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A geographic information system (GIS) consists of integrated computer hardware and software that store, manage, analyze, edit, output, and visualize geographic data. Much of this often happens within a spatial database, however, this is not essential to meet the definition of a GIS. In a broader sense, one may consider such a system also to include human users and support staff, procedures and workflows, the body of knowledge of relevant concepts and methods, and institutional organizations.
Pedology is a discipline within soil science which focuses on understanding and characterizing soil formation, evolution, and the theoretical frameworks for modeling soil bodies, often in the context of the natural environment. Pedology is often seen as one of two main branches of soil inquiry, the other being edaphology which is traditionally more agronomically oriented and focuses on how soil properties influence plant communities. In studying the fundamental phenomenology of soils, e.g. soil formation, pedologists pay particular attention to observing soil morphology and the geographic distributions of soils, and the placement of soil bodies into larger temporal and spatial contexts. In so doing, pedologists develop systems of soil classification, soil maps, and theories for characterizing temporal and spatial interrelations among soils. There are a few noteworthy sub-disciplines of pedology; namely pedometrics and soil geomorphology. Pedometrics focuses on the development of techniques for quantitative characterization of soils, especially for the purposes of mapping soil properties whereas soil geomorphology studies the interrelationships between geomorphic processes and soil formation.
Soil erosion is the denudation or wearing away of the upper layer of soil. It is a form of soil degradation. This natural process is caused by the dynamic activity of erosive agents, that is, water, ice (glaciers), snow, air (wind), plants, and animals. In accordance with these agents, erosion is sometimes divided into water erosion, glacial erosion, snow erosion, wind (aeolian) erosion, zoogenic erosion and anthropogenic erosion such as tillage erosion. Soil erosion may be a slow process that continues relatively unnoticed, or it may occur at an alarming rate causing a serious loss of topsoil. The loss of soil from farmland may be reflected in reduced crop production potential, lower surface water quality and damaged drainage networks. Soil erosion could also cause sinkholes.
Topography is the study of the forms and features of land surfaces. The topography of an area may refer to the land forms and features themselves, or a description or depiction in maps.
Precision agriculture (PA) is a farming management strategy based on observing, measuring and responding to temporal and spatial variability to improve agricultural production sustainability. It is used in both crop and livestock production. Precision agriculture often employs technologies to automate agricultural operations, improving their diagnosis, decision-making or performing. First conceptual work on PA and practical applications go back in the late 1980s. The goal of precision agriculture research is to define a decision support system for whole farm management with the goal of optimizing returns on inputs while preserving resources.
Ecological classification or ecological typology is the classification of land or water into geographical units that represent variation in one or more ecological features. Traditional approaches focus on geology, topography, biogeography, soils, vegetation, climate conditions, living species, habitats, water resources, and sometimes also anthropic factors. Most approaches pursue the cartographical delineation or regionalisation of distinct areas for mapping and planning.
Topsoil is the upper layer of soil. It has the highest concentration of organic matter and microorganisms and is where most of the Earth's biological soil activity occurs.
Soil classification deals with the systematic categorization of soils based on distinguishing characteristics as well as criteria that dictate choices in use.
Soil survey, soil mapping, is the process of classifying soil types and other soil properties in a given area and geo-encoding such information. It applies the principles of soil science, and draws heavily from geomorphology, theories of soil formation, physical geography, and analysis of vegetation and land use patterns. Primary data for the soil survey are acquired by field sampling and by remote sensing. Remote sensing principally uses aerial photography, but LiDAR and other digital techniques are steadily gaining in popularity. In the past, a soil scientist would take hard-copies of aerial photography, topo-sheets, and mapping keys into the field with them. Today, a growing number of soil scientists bring a ruggedized tablet computer and GPS into the field with them. The tablet may be loaded with digital aerial photos, LiDAR, topography, soil geodatabases, mapping keys, and more.
Soil map is a geographical representation showing diversity of soil types and/or soil properties in the area of interest. It is typically the end result of a soil survey inventory, i.e. soil survey. Soil maps are most commonly used for land evaluation, spatial planning, agricultural extension, environmental protection and similar projects. Traditional soil maps typically show only general distribution of soils, accompanied by the soil survey report. Many new soil maps are derived using digital soil mapping techniques. Such maps are typically richer in context and show higher spatial detail than traditional soil maps. Soil maps produced using (geo)statistical techniques also include an estimate of the model uncertainty.
Inference is a process of deriving logical conclusion from the basis of empirical evidence and prior knowledge rather than on the basis of direct observation. Soil inference system (SINFERS) is the term proposed by McBratney et al. (2002) as a knowledge base to infer soil properties and populate the digital soil databases. SINFERS takes measurements with a given level of certainty and infers data that is not known with minimal uncertainties by means of logically linked predictive functions. These predictive functions, in a non-spatial context are referred to as pedotransfer functions. The basic assumption underlying SINFERS is that if one knows or is able to predict the basic fundamental properties of a soil, one should be able to infer all other physical and chemical properties using PTFs. Pedotransfer functions relate basic soil properties to other more difficult or expensive to measure soil properties by means of regression and various data mining tools. Crucial to the operation of SINFERS are reliable inputs, the ability to link basic soil information, and the quantification of uncertainty.
Geomorphometry, or geomorphometrics, is the science and practice of measuring the characteristics of terrain, the shape of the surface of the Earth, and the effects of this surface form on human and natural geography. It gathers various mathematical, statistical and image processing techniques that can be used to quantify morphological, hydrological, ecological and other aspects of a land surface. Common synonyms for geomorphometry are geomorphological analysis, terrain morphometry, terrain analysis, and land surface analysis. Geomorphometrics is the discipline based on the computational measures of the geometry, topography and shape of the Earth's horizons, and their temporal change. This is a major component of geographic information systems (GIS) and other software tools for spatial analysis.
