scholarly journals Estimating Fire Background Temperature at a Geostationary Scale—An Evaluation of Contextual Methods for AHI-8

2018 ◽  
Vol 10 (9) ◽  
pp. 1368 ◽  
Author(s):  
Bryan Hally ◽  
Luke Wallace ◽  
Karin Reinke ◽  
Simon Jones ◽  
Chermelle Engel ◽  
...  
Keyword(s):  
Contextual Information ◽  
Window Size ◽  
Fire Detection ◽  
Estimation Accuracy ◽  
Significant Deterioration ◽  
Temperature Estimation ◽  
Landscape Characteristics ◽  
Detection And Attribution ◽  
Background Temperature

An integral part of any remotely sensed fire detection and attribution method is an estimation of the target pixel’s background temperature. This temperature cannot be measured directly independent of fire radiation, so indirect methods must be used to create an estimate of this background value. The most commonly used method of background temperature estimation is through derivation from the surrounding obscuration-free pixels available in the same image, in a contextual estimation process. This method of contextual estimation performs well in cloud-free conditions and in areas with homogeneous landscape characteristics, but increasingly complex sets of rules are required when contextual coverage is not optimal. The effects of alterations to the search radius and sample size on the accuracy of contextually derived brightness temperature are heretofore unexplored. This study makes use of imagery from the AHI-8 geostationary satellite to examine contextual estimators for deriving background temperature, at a range of contextual window sizes and percentages of valid contextual information. Results show that while contextual estimation provides accurate temperatures for pixels with no contextual obscuration, significant deterioration of results occurs when even a small portion of the target pixel’s surroundings are obscured. To maintain the temperature estimation accuracy, the use of no less than 65% of a target pixel’s total contextual coverage is recommended. The study also examines the use of expanding window sizes and their effect on temperature estimation. Results show that the accuracy of temperature estimation decreases significantly when expanding the examined window, with a 50% increase in temperature variability when using a larger window size than 5 × 5 pixels, whilst generally providing limited gains in the total number of temperature estimates (between 0.4%–4.4% of all pixels examined). The work also presents a number of case study regions taken from the AHI-8 disk in more depth, and examines the causes of excess temperature variation over a range of topographic and land cover conditions.

scholarly journals Spatial Analysis of the Drivers, Characteristics, and Effects of Forest Fragmentation

2021 ◽  
Vol 13 (6) ◽  
pp. 3246
Author(s):  
Zoe Slattery ◽  
Richard Fenner
Keyword(s):  
Forest Fragmentation ◽  
Forest Fires ◽  
Geographical Information ◽  
Agricultural Expansion ◽  
Forest Patches ◽  
Mato Grosso ◽  
Remote Imaging ◽  
Landscape Characteristics ◽  
High Level

Building on the existing literature, this study examines whether specific drivers of forest fragmentation cause particular fragmentation characteristics, and how these characteristics can be linked to their effects on forest-dwelling species. This research uses Landsat remote imaging to examine the changing patterns of forests. It focuses on areas which have undergone a high level of a specific fragmentation driver, in particular either agricultural expansion or commodity-driven deforestation. Seven municipalities in the states of Rondônia and Mato Grosso in Brazil are selected as case study areas, as these states experienced a high level of commodity-driven deforestation and agricultural expansion respectively. Land cover maps of each municipality are created using the Geographical Information System software ArcGIS Spatial Analyst extension. The resulting categorical maps are input into Fragstats fragmentation software to calculate quantifiable fragmentation metrics for each municipality. To determine the effects that these characteristics are likely to cause, this study uses a literature review to determine how species traits affect their responses to forest fragmentation. Results indicate that, in areas that underwent agricultural expansion, the remaining forest patches became more complex in shape with longer edges and lost a large amount of core area. This negatively affects species which are either highly dispersive or specialist to core forest habitat. In areas that underwent commodity-driven deforestation, it was more likely that forest patches would become less aggregated and create disjunct core areas. This negatively affects smaller, sedentary animals which do not naturally travel long distances. This study is significant in that it links individual fragmentation drivers to their landscape characteristics, and in turn uses these to predict effects on species with particular traits. This information will prove useful for forest managers, particularly in the case study municipalities examined in this study, in deciding which species require further protection measures. The methodology could be applied to other drivers of forest fragmentation such as forest fires.

