def run(self):
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',
}
#create ecoregion id
raster_utils.new_raster_from_base_uri(
GLOBAL_LANDCOVER_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"])
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)
def run(self):
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',
}
cell_size = raster_utils.get_cell_size_from_uri(
FOREST_EDGE_DISTANCE_URI)
forest_edge_nodata = raster_utils.get_nodata_from_uri(
FOREST_EDGE_DISTANCE_URI)
biomass_nodata = raster_utils.get_nodata_from_uri(GLOBAL_BIOMASS_URI)
outfile = open(BIOMASS_STATS_URI, 'w')
ecoregion_dataset = gdal.Open(ECOREGION_DATASET_URI)
ecoregion_band = ecoregion_dataset.GetRasterBand(1)
biomass_ds = gdal.Open(GLOBAL_BIOMASS_URI, gdal.GA_ReadOnly)
biomass_band = biomass_ds.GetRasterBand(1)
forest_edge_distance_ds = gdal.Open(FOREST_EDGE_DISTANCE_URI)
forest_edge_distance_band = forest_edge_distance_ds.GetRasterBand(1)
n_rows, n_cols = raster_utils.get_row_col_from_uri(GLOBAL_BIOMASS_URI)
base_srs = osr.SpatialReference(biomass_ds.GetProjection())
lat_lng_srs = base_srs.CloneGeogCS()
coord_transform = osr.CoordinateTransformation(
base_srs, lat_lng_srs)
geo_trans = biomass_ds.GetGeoTransform()
block_col_size, block_row_size = biomass_band.GetBlockSize()
n_global_block_rows = int(math.ceil(float(n_rows) / block_row_size))
n_global_block_cols = int(math.ceil(float(n_cols) / block_col_size))
last_time = time.time()
for global_block_row in xrange(n_global_block_rows):
current_time = time.time()
if current_time - last_time > 5.0:
print (
"aggregation %.1f%% complete" %
(global_block_row / float(n_global_block_rows) * 100))
last_time = current_time
for global_block_col in xrange(n_global_block_cols):
xoff = global_block_col * block_col_size
yoff = global_block_row * block_row_size
win_xsize = min(block_col_size, n_cols - xoff)
win_ysize = min(block_row_size, n_rows - yoff)
biomass_block = biomass_band.ReadAsArray(
xoff=xoff, yoff=yoff, win_xsize=win_xsize,
win_ysize=win_ysize)
forest_edge_distance_block = (
forest_edge_distance_band.ReadAsArray(
xoff=xoff, yoff=yoff, win_xsize=win_xsize,
win_ysize=win_ysize))
ecoregion_id_block = ecoregion_band.ReadAsArray(
xoff=xoff, yoff=yoff, win_xsize=win_xsize,
win_ysize=win_ysize)
for global_row in xrange(
global_block_row*block_row_size,
min((global_block_row+1)*block_row_size, n_rows)):
for global_col in xrange(
global_block_col*block_col_size,
min((global_block_col+1)*block_col_size, n_cols)):
row_coord = (
geo_trans[3] + global_row * geo_trans[5])
col_coord = (
geo_trans[0] + global_col * geo_trans[1])
local_row = (
global_row - global_block_row * block_row_size)
local_col = (
global_col - global_block_col * block_col_size)
lng_coord, lat_coord, _ = (
coord_transform.TransformPoint(
col_coord, row_coord))
ecoregion_id = ecoregion_id_block[local_row, local_col]
if (forest_edge_distance_block[local_row, local_col] !=
forest_edge_nodata and
forest_edge_distance_block
[local_row, local_col] > 0.0 and
biomass_block
[local_row, local_col] != biomass_nodata):
outfile.write("%f;%f;%f;%f;%s;%s;%s" % (
forest_edge_distance_block
[local_row, local_col] * cell_size,
biomass_block[local_row, local_col],
lat_coord, lng_coord,
ecoregion_lookup[ecoregion_id]['ECO_NAME'],
ecoregion_lookup[ecoregion_id]['ECODE_NAME'],
ecoregion_lookup[ecoregion_id]['WWF_MHTNAM']))
for global_grid_resolution in GRID_RESOLUTION_LIST:
#output a grid coordinate in the form
#'grid_row-grid_col'
grid_row = (
int((geo_trans[3] - row_coord) /
(global_grid_resolution*1000)))
grid_col = (
int((col_coord - geo_trans[0]) /
(global_grid_resolution*1000)))
grid_id = str(grid_row) + '-' + str(grid_col)
outfile.write(";%s" % grid_id)
outfile.write('\n')
outfile.close()
