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NASA has recently released an interactive webmapping tool to explore effect of global warming on the sea level rise and potential risks / damages associated.

“The NASA Sea Level Projection Tool allows users to visualize and download the sea level projection data from the IPCC 6th Assessment Report (AR6). The goal of this tool is to provide easy and improved access and visualization to the consensus projections found in the report. The target audience is intended to be broad, allowing a general audience and scientists alike to interact with the information contained in the AR6.”

To learn more about method and Digital Terrain Model source used for modeling, please refer to:

• Fox-Kemper, B., H. T. Hewitt, C. Xiao, G. Aðalgeirsdóttir, S. S. Drijfhout, T. L. Edwards, N. R. Golledge, M. Hemer, R. E. Kopp, G. Krinner, A. Mix, D. Notz, S. Nowicki, I. S. Nurhati, L. Ruiz, J-B. Sallée, A. B. A. Slangen, Y. Yu, (2021). Ocean, Cryosphere and Sea Level Change. In: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [MassonDelmotte, V., P. Zhai, A. Pirani, S. L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M. I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J. B. R. Matthews, T. K. Maycock, T. Waterfield, O. Yelekçi, R. Yu and B. Zhou (eds.)]. Cambridge University Press. In Press.

European Space Agency (ESA) has recently made available two global Digital Surface Models:

• GLO-30: the 30-m spatial resolution DSM,
• GLO-90: the 90-m spatial resolution DSM,

Both are available for download from cdsdata.copernicus.eu on a free basis for the general public under the terms and conditions of the Licence found here.

A copy of the GLO-30 and GLO-90 is now also available as Cloud Optimized GeoTIFFs from the Amazon AWS public data repository. The visualization shown above is from the sentinel-hub.com.

To learn more about the GLO-30 and it’s accuracy and production, refer to this article.

Within the OpenDataScience.eu (Geo-harmonizer) project we have recently produced an Digital Terrain Model for Continental Europe based on the four publicly available Digital Surface Models: MERITDEM, AW3D30, GLO-30, EU DEM. This is basically an Ensemble Machine Learning approach where GEDI level 2B points (Level 2A; “elev_lowestmode”) and ICESat-2 (ATL08; “h_te_mean”) were used to train a multisource model to predict “most probable height of terrain” including the prediction errors per pixel.

So which of the four big global DEM’s is the best match with terrain heights? Our results indicate that it is the MERITv1.0.1 (originally available at 90-m, but was downscaled here to 30-m using cubic splines) followed by the AW3Dv2012 and GLO-30. This confirms that Yamazaki et al. (2017) have done an excellent work in filtering out canopy and artifacts in the original SRTM/AWD30 data.

Read more about MERIT DEM in:

• Yamazaki D., D. Ikeshima, R. Tawatari, T. Yamaguchi, F. O’Loughlin, J.C. Neal, C.C. Sampson, S. Kanae & P.D. Bates, (2017). A high accuracy map of global terrain elevations. Geophysical Research Letters, vol.44, pp.5844-5853, 2017 doi: 10.1002/2017GL072874

To access the Continental Europe Digital Terrain Model at 30-m please visit https://maps.opendatascience.eu and select “terrain” from the layer menu.

You can also download the DTM for EU including the regression matrix with all training points (GEDI/ICESat) via:

• Hengl, T., Leal Parente, L., Krizan, J., & Bonannella, C. (2020). Continental Europe Digital Terrain Model at 30 m resolution based on GEDI, ICESat-2, AW3D, GLO-30, EUDEM, MERIT DEM and background layers (v0.3) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4724549

WEB APPLICATION and QUESTIONNAIRE at http://zgis205.plus.sbg.ac.at/PhysiographicClassificationApplication/default.aspx

Dear colleagues, We kindly ask for your help in evaluating the preliminary outputs of a global physiographic classification. The methodology has been designed for general purposes. We hope, however, that the results can be tuned to specific applications, by using the object attributes, without a need of running the classification again. Potential domains of application include Landscape Ecology, Ecology, Geomorphology, Geology, Hydrology, Soil Science, and Agriculture. Your evaluation would be useful in improving the current classification. The results of your evaluation will be part of a paper we intend to submit to a peer-reviewed journal. Classification results are embedded within a web application available at the following address

http://zgis205.plus.sbg.ac.at/PhysiographicClassificationApplication/default.aspx.