scholarly journals Game-Theoretic Decision Support for Cyber Forensic Investigations

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5300
Author(s):  
Antonia Nisioti ◽  
George Loukas ◽  
Stefan Rass ◽  
Emmanouil Panaousis
Keyword(s):  
Cyber Security ◽  
Strategic Decisions ◽  
Bayesian Games ◽  
Bayesian Game ◽  
Cyber Forensics ◽  
Detection And Attribution ◽  
Different Types ◽  
Game Theoretic ◽  
The Cost

The use of anti-forensic techniques is a very common practice that stealthy adversaries may deploy to minimise their traces and make the investigation of an incident harder by evading detection and attribution. In this paper, we study the interaction between a cyber forensic Investigator and a strategic Attacker using a game-theoretic framework. This is based on a Bayesian game of incomplete information played on a multi-host cyber forensics investigation graph of actions traversed by both players. The edges of the graph represent players’ actions across different hosts in a network. In alignment with the concept of Bayesian games, we define two Attacker types to represent their ability of deploying anti-forensic techniques to conceal their activities. In this way, our model allows the Investigator to identify the optimal investigating policy taking into consideration the cost and impact of the available actions, while coping with the uncertainty of the Attacker’s type and strategic decisions. To evaluate our model, we construct a realistic case study based on threat reports and data extracted from the MITRE ATT&CK STIX repository, Common Vulnerability Scoring System (CVSS), and interviews with cyber-security practitioners. We use the case study to compare the performance of the proposed method against two other investigative methods and three different types of Attackers.

scholarly journals Machine Learning for Sensorless Temperature Estimation of a BLDC Motor

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4655
Author(s):  
Dariusz Czerwinski ◽  
Jakub Gęca ◽  
Krzysztof Kolano
Keyword(s):  
Machine Learning ◽  
Temperature Measurement ◽  
Stochastic Gradient Descent ◽  
Estimation Accuracy ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Support Vector ◽  
Bldc Motor ◽  
Temperature Estimation ◽  
Motor Operation

In this article, the authors propose two models for BLDC motor winding temperature estimation using machine learning methods. For the purposes of the research, measurements were made for over 160 h of motor operation, and then, they were preprocessed. The algorithms of linear regression, ElasticNet, stochastic gradient descent regressor, support vector machines, decision trees, and AdaBoost were used for predictive modeling. The ability of the models to generalize was achieved by hyperparameter tuning with the use of cross-validation. The conducted research led to promising results of the winding temperature estimation accuracy. In the case of sensorless temperature prediction (model 1), the mean absolute percentage error MAPE was below 4.5% and the coefficient of determination R2 was above 0.909. In addition, the extension of the model with the temperature measurement on the casing (model 2) allowed reducing the error value to about 1% and increasing R2 to 0.990. The results obtained for the first proposed model show that the overheating protection of the motor can be ensured without direct temperature measurement. In addition, the introduction of a simple casing temperature measurement system allows for an estimation with accuracy suitable for compensating the motor output torque changes related to temperature.

scholarly journals Evaluating Landscape Attractiveness with Geospatial Data, a Case Study in Flanders, Belgium

Land ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 703
Author(s):  
Astrid Vannoppen ◽  
Jeroen Degerickx ◽  
Anne Gobin
Keyword(s):  
Ecosystem Services ◽  
Urban Areas ◽  
Policy Development ◽  
Bare Soil ◽  
Geospatial Data ◽  
Soil Conditions ◽  
Landscape Indicators ◽  
Landscape Characteristics ◽  
Landscape Quality

