Evaluation of statistical and geostatistical models of digital soil properties mapping in tropical mountain regions

Directory of Open Access Journals (Sweden)

Waldir de Carvalho Junior

2014-06-01

Full Text Available Soil properties have an enormous impact on economic and environmental aspects of agricultural production. Quantitative relationships between soil properties and the factors that influence their variability are the basis of digital soil mapping. The predictive models of soil properties evaluated in this work are statistical (multiple linear regression-MLR and geostatistical (ordinary kriging and co-kriging. The study was conducted in the municipality of Bom Jardim, RJ, using a soil database with 208 sampling points. Predictive models were evaluated for sand, silt and clay fractions, pH in water and organic carbon at six depths according to the specifications of the consortium of digital soil mapping at the global level (GlobalSoilMap. Continuous covariates and categorical predictors were used and their contributions to the model assessed. Only the environmental covariates elevation, aspect, stream power index (SPI, soil wetness index (SWI, normalized difference vegetation index (NDVI, and b3/b2 band ratio were significantly correlated with soil properties. The predictive models had a mean coefficient of determination of 0.21. Best results were obtained with the geostatistical predictive models, where the highest coefficient of determination 0.43 was associated with sand properties between 60 to 100 cm deep. The use of a sparse data set of soil properties for digital mapping can explain only part of the spatial variation of these properties. The results may be related to the sampling density and the quantity and quality of the environmental covariates and predictive models used.

Towards quantitative usage of EMI-data for Digital Soil Mapping

Science.gov (United States)

Nüsch, A.-K.; Wunderlich, T.; Kathage, S.; Werban, U.; Dietrich, P.

2009-04-01

As formulated in the Thematic Strategy for Soil Protection prepared by the European Commission soil degradation is a serious problem in Europe. The degradation is driven or exacerbated by human activity and has a direct impact on water and air quality, biodiversity, climate and human life-quality. High-resolution soil property maps are one major prerequisite for the specific protection of soil function and restoration of degraded soils as well as sustainable land use, water and environmental management. However, the currently available techniques for (digital) soil mapping still have deficiencies in terms of reliability and precision, the feasibility of investigation of large areas (e.g. catchments and landscapes) and the assessment of soil degradation threats at this scale. The focus of the iSOIL (Interactions between soil related science - Linking geophysics, soil science and digital soil mapping) project is on improving fast and reliable mapping of soil properties, soil functions and soil degradation threats. This requires the improvement as well as integration of geophysical and spectroscopic measurement techniques in combination with advanced soil sampling approaches, pedometrical and pedophysical approaches. Many commercially available geophysical sensors and equipment (EMI, DC, gamma-spectroscopy, magnetics) are ready to use for measurements of different parameters. Data collection with individual sensors is well developed and numerously described. However comparability of data of different sensor types as well as reproducibility of data is not self-evident. In particular handling of sensors has to be carried out accurately, e.g. consistent calibration. Soil parameters will be derived from geophysical properties to create comprehensive soil maps. Therefore one prerequisite is the comparison of different geophysical properties not only qualitative but also quantitative. At least reproducibility is one of the most important conditions for monitoring tasks. The

Progress towards GlobalSoilMap.net soil database of Denmark

DEFF Research Database (Denmark)

Adhikari, Kabindra; Bou Kheir, Rania; Greve, Mogens Humlekrog

2012-01-01

Denmark is an agriculture-based country where intensive mechanized cultivation has been practiced continuously for years leading to serious threats to the soils. Proper use and management of Danish soil resources, modeling and soil research activities need very detailed soil information. This study...... presents recent advancements in Digital Soil Mapping (DSM) activities in Denmark with an example of soil clay mapping using regression-based DSM techniques. Several environmental covariates were used to build regression rules and national scale soil prediction was made at 30 m resolution. Spatial...... content mapping, the plans for future soil mapping activities in support to GlobalSoilMap.net project initiatives are also included in this paper. Our study thought to enrich and update Danish soil database and Soil information system with new fine resolution soil property maps....

Componentes principais como preditores no mapeamento digital de classes de solos Principal components as predictor variables in digital mapping of soil classes

Directory of Open Access Journals (Sweden)

Alexandre ten Caten

2011-07-01

Full Text Available Tecnologias disponíveis para a observação da Terra oferecem uma grande gama de informações sobre componentes ambientais que, por estarem relacionadas com a formação dos solos, podem ser usadas como variáveis preditoras no Mapeamento Digital de Solos (MDS. No entanto, modelos com um grande número de preditores, bem como a existência de multicolinearidade entre os dados, podem ser ineficazes no mapeamento de classes e propriedades do solo. O objetivo deste estudo foi empregar a Análise de Componentes Principais (ACP visando a selecionar e diminuir o número de preditores na regressão logística múltipla multinomial (RLMM utilizada no mapeamento de classes de solos. Nove covariáveis ambientais, ligadas ao fator de formação relevo, foram derivadas de um Modelo Digital de Elevação e denominadas variáveis originais, estas foram submetidas à ACP e transformadas em Componentes Principais (CP. As RLMM foram desenvolvidas utilizando-se atributos de terreno e as CP como variáveis explicativas. O mapa de solos gerado a partir de três CP (65,6% da variância original obteve um índice kappa de 37,3%, inferior aos 48,5% alcançado pelo mapa de solos gerado a partir de todas as nove variáveis originais.Available technologies for Earth observation offer a wide range of predictors relevant to Digital Soil Mapping (DSM. However, models with a large number of predictors, as well as, the existence of multicollinearity among the data, may be ineffective in the mapping of classes and soil properties. The aim of this study was to use the Principal Component Analysis (PCA to reduce the number of predictors in the multinomial logistic regression (MLR used in soil mapping. Nine environmental covariates, related to the relief factor of soil formation, were derived from a digital elevation model and named the original variables, which were submitted to PCA and transformed into principal components (PC. The MLR were developed using the terrain

Creating a conceptual hydrological soil response map for the ...

African Journals Online (AJOL)

2014-03-03

Mar 3, 2014 ... a digital soil mapping (DSM) approach to soil mapping can speed up the mapping process and thereby extend soil map use in the field of ... This research uses an expert-knowledge DSM approach to create a soil map for Stevenson Hamilton .... the different bands of the Landsat and SPOT 5 images.

Creating a conceptual hydrological soil response map for the ...

African Journals Online (AJOL)

The use of a digital soil mapping (DSM) approach to soil mapping can speed up the mapping process and thereby extend soil map use in the field of hydrology. This research uses an expert-knowledge DSM approach to create a soil map for Stevenson Hamilton Research Supersite within the Kruger National Park, South ...

Digital soil mapping using remote sensing indices, terrain attributes, and vegetation features in the rangelands of northeastern Iran.

Science.gov (United States)

Mahmoudabadi, Ebrahim; Karimi, Alireza; Haghnia, Gholam Hosain; Sepehr, Adel

2017-09-11

Digital soil mapping has been introduced as a viable alternative to the traditional mapping methods due to being fast and cost-effective. The objective of the present study was to investigate the capability of the vegetation features and spectral indices as auxiliary variables in digital soil mapping models to predict soil properties. A region with an area of 1225 ha located in Bajgiran rangelands, Khorasan Razavi province, northeastern Iran, was chosen. A total of 137 sampling sites, each containing 3-5 plots with 10-m interval distance along a transect established based on randomized-systematic method, were investigated. In each plot, plant species names and numbers as well as vegetation cover percentage (VCP) were recorded, and finally one composite soil sample was taken from each transect at each site (137 soil samples in total). Terrain attributes were derived from a digital elevation model, different bands and spectral indices were obtained from the Landsat7 ETM+ images, and vegetation features were calculated in the plots, all of which were used as auxiliary variables to predict soil properties using artificial neural network, gene expression programming, and multivariate linear regression models. According to R 2 RMSE and MBE values, artificial neutral network was obtained as the most accurate soil properties prediction function used in scorpan model. Vegetation features and indices were more effective than remotely sensed data and terrain attributes in predicting soil properties including calcium carbonate equivalent, clay, bulk density, total nitrogen, carbon, sand, silt, and saturated moisture capacity. It was also shown that vegetation indices including NDVI, SAVI, MSAVI, SARVI, RDVI, and DVI were more effective in estimating the majority of soil properties compared to separate bands and even some soil spectral indices.

3D-Digital soil property mapping by geoadditive models

Science.gov (United States)

Papritz, Andreas

2016-04-01

In many digital soil mapping (DSM) applications, soil properties must be predicted not only for a single but for multiple soil depth intervals. In the GlobalSoilMap project, as an example, predictions are computed for the 0-5 cm, 5-15 cm, 15-30 cm, 30-60 cm, 60-100 cm, 100-200 cm depth intervals (Arrouays et al., 2014). Legacy soil data are often used for DSM. It is common for such datasets that soil properties were measured for soil horizons or for layers at varying soil depth and with non-constant thickness (support). This poses problems for DSM: One strategy is to harmonize the soil data to common depth prior to the analyses (e.g. Bishop et al., 1999) and conduct the statistical analyses for each depth interval independently. The disadvantage of this approach is that the predictions for different depths are computed independently from each other so that the predicted depth profiles may be unrealistic. Furthermore, the error induced by the harmonization to common depth is ignored in this approach (Orton et al. 2016). A better strategy is therefore to process all soil data jointly without prior harmonization by a 3D-analysis that takes soil depth and geographical position explicitly into account. Usually, the non-constant support of the data is then ignored, but Orton et al. (2016) presented recently a geostatistical approach that accounts for non-constant support of soil data and relies on restricted maximum likelihood estimation (REML) of a linear geostatistical model with a separable, heteroscedastic, zonal anisotropic auto-covariance function and area-to-point kriging (Kyriakidis, 2004.) Although this model is theoretically coherent and elegant, estimating its many parameters by REML and selecting covariates for the spatial mean function is a formidable task. A simpler approach might be to use geoadditive models (Kammann and Wand, 2003; Wand, 2003) for 3D-analyses of soil data. geoAM extend the scope of the linear model with spatially correlated errors to

Mapa digital de solos: uma proposta metodológica usando inferência fuzzy Digital soil map: a methodological proposal using fuzzy inference

Directory of Open Access Journals (Sweden)

Claudia C. Nolasco-Carvalho

2009-02-01

Full Text Available Elaborou-se um mapa digital de solos de uma área na região de Mucugê, BA, com o objetivo de avaliar o uso de geotecnologias na cartografia de solos. A metodologia desenvolvida a partir do modelo de inferência para solos - SoLIM , requer o conhecimento prévio da área por um especialista em mapeamento e está alicerçada na equação dos fatores de formação do solo e no modelo de distribuição dos solos na paisagem. Os dados, advindos do Modelo Digital do Terreno - MDT, da vegetação e da geologia, foram associados ao conhecimento do pedólogo e integrados em ambiente SIG (Sistema de Informações Geográficas sob inferência fuzzy. A modelagem por lógica fuzzy permitiu apontar as incertezas e transições da cobertura pedológica e gerou um mapa digital de solo que, quando comparado com o mapa convencional da área, mostrou menor generalização no domínio de espaços e parâmetros, ou seja, um refinamento da escala, porém a aplicabilidade da metodologia depende da validação de campo e da repetição em outras áreas.A digital soil map was elaborated for an area in the region of Mucugê-BA using data integration derived from a digital elevation model (DEM of the vegetation and geology that was associated with a soil scientist's knowledge and correlated in a GIS environment (Geography Information System under fuzzy inference, as a methodological proposal. The methodology was developed and based on the soil-land inference model - SoLIM, on the soil factor equation and the soil-landscape model. The fuzzy logic is able to simulate the uncertainty and transitions that often appear in pedologic systems. The results show that the methodology allows the generation of digital soil maps with increased scale and to reduce soil classe generalizations in the space and parameter domain. However, this methodology is very dependent upon the soil expert's knowledge and accuracy of the data base. To verify the applicability of the methodology the

Evaluation of digital soil mapping approaches with large sets of environmental covariates

Science.gov (United States)

Nussbaum, Madlene; Spiess, Kay; Baltensweiler, Andri; Grob, Urs; Keller, Armin; Greiner, Lucie; Schaepman, Michael E.; Papritz, Andreas

2018-01-01

The spatial assessment of soil functions requires maps of basic soil properties. Unfortunately, these are either missing for many regions or are not available at the desired spatial resolution or down to the required soil depth. The field-based generation of large soil datasets and conventional soil maps remains costly. Meanwhile, legacy soil data and comprehensive sets of spatial environmental data are available for many regions. Digital soil mapping (DSM) approaches relating soil data (responses) to environmental data (covariates) face the challenge of building statistical models from large sets of covariates originating, for example, from airborne imaging spectroscopy or multi-scale terrain analysis. We evaluated six approaches for DSM in three study regions in Switzerland (Berne, Greifensee, ZH forest) by mapping the effective soil depth available to plants (SD), pH, soil organic matter (SOM), effective cation exchange capacity (ECEC), clay, silt, gravel content and fine fraction bulk density for four soil depths (totalling 48 responses). Models were built from 300-500 environmental covariates by selecting linear models through (1) grouped lasso and (2) an ad hoc stepwise procedure for robust external-drift kriging (georob). For (3) geoadditive models we selected penalized smoothing spline terms by component-wise gradient boosting (geoGAM). We further used two tree-based methods: (4) boosted regression trees (BRTs) and (5) random forest (RF). Lastly, we computed (6) weighted model averages (MAs) from the predictions obtained from methods 1-5. Lasso, georob and geoGAM successfully selected strongly reduced sets of covariates (subsets of 3-6 % of all covariates). Differences in predictive performance, tested on independent validation data, were mostly small and did not reveal a single best method for 48 responses. Nevertheless, RF was often the best among methods 1-5 (28 of 48 responses), but was outcompeted by MA for 14 of these 28 responses. RF tended to over

Mapeamento digital de classes de solos: características da abordagem brasileira Digital soil mapping: characteristics of the brazilian approach

Directory of Open Access Journals (Sweden)

Alexandre ten Caten

2012-11-01

Full Text Available O solo é cada vez mais reconhecido como tendo um importante papel nos ecossistemas, assim como para a produção de alimentos e regulação do clima global. Por esse motivo, a demanda por informações relevantes e atualizadas em solos é crescente. Pesquisadores em ciência do solo estão sendo demandados a gerar informações em diferentes resoluções espaciais e com qualidade associada dentro do que está sendo chamado de Mapeamento Digital de Solos (MDS. Devido ao crescente número de trabalhos relacionados ao MDS, faz-se necessário reunir e discutir as principais características dos estudos relacionados ao mapeamento digital de classes de solos no Brasil, o que irá possibilitar uma perspectiva mais ampla dos caminhos, além de nortear trabalhos e demandas futuras. O mapeamento de classes de solos empregando técnicas de MDS é recente no país, com a primeira publicação em 2006. Entre as funções preditivas utilizadas, predomina o emprego da técnica de regressões logísticas. O fator de formação relevo foi empregado na totalidade dos estudos revisados. Quanto à avaliação da qualidade dos modelos preditivos, o emprego da matriz de erros e do índice kappa têm sido os procedimentos mais usuais. A consolidação dessa abordagem automatizada como ferramenta auxiliar ao mapeamento convencional passa pelo treinamento dos jovens pedólogos para a utilização de tecnologias da geoinformação e de ferramentas quantitativas dos aspectos de variabilidade do solo.Soil is increasingly being recognized as having an important role in ecosystems, as well as for food production and global climate regulation. For this reason, the demand for relevant and updated soil information is increasing. Soil science researchers are being demanded to produce information in different spatial resolutions with associated quality in what is being called Digital Soil Mapping (DSM. Due to an increasing number of papers related to the DSM in Brazil, it is

Digital soil mapping as a basis for climatically oriented agriculture a thematic on the territory of the national crop testing fields of the Republic of Tatarstan, Russia

Science.gov (United States)

Sahabiev, I. A.; Giniyatullin, K. G.; Ryazanov, S. S.

2018-01-01

The concept of climate-optimized agriculture (COA) of the UN FAO implies the transformation of agriculture techniques in conditions of changing climate. It is important to implement a timely transition to the concept of COA and sustainable development of soil resources, accurate digital maps of spatial distribution of soils and soil properties are needed. Digital mapping of soil humus content was carried out on the territory of the national crop testing fields (NCTF) of the Republic of Tatarstan (Russian Federation) and the accuracy of the maps obtained was estimated.

Latin Hypercube Sampling (LHS) at variable resolutions for enhanced watershed scale Soil Sampling and Digital Soil Mapping.

Science.gov (United States)

Hamalainen, Sampsa; Geng, Xiaoyuan; He, Juanxia

2017-04-01

Latin Hypercube Sampling (LHS) at variable resolutions for enhanced watershed scale Soil Sampling and Digital Soil Mapping. Sampsa Hamalainen, Xiaoyuan Geng, and Juanxia, He. AAFC - Agriculture and Agr-Food Canada, Ottawa, Canada. The Latin Hypercube Sampling (LHS) approach to assist with Digital Soil Mapping has been developed for some time now, however the purpose of this work was to complement LHS with use of multiple spatial resolutions of covariate datasets and variability in the range of sampling points produced. This allowed for specific sets of LHS points to be produced to fulfil the needs of various partners from multiple projects working in the Ontario and Prince Edward Island provinces of Canada. Secondary soil and environmental attributes are critical inputs that are required in the development of sampling points by LHS. These include a required Digital Elevation Model (DEM) and subsequent covariate datasets produced as a result of a Digital Terrain Analysis performed on the DEM. These additional covariates often include but are not limited to Topographic Wetness Index (TWI), Length-Slope (LS) Factor, and Slope which are continuous data. The range of specific points created in LHS included 50 - 200 depending on the size of the watershed and more importantly the number of soil types found within. The spatial resolution of covariates included within the work ranged from 5 - 30 m. The iterations within the LHS sampling were run at an optimal level so the LHS model provided a good spatial representation of the environmental attributes within the watershed. Also, additional covariates were included in the Latin Hypercube Sampling approach which is categorical in nature such as external Surficial Geology data. Some initial results of the work include using a 1000 iteration variable within the LHS model. 1000 iterations was consistently a reasonable value used to produce sampling points that provided a good spatial representation of the environmental

Digital mapping of soil properties in Zala County, Hungary for the support of county-level spatial planning and land management

Science.gov (United States)

Pásztor, László; Laborczi, Annamária; Szatmári, Gábor; Fodor, Nándor; Bakacsi, Zsófia; Szabó, József; Illés, Gábor

2014-05-01

The main objective of the DOSoReMI.hu (Digital, Optimized, Soil Related Maps and Information in Hungary) project is to significantly extend the potential, how demands on spatial soil related information could be satisfied in Hungary. Although a great amount of soil information is available due to former mappings and surveys, there are more and more frequently emerging discrepancies between the available and the expected data. The gaps are planned to be filled with optimized DSM products heavily based on legacy soil data, which still represent a valuable treasure of soil information at the present time. Impact assessment of the forecasted climate change and the analysis of the possibilities of the adaptation in the agriculture and forestry can be supported by scenario based land management modelling, whose results can be incorporated in spatial planning. This framework requires adequate, preferably timely and spatially detailed knowledge of the soil cover. For the satisfaction of these demands in Zala County (one of the nineteen counties of Hungary), the soil conditions of the agricultural areas were digitally mapped based on the most detailed, available recent and legacy soil data. The agri-environmental conditions were characterized according to the 1:10,000 scale genetic soil mapping methodology and the category system applied in the Hungarian soil-agricultural chemistry practice. The factors constraining the fertility of soils were featured according to the biophysical criteria system elaborated for the delimitation of naturally handicapped areas in the EU. Production related soil functions were regionalized incorporating agro-meteorological modelling. The appropriate derivatives of a 20m digital elevation model were used in the analysis. Multitemporal MODIS products were selected from the period of 2009-2011 representing different parts of the growing season and years with various climatic conditions. Additionally two climatic data layers, the 1

Elaboration of a framework for the compilation of countrywide, digital maps for the satisfaction of recent demands on spatial, soil related information in Hungary

Science.gov (United States)

Pásztor, László; Dobos, Endre; Szabó, József; Bakacsi, Zsófia; Laborczi, Annamária

2013-04-01

There is a heap of evidences that demands on soil related information have been significant worldwide and it is still increasing. Soil maps were typically used for long time to satisfy these demands. By the spread of GI technology, spatial soil information systems (SSIS) and digital soil mapping (DSM) took the role of traditional soil maps. Due to the relatively high costs of data collection, new conventional soil surveys and inventories are getting less and less frequent, which fact valorises legacy soil information and the systems which are serving the their digitally processed version. The existing data contain a wealth of information that can be exploited by proper methodology. Not only the degree of current needs for soil information has changed but also its nature. Traditionally the agricultural functions of soils were focussed on, which was also reflected in the methodology of data collection and mapping. Recently the multifunctionality of soils is getting to gain more and more ground; consequently information related to additional functions of soils becomes identically important. The new types of information requirements however cannot be fulfilled generally with new data collections at least not on such a level as it was done in the frame of traditional soil surveys. Soil monitoring systems have been established for the collection of recent information on the various elements of the DPSIR (Driving Forces-Pressures-State-Impacts-Responses) framework, but the primary goal of these systems has not been mapping by all means. And definitely this is the case concerning the two recently working Hungarian soil monitoring systems. In Hungary, presently soil data requirements are fulfilled with the recently available datasets either by their direct usage or after certain specific and generally fortuitous, thematic and/or spatial inference. Due to the more and more frequently emerging discrepancies between the available and the expected data, there might be notable

Neighborhood size of training data influences soil map disaggregation

Science.gov (United States)

Soil class mapping relies on the ability of sample locations to represent portions of the landscape with similar soil types; however, most digital soil mapping (DSM) approaches intersect sample locations with one raster pixel per covariate layer regardless of pixel size. This approach does not take ...

A soil map of a large watershed in China: applying digital soil mapping in a data sparse region

Science.gov (United States)

Barthold, F.; Blank, B.; Wiesmeier, M.; Breuer, L.; Frede, H.-G.

2009-04-01

Prediction of soil classes in data sparse regions is a major research challenge. With the advent of machine learning the possibilities to spatially predict soil classes have increased tremendously and given birth to new possibilities in soil mapping. Digital soil mapping is a research field that has been established during the last decades and has been accepted widely. We now need to develop tools to reduce the uncertainty in soil predictions. This is especially challenging in data sparse regions. One approach to do this is to implement soil taxonomic distance as a classification error criterion in classification and regression trees (CART) as suggested by Minasny et al. (Geoderma 142 (2007) 285-293). This approach assumes that the classification error should be larger between soils that are more dissimilar, i.e. differ in a larger number of soil properties, and smaller between more similar soils. Our study area is the Xilin River Basin, which is located in central Inner Mongolia in China. It is characterized by semi arid climate conditions and is representative for the natural occurring steppe ecosystem. The study area comprises 3600 km2. We applied a random, stratified sampling design after McKenzie and Ryan (Geoderma 89 (1999) 67-94) with landuse and topography as stratifying variables. We defined 10 sampling classes, from each class 14 replicates were randomly drawn and sampled. The dataset was split into 100 soil profiles for training and 40 soil profiles for validation. We then applied classification and regression trees (CART) to quantify the relationships between soil classes and environmental covariates. The classification tree explained 75.5% of the variance with land use and geology as most important predictor variables. Among the 8 soil classes that we predicted, the Kastanozems cover most of the area. They are predominantly found in steppe areas. However, even some of the soils at sand dune sites, which were thought to show only little soil formation

Soil organic carbon content assessment in a heterogeneous landscape: comparison of digital soil mapping and visible and near Infrared spectroscopy approaches

Science.gov (United States)

Michot, Didier; Fouad, Youssef; Pascal, Pichelin; Viaud, Valérie; Soltani, Inès; Walter, Christian

2017-04-01

This study aims are: i) to assess SOC content distribution according to the global soil map (GSM) project recommendations in a heterogeneous landscape ; ii) to compare the prediction performance of digital soil mapping (DSM) and visible-near infrared (Vis-NIR) spectroscopy approaches. The study area of 140 ha, located at Plancoët, surrounds the unique mineral spring water of Brittany (Western France). It's a hillock characterized by a heterogeneous landscape mosaic with different types of forest, permanent pastures and wetlands along a small coastal river. We acquired two independent datasets: j) 50 points selected using a conditioned Latin hypercube sampling (cLHS); jj) 254 points corresponding to the GSM grid. Soil samples were collected in three layers (0-5, 20-25 and 40-50cm) for both sampling strategies. SOC content was only measured in cLHS soil samples, while Vis-NIR spectra were measured on all the collected samples. For the DSM approach, a machine-learning algorithm (Cubist) was applied on the cLHS calibration data to build rule-based models linking soil carbon content in the different layers with environmental covariates, derived from digital elevation model, geological variables, land use data and existing large scale soil maps. For the spectroscopy approach, we used two calibration datasets: k) the local cLHS ; kk) a subset selected from the regional spectral database of Brittany after a PCA with a hierarchical clustering analysis and spiked by local cLHS spectra. The PLS regression algorithm with "leave-one-out" cross validation was performed for both calibration datasets. SOC contents for the 3 layers of the GSM grid were predicted using the different approaches and were compared with each other. Their prediction performance was evaluated by the following parameters: R2, RMSE and RPD. Both approaches led to satisfactory predictions for SOC content with an advantage for the spectral approach, particularly as regards the pertinence of the variation

Digital soil mapping in assessment of land suitability for organic farming

Science.gov (United States)

Ghambashidze, Giorgi; Kentchiashvili, Naira; Tarkhnishvili, Maia; Jolokhava, Tamar; Meskhi, Tea

2017-04-01

Digital soil mapping (DSM) is a fast-developing sub discipline of soil science which gets more importance along with increased availability of spatial data. DSM is based on three main components: the input in the form of field and laboratory observational methods, the process used in terms of spatial and non-spatial soil inference systems, and the output in the form of spatial soil information systems, which includes outputs in the form of rasters of prediction along with the uncertainty of prediction. Georgia is one of the countries who are under the way of spatial data infrastructure development, which includes soil related spatial data also. Therefore, it is important to demonstrate the capacity of DSM technics for planning and decision making process, in which assessment of land suitability is a major interest for those willing to grow agricultural crops. In that term land suitability assessment for establishing organic farms is in high demand as market for organically produced commodities is still increasing. It is the first attempt in Georgia to use DSM to predict areas with potential for organic farming development. Current approach is based on risk assessment of soil pollution with toxic elements (As, Hg, Pb, Cd, Cr) and prediction of bio-availability of those elements to plants on example of the region of Western Georgia, where detailed soil survey was conducted and spatial database of soil was created. The results of the study show the advantages of DSM at early stage assessment and depending on availability and quality of the input data, it can achieve acceptable accuracy.

A methodology for digital soil mapping in poorly-accessible areas

NARCIS (Netherlands)

Cambule, A.; Rossiter, D.G.; Stoorvogel, J.J.

2013-01-01

Effective soil management requires knowledge of the spatial patterns of soil variation within the landscape to enable wise land use decisions. This is typically obtained through time-consuming and costly surveys. The aim of this study was to develop a cost-efficient methodology for digital soil

Mapping soil deformation around plant roots using in vivo 4D X-ray Computed Tomography and Digital Volume Correlation.

