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library(terra)

## terra 1.7.71

# 1. Φόρτωση εικόνων ταξινόμησης

rf_raster <- rast("G:/parnitha_rf_classified.tif")
cart_raster <- rast("G:/parnitha_cart_classified.tif")

plot(rf_raster)

plot(cart_raster)

rf_raster <- rast("G:/parnitha_rf_classified.tif")
rf_raster <- as.factor(rf_raster)
levels(rf_raster) <- data.frame(value = 0:3, 
                                label = c("Fir", "Pine", "Open", "Urban"))
class_colors <- c("red", "green", "yellow", "blue")
class_labels <- c("Fir", "Pine", "Open", "Urban")

## Plot με χρώματα, ετικέτες, κλίμακα και βορρά
plot(rf_raster, col = class_colors, 
     legend = TRUE, main = "RF Classification")


library(terra)
library(ggplot2)

library(ggspatial)

library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:terra':
## 
##     intersect, union

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(patchwork)

## 
## Attaching package: 'patchwork'

## The following object is masked from 'package:terra':
## 
##     area

# 1. Φόρτωση εικόνων ταξινόμησης
rf_raster <- rast("G:/parnitha_rf_classified.tif")
cart_raster <- rast("G:/parnitha_cart_classified.tif")

# 2. Μετατροπή σε data.frame για ggplot
rf_df <- as.data.frame(rf_raster, xy = TRUE) %>% rename(class = 3)
cart_df <- as.data.frame(cart_raster, xy = TRUE) %>% rename(class = 3)

# 3. Κατηγορίες και χρώματα (σύμφωνα με τη σειρά που ζήτησες)

class_labels <- c("Fir", "Pine", "Open", "Urban")
class_colors <- c("red", "green", "yellow", "blue")

# Μετατροπή αριθμητικής κλάσης σε factor με σωστή σειρά και ονόματα
rf_df$class <- factor(rf_df$class, levels = 0:3, labels = class_labels)
cart_df$class <- factor(cart_df$class, levels = 0:3, labels = class_labels)

# 4. Θεματικοί χάρτες RF & CART με ggplot2 + ggspatial (χωρίς ROI)
plot_rf <- ggplot() +
  geom_raster(data = rf_df, aes(x = x, y = y, fill = class)) +
  scale_fill_manual(values = class_colors, name = "Κατηγορία") +
  coord_fixed() +
  labs(title = "Random Forest Classification", x = "Μήκος", y = "Πλάτος") +
  theme_minimal() +
  annotation_scale(pad_x = unit(1, "cm"),
                   pad_y = unit(0.1, "cm"),style = "ticks", width_hint = 0.5) +      # scale bar
  annotation_north_arrow(height = unit(0.8, "cm"),
                         width = unit(0.4, "cm"),
                         pad_x = unit(0.8, "cm"),
                         pad_y = unit(0.7, "cm"))  # βέλος βορρά


plot_rf

## Using plotunit = 'm'

## ΕΞΑΓΩΓΗ

ggsave("G:/01_rf_map.png", plot_rf, width = 8, height = 7, dpi = 300)

## Using plotunit = 'm'

### ΔΙΑΓΡΑΜΜΑΤΑ ΕΚΤΑΣΕΩΝ


# 5. Υπολογισμός εκτάσεων κλάσεων από RF (σε στρέμματα)
area_df <- rf_df %>%
  group_by(class) %>%
  summarise(count = n()) %>%
  mutate(area_m2 = count * 10 * 10,    # pixels 10x10 m
         area_ha = area_m2 / 10000,    # 1 ha = 10,000 m2
         perc = 100 * area_ha / sum(area_ha))

# 6. Διάγραμμα έκτασης κλάσεων
plot_area <- ggplot(area_df, aes(x = class, y = perc, fill = class)) +
  geom_col(show.legend = FALSE) +
  geom_text(aes(label = sprintf("%.1f%% (%.1f ha)", perc, area_ha)), vjust = -0.5) +
  scale_fill_manual(values = class_colors) +
  labs(title = "Ποσοστό Έκτασης ανά Κλάση (RF)", y = "Ποσοστό (%)", x = "Κλάση") +
  theme_minimal()

plot_area