Spatial Sampling Scheme Research Articles

Biogeography of Deep-Sea Benthic Bacteria at Regional Scale (LTER HAUSGARTEN, Fram Strait, Arctic)

Knowledge on spatial scales of the distribution of deep-sea life is still sparse, but highly relevant to the understanding of dispersal, habitat ranges and ecological processes. We examined regional spatial distribution patterns of the benthic bacterial community and covarying environmental parameters such as water depth, biomass and energy availability at the Arctic Long-Term Ecological Research (LTER) site HAUSGARTEN (Eastern Fram Strait). Samples from 13 stations were retrieved from a bathymetric (1,284–3,535 m water depth, 54 km in length) and a latitudinal transect (∼ 2,500 m water depth; 123 km in length). 454 massively parallel tag sequencing (MPTS) and automated ribosomal intergenic spacer analysis (ARISA) were combined to describe both abundant and rare types shaping the bacterial community. This spatial sampling scheme allowed detection of up to 99% of the estimated richness on phylum and class levels. At the resolution of operational taxonomic units (97% sequence identity; OTU3%) only 36% of the Chao1 estimated richness was recovered, indicating a high diversity, mostly due to rare types (62% of all OTU3%). Accordingly, a high turnover of the bacterial community was also observed between any two sampling stations (average replacement of 79% of OTU3%), yet no direct correlation with spatial distance was observed within the region. Bacterial community composition and structure differed significantly with increasing water depth along the bathymetric transect. The relative sequence abundance of Verrucomicrobia and Planctomycetes decreased significantly with water depth, and that of Deferribacteres increased. Energy availability, estimated from phytodetrital pigment concentrations in the sediments, partly explained the variation in community structure. Overall, this study indicates a high proportion of unique bacterial types on relatively small spatial scales (tens of kilometers), and supports the sampling design of the LTER site HAUSGARTEN to study bacterial community shifts in this rapidly changing area of the world’s oceans.

Assessing reference dataset representativeness through confidence metrics based on information density

Land cover maps obtained from classification of remotely sensed imagery provide valuable information in numerous environmental monitoring and modeling tasks. However, many uncertainties and errors can directly or indirectly affect the quality of derived maps. This work focuses on one key aspect of the supervised classification process of remotely sensed imagery: the quality of the reference dataset used to develop a classifier. More specifically, the representative power of the reference dataset is assessed by contrasting it with the full dataset (e.g. entire image) needing classification. Our method is applicable in several ways: training or testing datasets (extracted from the reference dataset) can be compared with the full dataset. The proposed method moves beyond spatial sampling schemes (e.g. grid, cluster) and operates in the multidimensional feature space (e.g. spectral bands) and uses spatial statistics to compare information density of data to be classified with data used in the reference process. The working hypothesis is that higher information density, not in general but with respect to the entire classified image, expresses higher confidence in obtained results. Presented experiments establish a close link between confidence metrics and classification accuracy for a variety of image classifiers namely maximum likelihood, decision tree, Backpropagation Neural Network and Support Vector Machine. A sensitivity analysis demonstrates that spatially-continuous reference datasets (e.g. a square window) have the potential to provide similar classification confidence as typically-used spatially-random datasets. This is an important finding considering the higher acquisition costs for randomly distributed datasets. Furthermore, the method produces confidence maps that allow spatially-explicit comparison of confidence metrics within a given image for identification of over- and under-represented image portions. The current method is presented for individual image classification but, with sufficient evaluation from the remote sensing community it has the potential to become a standard for reference dataset reporting and thus allowing users to assess representativeness of reference datasets in a consistent manner across different classification tasks.

