Modelling the spatial distribution of DEM Error

Assessment of a DEM’s quality is usually undertaken by deriving a measure of DEM accuracy – how close the DEM’s elevation values are to the true elevation. Measures such as Root Mean Squared Error and standard deviation of the error are frequently used. These measures summarise elevation errors in a DEM as a single value. A more detailed description of DEM accuracy would allow better understanding of DEM quality and the consequent uncertainty associated with using DEMs in analytical applications. The research presented addresses the limitations of using a single root mean squared error (RMSE) value to represent the uncertainty associated with a DEM by developing a new technique for creating a spatially distributed model of DEM quality – an accuracy surface. The technique is based on the hypothesis that the distribution and scale of elevation error within a DEM are at least partly related to morphometric characteristics of the terrain. The technique involves generating a set of terrain parameters to characterise terrain morphometry and developing regression models to define the relationship between DEM error and morphometric character. The regression models form the basis for creating standard deviation surfaces to represent DEM accuracy. The hypothesis is shown to be true and reliable accuracy surfaces are successfully created. These accuracy surfaces provide more detailed information about DEM accuracy than a single global estimate of RMSE

Using keystroke logging to understand writers’ processes on a reading-into-writing test

Background Integrated reading-into-writing tasks are increasingly used in large-scale language proficiency tests. Such tasks are said to possess higher authenticity as they reflect real-life writing conditions better than independent, writing-only tasks. However, to effectively define the reading-into-writing construct, more empirical evidence regarding how writers compose from sources both in real-life and under test conditions is urgently needed. Most previous process studies used think aloud or questionnaire to collect evidence. These methods rely on participants’ perceptions of their processes, as well as their ability to report them. Findings This paper reports on a small-scale experimental study to explore writers’ processes on a reading-into-writing test by employing keystroke logging. Two L2 postgraduates completed an argumentative essay on computer. Their text production processes were captured by a keystroke logging programme. Students were also interviewed to provide additional information. Keystroke logging like most computing tools provides a range of measures. The study examined the students’ reading-into-writing processes by analysing a selection of the keystroke logging measures in conjunction with students’ final texts and interview protocols. Conclusions The results suggest that the nature of the writers’ reading-into-writing processes might have a major influence on the writer’s final performance. Recommendations for future process studies are provided

Avoiding the uncanny valley : robot appearance, personality and consistency of behavior in an attention-seeking home scenario for a robot companion

“The original publication is available at www.springerlink.com”. Copyright Springer. DOI: 10.1007/s10514-007-9058-3This article presents the results of video-based Human Robot Interaction (HRI) trials which investigated people’s perceptions of different robot appearances and associated attention-seeking features and behaviors displayed by robots with different appearance and behaviors. The HRI trials studied the participants’ preferences for various features of robot appearance and behavior, as well as their personality attributions towards the robots compared to their own personalities. Overall, participants tended to prefer robots with more human-like appearance and attributes. However, systematic individual differences in the dynamic appearance ratings are not consistent with a universal effect. Introverts and participants with lower emotional stability tended to prefer the mechanical looking appearance to a greater degree than other participants. It is also shown that it is possible to rate individual elements of a particular robot’s behavior and then assess the contribution, or otherwise, of that element to the overall perception of the robot by people. Relating participants’ dynamic appearance ratings of individual robots to independent static appearance ratings provided evidence that could be taken to support a portion of the left hand side of Mori’s theoretically proposed ‘uncanny valley’ diagram. Suggestions for future work are outlined.Peer reviewe

Vegetation phenology and habitat discrimination : impacts for E.multilocularis transmission host modelling

Echinococcus multilocularis (Em), a parasitic tapeworm, is responsible for a significant burden of human disease across continental Asia. Here, we use a time-series of MODIS 16-day 250 m Enhanced Vegetation Index (EVI) satellite data to quantify the seasonal vegetation dynamics across a study area in Serxu County, Sichuan Province, China, in relation to the presence of the Em intermediate host Ochotona curzoniae (plateau pika) and Ochotona cansus (Gansu pika) (here merged to Ochotona spp.). A series of derived phenological metrics are analysed using the random forests statistical method to determine the relative importance of seasonal vegetation characteristics. Results indicate negative relationships between Ochotona spp. presence and EVI showing a preference for low-biomass habitats. However, EVI values during green-up and senescence periods are also shown to be important, potentially resulting from improved detectability of low-biomass grassland habitats at these times. Improved detection of Ochotona spp. preferred habitats via time-series EVI imagery offers better understanding of the distributions of this Em host, and the potential for monitoring the changes in Ochotona spp. optimal habitat distributions resulting from landscape change. This could aid the identification of villages at increased risk of infection, enabling preventive strategies to be adopted

