
|
||||
save_suffix <- if (quadrants){"quadrant"} else {"whole_field"}
|
||||
save_path <- here(save_dir, paste0("extracted_", date, "_", save_suffix, ".rds"))
|
||||
|
||||
saveRDS(pivot_stats, save_path)
|
||||
}
|
||||
|
||||
right = function(text, num_char) {
|
||||
substr(text, nchar(text) - (num_char-1), nchar(text))
|
||||
}
|
||||
|
||||
extract_CI_data <- function(field_names, harvesting_data, field_CI_data, season) {
|
||||
# field_names = "1.2A"
|
||||
# harvesting_data = harvesting_data
|
||||
# field_CI_data = pivot_stats_long
|
||||
# season= 2021
|
||||
|
||||
filtered_harvesting_data <- harvesting_data %>%
|
||||
filter(year == season, field %in% field_names)
|
||||
|
||||
filtered_field_CI_data <- field_CI_data %>%
|
||||
filter(field %in% field_names)
|
||||
|
||||
# CI <- map_df(field_names, ~ {
|
||||
ApproxFun <- approxfun(x = filtered_field_CI_data$Date, y = filtered_field_CI_data$value)
|
||||
Dates <- seq.Date(ymd(min(filtered_field_CI_data$Date)), ymd(max(filtered_field_CI_data$Date)), by = 1)
|
||||
LinearFit <- ApproxFun(Dates)
|
||||
|
||||
CI <- data.frame(Date = Dates, FitData = LinearFit) %>%
|
||||
left_join(., filtered_field_CI_data, by = "Date") %>%
|
||||
filter(Date > filtered_harvesting_data$season_start & Date < filtered_harvesting_data$season_end) %>%
|
||||
mutate(DOY = seq(1, n(), 1),
|
||||
model = paste0("Data", season, " : ", field_names),
|
||||
season = season,
|
||||
field = field_names)
|
||||
# }) #%>%
|
||||
#{if (length(field_names) > 0) message("Done!")}
|
||||
|
||||
return(CI)
|
||||
}
|
||||
#
|
||||
# load_fields <- function(geojson_path) {
|
||||
# field_geojson <- st_read(geojson_path) %>%
|
||||
# select(pivot, pivot_quadrant) %>%
|
||||
# vect()
|
||||
# return(field_geojson)
|
||||
# }
|
||||
#
|
||||
# load_harvest_data <- function(havest_data_path){
|
||||
# harvest_data <- readRDS(havest_data_path)
|
||||
# return(harvest_data)
|
||||
# }
|
||||
#
|
||||
# load_rasters <- function(raster_path, dates) {
|
||||
# raster_files <- list.files(raster_path, full.names = TRUE, pattern = ".tif")
|
||||
# filtered_files <- map(dates$days_filter, ~ raster_files[grepl(pattern = .x, x = raster_files)]) %>%
|
||||
# compact() %>%
|
||||
# flatten_chr()
|
||||
#
|
||||
# return(filtered_files)
|
||||
# }
|
||||
#
|
||||
# mask_and_set_names <- function(filtered_files, fields) {
|
||||
# rasters_masked <- map(filtered_files, mask_raster, fields = fields) %>% set_names(filtered_files)
|
||||
# rasters_masked[sapply(rasters_masked, is.null)] <- NULL
|
||||
# rasters_masked <- setNames(rasters_masked, map_chr(names(rasters_masked), date_extract))
|
||||
#
|
||||
# return(rasters_masked)
|
||||
# }
|
||||
#
|
||||
# calculate_total_pix_area <- function(filtered_files, fields_geojson) {
|
||||
# # total_pix_area <- rast(filtered_files[1]) %>%
|
||||
# # subset(1) %>%
|
||||
# # crop(fields_geojson, mask = TRUE)%>%
|
||||
# # global(.data, fun = "notNA")
|
||||
# total_pix_area <- rast(filtered_files[1]) %>% subset(1) %>% crop(fields_geojson, mask = TRUE) %>% freq(., usenames = TRUE)
|
||||
#
|
||||
# return(total_pix_area)
|
||||
# }
|
||||
#
|
||||
# cloud_layer_extract <- function(rasters_masked){
|
||||
# cloud_layer_rast <- map(rasters_masked, function(spatraster) {
|
||||
# spatraster[[5]]
|
||||
# }) %>% rast()
|
||||
#
|
||||
# return(cloud_layer_rast)
|
||||
# }
|
||||
#
|
||||
# calculate_cloud_coverage <- function(cloud_layer_rast, total_pix_area) {
|
||||
# cloud_perc_list <- freq(cloud_layer_rast, usenames = TRUE) %>%
|
||||
# mutate(cloud_perc = (100 -((count/total_pix_area$count)*100)),
|
||||
# cloud_thres_5perc = as.integer(cloud_perc < 5),
|
||||
# cloud_thres_40perc = as.integer(cloud_perc < 40)) %>%
|
||||
# rename(Date = layer) %>% select(-value, -count)
|
||||
#
|
||||
# cloud_index_5perc <- which(cloud_perc_list$cloud_thres_5perc == max(cloud_perc_list$cloud_thres_5perc))
|
||||
# cloud_index_40perc <- which(cloud_perc_list$cloud_thres_40perc == max(cloud_perc_list$cloud_thres_40perc))
|
||||
#
|
||||
# return(list(cloud_perc_list = cloud_perc_list, cloud_index_5perc = cloud_index_5perc, cloud_index_40perc = cloud_index_40perc))
|
||||
# }
|
||||
#
|
||||
# process_cloud_coverage <- function(cloud_coverage, rasters_masked) {
|
||||
# if (sum(cloud_coverage$cloud_perc_list$cloud_thres_5perc) > 1) {
|
||||
# message("More than 1 raster without clouds (<5%), max mosaic made ")
|
||||
#
|
||||
