ASRIS: the database

The Australian Soil Resources Information System (ASRIS) database compiles the best publicly available information available across Commonwealth, State, and Territory agencies into a national database of soil profile data, digital soil and land resources maps, and climate, terrain, and lithology datasets. These datasets are described in detail in this paper. Most datasets are thematic grids that cover the intensively used agricultural zones in Australia.

Statistics of the National soil test database of France

In France, farmers commission about 250,000 soil-testing analyses per year to assist them managing soil fertility. The number and diversity of origin of the samples make these analyses an interesting and original information source regarding cultivated topsoil variability. Moreover, these analyses relate to several parameters strongly influenced by human activity (macronutrient contents, pH...), for which existing cartographic information is not very relevant. Compiling the results of these analyses into a database makes it possible to re-use these data within both a national and temporal framework. A database compilation relating to data collected over the period 1990-2009 has been recently achieved. So far, commercial soil-testing laboratories approved by the Ministry of Agriculture have provided analytical results from more than 2,000,000 samples. After the initial quality control stage, analytical results from more than 1,900,000 samples were available in the database. The anonymity of the landholders seeking soil analyses is perfectly preserved, as the only identifying information stored is the location of the nearest administrative city to the sample site. We present in this dataset a set of statistical parameters of the spatial distributions for several agronomic soil properties. These statistical parameters are calculated for 4 different nested spatial entities (administrative areas: e.g. regions, departments, counties and agricultural areas) and for 4 time periods (1990-1994, 1995-1999, 2000-2004, 2005-2009). Two kinds of agronomic soil properties are available: the firs one correspond to the quantitative variables like the organic carbon content and the second one corresponds to the qualitative variables like the texture class. For each spatial unit and temporal period, we calculated the following statistics stets: the first set is calculated for the quantitative variables and corresponds to the number of samples, the mean, the standard deviation and, the 2-,4-,10-quantiles; the second set is calculated for the qualitative variables and corresponds to the number of samples, the value of the dominant class, the number of samples of the dominant class, the second dominant class, the number of samples of the second dominant class.

The GeoArchive handbook : a guide for developing a geographic database as an information foundation for community policing /

"Funded in part by the Department of Justice, Office of Justice Programs, Bureau of Justice Statistics, Analytic Projects for State-Level Criminal Justice Statistical Analysis Centers (SAC-2 program)."--Verso of t.p.

Preparation of consistent soil data sets for modelling purposes: Secondary SOTER data for four case study areas

The common GIS-based approach to regional analyses of soil organic carbon (SOC) stocks and changes is to define geographic layers for which unique sets of driving variables are derived, which include land use, climate, and soils. These GIS layers, with their associated attribute data, can then be fed into a range of empirical and dynamic models. Common methodologies for collating and formatting regional data sets on land use, climate, and soils were adopted for the project Assessment of Soil Organic Carbon Stocks and Changes at National Scale (GEFSOC). This permitted the development of a uniform protocol for handling the various input for the dynamic GEFSOC Modelling System. Consistent soil data sets for Amazon-Brazil, the Indo-Gangetic Plains (IGP) of India, Jordan and Kenya, the case study areas considered in the GEFSOC project, were prepared using methodologies developed for the World Soils and Terrain Database (SOTER). The approach involved three main stages: (1) compiling new soil geographic and attribute data in SOTER format; (2) using expert estimates and common sense to fill selected gaps in the measured or primary data; (3) using a scheme of taxonomy-based pedotransfer rules and expert-rules to derive soil parameter estimates for similar soil units with missing soil analytical data. The most appropriate approach varied from country to country, depending largely on the overall accessibility and quality of the primary soil data available in the case study areas. The secondary SOTER data sets discussed here are appropriate for a wide range of environmental applications at national scale. These include agro-ecological zoning, land evaluation, modelling of soil C stocks and changes, and studies of soil vulnerability to pollution. Estimates of national-scale stocks of SOC, calculated using SOTER methods, are presented as a first example of database application. Independent estimates of SOC stocks are needed to evaluate the outcome of the GEFSOC Modelling System for current conditions of land use and climate. (C) 2007 Elsevier B.V. All rights reserved.

