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John Triantafilis - One of the best experts on this subject based on the ideXlab platform.
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Mapping Soil Particle-Size Fractions Using Additive Log-Ratio (ALR) and Isometric Log-Ratio (ILR) Transformations and Proximally Sensed Ancillary Data
Clays and Clay Minerals, 2018Co-Authors: Muddassar Muzzamal, Rod Nielson, Michael Sefton, Jingyi Huang, John TriantafilisAbstract:Together, the three particle size fractions (PSFs) of clay, silt, and sand are the most fundamental soil properties because the relative abundance influences the physical, chemical, and biological activities in soil. Unfortunately, determining PSFs requires a laboratory method which is time-consuming. One way to add value is to use digital soil mapping, which relies on empirical models, such as multiple linear regression (MLR), to couple Ancillary Data to PSFs. This approach does not account for the special requirements of compositional Data. Here, Ancillary Data were coupled, via MLR modelling, to additive log-ratio (ALR) or isometric log-ratio (ILR) transformations of the PSFs to meet these requirements. These three approaches (MLR vs . ALR-MLR and ILR-MLR) were evaluated along with the use of different Ancillary Data that included proximally sensed gamma-ray spectrometry, electromagnetic induction, and elevation Data. In addition, how the prediction might be improved was examined using Ancillary Data that was measured on transects and was compared to Data interpolated from transects spaced far apart. Although the ALR-MLR approach did not produce significantly better results, it predicted soil PSFs that summed to 100 and had the advantage of interpreting the Ancillary Data relative to the original coordinates ( i.e. clay, silt, and sand). For the prediction of PSFs at various depths, all Ancillary Data were useful. Elevation and gamma-ray Data were slightly better for topsoil and elevation and electromagnetic (EM) Data were better for subsoil prediction. In addition, a smaller transect spacing (26 m) and number of samples (9–16) might be adopted for mapping soil PSFs and soil texture across the study field. The ALR-MLR approach can be applied elsewhere to map the spatial distribution of clay minerals.
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field level digital mapping of soil mineralogy using proximal and remote sensed Data
Soil Use and Management, 2017Co-Authors: G Nagra, Jingyi Huang, D Burkett, Colin R Ward, John TriantafilisAbstract:Primary (e.g., quartz) and secondary (clay) minerals are key factors determining the physical and chemical characteristics of soil. Understanding spatial distribution of minerals at the field scale would, therefore, be of potential benefit for soil management. However, current analysis requires time-consuming laboratory procedures and computational quantification analysis (e.g., SIROQUANT). Furthermore, mineral composition (e.g., quartz, kaolinite, illite and expandable clay minerals) must sum to 100. We aimed to add value to laboratory Data by developing multiple linear regression (MLR) relationships between mineralogy and Ancillary Data such as digital numbers (DNs) (i.e., Red [R], Green [G] and Blue [B]) acquired from remotely sensed air-photographs and soil apparent electrical conductivity (ECa – mS/m) measured from proximal sensing electromagnetic (EM) instruments (i.e., EM38 and EM31). To account for composition, we compare results from the MLR approach with those from additive log-ratio (ALR) transformation of mineralogy prior to MLR modelling. This approach together with various Ancillary Data and trend surface parameters (i.e., scaled Easting and Northing) has greater precision and less bias of prediction than the MLR approach using untransformed Data. Our approach also enables predictions to sum to 100. We conclude that the most useful Ancillary Data to predict the abundance of quartz, kaolinite and illite are B DNs and EM31, while expandable clays are best predicted with R DNs, EM38 and scaled Northing. The use of Ancillary Data to map mineralogical components combined with ALR-MLR is an effective approach, with resulting maps providing insights into soil and water management issues consistent with farmer experience.
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an error budget for soil salinity mapping using different Ancillary Data
Soil Research, 2015Co-Authors: Jingyi Huang, Ehsan Zare, R S Malik, John TriantafilisAbstract:Secondary soil salinisation occurs as a function of human interaction with the landscape. Increasing salinity is a major constraint to crop yield. The electrical conductivity of a saturated soil-paste extract (ECe, dS m–1) defines the level of salinity in soil. In order to manage salinity, farmers need to map its variation. However, ECe determination is time-consuming and expensive. Digital mapping of ECe is possible by using Ancillary Data such as easy-to-obtain digital elevation model, gamma-ray spectrometry and electromagnetic (EM) induction Data. In this paper, we used these Ancillary Data and empirical best linear unbiased prediction (E-BLUP) to make a digital map of ECe. In this regard, we found that elevation, radioelement of thorium (Th) and logEM38-v were the most statistically useful Ancillary Data. We also developed an error-budget procedure to quantify the relative contributions that model, input (for all the Ancillary Datasets), and combined and individual covariate (for each of the Ancillary Datasets) error made to the prediction error of our map of ECe. The error-budget procedure used ordinary kriging, E-BLUP and conditional simulation to produce numerous realisations of the Data and their underlying errors. Results show that the combined error of model error and input error was ~4.44 dS m–1. Compared with the standard deviation of observed soil ECe (3.61 dS m–1), the error was large. Of this error, most was attributable to the input error (1.38 dS m–1), which is larger than the model error (0.02 dS m–1). In terms of the input error, we determined that the larger standard deviation is attributable to the lack of Ancillary Data, namely the ECa in areas adjacent to the Darling River and on the aeolian dune where Data collection was difficult owing to dense native vegetation.