Global maps of soil temperature

Research in global change ecology relies heavily on global climatic grids derived from estimates of air temperature in open areas at around 2 m above the ground. These climatic grids do not reflect conditions below vegetation canopies and near the ground surface, where critical ecosystem functions occur and most terrestrial species reside. Here, we provide global maps of soil temperature and bioclimatic variables at a 1-km2 resolution for 0\u20135 and 5\u201315 cm soil depth. These maps were created by calculating the difference (i.e. offset) between in situ soil temperature measurements, based on time series from over 1200 1-km2 pixels (summarized from 8519 unique temperature sensors) across all the world's major terrestrial biomes, and coarse-grained air temperature estimates from ERA5-Land (an atmospheric reanalysis by the European Centre for Medium-Range Weather Forecasts). We show that mean annual soil temperature differs markedly from the corresponding gridded air temperature, by up to 10\ub0C (mean = 3.0 \ub1 2.1\ub0C), with substantial variation across biomes and seasons. Over the year, soils in cold and/or dry biomes are substantially warmer (+3.6 \ub1 2.3\ub0C) than gridded air temperature, whereas soils in warm and humid environments are on average slightly cooler ( 120.7 \ub1 2.3\ub0C). The observed substantial and biome-specific offsets emphasize that the projected impacts of climate and climate change on near-surface biodiversity and ecosystem functioning are inaccurately assessed when air rather than soil temperature is used, especially in cold environments. The global soil-related bioclimatic variables provided here are an important step forward for any application in ecology and related disciplines. Nevertheless, we highlight the need to fill remaining geographic gaps by collecting more in situ measurements of microclimate conditions to further enhance the spatiotemporal resolution of global soil temperature products for ecological applications

Global maps of soil temperature

Research in global change ecology relies heavily on global climatic grids derived from estimates of air temperature in open areas at around 2 m above the ground. These climatic grids do not reflect conditions below vegetation canopies and near the ground surface, where critical ecosystem functions occur and most terrestrial species reside. Here, we provide global maps of soil temperature and bioclimatic variables at a 1-km² resolution for 0–5 and 5–15 cm soil depth. These maps were created by calculating the difference (i.e., offset) between in-situ soil temperature measurements, based on time series from over 1200 1-km² pixels (summarized from 8500 unique temperature sensors) across all the world’s major terrestrial biomes, and coarse-grained air temperature estimates from ERA5-Land (an atmospheric reanalysis by the European Centre for Medium-Range Weather Forecasts). We show that mean annual soil temperature differs markedly from the corresponding gridded air temperature, by up to 10°C (mean = 3.0 ± 2.1°C), with substantial variation across biomes and seasons. Over the year, soils in cold and/or dry biomes are substantially warmer (+3.6 ± 2.3°C) than gridded air temperature, whereas soils in warm and humid environments are on average slightly cooler (-0.7 ± 2.3°C). The observed substantial and biome-specific offsets emphasize that the projected impacts of climate and climate change on near-surface biodiversity and ecosystem functioning are inaccurately assessed when air rather than soil temperature is used, especially in cold environments. The global soil-related bioclimatic variables provided here are an important step forward for any application in ecology and related disciplines. Nevertheless, we highlight the need to fill remaining geographic gaps by collecting more in-situ measurements of microclimate conditions to further enhance the spatiotemporal resolution of global soil temperature products for ecological applications

Global maps of soil temperature.

Research in global change ecology relies heavily on global climatic grids derived from estimates of air temperature in open areas at around 2 m above the ground. These climatic grids do not reflect conditions below vegetation canopies and near the ground surface, where critical ecosystem functions occur and most terrestrial species reside. Here, we provide global maps of soil temperature and bioclimatic variables at a 1-km <sup>2</sup> resolution for 0-5 and 5-15 cm soil depth. These maps were created by calculating the difference (i.e. offset) between in situ soil temperature measurements, based on time series from over 1200 1-km <sup>2</sup> pixels (summarized from 8519 unique temperature sensors) across all the world's major terrestrial biomes, and coarse-grained air temperature estimates from ERA5-Land (an atmospheric reanalysis by the European Centre for Medium-Range Weather Forecasts). We show that mean annual soil temperature differs markedly from the corresponding gridded air temperature, by up to 10°C (mean = 3.0 ± 2.1°C), with substantial variation across biomes and seasons. Over the year, soils in cold and/or dry biomes are substantially warmer (+3.6 ± 2.3°C) than gridded air temperature, whereas soils in warm and humid environments are on average slightly cooler (-0.7 ± 2.3°C). The observed substantial and biome-specific offsets emphasize that the projected impacts of climate and climate change on near-surface biodiversity and ecosystem functioning are inaccurately assessed when air rather than soil temperature is used, especially in cold environments. The global soil-related bioclimatic variables provided here are an important step forward for any application in ecology and related disciplines. Nevertheless, we highlight the need to fill remaining geographic gaps by collecting more in situ measurements of microclimate conditions to further enhance the spatiotemporal resolution of global soil temperature products for ecological applications