empirische berekening winFun voor vwf functie

data/Kruinen referentie/kruinen_Dilsen1.cpg

@@ -0,0 +1 @@
+UTF-8

data/Kruinen referentie/kruinen_Dilsen1.prj

@@ -0,0 +1 @@
+PROJCS["Belge_Lambert_1972",GEOGCS["GCS_Belge_1972",DATUM["D_Belge_1972",SPHEROID["International_1924",6378388.0,297.0]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Lambert_Conformal_Conic"],PARAMETER["False_Easting",150000.013],PARAMETER["False_Northing",5400088.438],PARAMETER["Central_Meridian",4.36748666666667],PARAMETER["Standard_Parallel_1",51.1666672333333],PARAMETER["Standard_Parallel_2",49.8333339],PARAMETER["Latitude_Of_Origin",90.0],UNIT["Meter",1.0]]

source/Calibration winFun.Rmd

@@ -0,0 +1,124 @@
+---
+title: "calibration winFun"
+author: "Stien Heremans"
+date: "2025-01-20"
+output: html_document
+---
+
+```{r}
+library(sf)       # For spatial vector data
+library(raster)   # For CHM (raster data)
+
+crowns <- st_read("../data/Kruinen referentie/kruinen_Dilsen1.shp")
+chm <- raster("../output/CHM/chm_rescaled Dilsen 1.tif")
+
+crowns <- st_transform(crowns, crs = crs(chm))
+```
+
+```{r}
+library(exactextractr)  # For extracting raster values by polygons
+
+crowns$area <- st_area(crowns)
+crowns$radius <- sqrt(as.numeric(crowns$area) / pi)
+crowns$height_75 <- exact_extract(chm, crowns, function(values, coverage_fraction) {
+  quantile(values, probs = 0.75, na.rm = TRUE)
+})
+crowns$max_height <- exact_extract(chm, crowns, function(values, coverage_fraction) {
+  max(values, na.rm = TRUE)
+})
+crowns$average_height <- exact_extract(chm, crowns, function(values, coverage_fraction) {
+  mean(values, na.rm = TRUE)
+})
+
+```
+
+
+```{r}
+head(crowns)
+```
+```{r}
+# Remove rows with missing values
+crowns <- crowns[!is.na(crowns$radius) & !is.na(crowns$height_75), ]
+```
+
+
+```{r}
+library(ggplot2)
+
+p1 <- ggplot(crowns, aes(x = height_75, y = radius)) +
+  geom_point() +
+  geom_smooth(method = "lm", se = FALSE, color = "blue") +
+  theme_minimal() +
+  labs(title = "Radius vs Height",
+       x = "Crown Height (75th Percentile)",
+       y = "Crown Radius (m)")
+p1
+```
+
+```{r}
+
+# Fit the model
+model_75height <- lm(radius ~ height_75, data = crowns)
+
+# Summarize the model
+summary(model_75height)
+```
+
+
+```{r}
+# Remove rows with missing values
+crowns <- crowns[!is.na(crowns$radius) & !is.na(crowns$max_height), ]
+```
+
+
+```{r}
+library(ggplot2)
+
+p1 <- ggplot(crowns, aes(x = max_height, y = radius)) +
+  geom_point() +
+  geom_smooth(method = "lm", se = FALSE, color = "blue") +
+  theme_minimal() +
+  labs(title = "Radius vs Height",
+       x = "Crown Height (max)",
+       y = "Crown Radius (m)")
+p1
+```
+
+```{r}
+
+# Fit the model
+model_maxheight <- lm(radius ~ max_height, data = crowns)
+
+# Summarize the model
+summary(model_maxheight)
+```
+
+
+
+```{r}
+# Remove rows with missing values
+crowns <- crowns[!is.na(crowns$radius) & !is.na(crowns$average_height), ]
+```
+
+
+```{r}
+library(ggplot2)
+
+p1 <- ggplot(crowns, aes(x = average_height, y = radius)) +
+  geom_point() +
+  geom_smooth(method = "lm", se = FALSE, color = "blue") +
+  theme_minimal() +
+  labs(title = "Radius vs Height",
+       x = "Crown Height (average)",
+       y = "Crown Radius (m)")
+p1
+```
+
+```{r}
+
+# Fit the model
+model_75height <- lm(radius ~ average_height, data = crowns)
+
+# Summarize the model
+summary(model_75height)
+```

source/TreeDelineation.Rmd

@@ -30,8 +30,8 @@ if (!dir.exists(file.path(output_dir, "tree_crowns"))) {
 
 
 # Load DSM and DTM
-dsm_drone <- rast("D:/Thesis Thomas/DHT/DEM update/m_geclipt_DEM Dilsen 1.tif")
-dtm <- rast("S:/Vlaanderen/Hoogte/DHMVII/DHMVIIDTMRAS1m.tif")
+dsm_drone <- rast("E:/Thesis Thomas/DHT/DEM update/m_geclipt_DEM Dilsen 1.tif")
+dtm <- rast("Z:/Vlaanderen/Hoogte/DHMVII/DHMVIIDTMRAS1m.tif")
 
 ```