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mapping particle size fractions as a composition using additive log ratio transformation and Ancillary Data
Soil Science Society of America Journal, 2014Co-Authors: Jingyi Huang, R Subasinghe, John TriantafilisAbstract:During morphological description, one of the first properties assessed is soil texture. This is because it does not change significantly with time and it has the greatest impact on management (e.g., determining nutrient holding capacity). There is therefore an increasing need for high-resolution digital prediction of soil texture. Unfortunately, determining the three particle size fractions (PSFs) using laboratory methods is time consuming. To add value to limited soil Data, Ancillary Data coupled with spatial and nonspatial statistical methods can be used. However, the most commonly used technique, multiple linear regression (MLR) of individual PSFs, does not consider the special requirements of a regionalized composition. We coupled Ancillary Data via MLR modeling to an additive log-ratio (ALR) transformation of the PSF to meet these requirements. The Ancillary Data included digitized air photo (e.g., Red digital numbers [DN]) and electromagnetic (EM38 and EM31) Data. We found that for predicting clay at various depths, the EM38 and EM31 Data were most useful. This was similarly the case for sand, with Red DN and the trend surface of some value. We also compared how prediction might be improved by using EM Data measured on transects (which simulate measurements made on 1-m transects) with interpolation from transects spaced 24 m apart. The results indicate that the use of EM Data on a 1-m transect using ALR-MLR can improve precision by around 24% for clay, 3% for silt, and 17% for sand with regard to topsoil prediction. We also conclude that the ALR-MLR technique has the advantage of adhering to the special requirements of a composition, with predicted values non-negative and PSFs summing to unity (i.e., = 100%).
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digital soil mapping of compositional particle size fractions using proximal and remotely sensed Ancillary Data
Geophysics, 2012Co-Authors: S. Buchanan, John Triantafilis, Inakwu O A Odeh, R SubansingheAbstract:ABSTRACTThe soil particle-size fractions (PSFs) are one of the most important attributes to influence soil physical (e.g., soil hydraulic properties) and chemical (e.g., cation exchange) processes. There is an increasing need, therefore, for high-resolution digital prediction of PSFs to improve our ability to manage agricultural land. Consequently, use of Ancillary Data to make cheaper high-resolution predictions of soil properties is becoming popular. This approach is known as “digital soil mapping.” However, most commonly employed techniques (e.g., multiple linear regression or MLR) do not consider the special requirements of a regionalized composition, namely PSF; (1) should be nonnegative (2) should sum to a constant at each location, and (3) estimation should be constrained to produce an unbiased estimation, to avoid false interpretation. Previous studies have shown that the use of the additive log-ratio transformation (ALR) is an appropriate technique to meet the requirements of a composition. In th...
Ana P Barros - One of the best experts on this subject based on the ideXlab platform.
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downscaling of remotely sensed soil moisture with a modified fractal interpolation method using contraction mapping and Ancillary Data
Remote Sensing of Environment, 2002Co-Authors: Gwangseob Kim, Ana P BarrosAbstract:Abstract Previous work showed that remotely sensed soil moisture fields exhibit multiscaling and multifractal behavior varying with the scales of observations and hydrometeorological forcing (Remote Sens. Environ. 81 (2002) 1). Specifically, it was determined that this multiscaling behavior is consistent with the scaling of soil hydraulic properties and vegetation cover, while the multifractal behavior is associated with the temporal evolution of soil moisture fields. Here, we apply these findings by directly incorporating information on the spatial structure of soil texture and vegetation water content to the spatial interpolation of remotely sensed soil moisture Data. A downscaling model is presented which consists of a modified fractal interpolation method based on contraction mapping. This methodology is different from other fractal interpolation schemes because it generates unique fractal surfaces. It is different from other contraction mapping models because it includes spatially and temporally varying scaling functions as opposed to single-valued scaling factors. The scaling functions are linear combinations of the spatial distributions of Ancillary Data. The model is demonstrated by downscaling soil moisture fields from 10 to 1 km resolution using remote-sensing Data from the Southern Great Plains 1997 (SGP'97) field experiment.