OPTIMIZATION OF DECISION-MAKING FOR SPATIAL SAMPLING IN THE NORTH CHINA PLAIN, BASED ON REMOTE-SENSING A PRIORI KNOWLEDGE

Abstract. In this paper, the MODIS remote sensing data, featured with low-cost, high-timely and moderate/low spatial resolutions, in the North China Plain (NCP) as a study region were firstly used to carry out mixed-pixel spectral decomposition to extract an useful regionalized indicator parameter (RIP) (i.e., an available ratio, that is, fraction/percentage, of winter wheat planting area in each pixel as a regionalized indicator variable (RIV) of spatial sampling) from the initial selected indicators. Then, the RIV values were spatially analyzed, and the spatial structure characteristics (i.e., spatial correlation and variation) of the NCP were achieved, which were further processed to obtain the scalefitting, valid a priori knowledge or information of spatial sampling. Subsequently, founded upon an idea of rationally integrating probability-based and model-based sampling techniques and effectively utilizing the obtained a priori knowledge or information, the spatial sampling models and design schemes and their optimization and optimal selection were developed, as is a scientific basis of improving and optimizing the existing spatial sampling schemes of large-scale cropland remote sensing monitoring. Additionally, by the adaptive analysis and decision strategy the optimal local spatial prediction and gridded system of extrapolation results were able to excellently implement an adaptive report pattern of spatial sampling in accordance with report-covering units in order to satisfy the actual needs of sampling surveys.

OPTIMIZATION OF DECISION-MAKING FOR SPATIAL SAMPLING IN THE NORTH CHINA PLAIN, BASED ON REMOTE-SENSING A PRIORI KNOWLEDGE

Abstract. In this paper, the MODIS remote sensing data, featured with low-cost, high-timely and moderate/low spatial resolutions, in the North China Plain (NCP) as a study region were firstly used to carry out mixed-pixel spectral decomposition to extract an useful regionalized indicator parameter (RIP) (i.e., an available ratio, that is, fraction/percentage, of winter wheat planting area in each pixel as a regionalized indicator variable (RIV) of spatial sampling) from the initial selected indicators. Then, the RIV values were spatially analyzed, and the spatial structure characteristics (i.e., spatial correlation and variation) of the NCP were achieved, which were further processed to obtain the scalefitting, valid a priori knowledge or information of spatial sampling. Subsequently, founded upon an idea of rationally integrating probability-based and model-based sampling techniques and effectively utilizing the obtained a priori knowledge or information, the spatial sampling models and design schemes and their optimization and optimal selection were developed, as is a scientific basis of improving and optimizing the existing spatial sampling schemes of large-scale cropland remote sensing monitoring. Additionally, by the adaptive analysis and decision strategy the optimal local spatial prediction and gridded system of extrapolation results were able to excellently implement an adaptive report pattern of spatial sampling in accordance with report-covering units in order to satisfy the actual needs of sampling surveys.

Inference of seed bank parameters in two wild tomato species using ecological and genetic data

Seed and egg dormancy is a prevalent life-history trait in plants and invertebrates whose storage effect buffers against environmental variability, modulates species extinction in fragmented habitats, and increases genetic variation. Experimental evidence for reliable differences in dormancy over evolutionary scales (e.g., differences in seed banks between sister species) is scarce because complex ecological experiments in the field are needed to measure them. To cope with these difficulties, we developed an approximate Bayesian computation (ABC) framework that integrates ecological information on population census sizes in the priors of the parameters, along with a coalescent model accounting simultaneously for seed banks and spatial genetic structuring of populations. We collected SNP data at seven nuclear loci (over 300 SNPs) using a combination of three spatial sampling schemes: population, pooled, and species-wide samples. We provide evidence for the existence of a seed bank in two wild tomato species (Solanum chilense and Solanum peruvianum) found in western South America. Although accounting for uncertainties in ecological data, we infer for each species (i) the past demography and (ii) ecological parameters, such as the germination rate, migration rates, and minimum number of demes in the metapopulation. The inferred difference in germination rate between the two species may reflect divergent seed dormancy adaptations, in agreement with previous population genetic analyses and the ecology of these two sister species: Seeds spend, on average, a shorter time in the soil in the specialist species (S. chilense) than in the generalist species (S. peruvianum).

