Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / The traditional soil analysis many techniques are used in order to determine the physical and chemical properties of the soil. The radiometry appears as a promising alternative technique in the analysis of soil properties. This technique has demonstrated great potential for identification and quantification of certain properties of the soil. It is a non-destructive and non-polluting tool, with the ability to collect data on large spatial dimensions with relative speed. The radiometry may in cases be simpler than the traditional analysis of the soil and on various occasions, more accurately. The main objective of this study was to determine pedotransfer functions to soil properties based on radiometric data. It was observed that the heterogeneity of the soil decreases the accuracy of the models, however it was possible to construct prediction functions for the content of sand, silt, clay and soil organic matter from the radiometry with a level of prediction models acceptable. Considering that, in the prediction of soil properties using radiometry, there is no reagents spending and less resource invested beyond the analysis time shorter than the traditional analysis, the results were promising. / Na análise tradicional do solo muitas técnicas são utilizadas na tentativa de determinar suas propriedades físicas e químicas. A radiometria aparece como uma técnica alternativa e promissora na análise de propriedades do solo. Essa técnica tem demonstrado grande potencial na identificação e quantificação de determinadas propriedades do solo. Trata-se, de uma ferramenta não destrutiva, não poluidora, com capacidade de coleta de dados em grandes dimensões espaciais com relativa velocidade. A radiometria pode, em muitos casos, ser mais simples do que a análise tradicional do solo e em várias ocasiões, mais precisa. O principal objetivo desse trabalho foi determinar funções de pedotransferência para as propriedades do solo tendo como base os dados da radiometria. Observou-se que a heterogeneidade do solo diminui a precisão dos modelos, porém foi possível construir funções de predição para o teor de argila, areia, silte e matéria orgânica do solo a partir da radiometria com um nível de predição dos modelos aceitável. Considerando que, na predição das propriedades do solo utilizando a radiometria, não há gastos com reagentes e menos recursos investidos além do tempo de análise menor que a análise tradicional, os resultados apresentados foram promissores.

Submitted by (lucia.rodrigues@ufrpe.br) on 2016-10-10T16:51:58Z No. of bitstreams: 1 Manoel Vieira de Franca.pdf: 1961261 bytes, checksum: 09755a0162b80a68c5762eb573c32531 (MD5) / Made available in DSpace on 2016-10-10T16:51:58Z (GMT). No. of bitstreams: 1 Manoel Vieira de Franca.pdf: 1961261 bytes, checksum: 09755a0162b80a68c5762eb573c32531 (MD5) Previous issue date: 2010-02-25 / Was studied an area located in the transition region between the Agreste and the Sertão regions of Pernambuco, owned by private property named Fazenda Arizona,in the municipality of Sertânia - PE. Was initially performed the georeferencing based on Law 10267/2001 of the INCRA, using for such a satellite tracking in GPS system, DL4 Plus model made by Novatel. Based on the topographical map produced, was conducted a soil survey, being the whole area surveyed using the auger hole and making annotations related to the slope, erosion, drainage, vegetation and relief. At a later stage were carried out trenching, for description of profiles and collecting samples of soil horizons, followed by physical and chemical analysis, aiming to classification and soil mapping based on the SIBCS (Brazilian System of Soil Classification). In addition to the soil map of Arizona farm, were also obtained the slope map and the map of cross-validation between the soil and slope classes. It was found that the main difficulties for the georeferencing were the deadline, the misinformation on the part of landowners and the bureaucracy in the official registries. Were found soils belonging to Neossolos, Cambisols, Luvisols,Alfisols and Oxisols classes, being the representant of the Oxisols, the best one suited for agricultural use. The Entisols are the main components, occupying about 61.36% of the total area. The predominant types of relief were the smooth and wavy terrain. From the point of view of environmental requirements, the areas for the permanent preservation occupy about 24,41% of the total. The fragility of the environment is increased as a function of shallow soils and high surface stoniness. / Foi estudada uma área localizada na região de transição entre as regiões Agreste e Sertão de Pernambuco, pertencente à propriedade particular de nome Fazenda Arizona, no município de Sertânia – PE. Foi inicialmente realizado o georreferenciamento com base