Spatial-temporal aspects of cost-benefit analysis for park management: an example from Khao Yai National Park, Thailand

Using a model calibrated to Khao Yai National Park in Thailand, this paper highlights the importance of generating explicitly spatial and temporal data for developing management plans for tropical protected forests. Spatial and temporal cost-benefit analysis should account for the interactions between different land uses – such as the benefits of contiguous areas of preserved land and edge effects – and the realities of villagers living near forests who rely on extracted resources. By taking a temporal perspective, this paper provides a rare empirical assessment of the importance of quasi-option values when determining optimal management plans.

The diffusion of grassroots innovations for sustainability in Italy and Great Britain: an exploratory spatial data analysis

Little research so far has been devoted to understanding the diffusion of grassroots innovation for sustainability across space. This paper explores and compares the spatial diffusion of two networks of grassroots innovations, the Transition Towns Network (TTN) and Gruppi di Acquisto Solidale (Solidarity Purchasing Groups – GAS), in Great Britain and Italy. Spatio-temporal diffusion data were mined from available datasets, and patterns of diffusion were uncovered through an exploratory data analysis. The analysis shows that GAS and TTN diffusion in Italy and Great Britain is spatially structured, and that the spatial structure has changed over time. TTN has diffused differently in Great Britain and Italy, while GAS and TTN have diffused similarly in central Italy. The uneven diffusion of these grassroots networks on the one hand challenges current narratives on the momentum of grassroots innovations, but on the other highlights important issues in the geography of grassroots innovations for sustainability, such as cross-movement transfers and collaborations, institutional thickness, and interplay of different proximities in grassroots innovation diffusion.

Towards more precise sugar beet management based on geostatistical analysis of spatial variability within fields

Within-field variation in sugar beet yield and quality was investigated in three commercial sugar beet fields in the east of England to identify the main associated variables and to examine the possibility of predicting yield early in the season with a view to spatially variable management of sugar beet crops. Irregular grid sampling with some purposively-located nested samples was applied. It revealed the spatial variability in each sugar beet field efficiently. In geostatistical analyses, most variograms were isotropic with moderate to strong spatial dependency indicating a significant spatial variation in sugar beet yield and associated growth and environmental variables in all directions within each field. The Kriged maps showed spatial patterns of yield variability within each field and visual association with the maps of other variables. This was confirmed by redundancy analyses and Pearson correlation coefficients. The main variables associated with yield variability were soil type, organic matter, soil moisture, weed density and canopy temperature. Kriged maps of final yield variability were strongly related to that in crop canopy cover, LAI and intercepted solar radiation early in the growing season, and the yield maps of previous crops. Therefore, yield maps of previous crops together with early assessment of sugar beet growth may make an early prediction of within-field variability in sugar beet yield possible. The Broom’s Barn sugar beet model failed to account for the spatial variability in sugar yield, but the simulation was greatly improved when corrected for early canopy development cover and when the simulated yield was adjusted for weeds and plant population. Further research to optimize inputs to maximise sugar yield should target the irrigation and fertilizing of areas within fields with low canopy cover early in the season.

Sensitivity of feature-based analysis methods of storm tracks to the form of background field removal

For the tracking of extrema associated with weather systems to be applied to a broad range of fields it is necessary to remove a background field that represents the slowly varying, large spatial scales. The sensitivity of the tracking analysis to the form of background field removed is explored for the Northern Hemisphere winter storm tracks for three contrasting fields from an integration of the U. K. Met Office's (UKMO) Hadley Centre Climate Model (HadAM3). Several methods are explored for the removal of a background field from the simple subtraction of the climatology, to the more sophisticated removal of the planetary scales. Two temporal filters are also considered in the form of a 2-6-day Lanczos filter and a 20-day high-pass Fourier filter. The analysis indicates that the simple subtraction of the climatology tends to change the nature of the systems to the extent that there is a redistribution of the systems relative to the climatological background resulting in very similar statistical distributions for both positive and negative anomalies. The optimal planetary wave filter removes total wavenumbers less than or equal to a number in the range 5-7, resulting in distributions more easily related to particular types of weather system. For the temporal filters the 2-6-day bandpass filter is found to have a detrimental impact on the individual weather systems, resulting in the storm tracks having a weak waveguide type of behavior. The 20-day high-pass temporal filter is less aggressive than the 2-6-day filter and produces results falling between those of the climatological and 2-6-day filters.

