ESSI1.4

One of the largest threats to the vast ecosystem of the Brazilian Amazon Forest is deforestation and forest degradation caused by human activity. The possibility to continuously monitor these degradation events has recently become more feasible through the use of freely available satellite remote sensing data and machine learning algorithms suited for big datasets.

A fundamental challenge of such large-scale monitoring tasks is the automatic generation of reliable and correct land use and land cover (LULC) maps. This is achieved by the development of robust deep learning models that generalize well on new data. However, these approaches require large amounts of labeled training data. We use the latest results of the MapBiomas project as the ‘ground-truth’ for developing new algorithms. In this project, Souza et al. [1] used yearly composites of USGS Landsat imagery to classify the LULC for the whole of Brazil. The latest iteration of their work became available for the years 1985–2020 as Collection 6 (https://mapbiomas.org). However, this reference data cannot be considered real ground truth, as it is itself generated from machine learning models and therefore requires novel approaches suited to overcome such problems of weakly supervised learning.

As tropical regions are often covered by clouds, radar data is better suited for continuous mapping than optical imagery, due to its cloud-penetrating capabilities. In a preliminary study, we combined data from ESA’s Sentinel-1 (radar) and Sentinel-2 (multispectral) missions for developing algorithms suited to act on multi-modal and -temporal data to obtain accurate LULC maps. The best performing proposed deep learning network, DeepForestM2, employed a seven-month radar time series combined with a single optical scene. This model configuration reached an overall accuracy of 75.0% on independent test data. A state-of-the-art (SotA) DeepLab model, trained on the very same data, reached an overall accuracy of 69.9%.

Currently, we are further developing this approach of fusing multi-modal data with a temporal aspect to improve on LULC classification. Larger amounts of more recent data, both Sentinel-1 and Sentinel-2 from 2020 are included in training experiments. Additional deep learning networks and approaches to deal with weakly supervised [2] learning are developed and tested on the data. The need for the weakly supervised methods arises from the reference data, which is both inaccurate and inexact, i.e., has a coarser spatial resolution than the training data. We aim to improve the classification results qualitatively, as well as quantitatively compared to SotA methods, especially with respect to generalizing well on new datasets. The resulting deep learning methods, together with the trained weights, will also be made accessible through a geoprocessing tool in Esri’s ArcGIS Pro for users without coding background.

• Carlos M. Souza et al. “Reconstructing Three Decades of Land Use and Land Cover Changes in Brazilian Biomes with Landsat Archive and Earth Engine”. en. In: Remote Sensing 17 (Jan. 2020). Number: 17 Publisher: Multidisciplinary Digital Publishing Institute, p. 2735. DOI: 10.3390/ rs12172735.
• Zhi-Hua Zhou. “A brief introduction to weakly supervised learning”. In: National Science Review 5.1 (Jan. 2018), pp. 44–53. ISSN: 2095-5138. DOI: 10.1093/nsr/nwx106.

How to cite: Brandmeier, M., Hell, M., Cherif, E., and Nüchter, A.: Synergetic use of Sentinel-1 and Sentinel-2 data for large-scale Land Use/Land Cover Mapping, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4300, https://doi.org/10.5194/egusphere-egu22-4300, 2022.

The technological developments in geoinformatics in recent decades have allowed the inclusion of geospatial data and analysis techniques in a wide range of scientific disciplines. One such field is associated with the study of urban green spaces (UGS). Those are defined as open, undeveloped areas that provide residents with recreational space, improving the aesthetic and environmental quality of the neighboring areas. Mapping accurately their spatial extent is absolutely essential requirement in urban planning and their preservation and expansion in Metropolitan areas are of high importance to protect the environment and public health.

