In [40]:
import geemap
import ee
import folium
import geopandas as gpd
In [41]:
ee.Initialize()
In [61]:
datadir = 'C:/Users/jtrum/world_bank/data/'
# Define the path to your GeoJSON file
aoi_path = datadir + 'luanda2clean.geojson'
# Load the GeoJSON as a Feature
aoi = geemap.geojson_to_ee(aoi_path)
# Create a map
Map = geemap.Map()
#choose a different basemap
Map.add_basemap('CartoDB.DarkMatter')
# Load the ESA Land Cover data
esa = ee.ImageCollection("ESA/WorldCover/v100").first()
# Filter the data to your AOI
esa = esa.clip(aoi)
esa_vis = {'bands': ['Map']}
# Add the ESA Land Cover layer to the map
Map.addLayer(esa, esa_vis, "ESA Land Cover")
# add legend with colors chosen from self-defined palette
Map.add_legend(builtin_legend='ESA_WorldCover',
#legend_title="ESA Land Cover",
#layer_name="ESA Land Cover",
colors=['#05450a', '#086a10', '#54a708', '#78d203', '#009900', '#c6b044', '#dcd159', '#dade48', '#fbff13','#d5n5n5', '#129279'],
labels=['trees', 'shrublands', 'grasslands', 'croplands', 'built-up', 'bare', 'snow and ice', 'water bodies', 'herbaceous vegetation', 'mangroves', 'moss and lichen']
)
#start the zoom at the center of the AOI
Map.centerObject(aoi, 10)
# Display the map
Map
Out[61]:
Map(center=[-8.980207755364754, 13.310742868350161], controls=(WidgetControl(options=['position', 'transparent…
In [62]:
df = geemap.image_area_by_group(esa, scale=1000, denominator=1e6, decimal_places=4, verbose=True)
Calculating area for group 10 ... Calculating area for group 20 ... Calculating area for group 30 ... Calculating area for group 40 ... Calculating area for group 50 ... Calculating area for group 60 ... Calculating area for group 80 ... Calculating area for group 90 ... Calculating area for group 95 ...
In [36]:
df.dtypes
Out[36]:
area float64 percentage float64 dtype: object
In [ ]:
In [63]:
df['percentage'] = df['percentage']*100
# reset the index and rename the columns to be the name of the Land Cover Type
df = df.reset_index().rename(columns={'group': 'type'})
# Create a mapping dictionary with numbers as strings
type_mapping = {
'10': 'Trees',
'20': 'Shrublands',
'30': 'Grasslands',
'40': 'Croplands',
'50': 'Built-Up',
'60': 'Barren',
'70': 'Snow and Ice',
'80': 'Water Bodies',
'90': 'Herbaceous Wetlands',
'95': 'Mangroves',
'100': 'Moss and Lichen'
}
# Use the mapping dictionary to replace values in the 'type' column
df['type'] = df['type'].replace(type_mapping)
df
Out[63]:
| type | area | percentage | |
|---|---|---|---|
| 0 | Trees | 42.6993 | 1.75 |
| 1 | Shrublands | 10.7903 | 0.44 |
| 2 | Grasslands | 1460.9425 | 59.87 |
| 3 | Croplands | 6.7740 | 0.28 |
| 4 | Built-Up | 505.1714 | 20.70 |
| 5 | Barren | 135.6323 | 5.56 |
| 6 | Water Bodies | 37.0484 | 1.52 |
| 7 | Herbaceous Wetlands | 195.5900 | 8.02 |
| 8 | Mangroves | 45.3820 | 1.86 |
In [114]:
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
# Make a color dictionary of the colors from the ESA Land Cover legend
color_dict = {
'Trees': '#006400',
'Shrublands': '#FFBB22',
'Grasslands': '#FFFF4C',
'Croplands': '#F096FF',
'Built-Up': '#FA0000',
'Barren': '#B4B4B4',
'Snow and Ice': '#F0F0F0',
'Water Bodies': '#0064C8',
'Herbaceous Wetlands': '#0096A0',
'Mangroves': '#00CF75',
'Moss and Lichen': '#FAE6A0'
}
# Sort the DataFrame by percentage in descending order
df = df.sort_values('percentage', ascending=False)
# Set the figure size
plt.figure(figsize=(10, 10))
# Create the barplot
sns.barplot(x='percentage', y='type', data=df, palette=color_dict)
# Add labels at the end of each bar
for i, v in enumerate(df['percentage']):
plt.text(v + 1, i, str(round(v, 2)) + '%', va='center', color='black', fontweight='bold')
# Customize labels and title
plt.xlabel('Percentage')
plt.ylabel('Land Cover Type')
plt.title('Land Cover in Luanda, Angola')
# Show the plot
plt.show()
In [81]:
# Define the location (Luanda, Angola) and Area of Interest (AOI)
# Replace the coordinates in the Point and Polygon geometries with those for Luanda, Angola.
Luanda = ee.Geometry.Point([13.2343, -8.8383])
Luanda_Extent = ee.Geometry.Polygon(
[[[13.1643, -8.9421],
[13.1643, -8.7278],
[13.3597, -8.7278],
[13.3597, -8.9421]]])
# Define the city name and convert it to lowercase
city_name = "Luanda"
id = "Luanda"
# Create Water Mask
WaterMask = ee.Image('COPERNICUS/S1_GRD')
waterMask = WaterMask.select('transition')
blank = ee.Image(0)
nonWater = blank.addBands(waterMask).unmask().select('transition').eq(0).rename('non_water')
# Create Slope Mask
srtm = ee.Image('USGS/SRTMGL1_003')
slope = ee.Terrain.slope(srtm)
Grade15 = slope.gt(15)
gtGrade15 = Grade15.updateMask(Grade15.neq(0))
slopeMask = gtGrade15.clip(Luanda_Extent).unmask(0).subtract(1).multiply(-1)
# Load Sentinel-1 C-band SAR Ground Range collection (log scaling, VV co-polar)
collection = (ee.ImageCollection('COPERNICUS/S1_GRD')
.filterBounds(Luanda)
.filter(ee.Filter.listContains('transmitterReceiverPolarisation', 'VV'))
.filter(ee.Filter.eq('orbitProperties_pass', 'DESCENDING'))
.select('VV'))
# Filter by date
during_flood_collection = collection.filterDate('2017-03-14', '2017-03-29')
during_flood = (during_flood_collection.first()
.clip(Luanda_Extent)
.updateMask(nonWater)
.updateMask(slopeMask))
before_flood_collection = collection.filterDate('2016-08-01', '2016-09-30')
before_flood = (before_flood_collection.median()
.clip(Luanda_Extent)
.updateMask(nonWater)
.updateMask(slopeMask))
# Define a smoothing box (30m is 3x size of S1 pixel)
smoothing_box = 30
# Create difference images (during flood - before flood)
difference_smoothed = (during_flood
.focal_median(smoothing_box, 'square', 'meters')
.subtract(before_flood.focal_median(smoothing_box, 'square', 'meters')))
difference_raw = during_flood.subtract(before_flood)
# Resample difference raster
difference_smoothed_resampled = (difference_smoothed
.resample('bilinear')
.reproject(crs=difference_smoothed.projection()))
# Calculate Upper and Lower Thresholds
UpperThreshold = (difference_smoothed_resampled
.reduceRegion(reducer=ee.Reducer.percentile([6]),
geometry=Luanda_Extent,
scale=10,
maxPixels=1e9)
.get('VV'))
LowerThreshold = (difference_smoothed_resampled
.reduceRegion(reducer=ee.Reducer.percentile([94]),
geometry=Luanda_Extent,
scale=10,
maxPixels=1e9)
.get('VV'))
print(UpperThreshold.getInfo())
print(LowerThreshold.getInfo())
# Make Difference Darker Rasters
difference_darker_smoothed_thresholded = difference_smoothed_resampled.lt(UpperThreshold)
difference_darker_thresholded = difference_raw.lt(UpperThreshold)
# Make Difference Brighter Rasters
difference_brighter_smoothed_thresholded = difference_smoothed_resampled.gt(LowerThreshold)
difference_brighter_thresholded = difference_raw.gt(LowerThreshold)
# Display map (you can visualize it in the GEE Code Editor)
Map = ee.Map()
Map.centerObject(Luanda, 13)
Map.addLayer(before_flood, {'min': -30, 'max': 0}, 'Before Flood', 1)
Map.addLayer(during_flood, {'min': -30, 'max': 0}, 'During Flood', 1)
