Intrinsic Spatial Variability Research Articles

Applying a Python script to predict the geotechnical properties of the Nasiriyah soil

Soil is a natural material that suffers from intrinsic spatial variability resulting from natural factors and their influence on the soil. It became controversial and debated how to estimate the characteristic value of soils to obtain a reliable geotechnical design with low cost and less effort. Usually, foundations are not built on the same site as the screening; investigations are carried out to excavate a little at essential sites. In this paper (423), test wells were collected in the study area to be obtained and tabulated in Excel. The kriging statistics is applied using a python script to predict the values of geotechnical site properties and reliability of the method in estimating spatially varying soil properties values based on measurement data and prior knowledge. The program implements probabilistic kriging statistics and predicts the desired value by entering the coordinates of the locations whose properties you want to know and based on the previously prepared Excel file of known points, coordinates, and property values. The program will be used in two soil sites in the city of Nasiriyah to predict its properties. These points were selected from the examination of soil investigation reports to determine the reliability and accuracy of the program in predicting values. To get more reliable probability values using the kriging method and python scripts. A huge database of prepared and analyzed engineering soil properties has been created based on field investigation reports for projects in Nasiriyah.

Grape Cluster Detection Using UAV Photogrammetric Point Clouds as a Low-Cost Tool for Yield Forecasting in Vineyards.

Yield prediction is crucial for the management of harvest and scheduling wine production operations. Traditional yield prediction methods rely on manual sampling and are time-consuming, making it difficult to handle the intrinsic spatial variability of vineyards. There have been significant advances in automatic yield estimation in vineyards from on-ground imagery, but terrestrial platforms have some limitations since they can cause soil compaction and have problems on sloping and ploughed land. The analysis of photogrammetric point clouds generated with unmanned aerial vehicles (UAV) imagery has shown its potential in the characterization of woody crops, and the point color analysis has been used for the detection of flowers in almond trees. For these reasons, the main objective of this work was to develop an unsupervised and automated workflow for detection of grape clusters in red grapevine varieties using UAV photogrammetric point clouds and color indices. As leaf occlusion is recognized as a major challenge in fruit detection, the influence of partial leaf removal in the accuracy of the workflow was assessed. UAV flights were performed over two commercial vineyards with different grape varieties in 2019 and 2020, and the photogrammetric point clouds generated from these flights were analyzed using an automatic and unsupervised algorithm developed using free software. The proposed methodology achieved R2 values higher than 0.75 between the harvest weight and the projected area of the points classified as grapes in vines when partial two-sided removal treatment, and an R2 of 0.82 was achieved in one of the datasets for vines with untouched full canopy. The accuracy achieved in grape detection opens the door to yield prediction in red grape vineyards. This would allow the creation of yield estimation maps that will ease the implementation of precision viticulture practices. To the authors’ knowledge, this is the first time that UAV photogrammetric point clouds have been used for grape clusters detection.

Analysing flash flood risk perception through a geostatistical approach in the village of Navaluenga, Central Spain

AbstractFlash floods are unexpected events, which evolve rapidly and affect relatively small areas. The short time available for minimising risks requires preparedness and active response. In this context of risk management, the flood risk perception of the local population is the first step towards achieving resilience of people and communities. Although flood risk perception has intrinsic spatial variability, few previous studies take this into account. This paper explores the spatial variability of flash flood risk perception in the village of Navaluenga in Central Spain, using nonparametric and multivariate geostatistical tools. How local people perceived the flash flood risk to their homes was assessed interviewing a representative sample. Results show that considering these flash flood risk related spatial variables enhances people's psychological interpretation of risk perception: the perception of flash flood risk in a short time event is congruent with those spatial variables. These findings determine priority areas for future risk communication plans. They could also be extrapolated to other urban areas with a similar hydrographic configuration, when there is a flood hazard from a main river and potentially flooded minor water courses, which are even closer to houses but are not considered dangerous by the local population.