# cloudy_rasters_list <- rasters_masked[cloud_coverage$cloud_index_5perc]
|
||||
# rsrc <- sprc(cloudy_rasters_list)
|
||||
# x <- mosaic(rsrc)
|
||||
# names(x) <- c("red", "green", "blue", "nir", "cloud", "CI")
|
||||
#
|
||||
# } else if (sum(cloud_coverage$cloud_perc_list$cloud_thres_5perc) == 1) {
|
||||
# message("Only 1 raster without clouds (<5%)")
|
||||
#
|
||||
# x <- rast(rasters_masked[cloud_coverage$cloud_index_5perc[1]])
|
||||
# names(x) <- c("red", "green", "blue", "nir", "cloud", "CI")
|
||||
#
|
||||
# } else if (sum(cloud_coverage$cloud_perc_list$cloud_thres_40perc) > 1) {
|
||||
# message("More than 1 image contains clouds, composite made of <40% cloud cover images")
|
||||
#
|
||||
# cloudy_rasters_list <- rasters_masked[cloud_coverage$cloud_index_40perc]
|
||||
# rsrc <- sprc(cloudy_rasters_list)
|
||||
# x <- mosaic(rsrc)
|
||||
# names(x) <- c("red", "green", "blue", "nir", "cloud", "CI")
|
||||
#
|
||||
# } else if (sum(cloud_coverage$cloud_perc_list$cloud_thres_40perc) == 0) {
|
||||
# message("No cloud free images available")
|
||||
# x <- rast(rasters_masked[1])
|
||||
# x[x] <- NA
|
||||
# names(x) <- c("red", "green", "blue", "nir", "cloud", "CI")
|
||||
# }
|
||||
#
|
||||
# return(x)
|
||||
# }
|
||||
|
||||
# extract_rasters_daily_func <- function(daily_vals_dir, filtered_files, fields_geojson) {
|
||||
# extracted_files <- walk(filtered_files, extract_rasters_daily, field_geojson = fields_geojson, quadrants = TRUE, daily_vals_dir)
|
||||
# return(extracted_files)
|
||||
# }
|
||||
|
||||
# CI_load <- function(daily_vals_dir, grouping_variable){
|
||||
# extracted_values <- list.files(here(daily_vals_dir), full.names = TRUE)
|
||||
#
|
||||
# field_CI_values <- extracted_values %>%
|
||||
# map_dfr(readRDS) %>%
|
||||
# group_by(.data[[grouping_variable]]) %>%
|
||||
# summarise(across(everything(), ~ first(na.omit(.))))
|
||||
# return(field_CI_values)
|
||||
# }
|
||||
#
|
||||
# CI_long <- function(field_CI_values, pivot_long_cols){
|
||||
# field_CI_long <- field_CI_values %>%
|
||||
# gather("Date", value, -pivot_long_cols) %>%
|
||||
# mutate(Date = right(Date, 8),
|
||||
# Date = ymd(Date)
|
||||
# ) %>%
|
||||
# drop_na(c("value","Date")) %>%
|
||||
# mutate(value = as.numeric(value))%>%
|
||||
# filter_all(all_vars(!is.infinite(.)))%>%
|
||||
# rename(field = pivot_quadrant)
|
||||
#
|
||||
# return(field_CI_long)
|
||||
# }
|
||||
#
|
||||
# process_year_data <- function(year, harvest_data, field_CI_long) {
|
||||
# pivots_dates_year <- harvest_data %>% na.omit() %>% filter(year == year)
|
||||
# pivot_select_model_year <- unique(pivots_dates_year$field)
|
||||
#
|
||||
# data <- map_dfr(pivot_select_model_year, ~ extract_CI_data(.x, harvest_data, field_CI_long, season = year))
|
||||
#
|
||||
# return(data)
|
||||
# }
|
||||
|
||||
|
||||
|
||||
#functions for CI_data_prep
|
||||
# create_mask_and_crop <- function(file, pivot_sf_q) {
|
||||
# # file <- filtered_files[5]
|
||||
# message("starting ", file)
|
||||
# loaded_raster <- rast(file)
|
||||
# names(loaded_raster) <- c("Red", "Green", "Blue", "NIR")
|
||||
# # names(CI) <- c("green","nir")
|
||||
# message("raster loaded")
|
||||
#
|
||||
# # CI <- CI[[2]] / CI[[1]] - 1
|
||||
# CI <- loaded_raster$NIR / loaded_raster$Green - 1
|
||||
#
|
||||
# loaded_raster$CI <- CI
|
||||
# # CI <- CI$nir/CI$green-1
|
||||
# message("CI calculated")
|
||||
# loaded_raster <- terra::crop(loaded_raster, pivot_sf_q, mask = TRUE) #%>% CI_func()
|
||||
#
|
||||
# loaded_raster[loaded_raster == 0] <- NA
|
||||
# # names(v_crop) <- c("red", "green", "blue","nir", "cloud" ,"CI")
|
||||
# # v_crop$CI <- v_crop$CI - 1
|
||||
# new_file <- here(merged_final, paste0(tools::file_path_sans_ext(basename(file)), ".tif"))
|
||||
# writeRaster(loaded_raster, new_file, overwrite = TRUE)
|
||||
#
|
||||
# vrt_file <- here(daily_vrt, paste0(tools::file_path_sans_ext(basename(file)), ".vrt"))
|
||||
# terra::vrt(new_file, vrt_file, overwrite = TRUE)
|
||||
#
|
||||
# # v_crop <- mask_raster(v, pivot_sf_q)
|
||||
# return(loaded_raster)
|
||||
# }
|
||||
|
||||
|
||||
# extract_rasters_daily <- function(file, field_geojson, quadrants = TRUE, save_dir) {
|
||||
# # x <- rast(filtered_files[1])%>% CI_func(drop_layers = TRUE)
|
||||
# # date <- date_extract(filtered_files[1])
|
||||
# # field_geojson <- sf::st_as_sf(pivot_sf_q)
|
||||
# field_geojson <- sf::st_as_sf(field_geojson)
|
||||
# x <- rast(file[1])
|
