From 0d85cd46d670a38448f0f0e759f4fc481c86409a Mon Sep 17 00:00:00 2001 From: Quarto GHA Workflow Runner Date: Tue, 2 Jul 2024 18:56:39 +0000 Subject: [PATCH] Built site for gh-pages --- .nojekyll | 2 +- about.html | 2 +- index.html | 16 +- .../index.html | 2 +- posts/2024-06-19-precipitation/index.html | 2 +- posts/2024-06-30-land-cover/index.html | 807 +++++++++++++----- .../index_files/figure-html/plot-stats-1.png | Bin 0 -> 81600 bytes .../figure-html/plot-stats-refined-1.png | Bin 0 -> 72531 bytes .../index_files/figure-html/plot2-1.png | Bin 98425 -> 111345 bytes .../figure-html/rasters-crop-combine-1.png | Bin 0 -> 57328 bytes posts/aws-storage-permissions/index.html | 2 +- posts/aws-storage-processx/index.html | 2 +- posts/logos-equipment/index.html | 2 +- posts/snakecase/index.html | 2 +- search.json | 25 +- sitemap.xml | 18 +- 16 files changed, 629 insertions(+), 253 deletions(-) create mode 100644 posts/2024-06-30-land-cover/index_files/figure-html/plot-stats-1.png create mode 100644 posts/2024-06-30-land-cover/index_files/figure-html/plot-stats-refined-1.png create mode 100644 posts/2024-06-30-land-cover/index_files/figure-html/rasters-crop-combine-1.png diff --git a/.nojekyll b/.nojekyll index c5b214e..924f2d3 100644 --- a/.nojekyll +++ b/.nojekyll @@ -1 +1 @@ -c0ee4e3c \ No newline at end of file +79137f47 \ No newline at end of file diff --git a/about.html b/about.html index 4554419..41148ef 100644 --- a/about.html +++ b/about.html @@ -2,7 +2,7 @@ - + diff --git a/index.html b/index.html index 3f0d766..f06d1da 100644 --- a/index.html +++ b/index.html @@ -2,7 +2,7 @@ - + @@ -206,7 +206,7 @@
Categories
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@@ -433,7 +435,7 @@

Retrieve the Land Cover Data


-

Nice. So now let’s read in these data, clip them to our area of interest with terra::crop then combine them into one tiff using terra::mosaic.

+

Nice. So now let’s read in these data, clip them to our area of interest with terra::crop then combine them into one tiff using terra::mosaic. In order to visualize NA values we can convert them to a mask and plot.

Code @@ -447,102 +449,209 @@

Retrieve the Land Cover Data

land_cover_raster_raw <- rast_files |> purrr::map(terra::rast) -# Clip the rasters to the AOI -land_cover_raster_clipped <- purrr::map( - land_cover_raster_raw, - ~ terra::crop(.x, aoi, snap = "in", mask = TRUE) -) - -# combine the rasters -land_cover_raster <- do.call(terra::mosaic, land_cover_raster_clipped) - -# Optionally, plot the merged raster -# terra::plot(land_cover_raster)
+#testing for NA +# v1 <- terra::values(land_cover_raster_raw[[1]]) +# v2 <- terra::values(land_cover_raster_raw[[2]]) +# +# # contains NA? No!! +# any(is.na(v1)) +# any(is.na(v2)) +# Clip the rasters to the AOI +land_cover_raster_clipped <- purrr::map( + land_cover_raster_raw, + ~ terra::crop(.x, aoi, snap = "in", mask = TRUE) +) + +# combine the rasters +land_cover_raster <- do.call(terra::mosaic, land_cover_raster_clipped) + +# in order to visualize NA values we need to convert them to a mask and plot +na_mask <- is.na(land_cover_raster) +plot(na_mask, main = "NA Values in the Raster", col = c("white", "red"))
+
+
+
+

+
+
+
-
-

Digital Elevation Model

-

Let’s grab the digital elevation model using elevatr::get_elev_raster so we can downsample the land cover raster to the same resolution as the DEM.

+
+

Resampling with Digital Elevation Model

+

If we want to we can grab a digital elevation model using elevatr::get_elev_raster so we can resample the land cover raster to the same resolution as the DEM. We could increase the resolution or decrease depending on the zoom level we choose.

+


+

The reason we might choose to resample (upsample or downsample) are summarized in the table below. Because the first map we wish to render (the entire Neexdzii Kwah) is quite large we will downsample so that it does not take 20 minutes to render the map. We can see the resolution of the original raster with terra::cellSize(land_cover_raster).

Code -
dem <- elevatr::get_elev_raster(
-    locations = aoi,
-    z = 11,
-    clip = "bbox"
-) |>
-    terra::rast()
+
resampling_table <- tibble::tibble(
+  Feature = c("Definition", "Purpose", "Method", "Effect", "Example"),
+  Upsampling = c(
+    "Increasing spatial resolution",
+    "Match higher-resolution data, enhance detail",
+    "Interpolation (nearest, bilinear, cubic)",
+    "More cells, finer detail",
+    "10m to 5m resolution"
+  ),
+  Downsampling = c(
+    "Decreasing spatial resolution",
+    "Reduce data size, speed up processing",
+    "Aggregation (average, sum, nearest)",
+    "Fewer cells, less detail",
+    "10m to 20m resolution"
+  )
+)
+
+knitr::kable(resampling_table, caption = "Summary of Upsampling and Downsampling")
+
+ + +++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Summary of Upsampling and Downsampling
FeatureUpsamplingDownsampling
DefinitionIncreasing spatial resolutionDecreasing spatial resolution
PurposeMatch higher-resolution data, enhance detailReduce data size, speed up processing
MethodInterpolation (nearest, bilinear, cubic)Aggregation (average, sum, nearest)
EffectMore cells, finer detailFewer cells, less detail
Example10m to 5m resolution10m to 20m resolution
-
-
-

Resample

-

Here we resample the land cover raster to the same resolution as the DEM.

