Joint use of monitoring and modelling

2007 ◽  
Vol 56 (8) ◽  
pp. 21-29 ◽  
Author(s):  
L.F. Jørgensen ◽  
J.C. Refsgaard ◽  
A.L. Højberg
Keyword(s):  
Remote Sensing ◽  
Data Analysis ◽  
Data Assimilation ◽  
Conceptual Models ◽  
Data Availability ◽  
Policy Makers ◽  
Daily Work ◽  
European Water ◽  
Process Understanding ◽  
Potential Benefits

There is much to gain in joining monitoring and modelling efforts, especially in the present process of implementing the European Water Framework Directive. Nevertheless, it is rare to see forces combined in these two disciplines. To bring the monitoring and the modelling communities together, a number of workshops have been arranged with discussions on benefits and constraints in joint use of monitoring and modelling. The workshops have been attended by scientists, water managers, policy makers as well as stakeholders and consultants. Emphasis has been put on data availability and accessibility, remote sensing and data assimilation techniques, monitoring programmes and modelling support to the design or optimisation of these as well as potential benefits of using supporting modelling tools in the process of designing Programmes of Measures by impact assessment etc. The way models can support in extrapolation in time and space, in data analysis, in process understanding (conceptual models), in accessing correct interaction between pressures and impacts etc. have also been elaborated. Although practitioners have been open-minded to the presented ideas, they are somewhat reluctant towards how to implement this in their daily work. This paper presents some experiences from the workshops.

Streamlined Environmental Assessments: Picking the Greenest Option Despite Limited Time and Data at the Federal Environment Agency in Germany

2015 ◽  
Vol 17 (03) ◽  
pp. 1550024 ◽  
Author(s):  
Markus Berger ◽  
Matthias Finkbeiner ◽  
Christiane Markard ◽  
Michael Angrick ◽  
Jakob Frommer ◽  
...  
Keyword(s):  
Environmental Performance ◽  
Ghg Emissions ◽  
Data Availability ◽  
Limited Time ◽  
Policy Makers ◽  
Daily Work ◽  
Federal Environment Agency ◽  
Ranking Of Alternatives ◽  
Environment Agency ◽  
Specific Impact

The Federal Environment Agency of Germany (Umweltbundesamt: UBA) is expected to provide information regarding the environmental performance of products and technologies — even in cases with limited time and data availability. Therefore, the streamlined environmental assessment (StreamEA) methodology has been developed which combines the competences available throughout the agency. Based on scientific assessment models, a ranking of alternatives can be determined for 15 impact categories, like greenhouse gas (GHG) emissions, nuisance, pathogenic emissions or physical killing of animals. Since the overall environmental burden depends on the specific impact per product and the total number of products, a macroeconomic assessment at the level of the product entirety is included. The applicability of the method, which can be adapted to other regions, and the robustness of results have been tested by means of case studies. The method is currently applied in the daily work of the agency to provide guidance to the general public and policy makers.

DARLA: Data Assimilation and Remote Sensing for Littoral Applications

2013 ◽  
Author(s):  
Andrew T. Jessup ◽  
Robert A. Holman ◽  
Steve Elgar
Keyword(s):  
Remote Sensing ◽  
Data Assimilation

More accurate less meaningful? A critical physical geographer’s reflection on interpreting remote sensing land-use analyses

2021 ◽  
pp. 030913332199181
Author(s):  
Andreas Christian Braun
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Geographic Information Systems ◽  
Image Classification ◽  
Satellite Image ◽  
Physical Geography ◽  
Data Availability ◽  
Scientific Results ◽  
Normative Considerations

Land-use and land-cover analyses based on satellite image classification are used in most, if not all, sub-disciplines of physical geography. Data availability and increasingly simple image classification techniques – nowadays, even implemented in simple geographic information systems – increase the use of such analyses. To assess the quality of such land-use analyses, accuracy metrics are applied. The results are considered to have sufficient quality, exceeding thresholds published in the literature. A typical practice in many studies is to confuse accuracy in remote sensing with quality, as required by physical geography. However, notions such as quality are subject to normative considerations and performative practices, which differ between scientific domains. Recent calls for critical physical geography have stressed that scientific results cannot be understood separately from the values and practices underlying them. This article critically discusses the specific understanding of quality in remote sensing, outlining norms and practices shaping it and their relation to physical geography. It points out that, as a seeming paradox, results considered more accurate in remote sensing terms can be less informative – or meaningful – in geographical terms. Finally, a roadmap of how to apply remote sensing land-use analyses more constructively in physical geography is proposed.

scholarly journals Wildfires Vegetation Recovery through Satellite Remote Sensing and Functional Data Analysis