Science.gov (United States)

Keyes, S D; Gillard, F; Soper, N; Mavrogordato, M N; Sinclair, I; Roose, T

2016-06-14

The mechanical impedance of soils inhibits the growth of plant roots, often being the most significant physical limitation to root system development. Non-invasive imaging techniques have recently been used to investigate the development of root system architecture over time, but the relationship with soil deformation is usually neglected. Correlative mapping approaches parameterised using 2D and 3D image data have recently gained prominence for quantifying physical deformation in composite materials including fibre-reinforced polymers and trabecular bone. Digital Image Correlation (DIC) and Digital Volume Correlation (DVC) are computational techniques which use the inherent material texture of surfaces and volumes, captured using imaging techniques, to map full-field deformation components in samples during physical loading. Here we develop an experimental assay and methodology for four-dimensional, in vivo X-ray Computed Tomography (XCT) and apply a Digital Volume Correlation (DVC) approach to the data to quantify deformation. The method is validated for a field-derived soil under conditions of uniaxial compression, and a calibration study is used to quantify thresholds of displacement and strain measurement. The validated and calibrated approach is then demonstrated for an in vivo test case in which an extending maize root in field-derived soil was imaged hourly using XCT over a growth period of 19h. This allowed full-field soil deformation data and 3D root tip dynamics to be quantified in parallel for the first time. This fusion of methods paves the way for comparative studies of contrasting soils and plant genotypes, improving our understanding of the fundamental mechanical processes which influence root system development. Copyright © 2016 Elsevier Ltd. All rights reserved.

Digital Terroir Mapping in the Tokaj Historical Wine Region

Science.gov (United States)

Pásztor, László; Lukácsy, György; Szabó, József; László, Péter; Burai, Péter; Bakacsi, Zsófia; Koós, Sándor; Laborczi, Annamária; Takács, Katalin; Bekő, László

2015-04-01

Tokaj is a historical region for botritized dessert wine making, the famed Tokaji Wine Region has the distinction of being Europe's first classified wine region. Very recently the sustainable quality wine production in the region was targeted, which requires detailed and "terroir-based approach" characterization of viticultural land. Tokaj region consists of 27 villages, the total producing vineyard surface area is 5,500 hectares, and the total vineyard land exceeds 11,000 hectares. The Tokaj Kereskedőház Ltd. is the only state owned winery in Hungary. The company is integrating grapes for wine production from 1,100 hectares of vineyard, which consist of 3,500 parcels with average size of 0.3 hectares. In 2013 the Hungarian Government has decided to elaborate a sustainable quality wine production in the Tokaj region coordinated by the Tokaj Kereskedőház Ltd, the biggest wine producer. To achieve the target it is indispensable to assess the viticultural potential of the land. In 2013 the characterization of the vineyard land potential was started collecting detailed, up-to-date information on the main environmental factors (geology, geomorphology and soil) which comprise the terroir effect and combined with legacy data of climate. The Council of Wine Communities of Tokaj Region has decided to widen the survey for the whole wine region in the year 2014. The primary objective of our work was the execution of an appropriate terroir zoning, which was carried out by digital terroir mapping. As a start-up we adapted some concepts recently applied in French wine regions. The implementation was however carried out totally in spatial, digital environment. Four main sources of information have been used (i) airborne laser scanning, (ii) hyperspectral imaginary, (iii) digital soil maps compiled based on detailed soil survey and (iv) interpolated climatic data. Based on them pedoclimate, mesoclimate and soil water reservoir were spatially predicted. The operational spatial

Soil map disaggregation improved by soil-landscape relationships, area-proportional sampling and random forest implementation

DEFF Research Database (Denmark)

Møller, Anders Bjørn; Malone, Brendan P.; Odgers, Nathan

implementation generally improved the algorithm’s ability to predict the correct soil class. The implementation of soil-landscape relationships and area-proportional sampling generally increased the calculation time, while the random forest implementation reduced the calculation time. In the most successful......Detailed soil information is often needed to support agricultural practices, environmental protection and policy decisions. Several digital approaches can be used to map soil properties based on field observations. When soil observations are sparse or missing, an alternative approach...... is to disaggregate existing conventional soil maps. At present, the DSMART algorithm represents the most sophisticated approach for disaggregating conventional soil maps (Odgers et al., 2014). The algorithm relies on classification trees trained from resampled points, which are assigned classes according...

LARGE-SCALE INDICATIVE MAPPING OF SOIL RUNOFF

Directory of Open Access Journals (Sweden)

E. Panidi

2017-11-01

Full Text Available In our study we estimate relationships between quantitative parameters of relief, soil runoff regime, and spatial distribution of radioactive pollutants in the soil. The study is conducted on the test arable area located in basin of the upper Oka River (Orel region, Russia. Previously we collected rich amount of soil samples, which make it possible to investigate redistribution of the Chernobyl-origin cesium-137 in soil material and as a consequence the soil runoff magnitude at sampling points. Currently we are describing and discussing the technique applied to large-scale mapping of the soil runoff. The technique is based upon the cesium-137 radioactivity measurement in the different relief structures. Key stages are the allocation of the places for soil sampling points (we used very high resolution space imagery as a supporting data; soil samples collection and analysis; calibration of the mathematical model (using the estimated background value of the cesium-137 radioactivity; and automated compilation of the map (predictive map of the studied territory (digital elevation model is used for this purpose, and cesium-137 radioactivity can be predicted using quantitative parameters of the relief. The maps can be used as a support data for precision agriculture and for recultivation or melioration purposes.

Combining hyperspectral imagery and legacy measured soil profiles to map subsurface soil properties in a Mediterranean area (Cap-Bon, Tunisia)

Science.gov (United States)

Lagacherie, Philippe; Sneep, Anne-Ruth; Gomez, Cécile

2013-04-01

Previous studies have demonstrated that Visible Near InfraRed (Vis-NIR) Hyperspectral imagery is a cost-efficient way for mapping soil properties at fine resolutions (~5m) over large areas. However, such mapping is only feasible for soil surface since the effective penetration depths of optical sensors do not exceed several millimetres. This study aimed to extend the use of Vis-NIR hyperspectral imagery to the mapping of subsurface properties at three intervals of depth (15-30 cm, 30-60 cm and 60-100 cm) as specified by the GlobalSoilMap project. To avoid additional data collection, our basic idea was to develop an original Digital Soil Mapping approach that combines the digital maps of surface soil properties obtained from Vis-NIR hyperspectral imagery with legacy soil profiles of the region and with easily available images of DEM-derived parameters. The study was conducted in a pedologically-contrasted 300km² cultivated area located in the Cap Bon region (Northern Tunisia). AISA-Dual Vis-NIR hyperspectral airborne data were acquired over the studied area with a fine spatial resolution (5 m) and fine spectral resolution (260 spectral bands from 450 to 2500nm). Vegetated surfaces were masked to conserve only bare soil surface which represented around 50% of the study area. Three soil surface properties (clay and sand contents, Cation Exchange Capacity) were successfully mapped over the bare soils, from these data using Partial Least Square Regression models (R2 > 0.7). We used as additional data a set of images of landscape covariates derived from a 30 meter DEM and a local database of 152 legacy soil profiles from which soil properties values at the required intervals of depths were computed using an equal-area-spline algorithm. Our Digital Soil Mapping approach followed two steps: i) the development of surface-subsurface functions - linear models and random forests - that estimates subsurface property values from surface ones and landscape covariates and that

GIS-based production of digital soil map for Nigeria | Nkwunonwo ...

African Journals Online (AJOL)

Soil, a valuable natural resource can be said to play a part across the range of human existence and its knowledge is fundamental to its utilization and management. Soil maps provide a means of gaining understanding about the soil, but limitations in accuracy, revision and mode of presentation– relating to graphics or ...

A guide for the use of digital elevation model data for making soil surveys

Science.gov (United States)

Klingebiel, A.A.; Horvath, Emil H.; Reybold, William U.; Moore, D.G.; Fosnight, E.A.; Loveland, Thomas R.

1988-01-01

The intent of this publication is twofold: (1) to serve as a user guide for soil scientists and others interested in learning about the value and use of digital elevation model (DEM) data in making soil surveys and (2) to provide documentation of the Soil Landscape Analysis Project (SLAP). This publication provides a step-by-step guide on how digital slope-class maps are adjusted to topographic maps and orthophotoquads to obtain accurate slope-class maps, and how these derivative maps can be used as a base for soil survey premaps. In addition, guidance is given on the use of aspect-class maps and other resource data in making pre-maps. The value and use of tabular summaries are discussed. Examples of the use of DEM products by the authors and by selected field soil scientists are also given. Additional information on SLAP procedures may be obtained from USDA, Soil Conservation Service, Soil Survey Division, P.O. Box 2890, Washington, D.C. 20013, and from references (Horvath and others, 1987; Horvath and others, 1983; Klingebiel and others, 1987; and Young, 1987) listed in this publication. The slope and aspect products and the procedures for using these products have evolved during 5 years of cooperative research with the USDA, Soil Conservation Service and Forest Service, and the USDI, Bureau of Land Management.

Digital mapping of soil organic carbon contents and stocks in Denmark.

Science.gov (United States)

Adhikari, Kabindra; Hartemink, Alfred E; Minasny, Budiman; Bou Kheir, Rania; Greve, Mette B; Greve, Mogens H

2014-01-01

Estimation of carbon contents and stocks are important for carbon sequestration, greenhouse gas emissions and national carbon balance inventories. For Denmark, we modeled the vertical distribution of soil organic carbon (SOC) and bulk density, and mapped its spatial distribution at five standard soil depth intervals (0-5, 5-15, 15-30, 30-60 and 60-100 cm) using 18 environmental variables as predictors. SOC distribution was influenced by precipitation, land use, soil type, wetland, elevation, wetness index, and multi-resolution index of valley bottom flatness. The highest average SOC content of 20 g kg(-1) was reported for 0-5 cm soil, whereas there was on average 2.2 g SOC kg(-1) at 60-100 cm depth. For SOC and bulk density prediction precision decreased with soil depth, and a standard error of 2.8 g kg(-1) was found at 60-100 cm soil depth. Average SOC stock for 0-30 cm was 72 t ha(-1) and in the top 1 m there was 120 t SOC ha(-1). In total, the soils stored approximately 570 Tg C within the top 1 m. The soils under agriculture had the highest amount of carbon (444 Tg) followed by forest and semi-natural vegetation that contributed 11% of the total SOC stock. More than 60% of the total SOC stock was present in Podzols and Luvisols. Compared to previous estimates, our approach is more reliable as we adopted a robust quantification technique and mapped the spatial distribution of SOC stock and prediction uncertainty. The estimation was validated using common statistical indices and the data and high-resolution maps could be used for future soil carbon assessment and inventories.

Mapping potential acid sulfate soils in Denmark using legacy data and LiDAR-based derivatives

DEFF Research Database (Denmark)

Beucher, Amélie; Adhikari, Kabindra; Breuning-Madsen, Henrik

2017-01-01

drainage of areas classified as potential a.s. soilswithout prior permission fromenvironmental authorities. Themapping of these soils was first conducted in the 1980’s.Wetlands, inwhich Danish potential a.s. soils mostly occur,were targeted and the soilswere surveyed through conventional mapping....... In this study, a probability map for potential a.s. soil occurrence was constructed for thewetlands located in Jutland, Denmark (c. 6500 km2), using the digital soilmapping (DSM) approach. Among the variety of available DSM techniques, artificial neural networks (ANNs) were selected. More than 8000 existing...... of environmental variables. The overall prediction accuracy based on a 30% hold-back validation data reached 70%. Furthermore, the conventional map indicated 32% of the study area (c. 2100 km2) as having a high frequency for potential a.s. soils while the digital map displayed about 46% (c. 3000 km2) as high...

Exploring the potential offered by legacy soil databases for ecosystem services mapping of Central African soils

Science.gov (United States)

Verdoodt, Ann; Baert, Geert; Van Ranst, Eric

2014-05-01

Central African soil resources are characterised by a large variability, ranging from stony, shallow or sandy soils with poor life-sustaining capabilities to highly weathered soils that recycle and support large amounts of biomass. Socio-economic drivers within this largely rural region foster inappropriate land use and management, threaten soil quality and finally culminate into a declining soil productivity and increasing food insecurity. For the development of sustainable land use strategies targeting development planning and natural hazard mitigation, decision makers often rely on legacy soil maps and soil profile databases. Recent development cooperation financed projects led to the design of soil information systems for Rwanda, D.R. Congo, and (ongoing) Burundi. A major challenge is to exploit these existing soil databases and convert them into soil inference systems through an optimal combination of digital soil mapping techniques, land evaluation tools, and biogeochemical models. This presentation aims at (1) highlighting some key characteristics of typical Central African soils, (2) assessing the positional, geographic and semantic quality of the soil information systems, and (3) revealing its potential impacts on the use of these datasets for thematic mapping of soil ecosystem services (e.g. organic carbon storage, pH buffering capacity). Soil map quality is assessed considering positional and semantic quality, as well as geographic completeness. Descriptive statistics, decision tree classification and linear regression techniques are used to mine the soil profile databases. Geo-matching as well as class-matching approaches are considered when developing thematic maps. Variability in inherent as well as dynamic soil properties within the soil taxonomic units is highlighted. It is hypothesized that within-unit variation in soil properties highly affects the use and interpretation of thematic maps for ecosystem services mapping. Results will mainly be based

Predicting and mapping soil available water capacity in Korea.

Science.gov (United States)

Hong, Suk Young; Minasny, Budiman; Han, Kyung Hwa; Kim, Yihyun; Lee, Kyungdo

2013-01-01

The knowledge on the spatial distribution of soil available water capacity at a regional or national extent is essential, as soil water capacity is a component of the water and energy balances in the terrestrial ecosystem. It controls the evapotranspiration rate, and has a major impact on climate. This paper demonstrates a protocol for mapping soil available water capacity in South Korea at a fine scale using data available from surveys. The procedures combined digital soil mapping technology with the available soil map of 1:25,000. We used the modal profile data from the Taxonomical Classification of Korean Soils. The data consist of profile description along with physical and chemical analysis for the modal profiles of the 380 soil series. However not all soil samples have measured bulk density and water content at -10 and -1500 kPa. Thus they need to be predicted using pedotransfer functions. Furthermore, water content at -10 kPa was measured using ground samples. Thus a correction factor is derived to take into account the effect of bulk density. Results showed that Andisols has the highest mean water storage capacity, followed by Entisols and Inceptisols which have loamy texture. The lowest water retention is Entisols which are dominated by sandy materials. Profile available water capacity to a depth of 1 m was calculated and mapped for Korea. The western part of the country shows higher available water capacity than the eastern part which is mountainous and has shallower soils. The highest water storage capacity soils are the Ultisols and Alfisols (mean of 206 and 205 mm, respectively). Validation of the maps showed promising results. The map produced can be used as an indication of soil physical quality of Korean soils.

Predicting and mapping soil available water capacity in Korea

Directory of Open Access Journals (Sweden)

Suk Young Hong

2013-04-01

Full Text Available The knowledge on the spatial distribution of soil available water capacity at a regional or national extent is essential, as soil water capacity is a component of the water and energy balances in the terrestrial ecosystem. It controls the evapotranspiration rate, and has a major impact on climate. This paper demonstrates a protocol for mapping soil available water capacity in South Korea at a fine scale using data available from surveys. The procedures combined digital soil mapping technology with the available soil map of 1:25,000. We used the modal profile data from the Taxonomical Classification of Korean Soils. The data consist of profile description along with physical and chemical analysis for the modal profiles of the 380 soil series. However not all soil samples have measured bulk density and water content at −10 and −1500 kPa. Thus they need to be predicted using pedotransfer functions. Furthermore, water content at −10 kPa was measured using ground samples. Thus a correction factor is derived to take into account the effect of bulk density. Results showed that Andisols has the highest mean water storage capacity, followed by Entisols and Inceptisols which have loamy texture. The lowest water retention is Entisols which are dominated by sandy materials. Profile available water capacity to a depth of 1 m was calculated and mapped for Korea. The western part of the country shows higher available water capacity than the eastern part which is mountainous and has shallower soils. The highest water storage capacity soils are the Ultisols and Alfisols (mean of 206 and 205 mm, respectively. Validation of the maps showed promising results. The map produced can be used as an indication of soil physical quality of Korean soils.

Evaluating the new soil erosion map of Hungary

Science.gov (United States)

Waltner, István; Centeri, Csaba; Takács, Katalin; Pirkó, Béla; Koós, Sándor; László, Péter; Pásztor, László

2017-04-01

With growing concerns on the effects of climate change and land use practices on our soil resources, soil erosion by water is becoming a significant issue internationally. Since the 1964 publication of the first soil erosion map of Hungary, there have been several attempts to provide a countrywide assessment of erosion susceptibility. However, there has been no up-to-date map produced in the last decade. In 2016, a new, 1:100 000 scale soil erosion map was published, based on available soil, elevation, land use and meteorological data for the extremely wet year of 2010. The map utilized combined outputs for two spatially explicit methods: the widely used empirical Universal Soil Loss Equation (USLE) and the process-based Pan-European Soil Erosion Risk Assessment (PESERA) models. The present study aims to provide a detailed analysis of the model results. In lieu of available national monitoring data, information from other sources were used. The Soil Degradation Subsystem (TDR) of the National Environmental Information System (OKIR) is a digital database based on a soil survey and farm dairy data collected from representative farms in Hungary. During the survey all kind of degradation forms - including soil erosion - were considered. Agricultural and demographic data was obtained from the Hungarian Central Statistical Office (KSH). Data from an interview-based survey was also used in an attempt to assess public awareness of soil erosion risks. Point-based evaluation of the model results was complemented with cross-regional assessment of soil erosion estimates. This, combined with available demographic information provides us with an opportunity to address soil erosion on a community level, with the identification of regions with the highest risk of being affected by soil erosion.

Soil properties mapping with the DIGISOIL multi-sensor system

Science.gov (United States)

Grandjean, G.

2012-04-01

The multidisciplinary DIGISOIL project aimed to integrate and improve in situ and proximal measurement technologies for the assessment of soil properties and soil degradation indicators, going from the sensing technologies to their integration and their application in (digital) soil mapping (DSM). In order to assess and prevent soil degradation and to benefit from the different ecological, economical and historical functions of the soil in a sustainable way, high resolution and quantitative maps of soil properties are needed. The core objective of the project is to explore and exploit new capabilities of advanced geophysical technologies for answering this societal demand. To this aim, DIGISOIL addresses four issues covering technological, soil science and economic aspects: (i) the validation of geophysical (in situ, proximal and airborne) technologies and integrated pedo-geophysical inversion techniques (mechanistic data fusion) (ii) the relation between the geophysical parameters and the soil properties, (iii) the integration of the derived soil properties for mapping soil functions and soil threats, (iv) the pre-evaluation, standardisation and sub-industrialization of the proposed methodologies, including technical and economical studies related to the societal demand. With respect to these issues, the DIGISOIL project allows to develop, test and validate the most relevant geophysical technologies for mapping soil properties. The system was tested on different field tests, and validated the proposed technologies and solutions for each of the identified methods: geoelectric, GPR, EMI, seismics, magnetic and hyperspectral. After data acquisition systems, sensor geometry, and advanced data processing techniques have been developed and validated, we present now the solutions for going from geophysical data to soil properties maps. For two test sites, located respectively in Luxembourg (LU) and Mugello (IT) a set of soil properties maps have been produced. They give

Can Process Understanding Help Elucidate The Structure Of The Critical Zone? Comparing Process-Based Soil Formation Models With Digital Soil Mapping.

Science.gov (United States)

Vanwalleghem, T.; Román, A.; Peña, A.; Laguna, A.; Giráldez, J. V.

2017-12-01

There is a need for better understanding the processes influencing soil formation and the resulting distribution of soil properties in the critical zone. Soil properties can exhibit strong spatial variation, even at the small catchment scale. Especially soil carbon pools in semi-arid, mountainous areas are highly uncertain because bulk density and stoniness are very heterogeneous and rarely measured explicitly. In this study, we explore the spatial variability in key soil properties (soil carbon stocks, stoniness, bulk density and soil depth) as a function of processes shaping the critical zone (weathering, erosion, soil water fluxes and vegetation patterns). We also compare the potential of traditional digital soil mapping versus a mechanistic soil formation model (MILESD) for predicting these key soil properties. Soil core samples were collected from 67 locations at 6 depths. Total soil organic carbon stocks were 4.38 kg m-2. Solar radiation proved to be the key variable controlling soil carbon distribution. Stone content was mostly controlled by slope, indicating the importance of erosion. Spatial distribution of bulk density was found to be highly random. Finally, total carbon stocks were predicted using a random forest model whose main covariates were solar radiation and NDVI. The model predicts carbon stocks that are double as high on north versus south-facing slopes. However, validation showed that these covariates only explained 25% of the variation in the dataset. Apparently, present-day landscape and vegetation properties are not sufficient to fully explain variability in the soil carbon stocks in this complex terrain under natural vegetation. This is attributed to a high spatial variability in bulk density and stoniness, key variables controlling carbon stocks. Similar results were obtained with the mechanistic soil formation model MILESD, suggesting that more complex models might be needed to further explore this high spatial variability.

Digital technical map of Košice city

Directory of Open Access Journals (Sweden)

Štefan Kuzevič

2007-06-01

Full Text Available The digital map of a city is comprised complex of the map. Process of creation of the digital map of a city is time consuming and financially demanding. The digital map is created as co-operation of the local authority, technology network administrator, companies, and local government. The exact and current digital map of the city utilizable for multilateral applications is the result of this co-operation. The digital map of city catch all important phenomenon and objects which are needed for making decisions and planning to all grades controlling the local authority. The Geographic information systems tools make possible updating digital map, analyses and syntheses spatial phenomena and theirs relationships. The digital map of city is created step by step. The digital technical map of city is one of very important part of the digital map. Illustration of the part digital technical map of Košice is showed in the Fig. 1.

Using Environmental Variables for Studying of the Quality of Sampling in Soil Mapping

OpenAIRE

A. Jafari; Norair Toomanian; R. Taghizadeh Mehrjerdi

2016-01-01

Introduction: Methods of soil survey are generally empirical and based on the mental development of the surveyor, correlating soil with underlying geology, landforms, vegetation and air-photo interpretation. Since there are no statistical criteria for traditional soil sampling; this may lead to bias in the areas being sampled. In digital soil mapping, soil samples may be used to elaborate quantitative relationships or models between soil attributes and soil covariates. Because the relationshi...

Comparison of Capability of Digitizing Methods to Predict Soil classification According to the Soil Taxonomy and World Reference Base for Soil Resources

Directory of Open Access Journals (Sweden)

zohreh mosleh

2017-02-01

Full Text Available Introduction: Soil classification generally aims to establish a taxonomy based on breaking the soil continuum into homogeneous groups that can highlight the essential differences in soil properties and functions between classes.The two most widely used modern soil classification schemes are Soil Taxonomy (ST and World Reference Base for Soil Resources (WRB.With the development of computers and technology, digital and quantitative approaches have been developed. These new techniques that include the spatial prediction of soil properties or classes, relies on finding the relationships between soil and the auxiliary information that explain the soil forming factors or processes and finally predict soil patterns on the landscape. These approaches are commonly referred to as digital soil mapping (DSM (14. A key component of any DSM mapping activity is the method used to define the relationship between soil observation and auxiliary information (4. Several types of machine learning approaches have been applied for digital soil mapping of soil classes, such as logistic and multinomial logistic regressions (10,12, random forests (15, neural networks (3,13 and classification trees (22,4. Many decisions about the soil use and management are based on the soil differences that cannot be captured by higher taxonomic levels (i.e., order, suborder and great group (4. In low relief areas such as plains, it is expected that the soil forming factors are more homogenous and auxiliary information explaining soil forming factors may have low variation and cannot show the soil variability. Materials and Methods: The study area is located in the Shahrekord plain of Chaharmahal-Va-Bakhtiari province. According tothe semi-detailed soil survey (16, 120 pedons with approximate distance of 750 m were excavated and described according to the “field book for describing and sampling soils” (19. Soil samples were taken from different genetic horizons, air dried and

Creating soil moisture maps based on radar satellite imagery

Science.gov (United States)

Hnatushenko, Volodymyr; Garkusha, Igor; Vasyliev, Volodymyr

2017-10-01

The presented work is related to a study of mapping soil moisture basing on radar data from Sentinel-1 and a test of adequacy of the models constructed on the basis of data obtained from alternative sources. Radar signals are reflected from the ground differently, depending on its properties. In radar images obtained, for example, in the C band of the electromagnetic spectrum, soils saturated with moisture usually appear in dark tones. Although, at first glance, the problem of constructing moisture maps basing on radar data seems intuitively clear, its implementation on the basis of the Sentinel-1 data on an industrial scale and in the public domain is not yet available. In the process of mapping, for verification of the results, measurements of soil moisture obtained from logs of the network of climate stations NOAA US Climate Reference Network (USCRN) were used. This network covers almost the entire territory of the United States. The passive microwave radiometers of Aqua and SMAP satellites data are used for comparing processing. In addition, other supplementary cartographic materials were used, such as maps of soil types and ready moisture maps. The paper presents a comparison of the effect of the use of certain methods of roughening the quality of radar data on the result of mapping moisture. Regression models were constructed showing dependence of backscatter coefficient values Sigma0 for calibrated radar data of different spatial resolution obtained at different times on soil moisture values. The obtained soil moisture maps of the territories of research, as well as the conceptual solutions about automation of operations of constructing such digital maps, are presented. The comparative assessment of the time required for processing a given set of radar scenes with the developed tools and with the ESA SNAP product was carried out.

Preliminary soil-slip susceptibility maps, southwestern California

Science.gov (United States)

Morton, Douglas M.; Alvarez, Rachel M.; Campbell, Russell H.; Digital preparation by Bovard, Kelly R.; Brown, D.T.; Corriea, K.M.; Lesser, J.N.

2003-01-01

This group of maps shows relative susceptibility of hill slopes to the initiation sites of rainfall-triggered soil slip-debris flows in southwestern California. As such, the maps offer a partial answer to one part of the three parts necessary to predict the soil-slip/debris-flow process. A complete prediction of the process would include assessments of “where”, “when”, and “how big”. These maps empirically show part of the “where” of prediction (i.e., relative susceptibility to sites of initiation of the soil slips) but do not attempt to show the extent of run out of the resultant debris flows. Some information pertinent to “when” the process might begin is developed. “When” is determined mostly by dynamic factors such as rainfall rate and duration, for which local variations are not amenable to long-term prediction. “When” information is not provided on the maps but is described later in this narrative. The prediction of “how big” is addressed indirectly by restricting the maps to a single type of landslide process—soil slip-debris flows. The susceptibility maps were created through an iterative process from two kinds of information. First, locations of sites of past soil slips were obtained from inventory maps of past events. Aerial photographs, taken during six rainy seasons that produced abundant soil slips, were used as the basis for soil slip-debris flow inventory. Second, digital elevation models (DEM) of the areas that were inventoried were used to analyze the spatial characteristics of soil slip locations. These data were supplemented by observations made on the ground. Certain physical attributes of the locations of the soil-slip debris flows were found to be important and others were not. The most important attribute was the mapped bedrock formation at the site of initiation of the soil slip. However, because the soil slips occur in surficial materials overlying the bedrocks units, the bedrock formation can only serve as

Uncertainty indication in soil function maps - transparent and easy-to-use information to support sustainable use of soil resources

Science.gov (United States)

Greiner, Lucie; Nussbaum, Madlene; Papritz, Andreas; Zimmermann, Stephan; Gubler, Andreas; Grêt-Regamey, Adrienne; Keller, Armin

2018-05-01

Spatial information on soil function fulfillment (SFF) is increasingly being used to inform decision-making in spatial planning programs to support sustainable use of soil resources. Soil function maps visualize soils abilities to fulfill their functions, e.g., regulating water and nutrient flows, providing habitats, and supporting biomass production based on soil properties. Such information must be reliable for informed and transparent decision-making in spatial planning programs. In this study, we add to the transparency of soil function maps by (1) indicating uncertainties arising from the prediction of soil properties generated by digital soil mapping (DSM) that are used for soil function assessment (SFA) and (2) showing the response of different SFA methods to the propagation of uncertainties through the assessment. For a study area of 170 km2 in the Swiss Plateau, we map 10 static soil sub-functions for agricultural soils for a spatial resolution of 20 × 20 m together with their uncertainties. Mapping the 10 soil sub-functions using simple ordinal assessment scales reveals pronounced spatial patterns with a high variability of SFF scores across the region, linked to the inherent properties of the soils and terrain attributes and climate conditions. Uncertainties in soil properties propagated through SFA methods generally lead to substantial uncertainty in the mapped soil sub-functions. We propose two types of uncertainty maps that can be readily understood by stakeholders. Cumulative distribution functions of SFF scores indicate that SFA methods respond differently to the propagated uncertainty of soil properties. Even where methods are comparable on the level of complexity and assessment scale, their comparability in view of uncertainty propagation might be different. We conclude that comparable uncertainty indications in soil function maps are relevant to enable informed and transparent decisions on the sustainable use of soil resources.

