Operationalization of Remote Sensing Solutions for Sustainable Forest Management

The great potential of remote sensing technologies for operational use in sustainable forest management is addressed in this book, which is the reprint of papers published in the Remote Sensing Special Issue “Operationalization of Remote Sensing Solutions for Sustainable Forest Management”. The studies come from three continents and cover multiple remote sensing systems (including terrestrial mobile laser scanning, unmanned aerial vehicles, airborne laser scanning, and satellite data acquisition) and a diversity of data processing algorithms, with a focus on machine learning approaches. The focus of the studies ranges from identification and characterization of individual trees to deriving national- or even continental-level forest attributes and maps. There are studies carefully describing exercises on the case study level, and there are also studies introducing new methodologies for transdisciplinary remote sensing applications. Even though most of the authors look forward to continuing their research, nearly all studies introduced are ready for operational use or have already been implemented in practical forestry

Advances in Remote Sensing-based Disaster Monitoring and Assessment

Remote sensing data and techniques have been widely used for disaster monitoring and assessment. In particular, recent advances in sensor technologies and artificial intelligence-based modeling are very promising for disaster monitoring and readying responses aimed at reducing the damage caused by disasters. This book contains eleven scientific papers that have studied novel approaches applied to a range of natural disasters such as forest fire, urban land subsidence, flood, and tropical cyclones

Application of Geographic Information Systems

The importance of Geographic Information Systems (GIS) can hardly be overemphasized in today’s academic and professional arena. More professionals and academics have been using GIS than ever – urban & regional planners, civil engineers, geographers, spatial economists, sociologists, environmental scientists, criminal justice professionals, political scientists, and alike. As such, it is extremely important to understand the theories and applications of GIS in our teaching, professional work, and research. “The Application of Geographic Information Systems” presents research findings that explain GIS’s applications in different subfields of social sciences. With several case studies conducted in different parts of the world, the book blends together the theories of GIS and their practical implementations in different conditions. It deals with GIS’s application in the broad spectrum of geospatial analysis and modeling, water resources analysis, land use analysis, infrastructure network analysis like transportation and water distribution network, and such. The book is expected to be a useful source of knowledge to the users of GIS who envision its applications in their teaching and research. This easy-to-understand book is surely not the end in itself but a little contribution to toward our understanding of the rich and wonderful subject of GIS

Soil-Water Conservation, Erosion, and Landslide

The predicted climate change is likely to cause extreme storm events and, subsequently, catastrophic disasters, including soil erosion, debris and landslide formation, loss of life, etc. In the decade from 1976, natural disasters affected less than a billion lives. These numbers have surged in the last decade alone. It is said that natural disasters have affected over 3 billion lives, killed on average 750,000 people, and cost more than 600 billion US dollars. Of these numbers, a greater proportion are due to sediment-related disasters, and these numbers are an indication of the amount of work still to be done in the field of soil erosion, conservation, and landslides. Scientists, engineers, and planners are all under immense pressure to develop and improve existing scientific tools to model erosion and landslides and, in the process, better conserve the soil. Therefore, the purpose of this Special Issue is to improve our knowledge on the processes and mechanics of soil erosion and landslides. In turn, these will be crucial in developing the right tools and models for soil and water conservation, disaster mitigation, and early warning systems

An evidential reasoning geospatial approach to transport corridor susceptibility zonation

PhD ThesisGiven the increased hazards faced by transport corridors such as climate induced extreme weather, it is essential that local spatial hot-spots of potential landslide susceptibility can be recognised. Traditionally, geotechnical survey and monitoring approaches have been used to recognise spatially landslide susceptibility zones. The increased availability of affordable very high resolution remotely-sensed datasets, such as airborne laser scanning (ALS) and multispectral aerial imagery, along with improved geospatial digital map data-sets, potentially allows the automated recognition of vulnerable earthwork slopes. However, the challenge remains to develop the analytical framework that allows such data to be integrated in an objective manner to recognise slopes potentially susceptible to failure. In this research, an evidential reasoning multi-source geospatial integration approach for the broad-scale recognition and prediction of landslide susceptibility in transport corridors has been developed. Airborne laser scanning and Ordnance Survey DTM data is used to derive slope stability parameters (slope gradient, aspect, terrain wetness index (TWI), stream power index (SPI) and curvature), while Compact Airborne Spectrographic Imager (CASI) imagery, and existing national scale digital map data-sets are used to characterise the spatial variability of land cover, land use and soil type. A novel approach to characterisation of soil moisture distribution within transport corridors is developed that incorporates the effects of the catchment contribution to local zones of moisture concentration in earthworks. In this approach, the land cover and soil type of the wider catchment are used to estimate the spatial contribution of precipitation contributing to surface runoff, which in turn is used to parameterise a weighted terrain accumulation flow model. The derived topographic and land use properties of the transport corridor are integrated within the evidential reasoning approach to characterise numeric measures of belief, disbelief and uncertainty regarding slope instability spatially within the transport corridor. Evidential reasoning was employed as it offers the ability to derive an objective weighting of the relative importance of each derived property to the final estimation of landslide susceptibility, whilst allowing the uncertainty of the properties to be taken into account. The developed framework was applied to railway transport earthworks located near Haltwhistle in northern England, UK. This section of the Carlisle-Newcastle rail line has a ii history of instability with the occurrence of numerous minor landslides in recent years. Results on spatial distribution of soil moisture indicate considerable contribution of the surrounding wider catchment topography to the localised zones of moisture accumulation. The degrees of belief and disbelief indicated the importance of slope with gradients between 250 to 350 and concave curvature. Permeable soils with variable intercalations accounted for over 80% of slope instability with 5.1% of the earthwork cuttings identified as relatively unstable in contrast to 47.5% for the earthwork embankment. The developed approach was found to have a goodness of fit of 88.5% with respect to the failed slopes used to parametrise the evidential reasoning model and an overall predictive capability of 77.75% based on independent validation dataset.TETFUND Nigeria, Nasarawa State University and my family members for their financial support towards the completion of the PhD programme

UAVs for the Environmental Sciences

This book gives an overview of the usage of UAVs in environmental sciences covering technical basics, data acquisition with different sensors, data processing schemes and illustrating various examples of application

Salinity hazard mapping and risk assessment in the Bourke irrigation district

At no point in history have we demanded so much from our agricultural land whilst simultaneously leaving so little room for management error. Of the many possible environmental impacts from agriculture, soil and water salinisation has some of the most long-lived and deleterious effects. Despite its importance, however, land managers are often unable to make informed decisions of how to manage the risk of salinisation due to a lack of data. Furthermore, there remains no universally agreed method for salinity risk mapping. This thesis addresses these issues by investigating new methods for producing high-resolution predictions of soil salinity, soil physical properties and groundwater depth using a variety of traditional and emerging ancillary data sources. The results show that the methodologies produce accurate predictions yielding natural resource information at a scale and resolution not previously possible. Further to this, a new methodology using fuzzy logic is developed that exploits this information to produce high-resolution salinity risk maps designed to aid both agricultural and natural resource management decisions. The methodology developed represents a new and effective way of presenting salinity risk and has numerous advantages over conventional risk models. The incorporation of fuzzy logic provides a meaningful continuum of salinity risk and allows for the incorporation of uncertainty. The method also allows salinity risk to be calculated relative to any vegetation community and shows where the risk is coming from (root-zone or groundwater) allowing more appropriate management decisions to be made. The development of this methodology takes us a step closer to closing what some have called our greatest gap in agricultural knowledge. That is, our ability to manage the salinity risk at the subcatchment scale