diff --git a/docs/examples/calculate-forest-metrics.ipynb b/docs/examples/calculate-forest-metrics.ipynb index 45dd64b..867af7b 100644 --- a/docs/examples/calculate-forest-metrics.ipynb +++ b/docs/examples/calculate-forest-metrics.ipynb @@ -14,11 +14,11 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": { "ExecuteTime": { - "end_time": "2025-04-09T22:16:05.213941Z", - "start_time": "2025-04-09T22:16:04.278346Z" + "end_time": "2025-10-02T01:46:11.326427Z", + "start_time": "2025-10-02T01:46:10.012225Z" } }, "outputs": [], @@ -26,7 +26,7 @@ "from pyforestscan.handlers import read_lidar\n", "from pyforestscan.visualize import plot_pad, plot_metric\n", "from pyforestscan.filters import filter_hag\n", - "from pyforestscan.calculate import assign_voxels, calculate_pad, calculate_pai, calculate_fhd, calculate_chm" + "from pyforestscan.calculate import assign_voxels, calculate_pad, calculate_pai, calculate_fhd, calculate_chm, calculate_canopy_cover" ] }, { @@ -39,7 +39,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -56,7 +56,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -74,7 +74,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -93,7 +93,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -102,7 +102,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -130,7 +130,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -139,17 +139,9 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 13, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/iosefa/repos/PyForestScan/pyforestscan/visualize.py:178: RuntimeWarning: All-NaN slice encountered\n", - " pad_2d = np.nanmax(pad, axis=1)\n" - ] - }, { "data": { "image/png": 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", @@ -175,7 +167,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -184,7 +176,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -202,6 +194,43 @@ "plot_metric('Plant Area Index', pai, extent, metric_name='PAI', cmap='viridis', fig_size=None)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Canopy Cover\n", + "We can compute canopy cover at a height threshold z using the Beer–Lambert relation. Here, we use z = 2 m and k = 0.5 (spherical leaf-angle assumption)." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "cover = calculate_canopy_cover(pad, voxel_height=voxel_resolution[-1], min_height=2.0, k=0.5)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_metric('Canopy Cover (z=2 m)', cover, extent, metric_name='Cover', cmap='viridis', fig_size=None)" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -212,7 +241,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -221,7 +250,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -238,6 +267,13 @@ "source": [ "plot_metric('Foliage Height Diversity', fhd, extent, metric_name='FHD', cmap='viridis', fig_size=None)" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/docs/usage/forest-structure/canopy_cover.md b/docs/usage/forest-structure/canopy_cover.md new file mode 100644 index 0000000..8577c01 --- /dev/null +++ b/docs/usage/forest-structure/canopy_cover.md @@ -0,0 +1,49 @@ +# Canopy Cover (GEDI-style) + +## Definition + +Canopy cover at height z is the fraction of ground area occluded by vegetation above z meters (Height Above Ground). +Following GEDI, we model the gap probability using the Beer–Lambert law: + +Cover(z) = 1 − exp(−k · PAI_above(z)) + +Where: +- `k` is the extinction coefficient (default 0.5 for spherical leaf-angle assumption). +- `PAI_above(z)` is the vertical integral of Plant Area Density (PAD) above height `z`. + +Common choices: +- Tree canopy cover (GEDI-like): z = 2.0 m. +- Total vegetative cover: z = 0.0 m. + +## Usage + +```python +from pyforestscan.handlers import read_lidar +from pyforestscan.filters import filter_hag +from pyforestscan.calculate import assign_voxels, calculate_pad, calculate_canopy_cover +from pyforestscan.visualize import plot_metric + +file_path = "../example_data/20191210_5QKB020880.laz" + +# Ensure HAG is present for PAD/PAI calculations +arrays = read_lidar(file_path, "EPSG:32605", hag=True) +arrays = filter_hag(arrays) +points = arrays[0] + +voxel_resolution = (5, 5, 1) # dx, dy, dz in meters +voxels, extent = assign_voxels(points, voxel_resolution) + +pad = calculate_pad(voxels, voxel_resolution[-1]) + +# GEDI-like canopy cover at z=2 m +cover = calculate_canopy_cover(pad, voxel_height=voxel_resolution[-1], min_height=2.0, k=0.5) + +plot_metric('Canopy Cover (z=2 m)', cover, extent, metric_name='Cover', cmap='viridis') +``` + +## Notes + +- As z increases, canopy cover decreases because less foliage lies above higher thresholds. +- `calculate_canopy_cover` returns NaN where PAD is entirely missing in the integration range. +- When tiling large EPT datasets, use `process_with_tiles(..., metric="cover", voxel_height=..., cover_min_height=2.0, cover_k=0.5)` to write GeoTIFF tiles. + diff --git a/docs/usage/forest-structure/intro.md b/docs/usage/forest-structure/intro.md index cf549e3..4c19bf7 100644 --- a/docs/usage/forest-structure/intro.md +++ b/docs/usage/forest-structure/intro.md @@ -13,6 +13,7 @@ Most calculations for the metrics follows the method outlined in Kamoske et al. * [Plant Area Density (PAD)](pad.md) * [Plant Area Index (PAI)](pai.md) * [Foliage Height Diversity (FHD)](fhd.md) +* [Canopy Cover](canopy_cover.md) ## References diff --git a/pyforestscan/__init__.py b/pyforestscan/__init__.py index e69de29..3b21c05 100644 --- a/pyforestscan/__init__.py +++ b/pyforestscan/__init__.py @@ -0,0 +1,19 @@ +from .calculate import ( + assign_voxels, + calculate_pad, + calculate_pai, + calculate_fhd, + calculate_chm, + generate_dtm, + calculate_canopy_cover, +) + +__all__ = [ + "assign_voxels", + "calculate_pad", + "calculate_pai", + "calculate_fhd", + "calculate_chm", + "generate_dtm", + "calculate_canopy_cover", +] diff --git a/pyforestscan/calculate.py b/pyforestscan/calculate.py index 62d071b..d25347e 100644 --- a/pyforestscan/calculate.py +++ b/pyforestscan/calculate.py @@ -130,7 +130,9 @@ def calculate_pad(voxel_returns, if drop_ground: pad[:, :, 0] = np.nan - pad[voxel_returns[:, :, 0] == 0, :] = np.nan + # Mask only columns that have zero returns across all Z (true empty columns) + empty_columns = (np.sum(voxel_returns, axis=2) == 0) + pad[empty_columns, :] = np.nan return pad @@ -168,6 +170,62 @@ def calculate_pai(pad, return pai +def calculate_canopy_cover(pad: np.ndarray, + voxel_height: float, + min_height: float = 2.0, + max_height: float | None = None, + k: float = 0.5) -> np.ndarray: + """ + Calculate GEDI-style canopy cover at a height threshold using PAD. + + Uses the Beer–Lambert relation: Cover(z) = 1 - exp(-k * PAI_above(z)), where + PAI_above(z) is the integrated Plant Area Index above height z. + + Args: + pad (np.ndarray): 3D array of PAD values with shape (X, Y, Z). + voxel_height (float): Height of each voxel in meters (> 0). + min_height (float, optional): Height-above-ground threshold z (in meters) at which + to compute canopy cover. Defaults to 2.0 m (GEDI convention). + max_height (float or None, optional): Maximum height to integrate up to. If None, + integrates to the top of the PAD volume. Defaults to None. + k (float, optional): Extinction coefficient (Beer–Lambert constant). Defaults to 0.5. + + Returns: + np.ndarray: 2D array (X, Y) of canopy