Comparison between artificial neural networks and maximum likelihood classification in digital soil mapping

Directory of Open Access Journals (Sweden)

César da Silva Chagas

2013-04-01

Full Text Available Soil surveys are the main source of spatial information on soils and have a range of different applications, mainly in agriculture. The continuity of this activity has however been severely compromised, mainly due to a lack of governmental funding. The purpose of this study was to evaluate the feasibility of two different classifiers (artificial neural networks and a maximum likelihood algorithm in the prediction of soil classes in the northwest of the state of Rio de Janeiro. Terrain attributes such as elevation, slope, aspect, plan curvature and compound topographic index (CTI and indices of clay minerals, iron oxide and Normalized Difference Vegetation Index (NDVI, derived from Landsat 7 ETM+ sensor imagery, were used as discriminating variables. The two classifiers were trained and validated for each soil class using 300 and 150 samples respectively, representing the characteristics of these classes in terms of the discriminating variables. According to the statistical tests, the accuracy of the classifier based on artificial neural networks (ANNs was greater than of the classic Maximum Likelihood Classifier (MLC. Comparing the results with 126 points of reference showed that the resulting ANN map (73.81 % was superior to the MLC map (57.94 %. The main errors when using the two classifiers were caused by: a the geological heterogeneity of the area coupled with problems related to the geological map; b the depth of lithic contact and/or rock exposure, and c problems with the environmental correlation model used due to the polygenetic nature of the soils. This study confirms that the use of terrain attributes together with remote sensing data by an ANN approach can be a tool to facilitate soil mapping in Brazil, primarily due to the availability of low-cost remote sensing data and the ease by which terrain attributes can be obtained.

Uncertainty indication in soil function maps – transparent and easy-to-use information to support sustainable use of soil resources

Directory of Open Access Journals (Sweden)

L. Greiner

2018-05-01

Full Text Available Spatial information on soil function fulfillment (SFF is increasingly being used to inform decision-making in spatial planning programs to support sustainable use of soil resources. Soil function maps visualize soils abilities to fulfill their functions, e.g., regulating water and nutrient flows, providing habitats, and supporting biomass production based on soil properties. Such information must be reliable for informed and transparent decision-making in spatial planning programs. In this study, we add to the transparency of soil function maps by (1 indicating uncertainties arising from the prediction of soil properties generated by digital soil mapping (DSM that are used for soil function assessment (SFA and (2 showing the response of different SFA methods to the propagation of uncertainties through the assessment. For a study area of 170 km2 in the Swiss Plateau, we map 10 static soil sub-functions for agricultural soils for a spatial resolution of 20 × 20 m together with their uncertainties. Mapping the 10 soil sub-functions using simple ordinal assessment scales reveals pronounced spatial patterns with a high variability of SFF scores across the region, linked to the inherent properties of the soils and terrain attributes and climate conditions. Uncertainties in soil properties propagated through SFA methods generally lead to substantial uncertainty in the mapped soil sub-functions. We propose two types of uncertainty maps that can be readily understood by stakeholders. Cumulative distribution functions of SFF scores indicate that SFA methods respond differently to the propagated uncertainty of soil properties. Even where methods are comparable on the level of complexity and assessment scale, their comparability in view of uncertainty propagation might be different. We conclude that comparable uncertainty indications in soil function maps are relevant to enable informed and transparent decisions on the sustainable use of soil

GEOSTATISTICAL BASED SUSCEPTIBILITY MAPPING OF SOIL EROSION AND OPTIMIZATION OF ITS CAUSATIVE FACTORS: A CONCEPTUAL FRAMEWORK

Directory of Open Access Journals (Sweden)

ABDULKADIR T. SHOLAGBERU

2017-11-01

Full Text Available Soil erosion hazard is the second biggest environmental challenges after population growth causing land degradation, desertification and water deterioration. Its impacts on watersheds include loss of soil nutrients, reduced reservoir capacity through siltation which may lead to flood risk, landslide, high water turbidity, etc. These problems become more pronounced in human altered mountainous areas through intensive agricultural activities, deforestation and increased urbanization among others. However, due to challenging nature of soil erosion management, there is great interest in assessing its spatial distribution and susceptibility levels. This study is thus intend to review the recent literatures and develop a novel framework for soil erosion susceptibility mapping using geostatistical based support vector machine (SVM, remote sensing and GIS techniques. The conceptual framework is to bridge the identified knowledge gaps in the area of causative factors’ (CFs selection. In this research, RUSLE model, field studies and the existing soil erosion maps for the study area will be integrated for the development of inventory map. Spatial data such as Landsat 8, digital soil and geological maps, digital elevation model and hydrological data shall be processed for the extraction of erosion CFs. GISbased SVM techniques will be adopted for the establishment of spatial relationships between soil erosion and its CFs, and subsequently for the development of erosion susceptibility maps. The results of this study include evaluation of predictive capability of GIS-based SVM in soil erosion mapping and identification of the most influential CFs for erosion susceptibility assessment. This study will serve as a guide to watershed planners and to alleviate soil erosion challenges and its related hazards.

Soil property maps of Africa at 250 m resolution

Science.gov (United States)

Kempen, Bas; Hengl, Tomislav; Heuvelink, Gerard B. M.; Leenaars, Johan G. B.; Walsh, Markus G.; MacMillan, Robert A.; Mendes de Jesus, Jorge S.; Shepherd, Keith; Sila, Andrew; Desta, Lulseged T.; Tondoh, Jérôme E.

2015-04-01

Vast areas of arable land in sub-Saharan Africa suffer from low soil fertility and physical soil constraints, and significant amounts of nutrients are lost yearly due to unsustainable soil management practices. At the same time it is expected that agriculture in Africa must intensify to meet the growing demand for food and fiber the next decades. Protection and sustainable management of Africa's soil resources is crucial to achieve this. In this context, comprehensive, accurate and up-to-date soil information is an essential input to any agricultural or environmental management or policy and decision-making model. In Africa, detailed soil information has been fragmented and limited to specific zones of interest for decades. To help bridge the soil information gap in Africa, the Africa Soil Information Service (AfSIS) project was established in 2008. AfSIS builds on recent advances in digital soil mapping, infrared spectroscopy, remote sensing, (geo)statistics, and integrated soil fertility management to improve the way soils are evaluated, mapped, and monitored. Over the period 2008-2014, the AfSIS project has compiled two soil profile data sets (about 28,000 unique locations): the Africa Soil Profiles (legacy) database and the AfSIS Sentinel Site (new soil samples) database -- the two data sets represent the most comprehensive soil sample database of the African continent to date. In addition a large set of high-resolution environmental data layers (covariates) was assembled. The point data were used in the AfSIS project to generate a set of maps of key soil properties for the African continent at 250 m spatial resolution: sand, silt and clay fractions, bulk density, organic carbon, total nitrogen, pH, cation-exchange capacity, exchangeable bases (Ca, K, Mg, Na), exchangeable acidity, and Al content. These properties were mapped for six depth intervals up to 2 m: 0-5 cm, 5-15 cm, 15-30 cm, 30-60 cm, 60-100 cm, and 100-200 cm. Random forests modelling was used to

Semi-automated landform classification for hazard mapping of soil liquefaction by earthquake

Science.gov (United States)

Nakano, Takayuki

2018-05-01

Soil liquefaction damages were caused by huge earthquake in Japan, and the similar damages are concerned in near future huge earthquake. On the other hand, a preparation of soil liquefaction risk map (soil liquefaction hazard map) is impeded by the difficulty of evaluation of soil liquefaction risk. Generally, relative soil liquefaction risk should be able to be evaluated from landform classification data by using experimental rule based on the relationship between extent of soil liquefaction damage and landform classification items associated with past earthquake. Therefore, I rearranged the relationship between landform classification items and soil liquefaction risk intelligibly in order to enable the evaluation of soil liquefaction risk based on landform classification data appropriately and efficiently. And I developed a new method of generating landform classification data of 50-m grid size from existing landform classification data of 250-m grid size by using digital elevation model (DEM) data and multi-band satellite image data in order to evaluate soil liquefaction risk in detail spatially. It is expected that the products of this study contribute to efficient producing of soil liquefaction hazard map by local government.

The History of Soil Mapping and Classification in Europe: The role of the European Commission

Science.gov (United States)

Montanarella, Luca

2014-05-01

Early systematic soil mapping in Europe dates back to the early times of soil science in the 19th Century and was developed at National scales mostly for taxation purposes. National soil classification systems emerged out of the various scientific communities active at that time in leading countries like Germany, Austria, France, Belgium, United Kingdom and many others. Different scientific communities were leading in the various countries, in some cases stemming from geological sciences, in others as a branch of agricultural sciences. Soil classification for the purpose of ranking soils for their capacity to be agriculturally productive emerged as the main priority, allowing in some countries for very detailed and accurate soil maps at 1:5,000 scale and larger. Detailed mapping was mainly driven by taxation purposes in the early times but evolved in several countries also as a planning and management tool for farms and local administrations. The need for pan-European soil mapping and classification efforts emerged only after World War II in the early 1950's under the auspices of FAO with the aim to compile a common European soil map as a contribution to the global soil mapping efforts of FAO at that time. These efforts evolved over the next decades, with the support of the European Commission, towards the establishment of a permanent network of National soil survey institutions (the European Soil Bureau Network). With the introduction of digital soil mapping technologies, the new European Soil Information System (EUSIS) was established, incorporating data at multiple scales for the EU member states and bordering countries. In more recent years, the formal establishment of the European Soil Data Centre (ESDAC) hosted by the European Commission, together with a formal legal framework for soil mapping and soil classification provided by the INSPIRE directive and the related standardization and harmonization efforts, has led to the operational development of advanced

Digital Mapping of Soil Organic Carbon Contents and Stocks in Denmark

DEFF Research Database (Denmark)

Adhikari, Kabindra; Hartemink, Alfred E.; Minasny, Budiman

2014-01-01

Estimation of carbon contents and stocks are important for carbon sequestration, greenhouse gas emissions and national carbon balance inventories. For Denmark, we modeled the vertical distribution of soil organic carbon (SOC) and bulk density, and mapped its spatial distribution at five standard ...

Comparing Kriging and Regression Approaches for Mapping Soil Clay Content in a diverse Danish Landscape

DEFF Research Database (Denmark)

Adhikari, Kabindra; Bou Kheir, Rania; Greve, Mette Balslev

2013-01-01

Information on the spatial variability of soil texture including soil clay content in a landscape is very important for agricultural and environmental use. Different prediction techniques are available to assess and map spatial variability of soil properties, but selecting the most suitable techn...... the prediction in OKst compared with that in OK, whereas RT showed the lowest performance of all (R2 = 0.52; RMSE = 0.52; and RPD = 1.17). We found RKrr to be an effective prediction method and recommend this method for any future soil mapping activities in Denmark....... technique at a given site has always been a major issue in all soil mapping applications. We studied the prediction performance of ordinary kriging (OK), stratified OK (OKst), regression trees (RT), and rule-based regression kriging (RKrr) for digital mapping of soil clay content at 30.4-m grid size using 6...

Evaluation of urban soils. Subproject 4: Bonding of heavy metals in technological soils - mapping of urban soils for the city of Rostock. Final report

International Nuclear Information System (INIS)

Kretschmer, H.; Coburger, E.; Kahle, P.; Neumann, A.; Surkus, A.

1995-01-01

Within the framework of the project a conceptional soil map for the urban area of Rostock was drawn up. The starting point was formed by the collection and analysis of available information. The following maps were digitised with the help of the geographical information system Arc/Info: Soil estimation, middle scaled map of agricultural sites, geology, maps of bogs and forest sites, map of the bog-depth sourrounding the river Warnow by Geinitz from 1887. To characterise the influence by man information about impermeable covered areas, actual land use, thrown up areas and disposal sites as well as war-destroyed sites were digitally used. Till the beginning of this project no information about impermeable covered areas and about the actual land use were available. That's why these two maps were created within the framework of the project on the base of topographical maps, aerial photographs and results of on-site-captures. Afterwards the thematic layers were overlapped. The general conceptional map for the urban area of Rostock was created out of the three separate conceptional maps about groundwater-influence, natural soil inventory and man-influence. Soil societies were assigned to the units of this general conceptional map. At the end 35 units were given for Rostock. Detailed mappings were taken on areas of the following kinds of use: Living areas, city centre, gardens, parks, graveyards, industrial and military sites. 26 main profiles were described and soil-physically and soil-chemically examined. The total contents of the heavy metals Zn, Cu, Pb and Cd were determined for the horizons of the main profiles. The subproject of Rostock is also concerned with investigations on the heavy metals (hM) Cu, Pb, Cd, Zn and Ni in technological substrates (tS) from Kiel, Eckernfoerde, Halle and Rostock (11 main soil profiles). (orig./SR) [de

Case studies: Soil mapping using multiple methods

Science.gov (United States)

Petersen, Hauke; Wunderlich, Tina; Hagrey, Said A. Al; Rabbel, Wolfgang; Stümpel, Harald

2010-05-01

application of geophysical methods, e.g. GPR on wet loessy soils will result in a high attenuation of signals. Furthermore, with this knowledge we support the development of geophysical pedo-transfer-functions, i.e. the link between geophysical to soil parameters, which is active researched in another work package of the iSOIL project. Acknowledgement: iSOIL-Interactions between soil related sciences - Linking geophysics, soil science and digital soil mapping is a Collaborative Project (Grant Agreement number 211386) co-funded by the Research DG of the European Commission within the RTD activities of the FP7 Thematic Priority Environment.

A stratified two-stage sampling design for digital soil mapping in a Mediterranean basin

Science.gov (United States)

Blaschek, Michael; Duttmann, Rainer

2015-04-01

ESRI software (ArcGIS) extended by Hawth's Tools and later on its replacement the Geospatial Modelling Environment (GME). 88% of all desired points could actually be reached in the field and have been successfully sampled. Our results indicate that the sampled calibration and validation sets are representative for each other and could be successfully used as interpolation data for spatial prediction purposes. With respect to soil textural fractions, for instance, equal multivariate means and variance homogeneity were found for the two datasets as evidenced by significant (P > 0.05) Hotelling T²-test (2.3 with df1 = 3, df2 = 193) and Bartlett's test statistics (6.4 with df = 6). The multivariate prediction of clay, silt and sand content using a neural network residual cokriging approach reached an explained variance level of 56%, 47% and 63%. Thus, the presented case study is a successful example of considering readily available continuous information on soil forming factors such as geology and relief as stratifying variables for designing sampling schemes in digital soil mapping projects.

Comparison of Three Supervised Learning Methods for Digital Soil Mapping: Application to a Complex Terrain in the Ecuadorian Andes

Directory of Open Access Journals (Sweden)

Martin Hitziger

2014-01-01

Full Text Available A digital soil mapping approach is applied to a complex, mountainous terrain in the Ecuadorian Andes. Relief features are derived from a digital elevation model and used as predictors for topsoil texture classes sand, silt, and clay. The performance of three statistical learning methods is compared: linear regression, random forest, and stochastic gradient boosting of regression trees. In linear regression, a stepwise backward variable selection procedure is applied and overfitting is controlled by minimizing Mallow’s Cp. For random forest and boosting, the effect of predictor selection and tuning procedures is assessed. 100-fold repetitions of a 5-fold cross-validation of the selected modelling procedures are employed for validation, uncertainty assessment, and method comparison. Absolute assessment of model performance is achieved by comparing the prediction error of the selected method and the mean. Boosting performs best, providing predictions that are reliably better than the mean. The median reduction of the root mean square error is around 5%. Elevation is the most important predictor. All models clearly distinguish ridges and slopes. The predicted texture patterns are interpreted as result of catena sequences (eluviation of fine particles on slope shoulders and landslides (mixing up mineral soil horizons on slopes.

Potential and limitations of using soil mapping information to understand landscape hydrology

Directory of Open Access Journals (Sweden)

F. Terribile

2011-12-01

Full Text Available This paper addresses the following points: how can whole soil data from normally available soil mapping databases (both conventional and those integrated by digital soil mapping procedures be usefully employed in hydrology? Answering this question requires a detailed knowledge of the quality and quantity of information embedded in and behind a soil map.

To this end a description of the process of drafting soil maps was prepared (which is included in Appendix A of this paper. Then a detailed screening of content and availability of soil maps and database was performed, with the objective of an analytical evaluation of the potential and the limitations of soil data obtained through soil surveys and soil mapping. Then we reclassified the soil features according to their direct, indirect or low hydrologic relevance. During this phase, we also included information regarding whether this data was obtained by qualitative, semi-quantitative or quantitative methods. The analysis was performed according to two main points of concern: (i the hydrological interpretation of the soil data and (ii the quality of the estimate or measurement of the soil feature.

The interaction between pedology and hydrology processes representation was developed through the following Italian case studies with different hydropedological inputs: (i comparative land evaluation models, by means of an exhaustive itinerary from simple to complex modelling applications depending on soil data availability, (ii mapping of soil hydrological behaviour for irrigation management at the district scale, where the main hydropedological input was the application of calibrated pedo-transfer functions and the Hydrological Function Unit concept, and (iii flood event simulation in an ungauged basin, with the functional aggregation of different soil units for a simplified soil pattern.

In conclusion, we show that special care is required in handling data from soil

Digital Mapping of Toxic Metals in Qatari Soils Using Remote Sensing and Ancillary Data

Directory of Open Access Journals (Sweden)

Yi Peng

2016-12-01

Full Text Available After decades of mining and industrialization in Qatar, it is important to estimate their impact on soil pollution with toxic metals. The study utilized 300 topsoil (0–30 cm samples, multi-spectral images (Landsat 8, spectral indices and environmental variables to model and map the spatial distribution of arsenic (As, chromium (Cr, nickel (Ni, copper (Cu, lead (Pb and zinc (Zn in Qatari soils. The prediction model used condition-based rules generated in the Cubist tool. In terms of R2 and the ratio of performance to interquartile distance (RPIQ, the models showed good predictive capabilities for all elements. Of all of the prediction results, Cu had the highest R2 = 0.74, followed by As > Pb > Cr > Zn > Ni. This study found that all of the models only chose images from January and February as predictors, which indicates that images from these two months are important for soil toxic metals’ monitoring in arid soils, due to the climate and the vegetation cover during this season. Topsoil maps of the six toxic metals were generated. The maps can be used to prioritize the choice of remediation measures and can be applied to other arid areas of similar environmental/socio-economic conditions and pollution causes.

Digital Mapping of Toxic Metals in Qatari Soils Using Remote Sensing and Ancillary Data

DEFF Research Database (Denmark)

Peng, Yi; Bou Kheir, Rania; Adhikari, Kabindra

2016-01-01

distribution of arsenic (As), chromium (Cr), nickel (Ni), copper (Cu), lead (Pb) and zinc (Zn) in Qatari soils. The prediction model used condition-based rules generated in the Cubist tool. In terms of R2 and the ratio of performance to interquartile distance (RPIQ), the models showed good predictive...... metals’ monitoring in arid soils, due to the climate and the vegetation cover during this season. Topsoil maps of the six toxic metals were generated. The maps can be used to prioritize the choice of remediation measures and can be applied to other arid areas of similar environmental...

Classifying and mapping wetlands and peat resources using digital cartography

Science.gov (United States)

Cameron, Cornelia C.; Emery, David A.

1992-01-01

Digital cartography allows the portrayal of spatial associations among diverse data types and is ideally suited for land use and resource analysis. We have developed methodology that uses digital cartography for the classification of wetlands and their associated peat resources and applied it to a 1:24 000 scale map area in New Hampshire. Classifying and mapping wetlands involves integrating the spatial distribution of wetlands types with depth variations in associated peat quality and character. A hierarchically structured classification that integrates the spatial distribution of variations in (1) vegetation, (2) soil type, (3) hydrology, (4) geologic aspects, and (5) peat characteristics has been developed and can be used to build digital cartographic files for resource and land use analysis. The first three parameters are the bases used by the National Wetlands Inventory to classify wetlands and deepwater habitats of the United States. The fourth parameter, geological aspects, includes slope, relief, depth of wetland (from surface to underlying rock or substrate), wetland stratigraphy, and the type and structure of solid and unconsolidated rock surrounding and underlying the wetland. The fifth parameter, peat characteristics, includes the subsurface variation in ash, acidity, moisture, heating value (Btu), sulfur content, and other chemical properties as shown in specimens obtained from core holes. These parameters can be shown as a series of map data overlays with tables that can be integrated for resource or land use analysis.

GlobalSoilMap France: High-resolution spatial modelling the soils of France up to two meter depth.

Science.gov (United States)

Mulder, V L; Lacoste, M; Richer-de-Forges, A C; Arrouays, D

2016-12-15

This work presents the first GlobalSoilMap (GSM) products for France. We developed an automatic procedure for mapping the primary soil properties (clay, silt, sand, coarse elements, pH, soil organic carbon (SOC), cation exchange capacity (CEC) and soil depth). The procedure employed a data-mining technique and a straightforward method for estimating the 90% confidence intervals (CIs). The most accurate models were obtained for pH, sand and silt. Next, CEC, clay and SOC were found reasonably accurate predicted. Coarse elements and soil depth were the least accurate of all models. Overall, all models were considered robust; important indicators for this were 1) the small difference in model diagnostics between the calibration and cross-validation set, 2) the unbiased mean predictions, 3) the smaller spatial structure of the prediction residuals in comparison to the observations and 4) the similar performance compared to other developed GlobalSoilMap products. Nevertheless, the confidence intervals (CIs) were rather wide for all soil properties. The median predictions became less reliable with increasing depth, as indicated by the increase of CIs with depth. In addition, model accuracy and the corresponding CIs varied depending on the soil variable of interest, soil depth and geographic location. These findings indicated that the CIs are as informative as the model diagnostics. In conclusion, the presented method resulted in reasonably accurate predictions for the majority of the soil properties. End users can employ the products for different purposes, as was demonstrated with some practical examples. The mapping routine is flexible for cloud-computing and provides ample opportunity to be further developed when desired by its users. This allows regional and international GSM partners with fewer resources to develop their own products or, otherwise, to improve the current routine and work together towards a robust high-resolution digital soil map of the world

Application of Remote Sensing for Mapping Soil Organic Matter Content

Directory of Open Access Journals (Sweden)

Bangun Muljo Sukojo

2010-10-01

Full Text Available Information organic content is important in monitoring and managing the environment as well as doing agricultural production activities. This research tried to map soil organic content in Malang using remote sensing technology. The research uses 6 bands of data captured by Landsat TM (Thematic Mapper satellite (band 1, 2, 3, 4, 5, 7. The research focuses on pixels having Normalized Difference Soil Index (NDSI more than 0.3. Ground-truth data were collected by analysing organic content of soil samples using Black-Walkey method. The result of analysis shows that digital number of original satellite image can be used to predict soil organic matter content. The implementation of regression equation in predicting soil organic content shows that 63.18% of research area contains of organic in a moderate category.

Decoding implicit information from the soil map of Belgium and implications for spatial modelling and soil classification

Science.gov (United States)

Dondeyne, Stefaan; Legrain, Xavier; Colinet, Gilles; Van Ranst, Eric; Deckers, Jozef

2014-05-01

A systematic soil survey of Belgium was conducted from 1948 to 1991. Field surveys were done at the detailed scale of 1:5000 with the final maps published at a 1:20,000 scale. Soil surveyors were classifying soils in the field according to physical and morphogenetic characteristics such as texture, drainage class and profile development. Mapping units are defined as a combination of these characteristics but to which modifiers can be added such as parent material, stoniness or depth to substrata. Interpretation of the map towards predicting soil properties seems straight forward. Consequently, since the soil map has been digitized, it has been used for e.g. hydrological modelling or for estimating soil organic carbon content at sub-national and national level. Besides the explicit information provided by the legend, a wealth of implicit information is embedded in the map. Based on three cases, we illustrate that by decoding this information, properties pertaining to soil drainage or soil organic carbon content can be assessed more accurately. First, the presence/absence of fragipans affects the soil hydraulic conductivity. Although a dedicated symbol exits for fragipans (suffix "...m"), it is only used explicitly in areas where fragipans are not all that common. In the Belgian Ardennes, where fragipans are common, their occurrence is implicitly implied for various soil types mentioned in explanatory booklets. Second, whenever seasonal or permanent perched water tables were observed, these were indicated by drainage class ".h." or ".i.", respectively. Stagnic properties have been under reported as typical stagnic mottling - i.e. when the surface of soil peds are lighter and/or paler than the more reddish interior - were not distinguished from mottling due to groundwater gley. Still, by combining information on topography and the occurrence of substratum layers, stagnic properties can be inferred. Thirdly, soils with deep anthropogenic enriched organic matter

Reproducibility of crop surface maps extracted from Unmanned Aerial Vehicle (UAV) derived digital surface maps

KAUST Repository

Parkes, Stephen

2016-10-25

Crop height measured from UAVs fitted with commercially available RGB cameras provide an affordable alternative to retrieve field scale high resolution estimates. The study presents an assessment of between flight reproducibility of Crop Surface Maps (CSM) extracted from Digital Surface Maps (DSM) generated by Structure from Motion (SfM) algorithms. Flights were conducted over a centre pivot irrigation system covered with an alfalfa crop. An important step in calculating the absolute crop height from the UAV derived DSM is determining the height of the underlying terrain. Here we use automatic thresholding techniques applied to RGB vegetation index maps to classify vegetated and soil pixels. From interpolation of classified soil pixels, a terrain map is calculated and subtracted from the DSM. The influence of three different thresholding techniques on CSMs are investigated. Median Alfalfa crop heights determined with the different thresholding methods varied from 18cm for K means thresholding to 13cm for Otsu thresholding methods. Otsu thresholding also gave the smallest range of crop heights and K means thresholding the largest. Reproducibility of median crop heights between flight surveys was 4-6cm for all thresholding techniques. For the flight conducted later in the afternoon shadowing caused soil pixels to be classified as vegetation in key locations around the domain, leading to lower crop height estimates. The range of crop heights was similar for both flights using K means thresholding (35-36cm), local minimum thresholding depended on whether raw or normalised RGB intensities were used to calculate vegetation indices (30-35cm), while Otsu thresholding had a smaller range of heights and varied most between flights (26-30cm). This study showed that crop heights from multiple survey flights are comparable, however, they were dependent on the thresholding method applied to classify soil pixels and the time of day the flight was conducted.

