Fisher–Shannon Complexity Analysis of High-Frequency Urban Wind Speed Time Series

One-hertz wind time series recorded at different levels (from 1.5–25.5 m) in an urban area are investigated by using the Fisher–Shannon (FS) analysis. FS analysis is a well-known method to gain insight into the complex behavior of nonlinear systems, by quantifying the order/disorder properties of time series. Our findings reveal that the FS complexity, defined as the product between the Fisher information measure and the Shannon entropy power, decreases with the height of the anemometer from the ground, suggesting a height-dependent variability in the order/disorder features of the high-frequency wind speed measured in urban layouts. Furthermore, the correlation between the FS complexity of wind speed and the daily variance of the ambient temperature shows a similar decrease with the height of the wind sensor. Such correlation is larger for the lower anemometers, indicating that ambient temperature is an important forcing of the wind speed variability in the vicinity of the ground

Advanced Analysis of Temporal Data Using Fisher-Shannon Information: Theoretical Development and Application in Geosciences

Complex non-linear time series are ubiquitous in geosciences. Quantifying complexity and non-stationarity of these data is a challenging task, and advanced complexity-based exploratory tool are required for understanding and visualizing such data. This paper discusses the Fisher-Shannon method, from which one can obtain a complexity measure and detect non-stationarity, as an efficient data exploration tool. The state-of-the-art studies related to the Fisher-Shannon measures are collected, and new analytical formulas for positive unimodal skewed distributions are proposed. Case studies on both synthetic and real data illustrate the usefulness of the Fisher-Shannon method, which can find application in different domains including time series discrimination and generation of times series features for clustering, modeling and forecasting. The paper is accompanied with Python and R libraries for the non-parametric estimation of the proposed measures

Information and entropy theory for the sustainability of coupled human and natural systems

For coupled human and natural systems (CHANS), sustainability can be defined operationally as a feasible, desirable set of flows (material, currency, information, energy, individuals, etc.) that can be maintained despite internal changes and changes in the environment. Sustainable development can be defined as the process by which CHANS can be moved toward sustainability. Specific indicators that give insight into the structure and behavior of feedbacks in CHANS are of particular interest because they would aid in the sustainable management of these systems through an understanding of the structures that govern system behavior. However, the use of specific feedbacks as monitoring tools is rare, possibly because of uncertainties regarding the nature of their dynamics and the diversity of types of feedbacks encountered in these systems. An information theory perspective may help to rectify this situation, as evidenced by recent research in sustainability science that supports the use of unit-free measures such as Shannon entropy and Fisher information to aggregate disparate indicators. These measures have been used for spatial and temporal datasets to monitor progress toward sustainability targets. Here, we provide a review of information theory and a theoretical framework for studying the dynamics of feedbacks in CHANS. We propose a combination of information-based indices that might productively inform our sustainability goals, particularly when related to key feedbacks in CHANS

Factors limiting sand dune restoration in Northwest Beach, Point Pelee National Park, Canada

Known as home to rare species of flora and fauna, and their critical habitats, Northwest beach of Point Pelee National Park has undergone significant ecological and infrastructural changes in the past decades. A number of important management challenges have emerged, including conservation of endangered Five-lined Skink (Plestiodon fasciatus) which inhabit the extensive dune system within the park. This research investigates key factors for sand dune ecosystem restoration in Northwest beach of Point Pelee with particular attention to the conservation of Skink habitat. Random stratified sampling method was used to collect sand and vegetation samples from the disturbed and natural areas. Sand samples were also collected from the sand piles, which is a part of dune restoration process initiated by the Parks Canada. Three aspects were considered: grain size distribution of dune sediments, vegetation assemblage and character of the dune associated species, land use and land cover change. Grain size distribution indicated that samples from most of the sand piles contained some amounts of clay/silt and pebble sized grains making it unfavourable for wind action, resulting in no significant contribution to dune formation. Most of the sand samples collected along the foredunes and water edge were appropriate for sediment transport. Shannon and Simpson’s Diversity Index was calculated as 1.48 and 0.67 for natural area as compared to 0.71 and 0.35 for the disturbed area, which indicate unfavourable species diversity for dune restoration in disturbed areas. The research also focused on the spatial and temporal changes in land use and land cover in NW beach area of Point Pelee using aerial photos for 1959, 1977, 2006 and 2015. Different time series of the aerial photos were chosen based on their availability. The Ecological land classification system for Southern Ontario were used to classify the aerial photos for land use and land cover (LULC). LULC classes included Shoreline vegetation, Deciduous thicket, Sand Barren and Dune Type, and Infrastructures (includes Transportation and services) for the entire Northwest Beach area. Segmentation and classification tools was used to classify four different time series of aerial photos. Grain size distribution and vegetation assemblage for dune associated species were calculated to determine the factors limiting habitat restoration process. Based on the results alternate management strategies for dune restoration in Point Pelee were recommended. The study offers key insights on the importance of timely detection, analysis and visualisation of dynamic changes for habitat restoration and maintaining ecological integrity of the Northwest beach area of Point Pelee