+
+
+
+Code +
terra::cellSize(land_cover_raster)
+
+
+
class       : SpatRaster 
+dimensions  : 6870, 10028, 1  (nrow, ncol, nlyr)
+resolution  : 8.333333e-05, 8.333333e-05  (x, y)
+extent      : -126.7469, -125.9112, 54.10183, 54.67433  (xmin, xmax, ymin, ymax)
+coord. ref. : lon/lat WGS 84 (EPSG:4326) 
+source(s)   : memory
+varname     : ESA_WorldCover_10m_2021_v200_N54W126_Map 
+name        :     area 
+min value   : 49.86984 
+max value   : 50.56391 
+
+
+


+

This translates to cells ~9m high x ~6m wide (~50m2). For the DEM we chose a zoom level of 11 for the DEM but we can go as high as 14. Zoom and resolution is related to latitude. For the latitude of our study area (approx 55 degrees) the resolution at zoom level 11 is ~30m high x ~18m wide. At zoom level 14 it is ~4m high x ~2m wide.

+
+
+Code +
dem <- elevatr::get_elev_raster(
+    locations = aoi,
+    z = 11,
+    clip = "bbox"
+) |>
+    terra::rast()
+
+
+
+

Downsample the Land Cover Raster

+

Here we downsample the land cover raster to the same resolution as the DEM for the purposes of rendering our map of the larger area in a reasonable amount of time.

Code -
land_cover_raster_resampled <- terra::resample(
-    land_cover_raster,
-    dem,
-    method = "near"
-)
-
-# terra::plot(land_cover_raster_resampled)
+
# Here we resample the land cover raster to the same resolution as the DEM.
+land_cover_raster_resampled <- terra::resample(
+    land_cover_raster,
+    dem,
+    method = "near",
+    threads = TRUE
+)
+
+# terra::plot(land_cover_raster_resampled)
-
-

Plot the Land Cover and DEM

+
+
+

Plot Land Cover for Entire Neexdzii Kwah

Get Additional Data

-

We could use some data for context such as major streams and the railway. We get the streams and railway from data distribution bc api using the bcdata package. Our rfp package calls just allow some extra sanity checks on the bcdata::bcdc_query_geodata function. It’s not really necessary but can be helpful when errors occur (ex. the name of the column to filter on is input incorrectly).

+

We could use some data for context such as major streams and the railway. We get the streams and railway from data distribution bc api using the bcdata package. Our rfp package calls just allow some extra sanity checks and convenience moves on the bcdata::bcdc_query_geodata function. It’s not really necessary but can be helpful (ex. can use small cap layer and column names and will throw an informative error if the name of the columns specified are input incorrectly).


Code -
# grab all the railways
-l_rail <- rfp::rfp_bcd_get_data(
-    bcdata_record_id = "whse_basemapping.gba_railway_tracks_sp"
-) |> 
-  sf::st_transform(4326) |> 
-  janitor::clean_names() 
-
-# streams in the bulkley and then filter to just keep the big ones
-l_streams <- rfp::rfp_bcd_get_data(
-  bcdata_record_id = "whse_basemapping.fwa_stream_networks_sp",
-  col_filter = "watershed_group_code",
-  col_filter_value = "BULK",
-  # grab a smaller object by including less columns
-  col_extract = c("linear_feature_id", "stream_order", "gnis_name", "downstream_route_measure", "blue_line_key", "length_metre")
-) |> 
-  sf::st_transform(4326) |> 
-  janitor::clean_names() |> 
-  dplyr::filter(stream_order > 4)
+
# grab all the railways
+l_rail <- rfp::rfp_bcd_get_data(
+    bcdata_record_id = "whse_basemapping.gba_railway_tracks_sp"
+) |> 
+  sf::st_transform(4326) |> 
+  janitor::clean_names() 
+
+# streams in the bulkley and then filter to just keep the big ones
+l_streams <- rfp::rfp_bcd_get_data(
+  bcdata_record_id = "whse_basemapping.fwa_stream_networks_sp",
+  col_filter = "watershed_group_code",
+  col_filter_value = "BULK",
+  # grab a smaller object by including less columns
+  col_extract = c("linear_feature_id", "stream_order", "gnis_name", "downstream_route_measure", "blue_line_key", "length_metre")
+) |> 
+  sf::st_transform(4326) |> 
+  janitor::clean_names() |> 
+  dplyr::filter(stream_order > 4)

Now we trim up those layers. We have some functions to validate and repair geometries and then we clip them to our area of interest.

Code -
layers_to_trim <- tibble::lst(l_rail, l_streams)
-
-# Function to validate and repair geometries
-validate_geometries <- function(layer) {
-  layer <- sf::st_make_valid(layer)
-  layer <- layer[sf::st_is_valid(layer), ]
-  return(layer)
-}
-
-# Apply validation to the AOI and layers
-aoi <- validate_geometries(aoi)
-layers_to_trim <- purrr::map(layers_to_trim, validate_geometries)
-
-# clip them  with purrr and sf
-layers_trimmed <- purrr::map(
-  layers_to_trim,
-  ~ sf::st_intersection(.x, aoi)
-) 
+
layers_to_trim <- tibble::lst(l_rail, l_streams)
+
+# Function to validate and repair geometries
+validate_geometries <- function(layer) {
+  layer <- sf::st_make_valid(layer)
+  layer <- layer[sf::st_is_valid(layer), ]
+  return(layer)
+}
+
+# Apply validation to the AOI and layers
+aoi <- validate_geometries(aoi)
+layers_to_trim <- purrr::map(layers_to_trim, validate_geometries)
+
+# clip them  with purrr and sf
+layers_trimmed <- purrr::map(
+  layers_to_trim,
+  ~ sf::st_intersection(.x, aoi)
+) 
@@ -552,59 +661,59 @@

