Renamed class property in compliance with stac standard. Implemented …

…test_layer_parameters in a manner intended to improve ease of code maintenance.

city_metrix/layers/albedo.py

@@ -5,11 +5,19 @@
 from .layer import Layer, get_utm_zone_epsg, get_image_collection
 
 class Albedo(Layer):
-    def __init__(self, start_date="2021-01-01", end_date="2022-01-01", scale_meters=10, threshold=None, **kwargs):
+    """
+    Attributes:
+        start_date: starting date for data retrieval
+        end_date: ending date for data retrieval
+        spatial_resolution: raster resolution in meters (see https://github.com/stac-extensions/raster)
+        threshold: threshold value for filtering the retrieval
+    """
+
+    def __init__(self, start_date="2021-01-01", end_date="2022-01-01", spatial_resolution=10, threshold=None, **kwargs):
         super().__init__(**kwargs)
         self.start_date = start_date
         self.end_date = end_date
-        self.scale_meters = scale_meters
+        self.spatial_resolution = spatial_resolution
         self.threshold = threshold
 
     def get_data(self, bbox):
@@ -30,7 +38,7 @@ def mask_and_count_clouds(s2wc, geom):
             nb_cloudy_pixels = is_cloud.reduceRegion(
                 reducer=ee.Reducer.sum().unweighted(),
                 geometry=geom,
-                scale=self.scale_meters,
+                scale=self.spatial_resolution,
                 maxPixels=1e9
             )
             return s2wc.updateMask(is_cloud.eq(0)).set('nb_cloudy_pixels',
@@ -89,7 +97,7 @@ def calc_s2_albedo(image):
         albedo_mean = s2_albedo.reduce(ee.Reducer.mean())
 
         data = (get_image_collection(
-            ee.ImageCollection(albedo_mean), bbox, self.scale_meters, "albedo")
+            ee.ImageCollection(albedo_mean), bbox, self.spatial_resolution, "albedo")
                 .albedo_mean)
 
         if self.threshold is not None:

city_metrix/layers/alos_dsm.py

@@ -6,9 +6,14 @@
 
 
 class AlosDSM(Layer):
-    def __init__(self, scale_meters=30, **kwargs):
+    """
+    Attributes:
+        spatial_resolution: raster resolution in meters (see https://github.com/stac-extensions/raster)
+    """
+
+    def __init__(self, spatial_resolution=30, **kwargs):
         super().__init__(**kwargs)
-        self.scale_meters = scale_meters
+        self.spatial_resolution = spatial_resolution
 
     def get_data(self, bbox):
         dataset = ee.ImageCollection("JAXA/ALOS/AW3D30/V3_2")
@@ -17,6 +22,6 @@ def get_data(self, bbox):
                                       .select('DSM')
                                       .mean()
                                       )
-        data = get_image_collection(alos_dsm, bbox, self.scale_meters, "ALOS DSM").DSM
+        data = get_image_collection(alos_dsm, bbox, self.spatial_resolution, "ALOS DSM").DSM
 
         return data

city_metrix/layers/average_net_building_height.py

@@ -6,9 +6,14 @@
 from .layer import Layer, get_utm_zone_epsg, get_image_collection
 
 class AverageNetBuildingHeight(Layer):
-    def __init__(self, scale_meters=100, **kwargs):
+    """
+    Attributes:
+        spatial_resolution: raster resolution in meters (see https://github.com/stac-extensions/raster)
+    """
+
+    def __init__(self, spatial_resolution=100, **kwargs):
         super().__init__(**kwargs)
-        self.scale_meters = scale_meters
+        self.spatial_resolution = spatial_resolution
 
     def get_data(self, bbox):
         # https://ghsl.jrc.ec.europa.eu/ghs_buH2023.php
@@ -19,7 +24,7 @@ def get_data(self, bbox):
 
         anbh = ee.Image("projects/wri-datalab/GHSL/GHS-BUILT-H-ANBH_GLOBE_R2023A")
         data = (get_image_collection(
-            ee.ImageCollection(anbh), bbox, self.scale_meters, "average net building height")
+            ee.ImageCollection(anbh), bbox, self.spatial_resolution, "average net building height")
                 .b1)
 
