Blume, Alina

Christofilakos, Spyridon

Lee, Chengfa Benjamin

Pertiwi, Avi Putri

Reinartz, Peter

Traganos, Dimosthenis

Institute of Transport Research:Publications

12/18/23, 4:30 PM AGU - iPosterSessions.comhttps://agu2022fallmeeting-agu.ipostersessions.com/Default.aspx?s=02-B4-01-80-04-EC-81-74-E2-51-8B-B2-33-97-22-10&pdfprint=true&guestview… 1/13Spatially-explicit Uncertainty of Remote SensingCoastal Biodiversity Products using a scalable cloud-based framework in the Google Earth EngineChristofilakos S., Blume A., Pertiwi A.P., Lee B.C., Traganos D., Reinartz P.German Aerospace Center (DLR), Institute of Remote Sensing Technology (IMF), Department of Photogrammetry and ImageAnalysis (PBA)PRESENTED AT:12/18/23, 4:30 PM AGU - iPosterSessions.comhttps://agu2022fallmeeting-agu.ipostersessions.com/Default.aspx?s=02-B4-01-80-04-EC-81-74-E2-51-8B-B2-33-97-22-10&pdfprint=true&guestview… 2/13INTRODUCTIONTHE CURRENT STAGEModern advances in remote sensing have enabled the global monitoring of Earth's biodiversity andof its wide-ranging dimensions.THE PROBLEMAlbeit the unprecedented advances, relevant uncertainty methods and information are still in theirinfancy which impedes effective understanding of the product biases and impactful policy makingefforts. THE SOLUTIONThe aim of this study is to estimate the spatial explicit information of machine learning tasks in boththematic and continuous products entirely through the cloud-based platform Google Earth Engine(GEE) and use it to optimize the accuracy of the model and moreover produce a map regarding theuncertainty level of the procedure.   12/18/23, 4:30 PM AGU - iPosterSessions.comhttps://agu2022fallmeeting-agu.ipostersessions.com/Default.aspx?s=02-B4-01-80-04-EC-81-74-E2-51-8B-B2-33-97-22-10&pdfprint=true&guestview… 3/13DATA & STUDY AREASSATELLITE DATAMulti-temporal and cloud free composites with the aid of Sentinel-2, lvl2a (S2) imagery. STUDY AREASThey are divided to two categories according to the nature of the distribution we want to produce.1. Benthic Habitat ClassificationBahamian Archipelago → ~18.800 images, 633.063 km         2. Satellite Derived Bathymetry (SDB)National scale of Belize (C. America)Quirimbas Archipelago (Mozambique)TRAINING DATAOn the one hand we use the Allen Coral Atlas product in order to export 1000 training points for the4-class classification and on the other hand, we ingest Ice-Sat2 depth values to our model for theSatellite-derived bathymetry workflow.VALIDATION DATAIn both cases field data assist as validation dataset. In the case of the habitat mapping the size of thevalidation data are 300 points per class while for the bathymetry they are 636 and 777 points forQuirimbas and Belize respectively 212/18/23, 4:30 PM AGU - iPosterSessions.comhttps://agu2022fallmeeting-agu.ipostersessions.com/Default.aspx?s=02-B4-01-80-04-EC-81-74-E2-51-8B-B2-33-97-22-10&pdfprint=true&guestview… 4/13METHODOLOGYThe workflow of the study is divided in three sequential stages.PREPARATION OF DATA1. Creation of Depth Invariance Index (DIV) based to the spectral ratio of green and bluechannels and the hue and value bands based to the optical spectrum.2. Segmenation of image. Product of which are the 21 bands that we use for the model and expressthe mean, median and standard deviation values of the original red, green and blue channels plusthe DIV, hue and value bands for each segment.3. Portional division of the training data to Training, Retraining and Testing. 60% of them remain as training data and help with the initial classification.30 % of them are set as re-training data which are the keystone of the next stage. The remaining 10 % serves as testing points to measures the alteration of uncertainty due theprocessMAIN PROCESSING STAGEWith the information of the classification and the probabilites of succesful classification per class weare able to estimate the spatial uncertainty per pixel. In context of a continuous distribution as aSDB, we estimate the spatially explicit uncertainty with the aid of the Probabily Density functionbased to the twenty different bathymetry estimations of the twenty trees of the Random Forestalgorithm of GEE. Both regression and probabilities values are accessable through the platform. TheRetraining data serves as a pool of data that we use for the second classification. By knowing theuncertainty value per pixel, we filter the retraining pool and accept new training data only fromareas with low uncertainty. RETRAINING OF THE MODELLast stage of the workflow is the retraining of the model where we use the initial training datasetmerged with the retraining data that come from low uncertainty areas. 