cover values in [0, 1], with NaN where + PAD is entirely missing for the integration range. + + Raises: + ValueError: If parameters are invalid (e.g., non-positive voxel_height, k < 0, + or min_height >= max_height). + """ + if voxel_height <= 0: + raise ValueError(f"voxel_height must be > 0 metres (got {voxel_height})") + if k < 0: + raise ValueError(f"k must be >= 0 (got {k})") + + # Compute PAI integrated from min_height up to max_height/top + pai_above = calculate_pai(pad, voxel_height, min_height=min_height, max_height=max_height) + + # Identify columns that are entirely NaN within the integration range + if max_height is None: + max_height = pad.shape[2] * voxel_height + if min_height >= max_height: + raise ValueError("Minimum height index must be less than maximum height index.") + start_idx = int(np.ceil(min_height / voxel_height)) + end_idx = int(np.floor(max_height / voxel_height)) + range_slice = pad[:, :, start_idx:end_idx] + all_nan_mask = np.all(np.isnan(range_slice), axis=2) + + # Beer–Lambert canopy cover + cover = 1.0 - np.exp(-k * pai_above) + + # Clamp to [0,1] and set invalids + cover = np.where(np.isfinite(cover), cover, np.nan) + cover = np.clip(cover, 0.0, 1.0) + cover[all_nan_mask] = np.nan + return cover + + def calculate_fhd(voxel_returns) -> np.ndarray: """ Calculate the Foliage Height Diversity (FHD) for a given set of voxel returns. diff --git a/pyforestscan/process.py b/pyforestscan/process.py index 9a72994..b243c11 100644 --- a/pyforestscan/process.py +++ b/pyforestscan/process.py @@ -5,7 +5,7 @@ from tqdm import tqdm -from pyforestscan.calculate import calculate_fhd, calculate_pad, calculate_pai, assign_voxels, calculate_chm +from pyforestscan.calculate import calculate_fhd, calculate_pad, calculate_pai, assign_voxels, calculate_chm, calculate_canopy_cover from pyforestscan.filters import remove_outliers_and_clean from pyforestscan.handlers import create_geotiff from pyforestscan.pipeline import _hag_raster, _hag_delaunay @@ -50,7 +50,8 @@ def _crop_dtm(dtm_path, tile_min_x, tile_min_y, tile_max_x, tile_max_y): def process_with_tiles(ept_file, tile_size, output_path, metric, voxel_size, voxel_height=1, buffer_size=0.1, srs=None, hag=False, - hag_dtm=False, dtm=None, bounds=None, interpolation=None, remove_outliers=False) -> None: + hag_dtm=False, dtm=None, bounds=None, interpolation=None, remove_outliers=False, + cover_min_height: float = 2.0, cover_k: float = 0.5) -> None: """ Process a large EPT point cloud by tiling, compute CHM or other metrics for each tile, and write the results to the specified output directory. @@ -59,7 +60,7 @@ def process_with_tiles(ept_file, tile_size, output_path, metric, voxel_size, ept_file (str): Path to the EPT file containing the point cloud data. tile_size (tuple): Size of each tile as (tile_width, tile_height). output_path (str): Directory where the output files will be saved. - metric (str): Metric to compute for each tile ("chm", "fhd", or "pai"). + metric (str): Metric to compute for each tile ("chm", "fhd", "pai", or "cover"). voxel_size (tuple): Voxel resolution as (x_resolution, y_resolution, z_resolution). voxel_height (float, optional): Height of each voxel in meters. Required if metric is "pai". buffer_size (float, optional): Fractional buffer size relative to tile size (e.g., 0.1 for 10% buffer). Defaults to 0.1. @@ -72,6 +73,8 @@ def process_with_tiles(ept_file, tile_size, output_path, metric, voxel_size, If None, tiling is done over the entire dataset. interpolation (str or None, optional): Interpolation method for CHM calculation ("linear", "cubic", "nearest", or None). remove_outliers (bool, optional): Whether