View-based approaches to spatial representation in human vision

In an immersive virtual environment, observers fail to notice the expansion of a room around them and consequently make gross errors when comparing the size of objects. This result is difficult to explain if the visual system continuously generates a 3-D model of the scene based on known baseline information from interocular separation or proprioception as the observer walks. An alternative is that observers use view-based methods to guide their actions and to represent the spatial layout of the scene. In this case, they may have an expectation of the images they will receive but be insensitive to the rate at which images arrive as they walk. We describe the way in which the eye movement strategy of animals simplifies motion processing if their goal is to move towards a desired image and discuss dorsal and ventral stream processing of moving images in that context. Although many questions about view-based approaches to scene representation remain unanswered, the solutions are likely to be highly relevant to understanding biological 3-D vision.

Spatial covariation of Azotobacter abundance and soil properties: A case study using the wavelet transform

The soil microflora is very heterogeneous in its spatial distribution. The origins of this heterogeneity and its significance for soil function are not well understood. A problem for understanding spatial variation better is the assumption of statistical stationarity that is made in most of the statistical methods used to assess it. These assumptions are made explicit in geostatistical methods that have been increasingly used by soil biologists in recent years. Geostatistical methods are powerful, particularly for local prediction, but they require the assumption that the variability of a property of interest is spatially uniform, which is not always plausible given what is known about the complexity of the soil microflora and the soil environment. We have used the wavelet transform, a relatively new innovation in mathematical analysis, to investigate the spatial variation of abundance of Azotobacter in the soil of a typical agricultural landscape. The wavelet transform entails no assumptions of stationarity and is well suited to the analysis of variables that show intermittent or transient features at different spatial scales. In this study, we computed cross-variograms of Azotobacter abundance with the pH, water content and loss on ignition of the soil. These revealed scale-dependent covariation in all cases. The wavelet transform also showed that the correlation of Azotobacter abundance with all three soil properties depended on spatial scale, the correlation generally increased with spatial scale and was only significantly different from zero at some scales. However, the wavelet analysis also allowed us to show how the correlation changed across the landscape. For example, at one scale Azotobacter abundance was strongly correlated with pH in part of the transect, and not with soil water content, but this was reversed elsewhere on the transect. The results show how scale-dependent variation of potentially limiting environmental factors can induce a complex spatial pattern of abundance in a soil organism. The geostatistical methods that we used here make assumptions that are not consistent with the spatial changes in the covariation of these properties that our wavelet analysis has shown. This suggests that the wavelet transform is a powerful tool for future investigation of the spatial structure and function of soil biota. (c) 2006 Elsevier Ltd. All rights reserved.

A geomatics-based approach for the derivation of the spatial distribution of sediment transport processes in periglacial mountain environments

Within this paper modern techniques such as satellite image analysis and tools provided by geographic information systems (GIS.) are exploited in order to extend and improve existing techniques for mapping the spatial distribution of sediment transport processes. The processes of interest comprise mass movements such as solifluction, slope wash, dirty avalanches and rock- and boulder falls. They differ considerably in nature and therefore different approaches for the derivation of their spatial extent are required. A major challenge is addressing the differences between the comparably coarse resolution of the available satellite data (Landsat TM/ETM+, 30 in x 30 m) and the actual scale of sediment transport in this environment. A three-stepped approach has been developed which is based on the concept of Geomorphic Process Units (GPUs): parameterization, process area delineation and combination. Parameters include land cover from satellite data and digital elevation model derivatives. Process areas are identified using a hierarchical classification scheme utilizing thresholds and definition of topology. The approach has been developed for the Karkevagge in Sweden and could be successfully transferred to the Rabotsbekken catchment at Okstindan, Norway using similar input data. Copyright (C) 2008 John Wiley & Sons, Ltd.

