From environment to landscape. Reconstructing environment perception using numerical data

The paper introduces a method that links environment to landscape. The environment-landscape divide appears because of epistemological differences: since studying the landscape involves describing the world as it was perceived by humans, it is difficult to access this dimension through the numerical data that we employ when studying the environment. We approach the issue of noncorrespondence between environment and landscape knowledge using fuzzy logic. The numerical data describing two geomorphometric parameters, slope and modified topographic index, are split each into three classes with overlapping borders. The classes are then fused into four qualitative categories: flat wet, steep dry, flat dry, and gradual moist. These four categories have direct correspondence in the real world and can be observed by people through simple perception. The correspondence of such categories to peoples’ perception is checked against evidence of past human settlement in three areas coming from Turkey, Serbia, and Syria. The identified qualitative categories resemble the way people categorized their landscape in all but the second case study. Humans were able to perceive and choose areas which correspond to gradual moist in Turkey and broadly to flatDigital Archaeolog

Solving for y: digital soil mapping using statistical models and improved models of land surface geometry

Digital soil mapping (DSM) is a rapidly growing area of soil research that has great potential for enhancing soil survey activities and advancing knowledge of soil-landscape relationships. To date many successful studies have shown that geographic datasets can be used to model soil spatial variation. This thesis addresses two issues relevant to DSM, scale effects on digital elevation models, and predicting soil properties. The first issue examined was the effect of spatial extent on the calculation of geometric land surface parameters (LSP) (e.g. slope gradient). This is a significant issue as they represent some of the most common predictors used in DSM. To examine this issue two case studies were designed. The first evaluated the systematic effects of varying both grid and neighborhood size on LSP, while the second examined how the correlation between soil and LSP vary with grid and neighborhood size. Results of the first case study demonstrate that finer grid sizes were more sensitive to the scale of LSP calculation than larger grid sizes. While the magnitude of effect was diminished when comparing a high relief landscape to a low relief landscape, the shape and location of the effect was similar. Results of the second case study showed that the correlation between soil properties and slope curvatures were similarly optimized when varying the spatial extent, but that the effect was more sensitive to grid size than neighborhood size. Slope gradient also showed significant correlations with some of the soil properties, but was not sensitive to changes in grid or neighborhood size.;The second study attempted to predict numerous physical and chemical soil properties for several depth intervals (0-15, 15-60, 60-100, and 100-150-centimeters), using generalized linear models (GLM) and geographic datasets. The area examined was the Upper Gauley Watershed on the Monongahela National Forest, which covers approximately 82,500 acres (33,400 hectares). This watershed represents a complex landscape with contrasting geologic strata, deciduous and coniferous forests, and steep slopes. Given this landscape diversity it was still possible to fit GLM which explained on average 38 percent of the adjusted deviance for rock fragment content, and exchangeable calcium and magnesium, and phosphorus. Some of the most commonly selected environmental predictors were slope curvatures, lithology types, and relative slope position indices. This seems to validate the prominence of these variables in theoretical soil-landscape models. Had the correlation between the soil properties and slope curvatures not been optimized by varying the spatial extent, it is likely that another less suitable LSP would have been selected

Geomorphometry 2020. Conference Proceedings

Geomorphometry is the science of quantitative land surface analysis. It gathers various mathematical, statistical and image processing techniques to quantify morphological, hydrological, ecological and other aspects of a land surface. Common synonyms for geomorphometry are geomorphological analysis, terrain morphometry or terrain analysis and land surface analysis. The typical input to geomorphometric analysis is a square-grid representation of the land surface: a digital elevation (or land surface) model. The first Geomorphometry conference dates back to 2009 and it took place in Zürich, Switzerland. Subsequent events were in Redlands (California), Nánjīng (China), Poznan (Poland) and Boulder (Colorado), at about two years intervals. The International Society for Geomorphometry (ISG) and the Organizing Committee scheduled the sixth Geomorphometry conference in Perugia, Italy, June 2020. Worldwide safety measures dictated the event could not be held in presence, and we excluded the possibility to hold the conference remotely. Thus, we postponed the event by one year - it will be organized in June 2021, in Perugia, hosted by the Research Institute for Geo-Hydrological Protection of the Italian National Research Council (CNR IRPI) and the Department of Physics and Geology of the University of Perugia. One of the reasons why we postponed the conference, instead of canceling, was the encouraging number of submitted abstracts. Abstracts are actually short papers consisting of four pages, including figures and references, and they were peer-reviewed by the Scientific Committee of the conference. This book is a collection of the contributions revised by the authors after peer review. We grouped them in seven classes, as follows: • Data and methods (13 abstracts) • Geoheritage (6 abstracts) • Glacial processes (4 abstracts) • LIDAR and high resolution data (8 abstracts) • Morphotectonics (8 abstracts) • Natural hazards (12 abstracts) • Soil erosion and fluvial processes (16 abstracts) The 67 abstracts represent 80% of the initial contributions. The remaining ones were either not accepted after peer review or withdrawn by their Authors. Most of the contributions contain original material, and an extended version of a subset of them will be included in a special issue of a regular journal publication

