Airborne Light Detection and Ranging (LIDAR) technology has become the primary method to derive high-resolution Digital Terrain Models (DTMs), which are essential for studying Earth’s surface processes, such as flooding and landslides. The critical step in generating a DTM is to separate ground and non-ground measurements in a voluminous point LIDAR dataset, using a filter, because the DTM is created by interpolating ground points. As one of widely used filtering methods, the progressive morphological (PM) filter has the advantages of classifying the LIDAR data at the point level, a linear computational complexity, and preserving the geometric shapes of terrain features. The filter works well in an urban setting with a gentle slope and a mixture of vegetation and buildings. However, the PM filter often removes ground measurements incorrectly at the topographic high area, along with large sizes of non-ground objects, because it uses a constant threshold slope, resulting in “cut-off” errors. A novel cluster analysis method was developed in this study and incorporated into the PM filter to prevent the removal of the ground measurements at topographic highs. Furthermore, to obtain the optimal filtering results for an area with undulating terrain, a trend analysis method was developed to adaptively estimate the slope-related thresholds of the PM filter based on changes of topographic slopes and the characteristics of non-terrain objects. The comparison of the PM and generalized adaptive PM (GAPM) filters for selected study areas indicates that the GAPM filter preserves the most “cut-off” points removed incorrectly by the PM filter. The application of the GAPM filter to seven ISPRS benchmark datasets shows that the GAPM filter reduces the filtering error by 20% on average, compared with the method used by the popular commercial software TerraScan. The combination of the cluster method, adaptive trend analysis, and the PM filter allows users without much experience in processing LIDAR data to effectively and efficiently identify ground measurements for the complex terrains in a large LIDAR data set. The GAPM filter is highly automatic and requires little human input. Therefore, it can significantly reduce the effort of manually processing voluminous LIDAR measurements.

Radio wave signal strength and associated propagation models are rarely analyzed across individual geographic provinces. This study evaluates the effectiveness of the Radio Mobile model to predict radio wave signal strength in the Blue Ridge and Valley and Ridge physiographic provinces. A spectrum analyzer was used on 19 FM transmitters to determine model accuracy. Statistical analysis determined the significance between different terrain factors and signal strength. Field signal strength was found to be related to test site elevation, transmitter azimuth, elevation angle, transmitter elevation, path loss, and distance. Using 76 signal strength receiver sites, Ordinary Least Square regression models predicted signal strength with 60% of variability explained in the Valley and Ridge province model and 43% of variability explained in the Blue Ridge province model. Region-specific statistical models were more accurate in determining a region’s transmitter placement and level of power for broadcasting compared to generic computer models.

Rip currents are the most dangerous hazard at surf beaches. Rip currents in South Florida have previously not been studied. Beach profiles for three Florida beaches (Miami Beach, Lido Beach, Sarasota, and Pensacola Beach) and one Georgia beach (South Cumberland Island) were chosen for surveying because of their variable sand bar heights. Rip current hazard at each beach was assessed by lifeguard rip rescue and drowning statistics. A relationship was found between sand bar height, beach slope and rip current hazard. Rip current measurements in South Florida, which involved utilizing GPS drifters, laser rangefinder and drone-imaged fluorescent tracer dye, showed that the speed ranged from 0.1-0.5 m/s, which is fairly slow compared to such measurements undertaken in California and Australia. The effect of rip currents on swimmers was analyzed based on the drag force acting on swimmers and the power they generate to overcome the currents when swimming against them. The drag force and power increase quadratically and cubically, respectively, with the increase of rip current and swimming speeds. Hence, even rip currents of low velocity can be dangerous and swimming against the current should be avoided if possible. Strong rips in California have been shown to exhibit a circulatory pattern, which could bring a floater back to the safety of a shallow sand bar. Field measurements of rip currents in South Florida clearly defined the flow characteristics of a nearly straight-line current, sometimes deflected to the east-southeast. Therefore, the traditional approach of swimming left or right, parallel to the shore is the best escape strategy, but not against the longshore current if present. A logistic regression analysis was conducted to predict the occurrence of rip currents based on beach conditions. The logistic model showed that wave height, wave period and wind speed were statistically significant factors in rip generation. Rips were found to be most commonly generated by relatively small, non-threatening waves (e.g., 0.6 to 0.9m in height). These physical factors, along with social and safety considerations, pose a significant problem for coastal management.

