From 93a9e4f2e1d8e30334234aca47827b0329c3a2e5 Mon Sep 17 00:00:00 2001 From: almac2022 Date: Sun, 17 Nov 2024 22:32:55 -1000 Subject: [PATCH] grab photos from 3 different years after plotting by year --- .../index/execute-results/html.json | 4 +- .../index/figure-html/map1-1.png | Bin 0 -> 241010 bytes .../index/figure-html/map2-1.png | Bin 582385 -> 610692 bytes .../index/figure-html/map3-1.png | Bin 269286 -> 373583 bytes .../index/figure-html/map4-1.png | Bin 199284 -> 426370 bytes .../index/figure-html/map5-1.png | Bin 0 -> 244813 bytes .../index/figure-html/unnamed-chunk-1-1.png | Bin 177385 -> 241010 bytes .../data/l_streams.geojson | 638 +++++++++++++++++- ...sv => airphotos_1968-05-09_1975-07-05.csv} | 76 ++- .../airphotos_1971-06-05_1975-09-18.csv | 145 ---- .../fig/Screenshot1.png | Bin 369118 -> 2843251 bytes .../index.qmd | 247 +++---- 12 files changed, 834 insertions(+), 276 deletions(-) create mode 100644 _freeze/posts/2024-11-15-bcdata-ortho-historic/index/figure-html/map1-1.png create mode 100644 _freeze/posts/2024-11-15-bcdata-ortho-historic/index/figure-html/map5-1.png rename posts/2024-11-15-bcdata-ortho-historic/exports/{airphotos_1963-08-07_1968-07-31.csv => airphotos_1968-05-09_1975-07-05.csv} (52%) delete mode 100644 posts/2024-11-15-bcdata-ortho-historic/exports/airphotos_1971-06-05_1975-09-18.csv diff --git a/_freeze/posts/2024-11-15-bcdata-ortho-historic/index/execute-results/html.json b/_freeze/posts/2024-11-15-bcdata-ortho-historic/index/execute-results/html.json index c0469fa..aba5078 100644 --- a/_freeze/posts/2024-11-15-bcdata-ortho-historic/index/execute-results/html.json +++ b/_freeze/posts/2024-11-15-bcdata-ortho-historic/index/execute-results/html.json @@ -1,8 +1,8 @@ { - "hash": "ce4083462c73006aa212a21024561275", + "hash": "07cad3ed80372fe50830d24f44828b6c", "result": { "engine": "knitr", - "markdown": "---\ntitle: \"Getting details of historic orthophoto imagery with R\"\nauthor: \"al\"\ndate: \"2024-11-15\"\ndate-modified: \"2024-11-17\"\ncategories: [fwapg, r, bcdata]\nimage: \"image.jpg\"\nparams:\n repo_owner: \"NewGraphEnvironment\"\n repo_name: \"new_graphiti\"\n post_name: \"2024-11-15-bcdata-ortho-historic\"\n update_gis: FALSE\nformat: \n html:\n code-fold: true\n---\n\n\nWe would like to obtain historic ortho photo imagery so that we can compare historic watershed conditions compared to current (ex. floodplain vegetation clearing, channel morphology, etc.). First we will generate an area of interest. In our first few code chunks we load our packages and load in some functions that will help us do this work.\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsuppressMessages(library(tidyverse))\nlibrary(ggplot2)\nlibrary(bcdata)\nlibrary(fwapgr)\nsuppressMessages(library(sf))\n# library(leaflet)\n# library(leafem)\n```\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\npath_post <- fs::path(\n here::here(),\n \"posts\",\n params$post_name\n)\n```\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\nstaticimports::import(\n dir = fs::path(\n path_post,\n \"scripts\"\n ),\n outfile = fs::path(\n path_post,\n \"scripts\",\n \"staticimports\",\n ext = \"R\"\n )\n)\n```\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\nsource(\n fs::path(\n path_post,\n \"scripts\",\n \"staticimports\",\n ext = \"R\"\n )\n)\n\n\nlfile_name <- function(dat_name = NULL, ext = \"geojson\") {\n fs::path(\n path_post,\n \"data\",\n paste0(dat_name, \".\", ext)\n )\n}\n\nlburn_sf <- function(dat = NULL, dat_name = NULL) {\n if (is.null(dat_name)) {\n cli::cli_abort(\"You must provide a name for the GeoJSON file using `dat_name`.\")\n }\n \n dat |>\n sf::st_write(\n lfile_name(dat_name),\n delete_dsn = TRUE\n # append = FALSE\n )\n}\n\n# Function to validate and repair geometries\nlngs_geom_validate <- function(layer) {\n layer <- sf::st_make_valid(layer)\n layer <- layer[sf::st_is_valid(layer), ]\n return(layer)\n}\n```\n:::\n\n\n\n## Download Spatial Data Layers\nHere we download our area of interest which is the Neexdzii Kwah River (a.k.a Upper Bulkley River) which is located between Houston, BC (just south of Smithers) and Topley, BC which is east of Houston and north of Burns Lake, BC. We hit up our remote database managed by Simon Norris with a package built by Poisson Consulting specifically for the task. We use the `downstream_route_measure` of the Bulkley River (identified through a unique `blue_line_key`) to query the watershed area upstream of the point where the Neexdzii Kwah River enters the Wedzin Kwah River (a.k.a Morice River).\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# lets build a custom watersehed just for upstream of the confluence of Neexdzii Kwa and Wetzin Kwa\n# blueline key\nblk <- 360873822\n# downstream route measure\ndrm <- 166030.4\n\naoi <- fwapgr::fwa_watershed_at_measure(blue_line_key = blk, \n downstream_route_measure = drm) |> \n sf::st_transform(4326)\n\n\n#get the bounding box of our aoi\n# aoi_bb <- sf::st_bbox(aoi)\n\n#lets burn this so we don't need to download each time\naoi <- lngs_geom_validate(aoi)\nlburn_sf(\n aoi,\n deparse(substitute(aoi)))\n```\n:::\n\n\nNext we grab a few key layers from the BC Data Catalougue API using convience functions in our `rfp` package (\"Reproducable Field Products\") which wrap the provincially maintained `bcdata` package. We grab:\n\n - Railways\n - Streams in the Bulkley Watershed group that are 5th order or greater.