DMS and SO2 at Baring Head, New Zealand: Implications for the Yield of SO2 from DMS

Atmospheric dimethyl sulfide (DMS) and sulfur dioxide (SO₂) concentrations were measured at Baring Head, New Zealandduring February and March 2000. Anti-correlated DMS and SO₂ diurnalcycles, consistent with the photochemical production of SO₂ from DMS, were observed in clean southerly air off the ocean. The data is used to infer a yield of SO₂ from DMS oxidation. The estimated yields are highly dependent on assumptions about the DMS oxidation rate. Fitting the measured data in a photochemical box model using model-generated OH levels and the Hynes et al. (1986) DMS + OH rate constant suggests that theSO₂ yield is 50–100%, similar to current estimates for the tropical Pacific.However, the observed amplitude of the DMS diurnal cycle suggests that the oxidation rate is higher than that used by the model, and therefore, that theSO₂ yield is lower in the range of 20–40%.     

Representation of Uncertainty and Integration of PGIS-based Grazing Intensity Maps Using Evidential Belief Functions

In a project to classify livestock grazing intensity using participatory geographic information systems (PGIS), we encountered the problem of how to synthesize PGIS-based maps of livestock grazing intensity that were prepared separately by local experts. We investigated the utility of evidential belief functions (EBFs) and Dempster's rule of combination to represent classification uncertainty and integrate the PGIS-based grazing intensity maps. These maps were used as individual sets of evidence in the application of EBFs to evaluate the proposition that " This area or pixel belongs to the high, medium, or low grazing intensity class because the local expert(s) says (say) so ". The class-area-weighted averages of EBFs based on each of the PGIS-based maps show that the lowest degree of classification uncertainty is associated with maps in which "vegetation species" was used as the mapping criterion. This criterion, together with local landscape attributes of livestock use may be considered as an appropriate standard measure for grazing intensity. The maps of integrated EBFs of grazing intensity show that classification uncertainty is high when the local experts apply at least two mapping criteria together. This study demonstrates the usefulness of EBFs to represent classification uncertainty and the possibility to use the EBF values in identifying and using criteria for PGIS-based mapping of livestock grazing intensity.     

Geospatial Information Integration for Authoritative and Crowd Sourced Road Vector Data

This article describes results from a research project undertaken to explore the technical issues associated with integrating unstructured crowd sourced data with authoritative national mapping data. The ultimate objective is to develop methodologies to ensure the feature enrichment of authoritative data, using crowd sourced data. Users increasingly find that they wish to use data from both kinds of geographic data sources. Different techniques and methodologies can be developed to solve this problem. In our previous research, a position map matching algorithm was developed for integrating authoritative and crowd sourced road vector data, and showed promising results ( Anand et al. 2010 ). However, especially when integrating different forms of data at the feature level, these techniques are often time consuming and are more computationally intensive than other techniques available. To tackle these problems, this project aims at developing a methodology for automated conflict resolution, linking and merging of geographical information from disparate authoritative and crowd‐sourced data sources. This article describes research undertaken by the authors on the design, implementation, and evaluation of algorithms and procedures for producing a coherent ontology from disparate geospatial data sources. To integrate road vector data from disparate sources, the method presented in this article first converts input data sets to ontologies, and then merges these ontologies into a new ontology. This new ontology is then checked and modified to ensure that it is consistent. The developed methodology can deal with topological and geometry inconsistency and provide more flexibility for geospatial information merging.     

Airborne MSS for land cover classification II

A basic methodology for land cover classification using airborne multispectral scanner (MSS) imagery is outlined. This includes waveband selection and radiometric calibration; correction for scan angle and atmosphere; training and classification and accuracy assessment. Refinements to this basic methodology include per‐field sampling and the addition of low‐pass filtering, image texture, prior probabilities and two dates of imagery.

For a study area in upland England, eight land covers were classified with a mean accuracy of 52.6 percent using the basic methodology. This was increased to 79.0 percent by using a suitability refined methodology. Per‐field sampling accounted for the largest proportion of this increase.     

Terrestrial laser scanning to estimate plot-level forest canopy fuel properties

This paper evaluates the potential of a terrestrial laser scanner (TLS) to characterize forest canopy fuel characteristics at plot level. Several canopy properties, namely canopy height, canopy cover, canopy base height and fuel strata gap were estimated. Different approaches were tested to avoid the effect of canopy shadowing on canopy height estimation caused by deployment of the TLS below the canopy. Estimation of canopy height using a grid approach provided a coefficient of determination of R² = 0.81 and an RMSE of 2.47 m. A similar RMSE was obtained using the 99th percentile of the height distribution of the highest points, representing the 1% of the data, although the coefficient of determination was lower (R² = 0.70). Canopy cover (CC) was estimated as a function of the occupied cells of a grid superimposed upon the TLS point clouds. It was found that CC estimates were dependent on the cell size selected, with 3 cm being the optimum resolution for this study. The effect of the zenith view angle on CC estimates was also analyzed. A simple method was developed to estimate canopy base height from the vegetation vertical profiles derived from an occupied/non-occupied voxels approach. Canopy base height was estimated with an RMSE of 3.09 m and an R² = 0.86. Terrestrial laser scanning also provides a unique opportunity to estimate the fuel strata gap (FSG), which has not been previously derived from remotely sensed data. The FSG was also derived from the vegetation vertical profile with an RMSE of 1.53 m and an R² = 0.87.     