Reproducibility of crop surface maps extracted from Unmanned Aerial Vehicle (UAV) derived digital surface maps

KAUST Repository

Parkes, Stephen; McCabe, Matthew; Al-Mashhawari, Samir K.; Rosas, Jorge

2016-01-01

Crop height measured from UAVs fitted with commercially available RGB cameras provide an affordable alternative to retrieve field scale high resolution estimates. The study presents an assessment of between flight reproducibility of Crop Surface Maps (CSM) extracted from Digital Surface Maps (DSM) generated by Structure from Motion (SfM) algorithms. Flights were conducted over a centre pivot irrigation system covered with an alfalfa crop. An important step in calculating the absolute crop height from the UAV derived DSM is determining the height of the underlying terrain. Here we use automatic thresholding techniques applied to RGB vegetation index maps to classify vegetated and soil pixels. From interpolation of classified soil pixels, a terrain map is calculated and subtracted from the DSM. The influence of three different thresholding techniques on CSMs are investigated. Median Alfalfa crop heights determined with the different thresholding methods varied from 18cm for K means thresholding to 13cm for Otsu thresholding methods. Otsu thresholding also gave the smallest range of crop heights and K means thresholding the largest. Reproducibility of median crop heights between flight surveys was 4-6cm for all thresholding techniques. For the flight conducted later in the afternoon shadowing caused soil pixels to be classified as vegetation in key locations around the domain, leading to lower crop height estimates. The range of crop heights was similar for both flights using K means thresholding (35-36cm), local minimum thresholding depended on whether raw or normalised RGB intensities were used to calculate vegetation indices (30-35cm), while Otsu thresholding had a smaller range of heights and varied most between flights (26-30cm). This study showed that crop heights from multiple survey flights are comparable, however, they were dependent on the thresholding method applied to classify soil pixels and the time of day the flight was conducted.

Digital modelling of landscape and soil in a mountainous region: A neuro-fuzzy approach

Science.gov (United States)

Viloria, Jesús A.; Viloria-Botello, Alvaro; Pineda, María Corina; Valera, Angel

2016-01-01

Research on genetic relationships between soil and landforms has largely improved soil mapping. Recent technological advances have created innovative methods for modelling the spatial soil variation from digital elevation models (DEMs) and remote sensors. This generates new opportunities for the application of geomorphology to soil mapping. This study applied a method based on artificial neural networks and fuzzy clustering to recognize digital classes of land surfaces in a mountainous area in north-central Venezuela. The spatial variation of the fuzzy memberships exposed the areas where each class predominates, while the class centres helped to recognize the topographic attributes and vegetation cover of each class. The obtained classes of terrain revealed the structure of the land surface, which showed regional differences in climate, vegetation, and topography and landscape stability. The land-surface classes were subdivided on the basis of the geological substratum to produce landscape classes that additionally considered the influence of soil parent material. These classes were used as a framework for soil sampling. A redundancy analysis confirmed that changes of landscape classes explained the variation in soil properties (p = 0.01), and a Kruskal-Wallis test showed significant differences (p = 0.01) in clay, hydraulic conductivity, soil organic carbon, base saturation, and exchangeable Ca and Mg between classes. Thus, the produced landscape classes correspond to three-dimensional bodies that differ in soil conditions. Some changes of land-surface classes coincide with abrupt boundaries in the landscape, such as ridges and thalwegs. However, as the model is continuous, it disclosed the remaining variation between those boundaries.

Farmer data sourcing. The case study of the spatial soil information maps in South Tyrol.

Science.gov (United States)

Della Chiesa, Stefano; Niedrist, Georg; Thalheimer, Martin; Hafner, Hansjörg; La Cecilia, Daniele

2017-04-01

Nord-Italian region South Tyrol is Europe's largest apple growing area exporting ca. 15% in Europe and 2% worldwide. Vineyards represent ca. 1% of Italian production. In order to deliver high quality food, most of the farmers in South Tyrol follow sustainable farming practices. One of the key practice is the sustainable soil management, where farmers collect regularly (each 5 years) soil samples and send for analyses to improve cultivation management, yield and finally profitability. However, such data generally remain inaccessible. On this regard, in South Tyrol, private interests and the public administration have established a long tradition of collaboration with the local farming industry. This has granted to the collection of large spatial and temporal database of soil analyses along all the cultivated areas. Thanks to this best practice, information on soil properties are centralized and geocoded. The large dataset consist mainly in soil information of texture, humus content, pH and microelements availability such as, K, Mg, Bor, Mn, Cu Zn. This data was finally spatialized by mean of geostatistical methods and several high-resolution digital maps were created. In this contribution, we present the best practice where farmers data source soil information in South Tyrol. Show the capability of a large spatial-temporal geocoded soil dataset to reproduce detailed digital soil property maps and to assess long-term changes in soil properties. Finally, implication and potential application are discussed.

Compilation of functional soil maps for the support of spatial planning and land management in Hungary

Science.gov (United States)

Pásztor, László; Laborczi, Annamária; Takács, Katalin; Szatmári, Gábor; Fodor, Nándor; Illés, Gábor; Bakacsi, Zsófia; Szabó, József

2015-04-01

The main objective of the DOSoReMI.hu (Digital, Optimized, Soil Related Maps and Information in Hungary) project is to significantly extend the potential, how demands on spatial soil related information could be satisfied in Hungary. Although a great amount of soil information is available due to former mappings and surveys, there are more and more frequently emerging discrepancies between the available and the expected data. The gaps are planned to be filled with optimized DSM products heavily based on legacy soil data. Delineation of Areas with Excellent Productivity in the framework of the National Regional Development Plan or delimitation of Areas with Natural Constraints in Hungary according to the common European biophysical criteria are primary issues in national level spatial planning. Impact assessment of the forecasted climate change and the analysis of the possibilities of the adaptation in the agriculture and forestry can be supported by scenario based land management modelling, whose results can be also incorporated in spatial planning. All these challenges require adequate, preferably timely and spatially detailed knowledge of the soil cover. For the satisfaction of these demands the soil conditions of Hungary have been digitally mapped based on the most detailed, available recent and legacy soil data, applying proper DSM techniques. Various soil related information were mapped in three distinct approaches: (i) basic soil properties determining agri-environmental conditions (e.g.: soil type according to the Hungarian genetic classification, rootable depth, sand, silt and clay content by soil layers, pH, OM and carbonate content for the plough layer); (ii) biophysical criteria of natural handicaps (e.g.: poor drainage, unfavourable texture and stoniness, shallow rooting depth, poor chemical properties and soil moisture balance) defined by common European system and (iii) agro-meteorologically modelled yield values for different crops, meteorological

Digital mapping techniques '06 - Workshop proceedings

Science.gov (United States)

Soller, David R.

2007-01-01

The Digital Mapping Techniques `06 (DMT`06) workshop was attended by more than 110 technical experts from 51 agencies, universities, and private companies, including representatives from 27 state geological surveys (see Appendix A of these Proceedings). This workshop was similar in nature to the previous nine meetings, which were held in Lawrence, Kansas (Soller, 1997), Champaign, Illinois (Soller, 1998), Madison, Wisconsin (Soller, 1999), Lexington, Kentucky (Soller, 2000), Tuscaloosa, Alabama (Soller, 2001), Salt Lake City, Utah (Soller, 2002), Millersville, Pennsylvania (Soller, 2003), Portland, Oregon (Soller, 2004), and Baton Rouge, Louisiana (Soller, 2005). This year?s meeting was hosted by the Ohio Geological Survey, from June 11-14, 2006, on the Ohio State University campus in Columbus, Ohio. As in the previous meetings, the objective was to foster informal discussion and exchange of technical information. It is with great pleasure that I note that the objective was successfully met, as attendees continued to share and exchange knowledge and information, and renew friendships and collegial work begun at past DMT workshops.Each DMT workshop has been coordinated by the Association of American State Geologists (AASG) and U.S. Geological Survey (USGS) Data Capture Working Group, the latter of which was formed in August 1996 to support the AASG and the USGS in their effort to build a National Geologic Map Database (see Soller, this volume, and http://ngmdb.usgs.gov/info/standards/datacapt/). The Working Group was formed because increased production efficiencies, standardization, and quality of digital map products were needed for the database - and for the State and Federal geological surveys - to provide more high-quality digital maps to the public.At the 2006 meeting, oral and poster presentations and special discussion sessions emphasized: 1) methods for creating and publishing map products (here, "publishing" includes Web-based release); 2) field data

Mapping soil erosion risk in Serra de Grândola (Portugal)

Science.gov (United States)

Neto Paixão, H. M.; Granja Martins, F. M.; Zavala, L. M.; Jordán, A.; Bellinfante, N.

2012-04-01

Geomorphological processes can pose environmental risks to people and economical activities. Information and a better knowledge of the genesis of these processes is important for environmental planning, since it allows to model, quantify and classify risks, what can mitigate the threats. The objective of this research is to assess the soil erosion risk in Serra de Grândola, which is a north-south oriented mountain ridge with an altitude of 383 m, located in southwest of Alentejo (southern Portugal). The study area is 675 km2, including the councils of Grândola, Santiago do Cacém and Sines. The process for mapping of erosive status was based on the guidelines for measuring and mapping the processes of erosion of coastal areas of the Mediterranean proposed by PAP/RAC (1997), developed and later modified by other authors in different areas. This method is based on the application of a geographic information system that integrates different types of spatial information inserted into a digital terrain model and in their derivative models. Erosive status are classified using information from soil erodibility, slope, land use and vegetation cover. The rainfall erosivity map was obtained using the modified Fournier index, calculated from the mean monthly rainfall, as recorded in 30 meteorological stations with influence in the study area. Finally, the soil erosion risk map was designed by ovelaying the erosive status map and the rainfall erosivity map.

Mapping earthworm communities in Europe

DEFF Research Database (Denmark)

Rutgers, Michiel; Orgiazzi, Alberto; Gardi, Ciro

Existing data sets on earthworm communities in Europe were collected, harmonized, modelled and depicted on a soil biodiversity map of Europe. Digital Soil Mapping was applied using multiple regressions relating relatively low density earthworm community data to soil characteristics, land use...

Mapping earthworm communities in Europe

NARCIS (Netherlands)

Rutgers, M.; Orgiazzi, A.; Gardi, C.; Römbke, J.; Jansch, S.; Keith, A.; Neilson, R.; Boag, B.; Schmidt, O.; Murchie, A.K.; Blackshaw, R.P.; Pérès, G.; Cluzeau, D.; Guernion, M.; Briones, M.J.I.; Rodeiro, J.; Pineiro, R.; Diaz Cosin, D.J.; Sousa, J.P.; Suhadolc, M.; Kos, I.; Krogh, P.H.; Faber, J.H.; Mulder, C.; Bogte, J.J.; Wijnen, van H.J.; Schouten, A.J.; Zwart, de D.

2016-01-01

Existing data sets on earthworm communities in Europe were collected, harmonized, collated, modelled and depicted on a soil biodiversity map. Digital Soil Mapping was applied using multiple regressions relating relatively low density earthworm community data to soil characteristics, land use,

Spatial Prediction of Soil Classes by Using Soil Weathering Parameters Derived from vis-NIR Spectroscopy

Science.gov (United States)

Ramirez-Lopez, Leonardo; Alexandre Dematte, Jose

2010-05-01

There is consensus in the scientific community about the great need of spatial soil information. Conventional mapping methods are time consuming and involve high costs. Digital soil mapping has emerged as an area in which the soil mapping is optimized by the application of mathematical and statistical approaches, as well as the application of expert knowledge in pedology. In this sense, the objective of the study was to develop a methodology for the spatial prediction of soil classes by using soil spectroscopy methodologies related with fieldwork, spectral data from satellite image and terrain attributes in simultaneous. The studied area is located in São Paulo State, and comprised an area of 473 ha, which was covered by a regular grid (100 x 100 m). In each grid node was collected soil samples at two depths (layers A and B). There were extracted 206 samples from transect sections and submitted to soil analysis (clay, Al2O3, Fe2O3, SiO2 TiO2, and weathering index). The first analog soil class map (ASC-N) contains only soil information regarding from orders to subgroups of the USDA Soil Taxonomy System. The second (ASC-H) map contains some additional information related to some soil attributes like color, ferric levels and base sum. For the elaboration of the digital soil maps the data was divided into three groups: i) Predicted soil attributes of the layer B (related to the soil weathering) which were obtained by using a local soil spectral library; ii) Spectral bands data extracted from a Landsat image; and iii) Terrain parameters. This information was summarized by a principal component analysis (PCA) in each group. Digital soil maps were generated by supervised classification using a maximum likelihood method. The trainee information for this classification was extracted from five toposequences based on the analog soil class maps. The spectral models of weathering soil attributes shown a high predictive performance with low error (R2 0.71 to 0.90). The spatial

Spatial Digital Database for the Geologic Map of Oregon

Science.gov (United States)

Walker, George W.; MacLeod, Norman S.; Miller, Robert J.; Raines, Gary L.; Connors, Katherine A.

2003-01-01

Introduction This report describes and makes available a geologic digital spatial database (orgeo) representing the geologic map of Oregon (Walker and MacLeod, 1991). The original paper publication was printed as a single map sheet at a scale of 1:500,000, accompanied by a second sheet containing map unit descriptions and ancillary data. A digital version of the Walker and MacLeod (1991) map was included in Raines and others (1996). The dataset provided by this open-file report supersedes the earlier published digital version (Raines and others, 1996). This digital spatial database is one of many being created by the U.S. Geological Survey as an ongoing effort to provide geologic information for use in spatial analysis in a geographic information system (GIS). This database can be queried in many ways to produce a variety of geologic maps. This database is not meant to be used or displayed at any scale larger than 1:500,000 (for example, 1:100,000). This report describes the methods used to convert the geologic map data into a digital format, describes the ArcInfo GIS file structures and relationships, and explains how to download the digital files from the U.S. Geological Survey public access World Wide Web site on the Internet. Scanned images of the printed map (Walker and MacLeod, 1991), their correlation of map units, and their explanation of map symbols are also available for download.

Mapping The Temporal and Spatial Variability of Soil Moisture Content Using Proximal Soil Sensing

Science.gov (United States)

Virgawati, S.; Mawardi, M.; Sutiarso, L.; Shibusawa, S.; Segah, H.; Kodaira, M.

2018-05-01

In studies related to soil optical properties, it has been proven that visual and NIR soil spectral response can predict soil moisture content (SMC) using proper data analysis techniques. SMC is one of the most important soil properties influencing most physical, chemical, and biological soil processes. The problem is how to provide reliable, fast and inexpensive information of SMC in the subsurface from numerous soil samples and repeated measurement. The use of spectroscopy technology has emerged as a rapid and low-cost tool for extensive investigation of soil properties. The objective of this research was to develop calibration models based on laboratory Vis-NIR spectroscopy to estimate the SMC at four different growth stages of the soybean crop in Yogyakarta Province. An ASD Field-spectrophotoradiometer was used to measure the reflectance of soil samples. The partial least square regression (PLSR) was performed to establish the relationship between the SMC with Vis-NIR soil reflectance spectra. The selected calibration model was used to predict the new samples of SMC. The temporal and spatial variability of SMC was performed in digital maps. The results revealed that the calibration model was excellent for SMC prediction. Vis-NIR spectroscopy was a reliable tool for the prediction of SMC.

Soils - Volusia County Soils (Polygons)

Data.gov (United States)

NSGIC Local Govt | GIS Inventory — Soils: 1:24000 SSURGO Map. Polygon boundaries of Soils in Volusia County, downloaded from SJRWMD and created by NRCS and SJRWMD. This data set is a digital version...

Alternate data sources for soil surveys on rangeland

Science.gov (United States)

Horvath, Emil H.; Klingebiel, A.A.; Moore, D.G.; Fosnight, E.A.

1983-01-01

Soil information is an essential theme in a digital information base for land management and resource monitoring, but public land management agencies seldom have detailed soil maps available for all of the area under their administration. Most of these agencies conduct soil surveys on a scheduled basis, but escalating costs and declining budgets are reducing the number of surveys that can be scheduled. Digital elevation and satellite spectral data are available or are obtainable for all areas in the continental United States and may be used as an aid to produce soils data. A study was conducted in the Grass Creek Resource Area in north-central Wyoming to assess the utility of incorporating digital elevation and Landsat data into an information base for soil survey and to evaluate the usefulness of these data as an input to an order-three soil survey. Slope-interval maps were produced from digital elevation data and topographic maps of three 7.5-minute quadrangle areas. These slope-interval maps were then overlaid on orthophotoquadrangles and used to produce photo-interpreted physiographic maps. These physiographic maps were digitized into a data base and used with Landsat multispectral scanner data to produce tabular summaries that describe each map polygon in terms of physiographic unit, slope, aspect, elevation, area, and spectral values. A good

Creation of a Cell-Based Digital Cadastral Mapping System (Digital ...

African Journals Online (AJOL)

Digital cadastre enhances land transaction activities to be conducted in a business manner. Similarly, land subdivision or boundary redefinition, land registration and land marketing are achieved with better accuracy. This paper discusses the need to introduce a national Cell-Based Digital Cadastral Mapping System model ...

Soil-Web: An online soil survey for California, Arizona, and Nevada

Science.gov (United States)

Beaudette, D. E.; O'Geen, A. T.

2009-10-01

Digital soil survey products represent one of the largest and most comprehensive inventories of soils information currently available. The complex structure of these databases, intensive use of codes and scientific jargon make it difficult for non-specialists to utilize digital soil survey resources. A project was initiated to construct a web-based interface to digital soil survey products (STATSGO and SSURGO) for California, Arizona, and Nevada that would be accessible to the general public. A collection of mature, open source applications (including Mapserver, PostGIS and Apache Web Server) were used as a framework to support data storage, querying, map composition, data presentation, and contextual links to related materials. Application logic was written in the PHP language to "glue" together the many components of an online soil survey. A comprehensive website ( http://casoilresource.lawr.ucdavis.edu/map) was created to facilitate access to digital soil survey databases through several interfaces including: interactive map, Google Earth and HTTP-based application programming interface (API). Each soil polygon is linked to a map unit summary page, which includes links to soil component summary pages. The most commonly used soil properties, land interpretations and ratings are presented. Graphical and tabular summaries of soil profile information are dynamically created, and aid with rapid assessment of key soil properties. Quick links to official series descriptions (OSD) and other such information are presented. All terminology is linked back to the USDA-NRCS Soil Survey Handbook which contains extended definitions. The Google Earth interface to Soil-Web can be used to explore soils information in three dimensions. A flexible web API was implemented to allow advanced users of soils information to access our website via simple web page requests. Soil-Web has been successfully used in soil science curriculum, outreach activities, and current research projects

Digital mapping in extreme and remote environments

Science.gov (United States)

Andersson, Joel; Bauer, Tobias; Sarlus, Zimer; Zainy, Maher; Brethes, Anais

2017-04-01

During the last few years, Luleå University of Technology has performed a series of research projects in remote areas with extreme climatic conditions using digital mapping technologies. The majority of past and ongoing research projects focus on the arctic regions of the Fennoscandian Shield and Greenland but also on the Zagros fold-and-thrust belt in northern Iraq. Currently, we use the Midland Valley application FieldMove on iPad mini devices with ruggedized casings. As all projects have a strong focus on geological field work, harsh climatic conditions are a challenge not only for the geologists but also for the digital mapping hardware. In the arctic regions especially cold temperatures affect battery lifetime and performance of the screens. But also high temperatures are restricting digital mapping. From experience, a typical temperature range where digital mapping, using iPad tablets, is possible lies between -20 and +40 degrees. Furthermore, the remote character of field areas complicates access but also availability of electricity. By a combination of robust solar chargers and ruggedized batteries we are able to work entirely autarkical. Additionally, we are currently installing a drone system that allows us to map outcrops normally inaccessible because of safety reasons or time deficiency. The produced data will subsequently be taken into our Virtual Reality studio for interpretation and processing. There we will be able to work also with high resolution DEM data from Lidar scanning allowing us to interpret structural features such as post-glacial faults in areas that are otherwise only accessible by helicopter. By combining digital field mapping with drone technique and a Virtual Reality studio we are able to work in hardly accessible areas, improve safety during field work and increase efficiency substantially.

Photogrammetry, Digital mapping and Land Informations Systems

DEFF Research Database (Denmark)

Frederiksen, Poul

1998-01-01

Monitoring activities on photogrammetry, digital mapping and land information systems in State Land Service in Latvia in relation to the EU Phare Project Phase II, Technical Assistance to land Privatisation and registration in Latvia.......Monitoring activities on photogrammetry, digital mapping and land information systems in State Land Service in Latvia in relation to the EU Phare Project Phase II, Technical Assistance to land Privatisation and registration in Latvia....

Using Vegetation Maps to Provide Information on Soil Distribution

Science.gov (United States)

José Ibáñez, Juan; Pérez-Gómez, Rufino; Brevik, Eric C.; Cerdà, Artemi

2016-04-01

Many different types of maps (geology, hydrology, soil, vegetation, etc.) are created to inventory natural resources. Each of these resources is mapped using a unique set of criteria, including scales and taxonomies. Past research has indicated that comparing the results of different but related maps (e.g., soil and geology maps) may aid in identifying deficiencies in those maps. Therefore, this study was undertaken in the Almería Province (Andalusia, Spain) to (i) compare the underlying map structures of soil and vegetation maps and (ii) to investigate if a vegetation map can provide useful soil information that was not shown on a soil map. To accomplish this soil and vegetation maps were imported into ArcGIS 10.1 for spatial analysis. Results of the spatial analysis were exported to Microsoft Excel worksheets for statistical analyses to evaluate fits to linear and power law regression models. Vegetative units were grouped according to the driving forces that determined their presence or absence (P/A): (i) climatophilous (climate is the only determinant of P/A) (ii); lithologic-climate (climate and parent material determine PNV P/A); and (iii) edaphophylous (soil features determine PNV P/A). The rank abundance plots for both the soil and vegetation maps conformed to Willis or Hollow Curves, meaning the underlying structures of both maps were the same. Edaphophylous map units, which represent 58.5% of the vegetation units in the study area, did not show a good correlation with the soil map. Further investigation revealed that 87% of the edaphohygrophylous units (which demand more soil water than is supplied by other soil types in the surrounding landscape) were found in ramblas, ephemeral riverbeds that are not typically classified and mapped as soils in modern systems, even though they meet the definition of soil given by the most commonly used and most modern soil taxonomic systems. Furthermore, these edaphophylous map units tend to be islands of biodiversity

Digital Mapping and Land Information Systems - Volume 6

DEFF Research Database (Denmark)

Frederiksen, Poul

1998-01-01

Introduction of digital mapping techniques in the 28 counties of Latvia related to the offices of the national mapping agency (State Land Service). Major components are: Training of regional staff, procurement of hard- and software, training of technical staff from State Land Service, HQ. Develop......Introduction of digital mapping techniques in the 28 counties of Latvia related to the offices of the national mapping agency (State Land Service). Major components are: Training of regional staff, procurement of hard- and software, training of technical staff from State Land Service, HQ...

Digital Soil Resource Inventories: Status and Prospects in 2015.

NARCIS (Netherlands)

Rossiter, David

2016-01-01

Eleven years ago, the author published a paper (Soil Use and Management 20(3): 296–301) titled “Digital soil resource inventories: status and prospects,” which concluded that, at the time, the quantity and quality of digital soil survey information at global, national, regional, and local scales was

GlobalSoilMap.net – a new digital soil map of the world

NARCIS (Netherlands)

Hartemink, A.E.; Hempel, J.; Lagacherie, P.; McBratney, A.B.; MacMillan, R.A.; Montanarella, L.; Sanchez, P.A.; Walsh, M.; Zhang, G.L.

2010-01-01

Knowledge of the world soil resources is fragmented and dated. There is a need for accurate, up-to-date and spatially referenced soil information as frequently expressed by the modelling community, farmers and land users, and policy and decision makers. This need coincides with an enormous leap in

State Base Map for GIS – New Digital Topographic Map of the Republic of Macedonia

Directory of Open Access Journals (Sweden)

Zlatko Srbinoski

2009-12-01

Full Text Available The basic aim of the National Spatial Data Infrastructure (NSDI built in accordance with INSPIRE directive is to standardize spatial data infrastructure on national level. In that direction, topographic maps are a basic platform for acquiring spatial data within geoinformation systems and one of the most important  segments of NSDI. This paper presents methodology of establishing the new digital topographic map of the Republic of Macedonia titled “State Base Map for GIS in Macedonia”. This paper analyzes geometrical accuracy of new digital topographic maps. Production of the new digital topographic map has been the most important cartographic project in the Republic of Macedonia since it became independent.

Multifractal and Singularity Maps of soil surface moisture distribution derived from 2D image analysis.

Science.gov (United States)

Cumbrera, Ramiro; Millán, Humberto; Martín-Sotoca, Juan Jose; Pérez Soto, Luis; Sanchez, Maria Elena; Tarquis, Ana Maria

2016-04-01

Soil moisture distribution usually presents extreme variation at multiple spatial scales. Image analysis could be a useful tool for investigating these spatial patterns of apparent soil moisture at multiple resolutions. The objectives of the present work were (i) to describe the local scaling of apparent soil moisture distribution and (ii) to define apparent soil moisture patterns from vertical planes of Vertisol pit images. Two soil pits (0.70 m long × 0.60 m width × 0.30 m depth) were excavated on a bare Mazic Pellic Vertisol. One was excavated in April/2011 and the other pit was established in May/2011 after 3 days of a moderate rainfall event. Digital photographs were taken from each Vertisol pit using a Kodak™ digital camera. The mean image size was 1600 × 945 pixels with one physical pixel ≈373 μm of the photographed soil pit. For more details see Cumbrera et al. (2012). Geochemical exploration have found with increasingly interests and benefits of using fractal (power-law) models to characterize geochemical distribution, using the concentration-area (C-A) model (Cheng et al., 1994; Cheng, 2012). This method is based on the singularity maps of a measure that at each point define areas with self-similar properties that are shown in power-law relationships in Concentration-Area plots (C-A method). The C-A method together with the singularity map ("Singularity-CA" method) define thresholds that can be applied to segment the map. We have applied it to each soil image. The results show that, in spite of some computational and practical limitations, image analysis of apparent soil moisture patterns could be used to study the dynamical change of soil moisture sampling in agreement with previous results (Millán et al., 2016). REFERENCES Cheng, Q., Agterberg, F. P. and Ballantyne, S. B. (1994). The separation of geochemical anomalies from background by fractal methods. Journal of Geochemical Exploration, 51, 109-130. Cheng, Q. (2012). Singularity theory and

The status of soil mapping for the Idaho National Engineering Laboratory

International Nuclear Information System (INIS)

Olson, G.L.; Lee, R.D.; Jeppesen, D.J.