na Lei 10.267/2001 do INCRA, utilizando-se para tal um rastreador de satélite no Sistema GPS, da marca Novatel e Modelo DL4 Plus. Com base na planta produzida após o levantamento topográfico, foi realizado um levantamento de solos, em que toda área foi percorrida realizando-se tradagens e fazendo-se anotações relativas à declividade, erosão, drenagem, vegetação e relevo. Numa etapa posterior foram realizadas aberturas de trincheiras, para descrição de perfis e coleta de amostras dos horizontes de solos, seguidas de análises físicas e químicas, objetivando-se a classificação e mapeamento de solos baseando-se no SIBCS (Sistema Brasileiro de Classificação de Solos). Além do mapa de solos da fazenda Arizona, foram também obtidos o mapa de declividade e o mapa com a validação cruzada entre os solos e as classes de declive. Ficou constatado que as principais dificuldades para o georreferenciamento foram o prazo estabelecido, a desinformação por parte dos proprietários rurais e a burocracia nos cartórios. Foram encontrados solos pertencentes as classes dos Neossolos, Cambissolos, Luvissolos, Planossolos e Latossolos, sendo o representante dos latossolos, o que melhor se presta para o cultivo agrícola. Os Neossolos litólicos são os principais componentes, ocupando cerca de 61,36% do total da área. Os tipos de relevo predominantes são o suave ondulado e o ondulado. Sob o ponto de vista das exigências ambientais, as áreas destinadas à preservação permanente ocupam cerca de 24,41% do total. A fragilidade do ambiente é aumentada em função da pouca profundidade dos solos e elevada pedregosidade à superfície.

Orientadores: Paulo Augusto Valente Ferreira, Alessandro Correa Victorino, Samuel Siqueira Bueno / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de Computação / Made available in DSpace on 2018-08-08T18:02:11Z (GMT). No. of bitstreams: 1 Castro_CesarDantasde_M.pdf: 3031758 bytes, checksum: 02179aa87aa18297b9b19bef7fb5b647 (MD5) Previous issue date: 2007 / Resumo: Este trabalho disserta sobre o desenvolvimento e a implementação de um sistema de localização e mapeamento simultâneos (SLAM) para um veículo robótico aéreo. Utilizando tal sistema, um robô que sobrevoe determinada área, até então desconhecida, deve ser capaz de conhecer sua postura no ambiente e mapeá-lo, sem o auxílio de mapas ou outras informações externas. Para alcançar este objetivo, o sistema recebe informações de uma unidade de medição inercial e de uma câmera, que observa características do ambiente e, indiretamente, a posição e a atitude do robô. Para fundir as informações dos dois conjuntos sensoriais embarcados, é utilizada uma arquitetura baseada no filtro de Kalman estendido, que atua como um estimador tanto da localização do dirigível quanto do mapa. Este sistema representa um primeiro passo em direção a uma solução de SLAM em seis graus de liberdade para o Projeto AURORA, que visa o desenvolvimento de tecnologia em robótica aérea. Desta forma, a abordagem proposta é validada em um ambiente de simulação composto de sensores virtuais e do simulador dinâmico do projeto AURORA. Os resultados apresentados mostram a eficácia da metodologia / Abstract: This work addresses the development and implementation of a simultaneous localization and mapping (SLAM) system for aerial robotic vehicles. Through this system, a robot flying over an unknown region must be capable of detecting its position accurately and, at the same time, constructing a map of the environment without the help of maps or any other external information. To reach that goal, the system receives input data from an inertial measurement unit and a single camera, which observes features in the environment and, indirectly, the robot¿s position and attitude. The data from both onboard sensors are then fused using an architecture based on an extended Kalman filter, which acts as an estimator of the robot pose and the map. This system represents a first step towards a six degrees of freedom SLAM solution for Project AURORA, whose goal is the development of technology on aerial robotics. As such, the proposed methodology is validated in a simulation environment composed of virtual sensors and the aerial platform simulator of the AURORA project based on a realistic dynamic model. The reported results show the efficiency of the approach / Mestrado / Automação / Mestre em Engenharia Elétrica