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space time characterization of soil moisture from passive microwave remotely sensed imagery and Ancillary Data
Remote Sensing of Environment, 2002Co-Authors: Gwangseob Kim, Ana P BarrosAbstract:Abstract The statistical structure of soil moisture fields was examined using large-scale images (40×250 km) obtained during the Southern Great Plains 1997 (SGP'97) hydrology experiment. In particular, empirical scaling analysis was conducted to investigate the linkages between the spatial and temporal variability of soil moisture, and landscape characteristics including terrain, soils, and vegetation. The results show that the soil moisture fields exhibit multiscaling and multifractal behavior varying with the scales of observation and hydrometeorological forcing. A break in statistical symmetry (multiscaling behavior) was identified, which separates the spatial and temporal evolution of the statistical structure of soil moisture fields for wavelengths below and above 10 km, the α- and β-scale ranges, respectively. Specifically, the multiscaling behavior is consistent with the scaling behavior of soil hydraulic properties as described by soil texture parameters such as sand and clay content. The multifractal behavior is associated with the temporal evolution of drying and wetting regimes, reflecting the nonlinear character of soil moisture dynamics. Finally, Empirical Orthogonal Function (EOF) analysis was conducted to explain the relationship between the spatial structure of estimated soil moisture and that of Ancillary Data including topography, soil texture, and vegetation cover. Topography appears to dominate the spatial structure of soil moisture only during and immediately after rainfall. In interstorm periods, the spatial evolution of soil moisture is closely associated with the spatial variability of soil hydraulic properties when the soil is above field capacity, while vegetation dominates the evolution of soil moisture fields through evapotranspiration as the landscape dries down.
Ronald E. Mcroberts - One of the best experts on this subject based on the ideXlab platform.
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probability and model based approaches to inference for proportion forest using satellite imagery as Ancillary Data
Remote Sensing of Environment, 2010Co-Authors: Ronald E. McrobertsAbstract:Estimates of forest area are among the most common and useful information provided by national forest inventories. The estimates are used for local and national purposes and for reporting to international agreements such as the Montreal Process, the Ministerial Conference on the Protection of Forests in Europe, and the Kyoto Protocol. The estimates are usually based on sample plot Data and are calculated using probability-based estimators. These estimators are familiar, generally unbiased, and entail only limited computational complexity, but they do not produce the maps that users are increasingly requesting, and they generally do not produce sufficiently precise estimates for small areas. Model-based estimators overcome these disadvantages, but they may be biased and estimation of variances may be computationally intensive. The study objective was to compare probability- and model-based estimators of mean proportion forest using maps based on a logistic regression model, forest inventory Data, and Landsat imagery. For model-based estimators, methods for evaluating bias and reducing the computational intensity were also investigated. Four conclusions were drawn: the logistic regression model exhibited no serious lack of fit to the Data; all the estimators produced comparable estimates for mean proportion forest, except for small areas; probability-based inferences enhanced using maps produced increased precision; and the computational intensity associated with estimating variances for model-based estimators can be greatly reduced with no detrimental effects.
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model based mean square error estimators for k nearest neighbour predictions and applications using remotely sensed Data for forest inventories
Remote Sensing of Environment, 2009Co-Authors: Ronald E. Mcroberts, Steen Magnussen, Erkki TomppoAbstract:article i nfo New model-based estimators of the uncertainty of pixel-level and areal k-nearest neighbour (knn) predictions of attribute Y from remotely-sensed Ancillary Data X are presented. Non-parametric functions predict Y from scalar 'Single Index Model' transformations of X. Variance functions generated estimates of the variance of Y. Three case studies, with Data from the Forest Inventory and Analysis program of the U.S. Forest Service, the Finnish National Forest Inventory, and Landsat ETM+ Ancillary Data, demonstrate applications of the proposed estimators. Nearly unbiased knn predictions of three forest attributes were obtained. Estimates of mean square error indicate that knn is an attractive technique for integrating remotely-sensed and ground Data for the provision of forest attribute maps and areal predictions.
