Fusion of different data layers, such as data from soil analysis and proximal soil sensing, is es... more Fusion of different data layers, such as data from soil analysis and proximal soil sensing, is essential to improve assessment of spatial variation in soil and yield. On-line visible and near infrared (Vis–NIR) spectroscopy have been proved to provide high resolution information about spatial variability of key soil properties. Multivariate geo-statistics tools were successfully implemented for the delineation of management zones (MZs) for precision application of crop inputs. This research was conducted in a 18 ha field to delineate MZs, using a multi-source data set, which consisted of eight laboratory measured soil variables (pH, available phosphorus (P), cation exchange capacity, total nitrogen (TN), total carbon (TC), exchangeable potassium (K), sand, silt) and four on-line collected Vis–NIR spectra-based predicted soil variables (pH, P, K and moisture content). The latter set of data was predicted using the partial least squares regression (PLSR) technique. The quality of the calibration models was evaluated by cross-validation. Multi-collocated cokriging was applied to the soil and spectral data set to produce thematic spatial maps, whereas multi-collocated factor cokriging was applied to delineate MZ. The Vis–NIR predicted K was chosen as the exhaustive variable, because it was the most correlated with the soil variables. A yield map of barley was interpolated by means of the inverse distance weighting method and was then classified into 3 iso-frequency classes (low, medium and high). To assess the productivity potential of the different zones of the field, spatial association between MZs and yield classes was calculated. Results showed that the prediction performance of PLSR calibration models for pH, P, MC and K were of excellent to moderate quality. The geostatistical model revealed good performance. The estimates of the first regionalised factor produced three MZs of equal size in the studied
Castrignanò, A., Buttafuoco, G., Comolli, R., & Ballabio, C. (2006). Error propagation an... more Castrignanò, A., Buttafuoco, G., Comolli, R., & Ballabio, C. (2006). Error propagation analysis of DEM-based slope and aspect. In Proceedings of the Second Global Workshop on Digital Soil Mapping for Regions and Countries with Sparse Soil Data Infrastructures, Rio de Janeiro, ...
The new high-resolution images from the satellites as IKONOS, SPOT5, Quickbird2 give us the oppor... more The new high-resolution images from the satellites as IKONOS, SPOT5, Quickbird2 give us the opportunity to map ground features, which were not detectable in the past, by using medium resolution remote sensed data (LANDSAT). More accurate and reliable maps of land cover can then be produced. However, classification procedure with these images is more complex than with the medium resolution remote sensing data for two main reasons: firstly, because of their exiguous number of spectral bands, secondly, owing to high spatial resolution, the assumption of pixel independence does not generally hold. It is then necessary to use new spectral classifiers taking into account also proximal information. In this view, it is necessary to combine both "spectral" and "spatial" features to optimise land use classification. Standard supervised classification techniques, so-called "per-pixel" classifiers, use only spectral information of remote sensing image, whereas negl...
The knowledge of hydraulic properties of soil is necessary in many environmental applications and... more The knowledge of hydraulic properties of soil is necessary in many environmental applications and land planning. These properties, however, are difficult to determine and often they demand high labour costs, for which the tendency is to estimate them on the base of other more easily measurable or already available soil data. The level of detail reached using this method is not always satisfactory for some applications to basin scale, where variables to measure the morphologic property of the landscape are required. This study is proposed to characterize the spatial distribution of the water retention of a soil on wide scale using data relative to the physical, topographical and chemical characteristics of the soil within a model based approach.
Sampling scheme is the major factor influencing the efficiency and costs of a survey. Moreover, i... more Sampling scheme is the major factor influencing the efficiency and costs of a survey. Moreover, in designing sampling scheme, earlier observations and knowledge on the area can provide valuable information.
The aim of this study is to describe a method to optimize the spatial sampling scheme, taking physics constraints and preliminary information into account. The method is based upon a spatial simulated annealing algorithm. Spatial sampling schemes can be optimised for minimal kriging variance or distance between observations. Three case studies are presented in different pedoclimatic conditions (mountain, plane, hill) to locate 100 samples.
