Rohallah Taghizadeh Mehrjardi; Fereydoon Sarmadian; Mehdi Tazeh; Mahmood Omid; Norair Toomanian; Mohammad Javad Rousta; Mohammad Hassan Rahimian
Abstract
Recently, researchers are increasingly employed Digital Soil Mapping (DSM) techniques to overcome traditional soil mapping difficulties. Apparently, due to the large heterogeneity of soil environments, sampling may be the most important step in digital soil mapping studies. Therefore, in this research, ...
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Recently, researchers are increasingly employed Digital Soil Mapping (DSM) techniques to overcome traditional soil mapping difficulties. Apparently, due to the large heterogeneity of soil environments, sampling may be the most important step in digital soil mapping studies. Therefore, in this research, we employed three different sampling strategies including Latin hypercube, Fuzzy-K-Means and random sampling to achieve the best spatial distribution of soil samples in an area around 720 km2 located in Ardakan region, Yazd province, Iran. Auxiliary data that used in this study, were including terrain attributes, Landsat 7 ETM+ images and a geomorphologic surfaces map. Based on statistical criteria (i.e. mean and standard deviation), results showed that Latin hypercube is the best sampling method. For instance, in the selected points, the mean of wetness index is 18.19 which is the same as the mean of all area. Similarly, the mean of Multi-resolution Valley Bottom Flatness (MrRVF) in the points selected by Latin hypercube strategy is very similar to all area. Moreover, histogram of auxiliary data in selected points (samples) was more similar to histogram of auxiliary data in all area. Also, the results indicated that a good geographical coverage (Fuzzy-K-Means) does not adequately represent the distribution of the variables. Therefore, Latin hypercube is the best strategy to determine sample locations in our study area and hence, it is recommended that researchers apply Latin hypercube method in future digital soil mapping studies.
Maral Khodadadi; Mohammad Sadegh Askari; Fereydoon Sarmadian; Hossein Gholi Refahi; Ali Akbar Norouzi; Ahmad Heidari; Hamid Reza Matinfar
Volume 1, Issue 2 , July 2009, , Pages 99-110
Abstract
Salinity is the major factors of soil degradation in semi arid and arid regions. The main aim of this study was to evaluate the capability of Landsat ETM+ data for soil Salinity mapping in the selected part of the Qazvin plain, an area of arid environment. In this study spectral classes carried out on ...
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Salinity is the major factors of soil degradation in semi arid and arid regions. The main aim of this study was to evaluate the capability of Landsat ETM+ data for soil Salinity mapping in the selected part of the Qazvin plain, an area of arid environment. In this study spectral classes carried out on remotely sensed data and with the help of field observation and soil analysis were regrouped to soil salinity classes to prepare soil salinity map.. Soil sampling was implemented using stratified random sampling method, depending on landscape complexity and homogeneity as well as on the representativeness of Landsat ETM+ data. Also in each soil map unit at least one profile was studied for subsoil salinity variations. Field samples taken by using augur and profiles were analyzed in laboratory for Na+ , Ca2+ , Mg2+ cations, as well as soil texture, ECe and pH. We have analyzed the effectiveness of additional data such as digital elevation model to improve the accuracy of classification. Also NDVI, SRVI, PVI, SAVI, SI, BI and NDSI indices, PCA and Tasseled cap were analyzed. Soil salinity map of each selected bands produced and with ground truth map crossed. The results indicated that combination of DEM with ETM+ bands has highest accuracy. This study addressed that thermal band of ETM+ can increase the classification accuracy which illustrated its effective role to classify the soil salinity. Tasseled cap and other indices had almost high accuracy among studied image processing techniques. The SI and BI indices had the highest correlation with EC and could distinguish the saline and non saline soils while the optimum index factor had overall low accuracy.
