rerenders website

.github/workflows/publish.yml

@@ -17,15 +17,15 @@ jobs:
       - name: Set up Quarto
         uses: quarto-dev/quarto-actions/setup@v2
 
-      - name: Install R
-        uses: r-lib/actions/setup-r@v2
+      # - name: Install R
+      #   uses: r-lib/actions/setup-r@v2
 
-      - name: Install R Dependencies
-        uses: r-lib/actions/setup-r-dependencies@v2
-        with:
-          packages:
-            any::knitr
-            any::rmarkdown
+      # - name: Install R Dependencies
+      #   uses: r-lib/actions/setup-r-dependencies@v2
+      #   with:
+      #     packages:
+      #       any::knitr
+      #       any::rmarkdown
 
       - name: Render and Publish
         uses: quarto-dev/quarto-actions/publish@v2

_freeze/R_session/Compare_BI_plots/execute-results/html.json

@@ -0,0 +1,15 @@
+{
+  "hash": "af8ab40fba1549a2c88d02c1dff88fb9",
+  "result": {
+    "engine": "knitr",
+    "markdown": "---\ntitle: \"Number_Trees_BI_nDOM\"\nformat: html\nknitr:\n  opts_chunk: \n    eval: false\neditor: visual\n---\n\n\n\n\n## Compare number of trees in BI plots: detected with remote sensing vs measured in field\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(here)\nlibrary(sf)\nlibrary(dplyr)\nlibrary(terra)\nlibrary(ggplot2)\nlibrary(foreign)\n```\n:::\n\n\n\n\nLoad BI plots, BI trees and trees detected in ndom (created in previous script).\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nplots_bi <- st_read(here(\"R_session\", \"data\", \"buffer13_bi_mittelpunkt_solling_2023.gpkg\"))\n\ntrees_bi <- st_read(here(\"R_session\", \"data\", \"bi_baeume_vorrat_solling_2023.gpkg\"))\n\ntrees_ndom <- st_read(here(\"R_session\", \"data\", \"ttops_bi_ndom.gpkg\"))\n```\n:::\n\n\n\n\nLoad table with median slope in the BI plot and bind with plots BI table\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nslope_bi_plot <- read.dbf(here(\"R_session\", \"data\", \"bi_plot_median_slope.dbf\"))\nplots_bi <- bind_cols(plots_bi, slope_bi_plot %>% select(X_median))\nrm(slope_bi_plot)\n```\n:::\n\n\n\n\nCount how many bi trees inside plot\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nintersection_bi_trees_plots <- st_intersection(x = plots_bi, y = trees_bi)\n\ncount_bi_trees_plot <- intersection_bi_trees_plots %>% \n    add_count(KSPNR, name = \"n_trees_bi\") %>% \n    group_by(KSPNR) %>% \n    slice(n()) %>% \n    ungroup() \n```\n:::\n\n\n\n\nOptional: Trees_bi 2638 observations, intersection_bi_trees_plots 2606 observations, check reason from this difference\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\n#st_erase = function(x, y) st_difference(x, st_union(st_combine(y)))\n\n#difference <- st_erase(trees_bi , intersection_bi_trees_plots)\n\n#writeVector(vect(difference), here(\"output\", \"trees_not_bi.gpkg\"), overwrite=TRUE)\n```\n:::\n\n\n\n\nCount how many ndom trees inside plot\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nintersection_ndom_trees_plots <- st_intersection(x = plots_bi, y = trees_ndom)\n\ncount_ndom_trees_plot <- intersection_ndom_trees_plots %>% \n    add_count(KSPNR, name = \"n_trees_ndom\") %>% \n    group_by(KSPNR) %>% \n    slice(n()) %>% \n    ungroup() \n```\n:::\n\n\n\n\nMerge table with number trees from ndom and table with number trees from BI\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\ncount_trees_bi_ndom <- merge(as.data.frame(count_ndom_trees_plot), as.data.frame(count_bi_trees_plot), by = \"KSPNR\")\n```\n:::\n\n\n\n\nPlot to compare the number of trees in each plot taking slope of plot into account\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nx <- ggplot(count_trees_bi_ndom, aes(x=n_trees_bi, y=n_trees_ndom,        colour=X_median.x)) + \n              xlim(0,30) + ylim(0,30) +\n              geom_abline(slope=1) +\n              geom_point() +\n              scale_colour_gradientn(colours=rainbow(4)) +\n              labs(title = \"Number of trees in each plot measured in BI vs detected with nDOM\", \n              x = \"Number of trees measured in BI\",\n              y = \"Number of trees detected in nDOM\",\n              color = \"Terrain slope [degree]\") +\n              theme(plot.title = element_text(hjust = 0.5))\n```\n:::\n\n\n\n\nWhat could be the reasons for the difference?\n",
+    "supporting": [],
+    "filters": [
+      "rmarkdown/pagebreak.lua"
+    ],
+    "includes": {},
+    "engineDependencies": {},
+    "preserve": {},
+    "postProcess": true
+  }
+}