 
The objective of this study is to explore the use of high spatial resolution satellite imagery from PlanetScope combined with the Geographic Object-Based Image Analysis (GEOBIA) classification approach in mapping UGS in Athens, Greece. For the UGS retrieval, an object-based classification (GEOBIA) method was developed utilizing a multispectral PlanetScope imagery acquired in June 2020. Accuracy assessment was performed with a confusion matrix utilizing a set of randomly selected control points within the image selected from field visits and image photo-interpretation. In addition, the obtained UGS were compared versus independent estimates of the Green Urban Areas from the Urban Atlas global operational product. All the geospatial data analysis was conducted in a GIS environment (ArcGIS Pro).

Results demonstrated the usefulness of GEOBIA technique when combined with very high spatial-resolution satellite imagery from PlanetScope in mapping UGS, as was demonstrated by the high accuracy results that were obtained from the statistical comparisons. With the technological evolution in the Earth Observation datasets acquisition and image processing techniques, mapping UGS has been optimized and facilitated and this study contributes in this direction. 

KEYWORDS: Urban Green Spaces, Athens, PlanetScope, Earth Observation, GEOBIA

How to cite: Dosiadis, E., Triantakonstantis, D., Popa, A.-M., Detsikas, S. E., Sandric, I., Petropoulos, G. P., Onose, D., and Chalkias, C.: A GEOBIA-based approach for mapping Urban Green Spaces using PlanetScope imagery: the case of Athens, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11946, https://doi.org/10.5194/egusphere-egu22-11946, 2022.

Unsupervised methods are a good entry point for satellite image classification, requiring little to no input, and outputting an analysis, in the form of a thematic map, that may act as a guide for more user input intensive methods. For this work, we use K-means methods to classify satellite and drone imagery that cover the Ossa-Morena Zone (OMZ), in Portugal, and assess their capacity for lithological and soil mapping. The drone is equipped with a High Precision NDVI Single Sensor and was flown over the ancient mines of Mociços, Mostardeira and Santa Eulália. The OMZ is a tectonostratigraphic domain shared between Portugal and Spain, divided in Sectors, extraordinarily rich and diverse from a lithological, stratigraphical, and structural point-of-view; for this work, we will focus on the Estremoz-Barrancos sector, comprised of a Neoproterozoic to Devonian metasedimentary succession, with a low-grade metamorphism in greenschist facies, and the Santa Eulália Plutonic Complex (SEPC), an elliptic late-Variscan granitic massif that crosscuts the Alter do Chão-Elvas Sector and the Blastomylonitic belt, constituted by two granitic facies, a few small mafic bodies, and some roof pendants that belong to the Alter do Chão-Elvas Sector.

The imagery used correspond to high-level satellite imagery products gathered between 2004 to 2006 (ASTER) and 2017 to 2021 (Landsat 8 and Sentinel-2), and drone imagery captured on May 6th and August 31st, 2021.

The K-means was applied to a variable number of selected bands, including band ratios, and tested for different number of initial clusters and different distance algorithms (Minimum Distance and Spectral Angle Mapping). Afterwards, it was assessed its ability to outlining and classify different geological structures by comparing the results to the geological map of OMZ.

The obtained thematic maps points towards poorer results when using a larger selection of bands - for instance, ASTER bands 1 to 9 (in which bands 1 to 3N were resampled to 30m) -, due to interspersion of different classes, whereas when using band ratio combinations, such as 4/2 and 6/(5+7) (ASTER), the produced map successfully classifies the major geological features present in the region, with increased sharpness between contacts with a higher number of classes.

Results show that K-means, when used under the correct conditions and parameters, has the potential for lithological and soil mapping through image classification, both for satellite and drone imagery.

Future work will focus on the integration of a pre-processing step for band selection using ML techniques, such as through Principal Component Analysis, Minimum Noise Fraction and Random Forest.

The authors acknowledge the funding provided by FCT through the Institute of Earth Sciences (ICT) with the reference UIDB/GEO/04683/2020.

How to cite: Silva, M., Nogueira, P., Henriques, R., and Gonçalves, M.: Application of unsupervised machine learning techniques for lithological and soil mapping in Ossa-Morena Zone, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9265, https://doi.org/10.5194/egusphere-egu22-9265, 2022.