The European Soil Database is the only harmonized soil database in Europe from which many other data information and services are derived. For instance, the European Soil Database v2 Raster Library contains raster (grid) data files with cell sizes of 1km x 1km for a large number of soil related parameters. Each grid is aligned with the INSPIRE reference grid. These rasters are in the public domain and allow expert users to use the data for instance to run soil-, water- and air related models.. The European Soil Database may be downloaded from the European Soil Data Center.
Subaqueous soils are soils formed in sediment found in shallow, permanently flooded environments or soils in any areas permanently covered by water too deep for the growth of rooted plants.
Pedodiversity is the variation of soil properties within an area. Pedodiversity studies were first started by analyzing soil series–area relationships. According to Guo et al. (2003) the term pedodiversity was developed by McBratney (1992) who discussed landscape preservation strategies based on pedodiversity. Recently, examinations of pedodiversity using indices commonly used to characterize bio-diversity have been made. Ibáñez et al. (1995) first introduced ecological diversity indices as measures of pedodiversity. They include species richness, relative species abundance, and Shannon index. Richness is the number of different soil types, which is the number of soil classes at particular level in a taxonomic system. Abundance is defined as the distribution of the number of soil individuals.
GIS in environmental contamination is the use of GIS software in mapping out the contaminants in soil and water using the spatial interpolation tools from GIS. Spatial interpolation allows for more efficient approach to remediation and monitoring of soil and water contaminants. Soil and water contamination by metals and other contaminants have become a major environmental problem after the industrialization across many parts of the world. As a result, environmental agencies are placed in charge in remediating, monitoring, and mitigating the soil contamination sites. GIS is used to monitor the sites for metal contaminants in the soil, and based on the GIS analysis, highest risk sites are identified in which majority of the remediation and monitoring takes place.
Pedometric mapping, or statistical soil mapping, is data-driven generation of soil property and class maps that is based on use of statistical methods. Its main objectives are to predict values of some soil variable at unobserved locations, and to access the uncertainty of that estimate using statistical inference i.e. statistically optimal approaches. From the application point of view, its main objective is to accurately predict response of a soil-plant ecosystem to various soil management strategies—that is, to generate maps of soil properties and soil classes that can be used for other environmental models and decision-making. It is largely based on applying geostatistics in soil science, and other statistical methods used in pedometrics.
In applied statistics and geostatistics, regression-kriging (RK) is a spatial prediction technique that combines a regression of the dependent variable on auxiliary variables with interpolation (kriging) of the regression residuals. It is mathematically equivalent to the interpolation method variously called universal kriging and kriging with external drift, where auxiliary predictors are used directly to solve the kriging weights.
Fabio Terribile is an Italian agricultural scientist and professor at the University of Naples Federico II. He is a pedologist and coordinator of the EU project Landsupport.
References
- ↑ Scull, P.; J. Franklin; O.A. Chadwick; D. McArthur (June 2003). "Predictive soil mapping - a review". Progress in Physical Geography. 27 (2): 171–197. CiteSeerX 10.1.1.137.3441 . doi:10.1191/0309133303pp366ra. S2CID 787741.
- ↑ Lagacherie, P.; McBratney, A. B.; Voltz, M., eds. (2006). Digital soil mapping: an introductory perspective. Amsterdam: Elsevier. p. 600. ISBN 978-0-444-52958-9. Archived from the original on 2012-01-16. Retrieved 2012-06-19.
- ↑ Dobos, E.; Carré, F.; Hengl, T.; Reuter, H.I.; Tóth, G., eds. (2006). Digital Soil Mapping as a support to production of functional maps (PDF). Luxemburg: Office for Official Publications of the European Communities. p. 68.
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- ↑ Boettinger, J.L.; Howell, D.W.; Moore, A.C.; Hartemink, A.E.; Kienast-Brown, S., eds. (2010). Digital Soil Mapping: Bridging Research, Environmental Application, and Operation. Springer. p. 473. ISBN 978-90-481-8862-8.
- 1 2 Hengl, Tom; Mendes de Jesus, Jorge; McMillan, R.A.; Batjes, Niels H.; Heuvelink, G.B.M.; Ribeiro, Eloi C.; Samuel-Rosa, Allesandro; Kempen, Bas; Leenaars, J.G.B.; Walsh, M.G.; Ruiperez Gonzalez, Maria G. (2014). "SoilGrids1km — global soil information based on automated mapping". PLOS ONE. 9 (8): e105992. Bibcode:2014PLoSO...9j5992H. doi: 10.1371/journal.pone.0105992 . PMC 4149475 . PMID 25171179.
- 1 2 3 McBratney, A.B.; M.L. Mendonça Santos; B. Minasny (1 November 2003). "On digital soil mapping". Geoderma. 117 (1–2): 3–52. Bibcode:2003Geode.117....3M. doi:10.1016/S0016-7061(03)00223-4.
- ↑ Ballabio, Cristiano; Panagos, Panos; Monatanarella, Luca (2016). "Mapping topsoil physical properties at European scale using the LUCAS database". Geoderma. 261: 110–123. Bibcode:2016Geode.261..110B. doi: 10.1016/j.geoderma.2015.07.006 .
- ↑ Orgiazzi, A.; Ballabio, C.; Panagos, P.; Jones, A.; Fernández-Ugalde, O. (2018). "LUCAS Soil, the largest expandable soil dataset for Europe: a review". European Journal of Soil Science. 69: 140–153. doi: 10.1111/ejss.12499 . ISSN 1365-2389.