Combination ecosystem green engineering and early life history processes to enhance the intertidal biodiversity in the Port of East London

Marine coastal ecosystems are highly impacted by anthropogenic activities, including the development of, and practices within, harbours and/or ports. Artificial structures associated with harbours have a different chemical composition, texture and orientation than that of the natural benthic habitats they have replaced, and are therefore not generally favourable for the settlement and occurrence of indigenous species. Attempts are being made to identify what structures and materials can be used to aid in the rehabilitation of native species within harbours. The aim of this study was to investigate the effects of utilising environmentally-friendly artificial structures (tiles) with varying complexity (crevices and ridges with different depths) with ecological engineers (the indigenous bivalve Perna perna), on the associated benthic biodiversity within an international port in South Africa (Port of East London). The objectives of the study were to test the effects of artificial habitat complexity and the presence of bivalves on 1) the growth, mortality and biomass of a selected model species of ecosystem engineer (P. perna), and 2) the associated intertidal biodiversity. Tiles seeded with bivalves (P. perna) were deployed at two sites in the Port of East London and monitored monthly over a period of 12 months (November 2016-October 2017) to assess survival, growth and associated biodiversity. Environmental variables were also measured every month. The results of the three-way repeated measures ANOVAs indicated that tile treatment (especially high complexity of 2.5 and 5 cm) had an effect on the diversity of mobile species, length, height and biomass of the bivalves used as model ecosystem engineer, indicating the potentials for protection from wave dislodgement and refuge provision. Treatment (especially flat tiles) also had an effect on sessile/sedentary species diversity, indicating the greater proportion of space provided by these non-complex tiles. In addition, month had an effect on the mortality, height and biomass of the bivalves. Mortality was highest in October 2017, while the growth in height and biomass of P. perna were largest in January and October 2017 respectively. The use of artificial tiles also had a positive effect on the survival, growth, and biomass of bivalves (especially the length, weight and dry weight in 2.5 cm and 5 cm complexity). Additionally, this research investigated patterns of settlement and recruitment of the local bivalve population (P. perna) in the Port of East London and adjacent natural coastline to understand the early life history temporal and spatial dynamics of this model ecosystem engineer species. Settlement and recruitment were assessed every month by deploying and replacing artificial collectors for a total period of 19 months (November 2016-May 2018). The results of the two-way ANOVAs indicated that month and site (port vs natural) had an effect on settlement and recruitment of bivalves. Settlement and recruitment of bivalves were highest in July 2017 (port) and March 2018 (natural rocky shore). This study has highlighted that the use of artificial concrete tiles with increased complexity, as well as the investigation of the early stages of mussel populations could be important to consider in a framework of rehabilitation of urban coastal environments such as the Port of East London. Ecological engineering (in terms of increased complexity and heterogeneity) has indeed the potentials to be incorporated in South African programmes aiming at improving natural biodiversity in coastal urban environments. Nonetheless, the spatio-temporal variability of early driver of mussel populations (settlement and recruitment) is also an important feature to be closely monitored if biodiversity in South African coastal armouring is to be enhanced effectively and in the long term

Novel Satellite-Based Methodologies for Multi-Sensor and Multi-Scale Environmental Monitoring to Preserve Natural Capital