12/18/23, 4:30 PM AGU - iPosterSessions.comhttps://agu2022fallmeeting-agu.ipostersessions.com/Default.aspx?s=02-B4-01-80-04-EC-81-74-E2-51-8B-B2-33-97-22-10&pdfprint=true&guestview… 5/13Benthic Habitat Classification Accuracy Matrix, BahamasRESULTSBased to the results, our approach achieves better accuracy scores than training the classifier withthe whole training dataset, especially on the thematic product.By comparing the results of retraining with different uncertainty threshold for the retraining filter, itseems that training points from certain areas (uncertainty < 0.25) achieve the highest accurasy forboth thematic and continuous products.We summarize the accuracy scores of the initial training and the retraining of the model in eachstudy area in the following tables.  Satellite Derived Bathymetry Accuracy Matrix, Quirimbas & Belize 12/18/23, 4:30 PM AGU - iPosterSessions.comhttps://agu2022fallmeeting-agu.ipostersessions.com/Default.aspx?s=02-B4-01-80-04-EC-81-74-E2-51-8B-B2-33-97-22-10&pdfprint=true&guestview… 6/13DISCUSSION AND CONCLUSIONSBetter Accuracy ScoresIn general, it is quite difficult to achieve good accuracy scores for marine remote sensing studies dueto physical parameters like the water column, the alteration of spectral reflactance because ofchange of medium and the depth that the target occur. Even if our Spatially-Explicit Uncertaintyworkflow is in a premature phase, our findings indicate that it can be a useful tool in theoptimization of Earth Observation products. It is believed that the selection of retraining points withlow uncertainty values helps the model to better perform due to the minimized noise that theyintroduce to the model.Uncertainty MapThe spatially explicit uncertainty maps seem to agree with anticipated high uncertainty in areas asdeep waters, delta of rivers and it is worth to mention that they highlight the borders of satelliteimagery tiles.In the same time, our approach seems to be the only by now which is able to visualize the level ofuncertainty in marine remote sensing studies in a way that policy makers can take advantage of forbetter decision making. Albeit the scalable potentials through GEE platform, the size and the geo-spatial arrangment of the initial training pool appears to be the only limiting factor.12/18/23, 4:30 PM AGU - iPosterSessions.comhttps://agu2022fallmeeting-agu.ipostersessions.com/Default.aspx?s=02-B4-01-80-04-EC-81-74-E2-51-8B-B2-33-97-22-10&pdfprint=true&guestview… 7/1312/18/23, 4:30 PM AGU - iPosterSessions.comhttps://agu2022fallmeeting-agu.ipostersessions.com/Default.aspx?s=02-B4-01-80-04-EC-81-74-E2-51-8B-B2-33-97-22-10&pdfprint=true&guestview… 8/1312/18/23, 4:30 PM AGU - iPosterSessions.comhttps://agu2022fallmeeting-agu.ipostersessions.com/Default.aspx?s=02-B4-01-80-04-EC-81-74-E2-51-8B-B2-33-97-22-10&pdfprint=true&guestview… 9/13FUTURE STEPS1. Exploit the spatially-explicit uncertainty information for the creation of independent trainingpoints. By doing that we hope to help the model to decide on its own on what data should beused as training points even if they are not included in the initial training pool.2. Investigation of the capabilities of uncertainty and have a better understanding on the source ofuncertainty and how to minimize it through deep-learning practises as backpropagation.12/18/23, 4:30 PM AGU - iPosterSessions.comhttps://agu2022fallmeeting-agu.ipostersessions.com/Default.aspx?s=02-B4-01-80-04-EC-81-74-E2-51-8B-B2-33-97-22-10&pdfprint=true&guestvie… 10/13AUTHOR INFORMATIONContact info of main author email : spyridon.christofilakos@dlr.deLinkdn: https://www.linkedin.com/in/spyros-christofilakos-1912aa22b/ (https://www.linkedin.com/in/spyros-christofilakos-1912aa22b/)Linkdn of our project : https://www.linkedin.com/company/global-seagrass-watch/ (https://www.linkedin.com/company/global-seagrass-watch/)Fellow co-author Alina Blume is currently working at ESA Centre for Earth Observation, Frascati, Italy12/18/23, 4:30 PM AGU - iPosterSessions.comhttps://agu2022fallmeeting-agu.ipostersessions.com/Default.aspx?s=02-B4-01-80-04-EC-81-74-E2-51-8B-B2-33-97-22-10&pdfprint=true&guestvie… 11/13ABSTRACTModern advances in remote sensing have enabled the global monitoring of Earth's biodiversity and of its wide-ranging dimensions. These scalableadvances are offering global information on the extent, structure, function and services of different ecosystem types, and their benefits to theenvironment and humans, in general. Albeit