Test leaf toolbox on selected polygons

[ronnyhdez]

After the flagging and filtering, I need to test the leaf toolbox on the selected polygons

[ronnyhdez]

Errors:

TypeError: 'FeatureCollection' object is not iterable

Then, converting to list:

TypeError: 'List' object is not iterable

[rfernand387]

Please send me the link to notebook you are using and make sure GEE collections of sites are accessible to all.

[ronnyhdez]

If I transform the feature collection to just ee.Geometry, it returns a different error:

EEException: Cannot convert a computed geometry to GeoJSON. Wrap a getInfo() call in json.dumps instead.

[ronnyhdez]

The function sampleSites only accepts assets from GEE. The errors above were mainly because I was calling the asset and transforming it to a FeatureCollection. The reason is that I need to filter by the intersecting features in the asset.

In GEE console I filtered the intersecting polygons, and then obtained the biggest 100 polygons by area, and then saved them as an asset.

From the notebook, I ran the LEAFtoolbox and it worked! This is part of the output

print(sitesDictionaryL08SR.values())

[863 rows x 25 columns]}, {'feature': {'area': 26643.913687916036, 'area_ha': 2.678175878285475, 'disp_nm': '', 'fetr_ty': 'WELL-ABAND', 'fieldnm': '', 'frst_spd_d': 1087801200000, 'frst_spd_y': 2004, 'hfi_id': '{BDB0B961-B79E-48E7-A397-96873FF0DD3B}', 'intersects_industrial': 0, 'intersects_industrial_buffer': 0, 'intersects_reservoirs': 0, 'intersects_reservoirs_buffer': 0, 'intersects_residential': 0, 'intersects_residential_buffer': 0, 'intersects_roads': 0, 'intersects_roads_buffer': 0, 'intersects_waterbodies': 0, 'intersects_waterbody_buffer': 0, 'lcu_id': '', 'mn_npr_': 2004, 'mx_bnd_': 2016, 'mx_ls__': 2005, 'nmbr_wl': 2, 'plygn_s': 9, 'prd_x__': 2, 'rclmtn_c': 1, 'rclmtn_d': 2021, 'rclmtn_s': 'reclaimed', 'shape_r': 26781.758782854748, 'shp_lng': 837.7389418259842, 'source': 'AHFMP', 'visible': 2, 'wll_stt': 1, 'wllst__': 151928, 'year': 2004}, 'leaftoolbox.SL2PV0':      SR_B1  SR_B2  SR_B3  SR_B4  SR_B5  SR_B6  SR_B7  SR_QA_AEROSOL  ST_B10  \
0     9919  10845  12307  13925  17113  21607  18207             96   45530   
1     9900  10760  12239  13814  16957  21376  18115             96   45520   
2     9862  10769  12237  13795  16829  21285  18056            160   45469   
3     9875  10730  12196  13718  16815  21353  18114             96   45400   
4     9890  10839  12348  13936  17116  21602  18232            160   45536   
..     ...    ...    ...    ...    ...    ...    ...            ...     ...   
696   8685   9471  11172  13049  18583  16849  12789             96   44500   
697   8652   9461  11190  13042  18698  16729  12694             96   44374   
698   8642   9436  11170  13025  18602  16774  12705            160   44403   
699   8617   9430  11118  13059  18598  16930  12800            128   44411   
700   8625   9410  11139  12938  18683  16693  12697             96   44418   

     ST_ATRAN  ...  ST_TRAD  ST_URAD  QA_PIXEL  QA_RADSAT           date  \
0        9389  ...     9772      317     21824          0  1620066160433   
1        9389  ...     9758      317     21824          0  1620066160433   
2        9388  ...     9733      317     21824          0  1620066160433   
3        9389  ...     9702      317     21824          0  1620066160433   
4        9389  ...     9775      317     21824          0  1620066160433   
..        ...  ...      ...      ...       ...        ...            ...   
696      8454  ...     9239     1168     21824          0  1662920626208   
697      8452  ...     9238     1169     21824          0  1662920626208   
698      8451  ...     9235     1170     21824          0  1662920626208   
699      8452  ...     9238     1169     21824          0  1662920626208   
700      8452  ...     9242     1169     21824          0  1662920626208   

      longitude   latitude  cosVZA  cosSZA  cosRAA  
0   -110.999399  53.341314    9979    7755       0  
1   -110.998949  53.341314    9979    7755       0  
2   -110.998498  53.341314    9979    7755       0  
3   -110.998047  53.341314    9979    7755       0  
4   -110.999399  53.341584    9979    7755       0  
..          ...        ...     ...     ...     ...  
696 -110.996696  53.342932    9981    6379       0  
697 -110.998047  53.343202    9981    6379       0  
698 -110.997597  53.343202    9981    6379       0  
699 -110.997146  53.343202    9981    6379       0  
700 -110.996696  53.343202    9981    6379       0  

For those 100 polygons, the running time is between 1 hour and 2 (didn't take the time accurately)

[ronnyhdez]

sitesDictionaryL08SR = LEAF.sampleSites(
    site,
    imageCollectionName="LANDSAT/LC08/C02/T1_L2",
    algorithm=SL2PV0,
    variableName="Surface_Reflectance",
    maxCloudcover=90,
    outputScaleSize=30,
    inputScaleSize=30,
    bufferSpatialSize=0,
    bufferTemporalSize=["2021-04-01", "2022-10-01"],
    subsamplingFraction=0.99,
)

Execution time sitesDictionaryL08SR: 4276.814430475235 seconds

sitesDictionaryL09SR = LEAF.sampleSites(
    site,
    imageCollectionName="LANDSAT/LC09/C02/T1_L2",
    algorithm=SL2PV0,
    variableName="Surface_Reflectance",
    outputScaleSize=30,
    inputScaleSize=30,
    bufferSpatialSize=0,
    bufferTemporalSize=["2021-04-01", "2022-10-01"],
    subsamplingFraction=0.99,
)

Execution time sitesDictionaryL09SR: 2787.4890642166138 seconds

[rfernand387]

Yes - you need a saved gee asset that is a feature collection. Make sure each feature has a correct 'system:time_start" property as this is used to guide sampling times.

[ronnyhdez]

Ref #88