         return data

city_metrix/layers/built_up_height.py

@@ -7,9 +7,14 @@
 
 
 class BuiltUpHeight(Layer):
-    def __init__(self, scale_meters=100, **kwargs):
+    """
+    Attributes:
+        spatial_resolution: raster resolution in meters (see https://github.com/stac-extensions/raster)
+    """
+
+    def __init__(self, spatial_resolution=100, **kwargs):
         super().__init__(**kwargs)
-        self.scale_meters = scale_meters
+        self.spatial_resolution = spatial_resolution
 
     def get_data(self, bbox):
         # Notes for Heat project:
@@ -19,6 +24,6 @@ def get_data(self, bbox):
         # ee.ImageCollection("projects/wri-datalab/GHSL/GHS-BUILT-H-ANBH_R2023A")
 
         built_height = ee.Image("JRC/GHSL/P2023A/GHS_BUILT_H/2018")
-        data = get_image_collection(ee.ImageCollection(built_height), bbox, self.scale_meters, "built up height")
+        data = get_image_collection(ee.ImageCollection(built_height), bbox, self.spatial_resolution, "built up height")
         return data.built_height
 

city_metrix/layers/esa_world_cover.py

@@ -20,29 +20,36 @@ class EsaWorldCoverClass(Enum):
     MOSS_AND_LICHEN = 100
 
 class EsaWorldCover(Layer):
+    """
+    Attributes:
+        land_cover_class: Enum value from EsaWorldCoverClass
+        year: year used for data retrieval
+        spatial_resolution: raster resolution in meters (see https://github.com/stac-extensions/raster)
+    """
+
     STAC_CATALOG_URI = "https://services.terrascope.be/stac/"
     STAC_COLLECTION_ID = "urn:eop:VITO:ESA_WorldCover_10m_2020_AWS_V1"
     STAC_ASSET_ID = "ESA_WORLDCOVER_10M_MAP"
 
-    def __init__(self, land_cover_class=None, year=2020, scale_meters=10, **kwargs):
+    def __init__(self, land_cover_class=None, year=2020, spatial_resolution=10, **kwargs):
         super().__init__(**kwargs)
         self.land_cover_class = land_cover_class
         self.year = year
-        self.scale_meters = scale_meters
+        self.spatial_resolution = spatial_resolution
 
     def get_data(self, bbox):
         if self.year == 2020:
             data = get_image_collection(
                 ee.ImageCollection("ESA/WorldCover/v100"),
                 bbox,
-                self.scale_meters,
+                self.spatial_resolution,
                 "ESA world cover"
             ).Map
         elif self.year == 2021:
             data = get_image_collection(
                 ee.ImageCollection("ESA/WorldCover/v200"),
                 bbox,
-                self.scale_meters,
+                self.spatial_resolution,
                 "ESA world cover"
             ).Map
 

city_metrix/layers/land_surface_temperature.py

@@ -6,11 +6,18 @@
 import xarray
 
 class LandSurfaceTemperature(Layer):
-    def __init__(self, start_date="2013-01-01", end_date="2023-01-01", scale_meters=30, **kwargs):
+    """
+    Attributes:
+        start_date: starting date for data retrieval
+        end_date: ending date for data retrieval
+        spatial_resolution: raster resolution in meters (see https://github.com/stac-extensions/raster)
+    """
+
+    def __init__(self, start_date="2013-01-01", end_date="2023-01-01", spatial_resolution=30, **kwargs):
         super().__init__(**kwargs)
         self.start_date = start_date
         self.end_date = end_date
-        self.scale_meters = scale_meters
+        self.spatial_resolution = spatial_resolution
 
     def get_data(self, bbox):
         def cloud_mask(image):
@@ -31,5 +38,5 @@ def apply_scale_factors(image):
             .map(apply_scale_factors) \
             .reduce(ee.Reducer.mean())
 