In Val Chiavenna (mountain, SO) minimal distance between observations criterion was used. Lithology, aerial photographs and two pedological profiles were preliminary information. In Lodi (plane) preliminary information was soil use and 158 soil samples. The optimization process was carried out in two steps: 50 samples were located with the minimal distance between observations criterion while additional 50 samples were located with the variance kriging criterion. In Mustigarufi (hill, CL) 50 samples were located with the minimal distance between observations criterion without take preliminary information into account. Additional 50 samples were located with the minimal distance between observations criterion and soil electrical conductivity variability, 6 pedological profiles and the earlier 50 observations as preliminary information.
Area-wide integrated pest management requires an understanding of insect population dynamics and ... more Area-wide integrated pest management requires an understanding of insect population dynamics and definition of suitable techniques to quantify spatio-temporal variability to make better pest management decisions. However, the viability of area-wide integrated pest management has often been questioned because of the high monitoring costs. The present study aimed to: (i) analyse the spatial and temporal dynamics of the olive fruit fly over a large olive growing area (Ormylia, Greece), and (ii) define a methodology to determine monitoring zones to optimize the monitoring effort over space and time in area-wide integrated pest management programmes. Data from an olive fruit fly monitoring network based on McPhail traps were utilized. The multi-variate spatial (elevation) and temporal (6 periods) data of olive fruit fly population density were analysed by principal component analysis, co-kriging and factor kriging to produce thematic maps and to delineate monitoring zones. Olive fruit fly density was spatially correlated from 200 to 4 000 m. The spatial pattern changed over the monitoring season. Areas with high density of olive fruit flies shifted from high altitudes in summer to lower altitudes towards autumn. Three recommended levels of monitoring intensity were defined, thus delineating
Abstract: Vineyards vary substantially in the quantity and quality of grapes they produce. The st... more Abstract: Vineyards vary substantially in the quantity and quality of grapes they produce. The study was undertaken in a commercial "Semidano" vineyard block (0.6 ha) in the municipality of Mogoro (Sardinia isle, Italy) during the vintages of 2008, 2009 and 2010. A total of 106 plants were sampled and georeferenced. To assess the joint spatial and temporal variation of the vine properties, a multivariate geostatistics technique was applied, called factor cokriging, which aims at decomposing the overall variance in a restricted number of regionalised scale-dependent factors. The thematic maps of the vineyard properties and the ones of the factors show a large variability on both space and time. All the measurements of spatial agreement reveal a lack of temporal stability of the variation patterns over the years.
A study was carried out in order to investigate the spatial relationships between durum wheat yie... more A study was carried out in order to investigate the spatial relationships between durum wheat yield components and soil properties in a field in Viterbo (Central Italy). Soil properties, plant development and biomass, LAI and Normalized Difference Vegetation Index (NDVI) were measured following a grid sampling scheme. Yield components were assessed at harvest for the same points. Factor Kriging Analysis (FKA) was applied in order to clarify the effect of environmental constraints on yield components.
A study was carried out to investigate the usefulness of multispectral and hyperspectral satellit... more A study was carried out to investigate the usefulness of multispectral and hyperspectral satellite information for the estimation of soil properties of agronomic importance such as soil texture and organic matter (SOM) in cultivated fields by comparing different estimation procedures. Images acquired from the Advanced Land Imager (ALI) and Hyperion sensors on board the EO-1 satellite were used, in combination with ground-sampling data from an agricultural field in central Italy, to evaluate the advantage of taking into account the spatial correlation between pixels. For this purpose, partial least squares regression (PLSR), ordinary least square (OLS) regression, regression with correlated errors (restricted maximum likelihood; REML) and ordinary kriging (OK) were compared through leave-one-out cross-validation. In order to predict soil variables by different models, the predictors of OLS and REML regressions were obtained from principal component analysis (PCA), PLSR and the minimum noise fraction (MNF) transformations of spectral data on bare soil or vegetation images. The PLSR did not provide satisfactory results in terms of root mean square error (RMSE) and ratio of performance to interquartile range (RPIQ) statistics, even with hyperspectral data, mainly because of the poor signal to noise ratio (SNR) of the Hyperion sensor. The estimation accuracy increased by using the MNF method in combination with a linear mixed effect model. A multivariate approach was sometimes better than univariate ordinary kriging (OK), demonstrating the value of including Hyperion bare soil or vegetation data in the estimation procedure. Hyperspectral data provided better results than multispectral data for clay, sand and especially for SOM estimation, highlighting the value of high-resolution spectral data for soil-related applications.
