Global Ensemble Digital Terrain Model (GEDTM30): a Tile Example of Local Enhanced Modeling via Transfer Learning
Description
This repository showcases an example to run a local enhanced modeling via transfer learning in Python. The script describes a pre-trained random forest model (global_model_legendtm.lz4) that is successively trained by the additional local samples (local_sample.csv). With the data from (covariates list), global and local prediction and stanard deviation has been derived in files (gendtm_pred_std.tiff, gendtm_prediction.tiff, legendtm_prediction.tiff, legendtm_pred_std.tiff). The standard deviation is scaled 100 and saved in INT16 and prediciton is scaled 10 and saved in INT32. The follow-up section explains the files and their references.
File description:
global_model_legendtm.lz4: random forest model trained by global stratified terrain samples of GEDI02 and ICESat-2 ATL08 v6.
local_sample.csv: the local samples of GEDI02 and ICESat-2 ATL08 v6. dtm_y: the terrain height from global Lidar
covariates list:
- GLO30_30m_v01272022.tiff: Copenricus GLO30 DEM (doi.org/10.5270/ESA-c5d3d65)
- dtm_elevation_aw3d30.dsm_m_30m_v01202022.tiff: ALOS AW3D30 DEM (Takaku et al. 2014, Tadono et al.)
- ghs_built_h_anbh_e2018_globe_r2022a_54009_100_v1_0.tiff: GHS building height ~100m resolution, resampled by cubicspline (10.2905/85005901-3A49-48DD-9D19-6261354F56FE)
- tree.cover_glad.umd_m_30m_s_20100101_20151231_go_epsg.4326_v1.0.tiff: tree cover precentile (Potapov et al. 2021)
- 3d_globfp.tiff: global three-dimensional building footprint dataset, vector dataset, rasterized to ~30m resolution (Che et al. 2024)
- canopy.height_glad.umd_m_30m_s_2017_2018_go_epsg.4326_v01242022.tiff: canopy height from GLAD (Potapov et al. 2021)
- edge.canopy.height_glad.umd_m_30m_s_2017_2018_go_epsg.4326_v01242022.tiff: edge canopy height by Sobel filter, derived from GLAD canopy height
- canopy.height_eth_m_30m_s_20000101_20230101_go_epsg.4326_v01242022.tiff: canopy height model from ETH (Lang et al. 2023)
- edge.canopy.height_eth_m_30m_s_20000101_20230101_go_epsg.4326_v01242022.tiff:edge canopy height by Sobel filter, derived from ETH canopy height
- ETOPO_slope_2022.tiff: earth topography 2022 (etopo 2022) global dem dataset ~500m resolution, resampled by cubicspline (MacFerrin et al. 2024)
- lc_glad.glcluc.change_c_30m_s_20000101_20201231_go_epsg.4326_v20230901.tiff: land use land cover change (Potapov et al. 2020)
- Landsat indices (Potapov 2020):
ndvi_swa.ard2_p025_30m_s_20060101_20101231_go_epsg.4326_v1.in.tiff
ndvi_swa.ard2_p50_30m_s_20060101_20101231_go_epsg.4326_v1.in.tiff
ndvi_swa.ard2_p975_30m_s_20060101_20101231_go_epsg.4326_v1.in.tiff
ndvi_swa.ard2_p025_30m_s_20110101_20151231_go_epsg.4326_v1.in.tiff
ndvi_swa.ard2_p50_30m_s_20110101_20151231_go_epsg.4326_v1.in.tiff
ndvi_swa.ard2_p975_30m_s_20110101_20151231_go_epsg.4326_v1.in.tiff
nir_swa.ard2_p025_30m_s_20060101_20101231_go_epsg.4326_v1.in.tiff
nir_swa.ard2_p50_30m_s_20060101_20101231_go_epsg.4326_v1.in.tiff
nir_swa.ard2_p975_30m_s_20060101_20101231_go_epsg.4326_v1.in.tifff
nir_swa.ard2_p025_30m_s_20110101_20151231_go_epsg.4326_v1.in.tiff
nir_swa.ard2_p50_30m_s_20110101_20151231_go_epsg.4326_v1.in.tiff
nir_swa.ard2_p975_30m_s_20110101_20151231_go_epsg.4326_v1.in.tif
ndwi_swa.ard2_p025_30m_s_20060101_20101231_go_epsg.4326_v1.in.tiff
ndwi_swa.ard2_p50_30m_s_20060101_20101231_go_epsg.4326_v1.in.tiff
ndwi_swa.ard2_p975_30m_s_20060101_20101231_go_epsg.4326_v1.in.tiff
ndwi_swa.ard2_p025_30m_s_20110101_20151231_go_epsg.4326_v1.in.tiff
ndwi_swa.ard2_p50_30m_s_20110101_20151231_go_epsg.4326_v1.in.tiff
ndwi_swa.ard2_p975_30m_s_20110101_20151231_go_epsg.4326_v1.in.tiff
nir,ndvi,ndwi: Near infra-red, Normalized Difference Vegetation Index, Normalized Difference Wetness Index
p025, p050,p975: precentile 2.5, 50, 97.5.
predictions:
- gendtm_predictiontiff: global model prediction
- gendtm_pred_std.tiff: standard deivation of global model prediction
- legendtm_pred_std.tiff: local enhanced global model prediction
- legendtm_pred_std.tiff: standard deivation of local enhanced global model prediction
Files
3d_globfp.tiff
Files
(11.7 GB)
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Additional details
Funding
References
- Potapov, P., Hansen, M. C., Kommareddy, I., Kommareddy, A., Turubanova, S., Pickens, A., . . . Ying, Q. (2020). Landsat analysis ready data for global land cover and land cover change mapping. Remote Sensing, 12(3), 426
- Lang, N., Jetz, W., Schindler, K., & Wegner, J. D. (2023). A high-resolution canopy height model of the earth. Nature Ecology & Evolution, 7(11), 1778–1789
- Potapov, P., Li, X., Hernandez-Serna, A., Tyukavina, A., Hansen, M. C., Kommareddy, A., . . . others (2021). Mapping global forest canopy height through integration of gedi and landsat data. Remote Sensing of Environment, 253, 112165
- Potapov, P., Hansen, M. C., Kommareddy, I., Kommareddy, A., Turubanova, S., Pickens, A., . . . Ying, Q. (2020). Landsat analysis ready data for global land cover and land cover change mapping. Remote Sensing, 12(3), 426
- Che, Y., Li, X., Liu, X., Wang, Y., Liao, W., Zheng, X., . . . others (2024). 3d-globfp: The first global three-dimensional building footprint dataset. Earth System Science Data Discussions, 2024, 1–28.
- MacFerrin, M., Amante, C., Carignan, K., Love, M., & Lim, E. (2024). The earth topography 2022 (etopo 2022) global dem dataset. Earth System Science Data Discussions, 2024, 1–24
- Tadono, T., Ishida, H., Oda, F., Naito, S., Minakawa, K., & Iwamoto, H. (2014). Precise global dem generation by alos prism. ISPRS annals of the photogrammetry, remote sensing and spatial1016 information sciences, 2, 71–76.
- Takaku, J., Tadono, T., & Tsutsui, K. (2014). Generation of high resolution global dsm from alos prism. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information1019 Sciences, 40, 243–248.