S. de Bruin

Soil-landscape modelling using fuzzy c-means clustering of attribute data derived from a Digital Elevation Model (DEM).

Abstract This study explores the use of fuzzy c -means clustering of attribute data derived from a digital elevation model to represent transition zones in the soil-landscape. The conventional geographic model used for soil-landscape description is not able to properly deal with these. Fuzzy c -means clustering was applied to a hillslope within a small drainage basin in southern Spain. Cluster validity evaluation was based on the coefficient of determination of regressing topsoil clay data on membership grades. The resulting clusters occupied spatially contiguous areas. We found a high degree of association with measured topsoil clay data ( r a 2 =0.68) for three clusters and a weighting exponent of 2.1. Location of the clusters coincided with observable terrain characteristics. Therefore we concluded that the coefficient of determination of regressing soil sample data on membership grades efficiently supports deciding upon the optimum fuzzy c -partition. The study confirms that fuzzy c -means clustering of terrain attribute data enhances conventional soil-landscape modelling, as it allows representation of fuzziness inherent to soil-landscape units.

Spatial relationship between climatologies and changes in global vegetation activity

Vegetation forms a main component of the terrestrial biosphere and plays a crucial role in land-cover and climate-related studies. Activity of vegetation systems is commonly quantified using remotely sensed vegetation indices (VI). Extensive reports on temporal trends over the past decades in time series of such indices can be found in literature. However, little remains known about the processes underlying these changes at large spatial scales. In this study, we aimed at quantifying the spatial relationship between changes in potential climatic growth constraints (i.e. temperature, precipitation and incident solar radiation) and changes in vegetation activity (1982-2008). We demonstrate an additive spatial model with 0.5° resolution, consisting of a regression component representing climate-associated effects and a spatially correlated field representing the combined influence of other factors, including land-use change. Little over 50% of the spatial variance could be attributed to changes in climatologies; conspicuously, many greening trends and browning hotspots in Argentina and Australia. The nonassociated model component may contain large-scale human interventions, feedback mechanisms or natural effects, which were not captured by the climatologies. Browning hotspots in this component were especially found in subequatorial Africa. On the scale of land-cover types, strongest relationships between climatologies and vegetation activity were found in forests, including indications for browning under warming conditions (analogous to the divergence issue discussed in dendroclimatology).

Integrating spatial statistics and remote sensing

This paper presents an integrated approach towards spatial statistics for remote sensing. Using the layer concept in Geographical Information Systems we treat successively elements of spatial statistics, scale, classification, sampling and decision support. The layer concept allows to combine continuous spatial properties with classified map units. The paper is illustrated with five case studies: one on heavy metals in groundwater at different scales, one on soil variability within seemingly homogeneous units, one on fuzzy classification for a soillandscape model, one on classification with geostatistical procedures and one on thermal images. The integrated approach offers a better understanding and quantification of uncertainties in remote sensing studies.

Optimization of mobile radioactivity monitoring networks

In case of a nuclear accident, decision makers rely on high-resolution and accurate information about the spatial distribution of radioactive contamination surrounding the accident site. However, the static nuclear monitoring networks of many European countries are generally too coarse to provide the desired level of spatial accuracy. In the Netherlands, authorities are considering a strategy in which measurement density is increased during an emergency using complementary mobile measuring devices. This raises the question, where should these mobile devices be placed? This article proposes a geostatistical methodology to optimize the allocation of mobile measurement devices, such that the expected weighted sum of false-positive and false-negative areas (i.e. false classification into safe and unsafe zones) is minimized. Radioactivity concentration is modelled as the sum of a deterministic trend and a zero-mean spatially correlated stochastic residual. The trend is defined as the outcome of a physical atmospheric dispersion model, NPK-PUFF. The residual is characterized by a semivariogram of differences between the outputs of various NPK-PUFF model runs, designed to reflect the effect of uncertainty in NPK-PUFF meteorological inputs (e.g. wind speed, wind direction). Spatial simulated annealing is used to obtain the optimal monitoring design, in which accessibility of sampling sites (e.g. distance to roads) is also considered. Although the methodology is computationally demanding, results are promising and the computational load may be considerably reduced to compute optimal mobile monitoring designs in nearly real time.

Significance and application of the multi-hierarchical landsystem in soil mapping

Abstract A methodological framework to formalise the landscape knowledge of the soil surveyor is presented. It requires structuring of terrain objects in a nested hierarchy, followed by inference and formalisation of knowledge rules. We demonstrate how these rules can be applied in a GIS environment for learning and communicating knowledge. Object subdivision is based on the technique of hierarchical subdivision derived from surveyor experience. Formalisation of contextual knowledge requires (1) description of the semantics of object levels in relation to scale, (2) definition of objects and attributes and their relation to higher and lower level objects, and (3) the knowledge inferred from the objects and its inclusion in the database. The Alora case study (South Spain) illustrates the value of the different object levels of the hierarchy for obtaining information on both soils and land evaluation. Often more than one context (object hierarchy) has to be considered to predict this information. We argue that a GIS database requires proper description of the different kinds of formal relationships that exist between objects and object classes, and between elementary and composite objects. We also show that the different scale levels of the multiple hierarchy play an essential role in conveying knowledge for an interdisciplinary application (land evaluation, land use, erosion, and hydrology).

