A. Stein

An overview of spatial sampling procedures and experimental design of spatial studies for ecosystem comparisons

Comparison of ecosystems and land use studies often require the use of non-classical statistics. This paper describes modern ways of approaching optimal sampling for ecological and environmental purposes. The first part of the paper deals with a description of different sampling procedures. A distinction is made between sampling surveys, optimal grid spacing and adaptive sampling. The second part of the manuscript uses a simulated example to illustrate the different sampling procedures. The third part contains an actual field study where various constraints were met that had to be incorporated in the lay out of the sampling scheme. Optimal statistical sampling procedures can be implemented and utilized to collect maximum information from limited resources.

Multivariate texture¿based segmentation of remotely sensed imagery for extraction of objects and their uncertainty

In this study, a segmentation procedure is proposed, based on grey‐level and multivariate texture to extract spatial objects from an image scene. Object uncertainty was quantified to identify transitions zones of objects with indeterminate boundaries. The Local Binary Pattern (LBP) operator, modelling texture, was integrated into a hierarchical splitting segmentation to identify homogeneous texture regions in an image. We proposed a multivariate extension of the standard univariate LBP operator to describe colour texture. The paper is illustrated with two case studies. The first considers an image with a composite of texture regions. The two LBP operators provided good segmentation results on both grey‐scale and colour textures, depicted by accuracy values of 96% and 98%, respectively. The second case study involved segmentation of coastal land cover objects from a multi‐spectral Compact Airborne Spectral Imager (CASI) image, of a coastal area in the UK. Segmentation based on the univariate LBP measure provided unsatisfactory segmentation results from a single CASI band (70% accuracy). A multivariate LBP‐based segmentation of three CASI bands improved segmentation results considerably (77% accuracy). Uncertainty values for object building blocks provided valuable information for identification of object transition zones. We conclude that the (multivariate) LBP texture model in combination with a hierarchical splitting segmentation framework is suitable for identifying objects and for quantifying their uncertainty.

Soil sampling strategies for spatial prediction by correlation with auxiliary maps

The paper evaluates spreading of observations in feature and geographical spaces as a key to sampling optimisation for spatial prediction by correlation with auxiliary maps. Although auxiliary data are commonly used for mapping soil variables, problems associated with the design of sampling strategies are rarely examined. When generalised least-squares estimation is used, the overall prediction error depends upon spreading of points in both feature and geographical space. Allocation of points uniformly over the feature space range proportionally to the distribution of predictor (equal range stratification, or ER design) is suggested as a prudent sampling strategy when the regression model between the soil and auxiliary variables is unknown. An existing 100-observation sample from a 50 by 50 km soil survey in central Croatia was used to illustrate these concepts. It was re-sampled to 25-point datasets using different experimental designs: ER and 2 response surface designs. The designs were compared for their performance in predicting soil organic matter from elevation (univariate example) using the overall prediction error as an evaluation criterion. The ER design gave overall prediction error similar to the minmax design, suggesting that it is a good compromise between accurate model estimation and minimisation of spatial autocorrelation of residuals. In addition, the ER design was extended to the multivariate case. Four predictors (elevation, temperature, wetness index, and NDVI) were transformed to standardised principal components. The sampling points were then assigned to the components in proportion to the variance explained by a principal component analysis and following the ER design. Since stratification of the feature space results in a large number of possible points in each cluster, the spreading in geographical space can also be maximised by selecting the best of several realisations.

Existential uncertainty of spatial objects segmented from satellite sensor imagery

This research addresses existential uncertainty of spatial objects derived from satellite sensor imagery. An image segmentation technique is applied at various values of splitting and merging thresholds. We test the hypothesis that objects occurring at many segmentation steps have less existential uncertainty than those occurring at only a few steps.

Prediction of base flows from basin characteristics: a case study from Zimbabwe

Abstract Abstract Base flows make up the flows of most rivers in Zimbabwe during the dry season. Prediction of base flows from basin characteristics is necessary for water resources planning of ungauged basins. Linear regression and artificial neural networks were used to predict the base flow index (BFI) from basin characteristics for 52 basins in Zimbabwe. Base flow index was positively related to mean annual precipitation (r = 0.71), basin slope (r = 0.76), and drainage density (r = 0.29), and negatively related to mean annual evapotranspiration (r = –0.74), and proportion of a basin with grasslands and wooded grasslands (r = –0.53). Differences in lithology did not significantly affect BFI. Linear regression and artificial neural networks were both suitable for predicting BFI values. The predicted BFI was used in turn to derive flow duration curves of the 52 basins and with R 2 being 0.89–0.99.