Submitted by Jorge Silva (jorgelmsilva@ufrrj.br) on 2017-06-01T19:57:23Z No. of bitstreams: 1 1991 - Terezinha Aldenora de Castro e Almeida Magalh?es.pdf: 8001622 bytes, checksum: 773c2d35c4e20e65c38f62e40caa36dc (MD5) / Made available in DSpace on 2017-06-01T19:57:23Z (GMT). No. of bitstreams: 1 1991 - Terezinha Aldenora de Castro e Almeida Magalh?es.pdf: 8001622 bytes, checksum: 773c2d35c4e20e65c38f62e40caa36dc (MD5) Previous issue date: 1991-03-26 / The area studied is located in the state of Rio de Janeiro, Brazil, including the drainage basin of Una River and part of the drainage basin of Sao Jo?o River. There were mapped the soils, the agricultural suitability of lands and class of lands for irrigation, based on preexisting soil surveys, modified and detailed. This paper comprises also the aerial photograph interpretation concerning the soil survey of the coastland region, the land-use mapping and the proposal of a new methodology for the evaluation of land-use distortions accompanied by the corresponding mapping. It is concluded that: 1) In most scanned districts the amount of overused land is not significant; 2) the steady state prevails in the district of Cabo Frio lands, but it is also significant in the districts of Araruama and S?o Pedro d'Aldeia; 3) there is a prevalence of underused land in the districts of Casimiro de Abreu, Rio Bonito and Araruama. The adoption of management practices according to the orientation of the present study, as well as the reorganization of aims of agricultural exploitation and potential, shall be of use in relieving social and environmental tensions pre-existing in the studied region and in promoting its self-sustainable development. / A ?rea estudada situa-se no estado do Rio de Janeiro _ Brasil, compreendendo a bacia de drenagem do rio Una e parte da bacia de drenagem do rio s?o Jo?o. No presente estudo s?o apresentados mapeamentos de Solos, Aptid?o agr?cola e irriga??o, baseados em levantamentos pr?-existentes, com modifica??es e detalhamento; ? apresentada aerofotointerpreta??o com fins de Levantamento de solos da zona litor?nea, in?dita; ? apresentado mapeamento de Uso da Terra utilizando metodologia in?dita; ? proposta metodologia in?dita de avalia??o das Distor??es de Uso da Terra, com o mapeamento correspondente. Concluiu-se que: 1) Na maioria dos munic?pios abrangidos pelo estudo, as terras sobre-utilizadas n?o s?o significativas; 2) As terras em equil?brio s?o predominantes somente no munic?pio de Cabo Frio, apesar de terem percentual significativo tamb?m nos munic?pios de Araruama e s?o Pedro d'Aldeia; 3) As terras subutilizadas predominam nos munic?pios de Casemiro de Abreu, Rio Bonito e Araruama; 4) A ado??o dos sistemas de manejo adequados ?s condi??es ambientais, conforme a orienta??o do presente estudo e o redirecionamento da explora??o agropecu?ria para adequ?-la ao afetivo potencial de explora??o econ?mica dessas terras, poder?o aliviar tens?es sociais e ambientais j? existentes na regi?o, e promover seu desenvolvimento sustent?vel

Submitted by Jorge Silva (jorgelmsilva@ufrrj.br) on 2017-07-18T18:30:23Z No. of bitstreams: 1 2015 - Helena Saraiva Koenow Pinheiro.pdf: 14533188 bytes, checksum: 58cff5581549af698fe42ba33bd8aa71 (MD5) / Made available in DSpace on 2017-07-18T18:30:23Z (GMT). No. of bitstreams: 1 2015 - Helena Saraiva Koenow Pinheiro.pdf: 14533188 bytes, checksum: 58cff5581549af698fe42ba33bd8aa71 (MD5) Previous issue date: 2015-07-30 / CAPES / CNPq / FAPERJ / Quantitative soil-landscape models represent a new trend in soil surveys. In this regard, the various digital mapping techniques are applied to predict the natural patterns of occurrence of soil types. The objective of this study was to apply digital mapping techniques to predict soil classes and attributes in a watershed, with wide range of landscape conditions, in Rio de Janeiro State, in Brazil. The approach was based on tacit soil knowledge, regarding the choice of landscape attributes that represent the variability of soil-forming factors in the region. In regard to construct the predictive models, terrain variables were generate from the digital elevation model, geology map and remote sensing data. Ten terrain attributes were created on softwareArcGIS Desktop v. 10, such as altimetry, slope, curvature, parental material map, topographic compound index and euclidean distance of hydrography. In the software ERDAS Imagine v.9 were generated three indices derived from remote sensing data (Landsat 5 TM). They are: clay minerals, iron oxide and vegetation index normalized difference - NDVI. To represent the landscape forms was generated map the "geomorphons" maps, the GRASS-GIS program. To provide enough datato predict soil properties, additional terrain variables were derived from a digital elevation model (DEM) generated in the software SAGA-GIS. The work development was organized into three steps, presented as chapters. The first chapter comprised bibliography review and presents the context of the study. The detailed analysis of soil-landscape relationships, considering the variability of environmental attributes and characteristics of pedo-enviroments are performed on the second chapter. The predominant soils in the area were Ferralsols, Acrisols, Gleysols, Cambissolos, Fluvisols and Regosols. The third chapter presented the application of