To increase detail in the polygon of conventional soil maps, we have produced a spatially disaggregated soil class map of a relatively flat agricultural plain using DSMART algorithm. DSMART works through resampled classification trees to estimate the probability of the existence of each possible soil classes and also to prepare the maps of the most probable soil class, second most probable, and so on in raster format.
In the Great Plains region of the USA, a recent conversion of the grasslands to cropland impacting the soil erosion and hence the crop productivity. The GIS-Enabled Revised Universal Soil Loss Equation was used to estimate the soil erosion at the watershed scale in the present study. Soil erosion in the current study was predicted for the Big Sioux River watershed scale using the spatial data downloaded from the easily available online sources.
In the Great Plains region of the USA, a recent conversion of the grasslands to cropland impacting the soil erosion and hence the crop productivity. The GIS-Enabled Revised Universal Soil Loss Equation was used to estimate the soil erosion at the watershed scale in the present study. Soil erosion in the current study was predicted for the Big Sioux River watershed scale using the spatial data downloaded from the easily available online sources.
The objectives of the present study are (i) to predict soil texture fractions, from the various particle size fractions data using data mining models, including a regression tree, an artificial neural network and a neuro-fuzzy system, and (ii) to study the impact of auxiliary data, such as terrain parameters, satellite images, geomorphological maps, and spectrometric data for predicting the spatial distribution of clay, sand, and silt contents.
The objectives of the present study are (i) to predict soil texture fractions, from the various particle size fractions data using data mining models, including a regression tree, an artificial neural network and a neuro-fuzzy system, and (ii) to study the impact of auxiliary data, such as terrain parameters, satellite images, geomorphological maps, and spectrometric data for predicting the spatial distribution of clay, sand, and silt contents.
Several broad types of data mining approaches through Digital Soil Mapping have been tested. The usual approach is to select a model that produces the best validation statistics. However, instead of choosing the best model, it is possible to combine all models realizing their strengths and weaknesses.
Several broad types of data mining approaches through Digital Soil Mapping have been tested. The usual approach is to select a model that produces the best validation statistics. However, instead of choosing the best model, it is possible to combine all models realizing their strengths and weaknesses.
Soil texture is the most well-known composition in soil science. When separate components of the texture are predicted independently in digital soil mapping, there is no guarantee that the separate estimates will sum to 100%. This study was conducted to investigate the effect of different soil texture modelling methods on the estimation of available soil water capacity and the total amount of irrigation water required for wheat production.
Soil texture is the most well-known composition in soil science. When separate components of the texture are predicted independently in digital soil mapping, there is no guarantee that the separate estimates will sum to 100%. This study was conducted to investigate the effect of different soil texture modelling methods on the estimation of available soil water capacity and the total amount of irrigation water required for wheat production.
An automated and a comprehensive validation system, which includes both the cutoff-dependent and –independent evaluation criteria for spatial modelling approaches, has not yet been developed for GIS based methodologies. This study, for the first time, aims to fill this gap by designing and evaluating a user-friendly model validation approach, denoted as Performance Measure Tool (PMT), and developed using freely available Python programming platform.