A spatiotemporal object-oriented data model for landslides (LOOM)

LOOM (landslide object-oriented model) is here presented as a data structure for landslide inventories based on the object-oriented paradigm. It aims at the effective storage, in a single dataset, of the complex spatial and temporal relations between landslides recorded and mapped in an area and at their manipulation. Spatial relations are handled through a hierarchical classification based on topological rules and two levels of aggregation are defined: (i) landslide complexes, grouping spatially connected landslides of the same type, and (ii) landslide systems, merging landslides of any type sharing a spatial connection. For the aggregation procedure, a minimal functional interaction between landslide objects has been defined as a spatial overlap between objects. Temporal characterization of landslides is achieved by assigning to each object an exact date or a time range for its occurrence, integrating both the time frame and the event-based approaches. The sum of spatial integrity and temporal characterization ensures the storage of vertical relations between landslides, so that the superimposition of events can be easily retrieved querying the temporal dataset. The here proposed methodology for landslides inventorying has been tested on selected case studies in the Cilento UNESCO Global Geopark (Italy). We demonstrate that the proposed LOOM model avoids data fragmentation or redundancy and topological inconsistency between the digital data and the real-world features. This application revealed to be powerful for the reconstruction of the gravity-induced deformation history of hillslopes, thus for the prediction of their evolution

GIS-based landform classification of Bronze Age archaeological sites on Crete Island

Various physical attributes of the Earth's surface are factors that influence local topography and indirectly influence human behaviour in terms of habitation locations. The determination of geomorphological setting plays an important role in archaeological landscape research. Several landform types can be distinguished by characteristic geomorphic attributes that portray the landscape surrounding a settlement and influence its ability to sustain a population. Geomorphometric landform information, derived from digital elevation models (DEMs), such as the ASTER Global DEM, can provide useful insights into the processes shaping landscapes. This work examines the influence of landform classification on the settlement locations of Bronze Age (Minoan) Crete, focusing on the districts of Phaistos, Kavousi and Vrokastro. The landform classification was based on the topographic position index (TPI) and deviation from mean elevation (DEV) analysis to highlight slope steepness of various landform classes, characterizing the surrounding landscape environment of the settlements locations. The outcomes indicate no interrelationship between the settlement locations and topography during the Early Minoan period, but a significant interrelationship exists during the later Minoan periods with the presence of more organised societies. The landform classification can provide insights into factors favouring human habitation and can contribute to archaeological predictive modelling

Mapping deep-sea biodiversity and good environmental status in the Azores: assisng with the implementaon of EU Marine Strategy Framework Direcve

Tese de Doutoramento, Ciências Biológicas, 26 de junho de 2023, Universidade dos Açores.ABSTRACT: One of the major shortfall of biodiversity knowledge steams from an incomplete description of the geographical distribution of species. Overcoming this shortfall is essential for conserving nature and its services and it is a required first step to tackle more complex ecological processes (e.g. dispersal, speciation, disturbance, biotic interactions, etc.) in remote and poorly studied regions such as the deep sea. In a region such as the Azores (NE Atlantic), where the deep sea represents a dominant component of the seascape, it is essential to characterize patterns and processes of deep-sea biodiversity. In fact, only by understanding how species and marine resources distribute it is possible to correctly inform area- and ecosystem-based management and achieve the goals of policies aiming at reversing the cycle of decline in ocean health. In particular, the European Commission has adopted a number of policies to grant a sustainable use of nature space and resources which include the Marine Strategy Framework Directive (MFSD) and the Maritime Spatial Planning Directive (MSPD). The overall goal of this thesis is to bring together existing and new biodiversity data from recent scientific surveys to deepen our understanding of biodiversity and biogeographic patterns of deep-sea Vulnerable Marine Ecosystems (VMEs) indicator taxa. The focus is on deep-sea hard-substrate communities of the Azores and, in particular, on ecosystem engineer species of the Phyla Cnidaria and Porifera. Four major environmental drivers of deep-sea benthic engineer species are recognized in the Azores: (i) a latitudinal gradient in primary production strongly influenced by the Azores Current-Azores Front (AzC-AzF) system; (ii) the depth-wise succession of the regional water masses and their stratification into different isopycnal (vertical) layers; (iii) the spatial distribution of prominent geomorphic features such as seamounts ridges and island slopes; (iv) the availability of hard substrate for attachment. The recognition of these environmental drivers sets an interesting background for future ecological research, ecosystem-based management and spatial monitoring. The response of deep-sea species to these environmental drivers is explored in detail in the different chapters of the present manuscript