River islands are important components of the river morpho-dynamics, which can provide essential information on fluvial processes, as well as on sediment and flow regimes. In the same time, river islands play an essential role from the political, environmental and socio-cultural points of view. Thus, understanding the temporal dynamics of the river islands is a required task for channel navigation safety, port functionality, agricultural production and biodiversity. The aim of this study is to analyse the spatial and temporal changes on the river islands during the last 40 years, based on satellite remotely sensed images. The study focuses on the Lower Danube River, downstream the Iron Gates dams altering the flow and sediment load, which also suffers from dredging for navigation. The islands of the Lower Danube River generate major impacts on riparian states relationship, interfere with the ports activity and EU investments (as it is the case of Rast port in Romania), or are the subject of ecological restoration. Multispectral satellite data, including Landsat and Sentinel-2 images, were used for river islands mapping at different temporal moments, with a medium spatial resolution (up to 15 m on Landsat pansharpened data and 10 m on Sentinel-2). Spectral indices, as NDVI and NDWI, allowed the automatic extraction of island boundaries and land cover information. On these, two processes were carried out: 1) the characterization of the river islands morphology, and 2) the quantification of the spatial and temporal changes over time. The resulted data are connected with in-situ measurements on flow regime and sediment supply, as well as with flood events and human activities in order to identify the potential drivers of change. The results demonstrate a strong correlation between river islands dynamics and flood events in the Lower Danube River, as the major flood event from 2006 significantly modified the islands size and shape. This research can allow the identification of the evolutionary model of the Danube River.

This research work was conducted as part of the project PCE 164/2021 “State, Communities and Nature of the Lower Danube Islands: An Environmental History (1830-2020)”, financed by the UEFISCDI.

How to cite: Virghileanu, M. and Ioana-Toroimac, G.: Remote sensing – based analysis of the islands dynamics in the Lower Danube River, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5333, https://doi.org/10.5194/egusphere-egu22-5333, 2022.

Earth observation (EO) - particularly so from hyperspectral imagers - gains increasing interest in wildfire mapping as it offers a prompt with high accuracy and low-cost delineation of a burnt area.  A key hyperspectral orbital sensor with over 20 years of operational life is Compact High-Resolution Imaging Spectrometer (CHRIS), onboard ESA’s PROBA platform. This mission sensor collects spectral data in the VNIR range (400 - 1050 nm) simultaneously at 5 viewing angles and at different spatial resolutions of 17 m and 34 m which contains 19 and 63 spectral bands respectively. The present study focuses on exploring the use of CHRIS PROBA legacy data combined with machine learning (ML) algorithms in obtaining a burnt area cartography. In this context, a further objective of the study has been to examine the contribution of the multi-angle sensor capabilities to enhance the burn scar detection. As a case study was selected a wildfire occurred during the summer of 2007 in the island of Evvoia, in central Greece for which imagery from the CHRIS PROBA archive shortly after the fire outbreak was available. For the accuracy assessment of the derived burnt area estimate the error matrix statistics were calculated in ENVI. Burnt area estimates from were also further validated against the operational product developed in the framework of ESA’s Global Monitoring for Environmental Security/Service Element. This study’s results evidenced the added value of satellite hyperspectral imagery combined with ML classifiers as a cost-effective and robust approach to evaluate a burnt area extent, particularly so of the multi-angle capability in this case. All in all, the study findings can also provide important insights towards the exploitation of hyperspectral imagery acquired from current missions (e.g. HySIS, PRISMA, CHRIS, DESIS) as well as upcoming ones (e.g. EnMAP, Shalom, HySpiri and Chime).

KEYWORDS: CHRIS-PROBA, hyperspectral, machine learning, burnt area mapping

How to cite: Lekka, C., Detsikas, S. E., Petropoulos, G. P., Katsafados, P., Triantakonstantis, D., and Srivastava, P. K.: Utilizing hyperspectral imagery for burnt area mapping in a Greek setting, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10163, https://doi.org/10.5194/egusphere-egu22-10163, 2022.

