API Reference#

This page provides an auto-generated summary of alsDB’s public API. For usage examples and conceptual background, refer to the alsDB User Guide guide.

Core classes#

`ALSDatabase`([storage_type, uri, schema_cfg, ...])	Write interface for the ALS TileDB array.
`ALSProvider`([storage_type, uri, url, ...])	Read interface for the ALS TileDB array.
`ALSTile`(path[, classification_filter, ...])	Processes a single LAZ tile into arrays ready for TileDB ingestion.

Storage#

storage.ALSZarrStore(path[, mode, ...])

Zarr-backed store for ALS gridded products.

TileDB backend#

`TileDBProvider`([storage_type, uri, url, ...])	Base class for managing low-level interactions with TileDB arrays for ALS data.
`TileDBSchemaConfig`([tile_extent_x, ...])	Schema and domain configuration for the ALS TileDB array.
`create_schema`(cfg)	Build a sparse TileDB array schema for LAS point-cloud data.

Tile and tile name#

`Tile`(path)	Wrapper around any LAZ/LAS tile file.
`PNOATileName`(year, region, tile_x_km, ...)	Metadata derived from a PNOA LAZ tile filename.
`parse_tile_filename`(filename)	Parse a PNOA LAZ filename and return a `PNOATileName`.

Processing#

Canopy Height Model#

`processing.chm.compute_chm`(provider, store)	Compute surface products on one fixed north-up grid and write to Zarr.
`processing.chm.compute_dtm`(provider, store)	Compute surface products on one fixed north-up grid and write to Zarr.
`processing.chm.compute_dsm`(provider, store)	Compute surface products on one fixed north-up grid and write to Zarr.
`processing.chm.compute_all`(provider, store)	Compute surface products on one fixed north-up grid and write to Zarr.

Gap fraction and LAI#

processing.gap.compute_gap_fraction(...[, ...])

Compute gap fraction (and optionally effective LAI) and write into store.

Structural metrics and biomass#

`processing.biomass.compute_metrics`(provider, ...)	Compute LiDAR structural metrics and write them into store.
`processing.biomass.compute_biomass`(provider, ...)	Estimate Above-Ground Biomass (AGB) and write into store.
`processing.biomass.naesset_model`(metrics[, ...])	Næsset-style power-law AGB model (Mg ha⁻¹).
`processing.biomass.calibrate_naesset`(h95, ...)	Fit Næsset power-law AGB coefficients (a, b, c) to field-plot data.
`processing.biomass.wrap_sklearn_model`(estimator)	Wrap a fitted scikit-learn estimator as a `model_fn` for `compute_biomass`.

Multi-temporal change detection#

processing.change.compute_change(store, ...)

Compute per-pixel change between two survey years for variable.

Individual tree segmentation#

processing.trees.segment_trees(provider[, ...])

Segment individual trees using filters.litree.

Waveform simulation#

`processing.waveform.simulate_waveform`(...[, ...])	Query a footprint and simulate it; see waveform_from_points for parameters.
`processing.waveform.simulate_batch`(provider, ...)	Simulate waveforms for a batch of shot locations in parallel.

Tiling utilities#

`processing._tiling.tile_bboxes`(bbox, ...)	Partition bbox into a grid of sub-tiles.
`processing._tiling.run_tiled`(worker_fn, ...)	Run worker_fn over all tiles, sequentially or in a thread pool.
`processing._tiling.array_crs`(provider)	Return the CRS string stored in the TileDB array metadata.
`processing._tiling.array_data_bbox`(provider)	Return the bounding box of actually stored data via `nonempty_domain`.
`processing._tiling.check_year_exists`(year, ...)	Return `True` if year falls within the ingested year range.
`processing._tiling.check_bbox_overlap`(...)	Return `True` if requested overlaps the stored data extent.

Visualisation#

2-D point-cloud plots#

`utils.viz.plot_overview`(df[, resolution, ...])	Four-panel overview figure: DSM with hillshade, RGB, intensity, classification.
`utils.viz.plot_dsm`(df[, resolution, ...])	Plot a Digital Surface Model (max Z per cell), optionally with hillshade.
`utils.viz.plot_rgb`(df[, resolution, ...])	Plot an RGB orthoimage from the colourised point cloud.
`utils.viz.plot_intensity`(df[, resolution, ...])	Plot a mean-intensity raster (greyscale).
`utils.viz.plot_classification`(df[, ...])	Plot a classification map with standard LAS colour coding.
`utils.viz.plot_waveform`(result[, ax_wave, ...])	Two-panel waveform plot for a `WaveformResult`.
`utils.viz.plot_rh_profile`(result[, ax_rh, ...])	Two-panel figure matching the GEDI L2A canonical waveform representation.
`utils.viz.plot_waveforms_3d`(results[, ...])	3-D waterfall plot of simulated GEDI-like waveforms.
`utils.viz.plot_pointcloud_3d`(df[, color_by, ...])	3-D scatter plot of the point cloud.

Gridded product plots#

`utils.viz_raster.plot_chm`(store[, ...])	Plot the Canopy Height Model from store.
`utils.viz_raster.plot_dtm`(store[, ...])	Plot the Digital Terrain Model from store, optionally with hillshade.
`utils.viz_raster.plot_dsm`(store[, ...])	Plot the Digital Surface Model from store, optionally with hillshade.
`utils.viz_raster.plot_agb`(store[, ...])	Plot Above-Ground Biomass (AGB) from store.
`utils.viz_raster.plot_gap`(store[, ...])	Plot gap fraction from store.
`utils.viz_raster.plot_lai`(store[, ...])	Plot effective LAI from store.
`utils.viz_raster.plot_metrics`(store[, ...])	Multi-panel overview of LiDAR structural metrics.
`utils.viz_raster.plot_products`(store[, ...])	Three-panel overview: DTM \| DSM \| CHM.
`utils.viz_raster.plot_products_agb`(store[, ...])	Four-panel overview: DTM \| DSM \| CHM \| AGB.

Utilities#

`utils.constants.ALSProduct`(*values)	Enum representing PNOA ALS data product types.
`setup_logging`([level])	Configure alsdb logging.