Get Legend Values

Code -
# extract the cross reference table from the pdf
-pdf_file <- "https://esa-worldcover.s3.eu-central-1.amazonaws.com/v200/2021/docs/WorldCover_PUM_V2.0.pdf"
-page <- 15
-
-# table_map <- tabulapdf::locate_areas(pdf_file, pages = page)
-# table_coords <- list(as.numeric(unlist(table_map[[1]])))
-
-table_coords <- list(c(94.55745,  74.66493, 755.06007, 550.41094))
-
-
-xref_raw <- tabulapdf::extract_tables(
-  pdf_file,
-  pages = page,
-  method = "lattice",
-  area = table_coords,
-  guess = FALSE
-)
-
-# ##this is how we make a clean dataframe
-xref_raw2 <- xref_raw |> 
-  purrr::pluck(1) |>
-  tibble::as_tibble() |>
-  janitor::row_to_names(1) |>
-  janitor::clean_names()
-
-xref_raw3 <- xref_raw2 |> 
-  tidyr::fill(code, .direction = "down")
-
-# Custom function to concatenate rows within each group
-collapse_rows <- function(df) {
-  df |> 
-    dplyr::summarise(across(everything(), ~ paste(na.omit(.), collapse = " ")))
-}
-
-# Group by code and apply the custom function
-xref <- xref_raw3 |>
-  dplyr::group_by(code) |>
-  dplyr::group_modify(~ collapse_rows(.x)) |>
-  dplyr::ungroup() |> 
-  dplyr::mutate(code = as.numeric(code)) |> 
-  dplyr::arrange(code) |> 
-  purrr::set_names(c("code", "land_cover_class", "lccs_code", "definition", "color_code")) |> 
-  # now we make a list of the color codes and convert to hex. Even though we don't actually need them here...
-  dplyr::mutate(color_code = purrr::map(color_code, ~ as.numeric(strsplit(.x, ",")[[1]])),
-                color = purrr::map_chr(color_code, ~ rgb(.x[1], .x[2], .x[3], maxColorValue = 255))) |> 
-  dplyr::relocate(definition, .after = last_col())
-
-
-my_dt_table(xref, cols_freeze_left = 0, page_length = 5)
+
# extract the cross reference table from the pdf
+pdf_file <- "https://esa-worldcover.s3.eu-central-1.amazonaws.com/v200/2021/docs/WorldCover_PUM_V2.0.pdf"
+page <- 15
+
+# table_map <- tabulapdf::locate_areas(pdf_file, pages = page)
+# table_coords <- list(as.numeric(unlist(table_map[[1]])))
+
+table_coords <- list(c(94.55745,  74.66493, 755.06007, 550.41094))
+
+
+xref_raw <- tabulapdf::extract_tables(
+  pdf_file,
+  pages = page,
+  method = "lattice",
+  area = table_coords,
+  guess = FALSE
+)
+
+# ##this is how we make a clean dataframe
+xref_raw2 <- xref_raw |> 
+  purrr::pluck(1) |>
+  tibble::as_tibble() |>
+  janitor::row_to_names(1) |>
+  janitor::clean_names()
+
+xref_raw3 <- xref_raw2 |> 
+  tidyr::fill(code, .direction = "down")
+
+# Custom function to concatenate rows within each group
+collapse_rows <- function(df) {
+  df |> 
+    dplyr::summarise(across(everything(), ~ paste(na.omit(.), collapse = " ")))
+}
+
+# Group by code and apply the custom function
+xref <- xref_raw3 |>
+  dplyr::group_by(code) |>
+  dplyr::group_modify(~ collapse_rows(.x)) |>
+  dplyr::ungroup() |> 
+  dplyr::mutate(code = as.numeric(code)) |> 
+  dplyr::arrange(code) |> 
+  purrr::set_names(c("code", "land_cover_class", "lccs_code", "definition", "color_code")) |> 
+  # now we make a list of the color codes and convert to hex. Even though we don't actually need them here...
+  dplyr::mutate(color_code = purrr::map(color_code, ~ as.numeric(strsplit(.x, ",")[[1]])),
+                color = purrr::map_chr(color_code, ~ rgb(.x[1], .x[2], .x[3], maxColorValue = 255))) |> 
+  dplyr::relocate(definition, .after = last_col())
+
+
+my_dt_table(xref, cols_freeze_left = 0, page_length = 5)
-
- +
+

We seem to get issues when the colors we have in our tiff does not match our cross-reference table. For this reason we will remove any values in the xref object that are not in the rasters that we are plotting.

@@ -617,45 +726,45 @@

Plot

Code -
color_table <- terra::coltab(land_cover_raster_raw[[1]])[[1]]
-
-coltab(land_cover_raster_resampled) <- color_table
-
-xref_cleaned <- xref |> 
-  filter(code %in% sort(unique(terra::values(land_cover_raster_resampled))))
-
-
-map <- ggplot() +
-  tidyterra::geom_spatraster(
-    data = as.factor(land_cover_raster_resampled),
-    use_coltab = TRUE,
-    maxcell = Inf
-  ) +
-  tidyterra::scale_fill_coltab(
-    data = as.factor(land_cover_raster_resampled),
-    name = "ESA Land Cover",
-    labels = xref_cleaned$land_cover_class
-  ) +
-  # geom_sf(
-  #     data = aoi,
-  #     fill = "transparent",
-  #     color = "white",
-  #     linewidth = .5
-  # ) +
-  geom_sf(
-    data = layers_trimmed$l_streams,
-    color = "blue",
-    size = 1
-  ) +
-  geom_sf(
-    data = layers_trimmed$l_rail,
-    color = "black",
-    size = 1
-  ) +
-  ggdark::dark_theme_void()
-
-
-map
+
color_table <- terra::coltab(land_cover_raster_raw[[1]])[[1]]
+
+coltab(land_cover_raster_resampled) <- color_table
+
+xref_cleaned <- xref |> 
+  filter(code %in% sort(unique(terra::values(land_cover_raster_resampled))))
+
+
+map <- ggplot() +
+  tidyterra::geom_spatraster(
+    data = as.factor(land_cover_raster_resampled),
+    use_coltab = TRUE,
+    maxcell = Inf
+  ) +
+  tidyterra::scale_fill_coltab(
+    data = as.factor(land_cover_raster_resampled),
+    name = "ESA Land Cover",
+    labels = xref_cleaned$land_cover_class
+  ) +
+  # geom_sf(
+  #     data = aoi,
+  #     fill = "transparent",
+  #     color = "white",
+  #     linewidth = .5
+  # ) +
+  geom_sf(
+    data = layers_trimmed$l_streams,
+    color = "blue",
+    size = 1
+  ) +
+  geom_sf(
+    data = layers_trimmed$l_rail,
+    color = "black",
+    size = 1
+  ) +
+  ggdark::dark_theme_void()
+
+
+map
@@ -672,41 +781,63 @@

