Development of an Earth Observation Cloud Platform in Support to Water Resources Monitoring

Bucur, Andreea; Wagner, Wolfgang; Elefante, Stefano; Naeimi, Vahid; Briese, Christian

doi:10.1007/978-3-319-65633-5_14

Cited by 8 publications

(4 citation statements)

References 20 publications

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“…Summary information about selected scenes can be found in Table 3. All scenes were atmospherically corrected using Sen2Cor [38] Version 2.4 using the data service platform operated by BOKU [39] on the Earth Observation Data Centre (EODC) [40]. The 20 m bands B5, B6, B7, B8a, B11 and B12 were resampled to 10 m and the 60 m-bands B1, B9 and B10 were excluded from the analyses.…”

Section: Sentinel-2 Data Setsmentioning

confidence: 99%

Optimal Input Features for Tree Species Classification in Central Europe Based on Multi-Temporal Sentinel-2 Data

et al. 2019

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Detailed knowledge about tree species composition is of great importance for forest management. The two identical European Space Agency (ESA) Sentinel-2 (S2) satellites provide data with unprecedented spectral, spatial and temporal resolution. Here, we investigated the potential benefits of using high temporal resolution data for classification of five coniferous and seven broadleaved tree species in a diverse Central European Forest. To run the classification, 18 cloud-free S2 acquisitions were analyzed in a two-step approach. The available scenes were first used to stratify the study area into six broad land-cover classes. Subsequently, additional classification models were created separately for the coniferous and the broadleaved forest strata. To permit a deeper analytical insight in the benefits of multi-temporal datasets for species identification, classification models were developed taking into account all 262,143 possible permutations of the 18 S2 scenes. Each model was fine-tuned using a stepwise recursive feature reduction. The additional use of vegetation indices improved the model performances by around 5 percentage points. Individual mono-temporal tree species accuracies range from 48.1% (January 2017) to 78.6% (June 2017). Compared to the best mono-temporal results, the multi-temporal analysis approach improves the out-of-bag overall accuracy from 72.9% to 85.7% for the broadleaved and from 83.8% to 95.3% for the coniferous tree species, respectively. Remarkably, a combination of six–seven scenes achieves a model quality equally high as the model based on all data; images from April until August proved most important. The classes European Beech and European Larch attain the highest user’s accuracies of 96.3% and 95.9%, respectively. The most important spectral variables to distinguish between tree species are located in the Red (coniferous) and short wave infrared (SWIR) bands (broadleaved), respectively. Overall, the study highlights the high potential of multi-temporal S2 data for species-level classifications in Central European forests.

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Section: Sentinel-2 Data Setsmentioning

confidence: 99%

Optimal Input Features for Tree Species Classification in Central Europe Based on Multi-Temporal Sentinel-2 Data

et al. 2019

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“…These services can be free, or subscription based (e.g., Google Earth Engine, Open Data Cube, etc.) [52,[55][56][57] Open Data/Analytics and Standards…”

Section: Cloud Servicesmentioning

confidence: 99%

Disruptive Technologies for Improving Water Security in Large River Basins

Harshadeep

Young

2020

Water

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Large river basins present significant challenges for water resource planning and management. They typically traverse a wide range of hydroclimatic regimes, are characterized by complex and variable hydrology, and span multiple jurisdictions with diverse water demands and values. They are often data-poor and in many developing economies are characterized by weak water governance. Rapid global change is seeing significant changes to the pressures on the water resources of large basins, exacerbating the challenge of sustainable water management. Diverse technologies have long supported water resource planning and development, from data collection, analytics, simulation, to decision-making, and real-time operations. In the last two decades however, a rapid increase in the range, capability, and accessibility of new technologies, coupled with large reductions in cost, mean there are increasing opportunities for emerging technologies to significantly “disrupt” traditional approaches to water resources management. In this paper, we consider the application of ‘disruptive technologies’ in water resources management in large river basins, through a lens of improving water security. We discuss the role of different actors and institutions for water management considering a range of emerging disruptive technologies. We consider the risks and benefits associated with the use of these technologies and discuss the barriers to their widespread adoption. We obverse a positive trend away from the reliance solely on centralized government institutions and traditional modeling for the collection and analysis of data, towards a more open and dynamic ‘data and knowledge ecosystem’ that draws upon data services at different levels (global to local) to support water planning and operations. We expect that technological advances and cost reductions will accelerate, fueling increased incremental adoption of new technologies in water resources planning and management. Large-basin analytics could become virtually free for users with global, regional, and national development agencies absorbing the costs of development and any subscription services for end users (e.g., irrigators) to help improve water management at user level and improve economic productivity. Collectively, these changes can help to ‘democratize’ water management through improved access to data and information. However, disruptive technologies can also be deployed in top-down or centralized processes, and so their use is sometimes contested or misunderstood. Increased attention therefore needs to be given to ensuring equity in technology access, and to strengthening the governance context for technology deployment. Widespread adoption of disruptive technologies will require adjustments to how water professionals are trained, increased adaptiveness in water resources planning and operations, and careful consideration of privacy and cybersecurity issues.

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“…On the other hand, due to the high data rate of these satellites, it is necessary to have high-performance computing facilities. These facilities are required to ful l several prerequisites, including adequate storage space, dependable internet access, su cient system hardware and software, and e cient algorithms for ood mapping (Bucur et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Near real-time flood inundation and hazard mapping of Baitarani River Basin using Google Earth Engine and SAR imagery

Atchyuth,

Swain,

Das

2023

Environ Monit Assess

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Flood Inundation mapping and satellite imagery monitoring are critical and effective responses during ood events. Mapping of a ood using optical data is limited due to the unavailability of cloud-free images. Because of its capacity to penetrate clouds and operate in all kinds of weather, synthetic aperture radar is preferred for water inundation mapping. Flood mapping in Eastern India's Baitarani River Basin for 2018, 2019, 2020, 2021, and 2022 was performed in this study using Sentinel-1 imagery and Google Earth Engine with Otsu's algorithm. Different machine-learning algorithms were used to map the LULC of the study region. Dual polarizations VH and VV and their combinations VV×VH, VV + VH, VH-VV, VV-VH, VV/VH, and VH/VV were examined to identify non-water and water bodies. The Normalized Difference Water Index (NDWI) map derived from Sentinel-2 data validated the surface water inundation with 80% accuracy. The total inundated areas were identi ed as 440.

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Development of an Earth Observation Cloud Platform in Support to Water Resources Monitoring

Cited by 8 publications

References 20 publications

Optimal Input Features for Tree Species Classification in Central Europe Based on Multi-Temporal Sentinel-2 Data

Optimal Input Features for Tree Species Classification in Central Europe Based on Multi-Temporal Sentinel-2 Data

Disruptive Technologies for Improving Water Security in Large River Basins

Near real-time flood inundation and hazard mapping of Baitarani River Basin using Google Earth Engine and SAR imagery

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