Morphometric characterisation of landform from DEMs

We describe a method of morphometric characterisation of landform from DEMs. The method is implemented by first classifying every location into morphometric classes based on the mathematical shape of a locally fitted quadratic surface and its positional relationship with the analysis window. Single-scale fuzzy terrain indices of peakness, pitness, passness, ridgeness, and valleyness are then calculated based on the distance of the analysis location from the ideal cases. These can then be combined into multi-scale terrain indices to summarise terrain information across different operational scales. The algorithm has four characteristics: (1) the ideal cases of different geomorphometric features are simply and clearly defined; (2) the output is spatially continuous to reflect the inherent fuzziness of geomorphometric features; (3) the output is easily combined into a multi-scale index across a range of operational scales; and (4) the standard general morphometric parameters are quantified as the first and second order derivatives of the quadratic surface. An additional benefit of the quadratic surface is the derivation of the R2 goodness of fit statistic, which allows an assessment of both the reliability of the results and the complexity of the terrain. An application of the method using a test DEM indicates that the single- and multi-scale terrain indices perform well when characterising the different geomorphometric features

Landscape Classification using Principal Component Analysis and Fuzzy Classification: Archaeological Sites and their Natural Surroundings in Central Mongolia

The middle and upper Orkhon Valley in Central Mongolia (47.5°N, 102.5°E) hosts a multitude of diverse archaeological features. Most of them – including the well-known ancient cities of Karakorum and Karabalgasun – have only rarely been described in their geographical setups. The aim of this study is to describe, classify and analyse their surrounding landscapes and consequently characterise these sites geographically. This analysis is based on freely available raster datasets that offer information about topography, surface reflectance and derivatives. Principal component analysis is applied as a dimensional reduction technique. Subsequently, a fuzzy-logic approach leads to a classification scheme in which archaeological features are embedded and therefore distinguishable. A distinct difference in preferences regarding to choose a site location can be made and confirmed by semiautomatic analysis, comparing burial and ritual places and settlements. Walled enclosures and settlements are connected to planar steppe regions, whereas burial and ritual places are embedded in mountainous and hilly environments

Training of Crisis Mappers and Map Production from Multi-sensor Data: Vernazza Case Study (Cinque Terre National Park, Italy)

This aim of paper is to presents the development of a multidisciplinary project carried out by the cooperation between Politecnico di Torino and ITHACA (Information Technology for Humanitarian Assistance, Cooperation and Action). The goal of the project was the training in geospatial data acquiring and processing for students attending Architecture and Engineering Courses, in order to start up a team of "volunteer mappers". Indeed, the project is aimed to document the environmental and built heritage subject to disaster; the purpose is to improve the capabilities of the actors involved in the activities connected in geospatial data collection, integration and sharing. The proposed area for testing the training activities is the Cinque Terre National Park, registered in the World Heritage List since 1997. The area was affected by flood on the 25th of October 2011. According to other international experiences, the group is expected to be active after emergencies in order to upgrade maps, using data acquired by typical geomatic methods and techniques such as terrestrial and aerial Lidar, close-range and aerial photogrammetry, topographic and GNSS instruments etc.; or by non conventional systems and instruments such us UAV, mobile mapping etc. The ultimate goal is to implement a WebGIS platform to share all the data collected with local authorities and the Civil Protectio

Proceedings of the GIS Research UK 18th Annual Conference GISRUK 2010

This volume holds the papers from the 18th annual GIS Research UK (GISRUK). This year the conference, hosted at University College London (UCL), from Wednesday 14 to Friday 16 April 2010. The conference covered the areas of core geographic information science research as well as applications domains such as crime and health and technological developments in LBS and the geoweb. UCL’s research mission as a global university is based around a series of Grand Challenges that affect us all, and these were accommodated in GISRUK 2010. The overarching theme this year was “Global Challenges”, with specific focus on the following themes: * Crime and Place * Environmental Change * Intelligent Transport * Public Health and Epidemiology * Simulation and Modelling * London as a global city * The geoweb and neo-geography * Open GIS and Volunteered Geographic Information * Human-Computer Interaction and GIS Traditionally, GISRUK has provided a platform for early career researchers as well as those with a significant track record of achievement in the area. As such, the conference provides a welcome blend of innovative thinking and mature reflection. GISRUK is the premier academic GIS conference in the UK and we are keen to maintain its outstanding record of achievement in developing GIS in the UK and beyond

Modelling geographic phenomena at multiple levels of detail: A model generalisation approach based on aggregation

