Proceedings Of The 18th Annual Meeting Of The Asia Oceania Geosciences Society (Aogs 2021)

The 18th Annual Meeting of the Asia Oceania Geosciences Society (AOGS 2021) was held from 1st to 6th August 2021. This proceedings volume includes selected extended abstracts from a challenging array of presentations at this conference. The AOGS Annual Meeting is a leading venue for professional interaction among researchers and practitioners, covering diverse disciplines of geosciences

Novel Approaches in Landslide Monitoring and Data Analysis

Significant progress has been made in the last few years that has expanded the knowledge of landslide processes. It is, therefore, necessary to summarize, share and disseminate the latest knowledge and expertise. This Special Issue brings together novel research focused on landslide monitoring, modelling and data analysis

Improving Flood Detection and Monitoring through Remote Sensing

As climate-change- and human-induced floods inflict increasing costs upon the planet, both in terms of lives and environmental damage, flood monitoring tools derived from remote sensing platforms have undergone improvements in their performance and capabilities in terms of spectral, spatial and temporal extents and resolutions. Such improvements raise new challenges connected to data analysis and interpretation, in terms of, e.g., effectively discerning the presence of floodwaters in different land-cover types and environmental conditions or refining the accuracy of detection algorithms. In this sense, high expectations are placed on new methods that integrate information obtained from multiple techniques, platforms, sensors, bands and acquisition times. Moreover, the assessment of such techniques strongly benefits from collaboration with hydrological and/or hydraulic modeling of the evolution of flood events. The aim of this Special Issue is to provide an overview of recent advancements in the state of the art of flood monitoring methods and techniques derived from remotely sensed data

Deep Learning Methods for Remote Sensing

Remote sensing is a field where important physical characteristics of an area are exacted using emitted radiation generally captured by satellite cameras, sensors onboard aerial vehicles, etc. Captured data help researchers develop solutions to sense and detect various characteristics such as forest fires, flooding, changes in urban areas, crop diseases, soil moisture, etc. The recent impressive progress in artificial intelligence (AI) and deep learning has sparked innovations in technologies, algorithms, and approaches and led to results that were unachievable until recently in multiple areas, among them remote sensing. This book consists of sixteen peer-reviewed papers covering new advances in the use of AI for remote sensing

Forest landscapes and global change. New frontiers in management, conservation and restoration. Proceedings of the IUFRO Landscape Ecology Working Group International Conference

This volume contains the contributions of numerous participants at the IUFRO Landscape Ecology Working Group International Conference, which took place in Bragança, Portugal, from 21 to 24 of September 2010. The conference was dedicated to the theme Forest Landscapes and Global Change - New Frontiers in Management, Conservation and Restoration. The 128 papers included in this book follow the structure and topics of the conference. Sections 1 to 8 include papers relative to presentations in 18 thematic oral and two poster sessions. Section 9 is devoted to a wide-range of landscape ecology fields covered in the 12 symposia of the conference. The Proceedings of the IUFRO Landscape Ecology Working Group International Conference register the growth of scientific interest in forest landscape patterns and processes, and the recognition of the role of landscape ecology in the advancement of science and management, particularly within the context of emerging physical, social and political drivers of change, which influence forest systems and the services they provide. We believe that these papers, together with the presentations and debate which took place during the IUFRO Landscape Ecology Working Group International Conference – Bragança 2010, will definitively contribute to the advancement of landscape ecology and science in general. For their additional effort and commitment, we thank all the participants in the conference for leaving this record of their work, thoughts and science

Novel Approaches for Structural Health Monitoring

The thirty-plus years of progress in the field of structural health monitoring (SHM) have left a paramount impact on our everyday lives. Be it for the monitoring of fixed- and rotary-wing aircrafts, for the preservation of the cultural and architectural heritage, or for the predictive maintenance of long-span bridges or wind farms, SHM has shaped the framework of many engineering fields. Given the current state of quantitative and principled methodologies, it is nowadays possible to rapidly and consistently evaluate the structural safety of industrial machines, modern concrete buildings, historical masonry complexes, etc., to test their capability and to serve their intended purpose. However, old unsolved problematics as well as new challenges exist. Furthermore, unprecedented conditions, such as stricter safety requirements and ageing civil infrastructure, pose new challenges for confrontation. Therefore, this Special Issue gathers the main contributions of academics and practitioners in civil, aerospace, and mechanical engineering to provide a common ground for structural health monitoring in dealing with old and new aspects of this ever-growing research field

Quantitative Modelling of Climate Change Impact on Hydro-climatic Extremes

In recent decades, climate change has caused a more volatile climate leading to more extreme events such as severe rainstorms, heatwaves and floods which are likely to become more frequent. Aiming to reveal climate change impact on the hydroclimatic extremes in a quantitative sense, this thesis presents a comprehensive analysis from three main strands. The first strand focuses on developing a quantitative modelling framework to quantify the spatiotemporal variation of hydroclimatic extremes for the areas of concern. A spatial random sampling toolbox (SRS-GDA) is designed for randomizing the regions of interest (ROIs) with different geographic locations, sizes, shapes and orientations where the hydroclimatic extremes are parameterised by a nonstationary distribution model whose parameters are assumed to be time-varying. The parameters whose variation with respect to different spatial features of ROIs and climate change are finally quantified by various statistical models such as the generalised linear model. The framework is applied to quantify the spatiotemporal variation of rainfall extremes in Great Britain (GB) and Australia and is further used in a comparison study to quantify the bias between observed and climate projected extremes. Then the framework is extended to a multivariate framework to estimate the time-varying joint probability of more than one hydroclimatic variable in the perspective of non-stationarity. A case study for evaluating compound floods in Ho Chi Minh City, Vietnam is applied for demonstrating the application of the framework. The second strand aims to recognise, classify and track the development of hydroclimatic extremes (e.g., severe rainstorms) by developing a stable computer algorithm (i.e., the SPER toolbox). The SPER toolbox can detect the boundary of the event area, extract the spatial and physical features of the event, which can be used not only for pattern recognition but also to support AI-based training for labelling/cataloguing the pattern from the large-sized, grid-based, multi-scaled environmental datasets. Three illustrative cases are provided; and as the front-end of AI study, an example for training a convolution neural network is given for classifying the rainfall extremes in the last century of GB. The third strand turns to support decision making by building both theory-driven and data-driven decision-making models to simulate the decisions in the context of flood forecasting and early warning, using the data collected via laboratory-style experiments based on various information of probabilistic flood forecasts and consequences. The research work demonstrated in this thesis has been able to bridge the knowledge gaps in the related field and it also provides a precritical insight in managing future risks arising from hydroclimatic extremes, which makes perfect sense given the urgent situation of climate change and the related challenges our societies are facing