||||
# date <- date_extract(file)
|
||||
# pivot_stats <- cbind(field_geojson, mean_CI = round(exactextractr::exact_extract(x$CI, field_geojson, fun = "mean"), 2)) %>%
|
||||
# st_drop_geometry() %>% rename("{date}" := mean_CI)
|
||||
# save_suffix <- if (quadrants){"quadrant"} else {"whole_field"}
|
||||
# save_path <- here(save_dir, paste0("extracted_", date, "_", save_suffix, ".rds"))
|
||||
# saveRDS(pivot_stats, save_path)
|
||||
# }
|
||||
|
||||
|
|
|
|||
|
|
@ -5,7 +5,7 @@ library(terra)
|
|||
library(tidyverse)
|
||||
library(lubridate)
|
||||
library(exactextractr)
|
||||
library(readxl)
|
||||
# library(readxl)
|
||||
|
||||
|
||||
# Vang alle command line argumenten op
|
||||
|
|
@ -37,10 +37,13 @@ weekly_CI_mosaic <- here(laravel_storage_dir, "weekly_mosaic")
|
|||
daily_vrt <- here(data_dir, "vrt")
|
||||
harvest_dir <- here(data_dir, "HarvestData")
|
||||
|
||||
source(here("r_app/parameters_project.R"))
|
||||
source(here("r_app/ci_extraction_utils.R"))
|
||||
# source(here("r_app/mosaic_creation_utils.R"))
|
||||
|
||||
source("parameters_project.R")
|
||||
source("ci_extraction_utils.R")
|
||||
|
||||
|
||||
pivot_stats2 <- readRDS(here(cumulative_CI_vals_dir,"combined_CI_data.rds")) %>%
|
||||
ungroup() %>%
|
||||
group_by(field, sub_field) %>%
|
||||
|
|
@ -61,76 +64,74 @@ pivot_stats_long <- pivot_stats2 %>%
|
|||
# sub_field = field) %>%
|
||||
unique()
|
||||
|
||||
|
||||
# #2021
|
||||
# pivot_select_model_Data_2021 <- harvesting_data %>% filter(Year == 2021) %>% pull(field)
|
||||
#
|
||||
# pivot_select_model_Data_2022 <- harvesting_data %>% filter(Year == 2022) %>% pull(field)
|
||||
|
||||
# pivot_select_model_Data_2023 <- harvesting_data %>% filter(year == 2023) %>% filter(!is.na(season_start)) %>% pull(sub_field)
|
||||
|
||||
pivot_select_model_Data_2024 <- harvesting_data %>% filter(year == 2024)%>% filter(!is.na(season_start)) %>% pull(sub_field)
|
||||
message(pivot_select_model_Data_2024)
|
||||
# pivots_dates_Data_2022 <- pivots_dates0 %>% filter(!is.na(season_end_2022))
|
||||
# pivot_select_model_Data_2022 <- unique(pivots_dates_Data_2022$pivot_quadrant )
|
||||
#
|
||||
# pivots_dates_Data_2023 <- pivots_dates0 %>% filter(!is.na(season_start_2023))
|
||||
# pivot_select_model_Data_2023 <- unique(pivots_dates_Data_2023$pivot_quadrant)
|
||||
#
|
||||
# pivots_dates_Data_2024 <- pivots_dates0 %>% filter(!is.na(season_start_2024))
|
||||
# pivot_select_model_Data_2024 <- unique(pivots_dates_Data_2024$pivot_quadrant)
|
||||
years <- harvesting_data %>%
|
||||
filter(!is.na(season_start)) %>%
|
||||
distinct(year) %>%
|
||||
pull(year)
|
||||
|
||||
extract_CI_data <- function(field_names, harvesting_data, field_CI_data, season) {
|
||||
# field_names = "Nandi A1a"
|
||||
# field_names = "1.13A"
|
||||
# harvesting_data = harvesting_data
|
||||
# field_CI_data = pivot_stats_long
|
||||
# season= 2024
|
||||
|
||||
# Filter harvesting data for the given season and field names
|
||||
filtered_harvesting_data <- harvesting_data %>%
|
||||
# na.omit() %>%
|
||||
filter(year == season, sub_field %in% field_names)
|
||||
|
||||
|
||||
# Filter field CI data for the given field names
|
||||
filtered_field_CI_data <- field_CI_data %>%
|
||||
filter(sub_field %in% field_names)
|
||||
if (nrow(filtered_field_CI_data) == 0) {
|
||||
return(data.frame()) # Return an empty data frame if no data is found
|
||||
}
|
||||
|
||||
# CI <- map_df(field_names, ~ {
|
||||
|
||||
# Return an empty data frame if no CI data is found
|
||||
if (nrow(filtered_field_CI_data) == 0) {
|
||||
return(data.frame())
|
||||
}
|
||||
|
||||
# Create a linear interpolation function for the CI data
|
||||
ApproxFun <- approxfun(x = filtered_field_CI_data$Date, y = filtered_field_CI_data$value)
|
||||
Dates <- seq.Date(ymd(min(filtered_field_CI_data$Date)), ymd(max(filtered_field_CI_data$Date)), by = 1)
|
||||
LinearFit <- ApproxFun(Dates)
|
||||
|
||||
CI <- data.frame(Date = Dates, FitData = LinearFit) %>%
|
||||
|
||||
# Combine interpolated data with the original CI data
|
||||
CI <- data.frame(Date = Dates, FitData = LinearFit) %>%
|
||||
left_join(., filtered_field_CI_data, by = "Date") %>%
|
||||
filter(Date > filtered_harvesting_data$season_start & Date < filtered_harvesting_data$season_end) %>%
|
||||
filter(Date > filtered_harvesting_data$season_start & Date < filtered_harvesting_data$season_end)
|
||||
|
||||