Soil Organic Carbon Stocks of Rio Grande do Sul, Brazil

Robust and accurate regional estimates of C storage in soils are currently an important research topic because of ongoing debate about human-induced changes in the terrestrial C cycle. Widely available geoprocessing tools were applied to estimate native soil organic C (SOC) stocks of Rio Grande do Sul state in southern Brazil to a depth of 30 cm from previously sampled soil pedons under undisturbed vegetation. The study used a statewide comprehensive soil survey comprising a small-scale soil map, a climate map, and a soil pedon database. Soil organic C stocks under native vegetation were calculated with two different approaches: the Tier 1 method of the Intergovernmental Panel on Climate Change (IPCC) and a refined method based on actual field measurements derived from soil profile data. Highest SOC stocks occurred in Neossolos Quartzarenico hidromorfico (Aquents), Organossolos Tiomorficos (Hemists), Latossolos Brunos (Udox), and Vertissolos Ebanicos (Uderts) soil classes. Before human use of soils, most C was stored in the Latossolos Vermelhos (Udox) and Neossolos Regoliticos (Orthents), which occupy a large area of Rio Grande do Sul. Generally, IPCC default reference SOC stocks compared well with SOC stocks calculated from soil pedons. The total SOC stock of Rio Grande do Sul was estimated at 1510.3 Tg C (5.8 kg C m(-2)) by the IPPC method and 1597.5 +/- 363.9 Tg C (7.4 +/- 1.9 kg C m(-2)) calculated from soil pedons. The SOC digital map and SOC database developed in this study provide crucial background information for state-level contemporary assessment of C stocks and soil C sequestration programs and initiatives.

Proposta de zoneamento ambiental visando a proteção dos recursos hídricos de uma bacia hidrográfica

One of the most important natural resources for sustaining human life, water, has been losing the basic requirements of quality and quantity sufficient enough to attend the population due to water contamination'problems, often caused by human beings themselves. Because of this, the sources of this resource are often located in remote places of the natural environment to ensure the quality of the water. However, when urban expansion began to occupy these areas, which were once regarded as distant, environmental pollution problems began to occur due to occupation of the land without planning. Based on this occurrence, this study aims to propose environmental zoning for the Maxaranguape river watershed in order to protect its water resources. This is important because this river can serve as a source of supply for the metropolitan area of Natal, the capital of Rio Grande do Norte. In accordance to this proposition, the model of natural soil loss vulnerability (CREPANI et al., 2001), the model of aquifer pollution vulnerability (FOSTER et al., 2006), and the legal incompatibility map (CREPANI et al., 2001) were used to delimit the zones. All this was done with Geographic Information System (GIS) and also created a geographic database update of the basin. The results of the first model mentioned indicated that 63.67% of the basin was classified as moderately stable / vulnerable, 35.66% as moderately vulnerable, and 0.67% as vulnerable. The areas with high vulnerability degree correspond with sand dunes and river channels areas. The second model indicated that 2.84% of the basin has low vulnerability, 70.27%) has median vulnerability, and 26.76% and 0.13% has high vulnerability and extreme vulnerability, respectively. The areas with the highest vulnerability values also refer to part of the sand dunes and river channels besides other areas such as Pureza urban area. The legal incompatibility map indicated that the basin has 85.02 km2 of Permanent Protection Area (PPA) and 14.62% of this area has some incongruity of use. Based on these results it was possible to draw three main zones: Protection and Sustainable Use Zone (PSUZ), Protection and Environmental Restoration Zone (PERZ) and Environmental Control Zone, which are divided into A, B and C. The PSUZ refer to the coastal areas of the basin, where the sand dunes are located. These sites should be areas of environmental protection and of sustainable urban expansion. The ZPRA refer to river channels, which are in high need of rehabilitation. The third zone corresponds to the rest of the basin which should have, in general, the mapping of possible sources of contamination for further control on the use and occupation of the river

Modeling and classifying variable width riparian zones utilizing digital elevation models, flood height data, digital soil data and national wetlands inventory : a new approach for riparian zone delineation