\n - [Historic Imagery Points](https://catalogue.data.gov.bc.ca/dataset/airborne-imagery-historical-index-map-points)\n - [Historic Imagery Polygons](https://catalogue.data.gov.bc.ca/dataset/airborne-imagery-historical-index-map-polygons)\n - [NTS 1:50,000 Grid](https://catalogue.data.gov.bc.ca/) (we will see why in a second)\n - [Air Photo Centroids](https://catalogue.data.gov.bc.ca/dataset/airphoto-centroids)\n \n\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# grab all the railways\nl_rail <- rfp::rfp_bcd_get_data(\n bcdata_record_id = \"whse_basemapping.gba_railway_tracks_sp\"\n) |> \n sf::st_transform(4326) |> \n janitor::clean_names() \n\n\n# streams in the bulkley and then filter to just keep the big ones\nl_streams <- rfp::rfp_bcd_get_data(\n bcdata_record_id = \"whse_basemapping.fwa_stream_networks_sp\",\n col_filter = \"watershed_group_code\",\n col_filter_value = \"BULK\",\n # grab a smaller object by including less columns\n col_extract = c(\"linear_feature_id\", \"stream_order\", \"gnis_name\", \"downstream_route_measure\", \"blue_line_key\", \"length_metre\")\n) |> \n sf::st_transform(4326) |> \n janitor::clean_names() |> \n dplyr::filter(stream_order > 4)\n\n# historic orthophotos\n# WHSE_IMAGERY_AND_BASE_MAPS.AIMG_HIST_INDEX_MAPS_POLY\n#https://catalogue.data.gov.bc.ca/dataset/airborne-imagery-historical-index-map-points\nl_imagery <- rfp::rfp_bcd_get_data(\n bcdata_record_id = \"WHSE_IMAGERY_AND_BASE_MAPS.AIMG_ORTHOPHOTO_TILES_POLY\") |> \n sf::st_transform(4326) |> \n janitor::clean_names()\n\nl_imagery_hist <- rfp::rfp_bcd_get_data(\n bcdata_record_id = \"WHSE_IMAGERY_AND_BASE_MAPS.AIMG_HIST_INDEX_MAPS_POINT\") |> \n sf::st_transform(4326) |> \n janitor::clean_names()\n\nl_imagery_grid <- rfp::rfp_bcd_get_data(\n bcdata_record_id = \"WHSE_BASEMAPPING.NTS_50K_GRID\") |> \n sf::st_transform(4326) |> \n janitor::clean_names()\n```\n:::\n\n\nFollowing download we run some clean up to ensure the geometry of our spatial files is \"valid\", trim to our area of interest and burn locally so that every time we rerun iterations of this memo we don't need to wait for the download process which takes a little longer than we want to wait.\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# get a list of the objects in our env that start with l_\nls <- ls()[stringr::str_starts(ls(), \"l_\")] \n\nlayers_all <- tibble::lst(\n !!!mget(ls)\n)\n\n# Apply validation to the AOI and layers\nlayers_all <- purrr::map(\n layers_all, \n lngs_geom_validate\n )\n\n# clip them with purrr and sf\nlayers_trimmed <- purrr::map(\n layers_all,\n ~ sf::st_intersection(.x, aoi)\n) \n\n# Burn each `sf` object to GeoJSON\npurrr::walk2(\n layers_trimmed,\n names(layers_trimmed),\n lburn_sf\n)\n```\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\n# lets use the nts mapsheet to query the photo centroids to avoid a massive file download\ncol_value <- layers_trimmed$l_imagery_grid |> \n dplyr::pull(map_tile) \n\n \n\nl_photo_centroids <- rfp::rfp_bcd_get_data(\n bcdata_record_id = \"WHSE_IMAGERY_AND_BASE_MAPS.AIMG_PHOTO_CENTROIDS_SP\",\n col_filter = \"nts_tile\",\n col_filter_value = col_value) |> \n sf::st_transform(4326) |> \n janitor::clean_names()\n\n# Apply validation to the AOI and layers\nl_photo_centroids <-lngs_geom_validate(l_photo_centroids)\n\n# clip to aoi - can use layers_trimmed$aoi \nl_photo_centroids <- sf::st_intersection(l_photo_centroids, aoi)\n\n\nlburn_sf(l_photo_centroids, \"l_photo_centroids\")\n```\n:::\n\n\nNext - we read the layers back in. The download step is skipped now unless we turn it on again by changing the `update_gis` param in our memo `yaml` header to `TRUE`.\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# now we read in all the sf layers that are local so it is really quick\nlayers_to_load <- fs::dir_ls(\n fs::path(\n path_post,\n \"data\"),\n glob = \"*.geojson\"\n)\n\nlayers_trimmed <- layers_to_load |>\n purrr::map(\n ~ sf::st_read(\n .x, quiet = TRUE)\n ) |> \n purrr::set_names(\n nm =tools::file_path_sans_ext(\n basename(\n names(\n layers_to_load\n )\n )\n )\n )\n```\n:::\n\n\nOK, seems we cannot get machine readable historical air photo information from the downloaded from the BC data catalogue [Historic Imagery Points](https://catalogue.data.gov.bc.ca/dataset/airborne-imagery-historical-index-map-points)\n and [Historic Imagery Polygons](https://catalogue.data.gov.bc.ca/dataset/airborne-imagery-historical-index-map-polygons) layers perhpas because the majority of the photos are not georeferenced? What we see in the map under the table below (red dot on map) is one point which contains 8 records including links to pdfs and kmls which are basically a georeferenced drawing of where the imagery overlaps. From as far as I can tell - if we wanted to try to use these kmls or pdfs to select orthoimagery we would need to manually eyeball where the photo polygons overlap where we want to see imagery for and manually write down identifiers for photo by hand. Maybe I am missing something but it sure seems that way. \n \n \n\n::: {.cell}\n\n```{.r .cell-code}\n#This what the information in the [Historic Imagery Points](https://catalogue.data.gov.bc.ca/dataset/airborne-imagery-historical-index-map-points) layer looks like.\n\nlayers_trimmed$l_imagery_hist |> \n sf::st_drop_geometry() |> \n knitr::kable()\n```\n\n::: {.cell-output-display}\n\n\n|id | historical_index_map_id|scale_category |geoextent_mapsheet |map_tag | start_year| end_year|kml_url |pdf_url | objectid|se_anno_cad_data | area_ha|localcode_ltree |refine_method |wscode_ltree |\n|:---------------------------------------------------------|-----------------------:|:--------------|:------------------|:--------|----------:|--------:|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------|--------:|:----------------|--------:|:-----------------|:-------------|:------------|\n|WHSE_IMAGERY_AND_BASE_MAPS.AIMG_HIST_INDEX_MAPS_POINT.557 | 557|large |093l_e |093l_e_1 | 1950| 1963|https://openmaps.gov.bc.ca/flight_indices/kml/large_scale/093/93l_e_1_1950_1963.kml |https://openmaps.gov.bc.ca/flight_indices/pdf/large_scale_block_10000_to_25000/093_nts/93l_e/93l_e_sheet_1_20ch_1950_1963.pdf | 1860|NA | 231944.7|400.431358.585806 |CUT |400.431358 |\n|WHSE_IMAGERY_AND_BASE_MAPS.AIMG_HIST_INDEX_MAPS_POINT.558 | 558|large |093l_e |093l_e_2 | 1971| 1974|https://openmaps.gov.bc.ca/flight_indices/kml/large_scale/093/93l_e_2_1971_1974.kml |https://openmaps.gov.bc.ca/flight_indices/pdf/large_scale_block_10000_to_25000/093_nts/93l_e/93l_e_sheet_2_20ch_1971_1974.pdf | 1861|NA | 231944.7|400.431358.585806 |CUT |400.431358 |\n|WHSE_IMAGERY_AND_BASE_MAPS.AIMG_HIST_INDEX_MAPS_POINT.559 | 559|large |093l_e |093l_e_3 | 1975| 1975|https://openmaps.gov.bc.ca/flight_indices/kml/large_scale/093/93l_e_3_1975.kml |https://openmaps.gov.bc.ca/flight_indices/pdf/large_scale_block_10000_to_25000/093_nts/93l_e/93l_e_sheet_3_20k_1975.pdf | 1862|NA | 231944.7|400.431358.585806 |CUT |400.431358 |\n|WHSE_IMAGERY_AND_BASE_MAPS.AIMG_HIST_INDEX_MAPS_POINT.560 | 560|large |093l_e |093l_e_4 | 1980| 1980|https://openmaps.gov.bc.ca/flight_indices/kml/large_scale/093/93l_e_4_1980.kml |https://openmaps.gov.bc.ca/flight_indices/pdf/large_scale_block_10000_to_25000/093_nts/93l_e/93l_e_sheet_4_20k_1980.pdf | 1863|NA | 231944.7|400.431358.585806 |CUT |400.431358 |\n|WHSE_IMAGERY_AND_BASE_MAPS.AIMG_HIST_INDEX_MAPS_POINT.561 | 561|large |093l_e |093l_e_5 | 1980| 1980|https://openmaps.gov.bc.ca/flight_indices/kml/large_scale/093/93l_e_5_1980.kml |https://openmaps.gov.bc.ca/flight_indices/pdf/large_scale_block_10000_to_25000/093_nts/93l_e/93l_e_sheet_5_10k_1980.pdf | 1864|NA | 231944.7|400.431358.585806 |CUT |400.431358 |\n|WHSE_IMAGERY_AND_BASE_MAPS.AIMG_HIST_INDEX_MAPS_POINT.562 | 562|large |093l_e |093l_e_6 | 1981| 1983|https://openmaps.gov.bc.ca/flight_indices/kml/large_scale/093/93l_e_6_1981_1983.kml |https://openmaps.gov.bc.ca/flight_indices/pdf/large_scale_block_10000_to_25000/093_nts/93l_e/93l_e_sheet_6_20k_1981_1983.pdf | 1865|NA | 231944.7|400.431358.585806 |CUT |400.431358 |\n|WHSE_IMAGERY_AND_BASE_MAPS.AIMG_HIST_INDEX_MAPS_POINT.563 | 563|large |093l_e |093l_e_7 | 1989| 1989|https://openmaps.gov.bc.ca/flight_indices/kml/large_scale/093/93l_e_7_1989.kml |https://openmaps.gov.bc.ca/flight_indices/pdf/large_scale_block_10000_to_25000/093_nts/93l_e/93l_e_sheet_7_15k_1989.pdf | 1866|NA | 231944.7|400.431358.585806 |CUT |400.431358 |\n|WHSE_IMAGERY_AND_BASE_MAPS.AIMG_HIST_INDEX_MAPS_POINT.564 | 564|large |093l_e |093l_e_8 | 1990| 1990|https://openmaps.gov.bc.ca/flight_indices/kml/large_scale/093/93l_e_8_1990.kml |https://openmaps.gov.bc.ca/flight_indices/pdf/large_scale_block_10000_to_25000/093_nts/93l_e/93l_e_sheet_8_10k_15k_1990_bw_1990_colour.pdf | 1867|NA | 231944.7|400.431358.585806 |CUT |400.431358 |\n\n\n:::\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\nmap <- ggplot() +\n geom_sf(\n data = layers_trimmed$aoi,\n fill = \"transparent\",\n color = \"black\",\n linewidth = .5\n ) +\n geom_sf(\n data = layers_trimmed$l_streams,\n color = \"blue\",\n size = 1\n ) +\n geom_sf(\n data = layers_trimmed$l_rail,\n color = \"black\",\n size = 1\n ) +\n # geom_sf(\n # data = layers_trimmed$l_imagery,\n # alpha = 0,\n # ) +\n geom_sf(\n data = layers_trimmed$l_imagery_hist,\n color = \"red\",\n size = 2\n ) +\n geom_sf(\n data = layers_trimmed$l_imagery_grid,\n alpha = 0.25,\n ) +\n geom_sf_text(\n data = layers_trimmed$l_imagery_grid,\n aes(label = map_tile),\n size = 3 # Adjust size of the text labels as needed\n )\n # ggdark::dark_theme_void()\n\n\nmap\n```\n\n::: {.cell-output-display}\n![](index_files/figure-html/unnamed-chunk-1-1.png){width=672}\n:::\n:::\n\n\nIt does however seem that the [Air Photo Centroids](https://catalogue.data.gov.bc.ca/dataset/airphoto-centroids) are georeferenced \n\n\n\n::: {.cell}\n\n```{.r .cell-code}\n#if we go through the online interface, we can use a NTS 1:50,000 map sheet to export a CSV file that does contain information about individual air photo locations (centre coordinate?). Our study area is huge so to begin with we can look at the area from somewhere around the confluence of McQuarie Creek and the Neexdzii Kwah up to say the upstream end of Bulkley Lake. For this area we just have two 1:50,000 map sheets (`093L09` and `093L08`). \n```\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\n# ## Download Photo Information\n\n# Next, we go into the provincial [WIMSI](https://www2.gov.bc.ca/gov/content/data/geographic-data-services/digital-imagery/air-photos) service and for each NTS 1:50,000 grid - we state the date range we would like to search and export. Thinking we may be able to hit the API directly with a query in the future which would eliminate the painful point and click portion of this workflow. For now though - we are doing this piece manually and chose the following options:\n# \n# - `Years from`: 1950 to 2024\n# - `Media`: All\n# - `Only show georeferenced images`: - No\n# \n# Running these options exports a csv for each query. We saved them in this repo with their original sane names `airphotos.{mapsheet}.csv`.\n\nknitr::include_graphics(fs::path(\n path_post,\n \"fig\",\n \"Screenshot1\",\n ext = \"png\"\n )\n)\n```\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\n# At this point we have downloaded two csvs (one for each NTS 1:50,000 mapsheet of course) with information about the airphotos including UTM coordinates that we will assume for now are the photo centres. In our next steps we read in what we have, turn into spatial object, trim to overall study area and plot.\n# list csvs\nls <- fs::dir_ls(\n fs::path(\n path_post,\n \"data\"),\n glob = \"*.csv\"\n)\n\nphotos_raw <- ls |> \n purrr::map_df(\n readr::read_csv\n ) |> \n sf::st_as_sf(\n coords = c(\"Longitude\", \"Latitude\"), crs = 4326\n ) |> \n janitor::clean_names() |> \n dplyr::mutate(photo_date = lubridate::mdy(photo_date)) \n\n\nphotos_aoi <- sf::st_intersection(\n photos_raw, \n layers_trimmed$aoi |> st_make_valid()\n )\n```\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\nmap +\n geom_sf(\n data = layers_trimmed$l_photo_centroids,\n alpha = 0.25\n ) \n```\n\n::: {.cell-output-display}\n![](index_files/figure-html/map2-1.png){width=672}\n:::\n\n```{.r .cell-code}\n # geom_sf_text(\n # data = photos_aoi,\n # aes(\n # label = \n # paste(\n # # roll_frame_series,\n # frame,\n # sep = \"-\"\n # )\n # ),\n # size = 2 # Adjust size of the text labels as needed\n # )\n```\n:::\n\n\nThat is a lot of photos! 