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‎04-models/notebooks/prediction-stats.ipynb

+199-45
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@@ -63,7 +63,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
@@ -75,7 +75,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
@@ -87,7 +87,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"execution_count": 7,
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"metadata": {},
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"outputs": [],
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"source": [
@@ -98,38 +98,61 @@
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]
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},
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{
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"cell_type": "markdown",
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"{'driver': 'GTiff', 'dtype': 'int16', 'nodata': -1.0, 'width': 20381, 'height': 22512, 'count': 1, 'crs': CRS.from_epsg(4326), 'transform': Affine(0.00026949458523585647, 0.0, -62.64186038139295,\n",
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" 0.0, -0.00026949458523585647, -19.287457970745013), 'tiled': False, 'interleave': 'band'}\n",
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"{'driver': 'GTiff', 'dtype': 'float32', 'nodata': -1.0, 'width': 20381, 'height': 22512, 'count': 1, 'crs': CRS.from_epsg(4326), 'transform': Affine(0.00026949458523585647, 0.0, -62.64186038139295,\n",
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" 0.0, -0.00026949458523585647, -19.287457970745013), 'tiled': False, 'interleave': 'band'}\n"
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]
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}
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],
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"source": [
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"# 25"
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"# Open the land use raster file\n",
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"with rasterio.open(sim25_lup) as src:\n",
119+
" land_use_array = src.read(1)\n",
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" p1 = src.profile\n",
121+
"\n",
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"# Open the deforestation probability raster file\n",
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"with rasterio.open(sim25_lup_prediction) as src:\n",
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" deforestation_probability_array = src.read(1)\n",
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" p2 = src.profile\n",
126+
"print(p1)\n",
127+
"print(p2)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"cell_type": "markdown",
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"metadata": {},
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"outputs": [],
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"source": []
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"source": [
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"# 25"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 20,
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"execution_count": 11,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Area of land use type 0: 0 square meters\n",
124-
"Area of land use type 1: 13140840600 square meters\n",
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"Area of land use type 2: 25707976200 square meters\n",
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"Area of land use type 3: 59036355000 square meters\n",
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"Area of land use type 4: 912870900 square meters\n",
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"Area of land use type 0: 0.0 square meters\n",
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"Area of land use type 1: 12236239734.029999 square meters\n",
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"Area of land use type 2: 23938267682.809998 square meters\n",
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"Area of land use type 3: 54972357917.75 square meters\n",
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"Area of land use type 4: 850029881.545 square meters\n",
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"Area of land use type 0: 0.0 hectares\n",
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"Area of land use type 1: 1314084.06 hectares\n",
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"Area of land use type 2: 2570797.62 hectares\n",
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"Area of land use type 3: 5903635.5 hectares\n",
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"Area of land use type 4: 91287.09 hectares\n"
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"Area of land use type 1: 1223623.9734029998 hectares\n",
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"Area of land use type 2: 2393826.7682809997 hectares\n",
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"Area of land use type 3: 5497235.791775 hectares\n",
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"Area of land use type 4: 85002.9881545 hectares\n"
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]
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}
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],
@@ -152,14 +175,14 @@
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"counts = np.bincount(land_use_array_flat)\n",
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"\n",
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"# Calculate the area of each land use type\n",
155-
"pixel_area = 900 # replace this with the actual area represented by each pixel\n",
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"pixel_area = 838.045 # 28.94785483442850804 m * 28.94785483442850804 m = 838.045 square meters EPSG:32721\n",
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"areas = counts * pixel_area\n",
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"\n",
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"# Print the area of each land use type\n",
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"for i, area in enumerate(areas):\n",
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" print(f\"Area of land use type {i}: {area} square meters\")\n",
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"# Calculate the deforested area for each pixel\n",
162-
"deforested_area_array = deforestation_probability_array * 900 # replace 900 with the actual area represented by each pixel\n",
185+
"deforested_area_array = deforestation_probability_array * 838.045 # replace 900 with the actual area represented by each pixel\n",
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"\n",
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"# Initialize arrays to hold the total deforested area, non-deforested area, and total area for each land use type\n",
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"deforested_areas = np.zeros(4) # number of land use types\n",
@@ -230,23 +253,23 @@
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},
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{
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"cell_type": "code",