the landform maps (?geomorphons?) as a covariate to pretic soil classes by neural network approach. The fourth chapter targets the application of trees-based models (decision trees and random forest) to predict soil classes. The evaluation of the inferred products to represent the soil classes was performed based on statistical indices (kappa, overall), generalization of soil classes and validation with control samples. The best performance was observed for the random forest model that showed better values to statistical indices and better generalization of mapping units. The fifth chapter comprised the prediction of soil texture components on topsoil layer by using multiple linear regressions and regression trees. The analyses indicated better performance by using regression trees algorithm to all soil attributes (sand, silt, and clay), independent of the database (harmonized or original). All predictive models were implemented in R software. Additional research is needed to select an appropriated set of predictive covariates; as so, collect more soil samples to use as input to models and also validate of the final products. Soil survey research is important in the actual context once can enhance the information generated by the soil surveys, as well as to obtain useful information to the final users, as example of the maps that represent the spatial variability of soil texture components. / Modelos solo-paisagem quantitativos representam uma nova tend?ncia nos levantamentos de solos. Neste sentido, as diferentes t?cnicas de mapeamento digital s?o aplicadas para prever os padr?es naturais de ocorr?ncia de classes de solo. O objetivo deste trabalho foi a aplica??o de geotecnologias no mapeamento de classes e atributos dos solos em uma bacia hidrogr?fica, que apresenta grande varia??o de condi??es de paisagem, no Estado do Rio de Janeiro, Brasil. A abordagem foi baseada em conhecimento pedol?gico t?cito, culminando na escolha de atributos da paisagem que representem a variabilidade dos fatores de forma??o de solos na regi?o. Na constru??o do modelo solo-paisagem foram gerados no programa de computa??o ArcGIS Desktop v. 10, atributos relacionados a pedog?nese na ?rea em estudo, como geologia altimetria, declividade, curvatura, ?ndice topogr?fico composto e dist?ncia euclidiana de hidrografia. No programa ERDAS Imagine v.9 foram gerados tr?s ?ndices derivados de dados de sensoriamento remoto (Landsat 5 TM). S?o eles: clay minerals, iron oxide e ?ndice de vegeta??o por diferen?a normalizada ? NDVI. Para representar as formas do relevo foi gerado mapa com as dez formas mais comuns do relevo (?geomorphons?), no programa GRASS-GIS. Adicionalmente, a predi??o de atributos do solo contou com co-vari?veis derivadas do modelo digital de eleva??o (MDE) geradas no programa SAGA-GIS. O trabalho de tese foi dividido em etapas, apresentadas na forma de cap?tulos. O primeiro cap?tulo apresenta a revis?o de literatura espec?fica de contextualiza??o do trabalho. O estudo das rela??es solo-paisagem e da variabilidade dos atributos do terreno, a caracteriza??o das unidades de mapeamento com base no levantamento de campo, constituem o segundo cap?tulo. Os solos predominantes na ?rea foram: Latossolos, Argissolos, Gleissolos, Cambissolos, Neossolos Fl?vicos e Lit?licos. O terceiro cap?tulo tratou do uso do mapa de formas da paisagem (?geomorphons?) como vari?vel preditora para o mapeamento de classes de solos, por abordagem de redes neurais artificiais. O quarto cap?tulo teve como objetivo a aplica??o de modelos baseados em ?rvores (?rvores de decis?o e random forest) para a predi??o de classes de solos. A avalia??o dos produtos inferidos para classes de solos foi baseada em ?ndices estat?sticos (kappa, exatid?o global), generaliza??o das classes de solos e valida??o com amostras de controle. O melhor desempenho foi observado para o modelo random forest que apresentou valor superior para os ?ndices estat?sticos e melhor generaliza??o das unidades de mapeamento. O quinto cap?tulo compreendeu a predi??o da composi??o da textura na camada superficial do solo atrav?s de regress?es lineares m?ltiplas e ?rvores de regress?o. As an?lises indicaram desempenho superior do algoritmo de ?rvores de regress?o, para todos os atributos testados (areia, silte, argila), utilizando dados harmonizados ou originais. Todos os modelos preditivos foram aplicados no programa R. An?lises adicionais s?o necess?rias para ajudar a definir conjunto de co-vari?veis preditoras adequado, assim como a coleta de mais amostras de solo, tanto para o processo de modelagem como para valida??o dos produtos. Trabalhos dessa natureza s?o importantes no contexto global de melhor aproveitamento das informa??es geradas em levantamento de solos, assim como para obten??o de mapas de car?ter pr?tico, como ? o caso da distribui??o espacial de atributos dos solos.

