Ziba Maghsodi; Hamid Reza Matinfar; Seyed Roohollah Mousavi
Abstract
The scale of environmental variables is one of the most important features to consider when selecting data. The aim of this study is to improve the accuracy of digital mapping by selecting the optimal scale for predicting six soil properties, For this purpose, 100 surface soil samples (0-30 cm depth) ...
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The scale of environmental variables is one of the most important features to consider when selecting data. The aim of this study is to improve the accuracy of digital mapping by selecting the optimal scale for predicting six soil properties, For this purpose, 100 surface soil samples (0-30 cm depth) were collected based on a random sampling pattern. Environmental variables related to topography and remote sensing were extracted from the digital elevation model (DEM) and Landsat-8 satellite. The optimal environmental variables were selected using the recursive feature elimination method in the Silakhor Plain region. Soil property modeling was conducted using machine learning models such as random forest (RF), Support Vector Regression (SVR), Cubist (CB), and hybrid modeling. The modeling results showed that the RF model performed best for predicting CCE, pH, sand, and silt with R² values of 0.64, 0.65, 0.59, and 0.70, respectively. Additionally, the SVR model showed the highest accuracy for predicting SOC with an R² of 0.62, while the CB model had the highest accuracy for predicting clay with an R² of 0.66. The most suitable cell sizes for CCE, pH, sand, and silt were identified as 30*30 m, for SOC as 60*60m, and for clay as 90*90m. The most important environmental variables for SOC, pH, silt, sand, and clay were valley depth, differential vegetation index, and modified vegetation index, respectively. Overall, the results indicated that in the study areas, the use of intermediate scales (cell sizes of 30 to 90 m) led to higher accuracy in predicting soil properties. This is because using larger cell sizes introduces noise that hinders accuracy.
Mahdieh Sanjari
Abstract
Given the increasing trend of application of Geographic Information System (GIS) for natural resources study in one hand and complication of biological, geomorphological, hydrological and ecosystem mechanisms on the other hand, scale is an overlooked but very impressive and flourishing concept. As for ...
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Given the increasing trend of application of Geographic Information System (GIS) for natural resources study in one hand and complication of biological, geomorphological, hydrological and ecosystem mechanisms on the other hand, scale is an overlooked but very impressive and flourishing concept. As for any natural resources study consistent with its phase, various maps are used and produced so in order to make the achieved results usable for planning as well as management of resources, determination of scale of the study and application domain for the results is very significant. Since using GIUH model in the basins without hydrological data have been widely recommended by hydrologist and this model developed in accordance to the relationship between geomorphological properties of basins and their effects on hydrological responses, so before using that it is essential to determine the optimal scale (in view points of accuracy, time and cost) which in this paper will be selected from 1:50000 and 1:25000 scales ,inclusively used in topographic maps in Iran, using multi-scale analysis. Of course, it should be mentioned that giving a comparison between the results of GIUH and the recorded data as well as the model’s effectiveness in our research basin has not been the purpose.
