Characterizing errors in digital elevation models and estimating the financial costs of accuracy

Digital topographic models are the foundation of more advanced modeling applications and ultimately inform planning and decision making in many fields. Despite this, the error associated with these models and derived attributes is commonly overlooked. Little attention has been given in the scientific literature to the benefits gained from having less error in a model or to the corresponding cost associated with reducing model error by choosing one product over another. To address these gaps in knowledge we evaluated the error associated with five digital elevation models (DEMs) and derived attributes of slope and aspect relative to the same attributes derived from LiDAR data. We also estimated the acquisition and processing costs per square kilometer of the five test models and the LiDAR models. We used three measures to characterize model error: (1) root mean square error, (2) mean error (and standard deviation), and (3) area of significant elevation error. We applied these measures to DEM products that are used extensively across a range of applications for planning and managing natural resources. We depicted the relationship between model accuracy (the inverse of error) and cost in two ways. One was accuracy/cost ratio for each model. The other used separate data on accuracy and cost to better guide potential users in choosing between models or deciding on necessary expenditure on models. The main conclusion of our work was that accounting for error in DEMs can inform choice of models and the need for financial outlays

Coarse-filter surrogates do not represent freshwater fish diversity at a regional scale in Queensland, Australia

Abiotic and biologically informed classifications are often used in conservation planning as coarse-filter surrogates for species. The relationship between these surrogates and the distribution of species is commonly assumed, but rarely assessed by planners. We derived four abiotic and eight biologically informed classifications of stream reaches to serve as surrogates for biodiversity patterns in the Wet Tropics bioregion, Queensland, Australia. We used stream reaches as planning units and, as conservation targets for each surrogate, we used two percentages – 10% and 30% – of the total stream reach length occupied by each class. We then derived minimum sets of planning units to meet targets for each surrogate and tested the effectiveness of the surrogates by calculating the average achievement of the same targets for predicted distributions of 28 fish species. Our results showed that neither abiotic nor biologically informed classifications were good at representing freshwater fish species; in fact none of the surrogates led to average representation of species better than randomly selected planning units. There were two main reasons for this poor performance. First, none of the surrogates had high classification strength or informativeness about compositional change in fish species within the study region. Second, frequency distributions of probabilities of occurrence for most fish species were strongly right-skewed, with few stream reaches having high probabilities. Combined, these results meant that selection of stream reaches to achieve surrogate targets was effectively random with respect to probabilities of fish species occurrence, leading to poor representation of fish species. We conclude there is a limited basis for using coarse-filter surrogates to represent freshwater fish diversity in this region, and that there is a clear need for research in this as well as other regions if planners are to understand the limitations associated with coarse-filter surrogates for representing freshwater biodiversity more broadly

On particulate characterization in a heavy-duty diesel engine by time-resolved laser-induced incandescence

Contains fulltext : 35588.pdf (publisher's version ) (Closed access)Time-resolved laser-induced incandescence (TR-LII) measurements have been performed inside the combustion chamber of a heavy-duty diesel engine running at low load and with regular diesel fuel. The LII traces were interpreted in terms of primary particle sizes, comparing two different assumed particle-size distributions: a mono-disperse and a log-normal distribution. The initial temperature of the particles (immediately after the laser pulse) is estimated by two-color pyrometry. We conclude that the initial temperature of the particles is not very critical for the outcome of the fitting procedure for the (mean) radius. Under the high-pressure conditions in the engine, the time dependence of the LII intensity contains sufficient structure to allow retrieval of details of the particle-size distribution