def run(self):
lulc_nodata = raster_utils.get_nodata_from_uri(GLOBAL_LANDCOVER_URI)
forest_lulc_codes = [1, 2, 3, 4, 5]
mask_uri = os.path.join(OUTPUT_DIR, "forest_mask.tif")
mask_nodata = 2
def mask_nonforest(lulc):
"""Takes in a numpy array of landcover values and returns 1s
where they match forest codes and 0 otherwise"""
mask = numpy.empty(lulc.shape, dtype=numpy.int8)
mask[:] = 1
for lulc_code in forest_lulc_codes:
mask[lulc == lulc_code] = 0
mask[lulc == lulc_nodata] = mask_nodata
return mask
cell_size = raster_utils.get_cell_size_from_uri(GLOBAL_LANDCOVER_URI)
raster_utils.vectorize_datasets(
[GLOBAL_LANDCOVER_URI,], mask_nonforest, mask_uri, gdal.GDT_Byte,
mask_nodata, cell_size, 'intersection', dataset_to_align_index=0,
dataset_to_bound_index=None, aoi_uri=None,
assert_datasets_projected=True, process_pool=None,
vectorize_op=False, datasets_are_pre_aligned=True)
raster_utils.distance_transform_edt(
mask_uri, FOREST_EDGE_DISTANCE_URI)
def process_ecoregion(prefix):
ecoregion_shapefile_uri = os.path.join(
DATA_DIR, 'ecoregions', 'ecoregions_projected.shp')
ecoregion_lookup = raster_utils.extract_datasource_table_by_key(
ecoregion_shapefile_uri, 'ECO_ID_U')
ecoregion_nodata = -1
ecoregion_lookup[ecoregion_nodata] = {
'ECO_NAME': 'UNKNOWN',
'ECODE_NAME': 'UNKNOWN',
'WWF_MHTNAM': 'UNKNOWN',
}
lulc_raw_uri = os.path.join(DATA_DIR, '%s%s' % (prefix, LULC_BASE))
biomass_raw_uri = os.path.join(DATA_DIR, '%s%s' % (prefix, BIOMASS_BASE))
cell_size = raster_utils.get_cell_size_from_uri(lulc_raw_uri)
lulc_uri = os.path.join(OUTPUT_DIR, "%s_lulc_aligned.tif" % (prefix))
biomass_uri = os.path.join(OUTPUT_DIR, "%s_biomass_aligned.tif" % (prefix))
raster_utils.align_dataset_list(
[lulc_raw_uri, biomass_raw_uri], [lulc_uri, biomass_uri], ['nearest']*2,
cell_size, 'intersection', 0, dataset_to_bound_index=None,
aoi_uri=None, assert_datasets_projected=True, process_pool=None)
#create ecoregion id
ecoregion_dataset_uri = os.path.join(
OUTPUT_DIR, "%s_ecoregion_id.tif" % (prefix))
raster_utils.new_raster_from_base_uri(
lulc_uri, ecoregion_dataset_uri, 'GTiff', ecoregion_nodata, gdal.GDT_Int16)
raster_utils.rasterize_layer_uri(
ecoregion_dataset_uri, ecoregion_shapefile_uri,
option_list=["ATTRIBUTE=ECO_ID_U"])
lulc_nodata = raster_utils.get_nodata_from_uri(lulc_uri)
forest_lulc_codes = [1, 2, 3, 4, 5]
mask_uri = os.path.join(OUTPUT_DIR, "%s_mask.tif" % prefix)
mask_nodata = 2
def mask_nonforest(lulc):
mask = numpy.empty(lulc.shape, dtype=numpy.int8)
mask[:] = 1
for lulc_code in forest_lulc_codes:
mask[lulc == lulc_code] = 0
mask[lulc == lulc_nodata] = mask_nodata
return mask
raster_utils.vectorize_datasets(
[lulc_uri,], mask_nonforest, mask_uri, gdal.GDT_Byte,
mask_nodata, cell_size, 'intersection', dataset_to_align_index=0,
dataset_to_bound_index=None, aoi_uri=None,
assert_datasets_projected=True, process_pool=None, vectorize_op=False,
datasets_are_pre_aligned=True)
forest_edge_distance_uri = os.path.join(OUTPUT_DIR, "%s_forest_edge.tif" % prefix)
raster_utils.distance_transform_edt(mask_uri, forest_edge_distance_uri)
biomass_stats_uri = os.path.join(OUTPUT_DIR, "%s_biomass_stats.csv" % prefix)
_aggregate_results(forest_edge_distance_uri, biomass_uri, ecoregion_dataset_uri, ecoregion_lookup, biomass_stats_uri)