Maximum likelihood estimation of the dynamic coregionalization model with heterotopic data

AbstractThe information content of multivariable spatio‐temporal data depends on the underlying spatial sampling scheme. The most informative case is represented by the isotopic configuration where all variables are measured at all sites. The opposite case is the completely heterotopic case where different variables are observed only at different locations. A well known approach to multivariate spatio‐temporal modelling is based on the linear coregionalization model (LCM).In this paper, the maximum likelihood estimation of the heterotopic spatio‐temporal model with spatial LCM components and temporal dynamics is developed. In particular, the computation of the estimates is based on the EM algorithm and two solutions are proposed: one is based on the more cumbersome exact maximization of the a posteriori expected log likelihood and the other is an approximate closed‐form solution. Their properties are assessed in terms of bias and efficiency through an example of air quality dinamic mapping using satellite data and a Monte Carlo simulation campaign based on a large data set. Copyright © 2011 John Wiley & Sons, Ltd.

Efficient collection of training data for sub-pixel land cover classification using neural networks

Artificial neural networks (ANNs) are a popular class of techniques for performing soft classifications of satellite images. They have successfully been applied for estimating crop areas through sub-pixel classification of medium to low resolution images. Before a network can be used for classification and estimation, however, it has to be trained. The collection of the reference area fractions needed to train an ANN is often both time-consuming and expensive. This study focuses on strategies for decreasing the efforts needed to collect the necessary reference data, without compromising the accuracy of the resulting area estimates. Two aspects were studied: the spatial sampling scheme (i) and the possibility for reusing trained networks in multiple consecutive seasons (ii). Belgium was chosen as the study area because of the vast amount of reference data available. Time series of monthly NDVI composites for both SPOT-VGT and MODIS were used as the network inputs. The results showed that accurate regional crop area estimation (R2>80%) is possible using only 1% of the entire area for network training, provided that the training samples used are representative for the land use variability present in the study area. Limiting the training samples to a specific subset of the population, either geographically or thematically, significantly decreased the accuracy of the estimates. The results also indicate that the use of ANNs trained with data from one season to estimate area fractions in another season is not to be recommended. The interannual variability observed in the endmembers’ spectral signatures underlines the importance of using up-to-date training samples. It can thus be concluded that the representativeness of the training samples, both regarding the spatial and the temporal aspects, is an important issue in crop area estimation using ANNs that should not easily be ignored.

Golden-Ratio Sampling for Scanning Circular Microphone Arrays

Circular microphone arrays have been recently studied for music recordings, beamforming, and sound-field analysis. Scanning circular arrays that use a single microphone to acquire measurements at a range of spatial sampling positions have been employed to gather an entire array data. Uniform sampling along the circle is typically used, but this scheme may not allow experimental flexibility. If, for any reason, the experiment was not completed in full, the data gathered may not be sufficient to perform the required processing. This paper presents an alternative spatial sampling scheme for scanning circular microphone arrays, which is more robust against uncertainty in the duration of the experiment. The proposed spatial sampling method is based on fixed-angle steps along the circle defined by the golden-ratio. Theoretical analysis of the golden-ratio sampling method is presented, supported by simulation examples, showing that experimental flexibility is achieved, but at the expanse of some reduction in numerical robustness.

Effect of sampling strategy on estimation of fine-scale spatial genetic structure in Androsace tapete (Primulaceae), an alpine plant endemic to Qinghai-Tibetan Plateau

The fine-scale spatial genetic structure (SGS) of alpine plants is receiving increasing attention, from which seed and pollen dispersal can be inferred. However, estimation of SGS may depend strongly on the sampling strategy, including the sample size and spatial sampling scheme. Here, we examined the effects of sample size and three spatial schemes, simple-random, line-transect, and random-cluster sampling, on the estimation of SGS in Androsace tapete, an alpine cushion plant endemic to Qinghai-Tibetan Plateau. Using both real data and simulated data of dominant molecular markers, we show that: (i) SGS is highly sensitive to sample strategy especially when the sample size is small (e.g., below 100); (ii) the commonly used SGS parameter (the intercept of the autocorrelogram) is more susceptible to sample error than a newly developed Sp statistic; and (iii) the random-cluster scheme is susceptible to obvious bias in parameter estimation even when the sample size is relatively large (e.g., above 200). Overall, the line-transect scheme is recommendable, in that it performs slightly better than the simple-random scheme in parameter estimation and is more efficient to encompass broad spatial scales. The consistency between simulated data and real data implies that these findings might hold true in other alpine plants and more species should be examined in future work.