7910 photos to be exact!!!\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nq_buffer <- 500\nq_drm_main <- 263795\nq_drm_other <- 5000\n```\n:::\n\n\n## Clip Photo Information with Streams Buffers\nHere are our query parameters to narrow down the area within our selected mapsheets in which we want to find photos for:\n\n - Buffer: 500m - size of buffer used on either side of stream lines selected\n - Stream segments: \n + Bulkley River (`gnis_name` in the stream layer)\n + Maxan Creek\n + Buck Creek\n + stream segments which begin before 5000 from the mainstem\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# We use the `downstream_route_measure` of the stream layer to exclude areas upstream of Bulkley Lake (also known as Taman Creek). We find it in QGIS by highlighting the stream layer and clicking on our segment of interest while we have the information tool selected - the resulting pop-up looks like this in QGIS.\nknitr::include_graphics(fs::path(\n path_post,\n \"fig\",\n \"Screenshot2\",\n ext = \"png\"\n )\n)\n```\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\naoi_refined_raw <- layers_trimmed$l_streams |> \n # removed & downstream_route_measure < q_drm_main for bulkley as doestn't cahnge 1960s query and increases beyond just by 5 photos\n dplyr::filter(gnis_name == \"Bulkley River\"|\n gnis_name != \"Bulkley River\" & downstream_route_measure < q_drm_other |\n gnis_name %in% c(\"Maxan Creek\", \"Buck Creek\")) |> \n # dplyr::arrange(downstream_route_measure) |>\n # calculate when we get to length_m by adding up the length_metre field and filtering out everything up to length_m\n # dplyr::filter(cumsum(length_metre) <= length_m) |>\n sf::st_union() |> \n # we need to run st_sf or we get a sp object in a list...\n sf::st_sf()\n \naoi_refined_buffered <- sf::st_buffer(\n aoi_refined_raw,\n q_buffer, endCapStyle = \"FLAT\"\n) \n\nphotos_aoi_refined <- sf::st_intersection(\n layers_trimmed$l_photo_centroids, \n aoi_refined_buffered\n )\n```\n:::\n\n\nLet's plot again and include our buffered areas around the first 5000m of streams (area in red) along with the location of the photo points that land within that area. Looks like this give us 968 photos.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nmap +\n geom_sf(\n data = aoi_refined_buffered,\n color = \"red\",\n alpha= 0\n ) +\n geom_sf(\n data = photos_aoi_refined,\n alpha = 0.25,\n ) \n```\n\n::: {.cell-output-display}\n![](index_files/figure-html/map3-1.png){width=672}\n:::\n:::\n\n\n## Filter Photos by Date\nNow lets filter by date to see what our options are including the earliest photos possible. Here is our range to choose from:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nrange(photos_aoi_refined$photo_date)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n[1] \"1963-08-07\" \"2019-09-18\"\n```\n\n\n:::\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\nq_date_end1 <- \"1969-12-31\"\nq_date_start1 <- \"1970-01-01\"\nq_date_end2 <- \"1975-12-31\"\n```\n:::\n\n\nThere is lots going on here so we will map a comparison of a couple of options. First we will look at all photos available for before 1970 (red dots on map below). We will also see what photos we have for between 1970-01-01 and on or before 1975-12-31 (blue dots on map below)?\n\n\n::: {.cell}\n\n```{.r .cell-code}\nphotos1 <- photos_aoi_refined |> \n dplyr::filter(\n photo_date <= q_date_end1\n )\n\nphotos2 <- photos_aoi_refined |> \n dplyr::filter(\n photo_date >= q_date_start1 &\n photo_date <= q_date_end2\n )\n```\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\nmap +\n geom_sf(\n data = photos1,\n color = \"red\",\n alpha = 0.25,\n ) \n```\n\n::: {.cell-output-display}\n![](index_files/figure-html/map4-1.png){width=672}\n:::\n\n```{.r .cell-code}\nmap + \n geom_sf(\n data = photos2,\n color = \"blue\",\n alpha = 0.25,\n ) \n```\n\n::: {.cell-output-display}\n![](index_files/figure-html/map4-2.png){width=672}\n:::\n:::\n\n\nWell we have some tough decisions to make as we seem to have better coverage in the Maxan Creek drainage in the 70s (144 photos) but good coverage of the mainstem Neexdzii Kwah in the 60s (83 photos)...\n\n
\n\n \nThe table below can be used to filter photos from any time (provided it's location falls in the watershed area we clipped our downloaded points to) and export the result to csv or excel file. This could be useful for narrowing down our search not only to timescales but also to specific mapsheets with options given for both NTS 1:50,000 as well as BCGS 1:20,000 grid mapsheets.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nphotos_aoi_refined |> \n my_dt_table(cols_freeze_left = 2)\n```\n\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n## Export `csv` Files with Photo Information\nLet's burn out some csvs that can be used to find the imagery for:\n\n - \"before 1969-12-31\" \n - 1970-01-01 to 1975-12-31 timeline\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlfile_name_photos <- function(dat = NULL){\n fs::path(\n path_post,\n \"exports\",\n paste(\n \"airphotos\",\n paste(range(dat$photo_date), collapse = \"_\"),\n sep = \"_\"\n ),\n ext = \"csv\"\n )\n}\n\n# path_output <- fs::path(\n# path_post,\n# \"exports\",\n# paste(\n# \"airphotos\",\n# paste(range(photos$photo_date), collapse = \"_\"),\n# sep = \"_\"\n# ),\n# ext = \"csv\"\n# )\n\nphotos1 |> \n readr::write_csv(\n lfile_name_photos(photos1), na =\"\"\n )\n\nphotos2 |> \n readr::write_csv(\n lfile_name_photos(photos2), na =\"\"\n )\n\nlpath_link <- function(dat = NULL){\n paste0(\n \"https://github.com/NewGraphEnvironment/new_graphiti/tree/main/posts/2024-11-15-bcdata-ortho-historic/exports/\",\n basename(\n lfile_name_photos(dat)\n )\n )\n}\n```\n:::\n\n\nWe can view and download exported csv files [here](https://github.com/NewGraphEnvironment/new_graphiti/tree/main/posts/2024-11-15-bcdata-ortho-historic/exports/)\n\n## Pick a Horse\nWe are absolutely privaledged to potentially have the assistance of [Mike Price](https://www.researchgate.net/profile/Michael-Price-14) to help us obtain this imagery from the UBC archives. Sooo... - unless he has a preference that 100% can override our recommendation - we would think that he would probably prefer clear direction from us. Soooo - to start - thinking the best plan is to go with the imagery in the timeframe of 1971-06-05, 1975-09-18\" since the coverage seems much better than the earlier images... The name of the csv with that information is `airphotos_1971-06-05_1975-09-18.csv` and the file can found along with the other export at the link above or individually [here](https://github.com/NewGraphEnvironment/new_graphiti/tree/main/posts/2024-11-15-bcdata-ortho-historic/exports/airphotos_1971-06-05_1975-09-18.csv).\n\n## Outstanding Issues\n\n - Why does the date range of the photos we can get IDs for through the [WIMSI](https://www2.gov.bc.ca/gov/content/data/geographic-data-services/digital-imagery/air-photos) online portal not overlap the dates listed in the BC Data Catalouge [Historic Imagery Points](https://catalogue.data.gov.bc.ca/dataset/airborne-imagery-historical-index-map-points) for the dates in the Historic Imagery Points that are listed as 1950 - 1963 vs our earliest records from the portal at 1963-08-07??!!\n\n\n", + "markdown": "---\ntitle: \"Getting details of historic orthophoto imagery with R\"\nauthor: \"al\"\ndate: \"2024-11-15\"\ndate-modified: \"2024-11-17\"\ncategories: [fwapg, r, bcdata, imagery, api]\nimage: \"image.jpg\"\nparams:\n repo_owner: \"NewGraphEnvironment\"\n repo_name: \"new_graphiti\"\n post_name: \"2024-11-15-bcdata-ortho-historic\"\n update_gis: FALSE\nformat: \n html:\n code-fold: true\n---\n\n\nWe would like to obtain historic ortho photo imagery so that we can compare historic watershed conditions compared to current (ex. floodplain vegetation clearing, channel morphology, etc.). First we will generate an area of interest. In our first few code chunks we load our packages and load in some functions that will help us do this work.\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsuppressMessages(library(tidyverse))\nlibrary(ggplot2)\nlibrary(bcdata)\nlibrary(fwapgr)\nlibrary(knitr)\nsuppressMessages(library(sf))\n# library(leaflet)\n# library(leafem)\n```\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\npath_post <- fs::path(\n here::here(),\n \"posts\",\n params$post_name\n)\n```\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\nstaticimports::import(\n dir = fs::path(\n path_post,\n \"scripts\"\n ),\n outfile = fs::path(\n path_post,\n \"scripts\",\n \"staticimports\",\n ext = \"R\"\n )\n)\n```\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\nsource(\n fs::path(\n path_post,\n \"scripts\",\n \"staticimports\",\n ext = \"R\"\n )\n)\n\n\nlfile_name <- function(dat_name = NULL, ext = \"geojson\") {\n fs::path(\n path_post,\n \"data\",\n paste0(dat_name, \".\", ext)\n )\n}\n\nlburn_sf <- function(dat = NULL, dat_name = NULL) {\n if (is.null(dat_name)) {\n cli::cli_abort(\"You must provide a name for the GeoJSON file using `dat_name`.\")\n }\n \n dat |>\n sf::st_write(\n lfile_name(dat_name),\n delete_dsn = TRUE\n # append = FALSE\n )\n}\n\n# Function to validate and repair geometries\nlngs_geom_validate <- function(layer) {\n layer <- sf::st_make_valid(layer)\n layer <- layer[sf::st_is_valid(layer), ]\n return(layer)\n}\n```\n:::\n\n\n\n## Download Spatial Data Layers\nHere we download our area of interest which is the Neexdzii Kwah River (a.k.a Upper Bulkley River) which is located between Houston, BC (just south of Smithers) and Topley, BC which is east of Houston and north of Burns Lake, BC. We hit up our remote database managed by Simon Norris with a package built by Poisson Consulting specifically for the task. We use the `downstream_route_measure` of the Bulkley River (identified through a unique `blue_line_key`) to query the watershed area upstream of the point where the Neexdzii Kwah River enters the Wedzin Kwah River (a.k.a Morice River).\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# lets build a custom watersehed just for upstream of the confluence of Neexdzii Kwa and Wetzin Kwa\n# blueline key\nblk <- 360873822\n# downstream route measure\ndrm <- 166030.4\n\naoi <- fwapgr::fwa_watershed_at_measure(blue_line_key = blk, \n downstream_route_measure = drm) |> \n sf::st_transform(4326)\n\n\n#get the bounding box of our aoi\n# aoi_bb <- sf::st_bbox(aoi)\n\n#lets burn this so we don't need to download each time\naoi <- lngs_geom_validate(aoi)\nlburn_sf(\n aoi,\n deparse(substitute(aoi)))\n```\n:::\n\n\nNext we grab a few key layers from the BC Data Catalougue API using convience functions in our `rfp` package (\"Reproducable Field Products\") which wrap the provincially maintained `bcdata` package. We grab:\n\n - Railways\n - Streams in the Bulkley Watershed group that are 4th order or greater.\n - [Historic Imagery Points](https://catalogue.data.gov.bc.ca/dataset/airborne-imagery-historical-index-map-points)\n - [Historic Imagery Polygons](https://catalogue.data.gov.bc.ca/dataset/airborne-imagery-historical-index-map-polygons)\n - [NTS 1:50,000 Grid](https://catalogue.data.gov.bc.ca/) (we will see why in a second)\n - [Air Photo Centroids](https://catalogue.data.gov.bc.ca/dataset/airphoto-centroids)\n \n\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# grab all the railways\nl_rail <- rfp::rfp_bcd_get_data(\n bcdata_record_id = \"whse_basemapping.gba_railway_tracks_sp\"\n) |> \n sf::st_transform(4326) |> \n janitor::clean_names() \n\n\n# streams in the bulkley and then filter to just keep the big ones\nl_streams <- rfp::rfp_bcd_get_data(\n bcdata_record_id = \"whse_basemapping.fwa_stream_networks_sp\",\n col_filter = \"watershed_group_code\",\n col_filter_value = \"BULK\",\n # grab a smaller object by including less columns\n col_extract = c(\"linear_feature_id\", \"stream_order\", \"gnis_name\", \"downstream_route_measure\", \"blue_line_key\", \"length_metre\")\n) |> \n sf::st_transform(4326) |> \n janitor::clean_names() |> \n dplyr::filter(stream_order >= 4)\n\n# historic orthophotos\n# WHSE_IMAGERY_AND_BASE_MAPS.AIMG_HIST_INDEX_MAPS_POLY\n#https://catalogue.data.gov.bc.ca/dataset/airborne-imagery-historical-index-map-points\nl_imagery <- rfp::rfp_bcd_get_data(\n bcdata_record_id = \"WHSE_IMAGERY_AND_BASE_MAPS.AIMG_ORTHOPHOTO_TILES_POLY\") |> \n sf::st_transform(4326) |> \n janitor::clean_names()\n\nl_imagery_hist <- rfp::rfp_bcd_get_data(\n bcdata_record_id = \"WHSE_IMAGERY_AND_BASE_MAPS.AIMG_HIST_INDEX_MAPS_POINT\") |> \n sf::st_transform(4326) |> \n janitor::clean_names()\n\nl_imagery_grid <- rfp::rfp_bcd_get_data(\n bcdata_record_id = \"WHSE_BASEMAPPING.NTS_50K_GRID\") |> \n sf::st_transform(4326) |> \n janitor::clean_names()\n```\n:::\n\n\nFollowing download we run some clean up to ensure the geometry of our spatial files is \"valid\", trim to our area of interest and burn locally so that every time we rerun iterations of this memo we don't need to wait for the download process which takes a little longer than we want to wait.\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# get a list of the objects in our env that start with l_\nls <- ls()[stringr::str_starts(ls(), \"l_\")] \n\nlayers_all <- tibble::lst(\n !!!mget(ls)\n)\n\n# Apply validation to the AOI and layers\nlayers_all <- purrr::map(\n layers_all, \n lngs_geom_validate\n )\n\n# clip them with purrr and sf\nlayers_trimmed <- purrr::map(\n layers_all,\n ~ sf::st_intersection(.x, aoi)\n) \n\n# Burn each `sf` object to GeoJSON\npurrr::walk2(\n layers_trimmed,\n names(layers_trimmed),\n lburn_sf\n)\n```\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\n# lets use the nts mapsheet to query the photo centroids to avoid a massive file download\ncol_value <- layers_trimmed$l_imagery_grid |> \n dplyr::pull(map_tile) \n\n \n\nl_photo_centroids <- rfp::rfp_bcd_get_data(\n bcdata_record_id = \"WHSE_IMAGERY_AND_BASE_MAPS.AIMG_PHOTO_CENTROIDS_SP\",\n col_filter = \"nts_tile\",\n col_filter_value = col_value) |> \n sf::st_transform(4326) |> \n janitor::clean_names()\n\n# Apply validation to the AOI and layers\nl_photo_centroids <-lngs_geom_validate(l_photo_centroids)\n\n# clip to aoi - can use layers_trimmed$aoi \nl_photo_centroids <- sf::st_intersection(l_photo_centroids, aoi)\n\n\nlburn_sf(l_photo_centroids, \"l_photo_centroids\")\n```\n:::\n\n\nNext - we read the layers back in. The download step is skipped now unless we turn it on again by changing the `update_gis` param in our memo `yaml` header to `TRUE`.\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# now we read in all the sf layers that are local so it is really quick\nlayers_to_load <- fs::dir_ls(\n fs::path(\n path_post,\n \"data\"),\n glob = \"*.geojson\"\n)\n\nlayers_trimmed <- layers_to_load |>\n purrr::map(\n ~ sf::st_read(\n .x, quiet = TRUE)\n ) |> \n purrr::set_names(\n nm =tools::file_path_sans_ext(\n basename(\n names(\n layers_to_load\n )\n )\n )\n )\n```\n:::\n\n\nOK, seems we cannot get machine readable historical air photo information from the downloaded from the BC data catalogue [Historic Imagery Points](https://catalogue.data.gov.bc.ca/dataset/airborne-imagery-historical-index-map-points) layer perhaps because the majority of the photos are not georeferenced? What we see in the map and table below (red dot on map) is one point which contains 8 records including links to pdfs and kmls which are basically a georeferenced drawing of where the imagery overlaps (@fig-1). From as far as I can tell - if we wanted to try to use the kmls or pdfs linked in the attribute tables of the \"Historic Imagery Points\" layer to select orthoimagery we would need to eyeball where the photo polygons overlap where we want to see imagery for and manually write down identifiers for photo by hand. Maybe I am missing something but it sure seems that way. \n\n\n::: {.cell}\n\n```{.r .cell-code}\nmap <- ggplot() +\n geom_sf(\n data = layers_trimmed$aoi,\n fill = \"transparent\",\n color = \"black\",\n linewidth = .5\n ) +\n geom_sf(\n data = layers_trimmed$l_streams,\n color = \"blue\",\n size = 1\n ) +\n geom_sf_text(\n data = layers_trimmed$l_streams |> dplyr::distinct(gnis_name, .keep_all = TRUE),\n aes(\n label = gnis_name\n ),\n size = 2 # Adjust size of the text labels as needed\n ) +\n geom_sf(\n data = layers_trimmed$l_rail,\n color = \"black\",\n size = 1\n ) +\n geom_sf(\n data = layers_trimmed$l_imagery_hist,\n color = \"red\",\n size = 2\n ) +\n geom_sf(\n data = layers_trimmed$l_imagery_grid,\n alpha = 0.25,\n ) +\n geom_sf_text(\n data = layers_trimmed$l_imagery_grid,\n aes(label = map_tile),\n size = 3 # Adjust size of the text labels as needed\n )\n\nmap\n```\n\n::: {.cell-output-display}\n![](index_files/figure-html/map1-1.png){width=672}\n:::\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\n#This what the information in the [Historic Imagery Points](https://catalogue.data.gov.bc.ca/dataset/airborne-imagery-historical-index-map-points) layer looks like.