r.trees.thresholds: addon to propose thresholds for creation of train…

…ing data and postprocessing

grass-gis-addons/m.analyse.trees/r.trees.thresholds/Makefile

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+MODULE_TOPDIR = ../../..
+
+PGM = r.trees.thresholds
+
+include $(MODULE_TOPDIR)/include/Make/Script.make
+
+default: script

grass-gis-addons/m.analyse.trees/r.trees.thresholds/r.trees.thresholds.html

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+<h2>DESCRIPTION</h2>
+
+<em>r.trees.thresholds</em> proposes thresholds for NDVI and NIR as these
+depend on the respective flight conditions.
+
+
+<h2>EXAMPLES</h2>
+
+<h3>Proposing thresholds for trainingdata generation and postprocessing</h3>
+
+<div class="code"><pre>
+r.trees.thresholds forest=fnk forest_column=code2020 \
+    nir_raster=top_nir_02 ndvi_raster=top_ndvi_02 \
+    ndsm=ndsm ndsm_threshold=4
+</pre></div>
+
+<h2>SEE ALSO</h2>
+
+<em>
+<a href="m.analyse.trees.html">m.analyse.trees</a>,
+<a href="https://grass.osgeo.org/grass-stable/manuals/r.geomorphon.html">r.quantile</a>,
+</em>
+
+<h2>AUTHOR</h2>
+
+<p>Anika Weinmann, <a href="https://www.mundialis.de/">mundialis</a>, weinmann at mundialis.de</p>
+<p>Victoria-Leandra Brunn, <a href="https://www.mundialis.de/">mundialis</a>, brunn at mundialis.de</p>