1995-01-01

This report discusses the production of a revised version of the general soil map of the 2304-km 2 (890-mi 2 ) Idaho National Engineering Laboratory (INEL) site in southeastern Idaho and the production of a geographic information system (GIS) soil map and supporting database. The revised general soil map replaces an INEL soil map produced in 1978 and incorporates the most current information on INEL soils. The general soil map delineates large soil associations based on National Resources Conservation Services [formerly the Soil Conservation Service (SCS)] principles of soil mapping. The GIS map incorporates detailed information that could not be presented on the general soil map and is linked to a database that contains the soil map unit descriptions, surficial geology codes, and other pertinent information

Concepts of soil mapping as a basis for the assessment of soil functions

Science.gov (United States)

Baumgarten, Andreas

2014-05-01

Soil mapping systems in Europe have been designed mainly as a tool for the description of soil characteristics from a morphogenetic viewpoint. Contrasting to the American or FAO system, the soil development has been in the main focus of European systems. Nevertheless , recent developments in soil science stress the importance of the functions of soils with respect to the ecosystems. As soil mapping systems usually offer a sound and extensive database, the deduction of soil functions from "classic" mapping parameters can be used for local and regional assessments. According to the used pedo-transfer functions and mapping systems, tailored approaches can be chosen for different applications. In Austria, a system mainly for spatial planning purposes has been developed that will be presented and illustrated by means of best practice examples.

Mathematical models application for mapping soils spatial distribution on the example of the farm from the North of Udmurt Republic of Russia

Science.gov (United States)

Dokuchaev, P. M.; Meshalkina, J. L.; Yaroslavtsev, A. M.

2018-01-01

Comparative analysis of soils geospatial modeling using multinomial logistic regression, decision trees, random forest, regression trees and support vector machines algorithms was conducted. The visual interpretation of the digital maps obtained and their comparison with the existing map, as well as the quantitative assessment of the individual soil groups detection overall accuracy and of the models kappa showed that multiple logistic regression, support vector method, and random forest models application with spatial prediction of the conditional soil groups distribution can be reliably used for mapping of the study area. It has shown the most accurate detection for sod-podzolics soils (Phaeozems Albic) lightly eroded and moderately eroded soils. In second place, according to the mean overall accuracy of the prediction, there are sod-podzolics soils - non-eroded and warp one, as well as sod-gley soils (Umbrisols Gleyic) and alluvial soils (Fluvisols Dystric, Umbric). Heavy eroded sod-podzolics and gray forest soils (Phaeozems Albic) were detected by methods of automatic classification worst of all.

Using Environmental Variables for Studying of the Quality of Sampling in Soil Mapping

Directory of Open Access Journals (Sweden)

A. Jafari

2016-02-01

Full Text Available Introduction: Methods of soil survey are generally empirical and based on the mental development of the surveyor, correlating soil with underlying geology, landforms, vegetation and air-photo interpretation. Since there are no statistical criteria for traditional soil sampling; this may lead to bias in the areas being sampled. In digital soil mapping, soil samples may be used to elaborate quantitative relationships or models between soil attributes and soil covariates. Because the relationships are based on the soil observations, the quality of the resulting soil map depends also on the soil observation quality. An appropriate sampling design for digital soil mapping depends on how much data is available and where the data is located. Some statistical methods have been developed for optimizing data sampling for soil surveys. Some of these methods deal with the use of ancillary information. The purpose of this study was to evaluate the quality of sampling of existing data. Materials and Methods: The study area is located in the central basin of the Iranian plateau (Figure 1. The geologic infrastructure of the area is mainly Cretaceous limestone, Mesozoic shale and sandstone. Air photo interpretation (API was used to differentiate geomorphic patterns based on their formation processes, general structure and morphometry. The patterns were differentiated through a nested geomorphic hierarchy (Fig. 2. A four-level geomorphic hierarchy is used to breakdown the complexity of different landscapes of the study area. In the lower level of the hierarchy, the geomorphic surfaces, which were formed by a unique process during a specific geologic time, were defined. A stratified sampling scheme was designed based on geomorphic mapping. In the stratified simple random sampling, the area was divided into sub-areas referred to as strata based on geomorphic surfaces, and within each stratum, sampling locations were randomly selected (Figure 2. This resulted in 191

Digital geologic map in the scale 1:50 000

International Nuclear Information System (INIS)

Kacer, S.; Antalik, M.

2005-01-01

In this presentation authors present preparation of new digital geologic map of the Slovak Republic. This map is prepared by the State Geological Institute of Dionyz Stur as a part of the project Geological information system GeoIS. One of the basic information geologic layers, which will be accessible on the web-site will be digital geologic map of the Slovak Republic in the scale 1: 50 000

Soils - SOILS_STATSGO_IN: Soil Associations in Indiana (U.S. Dept. of Agriculture, 1:250,000, Polygon Shapefile)

Data.gov (United States)

NSGIC State | GIS Inventory — Natural Resources Conservation Service, STATSGO metadata reports- "This data set is a digital general soil association map developed by the National Cooperative Soil...

The genotype-phenotype map of an evolving digital organism

OpenAIRE

Fortuna, Miguel A.; Zaman, Luis; Ofria, Charles; Wagner, Andreas

2017-01-01

To understand how evolving systems bring forth novel and useful phenotypes, it is essential to understand the relationship between genotypic and phenotypic change. Artificial evolving systems can help us understand whether the genotype-phenotype maps of natural evolving systems are highly unusual, and it may help create evolvable artificial systems. Here we characterize the genotype-phenotype map of digital organisms in Avida, a platform for digital evolution. We consider digital organisms fr...

Physico-empirical approach for mapping soil hydraulic behaviour

Directory of Open Access Journals (Sweden)

G. D'Urso

1997-01-01

Full Text Available Abstract: Pedo-transfer functions are largely used in soil hydraulic characterisation of large areas. The use of physico-empirical approaches for the derivation of soil hydraulic parameters from disturbed samples data can be greatly enhanced if a characterisation performed on undisturbed cores of the same type of soil is available. In this study, an experimental procedure for deriving maps of soil hydraulic behaviour is discussed with reference to its application in an irrigation district (30 km2 in southern Italy. The main steps of the proposed procedure are: i the precise identification of soil hydraulic functions from undisturbed sampling of main horizons in representative profiles for each soil map unit; ii the determination of pore-size distribution curves from larger disturbed sampling data sets within the same soil map unit. iii the calibration of physical-empirical methods for retrieving soil hydraulic parameters from particle-size data and undisturbed soil sample analysis; iv the definition of functional hydraulic properties from water balance output; and v the delimitation of soil hydraulic map units based on functional properties.

A Brief History of Soil Mapping and Classification in the USA

Science.gov (United States)

Brevik, Eric C.; Hartemink, Alfred E.

2014-05-01

Soil maps show the distribution of soils across an area but also depict soil science theory and ideas on soil formation and classification at the time the maps were created. The national soil mapping program in the USA was established in 1899. The first nation-wide soil map was published by M. Whitney in 1909 and showed soil provinces that were largely based on geology. In 1912, G.N. Coffey published the first country-wide map based on soil properties. The map showed 5 broad soil units that used parent material, color and drainage as diagnostic criteria. The 1913 national map was produced by C.F. Marbut, H.H. Bennett, J.E. Lapham, and M.H. Lapham and showed broad physiographic units that were further subdivided into soil series, soil classes and soil types. In 1935, Marbut drafted a series of maps based on soil properties, but these maps were replaced as official U.S. soil maps in 1938 with the work of M. Baldwin, C.E. Kellogg, and J. Thorp. A series of soil maps similar to modern USA maps appeared in the 1960s with the 7th Approximation followed by revisions with the 1975 and 1999 editions of Soil Taxonomy. This review has shown that soil maps in the United States produced since the early 1900s moved initially from a geologic-based concept to a pedologic concept of soils. Later changes were from property-based systems to process-based, and then back to property-based. The information in this presentation is based on Brevik and Hartemink (2013). Brevik, E.C., and A.E. Hartemink. 2013. Soil Maps of the United States of America. Soil Science Society of America Journal 77:1117-1132. doi:10.2136/sssaj2012.0390.

Using Digital Mapping Tool in Ill-Structured Problem Solving

Science.gov (United States)

Bai, Hua

2013-01-01

Scaffolding students' problem solving and helping them to improve problem solving skills are critical in instructional design courses. This study investigated the effects of students' uses of a digital mapping tool on their problem solving performance in a design case study. It was found that the students who used the digital mapping tool…

Deriving soil function maps to assess related ecosystem services using imaging spectroscopy in the Lyss agricultural area, Switzerland

Science.gov (United States)

Diek, Sanne; de Jong, Rogier; Braun, Daniela; Böhler, Jonas; Schaepman, Michael

2014-05-01

Soils play an important role in the benefits offered by ecosystems services. In densely populated Switzerland soils are a scarce resource, with high pressure on services ranging from urban expansion to over-utilization. Key change drivers include erosion, soil degradation, land management change and (chemical) pollution, which should be taken into consideration. Therefore there is an emerging need for an integrated, sustainable and efficient system assessing the management of soil and land as a resource. The use of remote sensing can offer spatio-temporal and quantitative information of extended areas. In particular imaging spectroscopy has shown to perfectly complement existing sampling schemes as secondary information for digital soil mapping. Although only the upper-most layer of soil interacts with light when using reflectance spectroscopy, it still can offer valuable information that can be utilized by farmers and decision makers. Fully processed airborne imaging spectrometer data from APEX as well as land cover classification for the agricultural area in Lyss were available. Based on several spectral analysis methods we derived multiple soil properties, including soil organic matter, soil texture, and mineralogy; complemented by vegetation parameters, including leaf area index, chlorophyll content, pigment distribution, and water content. The surface variables were retrieved using a combination of index-based and physically-based retrievals. Soil properties in partly to fully vegetated areas were interpolated using regression kriging based methods. This allowed the continuous assessment of potential soil functions as well as non-contiguous maps of abundances of combined soil and vegetation parameters. Based on a simple regression model we could make a rough estimate of ecosystem services. This provided the opportunity to look at the differences between the interpolated soil function maps and the non-contiguous (but combined) vegetation and soil function maps

High resolution mapping of soil organic carbon stocks using remote sensing variables in the semi-arid rangelands of eastern Australia.

Science.gov (United States)

Wang, Bin; Waters, Cathy; Orgill, Susan; Gray, Jonathan; Cowie, Annette; Clark, Anthony; Liu, De Li

2018-07-15

Efficient and effective modelling methods to assess soil organic carbon (SOC) stock are central in understanding the global carbon cycle and informing related land management decisions. However, mapping SOC stocks in semi-arid rangelands is challenging due to the lack of data and poor spatial coverage. The use of remote sensing data to provide an indirect measurement of SOC to inform digital soil mapping has the potential to provide more reliable and cost-effective estimates of SOC compared with field-based, direct measurement. Despite this potential, the role of remote sensing data in improving the knowledge of soil information in semi-arid rangelands has not been fully explored. This study firstly investigated the use of high spatial resolution satellite data (seasonal fractional cover data; SFC) together with elevation, lithology, climatic data and observed soil data to map the spatial distribution of SOC at two soil depths (0-5cm and 0-30cm) in semi-arid rangelands of eastern Australia. Overall, model performance statistics showed that random forest (RF) and boosted regression trees (BRT) models performed better than support vector machine (SVM). The models obtained moderate results with R 2 of 0.32 for SOC stock at 0-5cm and 0.44 at 0-30cm, RMSE of 3.51MgCha -1 at 0-5cm and 9.16MgCha -1 at 0-30cm without considering SFC covariates. In contrast, by including SFC, the model accuracy for predicting SOC stock improved by 7.4-12.7% at 0-5cm, and by 2.8-5.9% at 0-30cm, highlighting the importance of including SFC to enhance the performance of the three modelling techniques. Furthermore, our models produced a more accurate and higher resolution digital SOC stock map compared with other available mapping products for the region. The data and high-resolution maps from this study can be used for future soil carbon assessment and monitoring. Copyright © 2018 Elsevier B.V. All rights reserved.

Modelling soil organic carbon concentration of mineral soils in arable lands using legacy soil data

DEFF Research Database (Denmark)

Suuster, E; Ritz, Christian; Roostalu, H

2012-01-01

is appropriate if the study design has a hierarchical structure as in our scenario. We used the Estonian National Soil Monitoring data on arable lands to predict SOC concentrations of mineral soils. Subsequently, the model with the best prediction accuracy was applied to the Estonian digital soil map...

Use of digital images to estimate soil moisture

Directory of Open Access Journals (Sweden)

João F. C. dos Santos

Full Text Available ABSTRACT The objective of this study was to analyze the relation between the moisture and the spectral response of the soil to generate prediction models. Samples with different moisture contents were prepared and photographed. The photographs were taken under homogeneous light condition and with previous correction for the white balance of the digital photograph camera. The images were processed for extraction of the median values in the Red, Green and Blue bands of the RGB color space; Hue, Saturation and Value of the HSV color space; and values of the digital numbers of a panchromatic image obtained from the RGB bands. The moisture of the samples was determined with the thermogravimetric method. Regression models were evaluated for each image type: RGB, HSV and panchromatic. It was observed the darkening of the soil with the increase of moisture. For each type of soil, a model with best fit was observed and to use these models for prediction purposes, it is necessary to choose the model with best fit in advance, according to the soil characteristics. Soil moisture estimation as a function of its spectral response by digital image processing proves promising.

Mapping Soil Organic Matter with Hyperspectral Imaging

Science.gov (United States)

Moni, Christophe; Burud, Ingunn; Flø, Andreas; Rasse, Daniel

2014-05-01

Soil organic matter (SOM) plays a central role for both food security and the global environment. Soil organic matter is the 'glue' that binds soil particles together, leading to positive effects on soil water and nutrient availability for plant growth and helping to counteract the effects of erosion, runoff, compaction and crusting. Hyperspectral measurements of samples of soil profiles have been conducted with the aim of mapping soil organic matter on a macroscopic scale (millimeters and centimeters). Two soil profiles have been selected from the same experimental site, one from a plot amended with biochar and another one from a control plot, with the specific objective to quantify and map the distribution of biochar in the amended profile. The soil profiles were of size (30 x 10 x 10) cm3 and were scanned with two pushbroomtype hyperspectral cameras, one which is sensitive in the visible wavelength region (400 - 1000 nm) and one in the near infrared region (1000 - 2500 nm). The images from the two detectors were merged together into one full dataset covering the whole wavelength region. Layers of 15 mm were removed from the 10 cm high sample such that a total of 7 hyperspectral images were obtained from the samples. Each layer was analyzed with multivariate statistical techniques in order to map the different components in the soil profile. Moreover, a 3-dimensional visalization of the components through the depth of the sample was also obtained by combining the hyperspectral images from all the layers. Mid-infrared spectroscopy of selected samples of the measured soil profiles was conducted in order to correlate the chemical constituents with the hyperspectral results. The results show that hyperspectral imaging is a fast, non-destructive technique, well suited to characterize soil profiles on a macroscopic scale and hence to map elements and different organic matter quality present in a complete pedon. As such, we were able to map and quantify biochar in our

Digital Geological Mapping for Earth Science Students

Science.gov (United States)

England, Richard; Smith, Sally; Tate, Nick; Jordan, Colm

2010-05-01

This SPLINT (SPatial Literacy IN Teaching) supported project is developing pedagogies for the introduction of teaching of digital geological mapping to Earth Science students. Traditionally students are taught to make geological maps on a paper basemap with a notebook to record their observations. Learning to use a tablet pc with GIS based software for mapping and data recording requires emphasis on training staff and students in specific GIS and IT skills and beneficial adjustments to the way in which geological data is recorded in the field. A set of learning and teaching materials are under development to support this learning process. Following the release of the British Geological Survey's Sigma software we have been developing generic methodologies for the introduction of digital geological mapping to students that already have experience of mapping by traditional means. The teaching materials introduce the software to the students through a series of structured exercises. The students learn the operation of the software in the laboratory by entering existing observations, preferably data that they have collected. Through this the students benefit from being able to reflect on their previous work, consider how it might be improved and plan new work. Following this they begin fieldwork in small groups using both methods simultaneously. They are able to practise what they have learnt in the classroom and review the differences, advantages and disadvantages of the two methods, while adding to the work that has already been completed. Once the field exercises are completed students use the data that they have collected in the production of high quality map products and are introduced to the use of integrated digital databases which they learn to search and extract information from. The relatively recent development of the technologies which underpin digital mapping also means that many academic staff also require training before they are able to deliver the

DIGITAL FLOOD INSURANCE RATE MAP DATABASE,

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk Information And supporting data used to develop the risk data. The primary risk;...

ANALYSIS OF THE EFFECTS OF IMAGE QUALITY ON DIGITAL MAP GENERATION FROM SATELLITE IMAGES

Directory of Open Access Journals (Sweden)

H. Kim

2012-07-01

Full Text Available High resolution satellite images are widely used to produce and update a digital map since they became widely available. It is well known that the accuracy of digital map produced from satellite images is decided largely by the accuracy of geometric modelling. However digital maps are made by a series of photogrammetric workflow. Therefore the accuracy of digital maps are also affected by the quality of satellite images, such as image interpretability. For satellite images, parameters such as Modulation Transfer Function(MTF, Signal to Noise Ratio(SNR and Ground Sampling Distance(GSD are used to present images quality. Our previous research stressed that such quality parameters may not represent the quality of image products such as digital maps and that parameters for image interpretability such as Ground Resolved Distance(GRD and National Imagery Interpretability Rating Scale(NIIRS need to be considered. In this study, we analyzed the effects of the image quality on accuracy of digital maps produced by satellite images. QuickBird, IKONOS and KOMPSAT-2 imagery were used to analyze as they have similar GSDs. We measured various image quality parameters mentioned above from these images. Then we produced digital maps from the images using a digital photogrammetric workstation. We analyzed the accuracy of the digital maps in terms of their location accuracy and their level of details. Then we compared the correlation between various image quality parameters and the accuracy of digital maps. The results of this study showed that GRD and NIIRS were more critical for map production then GSD, MTF or SNR.

Evaluation of using digital gravity field models for zoning map creation

Science.gov (United States)

Loginov, Dmitry

2018-05-01

At the present time the digital cartographic models of geophysical fields are taking a special significance into geo-physical mapping. One of the important directions to their application is the creation of zoning maps, which allow taking into account the morphology of geophysical field in the implementation automated choice of contour intervals. The purpose of this work is the comparative evaluation of various digital models in the creation of integrated gravity field zoning map. For comparison were chosen the digital model of gravity field of Russia, created by the analog map with scale of 1 : 2 500 000, and the open global model of gravity field of the Earth - WGM2012. As a result of experimental works the four integrated gravity field zoning maps were obtained with using raw and processed data on each gravity field model. The study demonstrates the possibility of open data use to create integrated zoning maps with the condition to eliminate noise component of model by processing in specialized software systems. In this case, for solving problem of contour intervals automated choice the open digital models aren't inferior to regional models of gravity field, created for individual countries. This fact allows asserting about universality and independence of integrated zoning maps creation regardless of detail of a digital cartographic model of geo-physical fields.

Soil map density and a nation's wealth and income

NARCIS (Netherlands)

Hartemink, A.E.

2008-01-01

Little effort has been made to link soil mapping and soil data density to a nation’s welfare. Soil map density in 31 European countries and 44 low and middle income countries is linked to Gross Domestic Product (GDP) per capita and the number of soil scientists per country.

Digital soil mapping using multiple logistic regression on terrain parameters in southern Brazil Mapeamento digital de solos utilizando regressões logísticas múltiplas e parâmetros do terreno no sul do Brasil

Directory of Open Access Journals (Sweden)

Elvio Giasson

2006-06-01

Full Text Available Soil surveys are necessary sources of information for land use planning, but they are not always available. This study proposes the use of multiple logistic regressions on the prediction of occurrence of soil types based on reference areas. From a digitalized soil map and terrain parameters derived from the digital elevation model in ArcView environment, several sets of multiple logistic regressions were defined using statistical software Minitab, establishing relationship between explanatory terrain variables and soil types, using either the original legend or a simplified legend, and using or not stratification of the study area by drainage classes. Terrain parameters, such as elevation, distance to stream, flow accumulation, and topographic wetness index, were the variables that best explained soil distribution. Stratification by drainage classes did not have significant effect. Simplification of the original legend increased the accuracy of the method on predicting soil distribution.Os levantamentos de solos são fontes de informação necessárias para o planejamento de uso das terras, entretanto eles nem sempre estão disponíveis. Este estudo propõe o uso de regressões logísticas múltiplas na predição de ocorrência de classes de solos a partir de áreas de referência. Baseado no mapa original de solos em formato digital e parâmetros do terreno derivados do modelo numérico do terreno em ambiente ArcView, vários conjuntos de regressões logísticas múltiplas foram definidas usando o programa estatístico Minitab, estabelecendo relações entre as variáveis do terreno independentes e tipos de solos, usando tanto a legenda original como uma legenda simplificada, e usando ou não estratificação da área de estudo por classes de drenagem. Os parâmetros do terreno como elevação, distância dos rios, acúmulo de fluxo e índice de umidade topográfica foram as variáveis que melhor explicaram a distribuição das classes de

Digital Field Mapping with the British Geological Survey

Science.gov (United States)

Leslie, Graham; Smith, Nichola; Jordan, Colm

2014-05-01

The BGS•SIGMA project was initiated in 2001 in response to a major stakeholder review of onshore mapping within the British Geological Survey (BGS). That review proposed a significant change for BGS with the recommendation that digital methods should be implemented for field mapping and data compilation. The BGS•SIGMA project (System for Integrated Geoscience MApping) is an integrated workflow for geoscientific surveying and visualisation using digital methods for geological data visualisation, recording and interpretation, in both 2D and 3D. The project has defined and documented an underpinning framework of best practice for survey and information management, best practice that has then informed the design brief and specification for a toolkit to support this new methodology. The project has now delivered BGS•SIGMA2012. BGS•SIGMA2012 is a integrated toolkit which enables assembly and interrogation/visualisation of existing geological information; capture of, and integration with, new data and geological interpretations; and delivery of 3D digital products and services. From its early days as a system which used PocketGIS run on Husky Fex21 hardware, to the present day system which runs on ruggedized tablet PCs with integrated GPS units, the system has evolved into a complete digital mapping and compilation system. BGS•SIGMA2012 uses a highly customised version of ESRI's ArcGIS 10 and 10.1 with a fully relational Access 2007/2010 geodatabase. BGS•SIGMA2012 is the third external release of our award-winning digital field mapping toolkit. The first free external release of the award-winning digital field mapping toolkit was in 2009, with the third version (BGS-SIGMAmobile2012 v1.01) released on our website (http://www.bgs.ac.uk/research/sigma/home.html) in 2013. The BGS•SIGMAmobile toolkit formed the major part of the first two releases but this new version integrates the BGS•SIGMAdesktop functionality that BGS routinely uses to transform our field

Avaliação de modelos digitais de elevação para aplicação em um mapeamento digital de solos Evaluation of digital elevation models for application in a digital soil mapping

Directory of Open Access Journals (Sweden)

César S. Chagas

2010-02-01

Full Text Available No Brasil, normalmente os modelos digitais de elevação (MDEs são produzidos pelos próprios usuários e pouca atenção tem sido dada às suas limitações, como fonte de informação espacial. Este estudo propôs avaliar diferentes MDEs para subsidiar a escolha do modelo apropriado para derivar atributos topográficos utilizados em um mapeamento digital de solos, por redes neurais artificiais. A avaliação constou da determinação da raiz quadrada do erro médio quadrático da elevação (RMSE; análise das depressões espúrias; comparação entre drenagem mapeada e drenagem numérica, curvas de nível derivadas e curvas de nível originais, e análise das bacias de contribuição derivadas. Os resultados obtidos demonstraram que apenas o RMSE não foi suficiente para avaliar a qualidade desses modelos. O MDE, derivado de curvas de nível (CARTA, obtido com a utilização do módulo TOPOGRID apresentou qualidade superior aos MDEs derivados de sensores remotos (ASTER e SRTM. A análise qualitativa também identificou que o MDE CARTA é superior aos demais, pois estes apresentaram grande quantidade de erros que podem comprometer o estabelecimento das relações entre atributos do terreno e as condições locais de solos.In Brazil, the digital elevation models (DEMs are usually produced by users themselves and little attention has been given to their limitations as source of spatial information. The objective of this study was to evaluate different DEMs to help in choosing an appropriate model to derive topographical attributes used in a digital soil mapping based on a neural networks approach. The evaluation consisted of the following analysis: determination of root mean square error (RMSE of elevation; analysis of the spurious depressions; comparison between mapped drainage and numeric drainage and between derived contour lines and original contour lines; and analysis of the derived contribution basins. The results demonstrated that RMSE

Computer aided site management. Site use management by digital mapping

International Nuclear Information System (INIS)

Chupin, J.C.

1990-01-01

The logistics program developed for assisting the Hague site management is presented. A digital site mapping representation and geographical data bases are used. The digital site map and its integration into a data base are described. The program can be applied to urban and rural land management aid. Technical administrative and economic evaluations of the program are summarized [fr

BGS·SIGMA - Digital mapping at the British Geological Survey

Science.gov (United States)

Smith, Nichola; Lawrie, Ken

2017-04-01

Geological mapping methods have evolved significantly over recent decades and this has included the transition to digital field data capture. BGS has been developing methodologies and technologies for this since 2001, and has now reached a stage where our custom built data capture and map compilation system (BGS·SIGMAv2015) is the default toolkit, within BGS, for bedrock and superficial mapping across the UK and overseas. In addition, BGS scientists also use the system for other data acquisition projects, such as landslide assessment, geodiversity audits and building stone studies. BGS·SIGMAv2015 is an integrated toolkit which enables assembly, interrogation and visualisation of existing geological information; capture of, and integration with, new data and geological interpretations; and delivery of digital products and services. From its early days as a system which used PocketGIS run on Husky Fex21 hardware, to the present day system, developed using ESRI's ArcGIS built on top of a bespoke relational data model, running on ruggedized tablet PCs with integrated GPS units, the system has evolved into a comprehensive system for digital geological data capture, mapping and compilation. The benefits, for BGS, of digital data capture are huge. Not only are the data being gathered in a standardised format, with the use of dictionaries to ensure consistency, but project teams can start building their digital geological map in the field by merging data collected by colleagues, building line-work and polygons, and subsequently identifying areas for further investigation. This digital data can then be easily incorporated into corporate databases and used in 3D modelling and visualisation software once back in the office. BGS is now at a stage where the free external release of our digital mapping system is in demand across the world, with 3000 licences being issued to date, and is successfully being used by other geological surveys, universities and exploration companies

Digital bedrock geologic map of the Cavendish quadrangle, Vermont

Data.gov (United States)

Vermont Center for Geographic Information — Digital Data from VG95-203A Ratcliffe, NM, 1995,�Digital bedrock geologic map of the Cavendish quadrangle, Vermont: USGS Open-File Report 95-203, 2 plates, scale...

Digital bedrock geologic map of the Weston quadrangle, Vermont

Data.gov (United States)

Vermont Center for Geographic Information — Digital Data from VG96-526A Ratcliffe, NM�and Burton, WC, 1996,�Digital bedrock geologic map of the Weston quadrangle, Vermont: USGS Open-File Report 96-526, 2...

Digital bedrock geologic map of the Chester quadrangle, Vermont

Data.gov (United States)

Vermont Center for Geographic Information — Digital Data from VG95-576A Ratcliffe, N.M., 1995,�Digital bedrock geologic map of the Chester quadrangle, Vermont: USGS Open-File Report 95-576, 2 plates, scale...

development and testing of a capacitive digital soil moisture metre

African Journals Online (AJOL)

This paper presents a low cost, simple digital soil moisture meter, working on the principle of dielectric. A digital soil moisture meter using the NE555 timer and micro controller as a major electronic component was developed and tested, which display its output in a range of 0.0 to 99% on the 7-segment displayed unit.

