Digital soil mapping for Ismailia government using GIS

Digital soil mapping for ismailia government using GIS

 

Digital soil mapping as there maybe patterns of past erosion and deposition can be deduced from information contained in ismailia government Soil Map using GIS as there implies to qualitative validation of model result as possible, that simple model formulation allow to reproduce the basic pattern of erosion and deposition as it is observed in the landscape. Research confirm influence of topography on erosion and deposition patterns in such landscapes and indicates that in some cases simple model formulation may well be sufficient to allow adequate modeling of landscape evolution. The critical layer in geographic information systems (GIS), particularly when utilized in land management decisions, is soil survey information. The spatial component of information is generally input from digitized survey maps, usually in the form of polygons representing different soil map units. It is fact that the homogeneity of soils within soil map units varies. Conveying variability to users is essential to ensure proper use of soil survey information, using the transect method, soil map units will need to examine in order to assess homogeneity with respect to the variability of field determined soil taxonomy and physiography and the interpretive variability of selected soil properties for ismailia land management. Variability diagrams and interpretive maps is to be generated within GIS as the diagrams and maps, coupled with digitized soil maps, inform users of the degree of soil map unit variability and the variability of limiting soil properties.  The need to compare various prediction methods for mapping of soil action exchange capacity using different combinations of secondary information. The prediction methods used as noted by statistical analysis, geo-statistical interpolation and the hybrid techniques. The secondary spatial information used are terrain attributes, bare soil color aerial photograph, bare soil LANDSAT TM imagery, crop yield data and soil apparent electrical conductivity. A modification of jackknifing was used as the validation method in order to examine the stability of validation indices with different realizations of data set. The following illustrations can be applied to ismailia government as examples for soil mapping through GIS presence.

Soil Mapping Using GIS (2001).

 

 

 

Soil Mapping Using GIS (2001).

 

The illustration above denotes a SoLIM-derived map depicts soil spatial variation in much greater detail than the conventional soil map.  

 

 

The SoLIM methodology derives much of power and principles with the power of GIS under fuzzy logic. The similarity model overcomes the limitations of the in soil science, fuzzy measurements and fuzzy decisions conventional discrete conceptual model and allows the representation of soils as continua in both the spatial  soil survey, makes the survey updates more leaf area index estimates. Due to these advantages and with the continuing improvement of information gathering and process technology, there argue that the SoLIM pattern recognition with fuzzy application to classification has the potential to significantly advance the way soil and soil-landform interrelationships. However, there can be vital notion in pointing out qualify of soil information produced using GIS processes. Indeed, having great explosion in computation and information technology have vast amounts of data and tools in all fields of endeavor. Soil science is no exception, with the ongoing creation of regional, national, continental and worldwide databases.

 

The research will determine:

 

-       Environmental covariates for digital soil mapping

-       Spatial decomposition and/or lagging of soil and environmental data layers

-       Sampling methods for creating digital soil maps

-       Quantitative modeling for predicting soil classes and attributes

-       Quality assessment of digital soil maps and presentation of digital soil maps

 

 

The challenge of understanding these large stores of data has led to the development of new tools in the field of statistics and spawned new areas such as data mining and machine learning (2001). In addition, the increasing power of tools such as geographic information systems (GIS) and proximal sensors and data sources such as provided by digital elevation models are suggesting new ways forward as came when there is a global clamor for soil data and information for environmental monitoring and modeling. The principal manifestation is soil resource assessment using geographic information systems such as production of digital soil property and class maps with the constraint of limited relatively expensive fieldwork and subsequent laboratory analysis. The map of the Murray-Darling basin of Australia ( 2003) comprising some 19 million 250_250 m pixels cells and the digital Soil Map of Hungary (2000) are one of the notable examples of the present.

 

 

 

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