Added a plugin for processing level3 Land Cover product. (#151)

* Added a plugin for land cover level 3 and a unit test for this level.

* Add the level3 unit test

* Applied formatting.

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* Added comments on classes and passed cultivated classification as is.

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

Author: tebadi

odc/stats/plugins/_registry.py

@@ -40,6 +40,7 @@ def import_all():
     # TODO: make that more automatic
     modules = [
         "odc.stats.plugins.lc_treelite_cultivated.py",
+        "odc.stats.plugins.lc_level3",
         "odc.stats.plugins.lc_treelite_woody",
         "odc.stats.plugins.lc_tf_urban",
         "odc.stats.plugins.lc_veg_class_a1",

odc/stats/plugins/lc_level3.py

@@ -0,0 +1,57 @@
+"""
+Land Cover Level3 classification
+"""
+
+from typing import Tuple
+import xarray as xr
+from ._registry import StatsPluginInterface, register
+
+NODATA = 255
+
+
+class StatsLccsLevel3(StatsPluginInterface):
+    NAME = "ga_ls_lccs_level3"
+    SHORT_NAME = NAME
+    VERSION = "0.0.1"
+    PRODUCT_FAMILY = "lccs"
+
+    @property
+    def measurements(self) -> Tuple[str, ...]:
+        _measurements = ["level3_class"]
+        return _measurements
+
+    def reduce(self, xx: xr.Dataset) -> xr.Dataset:
+
+        l34_dss = xx.classes_l3_l4
+        urban_dss = xx.urban_classes
+        cultivated_dss = xx.cultivated_class
+
+        # Cultivated pipeline applies a mask which feeds only terrestrial veg (110) to the model
+        # Just exclude no data (255) and apply the cultivated results
+        cultivated_mask = cultivated_dss != int(NODATA)
+        l34_cultivated_masked = xr.where(cultivated_mask, cultivated_dss, l34_dss)
+
+        # Urban is classified on l3/4 surface output (210)
+        urban_mask = l34_dss == 210
+        l34_urban_cultivated_masked = xr.where(
+            urban_mask, urban_dss, l34_cultivated_masked
+        )
+
+        attrs = xx.attrs.copy()
+        attrs["nodata"] = NODATA
+        l34_urban_cultivated_masked = l34_urban_cultivated_masked.squeeze(dim=["spec"])
+        dims = l34_urban_cultivated_masked.dims
+
+        data_vars = {
+            "level3_class": xr.DataArray(
+                l34_urban_cultivated_masked.data, dims=dims, attrs=attrs
+            )
+        }
+
+        coords = dict((dim, xx.coords[dim]) for dim in dims)
+        level3 = xr.Dataset(data_vars=data_vars, coords=coords, attrs=attrs)
+
+        return level3
+
+
+register("lccs_level3", StatsLccsLevel3)

tests/test_lc_level3.py

@@ -0,0 +1,83 @@
+import numpy as np
+import pandas as pd
+import xarray as xr
+
+from odc.stats.plugins.lc_level3 import StatsLccsLevel3
+import pytest
+
+expected_l3_classes = [
+    [111, 112, 215],
+    [124, 112, 215],
+    [221, 215, 216],
+    [223, 255, 223],
+]
+
+
+@pytest.fixture(scope="module")
+def image_groups():
+    l34 = np.array(
+        [
+            [
+                [110, 110, 210],
+                [124, 110, 210],
+                [221, 210, 210],
+                [223, 255, 223],
+            ]
+        ],
+        dtype="int",
+    )
+
+    urban = np.array(
+        [
+            [
+                [215, 215, 215],
+                [216, 216, 215],
+                [116, 215, 216],
+                [216, 216, 216],
+            ]
+        ],
+        dtype="int",
+    )
+
+    cultivated = np.array(
+        [
+            [
+                [111, 112, 255],
+                [255, 112, 255],
+                [255, 255, 255],
+                [255, 255, 255],
+            ]
+        ],
+        dtype="int",
+    )
+
+    tuples = [
+        (np.datetime64("2000-01-01T00"), np.datetime64("2000-01-01")),
+    ]
+    index = pd.MultiIndex.from_tuples(tuples, names=["time", "solar_day"])
+    coords = {
+        "x": np.linspace(10, 20, l34.shape[2]),
+        "y": np.linspace(0, 5, l34.shape[1]),
+        "spec": index,
+    }
+
+    data_vars = {
+        "classes_l3_l4": xr.DataArray(
+            l34, dims=("spec", "y", "x"), attrs={"nodata": 255}
+        ),
+        "urban_classes": xr.DataArray(
+            urban, dims=("spec", "y", "x"), attrs={"nodata": 255}
+        ),
+        "cultivated_class": xr.DataArray(
+            cultivated, dims=("spec", "y", "x"), attrs={"nodata": 255}
+        ),
+    }
+    xx = xr.Dataset(data_vars=data_vars, coords=coords)
+    return xx
+
+
+def test_urban_class(image_groups):
+
+    lc_level3 = StatsLccsLevel3()
+    level3_classes = lc_level3.reduce(image_groups)
+    assert (level3_classes.level3_class.values == expected_l3_classes).all()