Soil survey is generally time-consuming, labour-intensive and costly. Optimization of sampling sc... more Soil survey is generally time-consuming, labour-intensive and costly. Optimization of sampling scheme allows one to reduce the number of sampling points without decreasing or even increasing the accuracy of investigated attribute. Maps of bulk soil electrical conductivity (ECa) recorded with EMI sensors could be effectively used to direct soil sampling design for characterizing spatial variability of soil moisture. A protocol, using a field-scale bulk ECa survey, has been applied to an agricultural field in Apulia region (south-eastern Italy). Continuous spatial simulated annealing was used as a method to optimize spatial soil sampling scheme taking into account sampling constraints, field boundaries and preliminary observations. Three optimization criteria were used: the first criterion (MMSD) optimizes the spreading of the point observations over the entire field by minimizing the expectation of the distance between an arbitrarily chosen point and its nearest observation, the seco...
The implementation of the EU Water Framework Directive (WFD) in Italy, produced a number of natio... more The implementation of the EU Water Framework Directive (WFD) in Italy, produced a number of national guidelines and decrees for a sustainable water resources management and safeguarding. Within these guidelines, the design and realization of reliable groundwater monitoring well networks are prescribed and a methodology for classifying groundwater qualitative state is defined, fundamentally based on the crossed-evaluation of seven physical and chemical parameters. Consequently, the classification is achieved just in the monitored locations. Some problem can yet arise when punctual information needs to be spatialised. Substantially, spatializing a variable consists in estimating its value at an unmonitored location using, firstly, the neighbouring monitored values and, possibly, any other related, available information. This paper proposes a probabilistic approach, based on geostatistical techniques, for the assessment of groundwater contamination level according to quality classifica...
This study concerned an experimental truffle bed of downy oaks infected by the ectomycorrhizal fu... more This study concerned an experimental truffle bed of downy oaks infected by the ectomycorrhizal fungus Tuber melanosporum and planted in 1983. The presence of T. melanosporum creates rounded areas with little herbaceous cover, termed AbrulisB, where carpophores are found. Thê investigation was aimed at relating the occurrence and carpophore production of T. melanosporum to soil properties, i.e., organic matter, structure, aeration and fertility, expressed in terms of total organic C, aggregate size classes, DTPA-extractable Fe and Mn, and host plant height. Data were Ž . processed by multivariate geostatistical techniques. A linear model of coregionalization LMC Ž . Ž . Ž . including i a nugget effect, ii a short-range spherical structure with a range of 7 m and iii a long-range spherical structure with a range of 32 m, was fitted to the experimental direct and Ž . cross-variograms of the investigated properties. Factorial kriging analysis FKA was used to separate the sources of variation of the data according to the spatial scale at which they operate, and to summarize and map them in terms of spatial factors. An indicator approach was adopted to estimate and map the conditional probability of presence and fructification of T. melanosporum. The visual comparison between the spatial pattern of the long-range structure of the first regionalized factor with the probability map of finding brulis plus carpophore production revealed ) Corresponding author. A. Castrignano . 0016-7061r00r$ -see front matter q 2000 Elsevier Science B.V. All rights reserved.
