def get_flowlines_ascii(self, tmp_dir):
cols = self.shp.columns.to_list()
cols.remove('geometry')
flow = self.shp.drop(cols, axis=1)
flow['value'] = 1
self.raster_path = tmp_dir + '/flow_raster.asc'
dem_file = str(config.dem_velma)
with rasterio.open(dem_file, 'r') as src:
in_arr = src.read(1)
in_arr[:] = 0
meta = src.meta.copy()
meta = src.meta
with rasterio.open(self.raster_path, 'w+', **meta) as out:
shapes = (
(geom, value)
for geom, value in zip(flow.geometry, flow.value))
burned = features.rasterize(shapes=shapes,
fill=np.nan,
out=in_arr,
transform=out.transform)
out.write_band(1, burned)
self.raster_header = readHeader(self.raster_path)
self.raster = np.loadtxt(self.raster_path, skiprows=6)
dem_file = str(config.dem_velma)
roi_raster = config.dem_velma.parents[1] / 'roi.asc'
# Take DEM and set all values to NaN, then burn species shp into empty DEM raster
with rasterio.open(dem_file, 'r') as src:
in_arr = src.read(1)
in_arr[:] = 0
meta = src.meta.copy()
meta = src.meta
with rasterio.open(roi_raster, 'w+', **meta) as out:
shapes = ((geom, value) for geom, value in zip(roi.geometry, roi.value))
burned = features.rasterize(shapes=shapes, fill=np.nan, out=in_arr, transform=out.transform)
out.write_band(1, burned)
roi_header = readHeader(roi_raster)
roi_asc = np.loadtxt(roi_raster, skiprows=6)
# =======================================================================
# Vectorize delineated DEM (area upstream of outpour point, as exported from JPDEM)
# =======================================================================
dem_path = config.dem_velma.parents[0] / 'delineated_dem.asc'
dem = np.loadtxt(dem_path, skiprows=6)
dem_simple = dem.astype('int16')
dem_simple[dem_simple > 1] = 1
dem_simple[dem_simple < 1] = 0
dem_simple = binary_fill_holes(dem_simple).astype(int)
# Replace bare, bpa, and nf that were in stands with conifer ccap[(ccap == bare_id)] = conifer_id ccap[(ccap == bpa_id)] = conifer_id ccap[(ccap == nf_id)] = conifer_id # Overlay NLCD deciduous forests on CCAP ccap[(nlcd == nlcd_forest_decid)] = nlcd_forest_decid # Replace CCAP shrub, herbaceous, dirt, and water with conifer ccap[(ccap == ccap_herby)] = conifer_id ccap[(ccap == ccap_shrub)] = conifer_id ccap[(ccap == ccap_dirt)] = conifer_id ccap[(ccap == ccap_water)] = conifer_id header = readHeader(stands_path) f = open(outfile, "w") f.write(header) np.savetxt(f, ccap, fmt="%i") f.close() # Create cover type map that is just conifer conifer = (ccap * 0) + 1 outfile = config.cover_type_ccap_merge_velma.parents[0] / 'conifer.asc' f = open(outfile, "w") f.write(header) np.savetxt(f, conifer, fmt='%i') f.close() # # Merge key files # ccap_key = pd.read_csv(config.ccap_out.parents[0] / 'ccap_classes.csv')
out=template,
transform=src.transform,
default_value=1)
protected = ((stand_id == 0) + (murrelet == 1))
harvest[protected.astype('bool')] = 0
if np.sum(harvest) > 0:
yearly_clearcuts.append(harvest)
# Export clearcut harvests
filter_dir = config.stand_id_velma.parents[0] / 'filter_maps'
try:
filter_dir.mkdir(parents=True)
except FileExistsError:
pass
header = readHeader(dem_file)
for i, harvest in enumerate(yearly_clearcuts):
outfile = filter_dir / 'random_35yr_clearcut_10pct_{}.asc'.format(i + 1)
f = open(outfile, "w")
f.write(header)
np.savetxt(f, harvest, fmt="%i")
f.close()
# # To check sizes of each yearly harvest
# areas = []
# for cut in yearly_clearcuts:
# area_sqm = np.sum(cut) * 100
# area_sqkm = area_sqm * 1e-6
# areas.append(area_sqkm)
# pd.DataFrame(areas, columns=['data']).describe()
# Marbled murrelet habitat is a protected area that can't be harvested
murrelet = np.loadtxt(config.data_path / 'landcover' / 'murrelet_no_harvest.asc', skiprows=6)