Modelling Geotechnical Heterogeneities Using Geostatistical Simulation and Finite Differences Analysis

Modelling a rock mass in an accurate and realistic way allows researchers to reduce the uncertainty associated with its characterisation and reproduce the intrinsic spatial variability and heterogeneities present in the rock mass. However, there is often a lack of a structured methodology to characterise heterogeneous rock masses using geotechnical information available from the prospection phase. This paper presents a characterization methodology based on the geostatistical simulation of geotechnical variables and the application of a scenario reduction technique aimed at selecting a reduced number of realisations able to statistically represent a large set of realisations obtained by the geostatistical approach. This type of information is useful for a further rock mass behaviour analysis. The methodology is applied to a gold deposit with the goal of understanding its main differences to traditional approaches based on a deterministic modelling of the rock mass. The obtained results show the suitability of the methodology to characterise heterogeneous rock masses, since there were considerable differences between the results of the proposed methodology, mainly concerning the theoretical tunnel displacements, and the ones obtained with a traditional approach.

Assessing the impact of the Asian mussel Arcuatula senhousia in the recently invaded Oristano Lagoon-Gulf system (W Sardinia, Italy)

In the marine environment, the introduction and spread of non-indigenous mussels may cause major modifications to native assemblages and alter the trophic flow within the food web. We analysed the impacts of the recently sighted Asian date mussel Arcuatula (=Musculista) senhousia on sediment features, native macrozoobenthic assemblages and the δ13C and δ15N values of dominant macrozoobenthic taxa in the Oristano Lagoon-Gulf system (western Sardinia, Italy). Results showed that the amount of variation generated by the occurrence of Arcuatula senhousia was lower than the intrinsic spatial variability in sediment features, macrozoobenthic assemblages and the δ13C values of dominant deposit feeders (Hediste diversicolor, Cirriphormia tentaculata, Haminoea navicula and Cyclope neritea) of this system. In addition, δ13C and δ15N values of A. senhousia were found to be similar to those of co-occurring suspension feeders Cerastoderma glaucum, Ruditapes decussatus and Scrobicularia plana, indicating exploitation of common food resources. The overall lack of effects of A. senhousia may be dependent on the moderate densities encountered in our study area (<1000 individuals m−2). We suggest that the low rate of new arrivals, owing to limited shellfish farming and maritime activities in the area, and unfavourable environmental conditions of the lagoons especially in summer (e.g. anoxia) which erode mussel populations, likely prevent A. senhousia from entering its expansion phase and impacting local benthic communities.

An approach for delineating homogeneous zones by using multi-sensor data

Spatial heterogeneity in soil properties has an impact on crop response. There is a growing demand for rapid and non-invasive acquisition of fine-scale information on soil and plant variation for site-specific management. Proximal sensing (Electromagnetic Induction (EMI), Ground Penetrating Radar (GPR), hyperspectral spectroscopy (HS)) and remote sensing (RS) can complement direct sampling. However, sensor data fusion techniques, jointly analysing data from different sources, are still being developed.The objective of this work was to define a multivariate and multi-sensor approach by combining EMI, GPR, RS and HS data, without any previous weighing, in order to differentiate an 1.5-ha arable field into homogenous zones.The multi-sensor data were split into four groups: 1) bulk electrical conductivity (EC) from EMI data, 2) amplitude of GPR signal data, 3) the first principal components relating to five bands (green, yellow, red, rededge, near-infrared (NIR) PCs) of hyperspectral reflectance data and 4) the vegetation indices (NDVI, NDRE and NIR/Green) calculated from the remote sensing image. The data of each group were separately analysed and interpolated at the nodes of a same grid by using cokriging or kriging. To obtain spatially contiguous clusters, a combined approach was used, based on multivariate geostatistics and a non‐parametric density function algorithm of clustering, applied to the overall multi-sensor data set of the estimates.The full approach allowed to identify three homogenous areas. In particular cluster 1, in the NW part of the field, with the lowest values of bulk electrical conductivity and GPR amplitude, and the highest red PC values. The other two clusters were delineated in the SE part of the field, with the highest values of green, yellow, red edge and NIR PCs for cluster 2, and the highest values of bulk electrical conductivity and vegetation indices for cluster 3. The delineation might be related to the intrinsic spatial variability of soil and the health status of plants and be used to produce a prescription map for site-specific management.