Regular Hierarchical Surface Models: A conceptual model of scale variation in a GIS and its application to hydrological geomorphometry

Environmental and geographical process models inevitably involve parameters that vary spatially. One example is hydrological modelling, where parameters derived from the shape of the ground such as flow direction and flow accumulation are used to describe the spatial complexity of drainage networks. One way of handling such parameters is by using a Digital Elevation Model (DEM), such modelling is the basis of the science of geomorphometry. A frequently ignored but inescapable challenge when modellers work with DEMs is the effect of scale and geometry on the model outputs. Many parameters vary with scale as much as they vary with position. Modelling variability with scale is necessary to simplify and generalise surfaces, and desirable to accurately reconcile model components that are measured at different scales. This thesis develops a surface model that is optimised to represent scale in environmental models. A Regular Hierarchical Surface Model (RHSM) is developed that employs a regular tessellation of space and scale that forms a self-similar regular hierarchy, and incorporates Level Of Detail (LOD) ideas from computer graphics. Following convention from systems science, the proposed model is described in its conceptual, mathematical, and computational forms. The RHSM development was informed by a categorisation of Geographical Information Science (GISc) surfaces within a cohesive framework of geometry, structure, interpolation, and data model. The positioning of the RHSM within this broader framework made it easier to adapt algorithms designed for other surface models to conform to the new model. The RHSM has an implicit data model that utilises a variation of Middleton and Sivaswamy (2001)’s intrinsically hierarchical Hexagonal Image Processing referencing system, which is here generalised for rectangular and triangular geometries. The RHSM provides a simple framework to form a pyramid of coarser values in a process characterised as a scaling function. In addition, variable density realisations of the hierarchical representation can be generated by defining an error value and decision rule to select the coarsest appropriate scale for a given region to satisfy the modeller’s intentions. The RHSM is assessed using adaptions of the geomorphometric algorithms flow direction and flow accumulation. The effects of scale and geometry on the anistropy and accuracy of model results are analysed on dispersive and concentrative cones, and Light Detection And Ranging (LiDAR) derived surfaces of the urban area of Dunedin, New Zealand. The RHSM modelling process revealed aspects of the algorithms not obvious within a single geometry, such as, the influence of node geometry on flow direction results, and a conceptual weakness of flow accumulation algorithms on dispersive surfaces that causes asymmetrical results. In addition, comparison of algorithm behaviour between geometries undermined the hypothesis that variance of cell cross section with direction is important for conversion of cell accumulations to point values. The ability to analyse algorithms for scale and geometry and adapt algorithms within a cohesive conceptual framework offers deeper insight into algorithm behaviour than previously achieved. The deconstruction of algorithms into geometry neutral forms and the application of scaling functions are important contributions to the understanding of spatial parameters within GISc

Proceedings of the 3rd Open Source Geospatial Research & Education Symposium OGRS 2014

The third Open Source Geospatial Research & Education Symposium (OGRS) was held in Helsinki, Finland, on 10 to 13 June 2014. The symposium was hosted and organized by the Department of Civil and Environmental Engineering, Aalto University School of Engineering, in partnership with the OGRS Community, on the Espoo campus of Aalto University. These proceedings contain the 20 papers presented at the symposium. OGRS is a meeting dedicated to exchanging ideas in and results from the development and use of open source geospatial software in both research and education.  The symposium offers several opportunities for discussing, learning, and presenting results, principles, methods and practices while supporting a primary theme: how to carry out research and educate academic students using, contributing to, and launching open source geospatial initiatives. Participating in open source initiatives can potentially boost innovation as a value creating process requiring joint collaborations between academia, foundations, associations, developer communities and industry. Additionally, open source software can improve the efficiency and impact of university education by introducing open and freely usable tools and research results to students, and encouraging them to get involved in projects. This may eventually lead to new community projects and businesses. The symposium contributes to the validation of the open source model in research and education in geoinformatics