Published: Jun 30, 2024 by C.H. Grohmann
There is a new paper (open access) describing a Machine Learning-based DTM for South America:
Laipelt L., Andrade B.C., Collischonn W., Teixeira A.A., Paiva R.C.D., Ruhoff A., 2024. ANADEM: A Digital Terrain Model for South America. Remote Sensing 16(13):2321. https://doi.org/10.3390/rs16132321
Paper abstract:
Digital elevation models (DEMs) have a wide range of applications and play a crucial role in many studies. Numerous public DEMs, frequently acquired using radar and optical satellite imagery, are currently available; however, DEM datasets tend to exhibit elevation values influenced by vegetation height and coverage, compromising the accuracy of models in representing terrain elevation. In this study, we developed a digital terrain model for South America using a novel methodology to remove vegetation bias in the Copernicus DEM GLO-30 (COPDEM) model using machine learning, Global Ecosystem Dynamics Investigation (GEDI) elevation data, and multispectral remote sensing products. Our results indicate considerable improvements compared to COPDEM in representing terrain elevation, reducing average errors (BIAS) from 9.6 m to 1.5 m. Furthermore, we evaluated our product (ANADEM) by comparison with other global DEMs, obtaining more accurate results for different conditions of vegetation fraction cover and land use. As a publicly available and open-source dataset, ANADEM will play a crucial role in advancing studies that demand accurate terrain elevation representations at large scales.
Data Access
ANADEM can be downloaded at https://hge-iph.github.io/anadem/ or accessed directly in GoogleEarth Engine (see instructions on the project link).