Refine the Area of Interest

Code -
buffer_size <- 250
-length_define <- 10000
+
buffer <- 250
+length_m <- 10000
-

Nice. So let’s say we want to zoom in on a particular area such as the mainstem of the Wetzin Kwa for the lowest 10km of the stream for a buffered area at approximately 250m either side of the stream. We can do that by getting the name of the stream. The smallest downstream route measure and the first segments that add to 10000m in linear length.

+

Nice. So let’s say we want to zoom in on a particular area such as the mainstem of the Neexdzii Kwah for the lowest 10000m of the stream for a buffered area at approximately 250m either side of the stream.
+We can do that by filtering by the name of the stream, arranging by downstream route measure and keeping only the first segments that add to less than or equal to 10000m in linear length.

+


+

Not totally sure what to do with the individual stream segments at this point. For now we merge them together into one and then buffer them. This reslts in one shape vsa bunch so that buffered segments don’t overlap each other. Interestingly we end up with a slightly larger raster when we merge the stream segments together before buffering. Below are the results:

+
dimensions  : 259, 951, 1  multiple stream segments
+dimensions  : 262, 958, 1  single stream segment 
Code -
aoi_neexdzii <- layers_trimmed$l_streams |> 
-  dplyr::filter(gnis_name == "Bulkley River") |> 
-  dplyr::arrange(downstream_route_measure) |> 
-  # calculate when we get to 10000m by adding up the length_metre field and filtering out everything up to 10000m
-  dplyr::filter(cumsum(length_metre) <= length_define) 
-
-aoi_neexdzii_buffered <- sf::st_buffer(aoi_neexdzii, buffer_size, endCapStyle = "FLAT")
+
#here we merge our features
+aoi_refined_raw <- layers_trimmed$l_streams |> 
+  dplyr::filter(gnis_name == "Bulkley River") |> 
+  dplyr::arrange(downstream_route_measure) |> 
+  # calculate when we get to length_m by adding up the length_metre field and filtering out everything up to length_m
+  dplyr::filter(cumsum(length_metre) <= length_m) |> 
+  sf::st_union()
+
+aoi_refined <- sf::st_sf(aoi_refined_raw)
+
+aoi_refined_buffered <- sf::st_buffer(aoi_refined, buffer, endCapStyle = "FLAT") 
+
+
+sum(sf::st_area(aoi_refined_buffered))
+
+
+
4310883 [m^2]
+
+
+Code +
#7323123 multiple stream segments
+#4310883 single stream segment

Plot Refined Area

-

Clip the resampled raster to the buffered area.

+

First we need to clip the landcover raster to the buffered area. We are not going to use the resampled raster because we want a more detailed view of the land cover classes for this much smaller area. The computational time to render the plot will be fine at the original resolution.

Code -
land_cover_sample <- terra::crop(land_cover_raster_resampled, aoi_neexdzii_buffered, snap = "in", mask = TRUE, extend = TRUE)
+
land_cover_sample <- terra::crop(land_cover_raster, aoi_refined_buffered, snap = "near", mask = TRUE, extend = TRUE)
+#dimensions  : 259, 951, 1  multiple stream segments
+#dimensions  : 262, 958, 1 single stream segment


-

So it looks like we lose our color values with the crop. We see that with has.colors(land_cover_sample).

+

We lose our color values with the crop. We see that with has.colors(land_cover_sample).

Code -
has.colors(land_cover_sample)
+
has.colors(land_cover_sample)
-
[1] TRUE
+
[1] FALSE


@@ -714,54 +845,50 @@

Plot Refined Area

Code -
coltab(land_cover_sample) <- color_table
-has.colors(land_cover_sample)
+
coltab(land_cover_sample) <- color_table
-
-
[1] TRUE
-


-

Now we should be able to plot what we have. Let’s re-trim up our extra data layers and add those in as well.

+

Now we should be able to plot what we have. Let’s re-trim up our extra data layers to the refined area of interest and add those in as well.