Riparian zones are dynamic, transitional ecosystems between aquatic and terrestrial ecosystems with well defined vegetation and soil characteristics. Development of an all-encompassing definition for riparian ecotones, because of their high variability, is challenging. However, there are two primary factors that all riparian ecotones are dependent on: the watercourse and its associated floodplain. Previous approaches to riparian boundary delineation have utilized fixed width buffers, but this methodology has proven to be inadequate as it only takes the watercourse into consideration and ignores critical geomorphology, associated vegetation and soil characteristics. Our approach offers advantages over other previously used methods by utilizing: the geospatial modeling capabilities of ArcMap GIS; a better sampling technique along the water course that can distinguish the 50-year flood plain, which is the optimal hydrologic descriptor of riparian ecotones; the Soil Survey Database (SSURGO) and National Wetland Inventory (NWI) databases to distinguish contiguous areas beyond the 50-year plain; and land use/cover characteristics associated with the delineated riparian zones. The model utilizes spatial data readily available from Federal and State agencies and geospatial clearinghouses. An accuracy assessment was performed to assess the impact of varying the 50-year flood height, changing the DEM spatial resolution (1, 3, 5 and 10m), and positional inaccuracies with the National Hydrography Dataset (NHD) streams layer on the boundary placement of the delineated variable width riparian ecotones area. The result of this study is a robust and automated GIS based model attached to ESRI ArcMap software to delineate and classify variable-width riparian ecotones.

Circumpolar dataset of Soil Organic Carbon north of treeline derived from ENVISAT ASAR GM, link to GeoTIFF

A new approach for the estimation of soil organic carbon (SOC) pools north of the tree line has been developed based on synthetic aperture radar (SAR; ENVISAT Advanced SAR Global Monitoring mode) data. SOC values are directly determined from backscatter values instead of upscaling using land cover or soil classes. The multi-mode capability of SAR allows application across scales. It can be shown that measurements in C band under frozen conditions represent vegetation and surface structure properties which relate to soil properties, specifically SOC. It is estimated that at least 29 Pg C is stored in the upper 30 cm of soils north of the tree line. This is approximately 25 % less than stocks derived from the soil-map-based Northern Circumpolar Soil Carbon Database (NCSCD). The total stored carbon is underestimated since the established empirical relationship is not valid for peatlands or strongly cryoturbated soils. The approach does, however, provide the first spatially consistent account of soil organic carbon across the Arctic. Furthermore, it could be shown that values obtained from 1 km resolution SAR correspond to accounts based on a high spatial resolution (2 m) land cover map over a study area of about 7 × 7 km in NE Siberia. The approach can be also potentially transferred to medium-resolution C-band SAR data such as ENVISAT ASAR Wide Swath with ~120 m resolution but it is in general limited to regions without woody vegetation. Global Monitoring-mode-derived SOC increases with unfrozen period length. This indicates the importance of this parameter for modelling of the spatial distribution of soil organic carbon storage.

How human schematization and systematic errors take effect on sketch map formalizations

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies

Posicionament de vehicles en mapes mitjançant GPS

El projecte consisteix en el desenvolupament d'un algorisme que millori el posicionament final d'un sistema que adquireix les dades d'una antena de GPS estàndard. Aquest sistema en certs moments té pèrdua total de senyal GPS o rep senyal amb pertorbacions, derivant en un mal posicionament. Nosaltres hem proposat una solució que utilitza les coordenades del GPS, el filtre Kalman per resoldre els problemes de pertorbacions de senyal, bases de dades digitals geogràfiques per garantir la circulació del vehicle per sobre la carretera, i finalment combina la informació temporal de posicions anteriors i la de les bases de dades per posicionar el vehicle quan hi ha pèrdua total de senyal. Els experiments realitzats ens indiquen que s'obté una millora del posicionement.