-        data = get_image_collection(ee.ImageCollection(l8_st), bbox, self.scale_meters, "LST").ST_B10_mean
+        data = get_image_collection(ee.ImageCollection(l8_st), bbox, self.spatial_resolution, "LST").ST_B10_mean
         return data

city_metrix/layers/nasa_dem.py

@@ -6,9 +6,14 @@
 
 
 class NasaDEM(Layer):
-    def __init__(self, scale_meters=30, **kwargs):
+    """
+    Attributes:
+        spatial_resolution: raster resolution in meters (see https://github.com/stac-extensions/raster)
+    """
+
+    def __init__(self, spatial_resolution=30, **kwargs):
         super().__init__(**kwargs)
-        self.scale_meters = scale_meters
+        self.spatial_resolution = spatial_resolution
 
     def get_data(self, bbox):
         dataset = ee.Image("NASA/NASADEM_HGT/001")
@@ -17,6 +22,6 @@ def get_data(self, bbox):
                                       .select('elevation')
                                       .mean()
                                       )
-        data = get_image_collection(nasa_dem, bbox, self.scale_meters, "NASA DEM").elevation
+        data = get_image_collection(nasa_dem, bbox, self.spatial_resolution, "NASA DEM").elevation
 
         return data

city_metrix/layers/natural_areas.py

@@ -5,12 +5,17 @@
 from .esa_world_cover import EsaWorldCover, EsaWorldCoverClass
 
 class NaturalAreas(Layer):
-    def __init__(self, scale_meters=10, **kwargs):
+    """
+    Attributes:
+        spatial_resolution: raster resolution in meters (see https://github.com/stac-extensions/raster)
+    """
+
+    def __init__(self, spatial_resolution=10, **kwargs):
         super().__init__(**kwargs)
-        self.scale_meters = scale_meters
+        self.spatial_resolution = spatial_resolution
 
     def get_data(self, bbox):
-        esa_world_cover = EsaWorldCover(scale_meters=self.scale_meters).get_data(bbox)
+        esa_world_cover = EsaWorldCover(spatial_resolution=self.spatial_resolution).get_data(bbox)
         reclass_map = {
             EsaWorldCoverClass.TREE_COVER.value: 1,
             EsaWorldCoverClass.SHRUBLAND.value: 1,

city_metrix/layers/ndvi_sentinel2_gee.py

@@ -4,16 +4,18 @@
 class NdviSentinel2(Layer):
     """"
     NDVI = Sentinel-2 Normalized Difference Vegetation Index
-    param: year: The satellite imaging year.
+    Attributes:
+        year: The satellite imaging year.
+        spatial_resolution: raster resolution in meters (see https://github.com/stac-extensions/raster)
     return: a rioxarray-format DataArray
     Author of associated Jupyter notebook: [email protected]
     Notebook: https://github.com/wri/cities-cities4forests-indicators/blob/dev-eric/scripts/extract-VegetationCover.ipynb
     Reference: https://en.wikipedia.org/wiki/Normalized_difference_vegetation_index
     """
-    def __init__(self, year=None, scale_meters=10, **kwargs):
+    def __init__(self, year=None, spatial_resolution=10, **kwargs):
         super().__init__(**kwargs)
         self.year = year
-        self.scale_meters = scale_meters
+        self.spatial_resolution = spatial_resolution
 
     def get_data(self, bbox):
         if self.year is None:
@@ -40,7 +42,7 @@ def calculate_ndvi(image):
         ndvi_mosaic = ndvi.qualityMosaic('NDVI')
 
         ic = ee.ImageCollection(ndvi_mosaic)
-        ndvi_data = get_image_collection(ic, bbox, self.scale_meters, "NDVI")
+        ndvi_data = get_image_collection(ic, bbox, self.spatial_resolution, "NDVI")
 
         xdata = ndvi_data.to_dataarray()
 

city_metrix/layers/tree_canopy_height.py

@@ -6,19 +6,24 @@
 import ee
 
 class TreeCanopyHeight(Layer):
+    """
+    Attributes:
+        spatial_resolution: raster resolution in meters (see https://github.com/stac-extensions/raster)
+    """
+
     name = "tree_canopy_height"
     NO_DATA_VALUE = 0
 