Global warming, as the biggest manifestation of climate change, has changed the distribution of water in the hydrological cycle by increasing the evapotranspiration rate resulting in anthropogenic and natural hazards adversely affecting modern and past human properties and heritage in different parts of the world. The comprehension of environmental issues is critical for ensuring our existence on Earth and environmental sustainability. Environmental modeling can be described as a simplified form of a real system that enhances our knowledge of how a system operates. Such models represent the functioning of various processes of the environment, such as processes related to the atmosphere, hydrology, land surface, and vegetation. The environmental models can be applied on a wide range of spatiotemporal scales (i.e. from local to global and from daily to decadal levels); and they can employ various types of models (e.g. process-driven, empirical or data-driven, deterministic, stochastic, etc.). Satellite remote sensing and Earth Observation techniques can be utilized as a powerful tool for flood mapping and monitoring. By increasing the number of satellites orbiting around the Earth, the spatial and temporal coverage of environmental phenomenon on the planet has in-creased. However, handling such a massive amount of data was a challenge for researchers in terms of data curation and pre-processing as well as required computational power. The advent of cloud computing platforms has eliminated such steps and created a great opportunity for rapid response to environmental crises. The purpose of this study was to gather state-of-the-art remote sensing and/or earth observation techniques and to further the knowledge concerned with any aspect of the use of remote sensing and/or big data in the field of geospatial analysis. In order to achieve the goals of this study, some of the water-related climate-change phenomena were studied via different mathematical, statistical, geomorphological and physical models using different satellite and in-situ data on different centralized and decentralized computational platforms. The structure of this study was divided into three chapters with their own materials, methodologies and results including: (1) flood monitoring; (2) soil water balance modeling; and (3) vegetation monitoring. The results of this part of the study can be summarize in: 1) presenting innovative procedures for fast and semi-automatic flood mapping and monitoring based on geomorphic methods, change detection techniques and remote sensing data; 2) modeling soil moisture and water balance components in the root zone layer using in-situ, drone and satellite data; incorporating downscaling techniques; 3) combining statistical methods with the remote sensing data for detecting inner anomalies in the vegetation covers such as pest emergence; 4) stablishing and disseminating the use of cloud computation platforms such as Google Earth Engine in order to eliminate the unnecessary steps for data curation and pre-processing as well as required computational power to handle the massive amount of RS data. As a conclusion, this study resulted in provision of useful information and methodologies for setting up strategies to mitigate damage and support the preservation of areas and landscape rich in cultural and natural heritage

Wavelet Scale Variance Analysis of Wind Extremes in Mountainous Terrains

The 10-min average wind speed series recorded at 130 stations distributed rather homogeneously in the territory of Switzerland are investigated. Fixing a percentile-based threshold of the wind speed distribution, a wind extreme is defined as the duration of the sequence of consecutive wind values above the threshold. This definition allows to analyze the sequence of extremes as a temporal point process marked by their duration. Representing the sequence of wind extremes by the inter-extreme interval series, the wavelet variance, a useful tool to investigate the variance of a time series across scales, was applied in order to find a link between the wavelet scales and several topographic parameters. Our findings suggest that the mean duration of wind extremes and mean inter-extreme time are positively correlated and that such relationship depends on the threshold of the wind speed. Furthermore, the threshold of the wind speed distribution correlates best with a terrain parameter related to the Laplacian of terrain elevations; and, in particular, for wavelet scales less than 3, the terrain exposure may explain the formation of extreme wind speeds

Information Theory and Its Application in Machine Condition Monitoring

Condition monitoring of machinery is one of the most important aspects of many modern industries. With the rapid advancement of science and technology, machines are becoming increasingly complex. Moreover, an exponential increase of demand is leading an increasing requirement of machine output. As a result, in most modern industries, machines have to work for 24 hours a day. All these factors are leading to the deterioration of machine health in a higher rate than before. Breakdown of the key components of a machine such as bearing, gearbox or rollers can cause a catastrophic effect both in terms of financial and human costs. In this perspective, it is important not only to detect the fault at its earliest point of inception but necessary to design the overall monitoring process, such as fault classification, fault severity assessment and remaining useful life (RUL) prediction for better planning of the maintenance schedule. Information theory is one of the pioneer contributions of modern science that has evolved into various forms and algorithms over time. Due to its ability to address the non-linearity and non-stationarity of machine health deterioration, it has become a popular choice among researchers. Information theory is an effective technique for extracting features of machines under different health conditions. In this context, this book discusses the potential applications, research results and latest developments of information theory-based condition monitoring of machineries