Comments (5)
giswqs
commented on June 19, 2024
Have you tried using the zonal_stats function?
https://geemap.org/notebooks/12_zonal_statistics/
from geemap.
Remolkeman
commented on June 19, 2024
Hi!
Following your suggestion, I tried the zonal_stats function, and the following are the results
No for loop:
import ee
import geemap
import os
ee.Authenticate()
ee.Initialize()
def calculateNDVI(image):
ndvi = image.normalizedDifference(["B8", "B4"])
return ndvi.rename("NDVI")
geometry = ee.Geometry.Polygon(
[
[
[-6.310153895594499, 36.89932544542902],
[-6.30912392733278, 36.89637395041979],
[-6.308093959071061, 36.892461327607315],
[-6.304574900843522, 36.89294183594877],
[-6.298480921961686, 36.894108772184815],
[-6.293674403406999, 36.895618898477814],
[-6.294618540980241, 36.89726627490264],
[-6.297536784388444, 36.903855425007976],
[-6.301399165369889, 36.90275727283513],
[-6.305519038416764, 36.90207091970124],
[-6.310411387659928, 36.90124728779126],
]
]
)
collection = (
ee.ImageCollection("COPERNICUS/S2_HARMONIZED")
.filterBounds(geometry)
.filterDate("2015-01-01", "2022-01-15")
.filterMetadata("CLOUDY_PIXEL_PERCENTAGE", "less_than", 10)
)
ndviCollection = collection.map(calculateNDVI)
geometry_feature = ee.FeatureCollection(geometry)
scale = 10
out_ndvi_stats = os.path.join(os.path.expanduser("~"), "Downloads", "ndvi_stats.csv")
geemap.zonal_stats(ndviCollection, geometry_feature, out_ndvi_stats, stat_type='MEAN', scale=scale)
geemap.create_download_link(out_ndvi_stats)
In this case, it does output a csv with the mean values properly and not empty.
"
Computing statistics ...
Generating URL ...
Downloading data from https://earthengine.googleapis.com/v1/projects/earthengine-legacy/tables/34324237bcb4dd798c95c943ae4842e8-3f0985e16b05496e4c0d4373a875a4c0:getFeatures
Please wait ...
Data downloaded to C:\Users\user\Downloads\ndvi_stats.csv
"
However, when using a for loop, it does not work properly.
import ee
import geemap
import os
ee.Authenticate()
ee.Initialize()
def calculateNDVI(image):
ndvi = image.normalizedDifference(["B8", "B4"])
return ndvi.rename("NDVI")
geometry = ee.Geometry.Polygon(
[
[
[-6.310153895594499, 36.89932544542902],
[-6.30912392733278, 36.89637395041979],
[-6.308093959071061, 36.892461327607315],
[-6.304574900843522, 36.89294183594877],
[-6.298480921961686, 36.894108772184815],
[-6.293674403406999, 36.895618898477814],
[-6.294618540980241, 36.89726627490264],
[-6.297536784388444, 36.903855425007976],
[-6.301399165369889, 36.90275727283513],
[-6.305519038416764, 36.90207091970124],
[-6.310411387659928, 36.90124728779126],
]
]
)
collection = (
ee.ImageCollection("COPERNICUS/S2_HARMONIZED")
.filterBounds(geometry)
.filterDate("2015-01-01", "2022-01-15")
.filterMetadata("CLOUDY_PIXEL_PERCENTAGE", "less_than", 10)
)
ndviCollection = collection.map(calculateNDVI)
geometry_feature = ee.FeatureCollection(geometry)
scale = 10
out_dir = os.path.join(os.path.expanduser("~"), "Downloads")
if not os.path.exists(out_dir):
os.makedirs(out_dir)
for i, image in enumerate(ndviCollection.toList(ndviCollection.size())):
image = ee.Image(image)
out_ndvi_stats = os.path.join(out_dir, f"ndvi_stats_{i}.csv")
geemap.zonal_stats(image, geometry_feature, out_ndvi_stats, stat_type='MEAN', scale=scale)
"
ConnectionError: ('Connection aborted.', RemoteDisconnected('Remote end closed connection without response'))"
}
"
What I am looking for is to extract the ndvi value of each pixel in each image of the collection inside the designed geometry. As I could check, this function returns mean, std, sum, etc. It seems that something is wrong in the for loop, and I might be failing to properly program it.
from geemap.
giswqs
commented on June 19, 2024
Use collection.toBands() instead of a for loop.
import ee
import geemap
import os
ee.Authenticate()
ee.Initialize()
def calculateNDVI(image):
ndvi = image.normalizedDifference(["B8", "B4"])
return ndvi.rename("NDVI")
geometry = ee.Geometry.Polygon(
[
[
[-6.310153895594499, 36.89932544542902],
[-6.30912392733278, 36.89637395041979],
[-6.308093959071061, 36.892461327607315],
[-6.304574900843522, 36.89294183594877],
[-6.298480921961686, 36.894108772184815],
[-6.293674403406999, 36.895618898477814],
[-6.294618540980241, 36.89726627490264],
[-6.297536784388444, 36.903855425007976],
[-6.301399165369889, 36.90275727283513],
[-6.305519038416764, 36.90207091970124],
[-6.310411387659928, 36.90124728779126],
]
]
)
collection = (
ee.ImageCollection("COPERNICUS/S2_HARMONIZED")
.filterBounds(geometry)
.filterDate("2015-01-01", "2022-01-15")
.filterMetadata("CLOUDY_PIXEL_PERCENTAGE", "less_than", 10)
)
ndviCollection = collection.map(calculateNDVI)
geometry_feature = ee.FeatureCollection(geometry)
scale = 10
out_dir = os.path.join(os.path.expanduser("~"), "Downloads")
if not os.path.exists(out_dir):
os.makedirs(out_dir)
image = ndviCollection.toBands()
out_ndvi_stats = "ndvi.csv"
geemap.zonal_stats(image, geometry_feature, out_ndvi_stats, stat_type='MEAN', scale=scale)from geemap.
Remolkeman
commented on June 19, 2024
Hi Qiusheng Wu!
It is very likely that I have not properly explained the problem I am facing. To that end, I want to give you some context to help with your understanding:
I am currently doing a final master thesis, in which I am applying common remote sensing techniques such as NDVI, NDMI, SAVI..... on several delimited areas within the Guadalquivir estuary(Spain). Thus far, I have created a time series that represents the mean value of the indices. What I am currently trying to do is to use each satellite image within these time series and represent the value of each pixel for NDVI, NDMI, etc. in a histogram, and thus check the distribution of the data outside a mean. For example, imagine a Sentinel-2 image for the date 25/12/2015. In this case, I want to know the NDVI value of each pixel in that defined area, represent it in a histogram, and check the distribution of this data for that particular day of that year. In the case of my study, I would have approximately 2000+ images between the areas. Therefore, I want to apply a for loop that uses these NDVI pixel values and represent them in a histogram for each of these images and, if possible, save the NDVI(I write NDVI, but it would be for each index) pixel values in a csv. In short, I am not looking for mean values, but the pixel values.
When I tried to apply this loop, I encountered the problem reported above, returning an empty output for using the loop.
from geemap.
Remolkeman
commented on June 19, 2024
up!
from geemap.
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