Code -
# clip them  with purrr and sf
-layers_trimmed <- purrr::map(
-  layers_to_trim,
-  ~ sf::st_intersection(.x, aoi_neexdzii_buffered)
-) 
-
-xref_cleaned <- xref |> 
-  filter(code %in% sort(unique(terra::values(land_cover_sample))))
-
-map <- ggplot2::ggplot() +
-    tidyterra::geom_spatraster(
-        data = as.factor(land_cover_sample),
-        use_coltab = TRUE,
-        maxcell = Inf
-    ) +
-    tidyterra::scale_fill_coltab(
-        data = as.factor(land_cover_sample),
-        name = "ESA Land Cover",
-        labels = xref_cleaned$land_cover_class
-    ) +
-  geom_sf(
-    data = layers_trimmed$l_rail,
-    color = "black",
-    size = 1
-  ) +
-    geom_sf(
-    data = layers_trimmed$l_streams,
-    color = "blue",
-    size = 1
-  ) +
-    ggdark::dark_theme_void()
-
-# save the plot
-# ggsave(here::here('posts', params$post_dir_name, "image.jpg"), width = 10, height = 10)
-    
-map
+
# clip them  with purrr and sf
+layers_trimmed <- purrr::map(
+  layers_to_trim,
+  ~ sf::st_intersection(.x, aoi_refined_buffered)
+) 
+
+xref_cleaned <- xref |> 
+  dplyr::filter(code %in% sort(unique(terra::values(land_cover_sample))))
+
+map <- ggplot2::ggplot() +
+  tidyterra::geom_spatraster(
+    data = as.factor(land_cover_sample),
+    use_coltab = TRUE,
+    maxcell = Inf
+  ) +
+  tidyterra::scale_fill_coltab(
+    data = as.factor(land_cover_sample),
+    name = "ESA Land Cover",
+    labels = xref_cleaned$land_cover_class
+  ) +
+  ggplot2::geom_sf(
+    data = layers_trimmed$l_rail,
+    color = "black",
+    size = 1
+  ) +
+  ggplot2::geom_sf(
+    data = layers_trimmed$l_streams,
+    color = "blue",
+    size = 1
+  ) +
+  ggdark::dark_theme_void()
+
+# save the plot
+# ggsave(here::here('posts', params$post_dir_name, "image.jpg"), width = 10, height = 10)
+    
+map
@@ -771,8 +898,236 @@

Plot Refined Area

-


-

Pretty sweet. Next up is to summarize the land cover classes for different areas to build our understanding of potential impacts due to land cover changes. We will likely use the terra::zonal function to do this. Stay tuned.

+
+
+
+

Summary Statistics

+

Next up is to summarize the land cover classes for different areas to build our understanding of potential impacts due to land cover changes. Let’s start with our refined area of interest.

+
+

Neexdzii Kwah

+

Now lets make a table and a simple bargraph to present the results.

+
+
+Code +
# Calculate the area of each cell (assuming your raster is in lat/lon coordinates)
+cell_area <- terra::cellSize(land_cover_raster_resampled, unit = "ha")
+
+# Summarize the raster values
+land_cover_summary_raw <- terra::freq(land_cover_raster_resampled, digits = 0) |> 
+  dplyr::mutate(area_ha = round(count * cell_area[1]$area),1) |> 
+  # make a column that is the percentage of the total area
+  dplyr::mutate(percent_area = round((area_ha / sum(area_ha) * 100), 1))
+
+# now we add the xref code and land_cover_class to the summary
+land_cover_summary <- land_cover_summary_raw |> 
+  dplyr::left_join(xref, by = c("value" = "code")) |> 
+  dplyr::select(land_cover_class, area_ha, percent_area) |> 
+  dplyr::arrange(desc(area_ha))
+
+
+
+
+Code +
my_caption <- "Land Cover Class by Refined Area of Interest"
+
+land_cover_summary |> 
+  knitr::kable(caption = my_caption)
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Land Cover Class by Refined Area of Interest
land_cover_classarea_hapercent_area
Tree cover19415884.3
Grassland2894012.6
Permanent water bodies37311.6
Moss and lichen28501.2
Built-up3290.1
Cropland2640.1
Bare / sparse vegetation1580.1
Herbaceous wetland90.0
Shrubland10.0
+
+
+
+
+Code +
plot_title <- "Land Cover Class for Area of Interest"
+
+# 
+land_cover_summary |> 
+  # convert land_cover_class to factor and arrange based on the area_ha
+  dplyr::mutate(land_cover_class = forcats::fct_reorder(land_cover_class, area_ha)) |>
+  ggplot2::ggplot(ggplot2::aes(x = land_cover_class, y = area_ha)) +
+  ggplot2::geom_col() +
+  ggplot2::coord_flip() +
+  ggplot2::labs(title= plot_title,
+                x = "Land Cover Class",
+                y = "Area (ha)") +
+  ggplot2::theme_minimal() 
+
+
+
+
+

+
+
+
+
+Code +
  # cowplot::theme_minimal_grid()
+
+
+
+
+

Refined Area of Interest

+

Now lets make a table and a simple bargraph to present the results.

+
+
+Code +
# Calculate the area of each cell (assuming your raster is in lat/lon coordinates)
+cell_area <- terra::cellSize(land_cover_sample, unit = "ha")
+
+# Summarize the raster values
+land_cover_summary_raw <- terra::freq(land_cover_sample, digits = 0) |> 
+  dplyr::mutate(area_ha = round(count * cell_area[1]$area),1) |> 
+  # make a column that is the percentage of the total area
+  dplyr::mutate(percent_area = round((area_ha / sum(area_ha) * 100), 1))
+
+# now we add the xref codde and land_cover_class to the summary
+land_cover_summary <- land_cover_summary_raw |> 
+  dplyr::left_join(xref, by = c("value" = "code")) |> 
+  dplyr::select(land_cover_class, area_ha, percent_area) |> 
+  dplyr::arrange(desc(area_ha))
+
+
+
+
+Code +
my_caption <- "Land Cover Class for Refined Area"
+
+land_cover_summary |> 
+  knitr::kable(caption = my_caption)
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Land Cover Class for Refined Area
land_cover_classarea_hapercent_area
Tree cover28366.6
Grassland8920.9
Built-up194.5
Permanent water bodies194.5
Cropland102.4
Bare / sparse vegetation40.9
Moss and lichen10.2
+
+
+
+
+Code +
plot_title <- "Land Cover Class for Refined Area"
+
+# 
+land_cover_summary |> 
+  # convert land_cover_class to factor and arrange based on the area_ha
+  dplyr::mutate(land_cover_class = forcats::fct_reorder(land_cover_class, area_ha)) |>
+  ggplot2::ggplot(ggplot2::aes(x = land_cover_class, y = area_ha)) +
+  ggplot2::geom_col() +
+  ggplot2::coord_flip() +
+  ggplot2::labs(title= plot_title,
+                x = "Land Cover Class",
+                y = "Area (ha)") +
+  ggplot2::theme_minimal() 
+
+
+
+
+