to remove statistical outliers before calculating metrics. Defaults to False. + cover_min_height (float, optional): Height threshold (in meters) for canopy cover (used when metric == "cover"). Defaults to 2.0. + cover_k (float, optional): Beer–Lambert extinction coefficient for canopy cover. Defaults to 0.5. Returns: None @@ -80,7 +83,7 @@ def process_with_tiles(ept_file, tile_size, output_path, metric, voxel_size, ValueError: If an unsupported metric is requested, if buffer or voxel sizes are invalid, or required arguments are missing. FileNotFoundError: If the EPT or DTM file does not exist, or a required file for processing is missing. """ - if metric not in ["chm", "fhd", "pai"]: + if metric not in ["chm", "fhd", "pai", "cover"]: raise ValueError(f"Unsupported metric: {metric}") (min_z, max_z) = (None, None) @@ -188,7 +191,7 @@ def process_with_tiles(ept_file, tile_size, output_path, metric, voxel_size, result_file = os.path.join(output_path, f"tile_{i}_{j}_chm.tif") create_geotiff(chm, result_file, srs, core_extent) - elif metric in ["fhd", "pai"]: + elif metric in ["fhd", "pai", "cover"]: voxels, spatial_extent = assign_voxels(tile_points, voxel_size) if metric == "fhd": @@ -204,6 +207,22 @@ def process_with_tiles(ept_file, tile_size, output_path, metric, voxel_size, else: result = calculate_pai(pad, voxel_height) result = np.where(np.isfinite(result), result, 0) + elif metric == "cover": + if not voxel_height: + raise ValueError(f"voxel_height is required for metric {metric}") + + pad = calculate_pad(voxels, voxel_size[-1]) + if np.all(pad == 0): + result = np.zeros((pad.shape[0], pad.shape[1])) + else: + result = calculate_canopy_cover( + pad, + voxel_height=voxel_height, + min_height=cover_min_height, + max_height=None, + k=cover_k, + ) + result = np.where(np.isfinite(result), result, 0) if current_buffer_size > 0: if buffer_pixels_x * 2 >= result.shape[1] or buffer_pixels_y * 2 >= result.shape[0]: diff --git a/pyforestscan/utils.py b/pyforestscan/utils.py index bafb4a0..624cdbf 100644 --- a/pyforestscan/utils.py +++ b/pyforestscan/utils.py @@ -48,25 +48,41 @@ def get_srs_from_ept(ept_file) -> str or None: def get_bounds_from_ept(ept_file) -> tuple[float, float, float, float, float, float]: """ - Extract the spatial bounds of a point cloud from an EPT (Entwine Point Tile) file using PDAL. + Extract dataset bounds from an EPT (Entwine Point Tile) source. Args: - ept_file (str): Path to the EPT file containing the point cloud data. + ept_file (str): Path or URL to the EPT JSON. Returns: - tuple: A tuple of bounds in the format (min_x, max_x, min_y, max_y, min_z, max_z). + tuple: (min_x, max_x, min_y, max_y, min_z, max_z) Raises: KeyError: If bounds information is not available in the EPT metadata. + ValueError: If the EPT bounds are malformed. """ ept_json = _read_ept_json(ept_file) - try: - bounds = ept_json["bounds"] - return bounds - except KeyError: + # Prefer "bounds"; some datasets may also include "boundsConforming". + raw_bounds = ept_json.get("bounds") + if raw_bounds is None: + raw_bounds = ept_json.get("boundsConforming") + if raw_bounds is None: raise KeyError("Bounds information is not available in the ept metadata.") + # Handle common representations: list/tuple of 6 numbers or a dict with keys. + if isinstance(raw_bounds, (list, tuple)) and len(raw_bounds) == 6: + min_x, min_y, min_z, max_x, max_y, max_z = raw_bounds + elif isinstance(raw_bounds, dict): + try: + min_x = raw_bounds["minx"]; min_y = raw_bounds["miny"]; min_z = raw_bounds["minz"] + max_x = raw_bounds["maxx"]; max_y = raw_bounds["maxy"]; max_z = raw_bounds["maxz"] + except Exception as e: + raise