Attractive landscapes are diverse and resilient landscapes that provide a multitude of essential ecosystem services. The development of landscape policy to protect and improve landscape attractiveness, thereby ensuring the provision of ecosystem services, is ideally adapted to region specific landscape characteristics. In addition, trends in landscape attractiveness may be linked to certain policies, or the absence of policies over time. A spatial and temporal evaluation of landscape attractiveness is thus desirable for landscape policy development. In this paper, landscape attractiveness was spatially evaluated for Flanders (Belgium) using landscape indicators derived from geospatial data as a case study. Large local differences in landscape quality in (i) rural versus urban areas and (ii) between the seven agricultural regions in Flanders were found. This observed spatial variability in landscape attractiveness demonstrated that a localized approach, considering the geophysical characteristics of each individual region, would be required in the development of landscape policy to improve landscape quality in Flanders. Some trends in landscape attractiveness were related to agriculture in Flanders, e.g., a slight decrease in total agricultural area, decrease in dominance of grassland, maize and cereals, a decrease in crop diversity, sharp increase in the adoption of agri-environmental agreements (AEA) and a decrease in bare soil conditions in winter. The observed trends and spatial variation in landscape attractiveness can be used as a tool to support policy analysis, assess the potential effects of future policy plans, identify policy gaps and evaluate past landscape policy.

scholarly journals Towards Zero Emissions Noosa

2019 ◽  
Vol 11 (17) ◽  
pp. 4679
Author(s):  
Carina Anderson ◽  
Robert Passey ◽  
Jeremy De Valck ◽  
Rakibuzzaman Shah
Keyword(s):  
Community Engagement ◽  
Focal Point ◽  
Contextual Information ◽  
Renewable Electricity ◽  
Community Group ◽  
Course Of Action ◽  
Engagement Process ◽  
Set Up ◽  
External Consultant

This paper reports on a case study of the community group Zero Emissions Noosa, whose goal is for 100% renewable electricity in the Noosa Shire (Queensland, Australia) by 2026. Described within this paper are the processes used by Zero Emissions Noosa to set up their zero emissions plan, involving community engagement and the use of an external consultant. The external consultant was employed to produce a detailed report outlining how to successfully achieve zero emissions from electricity in the Noosa Shire by 2026. This paper explains how and why the community engagement process used to produce the report was just as important as the outcomes of the report itself. Modeling was undertaken, and both detailed and contextual information was provided. Inclusion of the community in developing the scenario parameters for the modeling had a number of benefits including establishing the context within which their actions would occur and focusing their efforts on options that were technically feasible, financially viable and within their capabilities to implement. This provided a focal point for the community in calling meetings and contacting stakeholders. Rather than prescribing a particular course of action, it also resulted in a toolbox of options, a range of possible solutions that is flexible enough to fit into whatever actions are preferred by the community. The approach and outcomes discussed in this paper should, therefore, be useful to other communities with similar carbon emission reduction goals.

Assessing Triangulation Across Methodologies, Methods, and Stakeholder Groups: The Joys, Woes, and Politics of Interpreting Convergent and Divergent Data

2018 ◽  
Vol 41 (1) ◽  
pp. 125-144 ◽  
Author(s):  
Rebecca Campbell ◽  
Rachael Goodman-Williams ◽  
Hannah Feeney ◽  
Giannina Fehler-Cabral
Keyword(s):  
Large Scale ◽  
Contextual Information ◽  
Policy Makers ◽  
Specific Data ◽  
Qualitative And Quantitative ◽  
Stakeholder Groups ◽  
Data Points ◽  
Evaluation Project ◽  
Collection Methods

The purpose of this study was to develop triangulation coding methods for a large-scale action research and evaluation project and to examine how practitioners and policy makers interpreted both convergent and divergent data. We created a color-coded system that evaluated the extent of triangulation across methodologies (qualitative and quantitative), data collection methods (observations, interviews, and archival records), and stakeholder groups (five distinct disciplines/organizations). Triangulation was assessed for both specific data points (e.g., a piece of historical/contextual information or qualitative theme) and substantive findings that emanated from further analysis of those data points (e.g., a statistical model or a mechanistic qualitative assertion that links themes). We present five case study examples that explore the complexities of interpreting triangulation data and determining whether data are deemed credible and actionable if not convergent.

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