Spatial variability of soil pH and phosphorus in relation to soil run-off following slash-and-burn land clearing in Sumatra, Indonesia

Slash-and-burn land clearing on sloping land may lead to increased soil run-off following disappearance of the protective vegetative cover. In turn, soil run-off and redeposition affects soil fertility and spatial patterns of fertility parameters in a field. This study seeks to clarify the role of spatial patterns of post-burn dead biomass (necromass) in soil run-off and redeposition and their combined effect on spatial patterns in soil pH and resin-extractable P. The study is carried out on a post-productive rubber (Hevea brasiliensis) agroforest in Sumatra, Indonesia. Soils are classified as Dystric Fluvisols. After slash-and-burn of vegetation, the field was planted with rubber seedlings and rice (Oryza sativa). For comparison the adjacent rubber agroforest site was sampled. Soil run-off is expressed here as the quantity of downward moving soil that passed the specific location of a flow trap. Existing physical soil run-off barriers and crop performance were scored. Despite serious soil run-off from the steeper upper slopes little soil was actually lost because of the slope form of the field, presence of natural soil run-off barriers, and the planted crop. Spatial variability of soil pH decreased at the expense of small-scale, within-strata, variability mainly because of the patchy distribution of soil run-off barriers. Soil run-off, aggravated by slash-and-burn, did not result in development of a clear soil fertility gradient down slope. In areas of high soil run-off potential, clear burns should be avoided because soil run-off barriers like remnants of slash-and-burn and surface litter maintain the soil and its fertility.

Quantifying differences in biodiversity between a tropical forest area and a grassland area subject to traditional burning

Mosaics of natural forest and grassland tracts in sub-Saharan Africa provide differences in woody species biodiversity. These mosaics are of considerable interest as they are a major biodiversity bank. Their richness is felt to be threatened, for example by local burning. This study focuses on the impact of burning on biodiversity in the Budongo Forest Reserve in Uganda. Woody species at different development stages are compared between a forest stratum and the adjacent grassland stratum. Spatial variability of biodiversity indices is analysed within and between these strata, using statistical and geostatistical analysis methods. The forest stratum shows a higher woody species diversity on a per plot basis. With overall species diversity calculations, however, the grassland stratum has a higher woody species variability than the forest. For the long-term preservation of woody species diversity in this landscape, it is necessary to reconsider and adjust current management practices to maintain (fire) disturbances.

Effects of interpolation and data resolution on methane emission estimates from rice paddies

Rice paddies are an important source of the greenhouse gas methane (CH4). Global methane emission estimates are highly uncertain and do not account for effects of interpolation or data resolution errors. This paper determines such scaling effects for the influence of soil properties on calculated CH4 emissions for the island of Java, Indonesia. The effects of different interpolation techniques, variograms and neighbor optimization were tested for soil properties by cross-validation. Interpolated organic carbon values were not significantly different from the original soil samples, in contrast to interpolated soil iron contents. Interpolation of soil properties coupled to a process-based model on CH4emissions led to a significant change in distribution of calculated CH4 emissions, i.e., the variance decreased. Effects of data resolution were examined by interpolating soil properties to derive data at different data resolutions and then calculating CH4 emissions by applying the process-based model at these resolutions. The soil properties did not differ significantly for different data resolutions, in contrast to calculated CH4 emissions. These scaling effects were caused by the combination of interpolation and a non-linear model. Real scaling effects may even be larger because small-scale variability was not accounted for. Scaling effects, including those caused by small-scale variability, have to be considered to achieve unbiased and less uncertain global CH4emissions estimates from rice paddies.

Use of the Bradley-Terry model to quantify association in remotely sensed images

Thematic maps prepared from remotely sensed images require a statistical accuracy assessment. For this purpose, the /spl kappa/-statistic is often used. This statistic does not distinguish between whether one unit is classified as another, or vice versa. In this paper, the Bradley-Terry (BT) model is applied for accuracy assessment. This model compares categories pairwise. The probability of one class over another class is estimated as well as the expected values of class pixels. The study is illustrated with an Advanced Spaceborne Thermal Emission and Reflection Radiometer image from the Netherlands, to which a maximum-likelihood classification with the Euclidean distance is applied. An error matrix is generated using an IKONOS image from the same area as ground truth. It is shown to which degree the BT model extends the /spl kappa/-statistic. A comparison with the Mahalanobis distance is made. Standardization is carried out to overcome problems emerging from the fact that a common BT model does not include the number of correctly classified pixels. The study shows how the BT model serves as an alternative to the usual /spl kappa/-statistic.

Spatial distribution of populations of solitarious adult desert locust (Schistocerca gregaria Forsk.) on the coastal plain of Sudan

Abstract  1 Densities of solitarious adult desert locusts were measured on regular grids of up to 126 sample sites in the southern part of the coastal plain of Sudan during the winters of 1999/2000 and 2000/2001. Geostatistical procedures were used to characterize spatial dependence of locust density, to evaluate the possibility of estimating locust densities at unvisited sites, based on information obtained at surveyed sites, and to create density maps.