Predictive vegetation mapping in the Mediterranean context: Considerations and methodological issues

The need to map vegetation communities over large areas for nature conservation and to predict the impact of environmental change on vegetation distributions, has stimulated the development of techniques for predictive vegetation mapping. Predictive vegetation studies start with the development of a model relating vegetation units and mapped physical data, followed by the application of that model to a geographic database and over a wide range of spatial scales. This field is particularly important for identifying sites for rare and endangered species and locations of high biodiversity such as many areas of the Mediterranean Basin. The potential of the approach is illustrated with a mapping exercise in the alti-meditterranean zone of Lefka Ori in Crete. The study established the nature of the relationship between vegetation communities and physical data including altitude, slope and geomorphology. In this way the knowledge of community distribution was improved enabling a GIS-based model capable of predicting community distribution to be constructed. The paper describes the development of the spatial model and the methodological problems of predictive mapping for monitoring Mediterranean ecosystems. The paper concludes with a discussion of the role of predictive vegetation mapping and other spatial techniques, such as fuzzy mapping and geostatistics, for improving our understanding of the dynamics of Mediterranean ecosystems and for practical management in a region that is under increasing pressure from human impact.

Precipitation and latent heating distributions from satellite passive microwave radiometry. Part I: improved method and uncertainties

A revised Bayesian algorithm for estimating surface rain rate, convective rain proportion, and latent heating profiles from satellite-borne passive microwave radiometer observations over ocean backgrounds is described. The algorithm searches a large database of cloud-radiative model simulations to find cloud profiles that are radiatively consistent with a given set of microwave radiance measurements. The properties of these radiatively consistent profiles are then composited to obtain best estimates of the observed properties. The revised algorithm is supported by an expanded and more physically consistent database of cloud-radiative model simulations. The algorithm also features a better quantification of the convective and nonconvective contributions to total rainfall, a new geographic database, and an improved representation of background radiances in rain-free regions. Bias and random error estimates are derived from applications of the algorithm to synthetic radiance data, based upon a subset of cloud-resolving model simulations, and from the Bayesian formulation itself. Synthetic rain-rate and latent heating estimates exhibit a trend of high (low) bias for low (high) retrieved values. The Bayesian estimates of random error are propagated to represent errors at coarser time and space resolutions, based upon applications of the algorithm to TRMM Microwave Imager (TMI) data. Errors in TMI instantaneous rain-rate estimates at 0.5°-resolution range from approximately 50% at 1 mm h−1 to 20% at 14 mm h−1. Errors in collocated spaceborne radar rain-rate estimates are roughly 50%–80% of the TMI errors at this resolution. The estimated algorithm random error in TMI rain rates at monthly, 2.5° resolution is relatively small (less than 6% at 5 mm day−1) in comparison with the random error resulting from infrequent satellite temporal sampling (8%–35% at the same rain rate). Percentage errors resulting from sampling decrease with increasing rain rate, and sampling errors in latent heating rates follow the same trend. Averaging over 3 months reduces sampling errors in rain rates to 6%–15% at 5 mm day−1, with proportionate reductions in latent heating sampling errors.

GeOntoQuery - um mecanismo de busca em bancos de dados geográficos baseado em ontologias

Geographic Information System (GIS) are computational tools used to capture, store, consult, manipulate, analyze and print geo-referenced data. A GIS is a multi-disciplinary system that can be used by different communities of users, each one having their own interest and knowledge. This way, different knowledge views about the same reality need to be combined, in such way to attend each community. This work presents a mechanism that allows different community users access the same geographic database without knowing its particular internal structure. We use geographic ontologies to support a common and shared understanding of a specific domain: the coral reefs. Using these ontologies' descriptions that represent the knowledge of the different communities, mechanisms are created to handle with such different concepts. We use equivalent classes mapping, and a semantic layer that interacts with the ontologies and the geographic database, and that gives to the user the answers about his/her queries, independently of the used terms

Modelagem e desenvolvimento de um banco de dados geográficos : aplicação à elaboração de mapas de sensibilidade ambiental ao derramamento de óleo na área costeira entre galinhos e São Bento do Norte-RN