Mathematics ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1305
Author(s):  
Feliu Serra-Burriel ◽  
Pedro Delicado ◽  
Fernando M. Cucchietti
Keyword(s):  
Remote Sensing ◽  
Data Analysis ◽  
Functional Data Analysis ◽  
Functional Data ◽  
Vegetation Index ◽  
Vegetation Recovery ◽  
Functional Responses ◽  
Elapsed Time ◽  
Synthetic Controls ◽  
Available Information

In recent years, wildfires have caused havoc across the world, which are especially aggravated in certain regions due to climate change. Remote sensing has become a powerful tool for monitoring fires, as well as for measuring their effects on vegetation over the following years. We aim to explain the dynamics of wildfires’ effects on a vegetation index (previously estimated by causal inference through synthetic controls) from pre-wildfire available information (mainly proceeding from satellites). For this purpose, we use regression models from Functional Data Analysis, where wildfire effects are considered functional responses, depending on elapsed time after each wildfire, while pre-wildfire information acts as scalar covariates. Our main findings show that vegetation recovery after wildfires is a slow process, affected by many pre-wildfire conditions, among which the richness and diversity of vegetation is one of the best predictors for the recovery.

scholarly journals Quantitative Recognition and Characteristic Analysis of Production-Living-Ecological Space Evolution for Five Resource-Based Cities: Zululand, Xuzhou, Lota, Surf Coast and Ruhr

2021 ◽  
Vol 13 (8) ◽  
pp. 1563
Author(s):  
Yuanyuan Tao ◽  
Qianxin Wang
Keyword(s):  
Remote Sensing ◽  
Urban Areas ◽  
Large Scale ◽  
Remote Sensing Image ◽  
Data Availability ◽  
Function Evaluation ◽  
Characteristic Analysis ◽  
Accurate Identification ◽  
Living Space ◽  
Evolution Characteristics

The accurate identification of PLES changes and the discovery of their evolution characteristics is a key issue to improve the ability of the sustainable development for resource-based urban areas. However, the current methods are unsuitable for the long-term and large-scale PLES investigation. In this study, a modified method of PLES recognition is proposed based on the remote sensing image classification and land function evaluation technology. A multi-dimensional index system is constructed, which can provide a comprehensive evaluation for PLES evolution characteristics. For validation of the proposed methods, the remote sensing image, geographic information, and socio-economic data of five resource-based urbans (Zululand in South Africa, Xuzhou in China, Lota in Chile, Surf Coast in Australia, and Ruhr in Germany) from 1975 to 2020 are collected and tested. The results show that the data availability and calculation efficiency are significantly improved by the proposed method, and the recognition precision is better than 87% (Kappa coefficient). Furthermore, the PLES evolution characteristics show obvious differences at the different urban development stages. The expansions of production, living, and ecological space are fastest at the mining, the initial, and the middle ecological restoration stages, respectively. However, the expansion of living space is always increasing at any stage, and the disorder expansion of living space has led to the decrease of integration of production and ecological spaces. Therefore, the active polices should be formulated to guide the transformation of the living space expansion from jumping-type and spreading-type to filling-type, and the renovation of abandoned industrial and mining lands should be encouraged.

scholarly journals Soil Erosion Estimation Using Remote Sensing Techniques in Wadi Yalamlam Basin, Saudi Arabia

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Jarbou A. Bahrawi ◽  
Mohamed Elhag ◽  
Amal Y. Aldhebiani ◽  
Hanaa K. Galal ◽  
Ahmad K. Hegazy ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Saudi Arabia ◽  
Soil Erosion ◽  
Decision Support Tool ◽  
Data Availability ◽  
Productive Capacity ◽  
Support Tool ◽  
Erosion Risk ◽  
Soil Erosion Risk ◽  
Key Aspects

Soil erosion is one of the major environmental problems in terms of soil degradation in Saudi Arabia. Soil erosion leads to significant on- and off-site impacts such as significant decrease in the productive capacity of the land and sedimentation. The key aspects influencing the quantity of soil erosion mainly rely on the vegetation cover, topography, soil type, and climate. This research studies the quantification of soil erosion under different levels of data availability in Wadi Yalamlam. Remote Sensing (RS) and Geographic Information Systems (GIS) techniques have been implemented for the assessment of the data, applying the Revised Universal Soil Loss Equation (RUSLE) for the calculation of the risk of erosion. Thirty-four soil samples were randomly selected for the calculation of the erodibility factor, based on calculating theK-factor values derived from soil property surfaces after interpolating soil sampling points. Soil erosion risk map was reclassified into five erosion risk classes and 19.3% of the Wadi Yalamlam is under very severe risk (37,740 ha). GIS and RS proved to be powerful instruments for mapping soil erosion risk, providing sufficient tools for the analytical part of this research. The mapping results certified the role of RUSLE as a decision support tool.

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