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Using satellite imagery as Ancillary Data for increasing the precision of estimates for the Forest Inventory and Analysis program of the USDA Forest Service
Canadian Journal of Forest Research, 2005Co-Authors: Ronald E. Mcroberts, Geoffrey R. Holden, Mark D. Nelson, Greg C. Liknes, Dale D. GormansonAbstract:Forest inventory programs report estimates of forest variables for areas of interest ranging in size from municipalities, to counties, to states or provinces. Because of numerous factors, sample sizes are often insufficient to estimate attributes as precisely as is desired, unless the estimation process is enhanced using Ancillary Data. Classified satellite imagery has been shown to be an effective source of Ancillary Data that, when used with stratified estimation techniques, contributes to increased precision with little corresponding increase in cost. Stratification investigations conducted by the Forest Inventory and Analysis program of the USDA Forest Service are reviewed, and a new approach to stratification using satellite imagery is proposed. The results indicate that precision may be substantially increased for estimates of both forest area and volume per unit area.
Erkki Tomppo - One of the best experts on this subject based on the ideXlab platform.
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model based mean square error estimators for k nearest neighbour predictions and applications using remotely sensed Data for forest inventories
Remote Sensing of Environment, 2009Co-Authors: Ronald E. Mcroberts, Steen Magnussen, Erkki TomppoAbstract:article i nfo New model-based estimators of the uncertainty of pixel-level and areal k-nearest neighbour (knn) predictions of attribute Y from remotely-sensed Ancillary Data X are presented. Non-parametric functions predict Y from scalar 'Single Index Model' transformations of X. Variance functions generated estimates of the variance of Y. Three case studies, with Data from the Forest Inventory and Analysis program of the U.S. Forest Service, the Finnish National Forest Inventory, and Landsat ETM+ Ancillary Data, demonstrate applications of the proposed estimators. Nearly unbiased knn predictions of three forest attributes were obtained. Estimates of mean square error indicate that knn is an attractive technique for integrating remotely-sensed and ground Data for the provision of forest attribute maps and areal predictions.
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using coarse scale forest variables as Ancillary information and weighting of variables in k nn estimation a genetic algorithm approach
Remote Sensing of Environment, 2004Co-Authors: Erkki Tomppo, Merja HalmeAbstract:Abstract The non-parametric k-nearest neighbour (k-NN) multi-source estimation method is commonly employed in forest inventories that use satellite images and field Data. The method presumes the selection of a few estimation parameters. An important decision is the choice of the pixel-dependent geographical area from which the nearest field plots in the spectral space for each pixel are selected, the problem being that one spectral vector may correspond to several different ground Data vectors. The weighting of different spectral components is an obvious problem when defining the distance metric in the spectral space. The paper presents a new method. The first innovation is that the large-scale variation of forest variables is used as Ancillary Data that are added to the variables of the multi-source k-NN estimation. These Data are assigned weights in a way similar to the spectral information of satellite images when defining the applied distance metric. The second innovation is that “optimal” weights for spectral Data, as well as Ancillary Data, are computed by means of a genetic algorithm. Tests with practical forest inventory Data show that the method performs noticeably better than other applications of k-NN estimation methods in forest inventories, and that the problem of biases in the species volume predictions can for example, almost completely be overcome with this new approach.
Oliver J D Jewell - One of the best experts on this subject based on the ideXlab platform.
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Ancillary Data from animal borne cameras as an ecological survey tool for marine communities
Marine Biology, 2021Co-Authors: Taylor K Chapple, David Tickler, Ronan Roche, Daniel T I Bayley, Adrian C Gleiss, Paul E Kanive, Oliver J D JewellAbstract:Underwater visual surveys represent an essential component of coastal marine research and play a crucial role in supporting the management of marine systems. However, logistical and financial considerations can limit the availability of survey Data in some systems. While biologging camera tag devices are being attached to an increasing diversity of marine animals to collect behavioral information about the focal species, the Ancillary imagery collected can also be used in analytical techniques developed for diver-based surveys. We illustrate this approach by extracting Ancillary Data from shark-borne camera tag deployments focused on the behavior of a White shark (Carcharodon carcharias) off Gansbaai, South Africa, and a Grey Reef shark (Carcharhinus amblyrhynchos) within the Chagos Archipelago. Within the giant kelp forest environment of Gansbaai we could determine the spatial density of kelp thali and underlying substrate composition. Within the coral reef environment, the animal-borne video allowed us to determine the approximate percent and type of benthic cover, as well as growth form and genus of corals down to the upper mesophotic zone. We also enumerated fish species-level abundance over reef flat and wall environments. We used established dive-survey methods to analyze video Data and found the results to be broadly comparable in the two systems studied. Our work illustrates the broad applicability of Ancillary animal-borne video Data, which is analogous in type and quality to diver-based video Data, for analysis in established marine community survey frameworks. As camera tags and associated biologging technologies continue to develop and are adapted to new environments, utilising these Data could have wide-ranging applications and could maximise the overall cost–benefit ratio within biologging deployments.