Tidal flat systems with a diverse benthic community (e.g., bivalves, polychaetes and crustaceans) is important in the food chain for migratory birds and fish. The geographical distribution of macrozoobenthos depends on physical factors, among which sediment characteristics are key aspects. Although high-resolution and high-frequency mapping of benthic indices (i.e., sediment composition and benthic fauna) of these coastal systems are essential to coastal management plans, it is challenging to gather such information on tidal flats through in-situ measurements. The Synoptic Intertidal Benthic Survey (SIBES) database provides this field information for a 500m grid annual for the Dutch Wadden Sea, but continuous coverage and seasonal dynamics are still lacking. Remote sensing may be the only feasible monitoring method to fill in this gap, but it is hampered by the lack of spectral contrast and variation in this environment. In this study, we used a deep-learning model to enhance the information extraction from remote-sensing images for the prediction of environmental and ecological variables of the tidal flats of the Dutch Wadden Sea. A Variational Auto Encoder (VAE) deep-learning model was trained with Sentinel-2 satellite images with four bands (blue, green, red and near-infrared) over three years (2018, 2019 and 2020) of the tidal flats of the Dutch Wadden Sea. The model was trained to derive important characteristics of the tidal flats as image features by reproducing the input image. These features contain representative information from the four input bands, like spatial texture and band ratios, to complement the low-contrast spectral signatures. The VAE features, the spectral bands and the field-collected samples together were used to train a random forest model to predict the sediment characteristics: median grain size and silt content, and macrozoobenthic biomass and species richness. The prediction was done on the tidal flats of Pinkegat and Zoutkamperlaag of the Dutch Wadden sea. The encoded features consistently increased the accuracy of the predictive model. Compared to a model trained with just the spectral bands, the use of encoded features improved the prediction (coefficient of determination, R²) by 10-15% points for 2018, 2019 and 2020. Our approach improves the available techniques for mapping and monitoring of sediment and macrozoobenthic properties of tidal flat systems and thereby contribute towards their sustainable management.

How to cite: Madhuanand, L., Phillippart, K., Nijland, W., Wang, J., De Jong, S. M., Bijleveld, A. I., and Addink, E. A.: Predictive performance of deep-learning-enhanced remote-sensing data for ecological variables of tidal flats over time, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7859, https://doi.org/10.5194/egusphere-egu22-7859, 2022.

Increased observation frequencies are current trends in optical remote sensing. However, there are still challenges at the night side when sunlight is not available. Due to their powerful capabilities in low-light sensing, nightlight satellite sensors have been deployed to capture nightscapes of the Earth from space, observing anthropomorphic and natural activities at night. At present, most nightlight remote sensing applications have mostly focused on artificial lights, particularly within cities or self-luminous entities such as fisheries, oil, shale gas, offshore rigs, and other self-luminous bodies. Little attention has been paid to examining the potential of nightlight remote sensing for mapping land surfaces in low-light suburban areas using satellite remote sensing technology. Observations taken under moonlight are often discarded or corrected to reduce the lunar effects. Some researchers have discussed the possibility of moonlight as a useful illuminating source at night for the detection of nocturnal features on Earth, but no quantitative analysis has been reported so far. This study aims to systematically evaluate the potential of moonlight remote sensing with the whole month of mono-spectral Visible Infrared Imaging Radiometer Suite/Day-Night-Band (VIIRS/DNB) and multi-spectral Unmanned Aerial Vehicle (UAV) nighttime images. The present study aims to:1) to study the potential of moonlight remote sensing for mapping land surface in low-light suburban areas; 2) to investigate the Earth observation capability of moonlight data under different lunar phases;3) to make two daily uniform nightlight datasets(moonlight included and removed) for various night scenes researches, like weather diurnal forecast, circadian rhythms in plants and so on; 4) to discuss the requirements for the next-generation nightlight remote sensing satellite sensors.