Hungarian contribution to the Global Soil Organic Carbon Map (GSOC17) using advanced machine learning algorithms and geostatistics

Science.gov (United States)

Szatmári, Gábor; Laborczi, Annamária; Takács, Katalin; Pásztor, László

2017-04-01

The knowledge about soil organic carbon (SOC) baselines and changes, and the detection of vulnerable hot spots for SOC losses and gains under climate change and changed land management is still fairly limited. Thus Global Soil Partnership (GSP) has been requested to develop a global SOC mapping campaign by 2017. GSPs concept builds on official national data sets, therefore, a bottom-up (country-driven) approach is pursued. The elaborated Hungarian methodology suits the general specifications of GSOC17 provided by GSP. The input data for GSOC17@HU mapping approach has involved legacy soil data bases, as well as proper environmental covariates related to the main soil forming factors, such as climate, organisms, relief and parent material. Nowadays, digital soil mapping (DSM) highly relies on the assumption that soil properties of interest can be modelled as a sum of a deterministic and stochastic component, which can be treated and modelled separately. We also adopted this assumption in our methodology. In practice, multiple regression techniques are commonly used to model the deterministic part. However, this global (and usually linear) models commonly oversimplify the often complex and non-linear relationship, which has a crucial effect on the resulted soil maps. Thus, we integrated machine learning algorithms (namely random forest and quantile regression forest) in the elaborated methodology, supposing then to be more suitable for the problem in hand. This approach has enable us to model the GSOC17 soil properties in that complex and non-linear forms as the soil itself. Furthermore, it has enable us to model and assess the uncertainty of the results, which is highly relevant in decision making. The applied methodology has used geostatistical approach to model the stochastic part of the spatial variability of the soil properties of interest. We created GSOC17@HU map with 1 km grid resolution according to the GSPs specifications. The map contributes to the GSPs

Digital bedrock geologic map of the Andover quadrangle, Vermont

Data.gov (United States)

Vermont Center for Geographic Information — Digital Data from VG96-31A Ratcliffe, N.M., 1996,�Digital bedrock geologic map of the Andover quadrangle, Vermont: USGS Open-File Report 96-31-A, 2 plates, scale...

Digital bedrock geologic map of the Johnson quadrangle, Vermont

Data.gov (United States)

Vermont Center for Geographic Information — Digital Data from VG98-2 Thompson, PJ�and Thompson, TB, 1998,�Digital bedrock geologic map of the Johnson quadrangle, Vermont: VGS Open-File Report VG98-2, 2 plates,...

Digital bedrock geologic map of the Rochester quadrangle, Vermont

Data.gov (United States)

Vermont Center for Geographic Information — Digital Data from VG96-33A Walsh, GJ�and Falta, CK, 1996, Digital bedrock geologic map of the Rochester quadrangle, Vermont: USGS Open-File Report 96-33-A, 2 plates,...

Accounting for access costs in validation of soil maps

NARCIS (Netherlands)

Yang, Lin; Brus, Dick J.; Zhu, A.X.; Li, Xinming; Shi, Jingjing

2018-01-01

The quality of soil maps can best be estimated by collecting additional data at locations selected by probability sampling. These data can be used in design-based estimation of map quality measures such as the population mean of the squared prediction errors (MSE) for continuous soil maps and

Seeing the soil through the net: an eye-opener on the soil map of the Flemish region (Belgium)

Science.gov (United States)

Dondeyne, Stefaan; Vanierschot, Laura; Langohr, Roger; Van Ranst, Eric; Deckers, Jozef; Oorts, Katrien

2017-04-01

A systematic soil survey of Belgium was conducted from 1948 to 1991. Field surveys were done at the detailed scale of 1:5000 with the final maps published at a 1:20,000 scale. The legend of these detailed soil maps (scale 1:20,000) has been converted to the 3rd edition of the international soil classification system 'World Reference Base for Soil Resources' (WRB). Over the last years, the government of the Flemish region made great efforts to make these maps, along with other environmental data, available to the general audience through the internet. The soil maps are widely used and consulted by researchers, teachers, land-use planners, environmental consultancy agencies and archaeologists. The maps can be downloaded and consulted in the viewer 'Visual Soil Explorer' ('Bodemverkenner'). To increase the legibility of the maps, we assembled a collection of photographs from soil profiles representing 923 soil types and 413 photos of related landscape settings. By clicking on a specific location in the 'Visual Soil Explorer', pictures of the corresponding soil type and landscape appear in a pop-up window, with a brief explanation about the soil properties. The collection of photographs of soil profiles cover almost 80% of the total area of the Flemish region, and include the 100 most common soil types. Our own teaching experience shows that these information layers are particular valuable for teaching soil geography and earth sciences in general. Overall, such visual information layers should contribute to a better interpretation of the soil maps and legacy soil data by serving as an eye-opener on the soil map to the wider community.

Digital bedrock geologic map of the Plymouth quadrangle, Vermont

Data.gov (United States)

Vermont Center for Geographic Information — Digital Data from VG94-654A Walsh, G.J., and Ratcliffe, N.M., 1994,�Digital bedrock geologic map of the Plymouth quadrangle, Vermont: USGS Open-File Report 94-654, 2...

Digital bedrock geologic map of the Eden quadrangle, Vermont

Data.gov (United States)

Vermont Center for Geographic Information — Digital Data from VG98-3 Kim, J, Springston, G, and Gale, M, 1998,�Digital bedrock geologic map of the Eden quadrangle, Vermont: VGS Open-File Report VG98-3, 2...

Spectral signature selection for mapping unvegetated soils

Science.gov (United States)

May, G. A.; Petersen, G. W.

1975-01-01

Airborne multispectral scanner data covering the wavelength interval from 0.40-2.60 microns were collected at an altitude of 1000 m above the terrain in southeastern Pennsylvania. Uniform training areas were selected within three sites from this flightline. Soil samples were collected from each site and a procedure developed to allow assignment of scan line and element number from the multispectral scanner data to each sampling location. These soil samples were analyzed on a spectrophotometer and laboratory spectral signatures were derived. After correcting for solar radiation and atmospheric attenuation, the laboratory signatures were compared to the spectral signatures derived from these same soils using multispectral scanner data. Both signatures were used in supervised and unsupervised classification routines. Computer-generated maps using the laboratory and multispectral scanner derived signatures resulted in maps that were similar to maps resulting from field surveys. Approximately 90% agreement was obtained between classification maps produced using multispectral scanner derived signatures and laboratory derived signatures.

High Resolution Mapping of Soil Properties Using Remote Sensing Variables in South-Western Burkina Faso: A Comparison of Machine Learning and Multiple Linear Regression Models.

Science.gov (United States)

Forkuor, Gerald; Hounkpatin, Ozias K L; Welp, Gerhard; Thiel, Michael

2017-01-01

Accurate and detailed spatial soil information is essential for environmental modelling, risk assessment and decision making. The use of Remote Sensing data as secondary sources of information in digital soil mapping has been found to be cost effective and less time consuming compared to traditional soil mapping approaches. But the potentials of Remote Sensing data in improving knowledge of local scale soil information in West Africa have not been fully explored. This study investigated the use of high spatial resolution satellite data (RapidEye and Landsat), terrain/climatic data and laboratory analysed soil samples to map the spatial distribution of six soil properties-sand, silt, clay, cation exchange capacity (CEC), soil organic carbon (SOC) and nitrogen-in a 580 km2 agricultural watershed in south-western Burkina Faso. Four statistical prediction models-multiple linear regression (MLR), random forest regression (RFR), support vector machine (SVM), stochastic gradient boosting (SGB)-were tested and compared. Internal validation was conducted by cross validation while the predictions were validated against an independent set of soil samples considering the modelling area and an extrapolation area. Model performance statistics revealed that the machine learning techniques performed marginally better than the MLR, with the RFR providing in most cases the highest accuracy. The inability of MLR to handle non-linear relationships between dependent and independent variables was found to be a limitation in accurately predicting soil properties at unsampled locations. Satellite data acquired during ploughing or early crop development stages (e.g. May, June) were found to be the most important spectral predictors while elevation, temperature and precipitation came up as prominent terrain/climatic variables in predicting soil properties. The results further showed that shortwave infrared and near infrared channels of Landsat8 as well as soil specific indices of redness

Aircraft route planning based on digital map pre-treatment

Directory of Open Access Journals (Sweden)

Ran ZHEN

2015-04-01

Full Text Available Aiming at the flight path project in low complicated airspace, the influence of terrain conditions and surface threatening to aircraft flight are studied. Through the analysis of digital map and static threat, the paper explores the processing method of the digital map, and uses the Hermite function to process the map smoothly, reducing the searching range of optimal trajectory. By designing the terrain following, terrain avoidance and the way of avoiding a threat, the safety of aircraft can be guaranteed. In-depth analysis of particle swarm optimization (PSO algorithm realizes the three dimensional paths project before the aircraft performs a task. Through simulation, the difference of the maps before and after processing is shown, and offline programming of the three dimensional optimal path is achieved.

Insights into bird wing evolution and digit specification from polarizing region fate maps.

Science.gov (United States)

Towers, Matthew; Signolet, Jason; Sherman, Adrian; Sang, Helen; Tickle, Cheryll

2011-08-09

The proposal that birds descended from theropod dinosaurs with digits 2, 3 and 4 was recently given support by short-term fate maps, suggesting that the chick wing polarizing region-a group that Sonic hedgehog-expressing cells-gives rise to digit 4. Here we show using long-term fate maps that Green fluorescent protein-expressing chick wing polarizing region grafts contribute only to soft tissues along the posterior margin of digit 4, supporting fossil data that birds descended from theropods that had digits 1, 2 and 3. In contrast, digit IV of the chick leg with four digits (I-IV) arises from the polarizing region. To determine how digit identity is specified over time, we inhibited Sonic hedgehog signalling. Fate maps show that polarizing region and adjacent cells are specified in parallel through a series of anterior to posterior digit fates-a process of digit specification that we suggest is involved in patterning all vertebrate limbs with more than three digits.

Preliminary digital geologic maps of the Mariposa, Kingman, Trona, and Death Valley Sheets, California

International Nuclear Information System (INIS)

D'Agnese, F.A.; Faunt, C.C.; Turner, A.K.

1995-01-01

Parts of four 1:250,000-scale geologic maps by the California Department of Natural Resources, Division of Mines and Geology have been digitized for use in hydrogeologic characterization. These maps include the area of California between lat. 35 degree N; Long. 115 degree W and lat. 38 degree N, long. 118 degree W of the Kingman Sheet (Jennings, 1961), Trona Sheet (Jennings and others, 1962), Mariposa Sheet (Strand, 1967), and Death Valley Sheet (Streitz and Stinson, 1974). These digital maps are being released by the US Geological Survey in the ARC/INFO Version 6.1 Export format. The digitized data include geologic unit boundaries, fault traces, and identity of geologic units. The procedure outlined in US Geological Survey Circular 1054 (Soller and others, 1990) was sued during the map construction. The procedure involves transferring hard-copy data into digital format by scanning manuscript maps, manipulating the digital map data, and outputting the data. Most of the work was done using Environmental Systems Research Institute's ARC/INFO software. The digital maps are available in ARC/INFO Rev. 6.1 Export format, from the USGS, Yucca Mountain Project, in Denver, Colorado

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, , USA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk Information And supporting data used to develop the risk data. The primary risk;...

Predictive spatial modelling for mapping soil salinity at continental scale

Science.gov (United States)

Bui, Elisabeth; Wilford, John; de Caritat, Patrice

2017-04-01

Soil salinity is a serious limitation to agriculture and one of the main causes of land degradation. Soil is considered saline if its electrical conductivity (EC) is > 4 dS/m. Maps of saline soil distribution are essential for appropriate land development. Previous attempts to map soil salinity over extensive areas have relied on satellite imagery, aerial electromagnetic (EM) and/or proximally sensed EM data; other environmental (climate, topographic, geologic or soil) datasets are generally not used. Having successfully modelled and mapped calcium carbonate distribution over the 0-80 cm depth in Australian soils using machine learning with point samples from the National Geochemical Survey of Australia (NGSA), we took a similar approach to map soil salinity at 90-m resolution over the continent. The input data were the EC1:5 measurements on the randomly sampled trees were built using the training data. The results were good with an average internal correlation (r) of 0.88 between predicted and measured logEC1:5 (training data), an average external correlation of 0.48 (test subset), and a Lin's concordance correlation coefficient (which evaluates the 1:1 fit) of 0.61. Therefore, the rules derived were mapped and the mean prediction for each 90-m pixel was used for the final logEC1:5 map. This is the most detailed picture of soil salinity over Australia since the 2001 National Land and Water Resources Audit and is generally consistent with it. Our map will be useful as a baseline salinity map circa 2008, when the NGSA samples were collected, for future State of the Environment reports.

Digital bedrock geologic map of the Saxtons River quadrangle, Vermont

Data.gov (United States)

Vermont Center for Geographic Information — Digital Data from VG96-52A Ratcliffe, NM�and Armstrong, TR, 1996, Digital bedrock geologic map of the Saxtons River quadrangle, Vermont, USGS Open-File Report...

New modified map for digital image encryption and its performance

Science.gov (United States)

Suryadi, MT; Yus Trinity Irsan, Maria; Satria, Yudi

2017-10-01

Protection to classified digital data becomes so important in avoiding data manipulation and alteration. The focus of this paper is in data and information protection of digital images form. Protection is provided in the form of encrypted digital image. The encryption process uses a new map, {x}n+1=\\frac{rλ {x}n}{1+λ {(1-{x}n)}2}\\quad ({mod} 1), which is called MS map. This paper will show: the results of digital image encryption using MS map and how the performance is regarding the average time needed for encryption/decryption process; randomness of key stream sequence with NIST test, histogram analysis and goodness of fit test, quality of the decrypted image by PSNR, initial value sensitivity level, and key space. The results show that the average time of the encryption process is relatively same as the decryption process and it depends to types and sizes of the image. Cipherimage (encrypted image) is uniformly distributed since: it passes the goodness of fit test and also the histogram of the cipherimage is flat; key stream, that are generated by MS map, passes frequency (monobit) test, and runs test, which means the key stream is a random sequence; the decrypted image has same quality as the original image; and initial value sensitivity reaches 10-17, and key space reaches 3.24 × 10634. So, that encryption algorithm generated by MS map is more resistant to brute-force attack and known plaintext attack.

Structural equation modelling for digital soil mapping

NARCIS (Netherlands)

Angelini, Marcos E.

2018-01-01

Climate change and land degradation are of increasing societal and governmental concern. For this reason, several international programs have been initiated in the last decade, such as the 4 per 1000 initiative and the Sustainable Development Goals of United Nations. The soil science community is

Map of Nasca Geoglyphs

Science.gov (United States)

Hanzalová, K.; Pavelka, K.

2013-07-01

The Czech Technical University in Prague in the cooperation with the University of Applied Sciences in Dresden (Germany) work on the Nasca Project. The cooperation started in 2004 and much work has been done since then. All work is connected with Nasca lines in southern Peru. The Nasca project started in 1995 and its main target is documentation and conservation of the Nasca lines. Most of the project results are presented as WebGIS application via Internet. In the face of the impending destruction of the soil drawings, it is possible to preserve this world cultural heritage for the posterity at least in a digital form. Creating of Nasca lines map is very useful. The map is in a digital form and it is also available as a paper map. The map contains planimetric component of the map, map lettering and altimetry. Thematic folder in this map is a vector layer of the geoglyphs in Nasca/Peru. Basis for planimetry are georeferenced satellite images, altimetry is created from digital elevation model. This map was created in ArcGis software.

MAP OF NASCA GEOGLYPHS

Directory of Open Access Journals (Sweden)

K. Hanzalová

2013-07-01

Full Text Available The Czech Technical University in Prague in the cooperation with the University of Applied Sciences in Dresden (Germany work on the Nasca Project. The cooperation started in 2004 and much work has been done since then. All work is connected with Nasca lines in southern Peru. The Nasca project started in 1995 and its main target is documentation and conservation of the Nasca lines. Most of the project results are presented as WebGIS application via Internet. In the face of the impending destruction of the soil drawings, it is possible to preserve this world cultural heritage for the posterity at least in a digital form. Creating of Nasca lines map is very useful. The map is in a digital form and it is also available as a paper map. The map contains planimetric component of the map, map lettering and altimetry. Thematic folder in this map is a vector layer of the geoglyphs in Nasca/Peru. Basis for planimetry are georeferenced satellite images, altimetry is created from digital elevation model. This map was created in ArcGis software.

Digital bedrock geologic map of the Morrisville quadrangle,�Vermont

Data.gov (United States)

Vermont Center for Geographic Information — Digital Data from VG98-1 Springston, G., Kim, J., and Applegate, G.S., 1998,�Digital bedrock geologic map of the Morrisville quadrangle,�Vermont: VGS Open-File...

Digital compilation bedrock geologic map of the Milton quadrangle, Vermont

Data.gov (United States)

Vermont Center for Geographic Information — Digital Data from VG95-8A Dorsey, R, Doolan, B, Agnew, PC, Carter, CM, Rosencrantz, EJ, and Stanley, RS, 1995, Digital compilation bedrock geologic map of the Milton...

Digital compilation bedrock geologic map of the Lincoln quadrangle, Vermont

Data.gov (United States)

Vermont Center for Geographic Information — Digital Data from VG95-5A Stanley, R, DelloRusso, V, Haydock, S, Lapp, E, O'Loughlin, S, Prewitt, J,and Tauvers, PR, 1995, Digital compilation bedrock geologic map...

Successful Teaching, Learning, and Use of Digital Mapping Technology in Mazvihwa, Rural Zimbabwe

Science.gov (United States)

Eitzel Solera, M. V.; Madzoro, S.; Solera, J.; Mhike Hove, E.; Changarara, A.; Ndlovu, D.; Chirindira, A.; Ndlovu, A.; Gwatipedza, S.; Mhizha, M.; Ndlovu, M.

2016-12-01

Participatory mapping is now a staple of community-based work around the world. Particularly for indigenous and rural peoples, it can represent a new avenue for environmental justice and can be a tool for culturally appropriate management of local ecosystems. We present a successful example of teaching and learning digital mapping technology in rural Zimbabwe. Our digital mapping project is part of the long-term community-based participatory research of The Muonde Trust in Mazvihwa, Zimbabwe. By gathering and distributing local knowledge and also bringing in visitors to share knowledge, Muonde has been able to spread relevant information among rural farmers. The authors were all members of Muonde or were Muonde's visitors, and were mentors and learners of digital mapping technologies at different times. Key successful characteristics of participants included patience, compassion, openness, perseverance, respect, and humility. Important mentoring strategies included: 1) instruction in Shona and in English, 2) locally relevant examples, assignments, and analogies motivated by real needs, 3) using a variety of teaching methods for different learning modalities, 4) building on and modifying familiar teaching methods, and 5) paying attention to the social and relational aspects of teaching and learning. The Muonde mapping team has used their new skills for a wide variety of purposes, including: identifying, discussing, and acting on emerging needs; using digital mapping for land-use and agropastoral planning; and using mapping as a tool for recording and telling important historical and cultural stories. Digital mapping has built self-confidence as well as providing employable skills and giving Muonde more visibility to other local and national non-governmental organizations, utility companies, and educational institutions. Digital mapping, as taught in a bottom-up, collaborative way, has proven to be both accessible and of enormous practical use to rural Zimbabweans.

Dynamic Digital Maps as Vehicles for Distributing Digital Geologic Maps and Embedded Analytical Data and Multimedia

Science.gov (United States)

Condit, C. D.; Mninch, M.

2012-12-01

The Dynamic Digital Map (DDM) is an ideal vehicle for the professional geologist to use to describe the geologic setting of key sites to the public in a format that integrates and presents maps and associated analytical data and multimedia without the need for an ArcGIS interface. Maps with field trip guide stops that include photographs, movies and figures and animations, showing, for example, how the features seen in the field formed, or how data might be best visualized in "time-frame" sequences are ideally included in DDMs. DDMs distribute geologic maps, images, movies, analytical data, and text such as field guides, in an integrated cross-platform, web enabled format that are intuitive to use, easily and quickly searchable, and require no additional proprietary software to operate. Maps, photos, movies and animations are stored outside the program, which acts as an organizational framework and index to present these data. Once created, the DDM can be downloaded from the web site hosting it in the flavor matching the user's operating system (e.g. Linux, Windows and Macintosh) as zip, dmg or tar files (and soon as iOS and Android tablet apps). When decompressed, the DDM can then access its associated data directly from that site with no browser needed. Alternatively, the entire package can be distributed and used from CD, DVD, or flash-memory storage. The intent of this presentation is to introduce the variety of geology that can be accessed from the over 25 DDMs created to date, concentrating on the DDM of the Springerville Volcanic Field. We will highlight selected features of some of them, introduce a simplified interface to the original DDM (that we renamed DDMC for Classic) and give a brief look at a the recently (2010-2011) completed geologic maps of the Springerville Volcanic field to see examples of each of the features discussed above, and a display of the integrated analytical data set. We will also highlight the differences between the classic or

Bootstrapped neural nets versus regression kriging in the digital mapping of pedological attributes: the automatic and time-consuming perspectives

Science.gov (United States)

Langella, Giuliano; Basile, Angelo; Bonfante, Antonello; Manna, Piero; Terribile, Fabio

2013-04-01

Digital soil mapping procedures are widespread used to build two-dimensional continuous maps about several pedological attributes. Our work addressed a regression kriging (RK) technique and a bootstrapped artificial neural network approach in order to evaluate and compare (i) the accuracy of prediction, (ii) the susceptibility of being included in automatic engines (e.g. to constitute web processing services), and (iii) the time cost needed for calibrating models and for making predictions. Regression kriging is maybe the most widely used geostatistical technique in the digital soil mapping literature. Here we tried to apply the EBLUP regression kriging as it is deemed to be the most statistically sound RK flavor by pedometricians. An unusual multi-parametric and nonlinear machine learning approach was accomplished, called BAGAP (Bootstrap aggregating Artificial neural networks with Genetic Algorithms and Principal component regression). BAGAP combines a selected set of weighted neural nets having specified characteristics to yield an ensemble response. The purpose of applying these two particular models is to ascertain whether and how much a more cumbersome machine learning method could be much promising in making more accurate/precise predictions. Being aware of the difficulty to handle objects based on EBLUP-RK as well as BAGAP when they are embedded in environmental applications, we explore the susceptibility of them in being wrapped within Web Processing Services. Two further kinds of aspects are faced for an exhaustive evaluation and comparison: automaticity and time of calculation with/without high performance computing leverage.

Digital compilation bedrock geologic map of the Warren quadrangle, Vermont

Data.gov (United States)

Vermont Center for Geographic Information — Digital Data from VG95-4A Walsh, GJ, Haydock, S, Prewitt, J, Kraus, J, Lapp, E, O'Loughlin, S, and Stanley, RS, 1995, Digital compilation bedrock geologic map of the...

Digital geologic map database of the Nevada Test Site area, Nevada

Science.gov (United States)

Wahl, R.R.; Sawyer, D.A.; Minor, S.A.; Carr, M.D.; Cole, J.C.; Swadley, W.C.; Laczniak, R.J.; Warren, R.G.; Green, K.S.; Engle, C.M.

1997-01-01

Forty years of geologic investigations at the Nevada Test Site (NTS) have been digitized. These data include all geologic information that: (1) has been collected, and (2) can be represented on a map within the map borders at the map scale is included in the map digital coverages. The following coverages are included with this dataset: Coverage Type Description geolpoly Polygon Geologic outcrops geolflts line Fault traces geolatts Point Bedding attitudes, etc. geolcald line Caldera boundaries geollins line Interpreted lineaments geolmeta line Metamorphic gradients The above coverages are attributed with numeric values and interpreted information. The entity files documented below show the data associated with each coverage.

USGS QA Plan: Certification of digital airborne mapping products

Science.gov (United States)

Christopherson, J.

2007-01-01

To facilitate acceptance of new digital technologies in aerial imaging and mapping, the US Geological Survey (USGS) and its partners have launched a Quality Assurance (QA) Plan for Digital Aerial Imagery. This should provide a foundation for the quality of digital aerial imagery and products. It introduces broader considerations regarding processes employed by aerial flyers in collecting, processing and delivering data, and provides training and information for US producers and users alike.

The place of digital technology on the IEA's energy road-maps

International Nuclear Information System (INIS)

Ben-Naceur, Kamel

2017-01-01

The International Energy Agency (IEA) has drafted road-maps for the next four decades in collaboration with public and private producers and consumers of energy. For each type of energy, these road-maps indicate the key elements for compliance with the objectives of the Paris Climate Agreement. They emphasize the role of digital technology, in particular smart grids, in the transition toward a more digital and more intelligent energy system. The conditions necessary for successfully transforming this sector are mapped out, while attention is called to the risks inherent in this transition

UAV MULTISPECTRAL SURVEY TO MAP SOIL AND CROP FOR PRECISION FARMING APPLICATIONS

Directory of Open Access Journals (Sweden)

G. Sona

2016-06-01

Full Text Available New sensors mounted on UAV and optimal procedures for survey, data acquisition and analysis are continuously developed and tested for applications in precision farming. Procedures to integrate multispectral aerial data about soil and crop and ground-based proximal geophysical data are a recent research topic aimed to delineate homogeneous zones for the management of agricultural inputs (i.e., water, nutrients. Multispectral and multitemporal orthomosaics were produced over a test field (a 100 m x 200 m plot within a maize field, to map vegetation and soil indices, as well as crop heights, with suitable ground resolution. UAV flights were performed in two moments during the crop season, before sowing on bare soil, and just before flowering when maize was nearly at the maximum height. Two cameras, for color (RGB and false color (NIR-RG images, were used. The images were processed in Agisoft Photoscan to produce Digital Surface Model (DSM of bare soil and crop, and multispectral orthophotos. To overcome some difficulties in the automatic searching of matching points for the block adjustment of the crop image, also the scientific software developed by Politecnico of Milan was used to enhance images orientation. Surveys and image processing are described, as well as results about classification of multispectral-multitemporal orthophotos and soil indices.

High Resolution Mapping of Soil Properties Using Remote Sensing Variables in South-Western Burkina Faso: A Comparison of Machine Learning and Multiple Linear Regression Models.

Directory of Open Access Journals (Sweden)

Gerald Forkuor

Full Text Available Accurate and detailed spatial soil information is essential for environmental modelling, risk assessment and decision making. The use of Remote Sensing data as secondary sources of information in digital soil mapping has been found to be cost effective and less time consuming compared to traditional soil mapping approaches. But the potentials of Remote Sensing data in improving knowledge of local scale soil information in West Africa have not been fully explored. This study investigated the use of high spatial resolution satellite data (RapidEye and Landsat, terrain/climatic data and laboratory analysed soil samples to map the spatial distribution of six soil properties-sand, silt, clay, cation exchange capacity (CEC, soil organic carbon (SOC and nitrogen-in a 580 km2 agricultural watershed in south-western Burkina Faso. Four statistical prediction models-multiple linear regression (MLR, random forest regression (RFR, support vector machine (SVM, stochastic gradient boosting (SGB-were tested and compared. Internal validation was conducted by cross validation while the predictions were validated against an independent set of soil samples considering the modelling area and an extrapolation area. Model performance statistics revealed that the machine learning techniques performed marginally better than the MLR, with the RFR providing in most cases the highest accuracy. The inability of MLR to handle non-linear relationships between dependent and independent variables was found to be a limitation in accurately predicting soil properties at unsampled locations. Satellite data acquired during ploughing or early crop development stages (e.g. May, June were found to be the most important spectral predictors while elevation, temperature and precipitation came up as prominent terrain/climatic variables in predicting soil properties. The results further showed that shortwave infrared and near infrared channels of Landsat8 as well as soil specific indices

Spatial downscaling of soil prediction models based on weighted generalized additive models in smallholder farm settings.