Until now, the lack of time-continuous, terrestrial paleoenvironmental data from the Pleistocene Arctic has made model simulations of past interglacials difficult to assess. Here, we compare climate simulations of four warm interglacials at Marine Isotope Stage (MIS) 1 (9ka), 5e (127 ka), 11c (409 ka), and 31 (1072 ka) with new proxy climate data recovered from Lake El’gygytgyn, NE Russia. Climate reconstructions of the Mean Temperature of the Warmest Month (MTWM) indicate conditions 2.1, 0.5 and 3.1 ºC warmer than today during MIS 5e, 11c, and 31 respectively. While the climate model captures much of the observed warming during each interglacial, largely in response to boreal summer orbital forcing, the extraordinary warmth of MIS 11c relative to the other interglacials in the proxy records remain difficult to explain. To deconvolve the contribution of multiple influences on interglacial warming at Lake El’gygytgyn, we isolated the influence of vegetation, sea ice, and circum-Arctic land ice feedbacks on the climate of the Beringian interior. Vegetation-land surface feedback simulations during all four interglacials show expanding boreal forest cover with increasing summer insolation intensity. A deglaciated Greenland is shown to have a minimal effect on Northeast Asian temperature during the warmth of stage 11c and 31 (Melles et al., 2012). A prescribed enhancement of oceanic heat transport into the Arctic ocean has some effect on Beringian climate, suggesting intrahemispheric coupling seen in comparisons between Lake El’gygytgyn and Antarctic sediment records might be related to linkages between Antarctic ice volume and ocean circulation. The exceptional warmth of MIS 11c remains enigmatic however, relative to the modest orbital and greenhouse gas forcing during that interglacial. Large Northern Hemisphere ice sheets during Plio-Pleistocene glaciation causes a substantial decrease in Mean Temperature of the Coldest Month (MTCM) and Mean Annual Precipitation (PANN) causing significant Arctic aridification. Aridification and frigid conditions can be linked to a combination of mechanical forcing from the Laurentide and Fennoscandian ice sheets on mid-tropospheric westerly flow and expanded sea-ice cover causing albedo-enhanced feedback.

A tsunami generated by the C.E. 1707 Hōei earthquake is largely thought to be the flood event of record for southwestern Japan, yet historical documentation of the event is scarce. This is particularly true in northwestern Shikoku within the Bungo Channel, where significant inconsistencies exist between historical records and model-derived tsunami heights. To independently assess flooding from the C.E. 1707 Hōei tsunami in the context of the region’s long-term flooding history, we present complementary reconstructions of extreme coastal inundation from three back-barrier lakes in the northern Bungo Channel: Lake Ryuuoo, Lake Amida, and Lake Kamega. At all sites, the most prominent marine overwash deposit of the past ~1,000 years, as defined by grain size, density, and geochemical indicators, is consistent with the timing of the 1707 tsunami, providing strong evidence that the event caused the most significant flooding of the last millennium in this region. At Lake Ryuuoo, modern barrier beach elevations and grain sizes in the tsunami’s resultant deposit provide ~4 m as the first physically based height constraint for the 1707 tsunami in the northern Bungo Channel. Around 1,000 years ago, a concurrent and abrupt transition in lithology observed at all three sites is also consistent with rapid, regional geomorphic change. At Lake Ryuuoo, a marine overwash deposit comparable to the 1707 deposit directly overlies this transition. A 1,000-year-old lithological transition or deposit has not been observed at sites closer to the mouth of the Bungo Channel, suggesting that the deposit in Lake Ryuuoo is more consistent with a tsunami generated by local seismicity along the Japan Median Tectonic Line than with a Nankai Trough-derived tsunami. Our findings are significant in that they provide three new millennial-scale tsunami inundation reconstructions for a relatively understudied region of Japan, along with the first physically based height constraint for the Hōei tsunami in the northern Bungo Channel.

<p>This research is concerned with theoretical and methodological aspects of geographic information transformation between different user contexts. In this dissertation I present theories and methodological approaches that enable a context sensititve use and reuse of geographic data in geographic information systems.</p><p>A primary motive for the reported research is that the patrons interested in answering environmental questions have increased in number and been diversified during the last 10-15 years. The interest from international, national and regional authorities together with multinational and national corporations embrace a range of spatial and temporal scales from global to local, and from many-year/-decade perspectives to real time applications. These differences in spatial and temporal detail will be expressed as rather different questions towards existing data. It is expected that geographic information systems will be able to integrate a large number of diverse data to answer current and future geographic questions and support spatial decision processes. However, there are still important deficiencies in contemporary theories and methods for geographic information integration</p><p>Literature studies and preliminary experiments suggested that any transformation between different users’ contexts would change either the thematic, spatial or temporal detail, and the result would include some amount of semantic uncertainty. Consequently, the reported experiments are separated into studies of change in either spatial or thematic detail. The scope concerned with thematic detatil searched for approaches to represent indiscernibility between categories, and the scope concerned with spatial detail studied semantic effects caused by changing spatial granularity.</p><p>The findings make several contributions to the current knowledge about transforming geographic information between users’ contexts. When changing the categorical resolution of a geographic dataset, it is possible to represent cases of indiscernibility using novel methods of rough classification described in the thesis. The use of rough classification methods together with manual landscape interpretations made it possible to evaluate semantic uncertainty in geographic data. Such evaluations of spatially aggregated geographic data sets show both predictable and non-predictable effects. and these effects may vary for different environmental variables.</p><p>Development of methods that integrate crisp, fuzzy and rough data enables spatial decision support systems to consider various aspects of semantic uncertainty. By explicitly representing crisp, fuzzy and rough relations between datasets, a deeper semantic meaning is given to geographic databasses. The explicit representation of semantic relations is called a Geographic Concept Topology and is held as a viable tool for context transformation and full integration of geographic datasets.</p>