A computer program to simulate multilocus genotype data with spatially autocorrelated allele frequencies

Many models for inference of population genetic parameters are based on the assumption that the data set at hand consists of groups displaying within-group Hardy-Weinberg equilibrium at individual loci and linkage equilibrium between loci. This assumption is commonly violated by the presence of within-group spatial structure arising from nonrandom mating of individuals due to isolation by distance (IBD). This paper proposes a model and simulation method implemented in a computer program to flexibly simulate data displaying such patterns. The program permits displaying of smooth spatial variations of allele frequencies due to IBD and more abrupt variations due to presence of strong barriers to gene flow. It is useful in assessing performance of various statistical inference methods and in designing spatial sampling schemes. This is shown by a simulation study aimed at assessing the extent to which IBD patterns affect accuracy of cluster inferences performed in models assuming panmixia. The program is also used to study the effects of spatial sampling scheme (e.g. sampling individuals in clumps or uniformly across the spatial domain). The accuracy of such inferences is assessed in terms of number of inferred populations, assignment of individuals to populations and location of borders between populations. The effect of spatial sampling was weak while the effect of IBD may be substantial, leading to the inference of spurious populations, especially when IBD was strong with respect to the size of the sampling domain. The model and program are new and have been embedded in the R package Geneland, for user convenience and compliance with existing data formats.

Innovations Diffusion: A Spatial Sampling Scheme for Distributed Estimation and Detection

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> We consider a wireless network with distributed processing capabilities for estimation or detection applications. Due to limited communication resources, the network selects only a subset of sensor measurements for estimation or detection as long as the resulting fidelity is tolerable. We present a distributed sampling scheme based on the concept of <emphasis emphasistype="italic">innovations diffusion</emphasis> to select the sensor nodes. In the proposed scheme, sensor selection is accomplished through local communication and signal processing. In order to conserve energy and prolong system lifetime, the proposed algorithm selects a nearly minimum number of active sensors to ensure a desired fidelity for each working period. Extensive simulations illustrate the effectiveness of the proposed sampling scheme. </para>

Major ion chemistry and dissolved inorganic carbon cycling in a human-disturbed mountainous river (the Luodingjiang River) of the Zhujiang (Pearl River), China

Major ion chemistry and dissolved inorganic carbon system (DIC, mainly HCO 3 − and gaseous CO 2) in the Luodingjiang River, a mountainous tributary of the Zhujiang (Pearl River), China, were examined based on a seasonal and spatial sampling scheme in 2005. The diverse distribution of lithology and anthropogenic impacts in the river basin provided the basic idea to assess the effects of lithology vs. human activities on water chemistry and carbon biogeochemistry in river systems. Major ions showed great spatial variations, with higher concentrations of total dissolved solids (TDS) and DIC in the regions with carbonate rocks and clastic sedimentary rocks, while lower in the regions with metamorphic sandstones and schists as well as granites. pCO 2 at all sampling sites was oversaturated in June, ranging with a factor from 1.6 to 18.8 of the atmospheric concentration, reflecting the enhanced contribution from baseflow and interflow influx as well as in situ oxidation of organic matter. However, in April and December, undersaturated pCO 2 was found in some shallow, clean rivers in the upstream regions. δ 13C of DIC has a narrow range from − 9.07 to − 13.59‰, which was more depleted in the regions with metamorphic rocks and granites than in the carbonate regions. Seasonally, it was slightly more depleted in the dry season (December) than in the wet season (June). The results suggested that lithological variability had a dominant control on spatial variations of water chemistry and carbon geochemistry in river systems. Besides, anthropogenic activities, such as agricultural and urban activities and in-stream damming, as well as river physical properties, such as water depth and transparency, also indicated their impacts. The seasonal variations likely reflected the changes of hydrological regime, as well as metabolic processes in the river.