Exploring the spatial relations between soil physical properties and apparent electrical conductivity

Relations between the apparent electrical conductivity of the soil (ECa) and top- and sub-soil physical properties were examined for two arable fields in southern England (Crowmarsh Battle Farms and the Yattendon Estate). The spatial variation of ECa and the soil properties was explored geostatistically. The variogram ranges showed that ECa varied on a similar spatial scale to many of the soil physical properties in both fields. Several features in the map of kriged predictions of ECa were also evident in maps of the soil properties. In addition, the correlation coefficients showed a strong relation between ECa and several soil properties. A moving correlation analysis enabled differences in the relations between ECa and the soil properties to be examined within the fields. The results indicated that relations were inconsistent; they were stronger in some areas than others. A regression of ECa on the principal component scores of the leading components for both fields showed that the first two components accounted for a large proportion of the variance in ECa, whereas the others accounted for little or none. For Crowmarsh topsoil sand and clay, loss on ignition and volumetric water measured in the autumn had large correlations on the first component, and for Yattendon they were large for topsoil sand and clay, and autumn and spring volumetric water. The cross-variograms suggested strong coregionalization between ECa and several soil physical properties; in particular subsoil sand and silt at Crowmarsh, and subsoil sand and clay at Yattendon. The structural correlations from the linear model of coregionalization confirmed the strength of the relations between ECa and the subsoil properties. Nevertheless, no one property was consistently important for both fields. Although a map of ECa can indicate the general patterns of spatial variation in the soil, it is not a substitute for information on soil properties obtained by sampling and analysing the soil. Nevertheless, it could be used to guide further sampling. (c) 2005 Elsevier B.V. All rights reserved.

Sampling the soil in long-term forest plots: The implications of spatial variation

Long-term monitoring of forest soils as part of a pan-European network to detect environmental change depends on an accurate determination of the mean of the soil properties at each monitoring event. Forest soil is known to be very variable spatially, however. A study was undertaken to explore and quantify this variability at three forest monitoring plots in Britain. Detailed soil sampling was carried out, and the data from the chemical analyses were analysed by classical statistics and geostatistics. An analysis of variance showed that there were no consistent effects from the sample sites in relation to the position of the trees. The variogram analysis showed that there was spatial dependence at each site for several variables and some varied in an apparently periodic way. An optimal sampling analysis based on the multivariate variogram for each site suggested that a bulked sample from 36 cores would reduce error to an acceptable level. Future sampling should be designed so that it neither targets nor avoids trees and disturbed ground. This can be achieved best by using a stratified random sampling design.

Sensitivity analysis of a catchment-scale nitrogen model

There are now considerable expectations that semi-distributed models are useful tools for supporting catchment water quality management. However, insufficient attention has been given to evaluating the uncertainties inherent to this type of model, especially those associated with the spatial disaggregation of the catchment. The Integrated Nitrogen in Catchments model (INCA) is subjected to an extensive regionalised sensitivity analysis in application to the River Kennet, part of the groundwater-dominated upper Thames catchment, UK The main results are: (1) model output was generally insensitive to land-phase parameters, very sensitive to groundwater parameters, including initial conditions, and significantly sensitive to in-river parameters; (2) INCA was able to produce good fits simultaneously to the available flow, nitrate and ammonium in-river data sets; (3) representing parameters as heterogeneous over the catchment (206 calibrated parameters) rather than homogeneous (24 calibrated parameters) produced a significant improvement in fit to nitrate but no significant improvement to flow and caused a deterioration in ammonium performance; (4) the analysis indicated that calibrating the flow-related parameters first, then calibrating the remaining parameters (as opposed to calibrating all parameters together) was not a sensible strategy in this case; (5) even the parameters to which the model output was most sensitive suffered from high uncertainty due to spatial inconsistencies in the estimated optimum values, parameter equifinality and the sampling error associated with the calibration method; (6) soil and groundwater nutrient and flow data are needed to reduce. uncertainty in initial conditions, residence times and nitrogen transformation parameters, and long-term historic data are needed so that key responses to changes in land-use management can be assimilated. The results indicate the general, difficulty of reconciling the questions which catchment nutrient models are expected to answer with typically limited data sets and limited knowledge about suitable model structures. The results demonstrate the importance of analysing semi-distributed model uncertainties prior to model application, and illustrate the value and limitations of using Monte Carlo-based methods for doing so. (c) 2005 Elsevier B.V. All rights reserved.

