geemap.ee_to_numpy() Does not work

[PERCY-ESCOBAR]

• geemap version: 0.8.1
• Python version: 3.8
• Operating System: windows 10

Description

Dear profesor Qiusheng Wu. I am trying to extract a cloud masked image as numpy array but it does not work. The output message is the following:

<<Image.sampleRectangle: Fully masked pixels / pixels outside of the image footprint when sampling band 'B4' with no default value set. Note that calling sampleRectangle() on an image after ee.Image.clip() may result in a sampling bounding box outside the geometry passed to clip().>>

Thank you very much for your hep.

What I Did

Paste the command(s) you ran and the output.
If there was a crash, please include the traceback here.

[giswqs]

You probably need to set the default_value parameter for the ee_to_numpy() function, e.g., default_value=0

https://geemap.org/common/#geemap.common.ee_to_numpy

[PERCY-ESCOBAR]

Thank you for your answer. It works by setting the default_value = 0. However, the output region is a rectangular area not the limited area. Is there the way how to get only the limited region??

Thanks again for your time.

[giswqs]

Did you try masking out the area of the image outside your ROI before using the ee_to_numpy() function?

[PERCY-ESCOBAR]

Yes. I did. The same output happens when I try to export as Geo TIFF, the region exported is a rectangular area not the ROI limited which is an irregular polygon.

Thank you!

[giswqs]

Display your l8sr_coll_CloudMasked.first() on the Map. If it is not masked to your ROI, the output image might not be masked.

[PERCY-ESCOBAR]

This is the l8sr_coll_CloudMasked.first() displayed on the Map.

[giswqs]

I am very confused. All three images (plt, qigs, geemap) are all different. Not sure what you are trying to get.

[PERCY-ESCOBAR]

Yeah. I have been trying to get a masked landsat image clipping by irregular polygon ROI. The exported images is always rectangular area as it shows on QGIS and plt.. Here I display my ROI on the Map.

Thank you!

[giswqs]

Please share your source code for debugging.

[PERCY-ESCOBAR]

import ee
import os
import geemap

Map = geemap.Map()
Map

# roi
tambopata_roi = ee.Geometry.Polygon([[-69.152775, -12.596743],
    [-69.145598, -12.599121],
    [-69.138388, -12.600361],
    [-69.134336, -12.600964],
    [-69.119736, -12.601643],
    [-69.11153, -12.602179],
    [-69.100023, -12.603385],
    [-69.092846, -12.604022],
    [-69.084708, -12.603016],
    [-69.078493, -12.600369],
    [-69.071955, -12.59593],
    [-69.068762, -12.590502],
    [-69.06732, -12.584236],
    [-69.062925, -12.58494],
    [-69.064882, -12.590938],
    [-69.067801, -12.594757],
    [-69.071337, -12.598912],
    [-69.076204, -12.603042],
    [-69.082453, -12.605487],
    [-69.090581, -12.607586],
    [-69.09853, -12.607485],
    [-69.108651, -12.606074],
    [-69.122635, -12.60439],
    [-69.132487, -12.603423],
    [-69.139853, -12.603289],
    [-69.154267, -12.600152],
    [-69.152775, -12.596743]])

# Cloud, snow, and shallow mask function
def maskL8sr(image):
  # Bits 3 and 5 are cloud shadow and cloud, respectively.
  cloudShadowBitMask = 1 << 3
  cloudsBitMask = 1 << 5

  # Get the pixel QA band.
  qa = image.select('pixel_qa')

  # Both flags should be set to zero, indicating clear conditions.
  mask = qa.bitwiseAnd(cloudShadowBitMask).eq(0) \
      .And(qa.bitwiseAnd(cloudsBitMask).eq(0))

  # Return the masked image, scaled to reflectance, without the QA bands.
  return image.updateMask(mask).divide(10000) \
      .select("B[0-9]*") \
      .copyProperties(image, ["system:time_start"])

# Landsat 8 image collection
l8collection = ee.ImageCollection("LANDSAT/LC08/C01/T1_SR")\
.filterBounds(tambopata_roi)\

print('# Landsat 8 Images: ',l8collection.size().getInfo())

l8_vis = {
    'bands': ['B4','B3','B2'],
    'min': 0,
    'max': 4000
}

# Masked image collection
l8sr_coll_CloudMasked = l8collection.map(maskL8sr)
l8sr_coll_clipped = l8sr_coll_CloudMasked.map(lambda image: image.clip(tambopata_roi))

Map.addLayer(l8sr_coll_CloudMasked.first(), l8_vis, 'L8 collection')
Map.addLayer(l8sr_coll_clipped.first(), l8_vis, 'L8 clipped')
Map.addLayer(tambopata_roi, {}, 'ROI')
Map.centerObject(tambopata_roi, zoom=13)

out_dir = os.path.join(os.path.expanduser('~'), 'Downloads')
filename = os.path.join(out_dir, 'L8_Image.tif')

geemap.ee_export_image(l8sr_coll_clipped.first(), filename=filename, scale=30,
                      region=tambopata_roi, file_per_band=True)

import numpy as np
l8_array = geemap.ee_to_numpy(l8sr_coll_CloudMasked.first(), bands=['B4','B5'], region=tambopata_roi, default_value=0)

# Plotting the array
import matplotlib.pyplot as plt

plt.imshow(l8_array[:,:,0])

[giswqs]

import numpy as np
l8_array = geemap.ee_to_numpy(l8sr_coll_clipped.first(), bands=['B4','B5'], region=tambopata_roi, default_value=0)

# Plotting the array
import matplotlib.pyplot as plt

plt.imshow(l8_array[:,:,0])

[PERCY-ESCOBAR]

Dear Professor Qiusheng Wu, I was not aware of that. I appreciate your help so much.