# If CI is empty after filtering, return an empty dataframe
|
||||
if (nrow(CI) == 0) {
|
||||
return(data.frame())
|
||||
}
|
||||
|
||||
# Add additional columns if data exists
|
||||
CI <- CI %>%
|
||||
mutate(DOY = seq(1, n(), 1),
|
||||
model = paste0("Data", season, " : ", field_names),
|
||||
season = season,
|
||||
sub_field = field_names)
|
||||
# }) #%>%
|
||||
|
||||
subField = field_names)
|
||||
|
||||
return(CI)
|
||||
}
|
||||
|
||||
## Extracting the correct CI values
|
||||
# Data_2021 <- map(pivot_select_model_Data_2021, ~ extract_CI_data(.x, harvesting_data = harvesting_data, field_CI_data = pivot_stats_long, season = 2021)) %>% list_rbind()
|
||||
# message('2021')
|
||||
# Data_2022 <- map(pivot_select_model_Data_2022, ~ extract_CI_data(.x, harvesting_data = harvesting_data, field_CI_data = pivot_stats_long, season = 2022)) %>% list_rbind()
|
||||
# message('2022')
|
||||
# Data_2023 <- map(pivot_select_model_Data_2023, ~ extract_CI_data(.x, harvesting_data = harvesting_data, field_CI_data = pivot_stats_long, season = 2023)) %>% list_rbind()
|
||||
# message('2023')
|
||||
head(harvesting_data)
|
||||
Data_2024 <- map(pivot_select_model_Data_2024, ~ extract_CI_data(.x, harvesting_data = harvesting_data, field_CI_data = pivot_stats_long, season = 2024)) %>% list_rbind()
|
||||
message('2024')
|
||||
head(Data_2024)
|
||||
|
||||
|
||||
#CI_all <- rbind(Data_2023, Data_2024)
|
||||
CI_all <- Data_2024
|
||||
message('CI_all created')
|
||||
#CI_all <- Data_2023
|
||||
CI_all <- map_df(years, function(yr) {
|
||||
# yr = 2021
|
||||
message(yr)
|
||||
|
||||
# Get the fields harvested in this year
|
||||
sub_fields <- harvesting_data %>%
|
||||
filter(year == yr) %>%
|
||||
filter(!is.na(season_start)) %>%
|
||||
pull(sub_field)
|
||||
|
||||
# Filter sub_fields to only include those with value data in pivot_stats_long
|
||||
valid_sub_fields <- sub_fields %>%
|
||||
keep(~ any(pivot_stats_long$sub_field == .x))
|
||||
|
||||
# Extract data for each valid field
|
||||
map(valid_sub_fields, ~ extract_CI_data(.x, harvesting_data = harvesting_data, field_CI_data = pivot_stats_long, season = yr)) %>%
|
||||
list_rbind()
|
||||
})
|
||||
|
||||
CI_all <- CI_all %>% group_by(model) %>% mutate(CI_per_day = FitData - lag(FitData),
|
||||
cumulative_CI = cumsum(FitData))
|
||||
|
||||
|
||||
CI_all <- CI_all %>% group_by(model) %>% mutate(CI_per_day = FitData - lag(FitData),
|
||||
cumulative_CI = cumsum(FitData))
|
||||
|
|
|
|||
|
|
@ -11,7 +11,7 @@ library(terra)
|
|||
library(tidyverse)
|
||||
library(lubridate)
|
||||
# library(exactextractr)
|
||||
library(readxl)
|
||||
# library(readxl)
|
||||
#funcion CI_prep package
|
||||
|
||||
|
||||
|
|
@ -62,21 +62,24 @@ weekly_CI_mosaic <- here(laravel_storage_dir, "weekly_mosaic")
|
|||
daily_vrt <- here(data_dir, "vrt")
|
||||
harvest_dir <- here(data_dir, "HarvestData")
|
||||
|
||||
source(here("r_app/parameters_project.R"))
|
||||
source(here("r_app/mosaic_creation_utils.R"))
|
||||
|
||||
source("parameters_project.R")
|
||||
source("mosaic_creation_utils.R")
|
||||
|
||||
dir.create(here(laravel_storage_dir))
|
||||
dir.create(here(reports_dir))
|
||||
dir.create(here(data_dir))
|
||||
dir.create(here(extracted_CI_dir))
|
||||
dir.create(here(daily_CI_vals_dir))
|
||||
dir.create(here(cumulative_CI_vals_dir))
|
||||
dir.create(here(weekly_CI_mosaic))
|
||||
dir.create(here(daily_vrt))
|
||||
dir.create(merged_final)
|
||||
dir.create(harvest_dir)
|
||||
# dir.create(here(laravel_storage_dir))
|
||||
# dir.create(here(reports_dir))
|
||||
# dir.create(here(data_dir))
|
||||
# dir.create(here(extracted_CI_dir))
|
||||
# dir.create(here(daily_CI_vals_dir))
|
||||
# dir.create(here(cumulative_CI_vals_dir))
|
||||
# dir.create(here(weekly_CI_mosaic))
|
||||
# dir.create(here(daily_vrt))
|
||||
# dir.create(merged_final)
|
||||
# dir.create(harvest_dir)
|
||||
|
||||
# end_date <- lubridate::dmy("20-6-2024")
|
||||
# end_date <- lubridate::dmy("28-8-2024")
|
||||
week <- week(end_date)
|
||||
|
||||
|
||||
|
|
@ -101,78 +104,81 @@ vrt_list <- map(dates$days_filter, ~ vrt_files[grepl(pattern = .x, x = vrt_file
|
|||
compact() %>%
|
||||
flatten_chr()
|
||||
|
||||
raster_files_final <- list.files(merged_final,full.names = T, pattern = ".tif")
|
||||
if (length(vrt_list) > 0 ){
|
||||
raster_files_final <- list.files(merged_final,full.names = T, pattern = ".tif")
|
||||
|
||||
if (length(vrt_list) > 0) {
|
||||
