final 2024_Remote_Sensing

2024_Remote_Sensing/script.py

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+import geopandas as gpd
+import rasterio
+from rasterio.mask import mask
+import matplotlib.pyplot as plt
+import numpy as np
+from matplotlib.ticker import ScalarFormatter
+
+# Read the original shapefile
+gdf = gpd.read_file('continent_overlay/Continent_Outlines.shp')
+
+# Path to the raster file
+raster_path = 'gtopo_world_dem_6m.tif'
+
+# Open the raster file
+with rasterio.open(raster_path) as src:
+    plt.figure(figsize=(14, 8))  # Create a larger figure for better visualization
+
+    # Loop through each unique continent
+    for continent in gdf['CONTINENT'].unique():
+        # Filter GeoDataFrame for the current continent
+        gdf_continent = gdf[gdf['CONTINENT'] == continent]
+        
+        # Clip the raster with the geometry of the current continent
+        out_image, out_transform = mask(src, gdf_continent.geometry, crop=True)
+        
+        # Flatten the array and remove masked values
+        data = out_image[0].flatten()  # Assuming a single-band raster
+        data = data[(data != src.nodata) & (data >= 0) & (data <= 7000)]  # Remove nodata values and filter the range
+
+        # Calculate histogram data (bin counts)
+        counts, bin_edges = np.histogram(data, bins=50, range=(0, 7000))
+
+        # Plot the line for this continent
+        plt.plot(bin_edges[:-1], counts, label=continent, linewidth=1.5)
+
+    # Add x-axis and y-axis limits, legend, title, and labels
+    plt.xlim(0, 7000)
+    plt.ylim(0, 40000)
+    plt.gca().yaxis.set_major_formatter(ScalarFormatter(useOffset=False))  # Ensure y-axis shows whole numbers
+    plt.ticklabel_format(style='plain', axis='y')  # Disable scientific notation
+    plt.legend(loc='upper right')
+    plt.xlabel('Elevation (m)')
+    plt.ylabel('Count')
+
+    # Save the plot to a file
+    plt.savefig('plot1.png')
+
+    print('Line plot saved as "plot1.png"')