def analyze(threshold, geojson, begin, end, layer, count_pixels):
"""Take a user specified raster (currently only globcover) and a threshold,
and return a dictionary of loss pixel count per year and per raster
type for a given geometry.
TODO: build this out
"""
try:
hansen_asset_id = SETTINGS['gee']['assets']['hansen']
lulc_asset_id = SETTINGS['gee']['assets'][layer]
begin = int(begin.split('-')[0][2:])
end = int(end.split('-')[0][2:])
# Grab loss band depending on the threshold of interest
loss_band_name = 'loss_{0}'.format(threshold)
loss_band = ee.Image(hansen_asset_id).select(loss_band_name)
lulc_asset_id = SETTINGS['gee']['assets'][layer]
band_name = SETTINGS['gee']['lulc_band'].get(layer, 'b1')
lulc_band = ee.Image(lulc_asset_id).select(band_name)
masked_lulc = lulc_band.updateMask(loss_band.mask())
combine_image = loss_band.multiply(500).add(masked_lulc)
if count_pixels:
reducer = ee.Reducer.frequencyHistogram().unweighted()
else:
reducer = ee.Reducer.frequencyHistogram().unweighted().group()
combine_image = combine_image.addBands([ee.Image.pixelArea()])
reduce_args = {
'reducer': reducer,
'geometry': get_region(geojson),
'scale': 27.829872698318393,
'bestEffort': False,
'maxPixels': 1e10
}
area_stats = combine_image.reduceRegion(**reduce_args).getInfo()
if count_pixels:
area_stats = area_stats[loss_band_name]
else:
area_stats = flatten_area_hist(area_stats)
decoded_response = HistogramService.decode_response(
begin, end, area_stats, layer)
add_lulc_names = lookup(layer, decoded_response, count_pixels)
return {'result': add_lulc_names}
except Exception as error:
logging.error(str(error))
raise HistogramError(message='Error in Histogram Analysis')
def analyze(geojson, layer, count_pixels):
"""Take a user specified raster (currently only globcover)
and return a dictionary of land cover type and pixel count (or area)
type for a given geometry.
"""
try:
lulc_asset_id = SETTINGS['gee']['assets'][layer]
band_name = SETTINGS['gee']['lulc_band'].get(layer, 'b1')
logging.info(lulc_asset_id)
logging.info(band_name)
lulc_band = ee.Image(lulc_asset_id).select(band_name)
if count_pixels:
reducer = ee.Reducer.frequencyHistogram().unweighted()
else:
reducer = ee.Reducer.frequencyHistogram().unweighted().group()
lulc_band = lulc_band.addBands([ee.Image.pixelArea()])
reduce_args = {
'reducer': reducer,
'geometry': get_region(geojson),
'scale': 27.829872698318393,
'bestEffort': False,
'maxPixels': 1e13
}
area_stats = lulc_band.reduceRegion(**reduce_args).getInfo()
if count_pixels:
area_stats = area_stats[band_name]
if not count_pixels:
area_stats = flatten_area_hist(area_stats)
add_lulc_names = lookup(layer, area_stats, count_pixels)
return {'result': add_lulc_names}
except Exception as error:
logging.error(str(error))
raise LandcoverError(message='Error in Landcover Analysis')
def analyze(geojson):
"""
Analyze NLCD Landcover
"""
try:
logging.info(f'[nlcd-landcover-service]: Initialize analysis')
# nlcd land cover
d = {}
# Extract int years to work with for time range
valid_years = [{
'id': 'NLCD2001',
'year': 2001
}, {
'id': 'NLCD2006',
'year': 2006
}, {
'id': 'NLCD2011',
'year': 2011
}, {
'id': 'NLCD2016',
'year': 2016
}]
# Gather assets
band_name = 'landcover'
landcover_asset = SETTINGS.get('gee').get('assets').get(
'us_landcover')
image_list = [
ee.Image(f"{landcover_asset}/{year.get('id')}")
for year in valid_years
]
us_landcover = ee.ImageCollection(image_list).select(band_name)
# region = ee.Feature(geojson).geometry()
region = get_region(geojson)
scale = 30
logging.info(
f'[nlcd-landcover-service]: built assets for analysis, using {band_name}'
)
# Calculate landcover with a collection operation method
stats = us_landcover.map(
ImageColIntersect(region, scale,
ee.Reducer.frequencyHistogram())).getInfo()
logging.info(f'[nlcd-landcover-service]: retreived {stats}')
# Format data structure
data = [{
'id': d['id'],
'stats': {
k: v * 30 * 30 * 1e-4
for k, v in d['properties'][band_name].items()
}
} for d in stats['features']]
tmp = {}
for el in data:
year = [y['year'] for y in valid_years
if y['id'] == el['id']][0]
tmp[year] = lookup('nlcd_landcover', el['stats'], False)
d['nlcd_landcover'] = tmp
return d
except Exception as error:
logging.error(str(error))
raise NLCDLandcoverError(message='Error in LandCover Analysis')