\n\nlayers_trimmed$l_imagery_hist |> \n sf::st_drop_geometry() |> \n knitr::kable()\n```\n\n::: {.cell-output-display}\n\n\n|id | historical_index_map_id|scale_category |geoextent_mapsheet |map_tag | start_year| end_year|kml_url |pdf_url | objectid|se_anno_cad_data | area_ha|localcode_ltree |refine_method |wscode_ltree |\n|:---------------------------------------------------------|-----------------------:|:--------------|:------------------|:--------|----------:|--------:|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------|--------:|:----------------|--------:|:-----------------|:-------------|:------------|\n|WHSE_IMAGERY_AND_BASE_MAPS.AIMG_HIST_INDEX_MAPS_POINT.557 | 557|large |093l_e |093l_e_1 | 1950| 1963|https://openmaps.gov.bc.ca/flight_indices/kml/large_scale/093/93l_e_1_1950_1963.kml |https://openmaps.gov.bc.ca/flight_indices/pdf/large_scale_block_10000_to_25000/093_nts/93l_e/93l_e_sheet_1_20ch_1950_1963.pdf | 1860|NA | 231944.7|400.431358.585806 |CUT |400.431358 |\n|WHSE_IMAGERY_AND_BASE_MAPS.AIMG_HIST_INDEX_MAPS_POINT.558 | 558|large |093l_e |093l_e_2 | 1971| 1974|https://openmaps.gov.bc.ca/flight_indices/kml/large_scale/093/93l_e_2_1971_1974.kml |https://openmaps.gov.bc.ca/flight_indices/pdf/large_scale_block_10000_to_25000/093_nts/93l_e/93l_e_sheet_2_20ch_1971_1974.pdf | 1861|NA | 231944.7|400.431358.585806 |CUT |400.431358 |\n|WHSE_IMAGERY_AND_BASE_MAPS.AIMG_HIST_INDEX_MAPS_POINT.559 | 559|large |093l_e |093l_e_3 | 1975| 1975|https://openmaps.gov.bc.ca/flight_indices/kml/large_scale/093/93l_e_3_1975.kml |https://openmaps.gov.bc.ca/flight_indices/pdf/large_scale_block_10000_to_25000/093_nts/93l_e/93l_e_sheet_3_20k_1975.pdf | 1862|NA | 231944.7|400.431358.585806 |CUT |400.431358 |\n|WHSE_IMAGERY_AND_BASE_MAPS.AIMG_HIST_INDEX_MAPS_POINT.560 | 560|large |093l_e |093l_e_4 | 1980| 1980|https://openmaps.gov.bc.ca/flight_indices/kml/large_scale/093/93l_e_4_1980.kml |https://openmaps.gov.bc.ca/flight_indices/pdf/large_scale_block_10000_to_25000/093_nts/93l_e/93l_e_sheet_4_20k_1980.pdf | 1863|NA | 231944.7|400.431358.585806 |CUT |400.431358 |\n|WHSE_IMAGERY_AND_BASE_MAPS.AIMG_HIST_INDEX_MAPS_POINT.561 | 561|large |093l_e |093l_e_5 | 1980| 1980|https://openmaps.gov.bc.ca/flight_indices/kml/large_scale/093/93l_e_5_1980.kml |https://openmaps.gov.bc.ca/flight_indices/pdf/large_scale_block_10000_to_25000/093_nts/93l_e/93l_e_sheet_5_10k_1980.pdf | 1864|NA | 231944.7|400.431358.585806 |CUT |400.431358 |\n|WHSE_IMAGERY_AND_BASE_MAPS.AIMG_HIST_INDEX_MAPS_POINT.562 | 562|large |093l_e |093l_e_6 | 1981| 1983|https://openmaps.gov.bc.ca/flight_indices/kml/large_scale/093/93l_e_6_1981_1983.kml |https://openmaps.gov.bc.ca/flight_indices/pdf/large_scale_block_10000_to_25000/093_nts/93l_e/93l_e_sheet_6_20k_1981_1983.pdf | 1865|NA | 231944.7|400.431358.585806 |CUT |400.431358 |\n|WHSE_IMAGERY_AND_BASE_MAPS.AIMG_HIST_INDEX_MAPS_POINT.563 | 563|large |093l_e |093l_e_7 | 1989| 1989|https://openmaps.gov.bc.ca/flight_indices/kml/large_scale/093/93l_e_7_1989.kml |https://openmaps.gov.bc.ca/flight_indices/pdf/large_scale_block_10000_to_25000/093_nts/93l_e/93l_e_sheet_7_15k_1989.pdf | 1866|NA | 231944.7|400.431358.585806 |CUT |400.431358 |\n|WHSE_IMAGERY_AND_BASE_MAPS.AIMG_HIST_INDEX_MAPS_POINT.564 | 564|large |093l_e |093l_e_8 | 1990| 1990|https://openmaps.gov.bc.ca/flight_indices/kml/large_scale/093/93l_e_8_1990.kml |https://openmaps.gov.bc.ca/flight_indices/pdf/large_scale_block_10000_to_25000/093_nts/93l_e/93l_e_sheet_8_10k_15k_1990_bw_1990_colour.pdf | 1867|NA | 231944.7|400.431358.585806 |CUT |400.431358 |\n\n\n:::\n:::\n\n \n
\n\n\n::: {.cell}\n\n```{.r .cell-code}\nmy_caption <- \"Screenshot of kml downloaded from link provided in Historic Imagery Points.\"\nknitr::include_graphics(fs::path(\n path_post,\n \"fig\",\n \"Screenshot1\",\n ext = \"png\"\n )\n)\n```\n\n::: {.cell-output-display}\n![Screenshot of kml downloaded from link provided in Historic Imagery Points.](fig/Screenshot1.png){#fig-1 width=1492}\n:::\n:::\n\n\n\n\n
\n \nFor the [Historic Imagery Polygons](https://catalogue.data.gov.bc.ca/dataset/airborne-imagery-historical-index-map-polygons) layer the range of `year_operational` is 1996, 2013. This is not as far back as we would prefer to be looking.\n \n\n
\n\nIt does however seem that each of the [Air Photo Centroids](https://catalogue.data.gov.bc.ca/dataset/airphoto-centroids) are georeferenced with a date range of:\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nrange(layers_trimmed$l_photo_centroids$photo_date)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n[1] \"1963-08-07\" \"2019-09-18\"\n```\n\n\n:::\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\n# At this point we have downloaded two csvs (one for each NTS 1:50,000 mapsheet of course) with information about the airphotos including UTM coordinates that we will assume for now are the photo centres. In our next steps we read in what we have, turn into spatial object, trim to overall study area and plot.\n# list csvs\nls <- fs::dir_ls(\n fs::path(\n path_post,\n \"data\"),\n glob = \"*.csv\"\n)\n\nphotos_raw <- ls |> \n purrr::map_df(\n readr::read_csv\n ) |> \n sf::st_as_sf(\n coords = c(\"Longitude\", \"Latitude\"), crs = 4326\n ) |> \n janitor::clean_names() |> \n dplyr::mutate(photo_date = lubridate::mdy(photo_date)) \n\n\nphotos_aoi <- sf::st_intersection(\n photos_raw, \n layers_trimmed$aoi |> st_make_valid()\n )\n```\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\nmap +\n geom_sf(\n data = layers_trimmed$l_photo_centroids,\n alpha = 0.25\n ) \n```\n\n::: {.cell-output-display}\n![](index_files/figure-html/map2-1.png){width=672}\n:::\n:::\n\n\nThat is a lot of photos! 7910 photos to be exact!!!\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nq_buffer <- 500\nq_drm_main <- 263795\nq_drm_other <- 5000\n```\n:::\n\n\n## Clip Photo Information with Streams Buffers\nHere are our query parameters to narrow down the area within our study are watershed in which we want to find photos for:\n\n - Buffer: 500m - size of buffer used on either side of stream lines selected\n - Stream segments: \n + Bulkley River (`gnis_name` in the stream layer)\n + Maxan Creek\n + Buck Creek\n + for each remaining stream - segments of that stream which begin before 5000m from the downstream system\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# We use the `downstream_route_measure` of the stream layer to exclude areas upstream of Bulkley Lake (also known as Taman Creek). We find it in QGIS by highlighting the stream layer and clicking on our segment of interest while we have the information tool selected - the resulting pop-up looks like this in QGIS.