<p>Soil residual herbicides are a vital component of the best management practices (BMPs), to provide early-season weed control in most cropping systems. The availability of a biologically effective dose of a soil residual herbicide in the soil solution is dependent on several soil parameters including soil texture, organic matter (OM), and pH.  Soil residual herbicides are currently applied as a uniform application rate over an individual field; yet soil properties can vary spatially within agricultural fields. Therefore, areas of the field are being over- and under-applied when using a uniform application rate. By integrating variable-rate (VR) technology with soil residual herbicides, the correct rate could be applied based on the intra-field soil variability. However, the extent of spatial soil variability within a field and the impact on herbicide application rates has not been well-characterized to inform whether soil residual herbicide applications should move towards variable rate applications. Therefore, the objectives of this research were to 1) determine the extent of intra-field variability of soil texture and organic matter in ten commercial Indiana fields, 2) quantify the reliability of five different combinations of spatial soil data sources, 3) determine the impact of soil sample intensity on map development and the classification accuracy for VR applications of soil residual herbicides, 4) quantify the impact of VR herbicide application on the total amount and spatial accuracy of herbicide applied according to product labels, and 5) determine if the intensive spatial characterization of soil properties is related to weed seedbank abundance and species richness to improve predictive weed management using soil residual herbicides.</p> <p><br></p> <p>Commercial soil data was generated by intensively collecting 60 soil samples in a stratified random sampling pattern in 10 agricultural fields across Indiana. Analysis of this data from commercial fields confirmed inherent field variability that would benefit from multiple management zones according to the labeled rate structures of pendimethalin, s-metolachlor, and metribuzin. Therefore, further research was conducted to determine an accurate and reliable method to delineate the fields into management zones for variable-rate residual herbicide applications based on the spatial soil variability and herbicide labels. </p> <p><br></p> <p>A modified Monte Carlo cross-validation method was used to determine the best source of spatial soil data and sampling intensity for delineating management zones for variable-rate applications of pendimethalin, s- metolachlor, and metribuzin. These sources of spatial soil data included: Soil Survey Geographic database (SSURGO) data, intensive soil samples, electrical resistivity sensors, and implement mounted optical reflectance sensors using VNIR reflectance spectroscopy. The mean management zone classification accuracy for maps developed from soil samples with and without electrical conductivity was similar for 75% of all maps developed across each field, herbicide, and sampling intensity. The method of using soil sampling data combined with electrical conductivity (SSEC) maps was most frequently the top performing source of spatial soil data. The most reliable sampling intensity was one sample per hectare which resulted in lower root mean squared error (RMSE) OM values, higher management zone classification accuracy, and more reliable predictions for the number of management zones within each field. </p> <p><br></p> <p>Using VR maps developed from SSEC with one sample per hectare sampling intensity, additional research was conducted to compare the amount of herbicide and field area that was over-or under-applied with a uniform application rate compared to a VR application for 10 corn and soybean residual herbicides. Although research from our previous study documented that spatial soil variability was extensive enough to require two or more management zones for all fields, the same labeled herbicide dose defined for multiple soil conditions led to 20% of all maps not requiring a variable rate application (VRA). Additionally, no difference was shown in the total amount applied of herbicide in an individual field between a variable and uniform application rate for all herbicides. Nonetheless, nearly half of all VR maps had 10% or more of the field area misapplied with a uniform application rate and justifies further research to determine if the proper placement of residual herbicide adds value through increased weed control in the field areas