_freeze/R_session/Tree_detection/execute-results/html.json

@@ -0,0 +1,15 @@
+{
+  "hash": "3502f2b1e75cc6ba690310fe54e2858c",
+  "result": {
+    "engine": "knitr",
+    "markdown": "---\ntitle: \"Tree detection\"\nformat: html\nknitr:\n  opts_chunk: \n    eval: false\neditor: visual\n---\n\n\n\n\n## Tree detection\n\nGoal is to detect tree tops using a variable window size and using the ndom raster in the BI plots in Solling 2023.\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(lidR)\nlibrary(terra)\nlibrary(here)\n```\n:::\n\n\n\n\nLoad ndom previously clipped to BI plots in Solling (BI 2023)\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nndom_bi <- rast(here(\"R_session\", \"data\", \"ndom_bi_plots_solling_2023.tif\"))\nmapview(ndom_bi)\n```\n:::\n\n\n\n\nMake tiles because data too big for R\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\ntiles <- makeTiles(x=ndom_bi, \n          y=c(3000,3000),\n          filename=\n            here(\"R_session\", \"output\",\"tiles\",\"ndom_bi_.tif\"), \n          na.rm=TRUE)\n\n# In case the tiles are already there\ntiles <-list.files(here(\"R_session\", \"output\",\"tiles\"), pattern='*\\\\.tif', recursive=TRUE, full.names=TRUE)\n```\n:::\n\n\n\n\nTree detection function with variable window size. Any points below 2 m will equate to a window size of 3 m, while points above 20 meters equate to a window size of 5 m. Anything between 2 and 20 meter will have a non-linear relationship.\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nf <- function(x) {\n  y <- 2.6 * (-(exp(-0.08*(x-2)) - 1)) + 3 \n  # from https://r-lidar.github.io/lidRbook/itd.html\n  y[x < 2] <- 3\n  y[x > 20] <- 5\n  return(y)\n}\n\nheights <- seq(-5,30,0.5)\nws <- f(heights)\nplot(heights, ws, type = \"l\",  ylim = c(0,5))\n```\n:::\n\n\n\n\nTree detection using function for variable window size\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nout <- sapply(tiles, \\(tile) {\n        x <- rast(tile)\n        ttops <- lidR::locate_trees(x, lidR::lmf(f))\n        vect(ttops)\n    })\n\nout <- vect(out)\n```\n:::\n\n\n\n\nSave tree tops\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nwriteVector(out, here(\"R_session\", \"output\",\"ttops_ndom_.gpkg\"), overwrite=TRUE)\n```\n:::\n",
+    "supporting": [],
+    "filters": [
+      "rmarkdown/pagebreak.lua"
+    ],
+    "includes": {},
+    "engineDependencies": {},
+    "preserve": {},
+    "postProcess": true
+  }
+}