S oil OC is a key property within the ecosystem services framework . It serves as an indicator of... more S oil OC is a key property within the ecosystem services framework . It serves as an indicator of the capacity of a soil to provide nutrients, water, and energy for biological activity. Efficient and accurate methods for assessing soil OC stocks and fluxes are needed to address concerns about soil quality and the global C budget . Soil C is characterized by high spatial variability and is sensitive to management, making C assessment and change detection more difficult. While this problem can be overcome by spatially Assessment of soil organic C (OC) spatial variability with proximal and remote sensing is complicated by interactions with soil constituents and moisture content. The objectives of this study were to (i) assess how well C could be predicted across a wide range of soils, (ii) determine how varying soil moisture impacted OC predictions, and (iii) determine the spectral wavelengths useful for assessing OC. Soil samples from the North American Proficiency Testing (NAPT) Program soil library were utilized in this study. Spectral reflectance (800-2200 nm) was measured with a spectrometer for air-dried soil and with 15, 20, and 25% soil moisture. Several pretreatment spectral reflectances were analyzed with partial least square (PLS) regression. The best pretreatment was the first derivative, explaining 88% of OC variability with air-dried samples and 70% at 15% soil moisture. Predictions for the samples of 20% (r 2 = 0.64) and 25% (r 2 = 0.63) soil moisture were as good as the combined datasets. These datasets included the 15 and 20% (r 2 = 0.56), 15 and 25% (r 2 = 0.64), 20 and 25% (r 2 = 0.59), and 15, 20, and 25% (r 2 = 0.64) soil moistures. The variable importance for prediction identified wavelengths associated with organic components including aromatics, aliphatics, and amides. Clustering the latent vectors suggested that PLS was able to distinguish samples with different clay and Fe content despite that they were not included as predictors. This study suggests that spectral OC prediction with varying soil moisture content (i.e., between 15 and 25% moisture) is of acceptable quality (i.e., r 2 ³ 0.56) even across a range of soils from the United States. These findings have important implications for estimating OC with proximal and remote sensing techniques.
The main objectives of the project were to collect soil and crop data in order to: 1) calibrate a... more The main objectives of the project were to collect soil and crop data in order to: 1) calibrate a simulation model of crop growth and production and 2) implement a data fusion method aimed at integration of remote sensing data with ground-based data. The lack of granted projects obliged us to restrict the surveyed area to a field of 3 ha. Figure 1: Google photo of the experimental farm of the Research Unit for Cropping Systems in Dry Environments (CRA-SCA) Site Description The interest of our study is focused on "Capitanata area", a plain of about 4000 km 2 located in the northern part of Apulia Region (south-eastern Italy). This area is characterized by farms with average size up to 20 ha, highly productive soils cultivated under intensive and irrigated regime. Winter durum
Salinization is one of the most serious problems confronting sustainable agriculture in semi-arid... more Salinization is one of the most serious problems confronting sustainable agriculture in semi-arid and arid regions. Accurate mapping of soil salinization and the associated risk represent a fundamental step in planning agricultural and remediation activities. Geostatistical analysis is very useful for soil quality assessment because it makes it possible to determine the spatial relationships between selected variables and to produce synthetic maps of spatial variation. The main objective of this paper was to map the soil salinization risk in the Delia-Nivolelli alluvial basin (south-western Sicily, southern Italy), using multivariate geostatistical techniques and a set of topographical, physical and soil hydraulic properties. Elevation data were collected from existing topographic maps and analysed preliminarily to improve the estimate precision of sparsely sampled primary variables. For interpolation multi-collocated cokriging was applied to the dataset, including textural and hydraulic properties and electrical conductivity measurements carried out on 128 collected soil samples, using elevation data as auxiliary variable. Spatial dependence among elevation and physical soil properties was explored with factorial kriging analysis (FKA) that could isolate and display the sources of variation acting at different spatial scales. FKA isolated significant regionalised factors which give a concise description of the complex soil physical variability at the different selected spatial scales. These factors mapped, allowed the delineation of zones at different salinisation risk to be managed separately to control and prevent salinization risk. The proposed methodology could be a valid support for land use and soil remediation planning at regional scale.