Map.addLayer(difference_raw, {'min': -10, 'max': 10}, 'Difference - Raw', 0)
Map.addLayer(difference_smoothed, {'min': -10, 'max': 10}, 'Difference - Smoothed', 0)
Map.addLayer(difference_smoothed_resampled, {'min': -10, 'max': 10}, 'Difference - Smoothed & Resampled', 1)
# Save maps to Google Drive or export as assets as needed
--------------------------------------------------------------------------- HttpError Traceback (most recent call last) File c:\Users\jtrum\miniconda3\envs\wash_scan\lib\site-packages\ee\data.py:352, in _execute_cloud_call(call, num_retries) 351 try: --> 352 return call.execute(num_retries=num_retries) 353 except googleapiclient.errors.HttpError as e: File c:\Users\jtrum\miniconda3\envs\wash_scan\lib\site-packages\googleapiclient\_helpers.py:130, in positional.<locals>.positional_decorator.<locals>.positional_wrapper(*args, **kwargs) 129 logger.warning(message) --> 130 return wrapped(*args, **kwargs) File c:\Users\jtrum\miniconda3\envs\wash_scan\lib\site-packages\googleapiclient\http.py:938, in HttpRequest.execute(self, http, num_retries) 937 if resp.status >= 300: --> 938 raise HttpError(resp, content, uri=self.uri) 939 return self.postproc(resp, content) HttpError: <HttpError 400 when requesting https://earthengine.googleapis.com/v1/projects/earthengine-legacy/value:compute?prettyPrint=false&alt=json returned "Image.load: Asset 'COPERNICUS/S1_GRD' is not an Image.". Details: "Image.load: Asset 'COPERNICUS/S1_GRD' is not an Image."> During handling of the above exception, another exception occurred: EEException Traceback (most recent call last) Cell In[81], line 64 52 UpperThreshold = (difference_smoothed_resampled 53 .reduceRegion(reducer=ee.Reducer.percentile([6]), 54 geometry=Luanda_Extent, 55 scale=10, 56 maxPixels=1e9) 57 .get('VV')) 58 LowerThreshold = (difference_smoothed_resampled 59 .reduceRegion(reducer=ee.Reducer.percentile([94]), 60 geometry=Luanda_Extent, 61 scale=10, 62 maxPixels=1e9) 63 .get('VV')) ---> 64 print(UpperThreshold.getInfo()) 65 print(LowerThreshold.getInfo()) 66 # Make Difference Darker Rasters File c:\Users\jtrum\miniconda3\envs\wash_scan\lib\site-packages\ee\computedobject.py:103, in ComputedObject.getInfo(self) 97 def getInfo(self) -> Optional[Any]: 98 """Fetch and return information about this object. 99 100 Returns: 101 The object can evaluate to anything. 102 """ --> 103 return data.computeValue(self) File c:\Users\jtrum\miniconda3\envs\wash_scan\lib\site-packages\ee\data.py:971, in computeValue(obj) 968 body = {'expression': serializer.encode(obj, for_cloud_api=True)} 969 _maybe_populate_workload_tag(body) --> 971 return _execute_cloud_call( 972 _get_cloud_projects() 973 .value() 974 .compute(body=body, project=_get_projects_path(), prettyPrint=False) 975 )['result'] File c:\Users\jtrum\miniconda3\envs\wash_scan\lib\site-packages\ee\data.py:354, in _execute_cloud_call(call, num_retries) 352 return call.execute(num_retries=num_retries) 353 except googleapiclient.errors.HttpError as e: --> 354 raise _translate_cloud_exception(e) EEException: Image.load: Asset 'COPERNICUS/S1_GRD' is not an Image.