grass-gis-addons/m.analyse.trees/r.trees.thresholds/r.trees.thresholds.py

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+#!/usr/bin/env python3
+
+############################################################################
+#
+# MODULE:      r.trees.thresholds
+#
+# AUTHOR(S):   Victoria-Leandra Brunn, Anika Weinmann
+#
+# PURPOSE:     Proposes thresholds for NDVI and NIR as these depend on the
+#              respective flight conditions
+#
+# COPYRIGHT:   (C) 2024 by mundialis and the GRASS Development Team
+#
+#       This program is free software under the GNU General Public
+#       License (>=v2). Read the file COPYING that comes with GRASS
+#       for details.
+#
+#############################################################################
+
+# %Module
+# % description: Proposes thresholds for NDVI and NIR as these depend on the respective flight conditions.
+# % keyword: raster
+# % keyword: statistics
+# % keyword: threshold
+# % keyword: machine learning
+# % keyword: trees analysis
+# %end
+
+# %option G_OPT_V_INPUT
+# % key: forest
+# % label: Name of the vector map with forest inside e.g. FNK
+# % answer: ndsm
+# % guisection: Input
+# %end
+
+# %option G_OPT_DB_COLUMN
+# % key: forest_column
+# % label: Name of the column in the forest vector map to filter only forest e.g. for FNK code_2020
+# % guisection: Input
+# % required: no
+# %end
+
+# %option
+# % key: forest_values
+# % label: Values of the forest_column which to select from
+# % answer: 400,410,420,431,432,502
+# % multiple: yes
+# % guisection: Input
+# % required: no
+# %end
+
+# %option G_OPT_R_INPUT
+# % key: nir_raster
+# % label: Name of the NIR raster
+# % answer: top_nir_02
+# % guisection: Input
+# %end
+
+# %option G_OPT_R_INPUT
+# % key: ndvi_raster
+# % label: Name of the NDVI raster
+# % answer: top_ndvi_02
+# % guisection: Input
+# %end
+
+# %option G_OPT_R_INPUT
+# % key: ndsm
+# % label: Name of the nDSM raster
+# % answer: ndsm
+# % guisection: Input
+# %end
+
+# %option
+# % key: ndsm_threshold
+# % type: double
+# % required: yes
+# % label: nDSM threshold for potential trees
+# % answer: 1
+# % guisection: Parameters
+# %end
+
+
+import atexit
+import os
+import sys
+import grass.script as grass
+from grass.pygrass.utils import get_lib_path
+
+# initialize global vars
+rm_rasters = []
+rm_vectors = []
+tmp_mask_old = None
+PID = os.getpid()
+
+def cleanup():
+    nuldev = open(os.devnull, "w")
+    kwargs = {"flags": "f", "quiet": True, "stderr": nuldev}
+    for rmrast in rm_rasters:
+        if grass.find_file(name=rmrast, element="cell")["file"]:
+            grass.run_command("g.remove", type="raster", name=rmrast, **kwargs)
+    for rmv in rm_vectors:
+        if grass.find_file(name=rmv, element="vector")["file"]:
+            grass.run_command("g.remove", type="vector", name=rmv, **kwargs)
+    grass.del_temp_region()
+
+
+def get_percentiles(rast, percentiles=list(range(1, 100, 1))):
+    perc_out = grass.parse_command(
+        "r.quantile",
+        input=rast,
+        percentiles=percentiles,
+        quiet=True,
+    )
+    return {
+        float(k.split(":")[1]): float(k.split(":")[2])
+        for k in perc_out
+    }
+
+
+def main():
+    global rm_rasters, tmp_mask_old, rm_vectors
+
+    path = get_lib_path(modname="m.analyse.trees", libname="analyse_trees_lib")
+    if path is None:
+        grass.fatal("Unable to find the analyse trees library directory")
+    sys.path.append(path)
+    try:
+        from analyse_trees_lib import (
+            set_nprocs,
+            test_memory,
+        )
+    except Exception:
+        grass.fatal("m.analyse.trees library is not installed")
+
+    grass.message(_("Preparing input data..."))
+    if grass.find_file(name="MASK", element="cell")["file"]:
+        tmp_mask_old = f"tmp_mask_old_{PID}"
+        grass.run_command(
+            "g.rename", raster=f"MASK,{tmp_mask_old}", quiet=True
+        )
+
+    vect_map = options["forest"]
+    nir = options["nir_raster"]
+    ndvi = options["ndvi_raster"]
+    ndsm = options["ndsm"]
+    ndsm_threshold = float(options["ndsm_threshold"])
+    forest_column = options["forest_column"]
+    forest_values = options["forest_values"].split(",")
+
+    grass.use_temp_region()
+
+    # extract forest if "forest_column" is set
+    forest = vect_map
+    if forest_column:
+        forest = f"foest_{PID}"
+        forest_where = " or ".join([f"{forest_column}='{val}'" for val in forest_values])
+        rm_vectors.append(forest)
+        grass.run_command(
+            "v.extract",
+            input=vect_map,
+            output=forest,
+            where=f"{forest_where}",
+            quiet=True,
+        )
+
+    # get percentile of trees in the forests over the height
+    grass.run_command("g.region", vector=forest, align=nir)
+    grass.run_command("r.mask", vector=forest)
+    ndsm_percentiles = get_percentiles(ndsm)
+    ndsm_perc = 0
+    for ndsm_percentile, ndsm in ndsm_percentiles.items():
+        if ndsm < ndsm_threshold:
+            ndsm_perc = ndsm_percentile
+        else:
+            break
+
+    # percentiles for NDVI range
+    ndsm_perc_step = ndsm_perc/2. if ndsm_perc < 20 else 10
+    min_perc = ndsm_perc - ndsm_perc_step
+    max_perc = ndsm_perc + ndsm_perc_step
+    percs = [min_perc if min_perc > 0 else 0, ndsm_perc, ndsm_perc + 1, max_perc]
+
+    # get threshold for NDVI
+    ndvi_percentiles = get_percentiles(ndvi, percs)
+    ndvi_min = ndvi_percentiles[min_perc]
+    ndvi_val = (ndvi_percentiles[ndsm_perc] + ndvi_percentiles[ndsm_perc + 1]) / 2.
+    ndvi_max = ndvi_percentiles[max_perc]
+
+    # percentiles for NIR range
+    ndsm_perc_step = ndsm_perc/2. if ndsm_perc < 20 else 10
+    min_perc_nir = ndsm_perc
+    max_perc_nir = ndsm_perc + 2 * ndsm_perc_step
+    ndsm_perc_nir = ndsm_perc + 1 * ndsm_perc_step
+    percs_nir = [min_perc_nir if min_perc_nir > 0 else 0, ndsm_perc_nir, ndsm_perc_nir + 1, max_perc_nir]
+
+    # get threshold for NIR
+    nir_percentiles = get_percentiles(nir, percs_nir)
+    nir_min = nir_percentiles[min_perc_nir]
+    nir_val = (nir_percentiles[ndsm_perc_nir] + nir_percentiles[ndsm_perc_nir + 1]) / 2.
+    nir_max = nir_percentiles[max_perc_nir]
+    sys.stdout.write(
+        f"Proposal for NDVI threshold in the range of [{ndvi_min}, {ndvi_max}]; e.g.:\n"
+    )
+    sys.stdout.write(f"<NDVI_THRES={ndvi_val}>\n")
+    sys.stdout.write("Proposal for NIR threshold:\n")
+    sys.stdout.write(
+        f"Proposal for NIR threshold in the range of [{nir_min}, {nir_max}]; e.g.:\n"
+    )
+    sys.stdout.write(f"<NIR_THRES={nir_val}>\n")
+
+    # import pdb; pdb.set_trace()
+    grass.run_command("r.mask", flags="r")
+
+
+if __name__ == "__main__":
+    options, flags = grass.parser()
+    atexit.register(cleanup)
+    main()