Fusion of different data layers, such as data from soil analysis and proximal soil sensing, is es... more Fusion of different data layers, such as data from soil analysis and proximal soil sensing, is essential to improve assessment of spatial variation in soil and yield. On-line visible and near infrared (Vis–NIR) spectroscopy have been proved to provide high resolution information about spatial variability of key soil properties. Multivariate geo-statistics tools were successfully implemented for the delineation of management zones (MZs) for precision application of crop inputs. This research was conducted in a 18 ha field to delineate MZs, using a multi-source data set, which consisted of eight laboratory measured soil variables (pH, available phosphorus (P), cation exchange capacity, total nitrogen (TN), total carbon (TC), exchangeable potassium (K), sand, silt) and four on-line collected Vis–NIR spectra-based predicted soil variables (pH, P, K and moisture content). The latter set of data was predicted using the partial least squares regression (PLSR) technique. The quality of the calibration models was evaluated by cross-validation. Multi-collocated cokriging was applied to the soil and spectral data set to produce thematic spatial maps, whereas multi-collocated factor cokriging was applied to delineate MZ. The Vis–NIR predicted K was chosen as the exhaustive variable, because it was the most correlated with the soil variables. A yield map of barley was interpolated by means of the inverse distance weighting method and was then classified into 3 iso-frequency classes (low, medium and high). To assess the productivity potential of the different zones of the field, spatial association between MZs and yield classes was calculated. Results showed that the prediction performance of PLSR calibration models for pH, P, MC and K were of excellent to moderate quality. The geostatistical model revealed good performance. The estimates of the first regionalised factor produced three MZs of equal size in the studied
Castrignanò, A., Buttafuoco, G., Comolli, R., & Ballabio, C. (2006). Error propagation an... more Castrignanò, A., Buttafuoco, G., Comolli, R., & Ballabio, C. (2006). Error propagation analysis of DEM-based slope and aspect. In Proceedings of the Second Global Workshop on Digital Soil Mapping for Regions and Countries with Sparse Soil Data Infrastructures, Rio de Janeiro, ...
The new high-resolution images from the satellites as IKONOS, SPOT5, Quickbird2 give us the oppor... more The new high-resolution images from the satellites as IKONOS, SPOT5, Quickbird2 give us the opportunity to map ground features, which were not detectable in the past, by using medium resolution remote sensed data (LANDSAT). More accurate and reliable maps of land cover can then be produced. However, classification procedure with these images is more complex than with the medium resolution remote sensing data for two main reasons: firstly, because of their exiguous number of spectral bands, secondly, owing to high spatial resolution, the assumption of pixel independence does not generally hold. It is then necessary to use new spectral classifiers taking into account also proximal information. In this view, it is necessary to combine both "spectral" and "spatial" features to optimise land use classification. Standard supervised classification techniques, so-called "per-pixel" classifiers, use only spectral information of remote sensing image, whereas negl...
The knowledge of hydraulic properties of soil is necessary in many environmental applications and... more The knowledge of hydraulic properties of soil is necessary in many environmental applications and land planning. These properties, however, are difficult to determine and often they demand high labour costs, for which the tendency is to estimate them on the base of other more easily measurable or already available soil data. The level of detail reached using this method is not always satisfactory for some applications to basin scale, where variables to measure the morphologic property of the landscape are required. This study is proposed to characterize the spatial distribution of the water retention of a soil on wide scale using data relative to the physical, topographical and chemical characteristics of the soil within a model based approach.
Sampling scheme is the major factor influencing the efficiency and costs of a survey. Moreover, i... more Sampling scheme is the major factor influencing the efficiency and costs of a survey. Moreover, in designing sampling scheme, earlier observations and knowledge on the area can provide valuable information.
The aim of this study is to describe a method to optimize the spatial sampling scheme, taking physics constraints and preliminary information into account. The method is based upon a spatial simulated annealing algorithm. Spatial sampling schemes can be optimised for minimal kriging variance or distance between observations. Three case studies are presented in different pedoclimatic conditions (mountain, plane, hill) to locate 100 samples.
In Val Chiavenna (mountain, SO) minimal distance between observations criterion was used. Lithology, aerial photographs and two pedological profiles were preliminary information. In Lodi (plane) preliminary information was soil use and 158 soil samples. The optimization process was carried out in two steps: 50 samples were located with the minimal distance between observations criterion while additional 50 samples were located with the variance kriging criterion. In Mustigarufi (hill, CL) 50 samples were located with the minimal distance between observations criterion without take preliminary information into account. Additional 50 samples were located with the minimal distance between observations criterion and soil electrical conductivity variability, 6 pedological profiles and the earlier 50 observations as preliminary information.
