Identyfikatory
Języki publikacji
Abstrakty
Digital Soil Mapping (DSM) is a subdiscipline of pedology, where soil cover is modelled through use of spatial – temporal relations between environmental covariates and soil. The process of quantitative terrain description used in DSM is called terrain parametrization, where terrain attributes (morphometric factors, Digital Terrain Model derivatives) are the most used predicators. Terrain parameterisation was used as a tool in the hydrological survey workshop long before computers had been in use. With the development of digitisation, it also began to be used to determine selected soil attributes, which was greatly facilitated by GIS applications. A significant breakthrough in the importance of terrain attributes in the creation of soil maps and models took place with the formalisation of rules for digital soil mapping. Literature describes over 50 indices, although only a few of them are commonly applied. This applies to single soil attributes as well as more advanced implementations in more sophisticated models such as artificial intelligence algorithms. The aim of the following article is to present the main components of DSM and to describe characteristics of the most commonly derivatives of DTM applied there, also refers to several examples of the use of terrain parameters in the context of DSM in terms of the resolution of the elevation model used.
Słowa kluczowe
Czasopismo
Rocznik
Tom
Strony
87--99
Opis fizyczny
Bibliogr. 49 poz., rys., tab.
Twórcy
- Warsaw University of Technology, Department of Photogrammetry, Remote Sensing and Spatial Information Systems pl. Politechniki 1, 00-661 Warszawa
Bibliografia
- Abdel-Kader F.H. 2013. Digital Soil Mapping Using Spectral and Terrain Parameters and Statistical Modelling Integrated into GIS-Northwestern Coastal Region of Egypt. Developments in Soil Classification. Land Use Planning and Policy Implications, Springer, 353–371.
- Batson R.M., Edwards K., Eliason E.M. 1975. Computer-generated shaded – relief images. Journal of Research US Geology.
- Behrens T., Zhu A.X., Schmidt K., Scholten T. 2010. Multi-scale digital terrain analysis and feature selection for digital soil mapping. Geoderma, Elsevier.
- Behrens T., Schmidt K., MacMillan R.A., Viscarra Rossel R.A. 2018. Multi-scale digital soil mapping with deep learning. Naturesearch, Scientific Reports.
- Bell J.C., Cunningham R.L., Havens M.W. 1992. Calibration and Validation of a Soil‐Landscape Model for Predicting Soil Drainage Class. Soil Society of America Journal.
- Bell J.C., Cunningham R.L., Havens M.W. 1994. Soil Drainage Class Probability Mapping Using a Soil‐Landscape Model. Soil Society of America Journal.
- Białousz S., Chmiel J., Fijałkowska A., Różycki S., Pluto-Kossakowska J. 2010. Opracowanie i testowanie metod wykorzystania zdjęć satelitarnych oraz technologii GIS do aktualizacji małoskalowych baz danych przestrzennych o glebach i krajobrazie. Raport końcowy z projektu badawczego.
- Breiman L. 2001. Random Forest. Machine Learning, Springer.
- Burrough P.A., McDonell R.A. 1998. Principles of Geographical Information Systems. Oxford University Press, New York, 190.
- Carré F., Reuter H., Daroussin J., Scheurer O. 2008. From a Large to a Small-Scale Soil Map: Top-Down Against Bottom-Up Approaches. In: Digital Soil Mapping with Limited Data. A.E. Hartemink, A. McBratney, M. Mendonça-Santos (eds). Springer.
- Debella-Gilo M., Etzelmuller B., Klakegg O. 2007. Digital Soil Mapping Using Digital Terrain Analysis and Statistical Modeling Integrated into GIS: Examples from Vestfold County of Norway, Semantic Scholar.
- Dobos E., Micheli E., Baumgardner M.F., Biehl L., Helt T. 2000. Use of combined digital elevation model and satellite radiometric data for regional soil mapping. Geoderma, 97, 3–4, September, 367– 391.
- Dobos E., Montanarella L., Nègre T., Micheli E. 2001. A regional scale soil mapping approach using integrated AVHRR and DEM data. International Journal of Applied Earth Observation and Geoinformation.
- Dobos E., Norman B., Worstell B., and et al. 2002. The use of DEM and satellite data for regional scale soil databases. Agrokémia és Talajtan.
- Dobos E., Carré F., Hengl T., Reuter H.I., Tóth G. 2006. Digital Soil Mapping as a support to production of functional maps. Office for Official Publications of the European Communities, Luxemburg.
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- Ellili-Bargaoui Y., Malone B.P., Michot D., Minasny B., Vincent S., Walter C., Lemercier B. 2020. Comparing three approaches of spatial disaggregation of legacy soil maps based on the Disaggregation and Harmonisation of Soil Map Units Through Resampled Classification Trees (DSMART) algorithm. Soil, 6, 371–388.
- Fijałkowska A. 2021. Analysis of the Influence of DTM Source Data on the LS Factors of the Soil Water Erosion Model Values with the Use of GIS Technology, MDPI.
- Fleming M.D., Hoffer R.M. 1979. Machine Processing of Landsat MSS Data and DMA Topographic Data for Forest Cover Type Mapping, Laboratory for Applications of Remote Sensing, Purdue University, West Lafayette, IN, LARS Technical Report 062879.
- Gessler P.E., Chadwick O.A., Chamran F., Althouse L., Holmes K. 2000. Modeling Soil – Landscape and Ecosystem Properties Using Terrain Attributes. Soil Society of America Journal.
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- Hartemink A., McBratney A., Lourdes Mendoca-Santos M. 2008. Digital Soil Mapping with limited data, Springer.
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- Kot R. 2009. Próba ujednolicenia rangi jednostek rzeźby terenu dla delimitacji geokompleksów wybranych krajobrazów nizinnych. Problemy Ekologii Krajobrazu.
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- McBratney A., Santos M., Minasny B. 2003. On Digital Soil Mapping, Geoderma.
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- Padarian J., Minasny B., McBratney A. 2019. Using deep learning for digital soil mapping. Soil, 5, 79–89.
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- Radło-Kulisiewicz M. 2019. Wykorzystanie pochodnych DTM w modelowaniu pokrywy glebowej w krajobrazie młodoglacjalnym dla bazy danych o glebach o poziomie uogólnienia odpowiadającym mapom w skali 1: 250 000, rozprawa doktorska. Archiwum Politechniki Warszawskiej.
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- Teng H., Viscarra Rossel R., Behrens T. 2018. Updating a national soil classification with spectroscopic predictions and digital soil mapping. Catena.
- Thomas A.L., King D., Dambrine E., Couturier A., Roque J. 1999. Predicting soil classes with parameters derived from relief and geologic materials in a sandstone region of the Vosges mountains (Northeastern France). Geoderma, 90, 3–4, July, 291–305.
- Vaysse K., Lagacherie P. 2017. Using quantile regression forest to estimate uncertainty of digital soil mapping products. Geoderma.
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- Wadoux A., Padarian J., Minasny B. 2019. Multi-source data integration for soil mapping using deep learning. Soil, 5, 107–119. https://soil.copernicus.org/articles/5/107/2019
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- Xiong X., Grunwald S., Myers D.B., Kim J., Harris W.G., Comerford N.B. 2014. Holistic environmental soil-landscape modeling of soil organic carbon. Elsevier.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
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bwmeta1.element.baztech-61771c6a-3cde-4385-a522-aa33b6686b97