\nknitr::include_graphics(fs::path(\n path_post,\n \"fig\",\n \"Screenshot2\",\n ext = \"png\"\n )\n)\n```\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\nr_streams <- c(\"Maxan Creek\", \"Buck Creek\")\n\naoi_refined_raw <- layers_trimmed$l_streams |> \n # removed & downstream_route_measure < q_drm_main for bulkley as doestn't cahnge 1960s query and increases beyond just by 5 photos\n dplyr::filter(gnis_name == \"Bulkley River\"|\n gnis_name != \"Bulkley River\" & downstream_route_measure < q_drm_other |\n gnis_name %in% r_streams) |> \n # dplyr::arrange(downstream_route_measure) |>\n # calculate when we get to length_m by adding up the length_metre field and filtering out everything up to length_m\n # dplyr::filter(cumsum(length_metre) <= length_m) |>\n sf::st_union() |> \n # we need to run st_sf or we get a sp object in a list...\n sf::st_sf()\n \naoi_refined_buffered <- sf::st_buffer(\n aoi_refined_raw,\n q_buffer, endCapStyle = \"FLAT\"\n) \n\nphotos_aoi_refined <- sf::st_intersection(\n layers_trimmed$l_photo_centroids, \n aoi_refined_buffered\n )\n```\n:::\n\n\nLet's plot again and include our buffered areas around the first 5000m of streams (area in red) along with the location of the photo points that land within that area. Looks like this give us 1506 photos.\n\n\n::: {.cell}\n\n```{.r .cell-code}\nmap +\n geom_sf(\n data = aoi_refined_buffered,\n color = \"red\",\n alpha= 0\n ) +\n geom_sf(\n data = photos_aoi_refined,\n alpha = 0.25,\n ) \n```\n\n::: {.cell-output-display}\n![](index_files/figure-html/map3-1.png){width=672}\n:::\n:::\n\n\nThat is not as many photos - but still quite a few (1506). The table below can be used to filter these photos from any time and/or mapsheet and export the result to csv or excel file. \n\n\n::: {.cell}\n\n```{.r .cell-code}\nphotos_aoi_refined |> \n my_dt_table(cols_freeze_left = 2)\n```\n\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n## Filter Photos by Date\nNow lets map by year to see what our options are including the earliest photos possible. Here is our range to choose from:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nrange(photos_aoi_refined$photo_date)\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n[1] \"1963-08-07\" \"2019-09-18\"\n```\n\n\n:::\n:::\n\n`\n\n\n::: {.cell}\n\n```{.r .cell-code}\nmap +\ngeom_sf(\n data = photos_aoi_refined |> dplyr::filter(photo_year <= \"1975\")\n ) +\n facet_wrap(~ photo_year)\n```\n\n::: {.cell-output-display}\n![](index_files/figure-html/map4-1.png){width=672}\n:::\n:::\n\n\nWell - looks like we get really good coverage of the Bulkley River mainstem in 1968 then much better coverage of the Buck Creek drainage and Maxan Creek in 1971. For 1975 - the coverage of the Bulkley mainstem and Maxan Creek is pretty good...\n\n
\n\nThinking the ideal thing to do is to grab the photos from:\n\n - 1968 all\n - 1971 for the Buck Creek and Maxan Creek areas only\n - 1975 Maxan Creek only\n\n
\n\n\n::: {.cell}\n\n```{.r .cell-code}\n# spatially represent just Buck and Maxan, buffer and clip the 1971 photos\n# \"r_\" is for \"refine\"\nr_year1 <- \"1968\"\nr_year2 <- \"1971\"\nr_year3 <- \"1975\"\n\nr_streams2 <- c(\"Maxan Creek\")\n\nl_streams_refined1 <- layers_trimmed$l_streams |> \n # we defined r_streams in chunk way above \n dplyr::filter(gnis_name %in% r_streams) |> \n sf::st_union() |> \n # we need to run st_sf or we get a sp object in a list...\n sf::st_sf()\n \naoi_refined_buffered2 <- sf::st_buffer(\n l_streams_refined1,\n q_buffer, endCapStyle = \"FLAT\"\n) \n\nl_streams_refined2 <- layers_trimmed$l_streams |> \n # we defined r_streams in chunk way above \n dplyr::filter(gnis_name %in% r_streams2) |> \n sf::st_union() |> \n # we need to run st_sf or we get a sp object in a list...\n sf::st_sf()\n \naoi_refined_buffered3 <- sf::st_buffer(\n l_streams_refined2,\n q_buffer, endCapStyle = \"FLAT\"\n) \n\n# filter first year\nphotos1 <- photos_aoi_refined |> \n dplyr::filter(\n photo_year == r_year1\n )\n\n# filter second year using just the streams we want to include\nphotos2 <- sf::st_intersection(\n layers_trimmed$l_photo_centroids |> dplyr::filter(photo_year == r_year2), \n aoi_refined_buffered2\n )\n\n# filter second year using just the streams we want to include\nphotos3 <- sf::st_intersection(\n layers_trimmed$l_photo_centroids |> dplyr::filter(photo_year == r_year3), \n aoi_refined_buffered3\n )\n\nphotos_all <- dplyr::bind_rows(photos1, photos2, photos3)\n```\n:::\n\n\n\nNow let's have a look at what we have\n\n::: {.cell}\n\n```{.r .cell-code}\nmap +\n geom_sf(\n data = photos_all\n )\n```\n\n::: {.cell-output-display}\n![](index_files/figure-html/map5-1.png){width=672}\n:::\n:::\n\n\n## Export `csv` with Photo Information\nLet's burn out csv that can be used to find the imagery for the 137 photos above.\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlfile_name_photos <- function(dat = NULL){\n fs::path(\n path_post,\n \"exports\",\n paste(\n \"airphotos\",\n paste(range(dat$photo_date), collapse = \"_\"),\n sep = \"_\"\n ),\n ext = \"csv\"\n )\n}\n\nphotos_all |> \n readr::write_csv(\n lfile_name_photos(photos_all), na =\"\"\n )\n\n\nlpath_link <- function(dat = NULL){\n paste0(\n \"https://github.com/NewGraphEnvironment/new_graphiti/tree/main/posts/2024-11-15-bcdata-ortho-historic/exports/\",\n basename(\n lfile_name_photos(dat)\n )\n )\n}\n```\n:::\n\n\nWe can view and download exported csv files [here](https://github.com/NewGraphEnvironment/new_graphiti/tree/main/posts/2024-11-15-bcdata-ortho-historic/exports/). \n\n\n\n\n", "supporting": [ "index_files" ], diff --git a/_freeze/posts/2024-11-15-bcdata-ortho-historic/index/figure-html/map1-1.png b/_freeze/posts/2024-11-15-bcdata-ortho-historic/index/figure-html/map1-1.png new file mode 100644 index 0000000000000000000000000000000000000000..9163181e28179eaff514817c9a36c6eb2f155e48 GIT binary patch literal 241010 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