Area-wide integrated pest management requires an understanding of insect population dynamics and ... more Area-wide integrated pest management requires an understanding of insect population dynamics and definition of suitable techniques to quantify spatio-temporal variability to make better pest management decisions. However, the viability of area-wide integrated pest management has often been questioned because of the high monitoring costs. The present study aimed to: (i) analyse the spatial and temporal dynamics of the olive fruit fly over a large olive growing area (Ormylia, Greece), and (ii) define a methodology to determine monitoring zones to optimize the monitoring effort over space and time in area-wide integrated pest management programmes. Data from an olive fruit fly monitoring network based on McPhail traps were utilized. The multi-variate spatial (elevation) and temporal (6 periods) data of olive fruit fly population density were analysed by principal component analysis, co-kriging and factor kriging to produce thematic maps and to delineate monitoring zones. Olive fruit fly density was spatially correlated from 200 to 4 000 m. The spatial pattern changed over the monitoring season. Areas with high density of olive fruit flies shifted from high altitudes in summer to lower altitudes towards autumn. Three recommended levels of monitoring intensity were defined, thus delineating
Abstract: Vineyards vary substantially in the quantity and quality of grapes they produce. The st... more Abstract: Vineyards vary substantially in the quantity and quality of grapes they produce. The study was undertaken in a commercial "Semidano" vineyard block (0.6 ha) in the municipality of Mogoro (Sardinia isle, Italy) during the vintages of 2008, 2009 and 2010. A total of 106 plants were sampled and georeferenced. To assess the joint spatial and temporal variation of the vine properties, a multivariate geostatistics technique was applied, called factor cokriging, which aims at decomposing the overall variance in a restricted number of regionalised scale-dependent factors. The thematic maps of the vineyard properties and the ones of the factors show a large variability on both space and time. All the measurements of spatial agreement reveal a lack of temporal stability of the variation patterns over the years.
A study was carried out in order to investigate the spatial relationships between durum wheat yie... more A study was carried out in order to investigate the spatial relationships between durum wheat yield components and soil properties in a field in Viterbo (Central Italy). Soil properties, plant development and biomass, LAI and Normalized Difference Vegetation Index (NDVI) were measured following a grid sampling scheme. Yield components were assessed at harvest for the same points. Factor Kriging Analysis (FKA) was applied in order to clarify the effect of environmental constraints on yield components.
A study was carried out to investigate the usefulness of multispectral and hyperspectral satellit... more A study was carried out to investigate the usefulness of multispectral and hyperspectral satellite information for the estimation of soil properties of agronomic importance such as soil texture and organic matter (SOM) in cultivated fields by comparing different estimation procedures. Images acquired from the Advanced Land Imager (ALI) and Hyperion sensors on board the EO-1 satellite were used, in combination with ground-sampling data from an agricultural field in central Italy, to evaluate the advantage of taking into account the spatial correlation between pixels. For this purpose, partial least squares regression (PLSR), ordinary least square (OLS) regression, regression with correlated errors (restricted maximum likelihood; REML) and ordinary kriging (OK) were compared through leave-one-out cross-validation. In order to predict soil variables by different models, the predictors of OLS and REML regressions were obtained from principal component analysis (PCA), PLSR and the minimum noise fraction (MNF) transformations of spectral data on bare soil or vegetation images. The PLSR did not provide satisfactory results in terms of root mean square error (RMSE) and ratio of performance to interquartile range (RPIQ) statistics, even with hyperspectral data, mainly because of the poor signal to noise ratio (SNR) of the Hyperion sensor. The estimation accuracy increased by using the MNF method in combination with a linear mixed effect model. A multivariate approach was sometimes better than univariate ordinary kriging (OK), demonstrating the value of including Hyperion bare soil or vegetation data in the estimation procedure. Hyperspectral data provided better results than multispectral data for clay, sand and especially for SOM estimation, highlighting the value of high-resolution spectral data for soil-related applications.
Soil survey is generally time-consuming, labour-intensive and costly. Optimization of sampling sc... more Soil survey is generally time-consuming, labour-intensive and costly. Optimization of sampling scheme allows one to reduce the number of sampling points without decreasing or even increasing the accuracy of investigated attribute. Maps of bulk soil electrical conductivity (ECa) recorded with EMI sensors could be effectively used to direct soil sampling design for characterizing spatial variability of soil moisture. A protocol, using a field-scale bulk ECa survey, has been applied to an agricultural field in Apulia region (south-eastern Italy). Continuous spatial simulated annealing was used as a method to optimize spatial soil sampling scheme taking into account sampling constraints, field boundaries and preliminary observations. Three optimization criteria were used: the first criterion (MMSD) optimizes the spreading of the point observations over the entire field by minimizing the expectation of the distance between an arbitrarily chosen point and its nearest observation, the seco...