A geostatistical analysis of soil, vegetation, and image data characterizing land surface variation

The elucidation of spatial variation in the landscape can indicate potential wildlife habitats or breeding sites for vectors, such as ticks or mosquitoes, which cause a range of diseases. Information from remotely sensed data could aid the delineation of vegetation distribution on the ground in areas where local knowledge is limited. The data from digital images are often difficult to interpret because of pixel-to-pixel variation, that is, noise, and complex variation at more than one spatial scale. Landsat Thematic Mapper Plus (ETM+) and Satellite Pour l'Observation de La Terre (SPOT) image data were analyzed for an area close to Douna in Mali, West Africa. The variograms of the normalized difference vegetation index (NDVI) from both types of image data were nested. The parameters of the nested variogram function from the Landsat ETM+ data were used to design the sampling for a ground survey of soil and vegetation data. Variograms of the soil and vegetation data showed that their variation was anisotropic and their scales of variation were similar to those of NDVI from the SPOT data. The short- and long-range components of variation in the SPOT data were filtered out separately by factorial kriging. The map of the short-range component appears to represent the patterns of vegetation and associated shallow slopes and drainage channels of the tiger bush system. The map of the long-range component also appeared to relate to broader patterns in the tiger bush and to gentle undulations in the topography. The results suggest that the types of image data analyzed in this study could be used to identify areas with more moisture in semiarid regions that could support wildlife and also be potential vector breeding sites.

A singular vector perspective of 4DVAR: The spatial structure and evolution of baroclinic weather systems

The extent to which the four-dimensional variational data assimilation (4DVAR) is able to use information about the time evolution of the atmosphere to infer the vertical spatial structure of baroclinic weather systems is investigated. The singular value decomposition (SVD) of the 4DVAR observability matrix is introduced as a novel technique to examine the spatial structure of analysis increments. Specific results are illustrated using 4DVAR analyses and SVD within an idealized 2D Eady model setting. Three different aspects are investigated. The first aspect considers correcting errors that result in normal-mode growth or decay. The results show that 4DVAR performs well at correcting growing errors but not decaying errors. Although it is possible for 4DVAR to correct decaying errors, the assimilation of observations can be detrimental to a forecast because 4DVAR is likely to add growing errors instead of correcting decaying errors. The second aspect shows that the singular values of the observability matrix are a useful tool to identify the optimal spatial and temporal locations for the observations. The results show that the ability to extract the time-evolution information can be maximized by placing the observations far apart in time. The third aspect considers correcting errors that result in nonmodal rapid growth. 4DVAR is able to use the model dynamics to infer some of the vertical structure. However, the specification of the case-dependent background error variances plays a crucial role.

Airborne measurements of the spatial distribution of aerosol chemical composition across Europe and evolution of the organic fraction