Turks and Caicos Islands 2006 coral reef assessment: Large-scale environmental and ecological interactions and their management implications

La salud de los arrecifes coralinos de las Islas Turcas y Caicos (uno de los mejores sistemas arrecifales en la región) es vital para su bienestar económico y ambiental. Los métodos tradicionales de evaluación proporcionan relativamente poca información porque cubren un área muy pequeña para caracterizar con precisión complejos sistemas arrecifales, no pueden captar integralmente pautas a gran escala y además la alta variabilidad espacial intrínseca de los arrecifes inhibe la capacidad de los datos obtenidos en pequeñas áreas para identificar cambios o causas. Las investigaciones espacialmente extensivas suministran más información, sobre áreas mayores y en menos tiempo que las intensivas, revelando gradientes a gran escala que los métodos intensivos son inherentemente incapaces de identificar y brindan un nuevo paradigma que pudiera usarse ampliamente para el manejo ambiental, con mayor efectividad económica. A principios de junio de 2006 se llevaron a cabo investigaciones extensivas, evaluándose 26 parámetros ecológicos y ambientales en 47 sitios arrecifales a lo largo de las Islas. Se puso especial énfasis en identificar las diferentes zonas de algas a lo largo de gradientes ambientales. Los datos obtenidos están siendo utilizados para desarrollar estrategias nacionales de manejo de la calidad de agua y de restauración de corales.

Discussion of "Bearing capacity prediction of spatially random c – ϕ soils"

Popescu The writer would like to commend the authors for a particularly interesting analytical and numerical study of the effect of spatial variability of soil properties on the bearing capacity. It is generally acknowledged that the problem of accounting for the stochastic spatial variation of soil properties is a particularly complex one, both from the datacollection point of view and from the methodological point of view. Generally accepted techniques have not yet been established to accomplish these tasks. To initiate a constructive discussion on this subject, some comments are provided here on the selected range of coefficients of variation (COVs) of soil properties and on the methodology suggested by the authors to simulate the stochastic soil properties. Range of coefficients of variation Confidence in design soil properties, and therefore in predicted structural response, is affected by a series of uncertainties arising from several sources, such as intrinsic spatial variability (i.e., actual variation of soil properties from one location to another), measurement errors, and insufficient field information. Those sources of uncertainty are often considered together as one source of variability in the response. It was observed by the authors, and by other researchers, that the intrinsic spatial variability of soil properties affects not only the variability of the predicted structural response, but also the mechanical behaviour itself. As explained, for example, by Focht and Focht (2001), because of the presence of weaker zones in a soil deposit, “the actual failure surface can deviate from its theoretical position to pass through weaker material so that the average mobilized strength is less than the apparent average strength.” It appears that different types of uncertainties (insufficient information, measurement errors, etc., on the one hand, and actual spatial variability, on the other hand) affect the structural response in different manners, and they may have to be analyzed separately. It is the understanding of the writer that this was in fact the intention of the authors, namely to analyze the effects of intrinsic spatial variability alone. A number of researchers have provided information on ranges of COVs for spatial variability of different soil properties, as obtained from in situ soil investigations. In most cases, those ranges also include variability due to measurement errors. There is, however, a very comprehensive study in this respect (Phoon and Kulhawy 1999), mentioned also by the authors, in which approximate guidelines are provided for the COV of intrinsic spatial variability, separated from other sources of uncertainty. It is the writer’s opinion that, until more results become available, the guidelines provided by Phoon and Kulhawy (1999) are a good indication of the order of magnitude for the COV of soil variability. COVs as high as 5 (i.e., one order of magnitude larger than those usually reported in the literature) are assumed in the paper for the soil parameters. In justifying this range, the authors present a very interesting set of considerations related to soil variability at various scales, starting from the soil particle scale (of the order of micrometres) and going to the regional scale (of the order of kilometres). It is demonstrated that, within such limits, the range of COVs assumed in the paper is reasonable. The writer believes, however, that with respect to intrinsic soil spatial variability, the scales applicable here are bounded by the volume of soil involved in the bearing capacity failure and by the finite element mesh size. 367