being under-applied. </p> <p><br></p> <p>Similar to soil residual herbicides, weed seedbank abundance and species richness were impacted by the variable soil conditions present within the field area. The seedbanks favor the establishment in areas of the field that promote vigorous germination, growth, and reproduction next to the competing crop. Therefore, soil sampling and weed seedbank greenhouse grow-outs were conducted in four fields to gain a better understanding in the relationship between the spatial soil and weed seedbank variability. All weed seedbank characteristics were shown to be spatially aggregated. Even though no individual or combination of soil parameters consistently explained the variability of weed seedbank abundance, species richness, or individual weed species across all four fields. However, clay content was the most persistent soil parameter to negatively impact (lower seedbank values) the soil weed seedbank.</p> <p><br></p> <p>Further field studies should be conducted across multiple sites to determine if variable-rate residual herbicide applications aid farmers by reducing the risk of crop injury in over-applied field areas and increased weed control in the areas being under-applied.  These studies should also access whether earlier emergence and/or greater weed densities occur in field areas receiving sublethal herbicide doses compared to areas receiving the optimal application rate. Additional research should investigate the utility of VR residual herbicide applications when tank-mixing multiple products during an application. Particularly, when the soil parameters used for selecting the herbicide rate are not defined the same across herbicide labels </p>

Indiana University-Purdue University Indianapolis (IUPUI) / Up-to-date and accurate information on soil properties is important for precision farming and environmental management. The spatial information of soil properties allows adjustments of fertilizer applications to be made based on knowledge of local field conditions, thereby maximizing agricultural productivity and minimizing the risk of environmental pollution. While conventional soil sampling procedures are labor-intensive, time-consuming and expensive, remote sensing techniques provide a rapid and efficient tool for mapping soil properties. This study aimed at examining the capacity of hyperspectral reflectance data for mapping soil organic matter (SOM), total nitrogen (N) and total phosphorus (P). Soil samples collected from Eagle Creek Watershed, Cicero Creek Watershed, and Fall Creek Watershed were analyzed for organic matter content, total N and total P; their corresponding spectral reflectance was measured in the laboratory before and after oven drying and in the field using Analytical Spectral Devices spectrometer. Hyperion images for each of the watersheds were acquired, calibrated and corrected and Hyperion image spectra for individual sampled sites were extracted. These hyperspectral reflectance data were related to SOM, total N and total P concentration through partial least squares (PLS) regressions. The samples were split into two datasets: one for calibration, and the other for validation. High PLS performance was observed during the calibration for SOM and total N regardless of the type of the reflectance spectra, and for total P with Hyperion image spectra. The validation of PLS models was carried out with each type of reflectance to assess their predictive power. For laboratory reflectance spectra, PLS models of SOM and total N resulted in higher R2 values and lower RMSEP with oven-dried than those with field-moist soils. The results demonstrate that soil moisture degrades the performance of PLS in estimating soil constituents with spectral reflectance. For in-situ field spectra, PLS estimated SOM with an R2 of 0.74, N with an R2 of 0.79, and P with an R2 of 0.60. For Hyperion image spectra, PLS predictive models yielded an R2 of 0.74 between measured and predicted SOM, an R2 of 0.72 between measured and predicted total N, and an R2 of 0.67 between measured and predicted total P. These results reveal slightly decreased model performance when shifting from laboratory-measured spectra to satellite image spectra. Regardless of the spectral data, the models for estimating SOM and total N consistently outperformed those for estimating total P. These results also indicate that PLS is an effective tool for remotely estimating SOM, total N and P in agricultural soils, but more research is needed to improve the predictive power of the model when applied to satellite hyperspectral imagery.