How to cite: Liu, D. and Zhang, Q.: The Potential of Moonlight Remote Sensing: A Systematic Assessment with Multi-Source and Multi-Moon phase Nightlight Data, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3380, https://doi.org/10.5194/egusphere-egu22-3380, 2022.

In Song et al. (2021) [1] a framework for the retrieval of 10 m and 20 m spectral and 20 m broadband surface albedo products was described. This framework consists of four modules: 1) a machine learning based cloud detection method, Spectral ENcoder for SEnsor Independence (SEnSeI) [2]. 2) an advanced atmospheric correction model Sensor Invariant Atmospheric Correction (SIAC) [3]. 3) an endmember-based class extraction method, which enables the retrieval of 10 m/20 m albedos based on a regression between the MODIS Bidirectional Reflectance Distribution Function (BRDF) derived surface albedo and Sentinel-2 surface reflectance resampled to MODIS resolution. 4) a novel method of using the MODIS BRDF prior developed within the QA4ECV programme (http://www.qa4ecv.eu/) to fill in the gaps in a time series caused by cloud obscuration. We describe how ~1100 scenes were processed over 22 Sentinel-2 tiles at the STFC JASMIN facility. These tiles spanned different 4 month time periods for different users with a maximum of 22 dates per tile. These tiles cover Italy, Germany, South Africa, South Sudan, Ukraine and UK for 6 different users. For the Italian site, a detailed analysis was performed of the impact of this hr-albedo on the fAPAR and LAI derived using TIP [5] whilst a second user employed a method described in [6] to compare MODIS and Sentinel-2 and a third user looked at the impact on agricultural yield forecasting. Lessons learnt from these different applications will be described including both the opportunities and areas where further work is required to improve the data quality.

We thank ESA for their support through ESA-HR-AlbedoMap: Contract CO 4000130413 and the STFC JASMIN facility and in particular Victoria Bennett for their assistance.

[1] Song, R., Muller, J.-P., Francis, A., A Method of Retrieving 10-m Spectral Surface Albedo Products from Sentinel-2 and MODIS data," 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 2021, pp. 2381-2384, doi: 10.1109/IGARSS47720.2021.9554356

[2] Francis, A., Mrziglod, J., Sidiropoulos, P.  and J.-P. Muller, "SEnSeI: A Deep Learning Module for Creating Sensor Independent Cloud Masks," in IEEE Transactions on Geoscience and Remote Sensing, doi: 10.1109/TGRS.2021.3128280.

[3] Feng et al. (2019) A Sensor Invariant Atmospheric Correction: Sentinel-2/MSI AND Landsat 8/OLI https://doi.org/10.31223/osf.io/ps957.

[4] Song, R.; Muller, J.-P.; Kharbouche, S.; Yin, F.; Woodgate, W.; Kitchen, M.; Roland, M.; Arriga, N.; Meyer, W.; Koerber, G.; Bonal, D.; Burban, B.; Knohl, A.; Siebicke, L.; Buysse, P.; Loubet, B.; Leonardo, M.; Lerebourg, C.; Gobron, N. Validation of Space-Based Albedo Products from Upscaled Tower-Based Measurements Over Heterogeneous and Homogeneous Landscapes. Remote Sensing 2020, 12, 1–23.doi: 10.3390/rs12050833

[5] Gobron, N.; Marioni, M.; Muller, J.-P.; Song, R.; Francis, A. M.; Feng, Y.; Lewis, P. ESA Sentinel-2 Albedo Case Study: FAPAR and LAI downstream products.; 2021; pp. 1–30. JRC TR (in press)

[6] Peng, J.; Kharbouche, S.; Muller, J.-P.; Danne, O.; Blessing, S.; Giering, R.; Gobron, N.; Ludwig, R.; Mueller, B.; Leng, G.; Lees, T.; Dadson, S. Influences of leaf area index and albedo on estimating energy fluxes with HOLAPS framework. J Hydrol 2020, 580, 124245.