Characterization of spatial statistics of distributed targets in SAR data

Abstract A method for the analysis of spatial statistics in multifrequency polarimetric Synthetic Aperture Radar (SAR) data is presented. The objective is to extract the intrinsic variability of the target by removing the variability from other sources. Three sources which contribute to the spatial variability in the returned power from a distributed target are modelled, they are (1) image speckle, (2) system noise, and (3) the intrinsic spatial variability of the target or texture. Speckle and system noise are modelled based on an understanding of the physics of the SAR imaging and processing systems. Texture is modelled as a random variable which modulates the mean returned power from a distributed target. An image model which accounts for all three sources of variability is presented. The presence of texture is shown to increase the image variance-to-mean square ratio and to introduce deviations of the image autocovariance function from the expected SAR system response. Two textural parameters, the standard deviation of texture and its autocovariance coefficient, are examined. This statistical approach is illustrated using sea-ice SAR imagery acquired by the Jet Propulsion Laboratory three-frequency polarimetric airborne SAR. Textural modulation of the signal has been detected in the imagery. Results show that for different sea-ice types the spatial statistics seem to vary more across frequency than across polarization and the observed differences increase in magnitude with decreasing frequency. The results also suggest the potential of this approach for discrimination of various sea-ice types and open water in single frequency, single polarization SAR data. Correlation of the spatial statistics to field measurements will be important for the verification of these observations.

Textural Filtering For SAR Image Processing

Texture is an important feature which can be used in the interpretation of remotely sensed imagery. This paper presents exam- ples of the application of a new approach to the textural filtering and enhancing of digital images. Satisfactory results are obtained in pro- cessing images from both natural textures and airborne SAR scenes. Textural filtering of SAR images can be useful in improving the dis- crimination between lithologic units with different surface-roughness characteristics. One application example is discussed in which textural features show different discrimination performances before and after textural filtering. I. INTRODUCTION A digital image is usually characterized by two main aspects: Tone and texture. The image tone consists of background grey- level variations of the pixels throughout the entire image. The im- age texture represents the intrinsic spatial variability of neighbor- ing pixel values for each pixel within the image. It follows that methods of image analysis can be broadly divided into two cate- gories: The spectral one and the textural one. In spectral analysis the interest is in studying broad variations of the grey levels of the pixels (image tone) in mono-, or multispectral bands. The aim of textural analysis is to characterize spatially the grey-level relation- ships between the pixels of a neighborhood. Tone and texture are usually not independent in an image, so that when processing it we observe that one influences the other. The image tone characteris- tics can be separated from the texture, and these two features can be processed independently. This situation promises to be very use- ful in enhancing the texture perception on the one hand, and in improving the result of conventional texture analysis on the other. Digital filtering techniques are widely used in image processing and interpretation ( 11-(3). Based on the traditional concept of Fourier analysis, the three basic classical filters are the low-pass, the high-pass, and the band-pass. They are widely used in remote sensing for noise suppression, edge detection, smoothing, and en- hancement. However, these filters are unable to separate the tone and texture because in an image the two features affect each other. A new method of texture analysis has been recently proposed by He and Wang (4), (5), where a texture image can be described by the characteristics of its texture spectrum. The purpose of this pa- per is to design a textural filter in the texture spectrum domain to remove the regional intensity background variation, termed the tex- tural noise. The performance of the new textural filter on several of Brodatz's natural images (8) is discussed. In airborne SAR terms, the new filter has been shown to enhance the textural perception and improve the discrimination between different rock units.

Texture-based analysis techniques for the classification of radar images

Useful information for radar image interpretation and classification may be derived from a texture-based analysis, since their intrinsic spatial variability or texture is a valuable feature for discrimination among different regions. In the paper four different texture classification algorithms are described. The first two follow a structural approach to texture analysis and are based on the spectral texture properties detected from the Fourier spectrum. The last two are based on statistical texture properties, regarding texture as a two-dimensional random field defined by a suitable autoregressive model. These algorithms have been applied to meteorological images from a dual linear-polarisation radar and provided good results.