comparison land_cover.py @ 0:828c02027180 draft default tip

planemo upload for repository https://github.com/galaxyecology/tools-ecology/tree/master/tools/earth commit 4cbace8c3a71b7a1638cfd44a21f5d4b84d2fd15

0:828c02027180

# Land Cover TE Algorithm Without GEE

# How to Execute:
# - Load the two images (.TIFF) of the rasters
# to be processed in the "/data/land_cover/input" folder
# - Setting the two filenames in the specific cell of this script (0)
# - Run all cells of the script
# - Check the script results in the "/data/land_cover/output" folder
#    - Land Cover Degradation "/data/land_cover/output/lc_dg.tiff"
#   - Land Cover Transition "/data/land_cover/output/lc_tr.tiff"

# Librairies import
import argparse
import json
import os
import sys

import matplotlib.pyplot as plt

import numpy as np

import rasterio
from rasterio.plot import show

from te_schemas.land_cover import LCLegendNesting
from te_schemas.land_cover import LCTransitionDefinitionDeg

# Methods to manage Rasters


def remap(raster, problem_numbers, alternative_numbers):
    n_min, n_max = raster.min(), raster.max()
    replacer = np.arange(n_min, n_max + 1)
    mask = (problem_numbers >= n_min) & (problem_numbers <= n_max)
    replacer[problem_numbers[mask] - n_min] = alternative_numbers[mask]
    raster_replaced = replacer[raster - n_min]
    return raster_replaced


def saveRaster(dataset, datasetPath, cols, rows, projection, namedataset=None):
    if namedataset:
        rasterSet = rasterio.open(datasetPath,
                                  'w',
                                  driver='GTiff',
                                  height=rows,
                                  width=cols,
                                  count=1,
                                  dtype=np.int8,
                                  crs=projection,
                                  transform=transform, )
        rasterSet.write(dataset, 1)
        rasterSet.close()
    else:
        rasterSet = rasterio.open(datasetPath,
                                  'w',
                                  driver='GTiff',
                                  height=rows,
                                  width=cols,
                                  count=1,
                                  dtype=np.int8,
                                  crs=projection,
                                  transform=transform, )
        rasterSet.write(dataset, 1)
        rasterSet.set_band_description(1, namedataset)
        rasterSet.close()


def plot(ndviImage, cmap):
    src = rasterio.open(ndviImage, 'r')
    fig, ax = plt.subplots(1, figsize=(12, 12))
    show(src, cmap=cmap, ax=ax)
    ax.set_xlabel('Est')
    ax.set_ylabel('Nord')
    plt.show()


def plotContour(ndviImage, cmap):
    src = rasterio.open(ndviImage, 'r')
    fig, ax = plt.subplots(1, figsize=(12, 12))
    show(src, cmap=cmap, contour=True, ax=ax)
    ax.set_xlabel('Est')
    ax.set_ylabel('Nord')
    plt.show()


# Setting inputs
command_line_args = sys.argv[1:]


parser = argparse.ArgumentParser(description="landcover inputs and outputs")
# Add arguments
parser.add_argument("--raster_1", help="raster 1")
parser.add_argument("--raster_2", help="raster 2")
parser.add_argument("--json", help="json")

args = parser.parse_args(command_line_args)

# Parse the command line arguments

# Import data

path_raster_yi = args.raster_1
path_raster_yf = args.raster_2
fjson = args.json
# Input Rasters

# Outputs
out_dir = os.path.join(os.getcwd(), "data/land_cover/output/")
if not os.path.exists(out_dir):
    os.makedirs(out_dir)

# Output Rasters
path_lc_tr = os.path.join(out_dir, 'lc_tr.tiff')
path_lc_bl = os.path.join(out_dir, 'lc_bl.tiff')
path_lc_dg = os.path.join(out_dir, 'lc_dg.tiff')
path_lc_tg = os.path.join(out_dir, 'lc_tg.tiff')
path_change_yf_yi = os.path.join(out_dir, 'change_yf_yi.tiff')
path_lc_baseline_esa = os.path.join(out_dir, 'lc_baseline_esa.tiff')
path_lc_target_esa = os.path.join(out_dir, 'lc_target_esa.tiff')
path_out_img = os.path.join(out_dir, 'stack.tiff')