print("vrt list made, preparing mosaic creation by counting cloud cover")
|
||||
|
||||
total_pix_area <- rast(vrt_list[1]) %>% terra::subset(1) %>% setValues(1) %>%
|
||||
|
||||
total_pix_area <-
|
||||
rast(vrt_list[1]) %>% terra::subset(1) %>% setValues(1) %>%
|
||||
crop(field_boundaries, mask = TRUE) %>%
|
||||
global(., fun="notNA") #%>%
|
||||
|
||||
global(., fun = "notNA") #%>%
|
||||
|
||||
layer_5_list <- purrr::map(vrt_list, function(vrt_list) {
|
||||
rast(vrt_list[1]) %>% terra::subset(1)
|
||||
}) %>% rast()
|
||||
|
||||
missing_pixels_count <- layer_5_list %>% global(., fun="notNA") %>%
|
||||
|
||||
missing_pixels_count <-
|
||||
layer_5_list %>% global(., fun = "notNA") %>%
|
||||
mutate(
|
||||
total_pixels = total_pix_area$notNA,
|
||||
missing_pixels_percentage = round(100 -((notNA/total_pix_area$notNA)*100)),
|
||||
missing_pixels_percentage = round(100 - ((
|
||||
notNA / total_pix_area$notNA
|
||||
) * 100)),
|
||||
thres_5perc = as.integer(missing_pixels_percentage < 5),
|
||||
thres_40perc = as.integer(missing_pixels_percentage < 45)
|
||||
)
|
||||
message('hier')
|
||||
message(missing_pixels_count)
|
||||
index_5perc <- which(missing_pixels_count$thres_5perc == max(missing_pixels_count$thres_5perc) )
|
||||
|
||||
index_5perc <- which(missing_pixels_count$thres_5perc == max(missing_pixels_count$thres_5perc))
|
||||
index_40perc <- which(missing_pixels_count$thres_40perc == max(missing_pixels_count$thres_40perc))
|
||||
|
||||
|
||||
## Create mosaic
|
||||
|
||||
if(sum(missing_pixels_count$thres_5perc)>1){
|
||||
|
||||
if (sum(missing_pixels_count$thres_5perc) > 1) {
|
||||
message("More than 1 raster without clouds (<5%), max composite made")
|
||||
|
||||
|
||||
cloudy_rasters_list <- vrt_list[index_5perc]
|
||||
|
||||
|
||||
rsrc <- sprc(cloudy_rasters_list)
|
||||
x <- terra::mosaic(rsrc, fun = "max")
|
||||
names(x) <- c("Red", "Green", "Blue", "NIR", "CI")
|
||||
|
||||
}else if(sum(missing_pixels_count$thres_5perc)==1){
|
||||
|
||||
} else if (sum(missing_pixels_count$thres_5perc) == 1) {
|
||||
message("Only 1 raster without clouds (<5%)")
|
||||
|
||||
|
||||
x <- rast(vrt_list[index_5perc[1]])
|
||||
names(x) <- c("Red", "Green", "Blue", "NIR", "CI")
|
||||
|
||||
}else if(sum(missing_pixels_count$thres_40perc)>1){
|
||||
|
||||
} else if (sum(missing_pixels_count$thres_40perc) > 1) {
|
||||
message("More than 1 image contains clouds, composite made of <40% cloud cover images")
|
||||
|
||||
|
||||
cloudy_rasters_list <- vrt_list[index_40perc]
|
||||
|
||||
|
||||
rsrc <- sprc(cloudy_rasters_list)
|
||||
x <- mosaic(rsrc, fun = "max")
|
||||
names(x) <- c("Red", "Green", "Blue", "NIR", "CI")
|
||||
|
||||
}else if(sum(missing_pixels_count$thres_40perc)==1){
|
||||
|
||||
} else if (sum(missing_pixels_count$thres_40perc) == 1) {
|
||||
message("Only 1 image available but contains clouds")
|
||||
|
||||
|
||||
x <- rast(vrt_list[index_40perc[1]])
|
||||
names(x) <- c("Red", "Green", "Blue", "NIR", "CI")
|
||||
|
||||
}else if(sum(missing_pixels_count$thres_40perc)==0){
|
||||
|
||||
} else if (sum(missing_pixels_count$thres_40perc) == 0) {
|
||||
message("No cloud free images available, all images combined")
|
||||
|
||||
|
||||
rsrc <- sprc(vrt_list)
|
||||
x <- mosaic(rsrc, fun = "max")
|
||||
names(x) <- c("Red", "Green", "Blue", "NIR", "CI")
|
||||
|
||||
|
||||
}
|
||||
|
||||
|
||||
} else{
|
||||
|
||||
message("No images available this week, empty mosaic created")
|
||||
|
||||
|
||||
x <- rast(raster_files_final[1]) %>% setValues(0) %>%
|
||||
crop(field_boundaries, mask = TRUE)
|
||||
|
||||
|
||||
names(x) <- c("Red", "Green", "Blue", "NIR", "CI")
|
||||
}
|
||||
plot(x$CI, main = paste("CI map ", dates$week))
|
||||
|
|
|
|||
|
|
@ -11,207 +11,3 @@ date_list <- function(end_date, offset){
|
|||
|
||||
return(list("week" = week, "year" = year, "days_filter" = days_filter))
|
||||
}
|
||||
|
||||
# date_list <- function(weeks_in_the_paste){
|
||||
# week <- week(Sys.Date()- weeks(weeks_in_the_paste) )
|
||||
# year <- year(Sys.Date()- weeks(weeks_in_the_paste) )
|
||||
# days_filter <- Sys.Date() - weeks(weeks_in_the_paste) - days(0:6)
|
||||
#
|
||||
# return(c("week" = week,
|
||||
# "year" = year,
|
||||
# "days_filter" = list(days_filter)))
|
||||
#
|
||||
# }
|
||||
create_mask_and_crop <- function(file, field_boundaries) {
|
||||
# file <- filtered_files[5]
|
||||
message("starting ", file)
|
||||
loaded_raster <- rast(file)