You can visualize the results and let us know your opinion by filling in the form under the red button named ‘Please provide your feedback here.’ Apart of the classification itself, i.e. to which degree classes describe correctly given regions, we would like you evaluating the quality of object boundaries, i.e. to which degree boundaries match topographic discontinuities. After evaluation, both the database and the tool will be released for free download.

Please find below additional details on the methods and the web application.

The first 30 m resolution global ASTER-based DEM (GDEM) has recently been released. This is now the most detailed global GIS layer with public access (read more). The GDEM was created by stereo-correlating the 1.3 million-scene ASTER archive of optical images, covering almost 98% of Earth’s land surface (claimed 95% vertical accuracy: 20 meters, 95% horizontal accuracy: 30 meters). The one-by-one-degree tiles can be downloaded from NASA’s EOS data archive and/or Japan’s Ground Data System. The download of DEMs for large areas is at the moment difficult and limited to 100 tiles.

I have downloaded some GDEM tiles for the areas in the Netherlands, Italy, Serbia and USA, and compared these with the most accurate LIDAR-derived DEMs (aggregated to 25 m resolution) available for the same area. The data used for comparison and shown in plot below can be obtained from here. I was interested to see how accurate is the GDEM and what are the main limitations of using it for various mapping applications.

Conceptually speaking, accuracy of topography (or better to say relief) can be represented by examining (at least) the following three aspects of a DEM:

• Accuracy of absolute elevations (simply the difference between the GDEM and true elevation);
• Accuracy of hydrological features (deviance of stream networks, watershed polygons etc. from true lines);
• Accuracy of surface roughness (deviance of the nugget variation and/or difference in local relief quantified using e.g. difference from the mean value);

Fig: Comparison of the GDEM and LiDAR-based DEMs for four study areas: (1) fishcamp; (2) zlatibor; (3) calabria, and (4) boschord (all maps prepared in resolution 25-30 m).

The results of this small comparison show that:

1. The average RMSE for elevations for these for data sets is: 18.7 m;
2. The average error of locating streams is between 60-100 m;
3. Surface roughness is typically under-represented so that the effective resolution of GDEM is possibly 2-3 times coarser than the actual;

In addition, by visually comparing DEMs for the four case studies, you will notice that GDEM often carries some artificial lines and ghost-like features (GDEM tiles borders, vegetation cover etc.). The worst match between the GDEM and LiDAR-based DEM (reality) is in areas of low relief (boschord). Practically, GDEM looks to be of absolutely no use in areas where the average difference in elevations is <20 m. As the producers of GDEM themselves indicated: “The ASTER GDEM contains anomalies and artifacts that will reduce its usability for certain applications, because they can introduce large elevation errors on local scales”.

In summary, I can only conclude that (a) there is still a lot of filtering to be done with GDEM to remove the artificial breaks and ghost lines; (b) the effective resolution of the GDEM is probably 60-90 m and not 30 m, hence the whole layer should be aggregated to a more realistic resolution; (c) the first impression is that GDEM is not more accurate than the 90 m SRTM DEM, especially if one looks at the surface roughness and land surface objects. On the other hand, the horizontal accuracy of GDEM is more than satisfactory and GDEM has a near to complete global coverage, so that it can be used to fill the gaps and improve the global SRTM DEM. In addition, the GDEM comes also with a quality assessment (QA) map. Each QA file pixel contains either: (1) the number of scene-based DEMs contributing to the final GDEM value for each 30 m pixel (stack number); or (2) the source data set used to replace identified bad values in the ASTER GDEM.