the unprecedented advances, relevant uncertainty methods and information are still in their infancywhich impedes effective understanding of the product biases and impactful policy making efforts. In our study, we present a novel scalableuncertainty quantification framework, developed entirely within the Google Earth Engine, which assesses both thematic (e.g., ecosystempresence/absence, extent and distribution) and continuous products (e.g., satellite-derived bathymetry) related to coastal biodiversity using multi-temporal and cloud-free 10-m Sentinel-2, field data collections, and human-annotated data points. By exploiting the cloud-native machine learningclassifier and its outputs, we estimate the uncertainty of the procedure per pixel. With that information, our semi-automated model is able to re-train itself in a data driven way and produce better results. There are three areas of interest in this study. The first is the Archipelago of Bahamasisland complex, where we assess a four-class benthic habitat classification product per pixel. Our second and third study area is the national scaleof Belize and the Quirimbas Archipelago (Mozambique), respectively, in which a satellite-derived bathymetry map. In the case of classification,our model achieved a better overall accuracy of ~4% in comparison with the initial classification while the producer and user accuracy of thehabitat class that we are interested in, seagrass, rose by 13% and 7% respectively. On the regression results, our framework accurately highlightsthe areas with most uncertainty given the byproducts of the maximum likelihood regression that took place. While still in its alpha version, weenvisage that further developments of the framework could allow better quantification of the data and model uncertainty, reducing theuncertainties in the coastal biodiversity monitoring and enabling more effective policy making efforts towards safeguarding global biodiversityhealth.12/18/23, 4:30 PM AGU - iPosterSessions.comhttps://agu2022fallmeeting-agu.ipostersessions.com/Default.aspx?s=02-B4-01-80-04-EC-81-74-E2-51-8B-B2-33-97-22-10&pdfprint=true&guestvie… 12/13REFERENCESB. Lyons, M., M. Roelfsema, C., V. Kennedy, E., M. Kovacs, E., Borrego-Acevedo, R., Markey, K., Roe, M., M. Yuwono, D., L. Harris, D., R.Phinn, S., Asner, G.P., Li, J., E. Knapp, D., S. Fabina, N., Larsen, K., Traganos, D., J. Murray, N., 2020. Mapping the world’s coral reefs using aglobal multiscale earth observation framework. Remote Sens. Ecol. Conserv. 1–12. https://doi.org/10.1002/rse2.157A. Blume, 2021. composition and related carbon stocks in support of the Nationally Eidesstattliche Versicherung Statutory Declaration in Lieu ofan OathC.B. Lee, D. Traganos, P. Reinartz, 2022. A Simple Cloud-Native Spectral Transformation Method to Disentangle Optically Shallow and DeepWaters in Sentinel-2 Images. https://doi.org/10.3390/rs14030590D. Traganos, B. Aggarwal, D. Poursanidis, K. Topouzelis, N. Chrysoulakis, P. Reinartz, 2018. Towards Global-Scale Seagrass Mapping andMonitoring Using Sentinel-2 on Google Earth Engine: The Case Study of the Aegean and Ionian Seas. Remote Sens. 10, 1227.https://doi.org/10.3390/rs10081227N. Lang, N. Kalischek, J. Armston, K. Schindler, R. Dubayah, J.D. Wegner, 2022. Global canopy height regression and uncertainty estimationfrom GEDI LIDAR waveforms with deep ensembles. Remote Sens. Environ. 268, 112760. https://doi.org/10.1016/j.rse.2021.112760N. Gorelick, M. Hancher, M. Dixon, S. Ilyushchenko, D. Thau, R. Moore, 2017. Google Earth Engine: Planetary-scale geospatial analysis foreveryone. Remote Sens. Environ. https://doi.org/10.1016/j.rse.2017.06.031N. Marc Thomas et all., (2020).SPACE-BORNE CLOUD-NATIVE SATELLITE-DERIVED BATHYMETRY (SDB) MODELS USING ICESat-2and SENTINEL-2https://doi.org/10.1002/essoar.10504452.2Malinin, A. ,2019. Uncertainty Estimation in Deep Learning with application to Spoken Language Assessment (Doctoralthesis). https://doi.org/10.17863/CAM.45912 12/18/23, 4:30 PM AGU - iPosterSessions.comhttps://agu2022fallmeeting-agu.ipostersessions.com/Default.aspx?s=02-B4-01-80-04-EC-81-74-E2-51-8B-B2-33-97-22-10&pdfprint=true&guestvie… 13/13

Spatially-explicit Uncertainty of Remote Sensing Coastal Biodiversity Products using scalable cloud-based framework in the Google Earth Engine

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Spatially-explicit Uncertainty of Remote Sensing Coastal Biodiversity Products using scalable cloud-based framework in the Google Earth Engine

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