Zahra Masoudi; A. Jafari; Mohammad Hady Farpoor
Abstract
Introduction: Soil maps are a common source of information for land suitability studies. Land suitability studies are to compare land characteristics with the needs of land-use types and to select the best land-use productivity types for cultivation. Land evaluation analysis is considered as an interface ...
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Introduction: Soil maps are a common source of information for land suitability studies. Land suitability studies are to compare land characteristics with the needs of land-use types and to select the best land-use productivity types for cultivation. Land evaluation analysis is considered as an interface between land resources and land use planning and management. However, the conventional soil surveys are usually not useful for providing quantitative information about the spatial distribution of soil properties that are used in many environmental studies. Development of the computers and technology lead to develop the digital and quantitative approaches in soil studies. These new techniques rely on the relationships between soil and the environmental variables that explain the soil forming factors or processes and finally predict soil patterns on the landscape. Different types of the machine learning approaches have been applied for digital soil mapping of soil classes. To our knowledge, most of the previous studies applied land suitability evaluation based on the conventional approach. Therefore, the main objective of this study was to assess the performance of digital mapping approaches for the qualitative land suitability evaluation in the Jiroft plain of Kerman province. Materials and Methods: An area in the Jiroft plain of Kerman Province, Iran, across 28º14′ and 28º 26′N, and 57º 30′ and 57º 46′E was chosen. The study area is placed on alluvial plain, gravelly alluvial fans, eroded hills. Based on Google Earth image, geomorphology and topography maps and also field survey, 62 pedons were selected and excavated, and soil samples were taken from different soil horizons. Then, soil physicochemical properties were determined. To assess the climate, the climate information obtained from the Jiroft Synoptic Station. The average of soil properties was determined by considering the depth weighted coefficient up to 100 centimeters for potato. Qualitative land suitability evaluation for potato was determined by matching the site conditions (climatic, hydrology, vegetation and soil properties) with studied crop requirement tables presented by Givi (5). Land suitability classes were determined using parametric method. Land suitability classes reflect degree of suitability as S1 (suitable), S2 (moderately suitable), S3 (marginally suitable) and N (unsuitable). For digital approach, multinomial logistic regression (MLR) was used to test the predictive power for mapping the land suitability evaluation. Terrain attributes (elevation, slope, aspect, wetness index and multiresolution valley bottom flatness (MrVBF)), remote sensing indices (normalized difference vegetation index (NDVI), perpendicular vegetation index (PVI), and ratio vegetation index (RVI)), geology map, and geomorphology map were used as auxiliary information. Finally, all of the environmental covariates were projected onto the same reference system (WGS 84 UTM 40 N). Training and validating the model was done by leave-one-out cross validation. The accuracy of the predicted soil classes was determined using error matrices and overall accuracy. Results and Discussion: The results showed that climatic conditions are suitable (S1) for potato. The most important limiting factors were the gravel content, soil acidity and soil salinity for potato growing in the study area. Land suitability classes S2 to N were determined based on land index in the study area. The modelling results demonstrated overall accuracy 0.47 and 0.25 for class and subclass of land suitability, respectively. It seems that low number of soil samples for training and validating of the model were probably caused to low accuracy as compared to the other researches. In addition, the overall accuracy decreased from class to subclass. The terrain attributes (slope and aspect), remote sensing indices (normalized difference vegetation index) and geomorphology map were the most important auxiliary information to predict the land suitability classes and subclasses. This indicates the importance of geomorphological processes for determining the land suitability class in the study area. Conclusion: Results suggest that the land form, land position and geomorphology processes affect soil properties and then, land suitability classes. Therefore, variability of land suitability classes is function of variability of soil properties. Digital approaches could help to obtain the information with high resolution, provided that the criteria of suitability are associated with variability of soil properties. Although digital mapping approaches increase our knowledge about the variation of soil properties, integrating the management of the sparse lands with different owners should be considered as the first step for optimum soil and land use management.