Science.gov (United States)

Xu, Yiming; Smith, Scot E; Grunwald, Sabine; Abd-Elrahman, Amr; Wani, Suhas P; Nair, Vimala D

2017-09-11

Digital soil mapping (DSM) is gaining momentum as a technique to help smallholder farmers secure soil security and food security in developing regions. However, communications of the digital soil mapping information between diverse audiences become problematic due to the inconsistent scale of DSM information. Spatial downscaling can make use of accessible soil information at relatively coarse spatial resolution to provide valuable soil information at relatively fine spatial resolution. The objective of this research was to disaggregate the coarse spatial resolution soil exchangeable potassium (K ex ) and soil total nitrogen (TN) base map into fine spatial resolution soil downscaled map using weighted generalized additive models (GAMs) in two smallholder villages in South India. By incorporating fine spatial resolution spectral indices in the downscaling process, the soil downscaled maps not only conserve the spatial information of coarse spatial resolution soil maps but also depict the spatial details of soil properties at fine spatial resolution. The results of this study demonstrated difference between the fine spatial resolution downscaled maps and fine spatial resolution base maps is smaller than the difference between coarse spatial resolution base maps and fine spatial resolution base maps. The appropriate and economical strategy to promote the DSM technique in smallholder farms is to develop the relatively coarse spatial resolution soil prediction maps or utilize available coarse spatial resolution soil maps at the regional scale and to disaggregate these maps to the fine spatial resolution downscaled soil maps at farm scale.

A Double Perturbation Method for Reducing Dynamical Degradation of the Digital Baker Map

Science.gov (United States)

Liu, Lingfeng; Lin, Jun; Miao, Suoxia; Liu, Bocheng

2017-06-01

The digital Baker map is widely used in different kinds of cryptosystems, especially for image encryption. However, any chaotic map which is realized on the finite precision device (e.g. computer) will suffer from dynamical degradation, which refers to short cycle lengths, low complexity and strong correlations. In this paper, a novel double perturbation method is proposed for reducing the dynamical degradation of the digital Baker map. Both state variables and system parameters are perturbed by the digital logistic map. Numerical experiments show that the perturbed Baker map can achieve good statistical and cryptographic properties. Furthermore, a new image encryption algorithm is provided as a simple application. With a rather simple algorithm, the encrypted image can achieve high security, which is competitive to the recently proposed image encryption algorithms.

Soil Functional Mapping: A Geospatial Framework for Scaling Soil Carbon Cycling

Science.gov (United States)

Lawrence, C. R.

2017-12-01

Climate change is dramatically altering biogeochemical cycles in most terrestrial ecosystems, particularly the cycles of water and carbon (C). These changes will affect myriad ecosystem processes of importance, including plant productivity, C exports to aquatic systems, and terrestrial C storage. Soil C storage represents a critical feedback to climate change as soils store more C than the atmosphere and aboveground plant biomass combined. While we know plant and soil C cycling are strongly coupled with soil moisture, substantial unknowns remain regarding how these relationships can be scaled up from soil profiles to ecosystems. This greatly limits our ability to build a process-based understanding of the controls on and consequences of climate change at regional scales. In an effort to address this limitation we: (1) describe an approach to classifying soils that is based on underlying differences in soil functional characteristics and (2) examine the utility of this approach as a scaling tool that honors the underlying soil processes. First, geospatial datasets are analyzed in the context of our current understanding of soil C and water cycling in order to predict soil functional units that can be mapped at the scale of ecosystems or watersheds. Next, the integrity of each soil functional unit is evaluated using available soil C data and mapping units are refined as needed. Finally, targeted sampling is conducted to further differentiate functional units or fill in any data gaps that are identified. Completion of this workflow provides new geospatial datasets that are based on specific soil functions, in this case the coupling of soil C and water cycling, and are well suited for integration with regional-scale soil models. Preliminary results from this effort highlight the advantages of a scaling approach that balances theory, measurement, and modeling.

An overview on the history of pedology and soil mapping in Italy

Science.gov (United States)

Calzolari, C.

2012-04-01

In Italy, the word pedology (pedologia) was introduced in a text book as synonym of soil science for the first time in 1904 by Vinassa de Regny. In the literature, the term cohabitates with the words agrology (agrologia), agro-geology (agro-geologia), agricultural geognostic (geognostica agraria), geopedology (geo-pedologia) used in different historical moments by differently rooted soil scientists. When early pedologists started with systematic studies of soils, their characteristics and geography, they were strongly influenced by their cultural background, mainly geology and agro-chemistry. Along the time, the soil concept evolved, as did the concept of pedology, and this is somehow witnessed by the use of different Italian words with reference to soil: suolo, terreno, terra. Differently from agro-chemists, early pedologists based the soil study on the field description of soil profile. This was firstly based on the vertical differentiation between humus rich layers and "inactive" layers and later on, as long as the discipline evolved, on the presence of genetic horizons. The first complete soil map of Italy is dated 1928. Its Author, the geologist De Angelis d'Ossat, was the president of the organising committee of the 1924 International Soil Conference of Rome, where the International Society of Soil Science was founded. The map was based on the geological map of Italy, drafted in scale 1:1,000,000 after the creation of the Kingdom of Italy in 1861. The internal disputes within the Geological Society, together with the scarce interest of most of geologists for soil, did not facilitate the birth of a central soil survey. Soil mapping was mainly conducted by universities and research institutes, and we had to wait until 1953 for a new soil map (scale 1:3,125,000) at national level to be realised by Paolo Principi, based on literature data. In 1966 a new 1:1,000,000 soil map of Italy was eventually published by a national committee, led by Fiorenzo Mancini. This

A semester-long soil mapping project for an undergraduate pedology course

Science.gov (United States)

Brown, David J.

2015-04-01

Most students taking a pedology course will never work as soil mappers. But many will use soil maps at some point in their careers. At Montana State University, students spent 3 "lab" hours a week, complementing two lectures a week, in the field learning how to study soils literally from the ground up. The only prerequisites for enrollment were completion of an introductory soil science class and 3rd year standing at the university. The area to be mapped, just a km from campus, included a steep mountain backslope, and a complex footslope-toeslope area with diverse soils. Students were divided into teams of 3-4, with approximately 40 students altogether split over two sections that overlapped in the field by one hour. In the first lab session, groups completed a very basic description of just one soil profile. In subsequent weeks, they rotated through multiple pits excavated in a small area, and expanded their soil profile descriptions and interpretations. As students developed proficiency, they were assigned more dispersed locations to study, working for the most part independently as I hiked between pits. Throughout this process, every pit was geolocated using a GPS unit, and every profile description was copied and retained in a designated class file. Student groups delineated map units using stereo air photography, then used these delineations to guide the selection of their final locations to describe. At the end of the course, groups used all of the combined and georeferenced profile descriptions to construct a soil map of the study area complete with map unit descriptions. Most students struggled to make sense of the substantial variability within their map units, but through this struggle -- and their semester of field work -- they gained an appreciation for the value and limitations of a soil map that could not be obtained from even the most entertaining lecture. Both the class and particularly the field sessions received consistently high student reviews

Soil mapping and processes modelling for sustainable land management: a review

Science.gov (United States)

Pereira, Paulo; Brevik, Eric; Muñoz-Rojas, Miriam; Miller, Bradley; Smetanova, Anna; Depellegrin, Daniel; Misiune, Ieva; Novara, Agata; Cerda, Artemi

2017-04-01

Soil maps and models are fundamental for a correct and sustainable land management (Pereira et al., 2017). They are an important in the assessment of the territory and implementation of sustainable measures in urban areas, agriculture, forests, ecosystem services, among others. Soil maps represent an important basis for the evaluation and restoration of degraded areas, an important issue for our society, as consequence of climate change and the increasing pressure of humans on the ecosystems (Brevik et al. 2016; Depellegrin et al., 2016). The understanding of soil spatial variability and the phenomena that influence this dynamic is crucial to the implementation of sustainable practices that prevent degradation, and decrease the economic costs of soil restoration. In this context, soil maps and models are important to identify areas affected by degradation and optimize the resources available to restore them. Overall, soil data alone or integrated with data from other sciences, is an important part of sustainable land management. This information is extremely important land managers and decision maker's implements sustainable land management policies. The objective of this work is to present a review about the advantages of soil mapping and process modeling for sustainable land management. References Brevik, E., Calzolari, C., Miller, B., Pereira, P., Kabala, C., Baumgarten, A., Jordán, A. (2016) Historical perspectives and future needs in soil mapping, classification and pedological modelling, Geoderma, 264, Part B, 256-274. Depellegrin, D.A., Pereira, P., Misiune, I., Egarter-Vigl, L. (2016) Mapping Ecosystem Services in Lithuania. International Journal of Sustainable Development and World Ecology, 23, 441-455. Pereira, P., Brevik, E., Munoz-Rojas, M., Miller, B., Smetanova, A., Depellegrin, D., Misiune, I., Novara, A., Cerda, A. (2017) Soil mapping and process modelling for sustainable land management. In: Pereira, P., Brevik, E., Munoz-Rojas, M., Miller, B

Harmonisation of the soil map of Africa at the continental scale

DEFF Research Database (Denmark)

Dewitte, Olivier; Jones, Arwyn; Spaargaren, Otto

2013-01-01

In the context of major global environmental challenges such as food security, climate change, fresh water scarcity and biodiversity loss, the protection and the sustainable management of soil resources in Africa are of paramount importance. To raise the awareness of the general public...... with no information, soil patterns, river and drainage networks, and dynamic features such as sand dunes, water bodies and coastlines. In comparison to the initial map derived from HWSD, the new map represents a correction of 13% of the soil data for the continent. The map is available for downloading. (C) 2013......, stakeholders, policy makers and the science community to the importance of soil in Africa, the Joint Research Centre of the European Commission has produced the Soil Atlas of Africa. To that end, a new harmonised soil map at the continental scale has been produced. The steps of the construction of the new area...

development and testing of a capacitive digital soil moisture metre

African Journals Online (AJOL)

user

soil moisture meter using the NE555 timer and micro controller as a major electronic component ... relationship between the moisture content process and the digital soil moisture meter. ..... the moisture contents showing that the infiltration of.

Digital bedrock geologic map of the Mount Snow & Readsboro quadrangles, Vermont

Data.gov (United States)

Vermont Center for Geographic Information — Digital Data from VG95-DM1 Ratcliffe, NM, 1995, Digital bedrock geologic map of the Mount Snow & Readsboro quadrangles, Vermont, scale 1:24000, The bedrock...

Inception Report, Photogrammetry and Digital Mapping, LATIVA

DEFF Research Database (Denmark)

Frederiksen, Poul

1996-01-01

The report gives the current situation on photogrammetry and digital mapping by the end of 1996 in Latvia.Objectives and proposals are given for activities of the EU Phare project: Technical Assistance to Land Privatisation and Registration in Latvia.The project is executed by Kampsax Geoplan...

Digital and preliminary bedrock geologic map of the Wallingford quadrangle, Vermont

Data.gov (United States)

Vermont Center for Geographic Information — Digital Data from VG98-335A Burton, WC, and Ratcliffe, NM, 2000, Digital and preliminary bedrock geologic map of the Wallingford quadrangle, Vermont: USGS Open-File...

Small forest cuttings mapped with Landsat digital data

Science.gov (United States)

Bryant, E.; Dodge, A. G.; Eger, M. J. E.

1979-01-01

The Cooperative Landsat Applications Research Group used computer classification of Landsat digital data to map forest cuttings (clearcuts) in northern New Hampshire. Cuttings as small as 3 hectares were identified. Several ages or conditions of clearcuts could be distinguished. Progress in two methods of duplicating classification categories from one Landsat pass to another are discussed. One method was used in making maps of areas in 1973, 1975, and 1978.

NACP MsTMIP: Unified North American Soil Map

Data.gov (United States)

National Aeronautics and Space Administration — ABSTRACT: This data set provides soil maps for the United States (US) (including Alaska), Canada, Mexico, and a part of Guatemala. The map information content...

Digital and preliminary bedrock geologic map of the Chittenden quadrangle, Vermont

Data.gov (United States)

Vermont Center for Geographic Information — Digital Data from VG97-854A Ratcliffe, NM, 1997,�Digital and preliminary bedrock geologic map of the Chittenden quadrangle, Vermont: USGS Open-File Report 97-854, 1...

Characteristics and Classification of Soils Developed Over Coastal ...

African Journals Online (AJOL)

A semi-detailed soil survey of the land of Ikwuano Local Government Area Abia State South East Nigeria was made with the aid of the digitized map. Pedons in the identified mapping units were sampled and studied for their morphology, physical and chemical properties (e.g. soil colour, texture, pH, CEC, %OC, base ...

NACP MsTMIP: Unified North American Soil Map

Data.gov (United States)

National Aeronautics and Space Administration — This data set provides soil maps for the United States (US) (including Alaska), Canada, Mexico, and a part of Guatemala. The map information content includes maximum...

A comparison between probability and information measures of uncertainty in a simulated soil map and the economic value of imperfect soil information.

Science.gov (United States)

Lark, R. Murray

2014-05-01

Conventionally the uncertainty of a conventional soil map has been expressed in terms of the mean purity of its map units: the probability that the soil profile class examined at a site would be found to correspond to the eponymous class of the simple map unit that is delineated there (Burrough et al, 1971). This measure of uncertainty has an intuitive meaning and is used for quality control in soil survey contracts (Western, 1978). However, it may be of limited value to the manager or policy maker who wants to decide whether the map provides a basis for decision making, and whether the cost of producing a better map would be justified. In this study I extend a published analysis of the economic implications of uncertainty in a soil map (Giasson et al., 2000). A decision analysis was developed to assess the economic value of imperfect soil map information for agricultural land use planning. Random error matrices for the soil map units were then generated, subject to constraints which ensure consistency with fixed frequencies of the different soil classes. For each error matrix the mean map unit purity was computed, and the value of the implied imperfect soil information was computed by the decision analysis. An alternative measure of the uncertainty in a soil map was considered. This is the mean soil map information which is the difference between the information content of a soil observation, at a random location in the region, and the information content of a soil observation given that the map unit is known. I examined the relationship between the value of imperfect soil information and the purity and information measures of map uncertainty. In both cases there was considerable variation in the economic value of possible maps with fixed values of the uncertainty measure. However, the correlation was somewhat stronger with the information measure, and there was a clear upper bound on the value of an imperfect soil map when the mean information takes some

The role of soil quality maps in the reuse of lightly contaminated soil

OpenAIRE

Lamé, F.P.J.; Leenaers, H.; Zegwaard, J.

2000-01-01

In 1999 the Dutch government agreed on a new policy regarding the reuse of lightly contaminated soil. From now on, lightly contaminated soil may be reused under conditions of soil-quality management. The municipal authorities supervise the reuse under this new regime. Two basic criteria need to be met before reuse of lightly contaminated soil is allowed. Firstly, the quality of the soil has to be characterised on a soil quality map. Secondly, the soil that will be reused has to be of the same...

Digital and preliminary bedrock geologic map of the Rutland quadrangle, Vermont

Data.gov (United States)

Vermont Center for Geographic Information — Digital Data from VG98-121A Ratcliffe, N.M., 1998,�Digital and preliminary bedrock geologic map of the Rutland quadrangle, Vermont: USGS Open-File Report 98-121-A, 1...

Digital compilation bedrock geologic map of the Mt. Ellen quadrangle, Vermont

Data.gov (United States)

Vermont Center for Geographic Information — Digital Data from VG95-6A Stanley, RS, Walsh, G, Tauvers, PR, DiPietro, JA, and DelloRusso, V, 1995,�Digital compilation bedrock geologic map of the Mt. Ellen...

Digital compilation bedrock geologic map of the South Mountain quadrangle, Vermont

Data.gov (United States)

Vermont Center for Geographic Information — Digital Data from VG95-3A Stanley, R.S., DelloRusso, V., Tauvers, P.R., DiPietro, J.A., Taylor, S., and Prahl, C., 1995, Digital compilation bedrock geologic map of...

A Visual Framework for Digital Reconstruction of Topographic Maps

KAUST Repository

Thabet, Ali Kassem

2014-09-30

We present a framework for reconstructing Digital Elevation Maps (DEM) from scanned topographic maps. We first rectify the images to ensure that maps fit together without distortion. To segment iso-contours, we have developed a novel semi-automated method based on mean-shifts that requires only minimal user interaction. Contour labels are automatically read using an OCR module. To reconstruct the output DEM from scattered data, we generalize natural neighbor interpolation to handle the transfinite case (contours and points). To this end, we use parallel vector propagation to compute a discrete Voronoi diagram of the constraints, and a modified floodfill to compute virtual Voronoi tiles. Our framework is able to handle tens of thousands of contours and points and can generate DEMs comprising more than 100 million samples. We provide quantitative comparison to commercial software and show the benefits of our approach. We furthermore show the robustness of our method on a massive set of old maps predating satellite acquisition. Compared to other methods, our framework is able to accurately and efficiently generate a final DEM despite inconsistencies, sparse or missing contours even for highly complex and cluttered maps. Therefore, this method has broad applicability for digitization and reconstruction of the world\\'s old topographic maps that are often the only record of past landscapess and cultural heritage before their destruction under modern development.

Digital bedrock geologic map of the Gilson Mountain quadrangle,�Vermont

Data.gov (United States)

Vermont Center for Geographic Information — Digital Data from VG95-7A Doolan, B, 1995,�Digital bedrock geologic map of the Gilson Mountain quadrangle,�Vermont: VGS Open-File Report VG95-7A, 2 plates, scale...

Radarsat Antarctic Mapping Project Digital Elevation Model, Version 2

Data.gov (United States)

National Aeronautics and Space Administration — The high-resolution Radarsat Antarctic Mapping Project (RAMP) Digital Elevation Model (DEM) combines topographic data from a variety of sources to provide consistent...

County digital geologic mapping. Final report

Energy Technology Data Exchange (ETDEWEB)

Hess, R.H.; Johnson, G.L.; dePolo, C.M.

1995-12-31

The purpose of this project is to create quality-county wide digital 1:250,000-scale geologic maps from existing published 1:250,000-scale Geologic and Mineral Resource Bulletins published by the Nevada Bureau of Mines and Geology (NBMG). An additional data set, based on current NBMG research, Major and Significant Quaternary and Suspected Quaternary Faults of Nevada, at 1:250,000 scale has also been included.

County digital geologic mapping. Final report

International Nuclear Information System (INIS)

Hess, R.H.; Johnson, G.L.; dePolo, C.M.

1995-01-01

The purpose of this project is to create quality-county wide digital 1:250,000-scale geologic maps from existing published 1:250,000-scale Geologic and Mineral Resource Bulletins published by the Nevada Bureau of Mines and Geology (NBMG). An additional data set, based on current NBMG research, Major and Significant Quaternary and Suspected Quaternary Faults of Nevada, at 1:250,000 scale has also been included

Digital Mapping Techniques '11–12 workshop proceedings

Science.gov (United States)

Soller, David R.

2014-01-01

The Digital Mapping Techniques '11 (DMT'11) workshop was hosted by Virginia Division of Geology and Mineral Resources and The College of William & Mary, and coordinated by the National Geologic Map Database project. Conducted May 22-25 on the campus of The College of William & Mary, in Williamsburg, Virginia, it was attended by 77 technical experts from 30 agencies, universities, and private companies, including representatives from 19 State geological surveys (see "DMT'11 Presentations and Attendees" in these Proceedings).

Northern Circumpolar Soils Map, Version 1

Data.gov (United States)

National Aeronautics and Space Administration — This data set consists of a circumpolar map of dominant soil characteristics, with a scale of 1:10,000,000, covering the United States, Canada, Greenland, Iceland,...

Combining land use data acquired from Landsat with soil map data

Science.gov (United States)

Westin, F. C.; Brandner, T. M.

1981-01-01

A method currently used to derive agrophysical units (APUs), i.e., geographical areas having definable/comparable agronomic and physical parameters which reflect a range in agricultural use and management, is discussed with reference to results obtained for South Dakota and an area in China. The method consists of combining agricultural land use data acquired from Landsat with soil map data. The resulting map units are soil associations characterized by cropland use intensity, and they can be used to identify major cropland areas and to develop a rating reflecting the relative potential of the soils in the delineated area for crop production, as well as to update small-scale soil maps.

Ultrahigh Dimensional Variable Selection for Interpolation of Point Referenced Spatial Data: A Digital Soil Mapping Case Study

Science.gov (United States)

Lamb, David W.; Mengersen, Kerrie

2016-01-01

Modern soil mapping is characterised by the need to interpolate point referenced (geostatistical) observations and the availability of large numbers of environmental characteristics for consideration as covariates to aid this interpolation. Modelling tasks of this nature also occur in other fields such as biogeography and environmental science. This analysis employs the Least Angle Regression (LAR) algorithm for fitting Least Absolute Shrinkage and Selection Operator (LASSO) penalized Multiple Linear Regressions models. This analysis demonstrates the efficiency of the LAR algorithm at selecting covariates to aid the interpolation of geostatistical soil carbon observations. Where an exhaustive search of the models that could be constructed from 800 potential covariate terms and 60 observations would be prohibitively demanding, LASSO variable selection is accomplished with trivial computational investment. PMID:27603135

Digital Mapping Techniques '10-Workshop Proceedings, Sacramento, California, May 16-19, 2010

Science.gov (United States)

Soller, David R.; Soller, David R.

2012-01-01

The Digital Mapping Techniques '10 (DMT'10) workshop was attended by 110 technical experts from 40 agencies, universities, and private companies, including representatives from 19 State geological surveys (see Appendix A). This workshop, hosted by the California Geological Survey, May 16-19, 2010, in Sacramento, California, was similar in nature to the previous 13 meetings (see Appendix B). The meeting was coordinated by the U.S. Geological Survey's (USGS) National Geologic Map Database project. As in the previous meetings, the objective was to foster informal discussion and exchange of technical information. It is with great pleasure that I note that the objective was again successfully met, as attendees continued to share and exchange knowledge and information, and renew friendships and collegial work begun at past DMT workshops. At this meeting, oral and poster presentations and special discussion sessions emphasized (1) methods for creating and publishing map products ("publishing" includes Web-based release); (2) field data capture software and techniques, including the use of LiDAR; (3) digital cartographic techniques; (4) migration of digital maps into ArcGIS Geodatabase format; (5) analytical GIS techniques; and (6) continued development of the National Geologic Map Database.

Digital Technology in the protection of cultural heritage Bao Fan Temple mural digital mapping survey

Directory of Open Access Journals (Sweden)

Y. Zheng

2015-08-01

Full Text Available Peng Xi county, Sichuan province, the Bao Fan temple mural digitization survey mapping project: we use three-dimensional laserscanning, multi-baseline definition digital photography, multi-spectral digital image acquisition and other technologies for digital survey mapping. The purpose of this project is to use modern mathematical reconnaissance mapping means to obtain accurate mural shape, color, quality and other data. Combined with field investigation and laboratory analysis results, and based on a comprehensive survey and study, a comprehensive analysis of the historical Bao Fan Temple mural artistic and scientific value was conducted. A study of the mural’s many qualities (structural, material, technique, preservation environment, degradation, etc. reveal all aspects of the information carried by the Bao Fan Temple mural. From multiple angles (archeology, architecture, surveying, conservation science and other disciplines an assessment for the Bao Fan Temple mural provides basic data and recommendations for conservation of the mural. In order to achieve the conservation of cultural relics in the Bao Fan Temple mural digitization survey mapping process, we try to apply the advantages of three-dimensional laser scanning equipment. For wall murals this means obtaining three-dimensional scale data from the scan of the building and through the analysis of these data to help determine the overall condition of the settlement as well as the deformation of the wall structure. Survey analysis provides an effective set of conclusions and suggestions for appropriate mural conservation. But before data collection, analysis and research need to first to select the appropriate scanning equipment, set the appropriate scanning accuracy and layout position of stations necessary to determine the scope of required data. We use the fine features of the three-dimensional laser scanning measuring arm to scan the mural surface deformation degradation to reflect

Digital Technology in the protection of cultural heritage Bao Fan Temple mural digital mapping survey

Science.gov (United States)

Zheng, Y.

2015-08-01

Peng Xi county, Sichuan province, the Bao Fan temple mural digitization survey mapping project: we use three-dimensional laserscanning, multi-baseline definition digital photography, multi-spectral digital image acquisition and other technologies for digital survey mapping. The purpose of this project is to use modern mathematical reconnaissance mapping means to obtain accurate mural shape, color, quality and other data. Combined with field investigation and laboratory analysis results, and based on a comprehensive survey and study, a comprehensive analysis of the historical Bao Fan Temple mural artistic and scientific value was conducted. A study of the mural's many qualities (structural, material, technique, preservation environment, degradation, etc.) reveal all aspects of the information carried by the Bao Fan Temple mural. From multiple angles (archeology, architecture, surveying, conservation science and other disciplines) an assessment for the Bao Fan Temple mural provides basic data and recommendations for conservation of the mural. In order to achieve the conservation of cultural relics in the Bao Fan Temple mural digitization survey mapping process, we try to apply the advantages of three-dimensional laser scanning equipment. For wall murals this means obtaining three-dimensional scale data from the scan of the building and through the analysis of these data to help determine the overall condition of the settlement as well as the deformation of the wall structure. Survey analysis provides an effective set of conclusions and suggestions for appropriate mural conservation. But before data collection, analysis and research need to first to select the appropriate scanning equipment, set the appropriate scanning accuracy and layout position of stations necessary to determine the scope of required data. We use the fine features of the three-dimensional laser scanning measuring arm to scan the mural surface deformation degradation to reflect the actual state of

Review of research on remote sensing with digital map. Remote sensing to suchi chizu no ketsugo ni yoru kenkyu no shokai

Energy Technology Data Exchange (ETDEWEB)

Tanaka, S; Sugimura, T [Remote Sensing Technology Center of Japan, Tokyo (Japan)

1990-12-05

This paper describes the relationship between remote sensing and digital map. The relation between remote sensing and digital map is roughly classified into two kinds. One of them is utilization of remote sensing and digital map in combination to analyze phenomena, and the other is normalization of remote sensing data by use of digital map. For examples of utilizing remote sensing and digital map, there are the creation of a perspective image of ground scene from Landsat MSS data by use of a mesh type digital map of the orthogonal co-ordinates, and the creation of an image of the enviromental research along roads from satilite data by use of a vector type digital map. Furthermore, this paper introduces a procedure of correcting geographical strains by use of a digital map and converting a radar image to corrected plane image, and the use of a digital map in the global scale for the analysis of floods and other purposes. 20 refs., 5 figs., 1 tab.