murrelet[murrelet == -9999] = np.nan
# =======================================================================
# Create (binary) disturbance filter maps for each forest management scenario
# ===================================
disturbance = 'industrial_clearcut'
# All stands can be cut except protected areas
filter_map = ((stand_id == 0) + (murrelet == 1)) # The excluded cells here are TRUE
filter_map = np.invert(filter_map) * 1 # TRUE cells are inverted to false, and then binarized
outfile = filter_dir / '{}.asc'.format(disturbance)
f = open(outfile, 'w')
header = readHeader(stand_id_path)
f.write(header)
np.savetxt(f, filter_map, fmt='%i')
f.close()
# ===================================
disturbance = 'active_all'
# All stands can be cut except protected areas
filter_map = ((stand_id == 0) + (murrelet == 1))
filter_map = np.invert(filter_map) * 1
outfile = filter_dir / '{}.asc'.format(disturbance)
f = open(outfile, 'w')
header = readHeader(stand_id_path)
f.write(header)
np.savetxt(f, filter_map, fmt='%i')
f.close()
filter_map_dir = config.velma_data / 'landcover' / 'filter_maps'
for file in os.listdir(str(filter_map_dir)):
if file.endswith('.asc'):
old_paths.append(filter_map_dir / file)
# Resample categorical data files to desired resolution
new_files = []
for file in old_paths:
with rasterio.open(file, 'r') as ds:
data = ds.read(out_shape=(ds.count, int(ds.height * upscale_factor),
int(ds.width * upscale_factor)),
resampling=Resampling.nearest)
new_files.append(data)
# Export resampled files
header = readHeader(old_paths[0])
#for file in new_files:
with rasterio.open(new_paths[0], 'w', 'AAIGrid') as dst:
dst.write(new_files[0], 1)
# =======================================================================================
# Resample DEM files if necessary
orig_res_dem = 3
new_res_dem = 5
upscale_factor_dem = orig_res_dem / new_res_dem
old_dem_files = []
dem_dir = config.velma_data.parents[0] / 'ellsworth_3m_velma' / 'topography'
for file in os.listdir(str(dem_dir)):
nlcd_dev_openspace = 121 nlcd_dev_low = 122 nlcd_dev_med = 123 nlcd_dev_high = 124 nlcd_barren = 131 nlcd_forest_decid = 141 nlcd_forest_evergreen = 142 nlcd_forest_mixed = 143 nlcd_shrub = 152 nlcd_herby = 171 nlcd_woody_wet = 190 nlcd_emerg_herb_wet = 195 # Erode NLCD roads by 1 pixel - they look to be about 10-20m, not 30m road_mask = (nlcd == nlcd_dev_openspace) + (nlcd == nlcd_dev_low) roads = ndimage.binary_erosion(road_mask, iterations=1) # Combine CCAP developed with NLCD roads roads_merge = roads + (ccap == ccap_developed) + (ccap == ccap_dirt) # Convert to % permeability perm_fraction = 0.5 # % permeability of roads perm = np.invert(roads_merge) * 1 perm = np.where(perm == 0, 0.5, perm) header = readHeader(nlcd_path) f = open(outfile, "w") f.write(header) np.savetxt(f, perm, fmt="%f") f.close()
cover_age = np.loadtxt(config.cover_age_velma, skiprows=6)
age_count = cover_age - age_diff
# Subtract age difference between start and end period from current age raster.
# Age+1 each year, and cells are cut down when cells=0
prehansen_cuts = []
prehansen_years = []
for year in range(start, end):
loss_map = (age_count == 0)
prehansen_cuts.append(loss_map)
prehansen_years.append(year)
age_count += 1
# Export historical clearcut filter maps
header = readHeader(config.cover_age_velma)
for i, year in enumerate(range(start, end)):
prehansen_loss = prehansen_cuts[prehansen_years.index(year)]
hansen_loss = (hansen_yearly_loss == year % 100) * 1
total_loss = hansen_loss + prehansen_loss
if total_loss.sum() > 0:
print(year)
outfile = filter_dir / 'historical_clearcut_{}.asc'.format(year)
f = open(outfile, "w")
f.write(header)
np.savetxt(f, total_loss, fmt="%i")
f.close()