In [91]:
import spyndex
import geemap
import ee , eemont
In [103]:
In [112]:
import ee
import geemap
# Define the path to your GeoJSON file
datadir = 'C:/Users/jtrum/world_bank/data/'
aoi_path = datadir + 'luanda2clean.geojson'
# Load the GeoJSON as a FeatureCollection
aoi = ee.FeatureCollection(aoi_path)
# Create a map
Map = geemap.Map()
Map.centerObject(aoi, 10)
# Load the Sentinel-2 image collection
sentinel = ee.ImageCollection('COPERNICUS/S2_SR_HARMONIZED') \
.filterBounds(aoi) \
.filterDate('2019-01-01', '2019-12-31') \
.filterMetadata('CLOUDY_PIXEL_PERCENTAGE', 'less_than', 10) \
.first() \
.multiply(0.0001)
# Compute the NDVI index
ndvi = sentinel.normalizedDifference(['B8', 'B4'])
# Add the NDVI band to the image
img = sentinel.addBands(ndvi.rename('NDVI'))
# Add the NDVI layer to the map
Map.addLayer(img.select('NDVI'), {"min": -1, "max": 1, "palette": ['red', 'white', 'green']}, "NDVI")
# Display the map
Map
--------------------------------------------------------------------------- HttpError Traceback (most recent call last) File c:\Users\jtrum\miniconda3\envs\wash_scan\lib\site-packages\ee\data.py:352, in _execute_cloud_call(call, num_retries) 351 try: --> 352 return call.execute(num_retries=num_retries) 353 except googleapiclient.errors.HttpError as e: File c:\Users\jtrum\miniconda3\envs\wash_scan\lib\site-packages\googleapiclient\_helpers.py:130, in positional.<locals>.positional_decorator.<locals>.positional_wrapper(*args, **kwargs) 129 logger.warning(message) --> 130 return wrapped(*args, **kwargs) File c:\Users\jtrum\miniconda3\envs\wash_scan\lib\site-packages\googleapiclient\http.py:938, in HttpRequest.execute(self, http, num_retries) 937 if resp.status >= 300: --> 938 raise HttpError(resp, content, uri=self.uri) 939 return self.postproc(resp, content) HttpError: <HttpError 400 when requesting https://earthengine.googleapis.com/v1/projects/earthengine-legacy/value:compute?prettyPrint=false&alt=json returned "Collection.loadTable: Collection asset 'C:/Users/jtrum/world_bank/data/luanda2clean.geojson' not found.". Details: "Collection.loadTable: Collection asset 'C:/Users/jtrum/world_bank/data/luanda2clean.geojson' not found."> During handling of the above exception, another exception occurred: EEException Traceback (most recent call last) Cell In[112], line 13 11 # Create a map 12 Map = geemap.Map() ---> 13 Map.centerObject(aoi, 10) 15 # Load the Sentinel-2 image collection 16 sentinel = ee.ImageCollection('COPERNICUS/S2_SR_HARMONIZED') \ 17 .filterBounds(aoi) \ 18 .filterDate('2019-01-01', '2019-12-31') \ 19 .filterMetadata('CLOUDY_PIXEL_PERCENTAGE', 'less_than', 10) \ 20 .first() \ 21 .multiply(0.0001) File c:\Users\jtrum\miniconda3\envs\wash_scan\lib\site-packages\geemap\geemap.py:537, in Map.center_object(self, ee_object, zoom) 535 raise Exception("Zoom must be an integer.") 536 else: --> 537 centroid = geometry.centroid(maxError=maxError).getInfo()["coordinates"] 538 lat = centroid[1] 539 lon = centroid[0] File c:\Users\jtrum\miniconda3\envs\wash_scan\lib\site-packages\ee\computedobject.py:103, in ComputedObject.getInfo(self) 97 def getInfo(self) -> Optional[Any]: 98 """Fetch and return information about this object. 99 100 Returns: 101 The object can evaluate to anything. 102 """ --> 103 return data.computeValue(self) File c:\Users\jtrum\miniconda3\envs\wash_scan\lib\site-packages\ee\data.py:971, in computeValue(obj) 968 body = {'expression': serializer.encode(obj, for_cloud_api=True)} 969 _maybe_populate_workload_tag(body) --> 971 return _execute_cloud_call( 972 _get_cloud_projects() 973 .value() 974 .compute(body=body, project=_get_projects_path(), prettyPrint=False) 975 )['result'] File c:\Users\jtrum\miniconda3\envs\wash_scan\lib\site-packages\ee\data.py:354, in _execute_cloud_call(call, num_retries) 352 return call.execute(num_retries=num_retries) 353 except googleapiclient.errors.HttpError as e: --> 354 raise _translate_cloud_exception(e) EEException: Collection.loadTable: Collection asset 'C:/Users/jtrum/world_bank/data/luanda2clean.geojson' not found.
