Spatial Distribution Analysis of Soil Properties in Varzaneh Region of Isfahan Using Image Processing Techniques

آب و خاک

Article 11, Volume 29, Issue 4, September 2015, Pages 1004-1017   

Document Type: Research Article

DOI: 10.22067/jsw.v0i0.22782

Authors

F. Mahmoodi; R. Jafari^* ; H. Karimzadeh; N. Ramezani

Isfahan University of Technology

Abstract

Introduction: Use of remote sensing for soil assessment and monitoring started with the launch of the first Landsat satellite. Since then many other polar orbiting Earth-observation satellites such as the Landsat series, have been launched and their imagery have been used for a wide range of soil mapping. The broad swaths and regular revisit frequencies of these multispectral satellites mean that they can be used to rapidly detect changes in soil properties. Arid and semi-arid lands cover more than 70 percent of Iran and are very prone to desertification. Due to the broadness, remoteness, and harsh condition of these lands, soil studies using ground-based techniques appear to be limited. Remote sensing imagery with its cost and time-effectiveness has been suggested and used as an alternative approach for more than four decades. Flood irrigation is one of the most common techniques in Isfahan province in which 70% of water is lost through evaporation. This system has increased soil salinization and desert-like conditions in the region. For principled decision making on agricultural product management, combating desertification and its consequences and better use of production resources to achieve sustainable development; understanding and knowledge of the origin, amount and area of salinity, the percentage of calcite, gypsum and other mineral of soil in each region is essential. Therefore, this study aimed to map the physical and chemical characteristics of soils in Vazaneh region of Isfahan province, Iran.
Materials and Methods : Varzaneh region with 75000 ha located in central Iran and lies between latitudes 3550234 N and 3594309 N and longitudes 626530 E to 658338 E. The climate in the study area is characterized by hot summers and cold winters. The mean daily maximum temperature ranges from 35°C in summer to approximately 17°C in winter and mean daily minimum temperature ranges from 5°C in summer to about -24.5°C in winter. The mean annual evaporation rate is 3265 mm. In this study, image processing techniquess including band combinations, Principal Component Analysis (PC1, PC2 and PC3), and classification were applied to a TM image to map different soil properties. In order to prepare the satellite image, geometric correction was performed. A 1:25,000 map (UTM 39) was used as a base to georegister the Landsat image. 40 Ground Control Points (GCPs) were selected throughout the map and image. Road intersections or other man-made features were appropriate targets for this purpose. The raw image was transformed to the georectified image using a first order polynomial, and then resampled using the nearest neighbour method to preserve radiometry. The final Root Mean Square (RMS) error for the selected points was 0.3 pixels. To establish relationships between image and field data, stratified random sampling techniques were used to collect 53 soil samples at the GPS (Global Positioning System) points. The continuous map of soil properties was achieved using simple and multiple linear regression models by averaging 9 image pixels around sampling sites. Different image spectral indices were used as independent variables and the dependent variables were field- based data.
Results and Discussion: The results of multiple regression analysis showed that the strongest relationships was between sandy soil and TM bands 1, 2, 3, 4, and 5, explaining up to 83% of variation in this component. The weakest relationship was found between CaCo3 and 3, 5, and 7 TM bands. In some cases, the multiple regressions was not an appropriate predicting model of soil properties, therefore, the TM and PC bands that had the highest relationship with field data (confidence level, 99%) based on simple regression were classified by the maximum likelihood algorithm. According to error matrix, the overall accuracy of classified maps was between 85 and 93% for chlorine (Cl) and silt componets, repectively.
Conclusions: The results indicated that the discretely classified maps had higher accuracy than regression models. Therefore, to have an overview of soil properties in the region, classification techniques appears to be more applicable than regression models. The findings of this study shows that the extracted maps of the physical and chemical characteristics of soils can be used as a suitable tool for field operations, cambating desertification and rehabilitation purposes and compared to maps that are created by traditional methods, our final maps have more economically and time saving advantages. Therefore, they can be used as an adjunct to field methods to aid the assessment and monitoring of soil condition in the arid regions of Isfahan province.

Keywords

Image classification; Multiple linear regressions; Remote sensing; Soil properties

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