A comparative analysis of different spatial sampling schemes: Modelling of SSRB data

Low spatial resolution satellite sensors provide information over relatively large targets with typical pixel resolutions of hundreds of km2. However, the spatial scales of ground measurements are usually much smaller. Such differences in spatial scales makes the interpretation of comparisons between quantities derived from low resolution sensors and ground measurements particularly difficult. It also highlights the importance of developing appropriate sampling strategies when designing ground campaigns for validation studies of low resolution sensors. We make use of statistical modelling of high resolution surface shortwave radiation budget (SSRB) data to look into this problem. A spatial model that describes the SSRB over a selected region is proposed, and the impact of different sampling schemes in the performance of the model is analysed. Both systematic and random sampling schemes can efficiently represent the full observations set. **Current affiliation: Meteorological Office, Hadley Centre for Climate Prediction and Research, UK

Real-Time PCR-Based Monitoring of DNA Pools in the Tri-Trophic Interaction Between Norway Spruce, the Rust Thekopsora areolata, and an Opportunistic Ascomycetous Phomopsis sp.

The difficulty in subculturing biotrophic fungi complicates etiological studies related to the associated plant diseases. By employing internal transcribed spacer rDNA-targeted quantitative real-time polymerase chain reaction, we now show that the heteroecious rust Thekopsora areolata, commonly associated in natural conditions to sapling shoots and cones of Norway spruce and leaves of wild bird cherry, frequently infects nursery-grown seedlings of the conifer. A spatial sampling scheme was used to investigate seedlings and saplings of Norway spruce showing phloem necrosis: the highest concentration of DNA of T. areolata was recorded in the area with necrotic phloem. The separate analysis of bark and wood tissues suggested that the initial spread of the rust to healthy tissues neighboring the infection site takes place in the bark. A Phomopsis species found to coexist with T. areolata in several seedlings showed very high DNA levels in the upper part of the lesion, and even in the visually healthy proximal tissues above the lesions, which indicates that the ascomycete, most probably a secondary invader following primary infection by T. areolata, has a latent stage during early host colonization. We hypothesize that this hemibiotrophic mode of infection contributes to the successful coexistence of Phomopsis with a biotrophic rust.

Optimised Spatial Sampling Scheme for Soil Electriclal Conductivity Based on Variance Quad-Tree (VQT) Method

The acquisition of precise soil data representative of the entire survey area, is a critical issue for many treatments such as irrigation or fertilization in precision agriculture. The aim of this study was to investigate the spatial variability of soil bulk electrical conductivity (EC b) in a coastal saline field and design an optimized spatial sampling scheme of EC b based on a sampling design algorithm, the variance quad-tree (VQT) method. Soil EC b data were collected from the field at 20 m interval in a regular grid scheme. The smooth contour map of the whole field was obtained by ordinary kriging interpolation, VQT algorithm was then used to split the smooth contour map into strata of different number desired, the sampling locations can be selected within each stratum in subsequent sampling. The result indicated that the probability of choosing representative sampling sites was increased significantly by using VQT method with the sampling number being greatly reduced compared to grid sampling design while retaining the same prediction accuracy. The advantage of the VQT method is that this scheme samples sparsely in fields where the spatial variability is relatively uniform and more intensive where the variability is large. Thus the sampling efficiency can be improved, hence facilitate an assessment methodology that can be applied in a rapid, practical and cost-effective manner.

Kernel regression estimation for continuous spatial processes

We investigate here a kernel estimate of the spatial regression function r(x) = E(Yu | Xu = x), x ∈ ℝd, of a stationary multidimensional spatial process { Zu = (Xu, Yu), u ∈ ℝN}. The weak and strong consistency of the estimate is shown under sufficient conditions on the mixing coefficients and the bandwidth, when the process is observed over a rectangular domain of ℝN. Special attention is paid to achieve optimal and suroptimal strong rates of convergence. It is also shown that this suroptimal rate is preserved by using a suitable spatial sampling scheme.