How to cite: Muller, J.-P., Song, R., Francis, A., Gobron, N., Peng, J., and Torbick, N.: Assessment of 10m Spectral and Broadband Surface Albedo Products from Sentinel-2 and MODIS data, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12092, https://doi.org/10.5194/egusphere-egu22-12092, 2022.

High-accuracy Digital Elevation Models (DEMs) improve the quality of flood risk assessments and many other environmental applications, yet these products are often unavailable in developing countries due to high survey costs. Structure-from-Motion (SfM) photogrammetry combined with Unmanned Aerial Vehicles (UAVs) has been proven as an effective and low-cost technique that enables a wide audience to construct local-scale DEMs. However, the deviation from strict survey designs and guidelines regarding the number and distribution of Ground Control Points (GCPs) can result in linear and doming errors. Two surveys that suffer from these errors have been supplied for error-reduction, but both areas did not have an available high-accuracy DEM or could afford an additional differential Global Navigation Satellite System (dGNSS) ground survey to extract control points from to use in relative georeferencing approach. Little attention has been given to error-reduction using global open-access elevation data, such as: The TerraSAR-X add-on for Digital Elevation Measurements (TanDEM-X) 90; the Ice, Cloud and land Elevation Satellite-2 (ICESat-2); and Hydroweb.

The aim of this study was to improve and validate the two DEMs using control point extraction from the above data and analyze the validation results to determine the impact on error-reduction using regression analyses between the vertical error and distance from nearest control point. The outcomes shows that the ICESat-2 and Hydroweb can support surveys in absence of dGNSS GCPs with similar impact but cannot replace the necessity of dGNSS measurements in georeferencing and validation. These findings suggests that survey guidelines can be maintained with global open-access elevation data, but the effectiveness depends on both the number, distribution and estimated accuracy. Doming errors can be prevented by correct camera lens calibration, which depends on stable lens conditions or a stratified distribution of high-accuracy reference data. The validation of the SfM DEM in data-scarce areas proves difficult due to the lack of an independent validation dataset, but the Copernicus GLO-30 can give a quantification and show the spatial variability of the error. This study highlights the increasing accuracy of global open-access elevation data and shows that these databases allow the user to easily acquire more and independent data for georeferencing and validation, but the RSME is unable to be accurately reduced to sub-meter.

How to cite: Bakker, D., Phùng, P., van den Homberg, M., Veraverbeke, S., and Couasnon, A.: Error-reducing Structure-from-Motion derived Digital Elevation Models in data-scarce environments, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12524, https://doi.org/10.5194/egusphere-egu22-12524, 2022.

Datacubes form an accepted cornerstone for analysis (and visualization) ready spatio-temporal data offerings. The increase in user friendliness is achieved by abstracting away from the zillions of files in provider-specific organization. Data¬cube query languages additionally establish actionable datacubes enabling users to ask "any query, any time" with zero coding.

However, typically datacube deployments are aiming at large scale, data center environments accommodating Big Data and massive parallel processing capabilities for achieving decent performance. In this contribution, we conversely report about a downscaling experiment. In the ORBiDANSE project a datacube engine, rasdaman, has been ported to a cubesat, ESA OPS-SAT, and is operational in space. Effectively, the satellite thereby becomes a datacube service offering the standards-based query capabilities of the OGC Web Coverage Processing (WCPS) geo datacube analytics language.
We believe this will pave the way for on-board ad-hoc pro-cessing and filtering on Big EO Data, thereby unleashing them to a larger audience and in substantially shorter time.

In our talk, we report about the concept, technology, and experimental results of ad-hoc on-board datacube query processing.

How to cite: Baumann, P. and Misev, D.: ORBiDANSe: Orbital Big Datacube Analytics Service, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13002, https://doi.org/10.5194/egusphere-egu22-13002, 2022.