Representing major soil variability at regional scale by constrained Latin Hypercube Sampling of remote sensing data

This paper presents a sparse, remote sensing-based sampling approach making use of conditioned Latin Hypercube Sampling (cLHS) to assess variability in soil properties at regional scale. The method optimizes the sampling scheme for a defined spatial population based on selected covariates, which are assumed to represent the variability of the target variables. The optimization also accounts for specific constraints and costs expressing the field sampling effort. The approach is demonstrated using a case study in Morocco, where a small but representative sample record had to be collected over a 15,000 km2 area within 2 weeks. The covariate space of the Latin Hypercube consisted of the first three principal components of ASTER imagery as well as elevation. Comparison of soil properties taken from the topsoil with the existing soil map, a geological map and lithological data showed that the sampling approach was successful in representing major soil variability. The cLHS sample failed to express spatial correlation; constraining the LHS by a distance criterion favoured large spatial variability within a short distances resulting in an overestimation of the variograms nugget and short distance variability. However, the exhaustive covariate data appeared to be spatially correlated which supports our premise that once the relation between spatially explicit remote sensing data and soil properties has been modelled, the latter can be spatially predicted based on the densely sampled remotely sensed data. Therefore, the LHS approach is considered as time and cost efficient for regional scale surveys that rely on remote sensing-based prediction of soil properties.

Green Care Farms Promote Activity Among Elderly People With Dementia

In the Netherlands, an increasing number of green care farms are providing day care to community-dwelling elderly people with dementia. Currently, it is unknown whether activities, activity participation, and facility use of elderly people with dementia at green care farms differ from those at regular day care facilities. The authors performed group and individual observations at 11 green care farms and 12 regular day care facilities. Activities of elderly people at green care farms were more frequent, occurred outdoors more often, were of a higher physical intensity, and more often were aimed at individuals than activities at regular day care facilities. Therefore, the green care farms’ environment may be more beneficial for elderly people with dementia than the regular day care facility environment.

Quantitative mapping of global land degradation using Earth observations

Land degradation is a global issue on par with climate change and loss of biodiversity, but its extent and severity are only roughly known and there is little detail on the immediate processes – let alone the drivers. Earth-observation methods enable monitoring of land degradation in a consistent, physical way and on a global scale by making use of vegetation productivity and/or loss as proxies. Most recent studies indicate a general greening trend, but improved data sets and analysis also show a combination of greening and browning trends. Statistically based linear trends average out these effects. Improved understanding may be expected from data-driven and process-modelling approaches: new models, model integration, enhanced statistical analysis and modern sensor imagery at medium spatial resolution should substantially improve the assessment of global land degradation.

Querying probabilistic land cover data using fuzzy set theory

Queries expressed in verbal language often involve a mixture of uncertainties in the outcomes of events that are governed by chance and in the meaning of linguistic terms. This study exemplifies how probability and fuzzy sets can work together to deal with such queries in the spatial domain. It involves site selection on the basis of accessibility (travel time) estimates and per-pixel probabilities of land cover change derived from remotely sensed imagery. Relationships between probabilities and fuzzy sets were established on the basis of a linguistic probability qualifier (high probability) and the expectation of a membership function defined on stochastic travel time. Fuzzy query processing was compared with crisp processing to emphasise the difference between grade and probability of membership. Fuzzy set theory is used to deal with the vague meanings of linguistic terms. The fuzzy query response contained more information than the crisp response, namely the degree to which individual locations matched the selection criteria. This illustrates the gain in expressive power provided by combining probability and fuzzy sets.Queries expressed in verbal language often involve a mixture of uncertainties in the outcomes of events that are governed by chance and in the meaning of linguistic terms. This study exemplifies how probability and fuzzy sets can work together to deal with such queries in the spatial domain. It involves site selection on the basis of accessibility (travel time) estimates and per-pixel probabilities of land cover change derived from remotely sensed imagery. Relationships between probabilities and fuzzy sets were established on the basis of a linguistic probability qualifier (high probability) and the expectation of a membership function defined on stochastic travel time. Fuzzy query processing was compared with crisp processing to emphasise the difference between grade and probability of membership. Fuzzy set theory is used to deal with the vague meanings of linguistic terms. The fuzzy query response contained more information than the crisp response, namely the degree to which individual locations matched...

Formalisation of soil-landscape knowledge through interactive hierarchical disaggregation

The soil-landscape model strongly depends on scarcely documented expert knowledge. In this paper a methodological framework is formulated that takes advantage of a GIS to interactively formalise soil-landscape knowledge using stepwise image interpretation and inductive learning of soil-landscape relationships. It examines topology to keep record of potential part of relationships between terrain objects denoting discontinuities in soil formation regimes. The relationships are used to visualise the pathway along which terrain objects have been derived. They can be applied in similar areas to facilitate image interpretation by restricting possible lower-level terrain objects. The framework may adopt different inductive methods to describe soil variation in relation to a terrain description. It is illustrated using stratification of soil texture data according to terrain object classes in a case study within the Guadalhorce basin in southern Spain. The degree of association between terrain object classes and particle size classes increased from 6% to 38% in three steps of image interpretation.