In [113]:
aoi_path = datadir + 'luanda2clean.geojson'
aoi = ee.FeatureCollection(aoi)
coords = aoi.geometry().coordinates().getInfo()[0]
roi = ee.Geometry.Point([13.230162, -8.814921])
sentinel = ee.ImageCollection('COPERNICUS/S2_SR_HARMONIZED')\
.filterBounds(roi)\
.filterDate('2019-01-01', '2019-12-31')\
.filterMetadata('CLOUDY_PIXEL_PERCENTAGE', 'less_than', 10)\
.first()\
.multiply(0.0001)
idx = spyndex.computeIndex(
index = ['NDVI'],
params = {
"R": sentinel["B4"],
"N": sentinel["B8"],
"G": sentinel["B3"],
"B": sentinel["B2"]
}
)
img = sentinel.addBands(idx)
Map = geemap.Map()
Map.centerObject(img, 10)
Map.addLayer(img['NDVI'], {"min":-1, "max":1, "palette":['red', 'white', 'green']}, "NDVI")
Map
--------------------------------------------------------------------------- HttpError Traceback (most recent call last) File c:\Users\jtrum\miniconda3\envs\wash_scan\lib\site-packages\ee\data.py:352, in _execute_cloud_call(call, num_retries) 351 try: --> 352 return call.execute(num_retries=num_retries) 353 except googleapiclient.errors.HttpError as e: File c:\Users\jtrum\miniconda3\envs\wash_scan\lib\site-packages\googleapiclient\_helpers.py:130, in positional.<locals>.positional_decorator.<locals>.positional_wrapper(*args, **kwargs) 129 logger.warning(message) --> 130 return wrapped(*args, **kwargs) File c:\Users\jtrum\miniconda3\envs\wash_scan\lib\site-packages\googleapiclient\http.py:938, in HttpRequest.execute(self, http, num_retries) 937 if resp.status >= 300: --> 938 raise HttpError(resp, content, uri=self.uri) 939 return self.postproc(resp, content) HttpError: <HttpError 400 when requesting https://earthengine.googleapis.com/v1/projects/earthengine-legacy/value:compute?prettyPrint=false&alt=json returned "Collection.loadTable: Collection asset 'C:/Users/jtrum/world_bank/data/luanda2clean.geojson' not found.". Details: "Collection.loadTable: Collection asset 'C:/Users/jtrum/world_bank/data/luanda2clean.geojson' not found."> During handling of the above exception, another exception occurred: EEException Traceback (most recent call last) Cell In[113], line 3 1 aoi_path = datadir + 'luanda2clean.geojson' 2 aoi = ee.FeatureCollection(aoi) ----> 3 coords = aoi.geometry().coordinates().getInfo()[0] 5 roi = ee.Geometry.Point([13.230162, -8.814921]) 7 sentinel = ee.ImageCollection('COPERNICUS/S2_SR_HARMONIZED')\ 8 .filterBounds(roi)\ 9 .filterDate('2019-01-01', '2019-12-31')\ 10 .filterMetadata('CLOUDY_PIXEL_PERCENTAGE', 'less_than', 10)\ 11 .first()\ 12 .multiply(0.0001) File c:\Users\jtrum\miniconda3\envs\wash_scan\lib\site-packages\ee\computedobject.py:103, in ComputedObject.getInfo(self) 97 def getInfo(self) -> Optional[Any]: 98 """Fetch and return information about this object. 99 100 Returns: 101 The object can evaluate to anything. 