|
||||
names(loaded_raster) <- c("Red", "Green", "Blue", "NIR")
|
||||
# names(CI) <- c("green","nir")
|
||||
message("raster loaded")
|
||||
|
||||
# CI <- CI[[2]] / CI[[1]] - 1
|
||||
CI <- loaded_raster$NIR / loaded_raster$Green - 1
|
||||
|
||||
loaded_raster$CI <- CI
|
||||
# CI <- CI$nir/CI$green-1
|
||||
message("CI calculated")
|
||||
loaded_raster <- terra::crop(loaded_raster, field_boundaries, mask = TRUE) #%>% CI_func()
|
||||
|
||||
loaded_raster[loaded_raster == 0] <- NA
|
||||
# names(v_crop) <- c("red", "green", "blue","nir", "cloud" ,"CI")
|
||||
# v_crop$CI <- v_crop$CI - 1
|
||||
new_file <- here(merged_final, paste0(tools::file_path_sans_ext(basename(file)), ".tif"))
|
||||
writeRaster(loaded_raster, new_file, overwrite = TRUE)
|
||||
|
||||
vrt_file <- here(daily_vrt, paste0(tools::file_path_sans_ext(basename(file)), ".vrt"))
|
||||
terra::vrt(new_file, vrt_file, overwrite = TRUE)
|
||||
|
||||
# v_crop <- mask_raster(v, pivot_sf_q)
|
||||
return(loaded_raster)
|
||||
}
|
||||
|
||||
CI_func <- function(x, drop_layers = FALSE){
|
||||
CI <- x[[4]]/x[[2]]-1
|
||||
add(x) <- CI
|
||||
names(x) <- c("red", "green", "blue","nir", "cloud" ,"CI")
|
||||
if(drop_layers == FALSE){
|
||||
return(x)
|
||||
}else{
|
||||
return(x$CI)
|
||||
}
|
||||
}
|
||||
|
||||
mask_raster <- function(raster, fields){
|
||||
# x <- rast(filtered_files[1])
|
||||
x <- rast(raster)
|
||||
emtpy_or_full <- global(x, sum)
|
||||
|
||||
if(emtpy_or_full[1,] >= 2000000){
|
||||
names(x) <- c("red", "green", "blue","nir", "cloud")
|
||||
cloud <- x$cloud
|
||||
|
||||
cloud[cloud == 0 ] <- NA
|
||||
x_masked <- mask(x, cloud, inverse = T) %>% crop(.,fields, mask = TRUE )
|
||||
x_masked <- x_masked %>% CI_func()
|
||||
|
||||
message(raster, " masked")
|
||||
return(x_masked)
|
||||
}
|
||||
}
|
||||
|
||||
date_extract <- function(file_path) {
|
||||
str_extract(file_path, "\\d{4}-\\d{2}-\\d{2}")
|
||||
}
|
||||
|
||||
|
||||
|
||||
right = function(text, num_char) {
|
||||
substr(text, nchar(text) - (num_char-1), nchar(text))
|
||||
}
|
||||
|
||||
# load_fields <- function(geojson_path) {
|
||||
# field_geojson <- st_read(geojson_path) %>%
|
||||
# select(pivot, pivot_quadrant) %>%
|
||||
# vect()
|
||||
# return(field_geojson)
|
||||
# }
|
||||
#
|
||||
# load_harvest_data <- function(havest_data_path){
|
||||
# harvest_data <- readRDS(havest_data_path)
|
||||
# return(harvest_data)
|
||||
# }
|
||||
|
||||
# load_rasters <- function(raster_path, dates) {
|
||||
# raster_files <- list.files(raster_path, full.names = TRUE, pattern = ".tif")
|
||||
# filtered_files <- map(dates$days_filter, ~ raster_files[grepl(pattern = .x, x = raster_files)]) %>%
|
||||
# compact() %>%
|
||||
# flatten_chr()
|
||||
#
|
||||
# return(filtered_files)
|
||||
# }
|
||||
|
||||
# mask_and_set_names <- function(filtered_files, fields) {
|
||||
# rasters_masked <- map(filtered_files, mask_raster, fields = fields) %>% set_names(filtered_files)
|
||||
# rasters_masked[sapply(rasters_masked, is.null)] <- NULL
|
||||
# rasters_masked <- setNames(rasters_masked, map_chr(names(rasters_masked), date_extract))
|
||||
#
|
||||
# return(rasters_masked)
|
||||
# }
|
||||
|
||||
# calculate_total_pix_area <- function(filtered_files, fields_geojson) {
|
||||
# # total_pix_area <- rast(filtered_files[1]) %>%
|
||||
# # subset(1) %>%
|
||||
# # crop(fields_geojson, mask = TRUE)%>%
|
||||
# # global(.data, fun = "notNA")
|
||||
# total_pix_area <- rast(filtered_files[1]) %>% subset(1) %>% crop(fields_geojson, mask = TRUE) %>% freq(., usenames = TRUE)
|
||||
#
|
||||
# return(total_pix_area)
|
||||
# }
|
||||
|
||||
# cloud_layer_extract <- function(rasters_masked){
|
||||
# cloud_layer_rast <- map(rasters_masked, function(spatraster) {
|
||||
# spatraster[[5]]
|
||||
# }) %>% rast()
|
||||
#
|
||||
# return(cloud_layer_rast)
|
||||
# }
|
||||
|
||||
# calculate_cloud_coverage <- function(cloud_layer_rast, total_pix_area) {
|
||||
# cloud_perc_list <- freq(cloud_layer_rast, usenames = TRUE) %>%
|
||||
# mutate(cloud_perc = (100 -((count/total_pix_area$count)*100)),
|
||||
# cloud_thres_5perc = as.integer(cloud_perc < 5),
|
||||
# cloud_thres_40perc = as.integer(cloud_perc < 40)) %>%
|
||||
# rename(Date = layer) %>% select(-value, -count)
|
||||
#
|
||||
# cloud_index_5perc <- which(cloud_perc_list$cloud_thres_5perc == max(cloud_perc_list$cloud_thres_5perc))
|
||||