Considerable interest remains in capturing once geographical information at the fine scale, and from this, automatically deriving information at various levels of detail and scale via the process of map generalisation. This research aims to develop a methodology for transformation of geographic phenomena at a high level of detail directly into geographic phenomena at higher levels of abstraction. Intuitive and meaningful interpretation of geographical phenomena requires their representation at multiple levels of detail. This is due to the scale dependent nature of their properties. Prior to the cartographic portrayal of that information, model generalisation is required in order to derive higher order phenomena typically associated with the smaller scales. This research presents a model generalisation approach able to support the derivation of phenomena typically present at 1:250,000 scale mapping, directly from a large scale topographic database (1:1250/1:2500/1:10,000). Such a transformation involves creation of higher order or composite objects, such as settlement, forest, hills and ranges, from lower order or component objects, such as buildings, trees, streets, and vegetation, in the source database. In order to perform this transformation it is important to model the meaning and relationships among source database objects rather than to consider the object in terms of their geometric primitives (points, lines and polygons). This research focuses on two types of relationships: taxonomic and partonomic. These relationships provide different but complimentary strategies for transformation of source database objects into required target database objects. The proposed methodology highlights the importance of partonomic relations for transformation of spatial databases over large changes in levels of detail. The proposed approach involves identification of these relationships and then utilising these relationships to create higher order objects. The utility of the results obtained, via the implementation of the proposed methodology, is demonstrated using spatial analysis techniques and the creation of ‘links’ between objects at different representations needed for multiple representation databases. The output database can then act as input to cartographic generalisation in order to create maps (digital or paper). The results are evaluated using manually generalised datasets

Spatio-temporal correlation of extreme climate indices and river flood discharges

The occurrence of floods is strongly related to specific climatic conditions that favor extreme precipitation events. Although the impact of precipitation and temperature patterns on river flows is a well discussed topic in hydrology, few studies have focused on the rainfall and temperature extremes in their relation with peak discharges. This work presents a comparative analysis of Climate Change Indices (ETCCDI) annual time series, calculated using the NorthWestern Italy Optimal Interpolation (NWIOI) dataset, and annual maximum flows in the Piedmont Region. The Spearman’s rank correlation was used to determine which indices are temporally correlated with peak discharges, allowing to hypothesize the main physical processes involved in the production of floods. The correlation hypothesis was verified with the Spearman’s rank correlation test, considering a Student’s t-distribution with a 5% significance level. Moreover, the influence of climate variability on the tendency of annual maximum discharges was examined by correlating trends of climate indices with trends of the discharge series. These were calculated using the Theil-Sen slope estimator and tested with the Mann-Kendall test at the 5% significance level. The results highlight that while extreme precipitation indices are highly correlated with extreme discharges at the annual timescale, the interannual changes of extreme discharges may be better explained by the interannual changes of the total annual precipitation. This suggests that projections of the annual precipitation may be used as covariates for non-stationary flood frequency analysis

Classification and use of landform information to increase the accuracy of land condition monitoring in Western Australian pastoral rangelands

The aim of this research was to develop land unit scale data to assist land condition monitoring projects in pastoral rangelands in Western Australia. Landforms are a major components of land units and methods were explored to include landforms as a variable in land unit predictive modelling. Three land unit prediction models were tested, a Binary Weighted Overlay (BWO), a Fuzzy Weighted Overlay (FWO) and a Positive Weights of Evidence (PWofE) model

Landslide riskscapes in the Colorado Front Range: a quantitative geospatial approach for modeling human-environment interactions