ValueError(f"Malformed EPT bounds dictionary: {e}") from None + else: + raise ValueError(f"Unexpected EPT bounds format: {type(raw_bounds)}") + + return (min_x, max_x, min_y, max_y, min_z, max_z) + def tile_las_in_memory( las_file, diff --git a/tests/test_calculate.py b/tests/test_calculate.py index 039ba14..613c374 100644 --- a/tests/test_calculate.py +++ b/tests/test_calculate.py @@ -7,7 +7,8 @@ calculate_pad, calculate_pai, calculate_fhd, - calculate_chm + calculate_chm, + calculate_canopy_cover ) import math @@ -407,8 +408,13 @@ def test_calculate_fhd_high_diversity(): def test_calculate_fhd_partial_zero_distribution(): - voxel_returns = np.random.randint(0, 5, size=(5, 5, 5)) + # Construct deterministic columns to avoid flaky equality due to randomness. + voxel_returns = np.zeros((5, 5, 5), dtype=int) + # Column with all mass in a single bin -> entropy 0 voxel_returns[0, 0, :] = [1, 0, 0, 0, 0] + # Column with uniform distribution across bins -> maximal entropy + voxel_returns[1, 1, :] = [1, 1, 1, 1, 1] + fhd = calculate_fhd(voxel_returns) assert isinstance(fhd, np.ndarray) assert fhd.shape == (5, 5) @@ -601,3 +607,42 @@ def test_calculate_chm_large_heights(): assert isinstance(chm, np.ndarray) assert chm.ndim == 2 assert np.all(chm >= 1000) + + +# ---------------------------- +# Tests for calculate_canopy_cover +# ---------------------------- + +def test_calculate_canopy_cover_basic(): + pad = np.ones((4, 3, 10), dtype=float) + voxel_height = 1.0 + z = 2.0 + k = 0.5 + # PAI above z=2 with dz=1 and 10 layers => sum from idx 2..9 = 8 + expected_cover = 1.0 - np.exp(-k * 8.0) + cov = calculate_canopy_cover(pad, voxel_height, min_height=z, k=k) + assert cov.shape == (4, 3) + assert np.allclose(cov, expected_cover) + + +def test_calculate_canopy_cover_zero_pad(): + pad = np.zeros((2, 2, 5), dtype=float) + cov = calculate_canopy_cover(pad, voxel_height=1.0, min_height=2.0, k=0.5) + assert np.all(cov == 0.0) + + +def test_calculate_canopy_cover_nans_propagate(): + pad = np.ones((2, 2, 6), dtype=float) + pad[:, :, 3:] = np.nan # everything above z=3 m is NaN + cov = calculate_canopy_cover(pad, voxel_height=1.0, min_height=3.0, k=0.5) + # All NaN in integration range -> NaN in cover + assert np.all(np.isnan(cov)) + + +def test_calculate_canopy_cover_monotonic_with_height(): + pad = np.ones((3, 3, 10), dtype=float) + cov0 = calculate_canopy_cover(pad, voxel_height=1.0, min_height=0.0, k=0.5) + cov2 = calculate_canopy_cover(pad, voxel_height=1.0, min_height=2.0, k=0.5) + cov5 = calculate_canopy_cover(pad, voxel_height=1.0, min_height=5.0, k=0.5) + assert np.all(cov0 >= cov2) + assert np.all(cov2 >= cov5) diff --git a/tests/test_utils.py b/tests/test_utils.py index bc2cee6..d25d673 100644 --- a/tests/test_utils.py +++ b/tests/test_utils.py @@ -25,11 +25,16 @@ def test_get_srs_from_usgs_hawaii_ept(): def test_get_bounds_from_usgs_hawaii_ept(): """ Tests retrieving the EPT bounds from a real EPT JSON. + get_bounds_from_ept returns (min_x, max_x, min_y, max_y, min_z, max_z). """ ept_url = "https://s3-us-west-2.amazonaws.com/usgs-lidar-public/HI_Hawaii_Island_2017/ept.json" bounds = get_bounds_from_ept(ept_url) - assert len(bounds) == 6, "Bounds should be [minX, maxX, minY, maxY, minZ, maxZ]." - assert bounds == [-17405012, 2136822, -5677, -17229390, 2312444, 169945] + assert len(bounds) == 6, "Bounds should be (minX, maxX, minY, maxY, minZ, maxZ)." + expected = (-17405012, -17229390, 2136822, 2312444, -5677, 169945) + assert tuple(bounds) == expected + + min_x, max_x, min_y, max_y, min_z, max_z = bounds + assert min_x < max_x and min_y < max_y and min_z < max_z def test_tile_las_in_memory():