The implementation of the EU Water Framework Directive (WFD) in Italy, produced a number of natio... more The implementation of the EU Water Framework Directive (WFD) in Italy, produced a number of national guidelines and decrees for a sustainable water resources management and safeguarding. Within these guidelines, the design and realization of reliable groundwater monitoring well networks are prescribed and a methodology for classifying groundwater qualitative state is defined, fundamentally based on the crossed-evaluation of seven physical and chemical parameters. Consequently, the classification is achieved just in the monitored locations. Some problem can yet arise when punctual information needs to be spatialised. Substantially, spatializing a variable consists in estimating its value at an unmonitored location using, firstly, the neighbouring monitored values and, possibly, any other related, available information. This paper proposes a probabilistic approach, based on geostatistical techniques, for the assessment of groundwater contamination level according to quality classifica...
This study concerned an experimental truffle bed of downy oaks infected by the ectomycorrhizal fu... more This study concerned an experimental truffle bed of downy oaks infected by the ectomycorrhizal fungus Tuber melanosporum and planted in 1983. The presence of T. melanosporum creates rounded areas with little herbaceous cover, termed AbrulisB, where carpophores are found. Thê investigation was aimed at relating the occurrence and carpophore production of T. melanosporum to soil properties, i.e., organic matter, structure, aeration and fertility, expressed in terms of total organic C, aggregate size classes, DTPA-extractable Fe and Mn, and host plant height. Data were Ž . processed by multivariate geostatistical techniques. A linear model of coregionalization LMC Ž . Ž . Ž . including i a nugget effect, ii a short-range spherical structure with a range of 7 m and iii a long-range spherical structure with a range of 32 m, was fitted to the experimental direct and Ž . cross-variograms of the investigated properties. Factorial kriging analysis FKA was used to separate the sources of variation of the data according to the spatial scale at which they operate, and to summarize and map them in terms of spatial factors. An indicator approach was adopted to estimate and map the conditional probability of presence and fructification of T. melanosporum. The visual comparison between the spatial pattern of the long-range structure of the first regionalized factor with the probability map of finding brulis plus carpophore production revealed ) Corresponding author. A. Castrignano . 0016-7061r00r$ -see front matter q 2000 Elsevier Science B.V. All rights reserved.
S oil OC is a key property within the ecosystem services framework . It serves as an indicator of... more S oil OC is a key property within the ecosystem services framework . It serves as an indicator of the capacity of a soil to provide nutrients, water, and energy for biological activity. Efficient and accurate methods for assessing soil OC stocks and fluxes are needed to address concerns about soil quality and the global C budget . Soil C is characterized by high spatial variability and is sensitive to management, making C assessment and change detection more difficult. While this problem can be overcome by spatially Assessment of soil organic C (OC) spatial variability with proximal and remote sensing is complicated by interactions with soil constituents and moisture content. The objectives of this study were to (i) assess how well C could be predicted across a wide range of soils, (ii) determine how varying soil moisture impacted OC predictions, and (iii) determine the spectral wavelengths useful for assessing OC. Soil samples from the North American Proficiency Testing (NAPT) Program soil library were utilized in this study. Spectral reflectance (800-2200 nm) was measured with a spectrometer for air-dried soil and with 15, 20, and 25% soil moisture. Several pretreatment spectral reflectances were analyzed with partial least square (PLS) regression. The best pretreatment was the first derivative, explaining 88% of OC variability with air-dried samples and 70% at 15% soil moisture. Predictions for the samples of 20% (r 2 = 0.64) and 25% (r 2 = 0.63) soil moisture were as good as the combined datasets. These datasets included the 15 and 20% (r 2 = 0.56), 15 and 25% (r 2 = 0.64), 20 and 25% (r 2 = 0.59), and 15, 20, and 25% (r 2 = 0.64) soil moistures. The variable importance for prediction identified wavelengths associated with organic components including aromatics, aliphatics, and amides. Clustering the latent vectors suggested that PLS was able to distinguish samples with different clay and Fe content despite that they were not included as predictors. This study suggests that spectral OC prediction with varying soil moisture content (i.e., between 15 and 25% moisture) is of acceptable quality (i.e., r 2 ³ 0.56) even across a range of soils from the United States. These findings have important implications for estimating OC with proximal and remote sensing techniques.