102 """ --> 103 return data.computeValue(self) File c:\Users\jtrum\miniconda3\envs\wash_scan\lib\site-packages\ee\data.py:971, in computeValue(obj) 968 body = {'expression': serializer.encode(obj, for_cloud_api=True)} 969 _maybe_populate_workload_tag(body) --> 971 return _execute_cloud_call( 972 _get_cloud_projects() 973 .value() 974 .compute(body=body, project=_get_projects_path(), prettyPrint=False) 975 )['result'] File c:\Users\jtrum\miniconda3\envs\wash_scan\lib\site-packages\ee\data.py:354, in _execute_cloud_call(call, num_retries) 352 return call.execute(num_retries=num_retries) 353 except googleapiclient.errors.HttpError as e: --> 354 raise _translate_cloud_exception(e) EEException: Collection.loadTable: Collection asset 'C:/Users/jtrum/world_bank/data/luanda2clean.geojson' not found.
In [92]:
sentinel = ee.ImageCollection('COPERNICUS/S2_SR_HARMONIZED')\
.filterBounds(roi)\
.filterDate('2019-01-01', '2019-12-31')\
.filterMetadata('CLOUDY_PIXEL_PERCENTAGE', 'less_than', 10)\
.first()\
.multiply(0.0001)
In [95]:
idx = spyndex.computeIndex(index = 'NDVI',
params = {"R": sentinel["B4"],
"N": sentinel["B8"],
"G": sentinel["B3"],
"B": sentinel["B2"]
})
In [96]:
img = sentinel.addBands(idx)
In [98]:
Map = geemap.Map()
Map.centerObject(img, 10)
Map.addLayer(img['NDVI'], {"min":-1, "max":1, "palette":['red', 'white', 'blue']}, "NDVI")
Map
--------------------------------------------------------------------------- HttpError Traceback (most recent call last) File c:\Users\jtrum\miniconda3\envs\wash_scan\lib\site-packages\ee\data.py:352, in _execute_cloud_call(call, num_retries) 351 try: --> 352 return call.execute(num_retries=num_retries) 353 except googleapiclient.errors.HttpError as e: File c:\Users\jtrum\miniconda3\envs\wash_scan\lib\site-packages\googleapiclient\_helpers.py:130, in positional.<locals>.positional_decorator.<locals>.positional_wrapper(*args, **kwargs) 129 logger.warning(message) --> 130 return wrapped(*args, **kwargs) File c:\Users\jtrum\miniconda3\envs\wash_scan\lib\site-packages\googleapiclient\http.py:938, in HttpRequest.execute(self, http, num_retries) 937 if resp.status >= 300: --> 938 raise HttpError(resp, content, uri=self.uri) 939 return self.postproc(resp, content) HttpError: <HttpError 400 when requesting https://earthengine.googleapis.com/v1/projects/earthengine-legacy/value:compute?prettyPrint=false&alt=json returned "GeometryConstructors.Polygon, argument 'coordinates': Invalid type. Expected type: List<Object>. Actual type: FeatureCollection.". Details: "GeometryConstructors.Polygon, argument 'coordinates': Invalid type. Expected type: List<Object>. Actual type: FeatureCollection."> During handling of the above exception, another exception occurred: EEException Traceback (most recent call last) Cell In[98], line 2 1 Map = geemap.Map() ----> 2 Map.centerObject(img, 10) 3 Map.addLayer(img['NDVI'], {"min":-1, "max":1, "palette":['red', 'white', 'blue']}, "NDVI") 4 Map File c:\Users\jtrum\miniconda3\envs\wash_scan\lib\site-packages\geemap\geemap.py:537, in Map.center_object(self, ee_object, zoom) 535 raise Exception("Zoom must be an integer.") 536 else: --> 537 centroid = geometry.centroid(maxError=maxError).getInfo()["coordinates"] 538 lat = centroid[1] 539 lon = centroid[0] File c:\Users\jtrum\miniconda3\envs\wash_scan\lib\site-packages\ee\computedobject.py:103, in ComputedObject.getInfo(self) 97 def getInfo(self) -> Optional[Any]: 98 """Fetch and return information about this object. 