The variance quadtree algorithm: Use for spatial sampling design

Spatial sampling schemes are mainly developed to determine sampling locations that can cover the variation of environmental properties in the area of interest. Here we proposed the variance quadtree algorithm for sampling in an area with prior information represented as ancillary or secondary environmental data, and the covariance structure of the ancillary variable is non-stationary. The algorithm is based on the idea of a quadtree decomposition, where an area is successively divided into strata so each stratum has more-or-less equal variation. An observation point is then placed inside each stratum. This scheme samples sparsely in relatively uniform areas and more intensively where variation is large. It samples in the feature space and also takes into consideration the spread in the geographic space. We describe the algorithm, its software implementation, and present some examples of applications.

Robust Spatial-Sampling Controller Design for Banding Reduction in Electrophotographic Process

An improved controller design and implementation technique for electrophotographic process (EP) was proposed. The new controller aimed at solving two additional issues for generic EP platforms, i.e., reducing position-dependent or spatially periodic disturbances, and reducing system sensitivity to manufacturing variations in EP engine and consumables. To handle spatially periodic disturbances, a digital repetitive controller was synthesized and implemented using a spatial-sampling scheme. The result is a control algorithm that takes into account the variation of the nominal operating speed. Second, system variations due to manufacturing variations as well as consumable changes were incorporated into the design of a two degree of freedom robust controller. The controller is optimal in the sense that it minimizes the size of the sensitivity function from a set of disturbance signals to a set of measurable signals critical to print quality, e.g., photoconductor drum velocity or scan line spacing. A suitable trade-off between system performance and robustness to system modeling uncertainties was considered in the synthesis and optimization formulation. The effectiveness of the proposed controller design and implementation technique was numerically and experimentally verified. Significant reduction in visible banding was seen in printed images and was verified by reflectance measurement.

Optimal field sampling for targeting minerals using hyperspectral data

This paper presents a statistical method for deriving optimal spatial sampling schemes. It focuses on ground verification of minerals derived from hyperspectral data. Spectral angle mapper (SAM) and spectral feature fitting (SFF) classification techniques were applied to obtain rule mineral images. Each pixel in these rule images represents the similarity between the corresponding pixel in the hyperspectral image to a reference spectrum. The rule images provide weights that are utilized in objective functions of the sampling schemes which are optimized through a process of simulated annealing. A HyMAP 126-channel airborne hyperspectral data acquired in 2003 over the Rodalquilar area in Spain serves as an application to target those pixels with the highest likelihood of occurrence of a specific mineral and as a collection the location of these sampling points selected represent the distribution of that particular mineral. In this area, alunite being a predominant mineral in the alteration zones was chosen as the target mineral. Three weight functions are defined to intensively sample areas where a high probability and abundance of alunite occurs. Weight function I uses binary weights derived from the SAM classification image, leading to an even distribution of sampling points over the region of interest. Weight function II uses scaled weights derived from the SAM rule image. Sample points are arranged more intensely in areas of abundance of alunite. Weight function III combines information from several different rule image classifications. Sampling points are distributed more intensely in regions of high probable alunite as classified by both SAM and SFF, thus representing the purest of pixels. This method leads to an efficient distribution of sample points, on the basis of a user-defined objective.

Analysis and design of spherical microphone arrays

Spherical microphone arrays have been recently studied for sound-field recordings, beamforming, and sound-field analysis which use spherical harmonics in the design. Although the microphone arrays and the associated algorithms were presented, no comprehensive theoretical analysis of performance was provided. This work presents a spherical-harmonics-based design and analysis framework for spherical microphone arrays. In particular, alternative spatial sampling schemes for the positioning of microphones on a sphere are presented, and the errors introduced by finite number of microphones, spatial aliasing, inaccuracies in microphone positioning, and measurement noise are investigated both theoretically and by using simulations. The analysis framework can also provide a useful guide for the design and analysis of more general spherical microphone arrays which do not use spherical harmonics explicitly.