Christofilakos, Spyridon

Lee, Chengfa Benjamin

Pertiwi, Avi Putri

Traganos, Dimosthenis

Institute of Transport Research:Publications

SPATIALLY EXPLICIT UNCERTAINTY OF HIGH RESOLUTION NATIONWIDE SATELLITE DERIVED BATHYMETRY OF BELIZE AND ITS USAGE 1*Spyridon Christofilakos, 1Avi Putri Pertiwi, 1Chengfa Benjamin Lee, 1Dimosthenis Traganos 1German Aerospace Center (DLR), Remote Sensing Institute (IMF), Department of Photogrammetry and Image Analysis *Corresponding Author : spyridon.christofilakos@dlr.de Uncertainty in a nutshell   The usage of uncertainty in Cloud-based Remote Sensing Cloud-based techniques lack information on the spatially explicit uncertainty of Machine Learning products. Despite letting the researcher know which areas might be biased, spatially explicit uncertainty can also orient the researchers to the source of bias. The goal of the study  The estimation of uncertainty in a satellite derived bathymetry Random Forest product. Furthermore, we explore the benefits of such information in the context of better regression results through an ensemble model and the visualization of the uncertain areas in an attempt to provide insights on ‘hard to model areas’. A), B) Source: Pérez-Díaz, L., Alcalde, J., and Bond, C. E.: Introduction: Handling uncertainty in the geo-sciences: identification, mitigation and communication, Solid Earth, 11, 889–897, https://doi.org/10.5194/se-11-889-2020, 2020.  1.INTRODUCTION 3. RESULTS 2. MATERIALS AND METHODS 4. DISCUSSION 5. CONCLUSIONS STUDY AREA SATELLITE TYPE Satellite images TIME PERIOD BANDS THAT WERE USED Belize (Central America) Sentinel-2, LvL 2a (10m resolution) 876 Dec 2018 -  2021 ['B', 'G', 'R', 'N', 'B_mean', 'B_stdDev', 'B_median', 'G_mean',  'G_stdDev', 'G_median',  'R_mean', 'R_stdDev', 'R_median', 'N_mean',  'N_stdDev', 'N_median'] SDB MODEL RMSE (m) r2 Histogram width: 1m 1.142 0.614 Histogram width: 1.5m 1.133 0.627 Histogram width: 2m 1.134 0.617 Initial Training Dataset 1.214 0.619 SUBSET 1 ENSEMBLE ENSEMBLE ENSEMBLE Histogram width: 1m Histogram width: 1.5m Histogram width: 2m Spectral signatures who are not represented enough during training tend to have high uncertainty • The Probability Density Function is highly sensitive to the histogram cluster width during preprocessing • Majority of the training data belongs to the north tile, therefore in the uncertainty range of 1.5 -  2 m, areas in the south sector of image appear with high uncertainty Training points with high uncertainty are excluded in a certain point from the regression process • The high uncertainty of these specific points is justified by their abnormal spectral signatures (turbid waters, sunglind, overlay of satellite footprints etc • Minimizes the introduction of bias into the model  • Spatially Explicit Uncertainty seems a promising variable to improve the understanding of remote sensing data, models and applications • Spatially Explicit Uncertainty is able to provide policy makers with crucial information regarding the extent, the condition and the conditions that compile  the parameter under investigation. On top of that, its usage as a tool to distinguish easy and hard to model areas can be mitigate to other continuous distribution ML products like sea surface temperature, abnormality detection or even blue carbon accounting 6. BIBLIOGRAPHY Find us in Linkdln! 1. Thomas, N., Pertiwi, A. P., Traganos, D., Lagomasino, D., Poursanidis, D., Moreno, S., & Fatoyinbo, L. (2021). Space-borne cloud-native Satellite-Derived Bathymetry (SDB) models using ICESat-2 and Sentinel-2. Geophysical Research Letters, 48, e2020GL092170. https://doi.org/10.1029/2020GL092170 2. Lang, N., Kalischek, N.,  Armston, J.,  Schindler, K., Dubayah, R., and Jan Dirk Wegner. 2022. Global canopy height regression and uncertainty estima-tion from GEDI LIDAR waveforms with deep ensembles. Remote Sensing of Environment 268 (2022), 112760. 3. D. Poursanidis, D. Traganos, P. Reinartz, N. Chrysoulakis .2019. On the use of Sentinel-2 for coastal habitat mapping and satellite-derived bathymetry estimation using downscaled coastal aerosol band.Int. J. Appl. Earth Obs. Geoinf., 80 (2019), pp. 58-70, 10.1016/J.JAG.2019.03.012 4. Traganos, D.; Aggarwal, B.; Poursanidis, D.; Topouzelis, K.; Chrysoulakis, N.; Reinartz, P. Towards Global-Scale Seagrass Mapping and Monitoring Using Sentinel-2 on Google Earth Engine: The Case Study of the Aegean and Ionian Seas. Remote Sens. 2018, 10, 1227. https://doi.org/10.3390/rs10081227    6760 

Spatially Explicit Uncertainty of High Resolution Nationwide Satellite Derived Bathymetry of Belize and Its Usage

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