Yes / Archaeological research at Stonehenge (UK) is increasingly aimed at understanding the dynamic of the wider archaeological landscape. Through the application of state-of-the-art geophysical techniques, unprecedented insight is being gathered into the buried archaeological features of the area. However, applied survey techniques have rarely targeted natural soil variation, and the detailed knowledge of the palaeotopography is consequently less complete. In addition, metallic topsoil debris, scattered over different parts of the Stonehenge landscape, often impacts the interpretation of geophysical datasets. The research presented here demonstrates how a single multi-receiver electromagnetic induction (EMI) survey, conducted over a 22 ha area within the Stonehenge landscape, offers detailed insight into natural and anthropogenic soil variation at Stonehenge. The soil variations that were detected through recording the electrical and magnetic soil variability, shed light on the genesis of the landscape, and allow for a better definition of potential palaeoenvironmental and archaeological sampling locations. Based on the multi-layered dataset, a procedure was developed to remove the influence of topsoil metal from the survey data, which enabled a more straightforward identification of the detected archaeology. The results provide a robust basis for further geoarchaeological research, while potential to differentiate between modern soil disturbances and the underlying sub-surface variations can help in solving conservation and management issues. Through expanding this approach over the wider area, we aim at a fuller understanding of the human–landscape interactions that have shaped the Stonehenge landscape.

No description available.

Soil is one of the most precious resources on Earth because of its role in storing and recycling water and nutrients essential for life, providing a variety of ecosystem services. This vulnerable resource is at risk from degradation by erosion, salinity, contamination and other effects of mismanagement. Information from soil is therefore crucial for its sustainable management. While the demand for soil information is growing, the quantity of data collected in the field is reducing due to financial constraints. Digital Soil Mapping (DSM) supports the creation of geographically referenced soil databases generated by using field observations or legacy data coupled, through quantitative relationships, with environmental covariates. This enables the creation of soil maps at unexplored locations at reduced costs. The selection of an optimal scale for environmental covariates is still an unsolved issue affecting the accuracy of DSM. The overall aim of this research was to explore the effect of spatial scale alterations of environmental covariates in DSM. Three main targets were identified: assessing the impact of spatial scale alterations on classifying soil taxonomic units; investigating existing approaches from related scientific fields for the detection of scale patterns and finally enabling practitioners to find a suitable scale for environmental covariates by developing a new methodology for spatial scale analysis in DSM. Three study areas, covered by detailed reconnaissance soil survey, were identified in the Republic of Ireland. Their different pedological and geomorphological characteristics allowed to test scale behaviours across the spectrum of conditions present in the Irish landscape. The investigation started by examining the effects of scale alteration of the finest resolution environmental covariate, the Digital Elevation Model (DEM), on the classification of soil taxonomic units. Empirical approaches from related scientific fields were subsequently selected from the literature, applied to the study areas and compared with the experimental methodology. Wavelet analysis was also employed to decompose the DEMs into a series of independent components at varying scales and then used in DSM analysis of soil taxonomic units. Finally, a new multiscale methodology was developed and evaluated against the previously presented experimental results. The results obtained by the experimental methodology have proved the significant role of scale alterations in the classification accuracy of soil taxonomic units, challenging the common practice of using the finest available resolution of DEM in DSM analysis. The set of eight empirical approaches selected in the literature have been proved to have a detrimental effect on the selection of an optimal DEM scale for DSM applications. Wavelet analysis was shown effective in removing DEM sources of variation, increasing DSM model performance by spatially decomposing the DEM. Finally, my main contribution to knowledge has been developing a new multiscale methodology for DSM applications by combining a DEM segmentation technique performed by k-means clustering of local variograms parameters calculated in a moving window with an experimental methodology altering DEM scales. The newly developed multiscale methodology offers a way to significantly improve classification accuracy of soil taxonomic units in DSM. In conclusion, this research has shown that spatial scale analysis of environmental covariates significantly enhances the practice of DSM, improving overall classification accuracy of soil taxonomic units. The newly developed multiscale methodology can be successfully integrated in current DSM analysis of soil taxonomic units performed with data mining techniques, so advancing the practice of soil mapping. The future of DSM, as it successfully progresses from the early pioneering years into an established discipline, will have to include scale and in particular multiscale investigations in its methodology. DSM will have to move from a methodology of spatial data with scale to a spatial scale methodology. It is now time to consider scale as a key soil and modelling attribute in DSM.