# cloud_index_40perc <- which(cloud_perc_list$cloud_thres_40perc == max(cloud_perc_list$cloud_thres_40perc))
|
||||
#
|
||||
# return(list(cloud_perc_list = cloud_perc_list, cloud_index_5perc = cloud_index_5perc, cloud_index_40perc = cloud_index_40perc))
|
||||
# }
|
||||
|
||||
process_cloud_coverage <- function(cloud_coverage, rasters_masked) {
|
||||
if (sum(cloud_coverage$cloud_perc_list$cloud_thres_5perc) > 1) {
|
||||
message("More than 1 raster without clouds (<5%), max mosaic made ")
|
||||
|
||||
cloudy_rasters_list <- rasters_masked[cloud_coverage$cloud_index_5perc]
|
||||
rsrc <- sprc(cloudy_rasters_list)
|
||||
x <- mosaic(rsrc)
|
||||
names(x) <- c("red", "green", "blue", "nir", "cloud", "CI")
|
||||
|
||||
} else if (sum(cloud_coverage$cloud_perc_list$cloud_thres_5perc) == 1) {
|
||||
message("Only 1 raster without clouds (<5%)")
|
||||
|
||||
x <- rast(rasters_masked[cloud_coverage$cloud_index_5perc[1]])
|
||||
names(x) <- c("red", "green", "blue", "nir", "cloud", "CI")
|
||||
|
||||
} else if (sum(cloud_coverage$cloud_perc_list$cloud_thres_40perc) > 1) {
|
||||
message("More than 1 image contains clouds, composite made of <40% cloud cover images")
|
||||
|
||||
cloudy_rasters_list <- rasters_masked[cloud_coverage$cloud_index_40perc]
|
||||
rsrc <- sprc(cloudy_rasters_list)
|
||||
x <- mosaic(rsrc)
|
||||
names(x) <- c("red", "green", "blue", "nir", "cloud", "CI")
|
||||
|
||||
} else if (sum(cloud_coverage$cloud_perc_list$cloud_thres_40perc) == 0) {
|
||||
message("No cloud free images available")
|
||||
x <- rast(rasters_masked[1])
|
||||
x[x] <- NA
|
||||
names(x) <- c("red", "green", "blue", "nir", "cloud", "CI")
|
||||
}
|
||||
|
||||
return(x)
|
||||
}
|
||||
|
||||
|
||||
#functions for CI_data_prep
|
||||
# create_mask_and_crop <- function(file, pivot_sf_q) {
|
||||
# # file <- filtered_files[5]
|
||||
# message("starting ", file)
|
||||
# loaded_raster <- rast(file)
|
||||
# names(loaded_raster) <- c("Red", "Green", "Blue", "NIR")
|
||||
# # names(CI) <- c("green","nir")
|
||||
# message("raster loaded")
|
||||
#
|
||||
# # CI <- CI[[2]] / CI[[1]] - 1
|
||||
# CI <- loaded_raster$NIR / loaded_raster$Green - 1
|
||||
#
|
||||
# loaded_raster$CI <- CI
|
||||
# # CI <- CI$nir/CI$green-1
|
||||
# message("CI calculated")
|
||||
# loaded_raster <- terra::crop(loaded_raster, pivot_sf_q, mask = TRUE) #%>% CI_func()
|
||||
#
|
||||
# loaded_raster[loaded_raster == 0] <- NA
|
||||
# # names(v_crop) <- c("red", "green", "blue","nir", "cloud" ,"CI")
|
||||
# # v_crop$CI <- v_crop$CI - 1
|
||||
# new_file <- here(merged_final, paste0(tools::file_path_sans_ext(basename(file)), ".tif"))
|
||||
# writeRaster(loaded_raster, new_file, overwrite = TRUE)
|
||||
#
|
||||
# vrt_file <- here(daily_vrt, paste0(tools::file_path_sans_ext(basename(file)), ".vrt"))
|
||||
# terra::vrt(new_file, vrt_file, overwrite = TRUE)
|
||||
#
|
||||
# # v_crop <- mask_raster(v, pivot_sf_q)
|
||||
# return(loaded_raster)
|
||||
# }
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -1,111 +1,38 @@
|
|||
|
||||
library('readxl')
|
||||
#chemba
|
||||
if(project_dir == "chemba1"){
|
||||
message("Yield data for Chemba")
|
||||
|
||||
|
||||
field_boundaries_sf <- st_read(here(data_dir, "pivot.geojson")) %>% dplyr::select(pivot, pivot_quadrant)%>%
|
||||
mutate(sub_area = case_when(pivot %in% c("1.1", "1.2", "1.3", "1.4", "1.6", "1.7", "1.8", "1.9", "1.10", "1.11", "1.12", "1.13", "1.14" , "1.16" , "1.17" , "1.18" , "6.1", "6.2", "DL1.1", "DL1.3") ~ "estate",
|
||||
TRUE ~ "Cooperative"))
|
||||
names(field_boundaries_sf) <- c("field", "sub_field", "geometry", "sub_area")
|
||||
|
||||
field_boundaries <- field_boundaries_sf %>% vect()
|
||||
names(field_boundaries) <- c("field", "sub_field", "sub_area")
|
||||
|
||||
# field_boundaries_merged <- st_read(here(data_dir, "pivot.geojson")) %>% dplyr::select(pivot, pivot_quadrant) %>% group_by(pivot) %>% summarise() %>% vect()
|
||||
|
||||
joined_spans <-st_read(here(data_dir, "span.geojson")) %>% st_transform(crs(field_boundaries_sf))
|
||||
names(joined_spans) <- c("field", "area", "radius", "spans", "span", "geometry")
|
||||
|
||||
|
||||
pivots_dates0 <- readRDS(here(harvest_dir, "harvest_data_new")) %>% filter(
|
||||
pivot %in% c("1.1", "1.2", "1.3", "1.4", "1.6", "1.7", "1.8", "1.9", "1.10", "1.11", "1.12", "1.13",
|
||||
"1.14" , "1.16" , "1.17" , "1.18" ,"2.1", "2.2", "2.3" , "2.4", "2.5", "3.1", "3.2", "3.3",
|
||||
