EurekaMag.com logo
+ Site Statistics
References:
47,893,527
Abstracts:
28,296,643
+ Search Articles
+ Subscribe to Site Feeds
EurekaMag Most Shared ContentMost Shared
EurekaMag PDF Full Text ContentPDF Full Text
+ PDF Full Text
Request PDF Full TextRequest PDF Full Text
+ Follow Us
Follow on FacebookFollow on Facebook
Follow on TwitterFollow on Twitter
Follow on Google+Follow on Google+
Follow on LinkedInFollow on LinkedIn

+ Translate

Spatial data mining for enhanced soil map modelling


, : Spatial data mining for enhanced soil map modelling. International Journal of Geographical Information Science: , Pages 533-549. 2002.



Accession: 018458957

DOI: 10.1080/13658810210138715

Submit PDF Full Text: Here


Submit PDF Full Text

No spam - Every submission is manually reviewed

Due to poor quality, we do not accept files from Researchgate

Submitted PDF Full Texts will always be free for everyone
(We only charge for PDFs that we need to acquire)

Select a PDF file:
Close
Close

Related references

Klatka,S.; Boron,K., 2008: Modelling spatial variability of soil texture in areas subject to mining degradation. The geomechanical and hydrological degradation due to the underground mining of hard coal causes deformations to lands used for nature-related purposes (agriculture, forestry, etc.) and a high variability of soil properties. The present study anal...

Elnaggar Abelhamid A.; Noller Jay S.; Keller Mark, 2005: Spatial data mining and soil-landscape modeling applied to soil survey. Proceedings of the Pacific Division, American Association for the Advancement of Science 24, PART 1: 53

Behrens, T.; Schmidt, K.; Scholten, T., 2009: ConMap; a new spatial data mining framework for terrain based digital soil mapping. Geophysical Research Abstracts 11

Herbst,R.; Schneider,M.; Wagner,P., 2008: Investigations on the spatial variation of soil nutrients at different scales using geostatistics and data mining techniques. The basic question for this research was to compare and to quantify the differences of a smooth data reduction process of soil nutrient samples on a trial field in the mid of Germany. Therefore, 1258 probes were taken by a nested sampling strategy...

Lagacherie, P.; Cazemier, D.R.; Martin Clouaire, R.; Wassenaar, T., 2000: A spatial approach using imprecise soil data for modelling crop yields over vast areas. Estimations of crop yields using process-based crop models are area-limited because quantitative soil data are unavailable over vast areas. The spatial approach proposed in this study incorporates two novel aspects concerning the derivation of soi...

Anonymous, 2007: Development of methods for the derivation of spatial patterns of top layer soil moisture from modelling data

Svetlitchnyi A.A.; Plotnitskiy S.V.; Stepovaya O.Y., 2003: Spatial distribution of soil moisture content within catchments and its modelling on the basis of topographic data. This paper deals with the development of a model of the average seasonal spatial distribution of soil moisture content on slopes or in small catchments. The model is based on a quantitative assessment of the water and heat balances of the upper so...

Bui Elisabeth N.; Moran Christopher J., 2001: Disaggregation of polygons of surficial geology and soil maps using spatial modelling and legacy data. Examples from the Murray-Darling basin in Australia are used to illustrate different methods of disaggregation of reconnaissance-scale maps. One approach for disaggregation revolves around the de-convolution of the soil-landscape paradigm elaborat...

D.J.ng R.; Dumanski J.; Bootsma A., 1992: Implications of spatial averaging weather and soil moisture data for broad scale modelling activities. Spatial averaging of data before or after modelling has important implications for large area land evaluation studies. Two procedures are evaluated are evaluated for the spatial averaging of weather and soil moisture data before and after modellin...

Herbst M.; Diekkrueger B., 2003: Modelling the spatial variability of soil moisture in a micro-scale catchment and comparison with field data using geostatistics. The temporal and spatial variability of soil moisture in the micro-scale Berrensiefen catchment is simulated with SWMS_3D (three-dimensional variably saturated flow based on finite elements) and a loosely coupled model for the atmospheric boundary...