# Contours
contours_change_yf_yi = os.path.join(out_dir, '/change_yf_yi0.shp')


# Parsing Inputs
# Load static inputs
# Transition Matrix, ESA Legend, IPCC Legend
# Input Raster and Vector Paths
params = json.load(open(fjson))

crs = params.get("crs")

trans_matrix_val = params.get("trans_matrix")
trans_matrix = LCTransitionDefinitionDeg.Schema().load(trans_matrix_val)

esa_to_custom_nesting = LCLegendNesting.Schema().load(
    params.get("legend_nesting_esa_to_custom")
)

ipcc_nesting = LCLegendNesting.Schema().load(
    params.get("legend_nesting_custom_to_ipcc")
)

class_codes = sorted([c.code for c in esa_to_custom_nesting.parent.key])
class_positions = [*range(1, len(class_codes) + 1)]

# Load dynamic inputs
# Baseline ESA Raster
raster_yi = rasterio.open(path_raster_yi)
lc_baseline_esa = raster_yi.read(1)
yi_dict_profile = dict(raster_yi.profile)

for k in yi_dict_profile:
    print(k.upper(), yi_dict_profile[k])

# Target ESA Raster
raster_yf = rasterio.open(path_raster_yf)
lc_target_esa = raster_yf.read(1)
yf_dict_profile = dict(raster_yf.profile)

for k in yf_dict_profile:
    print(k.upper(), yf_dict_profile[k])

cols = raster_yi.width
rows = raster_yf.height
transform = raster_yi.transform
projection = raster_yi.crs

# Setting up output
saveRaster(lc_baseline_esa.astype('int8'),
           path_lc_baseline_esa,
           cols,
           rows,
           projection,
           "Land_cover_yi")

saveRaster(lc_target_esa.astype('int8'),
           path_lc_target_esa,
           cols,
           rows,
           projection,
           "Land_cover_yf")

# Algorithm execution
# Transition codes are based on the class code indices
# (i.e. their order when sorted by class code) -
# not the class codes themselves.
# So need to reclass the land cover used for the transition calculations
# from the raw class codes to the positional indices of those class codes.
# And before doing that, need to reclassified initial and
# final layers to the IPCC (or custom) classes.

# Processing baseline raster
bl_remap_1 = remap(lc_baseline_esa,
                   np.asarray(esa_to_custom_nesting.get_list()[0]),
                   np.asarray(esa_to_custom_nesting.get_list()[1]))
lc_bl = remap(bl_remap_1, np.asarray(class_codes), np.asarray(class_positions))

saveRaster(lc_bl.astype('int8'),
           path_lc_bl,
           cols,
           rows,
           projection,
           "Land_cover_yi")

# Processing Target Raster
tg_remap_1 = remap(lc_target_esa,
                   np.asarray(esa_to_custom_nesting.get_list()[0]),
                   np.asarray(esa_to_custom_nesting.get_list()[1]))
lc_tg = remap(tg_remap_1, np.asarray(class_codes), np.asarray(class_positions))

saveRaster(lc_tg.astype('int8'),
           path_lc_tg,
           cols,
           rows,
           projection,
           "Land_cover_yf")

# Processing Transition Map
# Compute transition map (first digit for baseline land cover,
# and second digit for target year land cover)
lc_tr = (lc_bl * esa_to_custom_nesting.parent.get_multiplier()) + lc_tg

# Compute land cover transition
# Remap persistence classes so they are sequential.
# This makes it easier to assign a clear color ramp in QGIS.
lc_tr_pre_remap = remap(lc_tr,
                        np.asarray(trans_matrix.get_persistence_list()[0]),
                        np.asarray(trans_matrix.get_persistence_list()[1]))

saveRaster(lc_tr_pre_remap.astype('int8'),
           path_lc_tr,
           cols,
           rows,
           projection,
           "Land_cover_transitions_yi-yf")

# Compute land cover degradation
# definition of land cover transitions as degradation (-1),
# improvement (1), or no relevant change (0)
lc_dg = remap(lc_tr,
              np.asarray(trans_matrix.get_list()[0]),
              np.asarray(trans_matrix.get_list()[1]))

saveRaster(lc_dg.astype('int8'),
           path_lc_dg,
           cols,
           rows,
           projection,
           "Land_cover_degradation")

# Compute  Mutlibands stack
# Land Cover Degradation + Baseline ESA
# + Target ESA + Land Cover Transition
dg_raster = rasterio.open(path_lc_dg, "r")
dg = dg_raster.read(1, masked=True)
baseline_esa = rasterio.open(path_lc_baseline_esa, "r").read(1, masked=True)
target_esa = rasterio.open(path_lc_target_esa, "r").read(1, masked=True)
tr = rasterio.open(path_lc_tr, "r").read(1, masked=True)

band_list = [dg, lc_baseline_esa, lc_target_esa, tr]
out_meta = dg_raster.meta.copy()
out_meta.update({"count": 4})

with rasterio.open(path_out_img, 'w', **out_meta) as dest:
    for band_nr, src in enumerate(band_list, start=1):
        dest.write(src, band_nr)