def vis_nbr(ee_product, image):
nir, swir, alpha = get_bands_by_name(ee_product, image,
['NIR', 'SWIR2', 'cloud_mask'])
level = (nir - swir) / (nir + swir + 1e-9)
out = apply_palette(level, ['black', 'red', 'yellow'])
out[-1] = alpha
return out
def handler(ee_product, image, comp_image=comp_image):
B, G, R, nir, swir, swir2 = get_bands_by_name(
ee_product, image,
['Blue', 'Green', 'Red', 'NIR', 'SWIR', 'SWIR2'])
index = ind_func(ee_product, image, already_normalised=True)
if comp_image is not None:
comp_index = ind_func(ee_product,
comp_image,
already_normalised=True)
index = index - comp_index
some_array, lots_array = l_func(B, G, R, nir, swir2)
no_array = get_natural_nirswirmix(B, G, R, nir, swir2)
combined_array = np.where(index > 1.0, some_array, no_array)
combined_array = np.where(index > 2.0, lots_array, combined_array)
return combined_array
def vis_firethresh(ee_product, image):
swir, swir2, mask = get_bands_by_name(ee_product, image,
['SWIR', 'SWIR2', 'cloud_mask'])
out = apply_palette((swir + swir2) / 4, ['black', 'red', 'yellow'])
out[-1] = mask
return out
def get_nbr_indicator(ee_product, image, sensitivity=1.0):
nir, swir2 = get_bands_by_name(ee_product, image, ['NIR', 'SWIR2'])
return sensitivity * (nir - swir2) / (nir + swir2 + 1e-9)
def get_veg_indicator(ee_product, image):
red, nir = get_bands_by_name(ee_product, image, ['Red', 'NIR'])
raw = (red - nir) / (red + nir + 1e-9)
return raw * 2 + .7 # Scale to fit 1,2 thresholds, .15-->1, .65-->2
def get_fire_indicator(ee_product, image, sensitivity=1.0):
swir, swir2 = get_bands_by_name(ee_product, image, ['SWIR', 'SWIR2'])
# Increase sensitivity for more possible fires and more wrong indications
return (swir + swir2) * sensitivity