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"execution_count": 21,
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"execution_count": 13,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Area of land use type 0: 0 square meters\n",
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"Area of land use type 1: 8532334800 square meters\n",
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"Area of land use type 2: 50576823000 square meters\n",
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"Area of land use type 3: 38781358200 square meters\n",
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"Area of land use type 4: 912873600 square meters\n",
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"Area of land use type 0: 0.0 square meters\n",
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"Area of land use type 1: 7944978352.74 square meters\n",
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"Area of land use type 2: 47095170701.149994 square meters\n",
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"Area of land use type 3: 36111692591.909996 square meters\n",
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"Area of land use type 4: 850032395.68 square meters\n",
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"Area of land use type 0: 0.0 hectares\n",
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"Area of land use type 1: 853233.48 hectares\n",
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"Area of land use type 2: 5057682.3 hectares\n",
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"Area of land use type 3: 3878135.82 hectares\n",
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"Area of land use type 4: 91287.36 hectares\n"
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"Area of land use type 1: 794497.835274 hectares\n",
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"Area of land use type 2: 4709517.070114999 hectares\n",
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"Area of land use type 3: 3611169.2591909994 hectares\n",
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"Area of land use type 4: 85003.23956799999 hectares\n"
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]
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}
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],
@@ -269,14 +292,14 @@
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"counts = np.bincount(land_use_array_flat)\n",
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"\n",
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"# Calculate the area of each land use type\n",
272-
"pixel_area = 900 # replace this with the actual area represented by each pixel\n",
295+
"pixel_area = 838.045 # replace this with the actual area represented by each pixel\n",
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"areas = counts * pixel_area\n",
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"\n",
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"# Print the area of each land use type\n",
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"for i, area in enumerate(areas):\n",
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" print(f\"Area of land use type {i}: {area} square meters\")\n",
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"# Calculate the deforested area for each pixel\n",
279-
"deforested_area_array = deforestation_probability_array * 900 # replace 900 with the actual area represented by each pixel\n",
302+
"deforested_area_array = deforestation_probability_array * 838.045 # replace 900 with the actual area represented by each pixel\n",
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"\n",
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"# Initialize arrays to hold the total deforested area, non-deforested area, and total area for each land use type\n",
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"deforested_areas = np.zeros(4) # number of land use types\n",
@@ -338,25 +361,32 @@
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"df_hectares.to_csv(output_file_hectares, index=False)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# 5%"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 22,
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"execution_count": 14,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Area of land use type 0: 0 square meters\n",
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"Area of land use type 1: 17575208100 square meters\n",
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"Area of land use type 2: 5497432200 square meters\n",
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"Area of land use type 3: 74801823300 square meters\n",
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"Area of land use type 4: 912858300 square meters\n",
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"Area of land use type 0: 0.0 square meters\n",
381+
"Area of land use type 1: 16365350302.404999 square meters\n",
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"Area of land use type 2: 5118995075.61 square meters\n",
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"Area of land use type 3: 69652548897.165 square meters\n",
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"Area of land use type 4: 850018148.915 square meters\n",
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"Area of land use type 0: 0.0 hectares\n",
356-
"Area of land use type 1: 1757520.81 hectares\n",
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"Area of land use type 2: 549743.22 hectares\n",
358-
"Area of land use type 3: 7480182.33 hectares\n",
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"Area of land use type 4: 91285.83 hectares\n"
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"Area of land use type 1: 1636535.0302404999 hectares\n",
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"Area of land use type 2: 511899.50756099995 hectares\n",
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"Area of land use type 3: 6965254.8897164995 hectares\n",
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"Area of land use type 4: 85001.81489149999 hectares\n"
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]
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}
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],
@@ -379,14 +409,14 @@
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"counts = np.bincount(land_use_array_flat)\n",
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"\n",
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"# Calculate the area of each land use type\n",
382-
"pixel_area = 900 # replace this with the actual area represented by each pixel\n",
412+
"pixel_area = 838.045 # replace this with the actual area represented by each pixel\n",
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"areas = counts * pixel_area\n",
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"\n",
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"# Print the area of each land use type\n",
386416
"for i, area in enumerate(areas):\n",
387417
" print(f\"Area of land use type {i}: {area} square meters\")\n",
388418
"# Calculate the deforested area for each pixel\n",
389-
