Multi-criteria characterization of recent digital soil mapping and modeling approaches

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@Article{Grunwald2009195,
  author =       "S. Grunwald",
  title =        "Multi-criteria characterization of recent digital soil
                 mapping and modeling approaches",
  journal =      "Geoderma",
  volume =       "152",
  number =       "3-4",
  pages =        "195--207",
  year =         "2009",
  ISSN =         "0016-7061",
  DOI =          "doi:10.1016/j.geoderma.2009.06.003",
  URL =          "http://www.sciencedirect.com/science/article/B6V67-4WSG2WJ-1/2/af92060815439203d2999e4ace2ae786",
  keywords =     "genetic algorithms, genetic programming, Digital soil
                 mapping, Digital soil modelling, Pedometrics,
                 Quantitative methods, Soils",
  abstract =     "The history of digital soil mapping and modelling
                 (DSMM) is marked by adoption of new mapping tools and
                 techniques, data management systems, innovative
                 delivery of soil data, and methods to analyse,
                 integrate, and visualise soil and environmental
                 datasets. DSMM studies are diverse with specialised,
                 mathematical prototype models tested on limited
                 geographic regions and/or datasets and simpler,
                 operational DSMM used for routine mapping over large
                 soil regions. Research-focused DSMM contrasts with
                 need-driven DSMM and agency-operated soil surveys.
                 Since there is no universal equation or digital soil
                 prediction model that fits all regions and purposes the
                 proposed strategy is to characterise recent DSMM
                 approaches to provide recommendations for future needs
                 at local, national and global scales. Such needs are
                 not solely soil-centered, but consider broader issues
                 such as land and water quality, carbon cycling and
                 global climate change, sustainable land management, and
                 more. A literature review was conducted to review 90
                 DSMM publications from two high-impact international
                 soil science journals -- Geoderma and Soil Science
                 Society of America Journal. A selective approach was
                 used to identify published studies that cover the
                 multi-factorial DSMM space. The following criteria were
                 used (i) soil properties, (ii) sampling setup, (iii)
                 soil geographic region, (iv) spatial scale, (v)
                 distribution of soil observations, (vi) incorporation
                 of legacy/historic data, (vii) methods/model type,
                 (viii) environmental covariates, (ix) quantitative and
                 pedological knowledge, and (x) assessment method.
                 Strengths and weaknesses of current DSMM, their
                 potential to be operationalized in soil
                 mapping/modelling programs, research gaps, and future
                 trends are discussed. Modeling of soils in 3D space and
                 through time will require synergistic strategies to
                 converge environmental landscape data and denser soil
                 data sets. There are needs for more sophisticated
                 technologies to measure soil properties and processes
                 at fine resolution and with accuracy. Although there
                 are numerous quantitative models rooted in factorial
                 models that predict soil properties with accuracy in
                 select geographic regions they lack consistency in
                 terms of environmental input data, soil properties,
                 quantitative methods, and evaluation strategies. DSMM
                 requires merging of quantitative, geographic and
                 pedological expertise and all should be ideally in
                 balance.",
  notes =        "survey",
}

Genetic Programming entries for Sabine Grunwald

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