+
+
+
+
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zN&DFb2o*pF^xXD^3dqm - + diff --git a/posts/aws-storage-processx/index.html b/posts/aws-storage-processx/index.html index 3969feb..58b7921 100644 --- a/posts/aws-storage-processx/index.html +++ b/posts/aws-storage-processx/index.html @@ -2,7 +2,7 @@ - + diff --git a/posts/logos-equipment/index.html b/posts/logos-equipment/index.html index 9b9b215..6b0a5b4 100644 --- a/posts/logos-equipment/index.html +++ b/posts/logos-equipment/index.html @@ -2,7 +2,7 @@ - + diff --git a/posts/snakecase/index.html b/posts/snakecase/index.html index 84fc649..98a3b80 100644 --- a/posts/snakecase/index.html +++ b/posts/snakecase/index.html @@ -2,7 +2,7 @@ - + diff --git a/search.json b/search.json index 6eba5c3..1209cd7 100644 --- a/search.json +++ b/search.json @@ -62,12 +62,19 @@ "section": "", "text": "Visualize and quantify remotely sense land cover data…. Here is a first start. We will use the European Space Agency’s WorldCover product which provides global land cover maps for the years 2020 and 2021 at 10 meter resolution based on the combination of Sentinel-1 radar data and Sentinel-2 imagery. We will use the 2021 dataset for mapping an area of the Skeena watershed near Houston, British Columbia.\nThis post was inspired - with much of the code copied - from a repository on GitHub from the wonderfully talented Milos Popovic.\nFirst thing we will do is load our packages. If you do not have the packages installed yet you can change the update_pkgs param in the yml of this file to TRUE. Using pak is great because it allows you to update your packages when you want to.\nCode\npkgs_cran <- c(\n \"usethis\",\n \"rstac\",\n \"here\",\n \"fs\",\n \"terra\",\n \"tidyverse\",\n \"rayshader\",\n \"sf\",\n \"classInt\",\n \"rgl\",\n \"tidyterra\",\n \"tabulapdf\",\n \"bcdata\",\n \"ggplot\",\n \"ggdark\",\n \"knitr\",\n \"DT\",\n \"htmlwidgets\")\n\npkgs_gh <- c(\n \"poissonconsulting/fwapgr\",\n \"NewGraphEnvironment/rfp\"\n )\n\npkgs <- c(pkgs_cran, pkgs_gh)\n\nif(params$update_pkgs){\n # install the pkgs\n lapply(pkgs,\n pak::pkg_install,\n ask = FALSE)\n}\n\n# load the pkgs\npkgs_ld <- c(pkgs_cran,\n basename(pkgs_gh))\ninvisible(\n lapply(pkgs_ld,\n require,\n character.only = TRUE)\n)\n\nsource(here::here(\"scripts/functions.R\"))" }, + { + "objectID": "posts/2024-06-30-land-cover/index.html#downsample-the-land-cover-raster", + "href": "posts/2024-06-30-land-cover/index.html#downsample-the-land-cover-raster", + "title": "Mapping Land Cover with R", + "section": "Downsample the Land Cover Raster", + "text": "Downsample the Land Cover Raster\nHere we downsample the land cover raster to the same resolution as the DEM for the purposes of rendering our map of the larger area in a reasonable amount of time.\n\n\nCode\n# Here we resample the land cover raster to the same resolution as the DEM.\nland_cover_raster_resampled <- terra::resample(\n land_cover_raster,\n dem,\n method = \"near\",\n threads = TRUE\n)\n\n# terra::plot(land_cover_raster_resampled)" + }, { "objectID": "posts/2024-06-30-land-cover/index.html#get-additional-data", "href": "posts/2024-06-30-land-cover/index.html#get-additional-data", "title": "Mapping Land Cover with R", "section": "Get Additional Data", - "text": "Get Additional Data\nWe could use some data for context such as major streams and the railway. We get the streams and railway from data distribution bc api using the bcdata package. Our rfp package calls just allow some extra sanity checks on the bcdata::bcdc_query_geodata function. It’s not really necessary but can be helpful when errors occur (ex. the name of the column to filter on is input incorrectly).\n\n\n\nCode\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# 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\nNow we trim up those layers. We have some functions to validate and repair geometries and then we clip them to our area of interest.\n\n\nCode\nlayers_to_trim <- tibble::lst(l_rail, l_streams)\n\n# Function to validate and repair geometries\nvalidate_geometries <- function(layer) {\n layer <- sf::st_make_valid(layer)\n layer <- layer[sf::st_is_valid(layer), ]\n return(layer)\n}\n\n# Apply validation to the AOI and layers\naoi <- validate_geometries(aoi)\nlayers_to_trim <- purrr::map(layers_to_trim, validate_geometries)\n\n# clip them with purrr and sf\nlayers_trimmed <- purrr::map(\n layers_to_trim,\n ~ sf::st_intersection(.x, aoi)\n)" + "text": "Get Additional Data\nWe could use some data for context such as major streams and the railway. We get the streams and railway from data distribution bc api using the bcdata package. Our rfp package calls just allow some extra sanity checks and convenience moves on the bcdata::bcdc_query_geodata function. It’s not really necessary but can be helpful (ex. can use small cap layer and column names and will throw an informative error if the name of the columns specified are input incorrectly).