The spatial distribution of aerosol chemical composition and the evolution of the Organic Aerosol (OA) fraction is investigated based upon airborne measurements of aerosol chemical composition in the planetary boundary layer across Europe. Sub-micron aerosol chemical composition was measured using a compact Time-of-Flight Aerosol Mass Spectrometer (cToF-AMS). A range of sampling conditions were evaluated, including relatively clean background conditions, polluted conditions in North-Western Europe and the near-field to far-field outflow from such conditions. Ammonium nitrate and OA were found to be the dominant chemical components of the sub-micron aerosol burden, with mass fractions ranging from 20--50% each. Ammonium nitrate was found to dominate in North-Western Europe during episodes of high pollution, reflecting the enhanced NO_x and ammonia sources in this region. OA was ubiquitous across Europe and concentrations generally exceeded sulphate by 30--160%. A factor analysis of the OA burden was performed in order to probe the evolution across this large range of spatial and temporal scales. Two separate Oxygenated Organic Aerosol (OOA) components were identified; one representing an aged-OOA, termed Low Volatility-OOA and another representing fresher-OOA, termed Semi Volatile-OOA on the basis of their mass spectral similarity to previous studies. The factors derived from different flights were not chemically the same but rather reflect the range of OA composition sampled during a particular flight. Significant chemical processing of the OA was observed downwind of major sources in North-Western Europe, with the LV-OOA component becoming increasingly dominant as the distance from source and photochemical processing increased. The measurements suggest that the aging of OA can be viewed as a continuum, with a progression from a less oxidised, semi-volatile component to a highly oxidised, less-volatile component. Substantial amounts of pollution were observed far downwind of continental Europe, with OA and ammonium nitrate being the major constituents of the sub-micron aerosol burden. Such anthropogenically perturbed air masses can significantly perturb regional climate far downwind of major source regions.

Biogenic nitric oxide emissions from soils: impact on NOx and ozone over West Africa during AMMA (African Monsoon Multidisciplinary Analysis) - observational study

Chemical and meteorological parameters measured on board the Facility for Airborne Atmospheric Measurements (FAAM) BAe 146 Atmospheric Research Aircraft during the African Monsoon Multidisciplinary Analysis (AMMA) campaign are presented to show the impact of NOx emissions from recently wetted soils in West Africa. NO emissions from soils have been previously observed in many geographical areas with different types of soil/vegetation cover during small scale studies and have been inferred at large scales from satellite measurements of NOx. This study is the first dedicated to showing the emissions of NOx at an intermediate scale between local surface sites and continental satellite measurements. The measurements reveal pronounced mesoscale variations in NOx concentrations closely linked to spatial patterns of antecedent rainfall. Fluxes required to maintain the NOx concentrations observed by the BAe-146 in a number of cases studies and for a range of assumed OH concentrations (1×106 to 1×107 molecules cm−3) are calculated to be in the range 8.4 to 36.1 ng N m−2 s−1. These values are comparable to the range of fluxes from 0.5 to 28 ng N m−2 s−1 reported from small scale field studies in a variety of non-nutrient rich tropical and sub-tropical locations reported in the review of Davidson and Kingerlee (1997). The fluxes calculated in the present study have been scaled up to cover the area of the Sahel bounded by 10 to 20 N and 10 E to 20 W giving an estimated emission of 0.03 to 0.30 Tg N from this area for July and August 2006. The observed chemical data also suggest that the NOx emitted from soils is taking part in ozone formation as ozone concentrations exhibit similar fine scale structure to the NOx, with enhancements over the wet soils. Such variability can not be explained on the basis of transport from other areas. Delon et al. (2008) is a companion paper to this one which models the impact of soil NOx emissions on the NOx and ozone concentration over West Africa during AMMA. It employs an artificial neural network to define the emissions of NOx from soils, integrated into a coupled chemistry-dynamics model. The results are compared to the observed data presented in this paper. Here we compare fluxes deduced from the observed data with the model-derived values from Delon et al. (2008).

Quantitative analysis of remote gas temperatures and concentrations from their infrared emission spectra

Techniques for obtaining quantitative values of the temperatures and concentrations of remote hot gaseous effluents from their measured passive emission spectra have been examined in laboratory experiments and on field trials. These emission spectra were obtained using an adapted FTIR spectrometer with 0.25 cm-1 spectral resolution. The CO2 and H2O vapour content in the plume from a 55 m smoke stack and the temperature of these gases were obtained by comparing the measured emission spectra with those modelled using the HITRAN atmospheric transmission database. The spatial distributions of CO2, CO and unburnt CH4 in a laboratory methane flame were reconstructed tomographically using a matrix inversion technique.