The area between Galinhos and São Bento do Norte beaches, located in the northern coast of the Rio Grande do Norte State is submitted to intense and constant processes of littoral and aeolian transport, causing erosion, alterations in the sediments balance and modifications in the shoreline. Beyond these natural factors, the human interference is huge in the surroundings due to the Guamaré Petroliferous Pole nearby, the greater terrestrial oil producing in Brazil. Before all these characteristics had been organized MAMBMARE and MARPETRO projects with the main objective to execute the geo-environmental monitoring of coastal areas on the northern portion of RN. There is a bulky amount of database from the study area such as geologic and geophysical multitemporal data, hydrodynamic measurements, remote sensing multitemporal images, thematic maps, among others; it is of extreme importance to elaborate a Geographic Database (GD), one of the main components of a Geographic Information System (GIS), to store this amount of information, allowing the access to researchers and users. The first part of this work consisted to elaborate a GD to store the data of the area between Galinhos and São Bento do Norte cities. The main goal was to use the potentiality of the GIS as a tool to support decisions in the environmental monitoring of this region, a valuable target for oil exploration, salt companies and shrimp farms. The collected data was stored as a virtual library to assist men decisions from the results presented as digital thematic maps, tables and reports, useful as source of data in the preventive planning and as guidelines to the future research themes both on regional and local context. The second stage of this work consisted on elaborate the Oil-Spill Environmental Sensitivity Maps. These maps based on the Environmental Sensitivity Index Maps to Oil Spill developed by the Ministry of Environment are cartographic products that supply full information to the decision making, contingency planning and assessment in case of an oil spilling incident in any area. They represent the sensitivity of the areas related to oil spilling, through basic data such as geology, geomorphology, oceanographic, social-economic and biology. Some parameters, as hydrodynamic data, sampling data, coastal type, declivity of the beach face, types of resources in risk (biologic, economic, human or cultural) and the land use of the area are some of the essential information used on the environmental sensitivity maps elaboration. Thus using the available data were possible to develop sensitivity maps of the study area on different dates (June/2000 and December/2000) and to perceive that there was a difference on the sensitivity index generated. The area on December presented more sensible to the oil than the June one because hydrodynamic data (wave and tide energy) allowed a faster natural cleaning on June. The use of the GIS on sensitivity maps showed to be a powerful tool, since it was possible to manipulate geographic data with correctness and to elaborate more accurate maps with a higher level of detail to the study area. This presented an medium index (3 to 4) to the long shore and a high index (10) to the mangrove areas highly vulnerable to oil spill

Desenvolvimento de um sistema computacional para geração automática de mapas de vulnerabilidade como ferramenta de auxílio às ações de prevenção ao derramamento de óleo em áreas costeiras

The northern portion of the Rio Grande do Norte State is characterized by intense coastal dynamics affecting areas with ecosystems of moderate to high environmental sensitivity. In this region are installed the main socioeconomic activities of RN State: salt industry, shrimp farm, fruit industry and oil industry. The oil industry suffers the effects of coastal dynamic action promoting problems such as erosion and exposure of wells and pipelines along the shore. Thus came the improvement of such modifications, in search of understanding of the changes which causes environmental impacts with the purpose of detecting and assessing areas with greater vulnerability to variations. Coastal areas under influence oil industry are highly vulnerable and sensitive in case of accidents involving oil spill in the vicinity. Therefore, it was established the geoenvironmental monitoring of the region with the aim of evaluating the entire coastal area evolution and check the sensitivity of the site on the presence of oil. The goal of this work was the implementation of a computer system that combines the needs of insertion and visualization of thematic maps for the generation of Environmental Vulnerability maps, using techniques of Business Intelligence (BI), from vector information previously stored in the database. The fundamental design interest was to implement a more scalable system that meets the diverse fields of study and make the appropriate system for generating online vulnerability maps, automating the methodology so as to facilitate data manipulation and fast results in cases of real time operational decision-making. In database development a geographic area was established the conceptual model of the selected data and Web system was done using the template database PostgreSQL, PostGis spatial extension, Glassfish Web server and the viewer maps Web environment, the GeoServer. To develop a geographic database it was necessary to generate the conceptual model of the selected data and the Web system development was done using the PostgreSQL database system, its spatial extension PostGIS, the web server Glassfish and GeoServer to display maps in Web