Creating a Population of 12‐Digit Headwater Basins within the Albemarle‐Pamlico Estuary System

Ecological research within the US Environmental Protection Agency's Office of Research and Development has recently changed its focus to quantifying and mapping ecosystem services provided to humans. Our local research group has been charged to develop a regional assessment of several ecosystem services in the Albemarle‐Pamlico Estuary System (APES). Time, data, and funding constraints precluded explicit modeling of the entire APES, so in Phase 1 of our research plan we chose to model ecosystem services in a random sample of headwater catchments. After observing numerous inconsistencies between the National Hydrography Dataset‐Plus (NHDPlus) stream network and the Virginia/North Carolina Watershed Boundary Dataset 12‐digit HUC coverage, we began by creating modified 12‐digit hydrologic units (HUCs) by aggregating smaller catchments delineated within the NHDPlus. In defining our population of interest (headwater 12‐digit HUCs with perennial, natural, wadeable pour points), we generally excluded HUCs that had multiple pour points, no pour points, or whose pour points were intermittent streams, artificial segments, ditches/canals, or lentic systems (lakes and reservoirs). After taking these actions, 318 HUCs remained and a stratified random sample (Omnerik ecoregions as strata) of 50 HUCs was chosen from this population.     

Joint parameter estimation from magnetic resonance and vertical electric soundings using a multi‐objective genetic algorithm

Magnetic resonance sounding (MRS) has increasingly become an important method in hydrogeophysics because it allows for estimations of essential hydraulic properties such as porosity and hydraulic conductivity. A resistivity model is required for magnetic resonance sounding modelling and inversion. Therefore, joint interpretation or inversion is favourable to reduce the ambiguities that arise in separate magnetic resonance sounding and vertical electrical sounding (VES) inversions. A new method is suggested for the joint inversion of magnetic resonance sounding and vertical electrical sounding data. A one‐dimensional blocky model with varying layer thicknesses is used for the subsurface discretization. Instead of conventional derivative‐based inversion schemes that are strongly dependent on initial models, a global multi‐objective optimization scheme (a genetic algorithm [GA] in this case) is preferred to examine a set of possible solutions in a predefined search space. Multi‐objective joint optimization avoids the domination of one objective over the other without applying a weighting scheme. The outcome is a group of non‐dominated optimal solutions referred to as the Pareto‐optimal set. Tests conducted using synthetic data show that the multi‐objective joint optimization approximates the joint model parameters within the experimental error level and illustrates the range of trade‐off solutions, which is useful for understanding the consistency and conflicts between two models and objectives. Overall, the Levenberg‐Marquardt inversion of field data measured during a survey on a North Sea island presents similar solutions. However, the multi‐objective genetic algorithm method presents an efficient method for exploring the search space by producing a set of non‐dominated solutions. Borehole data were used to provide a verification of the inversion outcomes and indicate that the suggested genetic algorithm method is complementary for derivative‐based inversions.     

Ultra‐rapid topographic surveying for complex environments: the hand‐held mobile laser scanner (HMLS)

Terrestrial laser scanning is the current technique of choice for acquiring high resolution topographic data at the site scale (i.e. over tens to hundreds of metres), for accurate volume measurements or process modelling. However, in regions of complex topography with multiple local horizons, restricted lines of sight significantly hinder use of such tripod‐based instruments by requiring multiple setups to achieve full coverage of the area. We demonstrate a novel hand‐held mobile laser scanning technique that offers particular promise for site‐scale topographic surveys of complex environments. To carry out a survey, the hand‐held mobile laser scanner (HMLS) is walked across a site, mapping around the surveyor continuously en route. We assess the accuracy of HMLS data by comparing survey results from an eroding coastal cliff site with those acquired by a state‐of‐the‐art terrestrial laser scanner (TLS) and also with the results of a photo‐survey, processed by structure from motion and multi‐view stereo (SfM‐MVS) algorithms. HMLS data are shown to have a root mean square (RMS) difference to the benchmark TLS data of 20 mm, not dissimilar to that of the SfM‐MVS survey (18 mm). The efficiency of the HMLS system in complex terrain is demonstrated by acquiring topographic data covering ~780 m² of salt‐marsh gullies, with a mean point spacing of 4.4 cm, in approximately six minutes. We estimate that HMLS surveying of gullies is approximately 40 times faster than using a TLS and six times faster than using SfM‐MVS. © 2013 The Authors. Earth Surface Processes and Landforms Published by John Wiley & Sons Ltd.     

Quantifying the rate and depth dependence of bioturbation based on optically‐stimulated luminescence (OSL) dates and meteoric 10Be

Both the rate and the vertical distribution of soil disturbance modify soil properties such as porosity, particle size, chemical composition and age structure; all of which play an important role in a soil's biogeochemical functioning. Whereas rates of mixing have been previously quantified, the nature of bioturbation's depth dependence remains poorly constrained. Here we constrain, for the first time, the relationship between mixing rate and depth in a bioturbated soil in northeast Queensland, Australia using a novel method combining OSL (optically‐stimulated luminescence) ages and meteoric beryllium‐10 (¹⁰Be) inventories. We find that the best fit mixing rate decreases non‐linearly with increasing soil depth in this soil and the characteristic length scale of 0.28 m over which the mixing coefficient decays is comparable to reported rooting depth coefficients. In addition we show that estimates of surface mixing rates from OSL data are highly dependent on erosion rate and that erosion rate must be constrained if accurate mixing rates are to be quantified. We calculate surface diffusion‐like mixing coefficients of 1.8 × 10^?4 and 2.1 × 10^?4 m² yr^?1 for the studied soil for two different estimates of soil erosion. © 2014 The Authors. Earth Surface Processes and Landforms Published by John Wiley & Sons Ltd.