--- pretty_name: "Canopy height data: NEON and five international sites" license: other license_name: cc-by-4.0-and-gpl-3.0-only license_link: LICENSE tags: - remote-sensing - canopy-height - lidar - gedi - sentinel-1 - sentinel-2 viewer: false --- # Canopy height data: NEON and five international sites Training and evaluation data from our canopy height mapping study, packaged for testing. All source data are public (see Sources and licences). | Archive | Content | |---|---| | `Canopy_height_data_NEON.tar.gz` | `neon_test/` | | `Canopy_height_data_MRF.tar.gz` | `intl_test/MRF_stack/`, `intl_train/MRF_train_stack/` | | `Canopy_height_data_EBR.tar.gz` | `intl_test/EBR_stack/`, `intl_train/EBR_train_cover400_stack/` | | `Canopy_height_data_SER.tar.gz` | `intl_test/SER_stack/`, `intl_train/SER_train_cover400_stack/` | | `Canopy_height_data_SPC.tar.gz` | `intl_test/SPC_stack/`, `intl_train/SPC_train_cover400_stack/` | | `Canopy_height_data_MUR.tar.gz` | `intl_test/MUR_stack/`, `intl_train/MUR_train_cover400_stack/` | All archives unpack into `Canopy_height_data/` and include `stats/` (per-channel mean and standard deviation of the 76-channel input). Every array is a stack of 256 × 256 chips at 10 m resolution, stored as a NumPy `.npy` file with the chips on the first axis. Stacks of more than 400 chips are split into `part001` and `part002`. International sites: MRF = Mount Richmond Forest (New Zealand), EBR = Entlebuch Biosphere Reserve (Switzerland), SER = Sepilok and Danum Valley (Malaysia), SPC = São Paulo (Brazil), MUR = Middle Usumacinta (Mexico). ## Weights `weights/`: trained models for [chm-tool](https://github.com/Link-dev/chm-tool). | Folder | Files | |---|---| | `source/` | `UNet-ALS.pth` (input AE), `UNet-A-ALS.pth`, `UNet-E-ALS.pth`, `UNet-T-ALS.pth`, `UNet-TE-ALS.pth` | | `NEON/` | `UNet-SLS.pth`, `RF-SLS.joblib` | | `MRF/`, `EBR/`, `SER/`, `SPC/`, `MUR/` | `UNet-SLS.pth`, `RF-SLS.joblib`, `KG-UNet1.pth`, `KG-UNet2.pth` | ## Files `neon_test/`: chips are ordered by site: ABBY (18), BART (30), HARV (61), LENO (25), MLBS (36), NIWO (28), SCBI (25), SJER (25), SRER (37), TALL (30), TEAK (46), WREF (43). The site-year is 2019, except NIWO (2020) and MLBS (2021). | File | Content | |---|---| | `X_part001.npy` | annual input, 76 channels | | `X_part001_TS.npy` | seasonal input, 44 channels | | `y_part001.npy`, `chm_p95.npy`, `chm_p98.npy` | ALS canopy height, p90 / p95 / p98 | | `y_GEDI_part001.npy` | GEDI RH95 | | `y_eth_part001.npy`, `y_umd_part001.npy`, `tolan_p90.npy` | HRCH, GFCH, GMTCH | | `chip_index.csv` | site and biome of each chip | `intl_test/_stack/`: | File | Content | |---|---| | `_partNNN.npy` | annual input, 76 channels | | `_chm_partNNN.npy`, `_chm_p95_partNNN.npy`, `_chm_p98_partNNN.npy` | ALS canopy height, p90 / p95 / p98 | | `_GEDI_part001.npy` (SER, SPC, MUR) | GEDI RH95 | | `_eth_partNNN.npy`, `_umd_partNNN.npy`, `_gmtch_partNNN.npy` | HRCH, GFCH, GMTCH | | `_index.csv` (`_label_index.csv` for EBR and MRF) | chip locations | `intl_train/`: | File | Content | |---|---| | `_train_cover400_stack/_train_cover400_partNNN.npy` (EBR, SER, SPC, MUR) | annual input, 76 channels | | `_train_cover400_stack/_train_cover400_GEDI_partNNN.npy` | GEDI RH95 training labels | | `_train_cover400_stack/_train_cover400_index.csv` | chip locations | | `MRF_train_stack/MRF_GEDI_partNNN.npy` | GEDI RH95 training