99 100 Returns: 101 The object can evaluate to anything. 102 """ --> 103 return data.computeValue(self) File c:\Users\jtrum\miniconda3\envs\wash_scan\lib\site-packages\ee\data.py:971, in computeValue(obj) 968 body = {'expression': serializer.encode(obj, for_cloud_api=True)} 969 _maybe_populate_workload_tag(body) --> 971 return _execute_cloud_call( 972 _get_cloud_projects() 973 .value() 974 .compute(body=body, project=_get_projects_path(), prettyPrint=False) 975 )['result'] File c:\Users\jtrum\miniconda3\envs\wash_scan\lib\site-packages\ee\data.py:354, in _execute_cloud_call(call, num_retries) 352 return call.execute(num_retries=num_retries) 353 except googleapiclient.errors.HttpError as e: --> 354 raise _translate_cloud_exception(e) EEException: GeometryConstructors.Polygon, argument 'coordinates': Invalid type. Expected type: List<Object>. Actual type: FeatureCollection.
In [109]:
import ee
import geopandas as gpd
import geemap
# Define the path to your GeoJSON file
datadir = 'C:/Users/jtrum/world_bank/data/'
aoi_path = datadir + 'luanda2clean.geojson'
# Load the GeoJSON file locally using geopandas
aoi_gdf = gpd.read_file(aoi_path)
# Convert the geopandas GeoDataFrame to a FeatureCollection for Earth Engine
aoi = ee.FeatureCollection(aoi_gdf.__geo_interface__)
# Rest of your code remains the same
sentinel = ee.ImageCollection('COPERNICUS/S2_SR_HARMONIZED')\
.filterBounds(aoi)\
.filterDate('2019-01-01', '2019-12-31')\
.filterMetadata('CLOUDY_PIXEL_PERCENTAGE', 'less_than', 10)\
.first()\
.multiply(0.0001)
idx = spyndex.computeIndex(index=['VARI', 'NDWI', 'NDVI'],
params={"R": sentinel.select("B4"),
"N": sentinel.select("B8"),
"G": sentinel.select("B3"),
"B": sentinel.select("B2")
})
img = sentinel.addBands(idx)
Map = geemap.Map()
Map.centerObject(img, 10)
Map.addLayer(img.select('NDVI'), {"min": -1, "max": 1, "palette": ['red', 'white', 'blue']}, "NDVI")
Map.addLayer(img.select('NDWI'), {"min": -1, "max": 1, "palette": ['red', 'white', 'blue']}, "NDWI")
Map.addLayer(img.select('VARI'), {"min": -1, "max": 1, "palette": ['red', 'white', 'blue']}, "VARI")
Map
--------------------------------------------------------------------------- HttpError Traceback (most recent call last) File c:\Users\jtrum\miniconda3\envs\wash_scan\lib\site-packages\ee\data.py:352, in _execute_cloud_call(call, num_retries) 351 try: --> 352 return call.execute(num_retries=num_retries) 353 except googleapiclient.errors.HttpError as e: File c:\Users\jtrum\miniconda3\envs\wash_scan\lib\site-packages\googleapiclient\_helpers.py:130, in positional.<locals>.positional_decorator.<locals>.positional_wrapper(*args, **kwargs) 129 logger.warning(message) --> 130 return wrapped(*args, **kwargs) File c:\Users\jtrum\miniconda3\envs\wash_scan\lib\site-packages\googleapiclient\http.py:938, in HttpRequest.execute(self, http, num_retries) 937 if resp.status >= 300: --> 938 raise HttpError(resp, content, uri=self.uri) 939 return self.postproc(resp, content) HttpError: <HttpError 400 when requesting https://earthengine.googleapis.com/v1/projects/earthengine-legacy/maps?fields=name&alt=json returned "Image.select: Pattern 'NDVI' did not match any bands.". Details: "Image.select: Pattern 'NDVI' did not match any bands."