\n\n\n\nCode\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# 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\nNow we trim up those layers. We have some functions to validate and repair geometries and then we clip them to our area of interest.\n\n\nCode\nlayers_to_trim <- tibble::lst(l_rail, l_streams)\n\n# Function to validate and repair geometries\nvalidate_geometries <- function(layer) {\n layer <- sf::st_make_valid(layer)\n layer <- layer[sf::st_is_valid(layer), ]\n return(layer)\n}\n\n# Apply validation to the AOI and layers\naoi <- validate_geometries(aoi)\nlayers_to_trim <- purrr::map(layers_to_trim, validate_geometries)\n\n# clip them with purrr and sf\nlayers_trimmed <- purrr::map(\n layers_to_trim,\n ~ sf::st_intersection(.x, aoi)\n)" }, { "objectID": "posts/2024-06-30-land-cover/index.html#get-legend-values", @@ -88,7 +95,21 @@ "href": "posts/2024-06-30-land-cover/index.html#plot-refined-area", "title": "Mapping Land Cover with R", "section": "Plot Refined Area", - "text": "Plot Refined Area\nClip the resampled raster to the buffered area.\n\n\nCode\nland_cover_sample <- terra::crop(land_cover_raster_resampled, aoi_neexdzii_buffered, snap = \"in\", mask = TRUE, extend = TRUE)\n\n\n\nSo it looks like we lose our color values with the crop. We see that with has.colors(land_cover_sample).\n\n\nCode\nhas.colors(land_cover_sample)\n\n\n[1] TRUE\n\n\n\nLet’s add them back in with the terra::coltab function.\n\n\nCode\ncoltab(land_cover_sample) <- color_table\nhas.colors(land_cover_sample)\n\n\n[1] TRUE\n\n\n\nNow we should be able to plot what we have. Let’s re-trim up our extra data layers and add those in as well.\n\n\nCode\n# clip them with purrr and sf\nlayers_trimmed <- purrr::map(\n layers_to_trim,\n ~ sf::st_intersection(.x, aoi_neexdzii_buffered)\n) \n\nxref_cleaned <- xref |> \n filter(code %in% sort(unique(terra::values(land_cover_sample))))\n\nmap <- ggplot2::ggplot() +\n tidyterra::geom_spatraster(\n data = as.factor(land_cover_sample),\n use_coltab = TRUE,\n maxcell = Inf\n ) +\n tidyterra::scale_fill_coltab(\n data = as.factor(land_cover_sample),\n name = \"ESA Land Cover\",\n labels = xref_cleaned$land_cover_class\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_streams,\n color = \"blue\",\n size = 1\n ) +\n ggdark::dark_theme_void()\n\n# save the plot\n# ggsave(here::here('posts', params$post_dir_name, \"image.jpg\"), width = 10, height = 10)\n \nmap\n\n\n\n\n\n\n\n\n\n\nPretty sweet. Next up is to summarize the land cover classes for different areas to build our understanding of potential impacts due to land cover changes. We will likely use the terra::zonal function to do this. Stay tuned." + "text": "Plot Refined Area\nFirst we need to clip the landcover raster to the buffered area. We are not going to use the resampled raster because we want a more detailed view of the land cover classes for this much smaller area. The computational time to render the plot will be fine at the original resolution.\n\n\nCode\nland_cover_sample <- terra::crop(land_cover_raster, aoi_refined_buffered, snap = \"near\", mask = TRUE, extend = TRUE)\n#dimensions : 259, 951, 1 multiple stream segments\n#dimensions : 262, 958, 1 single stream segment\n\n\n\nWe lose our color values with the crop. We see that with has.colors(land_cover_sample).\n\n\nCode\nhas.colors(land_cover_sample)\n\n\n[1] FALSE\n\n\n\nLet’s add them back in with the terra::coltab function.\n\n\nCode\ncoltab(land_cover_sample) <- color_table\n\n\n\nNow we should be able to plot what we have. Let’s re-trim up our extra data layers to the refined area of interest and add those in as well.\n\n\nCode\n# clip them with purrr and sf\nlayers_trimmed <- purrr::map(\n layers_to_trim,\n ~ sf::st_intersection(.x, aoi_refined_buffered)\n) \n\nxref_cleaned <- xref |> \n dplyr::filter(code %in% sort(unique(terra::values(land_cover_sample))))\n\nmap <- ggplot2::ggplot() +\n tidyterra::geom_spatraster(\n data = as.factor(land_cover_sample),\n use_coltab = TRUE,\n maxcell = Inf\n ) +\n tidyterra::scale_fill_coltab(\n data = as.factor(land_cover_sample),\n name = \"ESA Land Cover\",\n labels = xref_cleaned$land_cover_class\n ) +\n ggplot2::geom_sf(\n data = layers_trimmed$l_rail,\n color = \"black\",\n size = 1\n ) +\n ggplot2::geom_sf(\n data = layers_trimmed$l_streams,\n color = \"blue\",\n size = 1\n ) +\n ggdark::dark_theme_void()\n\n# save the plot\n# ggsave(here::here('posts', params$post_dir_name, \"image.jpg\"), width = 10, height = 10)\n \nmap" + }, + { + "objectID": "posts/2024-06-30-land-cover/index.html#neexdzii-kwah", + "href": "posts/2024-06-30-land-cover/index.html#neexdzii-kwah", + "title": "Mapping Land Cover with R", + "section": "Neexdzii Kwah", + "text": "Neexdzii Kwah\nNow lets make a table and a simple bargraph to present the results.