In order to cope with system complexity and dynamic environments, modern industries are investing in a variety of sensor networks and data acquisition systems to increase information visibility. Multi-sensor systems bring the proliferation of high-dimensional functional Big Data that capture rich information on the evolving dynamics of natural and engineered processes. With spatially and temporally dense data readily available, there is an urgent need to develop advanced methodologies and associated tools that will enable and assist (i) the handling of the big data communicated by the contemporary complex systems, (ii) the extraction and identification of pertinent knowledge about the environmental and operational dynamics driving these systems, and (iii) the exploitation of the acquired knowledge for more enhanced design, analysis, monitoring, diagnostics and control. My methodological and theoretical research as well as a considerable portion of my applied and collaborative work in this dissertation aims at addressing high-dimensional functional big data communicated by the systems. An innovative contribution of my work is the establishment of a series of systematic methodologies to investigate the complex system informatics including multi-dimensional modeling, feature extraction and selection, model-based monitoring and root cause diagnostics. This study presents systematic methodologies to investigate spatiotemporal informatics of complex systems from multi-dimensional modeling and feature extraction to model-driven monitoring, fault identification and root cause diagnostics. In particular, we developed a multiscale adaptive basis function model to represent and characterize the high-dimensional nonlinear functional profiles, thereby reducing the large amount of data to a parsimonious set of variables (i.e., model parameters) while preserving the information. Furthermore, the complex interdependence structure among variables is identified by a novel self-organizing network algorithm, in which the homogeneous variables are clustered into sub-network communities. Then we minimize the redundancy of variables in each cluster and integrate the new set of clustered variables with predictive models to identify a sparse set of sensitive variables for process monitoring and fault diagnostics. We evaluated and validated our methodologies using real-world case studies that extract parameters from representation models of vectorcardiogram (VCG) signals for the diagnosis of myocardial infarctions. The proposed systematic methodologies are generally applicable for modeling, monitoring and diagnosis in many disciplines that involve a large number of highly-redundant variables extracted from the big data. The self-organizing approach was also innovatively developed to derive the steady geometric structure of a network from the recurrence-based adjacency matrix. As such, novel network-theoretic measures can be achieved based on actual node-to-node distances in the self-organized network topology.

Landslides cause significant damage to property and human lives throughout the world. Rainfall is the most common triggering factor for the occurrence of landslides. This dissertation presents two novel methodologies for assessment of rainfall-triggered shallow landslide hazard. The first method focuses on using remotely sensed soil moisture and soil surface properties in developing a framework for real-time regional scale landslide hazard assessment while the second method is a deterministic approach to landslide hazard assessment of the specific sites identified during first assessment. In the latter approach, landslide inducing transient seepage in soil during rainfall and its effect on slope stability are modeled using numerical analysis. Traditionally, the prediction of rainfall-triggered landslides has been performed using pre-determined rainfall intensity-duration thresholds. However, it is the infiltration of rainwater into soil slopes which leads to an increase of porewater pressure and destruction of matric suction that causes a reduction in soil shear strength and slope instability. Hence, soil moisture, pore pressure and infiltration properties of soil must be direct inputs to reliable landslide hazard assessment methods. In-situ measurement of pore pressure for real-time landslide hazard assessment is an expensive endeavor and thus, the use of more practical remote sensing of soil moisture is constantly sought. In past studies, a statistical framework for regional scale landslide hazard assessment using remotely sensed soil moisture has not been developed. Thus, the first major objective of this study is to develop a framework for using downscaled remotely sensed soil moisture available on a daily basis to monitor locations that are highly susceptible to rainfall- triggered shallow landslides, using a well-structured assessment procedure. Downscaled soil moisture, the relevant geotechnical properties of saturated hydraulic conductivity and soil type, and the conditioning factors of elevation, slope, and distance to roads are used to develop an improved logistic regression model to predict the soil slide hazard of soil slopes using data from two geographically different regions. A soil moisture downscaling model with a proven superior prediction accuracy than the downscaling models that have been used in previous landslide studies is employed in this study. Furthermore, this model provides satisfactory classification accuracy and performs better than the alternative water drainage-based indices that are conventionally used to quantify the effect that elevated soil moisture has upon the soil sliding. Furthermore, the downscaling of soil moisture content is shown to improve the prediction accuracy. Finally, a technique that can determine the threshold probability for identifying locations with a high soil slide hazard is proposed. On the other hand, many deterministic methods based on analytical and numerical methodologies have been developed in the past to model the effects of infiltration and subsequent transient seepage during rainfall on the stability of natural and manmade slopes. However, the effects of continuous interplay between surface and subsurface water flows on slope stability is seldom considered in the above-mentioned numerical and analytical models. Furthermore, the existing seepage models are based on the Richards equation, which is derived using Darcy’s law, under a pseudo-steady state assumption. Thus, the inertial components of flow have not been incorporated typically in modeling the flow of water through the subsurface. Hence, the second objective of this study is to develop a numerical model which has the capability to model surface, subsurface and infiltration water flows based on a unified approach, employing fundamental fluid dynamics, to assess slope stability during rainfall-induced transient seepage conditions. The developed model is based on the Navier-Stokes equations, which possess the capability to model surface, subsurface and infiltration water flows in a unified manner. The extended Mohr-Coulomb criterion is used in evaluating the shear strength reduction due to infiltration. Finally, the effect of soil hydraulic conductivity on slope stability is examined. The interplay between surface and subsurface water flows is observed to have a significant impact on slope stability, especially at low hydraulic conductivity values. The developed numerical model facilitates site-specific calibration with respect to saturated hydraulic conductivity, remotely sensed soil moisture content and rainfall intensity to predict landslide inducing subsurface pore pressure variations in real time.