Soil mapping and modelling for evaluation of the effects of historical and present-day soil erosion

Science.gov (United States)

Smetanova, Anna; Szwarczewski, Piotr

2016-04-01

The loess hilly lands in Danube Lowland are characterized by patchy soil-scape. The soil erosion processes uncover the subsurface, bright loess horizon, while non-eroded and colluvial soils are of the dark colour, in the chernozem area. With the modernisation of agriculture since the 1950's and in the process of collectivization, when small fields were merged into bigger, the soil degradation progressed. However, the analysis of historical sources and sediment archives showed the proofs of historical soil erosion. The objective of this study is to map the soil erosion patterns in connection of both pre- and post-collectivization landscape and to understand the accordingly developed soil erosion patterns. The combined methods of soil mapping and soil erosion modelling were applied in the part of the Trnavska pahorkatina Hilly Land in Danube Lowland. The detailed soil mapping in a zero-order catchment (0.28 km²) uncovered the removal of surface soil horizon of 0.6m or more, while the colluvial soils were about 1.1m deep. The soil properties and dating helped to describe the original soil profile in the valley bottom, and reconstruct the history of soil erosion in the catchment. The soil erosion model was applied using the reconstructed land use patterns in order to understand the effect of recent and historical soil erosion in the lowland landscape. This work was supported by the Slovak Research and Development Agency under the contract ESF-EC-0006-07 and APVV-0625-11; Anna Smetanová has received the support of the AgreenSkills fellowship (under grant agreement n°267196).

Detailed predictive mapping of acid sulfate soil occurrence using electromagnetic induction data

DEFF Research Database (Denmark)

Beucher, Amélie; Boman, A; Mattbäck, S

impact through the resulting corrosion of concrete and steel infrastructures, or their poor geotechnical qualities. Therefore, mapping acid sulfate soil occurrence constitutes a key step to target the strategic areas for subsequent environmental risk management and mitigation. Conventional mapping (i...... obtained from a EM38 proximal sensor enabled the refined mapping of acid sulfate soils over a field (Huang et al. 2014). The present study aims at developing an efficient and reliable method for the detailed predictive mapping of acid sulfate soil occurrence in a field located in western Finland. Different...

Soil maps, field knowledge, forest inventory and Ecological-Economic Zoning as a basis for agricultural suitability of lands in Minas Gerais elaborated in GIS

Directory of Open Access Journals (Sweden)

Vladimir Antonio Silva

2013-12-01

Full Text Available Lands (broader concept than soils, including all elements of the environment: soils, geology, topography, climate, water resources, flora and fauna, and the effects of anthropogenic activities of the state of Minas Gerais are in different soil, climate and socio-economics conditions and suitability for the production of agricultural goods is therefore distinct and mapping of agricultural suitability of the state lands is crucial for planning guided sustainability. Geoprocessing uses geographic information treatment techniques and GIS allows to evaluate geographic phenomena and their interrelationships using digital maps. To evaluate the agricultural suitability of state lands, we used soil maps, field knowledge, forest inventories and databases related to Ecological-Economic Zoning (EEZ of Minas Gerais, to develop a map of land suitability in GIS. To do this, we have combined the maps of soil fertility, water stress, oxygen deficiency, vulnerability to erosion and impediments to mechanization. In terms of geographical expression, the main limiting factor of lands is soil fertility, followed by lack of water, impediments to mechanization and vulnerability to erosion. Regarding agricultural suitability, the group 2 (regular suitability for crops is the most comprehensive, representing 45.13% of the state. For management levels A and B, low and moderate technological level, respectively, the most expressive suitability class is the regular, followed by the restricted class and last, the adequate class, while for the management level C (high technological level the predominant class is the restricted. The predominant most intensive use type is for crops, whose area increases substantially with capital investment and technology (management levels B and C.

Soil organic carbon mapping of partially vegetated agricultural fields with imaging spectroscopy

NARCIS (Netherlands)

Bartholomeus, H.; Kooistra, L.; Stevens, A.; Leeuwen, van M.; Wesemael, van B.; Ben-Dor, E.; Tychon, B.

2011-01-01

Soil Organic Carbon (SOC) is one of the key soil properties, but the large spatial variation makes continuous mapping a complex task. Imaging spectroscopy has proven to be an useful technique for mapping of soil properties, but the applicability decreases rapidly when fields are partially covered

Soil erodibility mapping using three approaches in the Tangiers province –Northern Morocco

Directory of Open Access Journals (Sweden)

Hamza Iaaich

2016-09-01

Full Text Available Soil erodibility is a key factor in assessing soil loss rates. In fact, soil loss is the most occurring land degradation form in Morocco, affecting rural and urban vulnerable areas. This work deals with large scale mapping of soil erodibility using three mapping approaches: (i the CORINE approach developed for Europe by the JRC; (ii the UNEP/FAO approach developed within the frame of the United Nations Environmental Program for the Mediterranean area; (iii the Universal Soil Loss Equation (USLE K factor. Our study zone is the province of Tangiers, North-West of Morocco. For each approach, we mapped and analyzed different erodibility factors in terms of parent material, topography and soil attributes. The thematic maps were then integrated using a Geographic Information System to elaborate a soil erodibility map for each of the three approaches. Finally, the validity of each approach was checked in the field, focusing on highly eroded areas, by confronting the estimated soil erodibility and the erosion state as observed in the field. We used three statistical indicators for validation: overall accuracy, weighted Kappa factor and omission/commission errors. We found that the UNEP/FAO approach, based principally on lithofacies and topography as mapping inputs, is the most adapted for the case of our study zone, followed by the CORINE approach. The USLE K factor underestimated the soil erodibility, especially for highly eroded areas.

Predictive mapping of the acidifying potential for acid sulfate soils

DEFF Research Database (Denmark)

Boman, A; Beucher, Amélie; Mattbäck, S

Developing methods for the predictive mapping of the potential environmental impact from acid sulfate soils is important because recent studies (e.g. Mattbäck et al., under revision) have shown that the environmental hazards (e.g. leaching of acidity) related to acid sulfate soils vary depending...... on their texture (clay, silt, sand etc.). Moreover, acidity correlates, not only with the sulfur content, but also with the electrical conductivity (EC) measured after incubation. Electromagnetic induction (EMI) data collected from an EM38 proximal sensor also enabled the detailed mapping of acid sulfate soils...... over a field (Huang et al., 2014).This study aims at assessing the use of EMI data for the predictive mapping of the acidifying potential in an acid sulfate soil area in western Finland. Different supervised classification modelling techniques, such as Artificial Neural Networks (Beucher et al., 2015...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, OSCEOLA COUNTY, FL

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HART COUNTY, KY

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE,GRAVES COUNTY, KY

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, LYON COUNTY, KY

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WOLFE COUNTY, KY

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WEBSTER COUNTY, KY

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CHRISTIAN COUNTY, KY

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, DAVIESS COUNTY, KY

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MARLBORO COUNTY, SC

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MIDDLESEX, VA, USA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WARREN COUNTY, USA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SCOTT COUNTY, KY

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, NEWTON COUNTY, GEORGIA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, LUCAS COUNTY, OHIO

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HARDIN COUNTY, TX

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SARPY COUNTY, USA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SUFFOLK COUNTY, MASSACHUSETTS

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, FAIRFIELD COUNTY, CONNECTICUT

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, BUTLER COUNTY, NE

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WALKER COUNTY, TX

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WALKER COUNTY, AL

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Wilcox COUNTY, AL

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WASHINGTON COUNTY, USA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, BALLARD COUNTY, KY

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HARRISON COUNTY, KY

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, POWELL COUNTY, KY

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Eddy County, NM

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Winston COUNTY, AL

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Mitchell County, GA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ANGELINA COUNTY, TX

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ATASCOSA COUNTY, TEXAS

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, DOUGLAS COUNTY, USA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, FLEMING COUNTY, KY

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SEWARD COUNTY, USA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MCCRACKEN COUNTY, KY

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SAUNDERS COUNTY, NEBRASKA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CHEROKEE COUNTY, KANSAS

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, POLK COUNTY, NEBRASKA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HARLAN COUNTY, NEBRASKA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, RENO COUNTY, KANSAS

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, FURNAS COUNTY, NEBRASKA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ALLAMAKEE COUNTY, IOWA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CASS COUNTY, NEBRASKA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MCPHERSON COUNTY, KANSAS

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, DAWES COUNTY, NEBRASKA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, VALLEY COUNTY, NEBRASKA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ELLSWORTH COUNTY, KANSAS

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SHERMAN COUNTY, NEBRASKA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HARVEY COUNTY, KANSAS

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, PLATTE COUNTY, NEBRASKA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, COLFAX COUNTY, NEBRASKA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, THURSTON COUNTY, NEBRASKA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, TAMA COUNTY, IOWA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WEBSTER COUNTY, NEBRASKA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, BONNER COUNTY, IDAHO

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ROBERTSON COUNTY, KY

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CHEROKEE COUNTY, TX

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, GRANT COUNTY, KY

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ESSEX COUNTY, MA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SEBASTIAN COUNTY, AR

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SEBASTIAN COUNTY, ARKANSAS

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CHAMBERS COUNTY, TEXAS

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, NELSON COUNTY, KY

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MCLEAN COUNTY, KY

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, RUSK COUNTY, TX

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, PEMBINA COUNTY, USA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SHELBY COUNTY, AL

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, JOHNSON COUNTY, KY

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Harris COUNTY, TX

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, PIKE COUNTY, AL

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MARION COUNTY, FLORIDA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HOUSTON COUNTY, TX

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Marshall COUNTY, AL

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, GRIMES COUNTY, TX

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WALLER COUNTY, TX

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CARBON COUNTY, UTAH

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Liberty County, TX

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, TYLER COUNTY, TX

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Stafford County , VIRGINIA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SUMNER COUNTY, USA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HILLSBOROUGH COUNTY, FLORIDA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, LEE COUNTY, FLORIDA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WAYNE COUNTY, USA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Jefferson COUNTY, AL

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Kenton COUNTY, Kentucky

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, RUSSELL COUNTY, KY

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ST. LOUIS, USA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

A Visual Framework for Digital Reconstruction of Topographic Maps

KAUST Repository

Thabet, Ali Kassem; Smith, Neil; Wittmann, Roland; Schneider, Jens

2014-01-01

, this method has broad applicability for digitization and reconstruction of the world's old topographic maps that are often the only record of past landscapess and cultural heritage before their destruction under modern development.

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ALPENA COUNTY, MI

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

Digital Flood Insurance Rate Map for Vermillion County, IN

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Dougherty County, GA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, POLK COUNTY, TX

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MCDONALD COUNTY, USA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MERCER COUNTY, KY

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

Digital Flood Insurance Rate Map Database, Crawford County, PA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, BATH COUNTY, KY

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Douglas COUNTY, Nevada

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Elbert County, Colorado

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, JASPER COUNTY, TX

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Cleburne COUNTY, AL

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE,CAMDEN COUNTY, GEORGIA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk Information And supporting data used to develop the risk data. The primary risk;...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MENDOCINO COUNTY, CALIFORNIA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ETOWAH COUNTY, AL

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HUNTERDON CO., NJ

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, FINNEY COUNTY, USA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CULLMAN COUNTY, AL

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MARION COUNTY, KY

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, LEBANON COUNTY, PENNSYLVANIA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ELLIOTT COUNTY, KY

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, GAGE COUNTY, NEBRASKA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CLARK COUNTY, USA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, POLK COUNTY, USA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MIDDLESEX COUNTY, MASSACHUSETTS

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ESCAMBIA COUNTY, AL

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Baldwin COUNTY, AL

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SONOMA COUNTY, CALIFORNIA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, BENTON COUNTY, ARKANSAS

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, GORDON COUNTY, GEORGIA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information And supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MARIN COUNTY, CALIFORNIA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, BOYLE COUNTY, KY

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Bell COUNTY, Kentucky

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, BATH COUNTY, VIRGINIA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SIMPSON COUNTY, KY

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, BANDERA COUNTY, TEXAS

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Washington COUNTY, NE

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

Digital Flood Insurance Rate Map Database, Mercer County, PA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, DUKES COUNTY, MA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Terrell County, GA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, GLOUCESTER, VA, USA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WORCESTER COUNTY, MA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Cherokee COUNTY, AL

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, JACKSON COUNTY, AL

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, NORFOLK COUNTY, MASSACHUSETTS

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, COWLEY COUNTY, USA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, TALBOT, MARYLAND, USA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, NANTUCKET COUNTY, MASSACHUSETTS

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CHESTERFIELD, VA, USA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WORCESTER COUNTY, MASSACHUSETTS

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, OTTAWA COUNTY, USA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, GRAND COUNTY, USA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, DILLON COUNTY, USA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ALLEN COUNTY, USA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HOLMES COUNTY, OHIO

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HOLMES COUNTY, USA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, KOOTENAI COUNTY, USA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Accomack County, VIRGINIA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, LYCOMING COUNTY, PENNSYLVANIA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WHATCOM COUNTY, WASHINGTON

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HARRISON COUNTY, TEXAS

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MATHEWS COUNTY, VIRGINIA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HIGHLAND COUNTY, VIRGINIA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SUMTER COUNTY, AL

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, RALLS COUNTY, USA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MARION COUNTY, USA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HOUSTON COUNTY, Georgia

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, YOLO COUNTY, CALIFORNIA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Tuolumne County, USA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MADERA COUNTY, CALIFORNIA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, TEHAMA COUNTY, CALIFORNIA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SACRAMENTO COUNTY, CALIFORNIA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HOPKINS COUNTY, KY

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Butts County, GA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MAYES COUNTY, OK

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, GRADY COUNTY, OKLAHOMA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SHASTA COUNTY, CALIFORNIA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ROSS COUNTY, USA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, BALTIMORE CITY, MARYLAND

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Chambers COUNTY, AL

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ROGERS COUNTY, OKLAHOMA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MEDINA COUNTY, TX

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, STARK COUNTY, OHIO

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ALBEMARLE COUNTY, VIRGINIA

Data.gov (United States)

Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

Content Layer progressive Coding of Digital Maps

DEFF Research Database (Denmark)

Forchhammer, Søren; Jensen, Ole Riis

2002-01-01

A new lossless context based method is presented for content progressive coding of limited bits/pixel images, such as maps, company logos, etc., common on the World Wide Web. Progressive encoding is achieved by encoding the image in content layers based on color level or other predefined...... information. Information from already coded layers are used when coding subsequent layers. This approach is combined with efficient template based context bilevel coding, context collapsing methods for multilevel images and arithmetic coding. Relative pixel patterns are used to collapse contexts. Expressions...... for calculating the resulting number of contexts are given. The new methods outperform existing schemes coding digital maps and in addition provide progressive coding. Compared to the state-of-the-art PWC coder, the compressed size is reduced to 50-70% on our layered map test images....

Principles of soil mapping of a megalopolis with St. Petersburg as an example

Science.gov (United States)

Aparin, B. F.; Sukhacheva, E. Yu.

2014-07-01

For the first time, a soil map of St. Petersburg has been developed on a scale of 1 : 50000 using MicroStation V8i software. The legend to this map contains more than 60 mapping units. The classification of urban soils and information on the soil cover patterns are principally new elements of this legend. New concepts of the urbanized soil space and urbopedocombinations have been suggested for soil mapping of urban territories. The typification of urbopedocombinations in St. Petersburg has been performed on the basis of data on the geometry and composition of the polygons of soils and nonsoil formations. The ratio between the areas of soils and nonsoil formations and their spatial distribution patterns have been used to distinguish between six types of the urbanized soil space. The principles of classification of the soils of urban territories have been specified, and a separate order of pedo-allochthonous soils has been suggested for inclusion into the Classification and Diagnostic System of Russian Soils (2004). Six types of pedo-allochthonous soils have been distinguished on the basis of data on their humus and organic horizons and the character of the underlying mineral substrate.

The Use of Electromagnetic Induction Techniques for Soil Mapping

Science.gov (United States)

Brevik, Eric C.; Doolittle, Jim

2015-04-01

Soils have high natural spatial variability. This has been recognized for a long time, and many methods of mapping that spatial variability have been investigated. One technique that has received considerable attention over the last ~30 years is electromagnetic induction (EMI). Particularly when coupled with modern GPS and GIS systems, EMI techniques have allowed the rapid and relatively inexpensive collection of large spatially-related data sets that can be correlated to soil properties that either directly or indirectly influence electrical conductance in the soil. Soil electrical conductivity is directly controlled by soil water content, soluble salt content, clay content and mineralogy, and temperature. A wide range of indirect controls have been identified, such as soil organic matter content and bulk density; both influence water relationships in the soil. EMI techniques work best in areas where there are large changes in one soil property that influences soil electrical conductance, and don't work as well when soil properties that influence electrical conductance are largely homogenous. This presentation will present examples of situations where EMI techniques were successful as well as a couple of examples of situations where EMI was not so useful in mapping the spatial variability of soil properties. Reasons for both the successes and failures will be discussed.

Digital and preliminary bedrock geologic map of the Pico Peak quadrangle, Vermont

Data.gov (United States)

Vermont Center for Geographic Information — Digital Data from VG98-226A Walsh, G. J., and Ratcliffe, N.M., 1998,�Digital and preliminary bedrock geologic map of the Pico Peak quadrangle, Vermont: USGS...

Digital and preliminary bedrock geologic map of the Mount Carmel quadrangle, Vermont

Data.gov (United States)

Vermont Center for Geographic Information — Digital Data from VG98-330A Ratcliffe, N.M., and Walsh, G. J., 1998,�Digital and preliminary bedrock geologic map of the Mount Carmel quadrangle, Vermont: USGS...

Digital Subtraction Angiography (DSA) "Road Map": An Angiographic Tool

Science.gov (United States)

Turski, P. A.; Stieghorst, M. F.; Strother, C. M.; Crummy, A. B.; Lieberman, R. P.; Mistretta, C. A.

1982-12-01

Continuous Digital subtraction combined with intraarterial injections of contrast medium permits the display of arterial structures during real time fluoroscopy. This DSA "road map" facilitates selective catheterization and has proved useful in interventional procedures.

Digital compilation bedrock geologic map of part of the Waitsfield quadrangle, Vermont

Data.gov (United States)

Vermont Center for Geographic Information — Digital Data from VG96-03�Digital compilation bedrock geologic map of part of the Waitsfield quadrangle, Vermont: VGS Open-File Report VG96-3A, 2 plates, scale...

The use of proximal soil sensor data fusion and digital soil mapping for precision agriculture

OpenAIRE

Ji, Wenjun; Adamchuk, Viacheslav; Chen, Songchao; Biswas, Asim; Leclerc, Maxime; Viscarra Rossel, Raphael

2017-01-01

Proximal soil sensing (PSS) is a promising approach when it comes to detailed characterization of spatial soil heterogeneity. Since none of existing PSS systems can measure all soil information needed for implementation precision agriculture, sensor data fusion can provide a reasonable al- ternative to characterize the complexity of soils. In this study, we fused the data measured using a gamma-ray sensor, an apparent electrical conductivity (ECa) sensor, and a commercial Veris MS...

Securing Digital Audio using Complex Quadratic Map

Science.gov (United States)

Suryadi, MT; Satria Gunawan, Tjandra; Satria, Yudi

2018-03-01

In This digital era, exchanging data are common and easy to do, therefore it is vulnerable to be attacked and manipulated from unauthorized parties. One data type that is vulnerable to attack is digital audio. So, we need data securing method that is not vulnerable and fast. One of the methods that match all of those criteria is securing the data using chaos function. Chaos function that is used in this research is complex quadratic map (CQM). There are some parameter value that causing the key stream that is generated by CQM function to pass all 15 NIST test, this means that the key stream that is generated using this CQM is proven to be random. In addition, samples of encrypted digital sound when tested using goodness of fit test are proven to be uniform, so securing digital audio using this method is not vulnerable to frequency analysis attack. The key space is very huge about 8.1×l031 possible keys and the key sensitivity is very small about 10-10, therefore this method is also not vulnerable against brute-force attack. And finally, the processing speed for both encryption and decryption process on average about 450 times faster that its digital audio duration.

Digital bedrock geologic map of the Mount Holly and Ludlow quadrangles, Vermont

Data.gov (United States)

Vermont Center for Geographic Information — Digital Data from VG94-229A Walsh, G.J., Ratcliffe, N.M., Dudley, J.B., and Merrifield, T., 1994,�Digital bedrock geologic map of the Mount Holly and Ludlow...

Estimating soil water-holding capacities by linking the Food and Agriculture Organization Soil map of the world with global pedon databases and continuous pedotransfer functions

Science.gov (United States)

Reynolds, C. A.; Jackson, T. J.; Rawls, W. J.

2000-12-01

Spatial soil water-holding capacities were estimated for the Food and Agriculture Organization (FAO) digital Soil Map of the World (SMW) by employing continuous pedotransfer functions (PTF) within global pedon databases and linking these results to the SMW. The procedure first estimated representative soil properties for the FAO soil units by statistical analyses and taxotransfer depth algorithms [Food and Agriculture Organization (FAO), 1996]. The representative soil properties estimated for two layers of depths (0-30 and 30-100 cm) included particle-size distribution, dominant soil texture, organic carbon content, coarse fragments, bulk density, and porosity. After representative soil properties for the FAO soil units were estimated, these values were substituted into three different pedotransfer functions (PTF) models by Rawls et al. [1982], Saxton et al. [1986], and Batjes [1996a]. The Saxton PTF model was finally selected to calculate available water content because it only required particle-size distribution data and results closely agreed with the Rawls and Batjes PTF models that used both particle-size distribution and organic matter data. Soil water-holding capacities were then estimated by multiplying the available water content by the soil layer thickness and integrating over an effective crop root depth of 1 m or less (i.e., encountered shallow impermeable layers) and another soil depth data layer of 2.5 m or less.

Introducing students to digital geological mapping: A workflow based on cheap hardware and free software

Science.gov (United States)

Vrabec, Marko; Dolžan, Erazem

2016-04-01

The undergraduate field course in Geological Mapping at the University of Ljubljana involves 20-40 students per year, which precludes the use of specialized rugged digital field equipment as the costs would be way beyond the capabilities of the Department. A different mapping area is selected each year with the aim to provide typical conditions that a professional geologist might encounter when doing fieldwork in Slovenia, which includes rugged relief, dense tree cover, and moderately-well- to poorly-exposed bedrock due to vegetation and urbanization. It is therefore mandatory that the digital tools and workflows are combined with classical methods of fieldwork, since, for example, full-time precise GNSS positioning is not viable under such circumstances. Additionally, due to the prevailing combination of complex geological structure with generally poor exposure, students cannot be expected to produce line (vector) maps of geological contacts on the go, so there is no need for such functionality in hardware and software that we use in the field. Our workflow therefore still relies on paper base maps, but is strongly complemented with digital tools to provide robust positioning, track recording, and acquisition of various point-based data. Primary field hardware are students' Android-based smartphones and optionally tablets. For our purposes, the built-in GNSS chips provide adequate positioning precision most of the time, particularly if they are GLONASS-capable. We use Oruxmaps, a powerful free offline map viewer for the Android platform, which facilitates the use of custom-made geopositioned maps. For digital base maps, which we prepare in free Windows QGIS software, we use scanned topographic maps provided by the National Geodetic Authority, but also other maps such as aerial imagery, processed Digital Elevation Models, scans of existing geological maps, etc. Point data, like important outcrop locations or structural measurements, are entered into Oruxmaps as

Mapping the Soil Texture in the Heihe River Basin Based on Fuzzy Logic and Data Fusion

Directory of Open Access Journals (Sweden)

Ling Lu

2017-07-01

Full Text Available Mapping soil texture in a river basin is critically important for eco-hydrological studies and water resource management at the watershed scale. However, due to the scarcity of in situ observation of soil texture, it is very difficult to map the soil texture in high resolution using traditional methods. Here, we used an integrated method based on fuzzy logic theory and data fusion to map the soil texture in the Heihe River basin in an arid region of Northwest China, by combining in situ soil texture measurement data, environmental factors, a previous soil texture map, and other thematic maps. Considering the different landscape characteristics over the whole Heihe River basin, different mapping schemes have been used to extract the soil texture in the upstream, middle, and downstream areas of the Heihe River basin, respectively. The validation results indicate that the soil texture map achieved an accuracy of 69% for test data from the midstream area of the Heihe River basin, which represents a much higher accuracy than that of another existing soil map in the Heihe River basin. In addition, compared with the time-consuming and expensive traditional soil mapping method, this new method could ensure greater efficiency and a better representation of the explicitly spatial distribution of soil texture and can, therefore, satisfy the requirements of regional modeling.

Digital mapping techniques '00, workshop proceedings - May 17-20, 2000, Lexington, Kentucky

Science.gov (United States)

Soller, David R.

2000-01-01

Introduction: The Digital Mapping Techniques '00 (DMT'00) workshop was attended by 99 technical experts from 42 agencies, universities, and private companies, including representatives from 28 state geological surveys (see Appendix A). This workshop was similar in nature to the first three meetings, held in June, 1997, in Lawrence, Kansas (Soller, 1997), in May, 1998, in Champaign, Illinois (Soller, 1998a), and in May, 1999, in Madison, Wisconsin (Soller, 1999). This year's meeting was hosted by the Kentucky Geological Survey, from May 17 to 20, 2000, on the University of Kentucky campus in Lexington. As in the previous meetings, the objective was to foster informal discussion and exchange of technical information. When, based on discussions at the workshop, an attendee adopts or modifies a newly learned technique, the workshop clearly has met that objective. Evidence of learning and cooperation among participating agencies continued to be a highlight of the DMT workshops (see example in Soller, 1998b, and various papers in this volume). The meeting's general goal was to help move the state geological surveys and the USGS toward development of more cost-effective, flexible, and useful systems for digital mapping and geographic information systems (GIS) analysis. Through oral and poster presentations and special discussion sessions, emphasis was given to: 1) methods for creating and publishing map products (here, 'publishing' includes Web-based release); 2) continued development of the National Geologic Map Database; 3) progress toward building a standard geologic map data model; 4) field data-collection systems; and 5) map citation and authorship guidelines. Four representatives of the GIS hardware and software vendor community were invited to participate. The four annual DMT workshops were coordinated by the AASG/USGS Data Capture Working Group, which was formed in August, 1996, to support the Association of American State Geologists and the USGS in their effort

How to feed environmental studies with soil information to address SDG 'Zero hunger'

Science.gov (United States)

Hendriks, Chantal; Stoorvogel, Jetse; Claessens, Lieven

2017-04-01

As pledged by UN Sustainable Development Goal (SDG) 2, there should be zero hunger, food security, improved food nutrition and sustainable agriculture by 2030. Environmental studies are essential to reach SDG 2. Soils play a crucial role, especially in addressing 'Zero hunger'. This study aims to discuss the connection between the supply and demand of soil data for environmental studies and how this connection can be improved illustrating different methods. As many studies are resource constrained, the options to collect new soil data are limited. Therefore, it is essential to use existing soil information, auxiliary data and collected field data efficiently. Existing soil data are criticised in literature as i) being dominantly qualitative, ii) being often outdated, iii) being not spatially exhaustive, iv) being only available at general scales, v) being inconsistent, and vi) lacking quality assessments. Additional field data can help to overcome some of these problems. Outdated maps can, for example, be improved by collecting additional soil data in areas where changes in soil properties are expected. Existing soil data can also provide insight in the expected soil variability and, as such, these data can be used for the design of sampling schemes. Existing soil data are also crucial input for studies on digital soil mapping because they give information on parent material and the relative age of soils. Digital soil mapping is commonly applied as an efficient method to quantitatively predict the spatial variation of soil properties. However, the efficiency of digital soil mapping may increase if we look at functional soil properties (e.g. nutrient availability, available water capacity) for the soil profile that vary in a two-dimensional space rather than at basic soil properties of individual soil layers (e.g. texture, organic matter content, nitrogen content) that vary in a three-dimensional space. Digital soil mapping techniques are based on statistical