\n\n\nCode\n# Calculate the area of each cell (assuming your raster is in lat/lon coordinates)\ncell_area <- terra::cellSize(land_cover_raster_resampled, unit = \"ha\")\n\n# Summarize the raster values\nland_cover_summary_raw <- terra::freq(land_cover_raster_resampled, digits = 0) |> \n dplyr::mutate(area_ha = round(count * cell_area[1]$area),1) |> \n # make a column that is the percentage of the total area\n dplyr::mutate(percent_area = round((area_ha / sum(area_ha) * 100), 1))\n\n# now we add the xref code and land_cover_class to the summary\nland_cover_summary <- land_cover_summary_raw |> \n dplyr::left_join(xref, by = c(\"value\" = \"code\")) |> \n dplyr::select(land_cover_class, area_ha, percent_area) |> \n dplyr::arrange(desc(area_ha))\n\n\n\n\nCode\nmy_caption <- \"Land Cover Class by Refined Area of Interest\"\n\nland_cover_summary |> \n knitr::kable(caption = my_caption)\n\n\n\nLand Cover Class by Refined Area of Interest\n\n\nland_cover_class\narea_ha\npercent_area\n\n\n\n\nTree cover\n194158\n84.3\n\n\nGrassland\n28940\n12.6\n\n\nPermanent water bodies\n3731\n1.6\n\n\nMoss and lichen\n2850\n1.2\n\n\nBuilt-up\n329\n0.1\n\n\nCropland\n264\n0.1\n\n\nBare / sparse vegetation\n158\n0.1\n\n\nHerbaceous wetland\n9\n0.0\n\n\nShrubland\n1\n0.0\n\n\n\n\n\n\n\nCode\nplot_title <- \"Land Cover Class for Area of Interest\"\n\n# \nland_cover_summary |> \n # convert land_cover_class to factor and arrange based on the area_ha\n dplyr::mutate(land_cover_class = forcats::fct_reorder(land_cover_class, area_ha)) |>\n ggplot2::ggplot(ggplot2::aes(x = land_cover_class, y = area_ha)) +\n ggplot2::geom_col() +\n ggplot2::coord_flip() +\n ggplot2::labs(title= plot_title,\n x = \"Land Cover Class\",\n y = \"Area (ha)\") +\n ggplot2::theme_minimal() \n\n\n\n\n\n\n\n\n\nCode\n # cowplot::theme_minimal_grid()" + }, + { + "objectID": "posts/2024-06-30-land-cover/index.html#refined-area-of-interest", + "href": "posts/2024-06-30-land-cover/index.html#refined-area-of-interest", + "title": "Mapping Land Cover with R", + "section": "Refined Area of Interest", + "text": "Refined Area of Interest\nNow lets make a table and a simple bargraph to present the results.\n\n\nCode\n# Calculate the area of each cell (assuming your raster is in lat/lon coordinates)\ncell_area <- terra::cellSize(land_cover_sample, unit = \"ha\")\n\n# Summarize the raster values\nland_cover_summary_raw <- terra::freq(land_cover_sample, digits = 0) |> \n dplyr::mutate(area_ha = round(count * cell_area[1]$area),1) |> \n # make a column that is the percentage of the total area\n dplyr::mutate(percent_area = round((area_ha / sum(area_ha) * 100), 1))\n\n# now we add the xref codde and land_cover_class to the summary\nland_cover_summary <- land_cover_summary_raw |> \n dplyr::left_join(xref, by = c(\"value\" = \"code\")) |> \n dplyr::select(land_cover_class, area_ha, percent_area) |> \n dplyr::arrange(desc(area_ha))\n\n\n\n\nCode\nmy_caption <- \"Land Cover Class for Refined Area\"\n\nland_cover_summary |> \n knitr::kable(caption = my_caption)\n\n\n\nLand Cover Class for Refined Area\n\n\nland_cover_class\narea_ha\npercent_area\n\n\n\n\nTree cover\n283\n66.6\n\n\nGrassland\n89\n20.9\n\n\nBuilt-up\n19\n4.5\n\n\nPermanent water bodies\n19\n4.5\n\n\nCropland\n10\n2.4\n\n\nBare / sparse vegetation\n4\n0.9\n\n\nMoss and lichen\n1\n0.2\n\n\n\n\n\n\n\nCode\nplot_title <- \"Land Cover Class for Refined Area\"\n\n# \nland_cover_summary |> \n # convert land_cover_class to factor and arrange based on the area_ha\n dplyr::mutate(land_cover_class = forcats::fct_reorder(land_cover_class, area_ha)) |>\n ggplot2::ggplot(ggplot2::aes(x = land_cover_class, y = area_ha)) +\n ggplot2::geom_col() +\n ggplot2::coord_flip() +\n ggplot2::labs(title= plot_title,\n x = \"Land Cover Class\",\n y = \"Area (ha)\") +\n ggplot2::theme_minimal()" }, { "objectID": "posts/aws-storage-processx/index.html", diff --git a/sitemap.xml b/sitemap.xml index 429970a..b0113c2 100644 --- a/sitemap.xml +++ b/sitemap.xml @@ -2,38 +2,38 @@ https://NewGraphEnvironment.github.io/new_graphiti/about.html - 2024-07-01T23:47:20.694Z + 2024-07-02T18:56:12.942Z https://NewGraphEnvironment.github.io/new_graphiti/posts/2024-06-19-precipitation/index.html - 2024-07-01T23:47:20.718Z + 2024-07-02T18:56:12.970Z https://NewGraphEnvironment.github.io/new_graphiti/posts/snakecase/index.html - 2024-07-01T23:47:20.750Z + 2024-07-02T18:56:12.998Z https://NewGraphEnvironment.github.io/new_graphiti/posts/logos-equipment/index.html - 2024-07-01T23:47:20.746Z + 2024-07-02T18:56:12.998Z https://NewGraphEnvironment.github.io/new_graphiti/posts/aws-storage-permissions/index.html - 2024-07-01T23:47:20.746Z + 2024-07-02T18:56:12.998Z https://NewGraphEnvironment.github.io/new_graphiti/posts/2024-05-27-references-bib-succinct/index.html - 2024-07-01T23:47:20.714Z + 2024-07-02T18:56:12.966Z https://NewGraphEnvironment.github.io/new_graphiti/posts/2024-06-30-land-cover/index.html - 2024-07-01T23:47:20.734Z + 2024-07-02T18:56:12.986Z https://NewGraphEnvironment.github.io/new_graphiti/posts/aws-storage-processx/index.html - 2024-07-01T23:47:20.746Z + 2024-07-02T18:56:12.998Z https://NewGraphEnvironment.github.io/new_graphiti/index.html - 2024-07-01T23:47:20.706Z + 2024-07-02T18:56:12.958Z