<p>In 1999 the Swedish Parliament decided about 15 environmental goals aimed to be achieved in time for the next generation. For example physical social planning must be based on programs and strategies how cultural-historical values has been attended, so called programs of cultural heritage. By law the Swedish municipalities are responsible for reaching these goals by creating program of cultural heritage. But only a few of the municipalities have these programs, meaning that many municipalities need to create programs of cultural heritage in the next five years.</p><p>The aim of this report is to present a model of analysis on programs of cultural heritage in municipalities for preservation and development of cultural environments. By testing the model on the municipalities of Osby and Tomelilla methods on how to create a new program of cultural heritage or how to complete existing documents are formulated.</p><p>One of the conclusions is that one document, program of cultural heritage, functions as landscape analysis, model of cultural valuation and strategy of developing cultural values. One condition is that the program of cultural heritage is established in social planning in the municipality.</p><p>Two case-studies, the Osby and Tomelilla programs of cultural heritage with text-analysis and interviews, constitutes the study. The municipalities do not use these programs in full extension, often based on ignorance. When the program isn’t established with politicians or employees it can’t be established with citizens. The study shows great differences between the program in Tomelilla which can be applied to the physical social planning, and the program in Osby which can’t. The conclusion is that the program needs to be adjusted for the cause if the program can’t be applied in the hysical social planning in the municipality.</p><p>The common meaning in the municipalities is that cultural heritage is a positive factor, which makes it possible to reach the environmental goals, regional development, tourism et cetera. One important part in a program of cultural heritage is to erase the boundaries between nature, recreation and culture by finding the starting point in the specific environment, not based on the administrative responsibility.</p>

<p>This thesis aims to show how biogeophysical and social processes are interlinked in landscape change, and to propose approaches for interdisciplinary environmental assessments (such as EIAs), concentrating on developing countries’ situations, and representation of findings from such studies. </p><p>Landscape in its holistic sense is a very good concept and basis for intellectual and practical use in environmental dialogues. However, landscapes are valued and assessed differently, depending on cultural background along with individual characteristics. </p><p>Methods of conducting interdisciplinary environmental assessments need to vary, but it is important to follow a structure to avoid too broad and general studies that only assemble a few factors and present them without an integrated synthesis. This thesis has suggested one research sequence and structure that has proven to be practical and possible to execute in areas where data is scarce and where local involvement is a major component. It extends the observation period in time and space where remote sensing analyses are integrated with interviews, archive material, land-cover assessments and soil analyses.</p><p>Case studies from Tanzania have been used to investigate how perceptions of land and resources manifest themselves at local scales and how this information can contribute to sustainable environmental planning. Preferences and perceptions of land as being ‘important’ and ‘good’ do not always correlate with favourable biogeophysical conditions, indicating that both social services, such as health care, access to markets, education and employment, as well as “non-rational” factors are essential to consider in environmental planning and management.</p><p>This study has partly been part of a larger research project investigating the links between human livelihood and biodiversity in <i>miombo</i> woodlands. It has been shown how <i>miombo</i> woodland is important to local populations as it provides material goods as well as many intangible services. However, it is also associated with problems and dangers, which are important to consider and understand in planning for the environment and sustainable development.</p>