-    def __init__(self, scale_meters=1, **kwargs):
+    def __init__(self, spatial_resolution=1, **kwargs):
         super().__init__(**kwargs)
-        self.scale_meters = scale_meters
+        self.spatial_resolution = spatial_resolution
 
     def get_data(self, bbox):
         canopy_ht = ee.ImageCollection("projects/meta-forest-monitoring-okw37/assets/CanopyHeight")
         # aggregate time series into a single image
         canopy_ht = canopy_ht.reduce(ee.Reducer.mean()).rename("cover_code")
 
         data = get_image_collection(ee.ImageCollection(canopy_ht), bbox,
-                                    self.scale_meters, "tree canopy height")
+                                    self.spatial_resolution, "tree canopy height")
 
         return data.cover_code

city_metrix/layers/tree_cover.py

@@ -8,23 +8,27 @@
 class TreeCover(Layer):
     """
     Merged tropical and nontropical tree cover from WRI
+    Attributes:
+        min_tree_cover: minimum tree-cover values used for filtering results
+        max_tree_cover: maximum tree-cover values used for filtering results
+        spatial_resolution: raster resolution in meters (see https://github.com/stac-extensions/raster)
     """
-    
+
     NO_DATA_VALUE = 255
 
-    def __init__(self, min_tree_cover=None, max_tree_cover=None, scale_meters=10, **kwargs):
+    def __init__(self, min_tree_cover=None, max_tree_cover=None, spatial_resolution=10, **kwargs):
         super().__init__(**kwargs)
         self.min_tree_cover = min_tree_cover
         self.max_tree_cover = max_tree_cover
-        self.scale_meters = scale_meters
+        self.spatial_resolution = spatial_resolution
 
     def get_data(self, bbox):
         tropics = ee.ImageCollection('projects/wri-datalab/TropicalTreeCover')
         nontropics = ee.ImageCollection('projects/wri-datalab/TTC-nontropics')
         merged_ttc = tropics.merge(nontropics)
         ttc_image = merged_ttc.reduce(ee.Reducer.mean()).rename('ttc')
 
-        data = get_image_collection(ee.ImageCollection(ttc_image), bbox, self.scale_meters, "tree cover").ttc
+        data = get_image_collection(ee.ImageCollection(ttc_image), bbox, self.spatial_resolution, "tree cover").ttc
 
         if self.min_tree_cover is not None:
             data = data.where(data >= self.min_tree_cover)

city_metrix/layers/urban_land_use.py

@@ -7,10 +7,16 @@
 
 
 class UrbanLandUse(Layer):
-    def __init__(self, band='lulc', scale_meters=5, **kwargs):
+    """
+    Attributes:
+        band: raster band used for data retrieval
+        spatial_resolution: raster resolution in meters (see https://github.com/stac-extensions/raster)
+    """
+
+    def __init__(self, band='lulc', spatial_resolution=5, **kwargs):
         super().__init__(**kwargs)
         self.band = band
-        self.scale_meters = scale_meters
+        self.spatial_resolution = spatial_resolution
 
     def get_data(self, bbox):
         dataset = ee.ImageCollection("projects/wri-datalab/cities/urban_land_use/V1")
@@ -28,6 +34,6 @@ def get_data(self, bbox):
                                      .rename('lulc')
                                      )
 
-        data = get_image_collection(ulu, bbox, self.scale_meters, "urban land use").lulc
+        data = get_image_collection(ulu, bbox, self.spatial_resolution, "urban land use").lulc
 
         return data

city_metrix/layers/world_pop.py

@@ -6,9 +6,14 @@
 from .layer import Layer, get_utm_zone_epsg, get_image_collection
 
 class WorldPop(Layer):
-    def __init__(self, scale_meters=100, **kwargs):
+    """
+    Attributes:
+        spatial_resolution: raster resolution in meters (see https://github.com/stac-extensions/raster)
+    """
+
+    def __init__(self, spatial_resolution=100, **kwargs):
         super().__init__(**kwargs)
-        self.scale_meters = scale_meters
+        self.spatial_resolution = spatial_resolution
 
     def get_data(self, bbox):
         # load population
@@ -20,5 +25,5 @@ def get_data(self, bbox):
                      .mean()
                      )
 
-        data = get_image_collection(world_pop, bbox, self.scale_meters, "world pop")
+        data = get_image_collection(world_pop, bbox, self.spatial_resolution, "world pop")
         return data.population