2021 Spring.Includes bibliographical references.This research investigated the application of riskscapes to landslides in the context of geospatial inquiry. Riskscapes are framed as a landscape of risk to represent risk spatially. Geospatial models for landslide riskscapes were developed to improve our understanding of the spatial context for landslides and their risks as part of the system of human-environment interactions. Spatial analysis using Geographic Information Systems (GIS) leveraged modeling methods and the distributed properties of riskscapes to identify and preserve these spatial relationships. This dissertation is comprised of four separate manuscripts. These projects defined riskscapes in the context of landslides, applied geospatial analyses to create a novel riskscape model to introduce spatial autocorrelation methods to the riskscape framework, compared geostatistical analysis methods in these landslide riskscape assessments, and described limitations of spatial science identified in the riskscape development process. The first project addressed the current literature for riskscapes and introduced landslides as a measurable feature for riskscapes. Riskscapes are founded in social constructivist theory and landslide studies are frequently based on quantitative risk assessment practices. The uniqueness of a riskscape is the inclusion of human geography and environmental factors, which are not consistently incorporated in geologic or natural hazard studies. I proposed the addition of spatial theory constructs and methods to create spatially measurable products. I developed a conceptual framework for a landslide riskscape by describing the current riskscape applications as compared to existing landslide and GIS risk model processes. A spatial modeling formula to create a weighted sum landslide riskscape was presented as a modification to a natural hazard risk equation to incorporate the spatial dimension of risk factors. The second project created a novel method for three geospatial riskscapes as an approach to model landslide susceptibility areas in Boulder and Larimer Counties, Colorado. This study synthesized physical and human geography to create multiple landslide riskscape models using GIS methods. These analysis methods used a process model interface in GIS. Binary, ranked, and human factor weighted sum riskscapes were created, using frequency ratio as the basis for developing a weighting scheme. Further, spatial autocorrelation was introduced as a recommended practice to quantify the spatial relationships in landslide riskscape development. Results demonstrated that riskscapes, particularly those for ranked and human factor riskscapes, were highly autocorrelated, non-random, and exhibited clustering. These findings indicated that a riskscape model can support improvements to response modeling, based on the identification of spatially significant clustering of hazardous areas. The third project extended landslide riskscapes to measurable geostatistical comparisons using geostatistical tools within a GIS platform. Logistic regression, weights of evidence, and probabilistic neural networks methods were used to analyze the weighted sum landslide riskscape models using ArcGIS and Spatial Data Modeler (ArcSDM). Results showed weights of evidence models performed better than both logistic regression and neural networks methods. Receiver Operator Characteristic (ROC) curves and Area Under the Curve validation tests were performed and found the weights of evidence model performed best in both posterior probability prediction and AUC validation. A fourth project was developed based on the limitations discovered during the analytical process evaluations from the riskscape model development and geostatistical analysis. This project reviewed the issues with data quality, the variations in results predicated on the input parameters within the analytical toolsets, and the issues surrounding open-source application tools. These limitations stress the importance of parameter selection in a geospatial analytical environment. These projects collectively determined methods for riskscape development related to landslide features. The models presented demonstrate the importance and influence of spatial distributions on landslide riskscapes. Based on the proposed conceptual framework of a spatial riskscape for landslides, weighted sum riskscapes can provide a basis for prioritization of resources for landslides. Ranked and human factor riskscapes indicate the need to provide planning and protection for areas at increased risk for landslides. These studies provide a context for riskscapes to further our understanding of the benefits and limitations of a quantitative riskscape approach. The development of a methodological framework for quantitative riskscape models provides an approach that can be applied to other hazards or study areas to identify areas of increased human-environment interaction. Riskscape models can then be evaluated to inform mitigation and land-use planning activities to reduce impacts of natural hazards in the anthropogenic environment

Knowledge integration in transdisciplinary research: a case study of the socio-ecological complexity project

Includes bibliographical references.2015 Fall.Knowledge integration has been crucial for gaining a holistic picture of the inner workings of socio-ecological systems. Integrating local and scientific knowledge sustains biological and global cultural diversity, and may fill gaps in understanding that cannot be elucidated by individual scientific disciplines. Interdisciplinary and transdisciplinary research teams face the challenge of collaborating and integrating their varying disciplinary paradigms and epistemologies along with stakeholders' local knowledge for understanding and adapting to global and local environmental issues. Communication and knowledge integration across funders, researchers, and research end-users in transdisciplinary research are critical for meeting diverse stakeholder needs and genuinely engaging multiple knowledge systems. These knowledge systems may include a combination of researcher and local ecological knowledge embedded in institutions, disciplines, and cultures. The purpose of this dissertation is to investigate and apply knowledge integration tools for examining socio-ecological systems and transdisciplinary research communication. Specifically, I examine the Socio-ecological Complexity (SEC) project as a case study. The SEC is a pseudonym for an actual project examining the role of Community-Based Rangeland Management (CBRM) institutions in influencing the resilience of Mongolian socio-ecological rangeland systems to climate change. I apply two tools for the integration of knowledge within SEC: participatory reflection and participatory mapping. I apply participatory reflection among the SEC research team and provide stakeholder engagement indicators for reflecting, communicating, and incorporating the needs of funders, researchers, and research end users as major stakeholder groups in transdisciplinary research. These specific indicators allow transdisciplinary research teams to assess the current level of knowledge integration, communicate and target stakeholder needs that may influence project outcomes in communicating their research. To integrate the local ecological knowledge (LEK) of research end users, I apply participatory mapping to explore herders' knowledge of their rangelands and their perceptions of socio-ecological boundaries imbedded in their pastures. The process of participatory mapping revealed emic narratives on physical and human demarcated boundaries influencing landscapes, adaptive practices, and local governance arrangements for accessing pasture resources. Participatory mapping and participatory reflection serve as tools for integrating and communicating diverse knowledge systems in transdisciplinary research. To examine how knowledge and world views may be communicated among diverse actors in transdisciplinary research, I provide a reflexive account of the role of voice in transdisciplinary fieldwork. My reflexive account reveals the complex network of actors and how identity, language, financial structures and hierarchy within a multi-cultural and transdisciplinary project shape actors' voices and opinions. The application of knowledge integration tools (participatory reflection and participatory mapping) and the open dialogue about the role of voice in transdisciplinary research provide diverse views for evaluating transdisciplinary research outcomes and analyzing coupled human-environment relationships in socio-ecological systems