> During handling of the above exception, another exception occurred: EEException Traceback (most recent call last) Cell In[109], line 34 32 Map = geemap.Map() 33 Map.centerObject(img, 10) ---> 34 Map.addLayer(img.select('NDVI'), {"min": -1, "max": 1, "palette": ['red', 'white', 'blue']}, "NDVI") 35 Map File c:\Users\jtrum\miniconda3\envs\wash_scan\lib\site-packages\geemap\geemap.py:447, in Map.add_ee_layer(self, ee_object, vis_params, name, shown, opacity) 445 layer_count = len(self.layers) 446 name = "Layer " + str(layer_count + 1) --> 447 tile_layer = EELeafletTileLayer(ee_object, vis_params, name, shown, opacity) 449 layer = self.find_layer(name=name) 450 if layer is not None: File c:\Users\jtrum\miniconda3\envs\wash_scan\lib\site-packages\geemap\ee_tile_layers.py:134, in EELeafletTileLayer.__init__(self, ee_object, vis_params, name, shown, opacity, **kwargs) 116 def __init__( 117 self, 118 ee_object, (...) 123 **kwargs, 124 ): 125 """Initialize the ipyleaflet tile layer. 126 127 Args: (...) 132 opacity (float, optional): The layer's opacity represented as a number between 0 and 1. Defaults to 1. 133 """ --> 134 self.url_format = _get_tile_url_format( 135 ee_object, _validate_vis_params(vis_params) 136 ) 137 super().__init__( 138 url=self.url_format, 139 attribution="Google Earth Engine", (...) 144 **kwargs, 145 ) File c:\Users\jtrum\miniconda3\envs\wash_scan\lib\site-packages\geemap\ee_tile_layers.py:18, in _get_tile_url_format(ee_object, vis_params) 16 def _get_tile_url_format(ee_object, vis_params): 17 image = _ee_object_to_image(ee_object, vis_params) ---> 18 map_id_dict = ee.Image(image).getMapId(vis_params) 19 return map_id_dict["tile_fetcher"].url_format File c:\Users\jtrum\miniconda3\envs\wash_scan\lib\site-packages\ee\image.py:126, in Image.getMapId(self, vis_params) 124 vis_image, request = self._apply_visualization(vis_params) 125 request['image'] = vis_image --> 126 response = data.getMapId(request) 127 response['image'] = self 128 return response File c:\Users\jtrum\miniconda3\envs\wash_scan\lib\site-packages\ee\data.py:662, in getMapId(params) 657 queryParams = { 658 'fields': 'name', 659 'body': request, 660 } 661 _maybe_populate_workload_tag(queryParams) --> 662 result = _execute_cloud_call( 663 _get_cloud_projects() 664 .maps() 665 .create(parent=_get_projects_path(), **queryParams) 666 ) 667 map_name = result['name'] 668 url_format = '%s/%s/%s/tiles/{z}/{x}/{y}' % ( 669 _tile_base_url, _cloud_api_utils.VERSION, map_name) File c:\Users\jtrum\miniconda3\envs\wash_scan\lib\site-packages\ee\data.py:354, in _execute_cloud_call(call, num_retries) 352 return call.execute(num_retries=num_retries) 353 except googleapiclient.errors.HttpError as e: --> 354 raise _translate_cloud_exception(e) EEException: Image.select: Pattern 'NDVI' did not match any bands.