"deforested_area_array = deforestation_probability_array * 900 # replace 900 with the actual area represented by each pixel\n",
419+
"deforested_area_array = deforestation_probability_array * 838.045 # replace 900 with the actual area represented by each pixel\n",
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"\n",
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"# Initialize arrays to hold the total deforested area, non-deforested area, and total area for each land use type\n",
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"deforested_areas = np.zeros(4) # number of land use types\n",
@@ -447,6 +477,130 @@
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"# Export the DataFrame to a CSV file\n",
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"df_hectares.to_csv(output_file_hectares, index=False)"
449479
]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Hedgerows"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Area of land use type 0: 0.0 square meters\n",
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"Area of land use type 1: 20277853893.765 square meters\n",
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"Area of land use type 2: 2886681200.39 square meters\n",
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"Area of land use type 3: 67989778784.395 square meters\n",
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"Area of land use type 4: 850034071.77 square meters\n",
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"Area of land use type 0: 0.0 hectares\n",
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"Area of land use type 1: 2027785.3893765 hectares\n",
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"Area of land use type 2: 288668.120039 hectares\n",
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"Area of land use type 3: 6798977.8784395 hectares\n",
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"Area of land use type 4: 85003.407177 hectares\n"
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]
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}
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],
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"source": [
511+
"# Open the land use raster file\n",
512+
"with rasterio.open(simhedges_lup) as src:\n",
513+
" land_use_array = src.read(1)\n",
514+
"\n",
515+
"# Open the deforestation probability raster file\n",
516+
"with rasterio.open(simhedges_lup_prediction) as src:\n",
517+
" deforestation_probability_array = src.read(1)\n",
518+
"\n",
519+
"# Mask the NoData values\n",
520+
"'''This line is creating a masked array from the land_use_array. \n",
521+
"A masked array is a numpy array that has a separate Boolean mask that \n",
522+
"indicates whether each value in the array is valid or not. \n",
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"In this case, the masked_less function is used to create a mask \n",
524+
"that is True for all values in the array that are less than 0, \n",
525+
"and False for all other values. This effectively ignores all values less than 0 in the \n",
526+
"subsequent calculations.'''\n",
527+
"land_use_array_masked = np.ma.masked_less(land_use_array, 0)\n",
528+
"\n",
529+
"# Flatten the array\n",
530+
"land_use_array_flat = land_use_array_masked.compressed()\n",
531+
"\n",
532+
"# Count the number of pixels of each land use type\n",
533+
"counts = np.bincount(land_use_array_flat)\n",
534+
"\n",
535+
"# Calculate the area of each land use type\n",
536+
"pixel_area = 838.045 # replace this with the actual area represented by each pixel\n",
537+
"areas = counts * pixel_area\n",
538+
"\n",
539+
"# Print the area of each land use type\n",
540+
"for i, area in enumerate(areas):\n",
541+
" print(f\"Area of land use type {i}: {area} square meters\")\n",
542+
"# Calculate the deforested area for each pixel\n",
543+
"deforested_area_array = deforestation_probability_array * 838.045 # replace 900 with the actual area represented by each pixel\n",
544+
"\n",
545+
"# Initialize arrays to hold the total deforested area, non-deforested area, and total area for each land use type\n",
546+
"deforested_areas = np.zeros(4) # number of land use types\n",
547+
"non_deforested_areas = np.zeros(4) # number of land use types\n",
548+
"total_areas = np.zeros(4) # number of land use types\n",
549+
"\n",
550+
"# Loop over the land use types\n",
551+
"for i in range(4): # number of land use types\n",
552+
" # Select the deforested area for the pixels of the current land use type\n",
553+
" deforested_area = deforested_area_array[land_use_array == i+1]\n",
554+
" \n",
555+
" # Sum the deforested area\n",
556+
" deforested_areas[i] = deforested_area.sum()\n",
557+
" \n",
558+
" # Calculate the total area\n",
559+
" total_areas[i] = (land_use_array == i+1).sum() * pixel_area\n",
560+
"\n",
561+
" # Calculate the non-deforested area\n",
562+
" non_deforested_areas[i] = total_areas[i] - deforested_areas[i]\n",
563+
" # Calculate the area of each land use type in hectares\n",
564+
"areas_hectares = areas / 10000 # 1 hectare = 10,000 square meters\n",
565+
"\n",
566+
"# Print the area of each land use type in hectares\n",
567+
"for i, area in enumerate(areas_hectares):\n",
568+
" print(f\"Area of land use type {i}: {area} hectares\")\n",
569+
"\n",
570+
"# Calculate the deforested area for each pixel in hectares\n",
571+
"deforested_area_array_hectares = deforestation_probability_array * pixel_area / 10000\n",
572+
"\n",
573+
"# Initialize arrays to hold the total deforested area, non-deforested area, and total area for each land use type in hectares\n",
574+
"deforested_areas_hectares = np.zeros(4) # number of land use types\n",
575+
"non_deforested_areas_hectares = np.zeros(4) # number of land use types\n",
576+
"total_areas_hectares = np.zeros(4) # number of land use types\n",
577+
"\n",
578+
"# Loop over the land use types\n",
579+
"for i in range(4): # number of land use types\n",
580+
" # Select the deforested area for the pixels of the current land use type\n",
581+
" deforested_area_hectares = deforested_area_array_hectares[land_use_array == i+1]\n",
582+
" \n",
583+
" # Sum the deforested area\n",
584+
" deforested_areas_hectares[i] = deforested_area_hectares.sum()\n",
585+
" \n",
586+
" # Calculate the total area in hectares\n",
587+
" total_areas_hectares[i] = (land_use_array == i+1).sum() * pixel_area / 10000\n",
588+
"\n",
589+
" # Calculate the non-deforested area in hectares\n",
590+
" non_deforested_areas_hectares[i] = total_areas_hectares[i] - deforested_areas_hectares[i]\n",
591+
" \n",
592+
"# Create a DataFrame with the deforested area of each land use type in hectares\n",
593+
"df_hectares = pd.DataFrame({\n",
594+
" 'LandUseType': ['Hedgerow', 'Forest Reserve', 'Paddocks', 'Riparian Corridor'],\n",
595+
" 'DeforestedArea': deforested_areas_hectares,\n",
596+
" 'NonDeforestedArea': non_deforested_areas_hectares,\n",
597+
" 'TotalArea': total_areas_hectares\n",
598+
"})\n",
599+
"\n",
600+
"output_file_hectares = os.path.join(output_folder, \"sim-hedges-log-lut-area-hectares.csv\")\n",
601+
"# Export the DataFrame to a CSV file\n",
602+
"df_hectares.to_csv(output_file_hectares, index=False)"
603+
]
450604
}
451605
],
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"metadata": {

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