The main objectives of the project were to collect soil and crop data in order to: 1) calibrate a... more The main objectives of the project were to collect soil and crop data in order to: 1) calibrate a simulation model of crop growth and production and 2) implement a data fusion method aimed at integration of remote sensing data with ground-based data. The lack of granted projects obliged us to restrict the surveyed area to a field of 3 ha. Figure 1: Google photo of the experimental farm of the Research Unit for Cropping Systems in Dry Environments (CRA-SCA) Site Description The interest of our study is focused on "Capitanata area", a plain of about 4000 km 2 located in the northern part of Apulia Region (south-eastern Italy). This area is characterized by farms with average size up to 20 ha, highly productive soils cultivated under intensive and irrigated regime. Winter durum
Salinization is one of the most serious problems confronting sustainable agriculture in semi-arid... more Salinization is one of the most serious problems confronting sustainable agriculture in semi-arid and arid regions. Accurate mapping of soil salinization and the associated risk represent a fundamental step in planning agricultural and remediation activities. Geostatistical analysis is very useful for soil quality assessment because it makes it possible to determine the spatial relationships between selected variables and to produce synthetic maps of spatial variation. The main objective of this paper was to map the soil salinization risk in the Delia-Nivolelli alluvial basin (south-western Sicily, southern Italy), using multivariate geostatistical techniques and a set of topographical, physical and soil hydraulic properties. Elevation data were collected from existing topographic maps and analysed preliminarily to improve the estimate precision of sparsely sampled primary variables. For interpolation multi-collocated cokriging was applied to the dataset, including textural and hydraulic properties and electrical conductivity measurements carried out on 128 collected soil samples, using elevation data as auxiliary variable. Spatial dependence among elevation and physical soil properties was explored with factorial kriging analysis (FKA) that could isolate and display the sources of variation acting at different spatial scales. FKA isolated significant regionalised factors which give a concise description of the complex soil physical variability at the different selected spatial scales. These factors mapped, allowed the delineation of zones at different salinisation risk to be managed separately to control and prevent salinization risk. The proposed methodology could be a valid support for land use and soil remediation planning at regional scale.
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Papers by A. Castrignanò
The aim of this study is to describe a method to optimize the spatial sampling scheme, taking physics constraints and preliminary information into account. The method is based upon a spatial simulated annealing algorithm. Spatial sampling schemes can be optimised for minimal kriging variance or distance between observations. Three case studies are presented in different pedoclimatic conditions (mountain, plane, hill) to locate 100 samples.
In Val Chiavenna (mountain, SO) minimal distance between observations criterion was used. Lithology, aerial photographs and two pedological profiles were preliminary information. In Lodi (plane) preliminary information was soil use and 158 soil samples. The optimization process was carried out in two steps: 50 samples were located with the minimal distance between observations criterion while additional 50 samples were located with the variance kriging criterion. In Mustigarufi (hill, CL) 50 samples were located with the minimal distance between observations criterion without take preliminary information into account. Additional 50 samples were located with the minimal distance between observations criterion and soil electrical conductivity variability, 6 pedological profiles and the earlier 50 observations as preliminary information.
The aim of this study is to describe a method to optimize the spatial sampling scheme, taking physics constraints and preliminary information into account. The method is based upon a spatial simulated annealing algorithm. Spatial sampling schemes can be optimised for minimal kriging variance or distance between observations. Three case studies are presented in different pedoclimatic conditions (mountain, plane, hill) to locate 100 samples.
In Val Chiavenna (mountain, SO) minimal distance between observations criterion was used. Lithology, aerial photographs and two pedological profiles were preliminary information. In Lodi (plane) preliminary information was soil use and 158 soil samples. The optimization process was carried out in two steps: 50 samples were located with the minimal distance between observations criterion while additional 50 samples were located with the variance kriging criterion. In Mustigarufi (hill, CL) 50 samples were located with the minimal distance between observations criterion without take preliminary information into account. Additional 50 samples were located with the minimal distance between observations criterion and soil electrical conductivity variability, 6 pedological profiles and the earlier 50 observations as preliminary information.