Use of cartographic images by expert and novice field geologists in planning fieldwork routes

Topographic maps and aerial photographs are particularly useful when geoscientists are faced with fieldwork tasks such as selecting paths for observation, establishing sampling schemes, or defining field regions. These types of images are crucial in bedrock geologic mapping, a cognitively complex field-based problem-solving task. Geologic mapping requires the geologist to correctly identify rock types and three-dimensional bedrock structures from often partial or poor-quality outcrop data while navigating through unfamiliar terrain. This paper compares the walked routes of novice to expert geologists working in the field (n = 66) with the results of a route planning and navigation survey of a similar population of geologists (n = 77). Results show clearly that those geologists with previous mapping experience make quick and decisive determinations about field areas from available imagery and maps, regardless of whether they are or not physically present in the field area. Recognition of geologic features enabled experts to form and verbalize a specific plan for travel through a landscape based on those features. Novices were less likely to develop specific travel route plans and were less likely to identify critical landscape cues from aerial photographs.     

Modeling the risk of spread and establishment for Asian longhorned beetle (Anoplophora glabripennis) in Massachusetts from 2008-2009

Land managers responsible for invasive species removal in the USA require tools to prevent the Asian longhorned beetle (Anoplophora glabripennis) (ALB) from decimating the maple-dominant hardwood forests of Massachusetts and New England. Species distribution models (SDMs) and spread models have been applied individually to predict the invasion distribution and rate of spread, but the combination of both models can increase the accuracy of predictions of species spread over time when habitat suitability is heterogeneous across landscapes. First, a SDM was fit to 2008 ALB presence-only locations. Then, a stratified spread model was generated to measure the probability of spread due to natural and human causes. Finally, the SDM and spread models were combined to evaluate the risk of ALB spread in Central Massachusetts in 2008–2009. The SDM predicted many urban locations in Central Massachusetts as having suitable environments for species establishment. The combined model shows the greatest risk of spread and establishment in suitable locations immediately surrounding the epicentre of the ALB outbreak in Northern Worcester with lower risk areas in suitable locations only accessible through long-range dispersal from access to human transportation networks. The risk map achieved an accuracy of 67% using 2009 ALB locations for model validation. This model framework can effectively provide risk managers with valuable information concerning the timing and spatial extent of spread/establishment risk of ALB and potential strategies needed for effective future risk management efforts.     

Characterization,modeling, and remediation of karst in a changing environment

This introductory editorial paper provides a review and prospective outlook of the achievements and challenges in karst research under a changing environment. A brief discussion of the past and future karst research has been focused on: (1) data and new technologies; (2) modeling of karst flow and reactive transport; (3) responses of karst hydrosystems to climate variability and changes across scales.     

Characterization of fluid inclusions from mineralized pegmatites of the Damara Belt,Namibia: insight into late-stage fluid evolution and implications for mineralization

Mineralogy and Petrology - Mineralized NYF and LCT pegmatites occur throughout the northeast-trending Neoproterozoic Damara Belt, Namibia. Mineralization in the pegmatites varies geographically,...     

Estimating urban above ground biomass with multi-scale LiDAR

Background

Urban trees have long been valued for providing ecosystem services (mitigation of the “heat island” effect, suppression of air pollution, etc.); more recently the potential of urban forests to store significant above ground biomass (AGB) has also be recognised. However, urban areas pose particular challenges when assessing AGB due to plasticity of tree form, high species diversity as well as heterogeneous and complex land cover. Remote sensing, in particular light detection and ranging (LiDAR), provide a unique opportunity to assess urban AGB by directly measuring tree structure. In this study, terrestrial LiDAR measurements were used to derive new allometry for the London Borough of Camden, that incorporates the wide range of tree structures typical of an urban setting. Using a wall-to-wall airborne LiDAR dataset, individual trees were then identified across the Borough with a new individual tree detection (ITD) method. The new allometry was subsequently applied to the identified trees, generating a Borough-wide estimate of AGB.

Results

Camden has an estimated median AGB density of 51.6 Mg ha^–1 where maximum AGB density is found in pockets of woodland; terrestrial LiDAR-derived AGB estimates suggest these areas are comparable to temperate and tropical forest. Multiple linear regression of terrestrial LiDAR-derived maximum height and projected crown area explained 93% of variance in tree volume, highlighting the utility of these metrics to characterise diverse tree structure. Locally derived allometry provided accurate estimates of tree volume whereas a Borough-wide allometry tended to overestimate AGB in woodland areas. The new ITD method successfully identified individual trees; however, AGB was underestimated by ≤?25% when compared to terrestrial LiDAR, owing to the inability of ITD to resolve crown overlap. A Monte Carlo uncertainty analysis identified assigning wood density values as the largest source of uncertainty when estimating AGB.

Conclusion

Over the coming century global populations are predicted to become increasingly urbanised, leading to an unprecedented expansion of urban land cover. Urban areas will become more important as carbon sinks and effective tools to assess carbon densities in these areas are therefore required. Using multi-scale LiDAR presents an opportunity to achieve this, providing a spatially explicit map of urban forest structure and AGB.

     

MARLI: a mobile application for regional landslide inventories in Ecuador

The regions of Central and South America most susceptible to the occurrence of landslides will become even more vulnerable in the context of climate change. The Josefina disaster, in 1993, demonstrated both the vulnerability of local infrastructures and communities in the Paute River basin (Ecuador). Since this natural phenomena, several landslide inventories and susceptibility studies were developed, revealing the vulnerability of the Paute River basin to unstable terrain and the need for further studies throughout the basin. Despite this, no studies have been done since then to update the information generated. This paper describes a Mobile Application for Regional Landslide Inventories (MARLI), a simple but efficient open-access platform to report landslide events using the Open Data Kit system. Its design makes reporting fast, simple and cost-effective with an added benefit, and a specialized knowledge is not required for its use. MARLI was tested for the collection of landslides in Cuenca (Ecuador). From the data taken in the field, it was possible to analyze the performance and suitability of collected data and compare the results with regional inventories in the same area. Additionally, these results can be used for the elaboration and update of large-scale inventories or the training of automatic identification systems of landslides and later evaluation of their precision in a small-medium scale. Likewise, this product constitutes a fundamental input for the formulation of mitigation strategies, to formulate the appropriate response and in time, also the elaboration of reconstruction plans before the increase in the occurrence of such phenomena.

     

Stochastic Local Interaction Model: An Alternative to Kriging for Massive Datasets

Mathematical Geosciences - Classical geostatistical methods face serious computational challenges if they are confronted with large spatial datasets. The stochastic local interaction (SLI) approach...