"4.1", "4.2", "4.3", "4.4", "4.5", "4.6", "5.1" ,"5.2", "5.3", "5.4", "6.1", "6.2", "DL1.1", "DL1.3")
|
||||
field_boundaries_sf <- st_read(here(data_dir, "pivot.geojson"))
|
||||
|
||||
names(field_boundaries_sf) <- c("field", "sub_field", "geometry")
|
||||
field_boundaries <- field_boundaries_sf %>% terra::vect()
|
||||
|
||||
harvesting_data <- read_excel(here(data_dir, "harvest.xlsx")) %>%
|
||||
dplyr::select(
|
||||
c(
|
||||
"field",
|
||||
"sub_field",
|
||||
"year",
|
||||
"season_start",
|
||||
"season_end",
|
||||
"age",
|
||||
"sub_area",
|
||||
"tonnage_ha"
|
||||
)
|
||||
) %>%
|
||||
mutate(
|
||||
field = as.character(field),
|
||||
sub_field = as.character(sub_field),
|
||||
year = as.numeric(year),
|
||||
season_start = as.Date(season_start),
|
||||
season_end = as.Date(season_end),
|
||||
age = as.numeric(age),
|
||||
sub_area = as.character(sub_area),
|
||||
tonnage_ha = as.numeric(tonnage_ha)
|
||||
) %>%
|
||||
mutate(
|
||||
season_end = case_when(season_end > Sys.Date() ~ Sys.Date(),
|
||||
TRUE ~ season_end),
|
||||
age = round(as.numeric(season_end - season_start) / 7, 0)
|
||||
)
|
||||
|
||||
harvesting_data <- pivots_dates0 %>%
|
||||
dplyr::select(c("pivot_quadrant", "pivot", "season_start_2021", "season_end_2021", "season_start_2022",
|
||||
"season_end_2022", "season_start_2023", "season_end_2023", "season_start_2024", "season_end_2024", "Age")) %>%
|
||||
pivot_longer(cols = starts_with("season"), names_to = "Year", values_to = "value") %>%
|
||||
separate(Year, into = c("name", "Year"), sep = "(?<=season_start|season_end)\\_", remove = FALSE) %>%
|
||||
mutate(name = str_to_title(name)) %>%
|
||||
pivot_wider(names_from = name, values_from = value) %>%
|
||||
rename(Field = pivot,
|
||||
subField = pivot_quadrant)
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
} else if (project_dir == "xinavane"){
|
||||
library(readxl)
|
||||
message("Yield data for Xinavane")
|
||||
field_boundaries_sf <- st_read(here(data_dir, "pivot.geojson")) %>% dplyr::select(-Pivot)
|
||||
names(field_boundaries_sf) <- c("field", "sub_field", "geometry")
|
||||
|
||||
field_boundaries <- field_boundaries_sf %>% vect()
|
||||
|
||||
joined_spans <- field_boundaries_sf %>% dplyr::select(Field)
|
||||
|
||||
pivots_dates0 <- read_excel(here(harvest_dir, "Yield data.xlsx"),
|
||||
skip = 3,
|
||||
col_types = c("numeric", "text", "skip", "text", "numeric", "numeric", "numeric", "numeric", "date",
|
||||
"numeric", "skip", "numeric")) %>%
|
||||
rename(
|
||||
year = 1,
|
||||
field = 2,
|
||||
sub_area = 3,
|
||||
hectares = 4,
|
||||
tons = 5,
|
||||
tcha = 6,
|
||||
tchy = 7,
|
||||
season_end = 8,
|
||||
age = 9,
|
||||
ratoon = 10
|
||||
) %>%
|
||||
mutate(Season_end = ymd(season_end),
|
||||
Season_start = as.Date(season_end - (duration(months = age))),
|
||||
subField = Field) #don't forget to add 2022 as a year for the 'curent' season
|
||||
|
||||
pivots_dates0 <- pivots_dates0 %>%
|
||||
mutate(year = year + 6)
|
||||
|
||||
# Add 6 years to Season_end column
|
||||
pivots_dates0 <- pivots_dates0 %>%
|
||||
mutate(season_end = season_end + years(6))
|
||||
|
||||
# Add 6 years to Season_start column
|
||||
pivots_dates0 <- pivots_dates0 %>%
|
||||
mutate(season_start = season_start + years(6))
|
||||
|
||||
|
||||
harvesting_data <- pivots_dates0 %>% dplyr::select(c("field","sub_field", "year", "season_start","season_end", "age" , "sub_area", "tcha"))
|
||||
|
||||
|
||||
} else {
|
||||
field_boundaries_sf <- st_read(here(data_dir, "pivot.geojson"))
|
||||
head(field_boundaries_sf)
|
||||
names(field_boundaries_sf) <- c("field", "sub_field", "geometry")
|
||||
field_boundaries <- field_boundaries_sf %>% terra::vect()
|
||||
harvesting_data <- read_excel(here(data_dir, "harvest.xlsx")) %>%
|
||||
dplyr::select(c("field", "sub_field", "year", "season_start", "season_end", "age", "sub_area", "tonnage_ha")) %>%
|
||||
mutate(field = as.character(field),
|
||||
sub_field = as.character(sub_field),
|
||||
year = as.numeric(year),
|
||||
season_start = as.Date(season_start),
|
||||
season_end = as.Date(season_end),
|
||||
age = as.numeric(age),
|
||||
sub_area = as.character(sub_area),
|
||||
tonnage_ha = as.numeric(tonnage_ha)
|
||||
) %>%
|
||||
mutate(season_end = case_when(
|
||||
season_end > Sys.Date() ~ Sys.Date(),
|
||||
TRUE ~ season_end),
|
||||
age = round(as.numeric(season_end - season_start)/7,0))
|
||||
}
|
||||
|
||||
|
|
|
|||
Loading…
Reference in a new issue