labels for MRF; MRF is trained on its evaluation chips, whose input is `intl_test/MRF_stack/MRF_partNNN.npy` | | `MRF_train_stack/MRF_train_index.csv` | chip locations | ## Data Annual input (uint16; 0 = no data): | Channels | Layer | Decode | |---|---|---| | 0–63 | Satellite Embedding, annual | value / 10000 − 1 | | 64 | SRTM elevation | m | | 65–66 | Sentinel-1 VV, VH, annual median | value / 100 − 50 (dB) | | 67–75 | Sentinel-2 L2A B2, B3, B4, B5, B6, B7, B8, B11, B12, annual median | value / 10000 (reflectance) | Seasonal input (`X_part001_TS.npy`, same encoding): channels 0–7 are Sentinel-1 VV and VH for seasons 1–4 (VV1, VH1, VV2, VH2, …); channels 8–43 are the nine Sentinel-2 bands for seasons 1–4. The seasons are December (of the previous year) to February, March–May, June–August and September–November. ALS canopy height (float32, m; −999 = no data): the 90th, 95th and 98th percentile of the 1 m ALS canopy height within each 10 m cell. GEDI RH95 (float32, m; −999 = no footprint): median GEDI L2A RH95 of 2019–2021 on the 10 m grid. Global products (float32, m): `umd` = GFCH, `eth` = HRCH, `gmtch` / `tolan_p90` = GMTCH (90th percentile of the 1 m map within each 10 m cell). International index files: one row per chip in stack order (`part`, `row_in_part`), with the CRS (`epsg` or `crs`) and the upper-left corner of the chip (`tile_x0`, `tile_y1` or `x_ul`, `y_ul`). Biome codes in `chip_index.csv`: TRF = temperate rain forest, TSF = temperate seasonal forest, BF = boreal forest, WS = woodland/shrubland, SD = subtropical desert. ## Sources and licences | Data | Source | Licence | |---|---|---| | NEON ALS | NEON, Ecosystem structure (DP3.30015.001): RELEASE-2025 https://doi.org/10.48443/jqqd-1n30; RELEASE-2024 https://doi.org/10.48443/zzz8-pr54 (SCBI); provisional data (MLBS 2021) | CC0 1.0 when obtained | | SPC ALS | São Paulo City Hall (2024), OpenTopography, https://doi.org/10.5069/G9NV9GD1 | GPL-3.0 | | MUR ALS | Inomata (2022), OpenTopography, https://doi.org/10.5069/G95B00NF | CC BY 4.0 | | SER ALS | Coomes & Jackson (2022), NERC EDS CEDA, https://doi.org/10.5285/dd4d20c8626f4b9d99bc14358b1b50fe | OGL v3.0 | | EBR ALS | swisstopo swissSURFACE3D Raster and swissALTI3D, https://www.swisstopo.admin.ch | swisstopo OGD | | MRF ALS | LINZ / Marlborough District Council (2020–2022), OpenTopography, https://doi.org/10.5069/G97D2SB0 | CC BY 4.0 | | Satellite Embedding | Google and Google DeepMind (2025), https://developers.google.com/earth-engine/datasets/catalog/GOOGLE_SATELLITE_EMBEDDING_V1_ANNUAL | CC BY 4.0 | | SRTM | NASA JPL (2013), https://doi.org/10.5067/MEaSUREs/SRTM/SRTMGL1.003 | public domain | | Sentinel-1, Sentinel-2 | Copernicus Sentinel data 2018–2021 | Copernicus Sentinel data terms | | GEDI L2A | Dubayah et al. (2021), https://doi.org/10.5067/GEDI/GEDI02_A.002 | public domain | | GFCH | Potapov et al. (2021), https://doi.org/10.1016/j.rse.2020.112165 | CC BY | | HRCH | Lang et al. (2023), https://doi.org/10.1038/s41559-023-02206-6 | CC BY 4.0 | | GMTCH | Tolan et al. (2024), https://doi.org/10.1016/j.rse.2023.113888 | CC BY 4.0 | ## Licence CC BY 4.0, except the three `intl_test/SPC_stack/SPC_chm_*` files, which are derived from the GPL-3.0 São Paulo lidar survey and are released under GPL-3.0-only. See `LICENSE` and `LICENSE-GPL-3.0.txt`. The weights in `weights/` are released under the MIT licence.