javad khanifar

 Haji Mohammad Gilani is one of the 10th century poets of Indian style. He learned mysticism from Allameh Maulana Mohammad Baqer Khorasani and Mullah Hasan Gilani, and his lyrical poems are full of mystic-romantic themes. The purpose of this article is to examine the style. The study of Haji Mohammad Gilani"s Diwan and the introduction of its unique manuscript.

 This research is descriptive-analytical.

 During the analysis of the style of Divan, it was found that the poet used different weights to convey mystical-romantic concepts. The use of current lines is noticeably repeated in his poems. At the lexical level, the use of slang words and terms has a high frequency. Also, his poems show a relatively high deviation in new compositions. At the intellectual level, mystic-romantic poems have the highest frequency.

 Haji Mohammad Gilani is one of the poets of Indian style and his poems are simple and free of difficulties. He has paid more attention to mystical and romantic topics and his poems have relatively little coherence in the vertical axis. Almost 90% of his poems are mirdf.

The aim of this research was to study the importance of the neighborhood scale in modeling the relationship between vegetation cover and morphometric variables using the classification and regression trees algorithm (CART) in southwestern of Iran. For this purpose, the second Modified Soil-Adjusted Vegetation Index (MSAVI2) was calculated from a Landsat 8 image, and eight morphometric variables were derived using the Wood method in four neighborhood scales (90×90, 150×150, 210×210, and 270×270 m) from a 30 m SRTM digital elevation model. The results of the Kruskal-Wallis test confirmed that in some sub-watersheds, neighborhood-scale change can have a significant effect on slope gradient, profile curvature, specific catchment area, LS factor, and topographic wetness index. The results showed that in each sub-watershed different morphometric variables are most related to the spatial distribution of the MSAVI2 index and the value of the Spearman correlation coefficient between them is slightly affected by the neighborhood scale. CART models based on the MSAVI2 index and 270×270 m morphometric variables with a kappa coefficient of 0.55 and 0.78, respectively, had the best performance in classifying vegetation types. The elevation smoothed, which is the least affected by the neighborhood scale, was recognized as the most important predictor in the CART model. However upscaling led to the increasing importance of other morphometric variables, especially slope gradient, in classifying vegetation types and finally improving the accuracy of the CART model. Overall, the present results indicate that the application of multi-scale geomorphometric analysis with respect to the geomorphology of the study area can improve the performance of prediction models related to vegetation cover to an appropriate extent.

Aggregate stability is one the most important soil physical properties, which is estimated using easily accessible parameters. Remote sensing (RS) is a useful technique that has meaningful capabilities to monitor soil and water resources. Moreover, soil aggregate stability is an index of soil erodibility which can be used as a useful tool to manage and control the soil against destructive factors. Therefore, it is really important to model the effective factors on soil aggregate stability. The present study conducted because of importance of soil physical properties and land use effects on crop cover status and its modeling.

Methods and material:

In this research, the relation between vegetation indices and mean weight diameter (MWD) as an index of soil aggregate stability was investigated in forest and rangeland land uses. In order to evaluate the relation between land uses and vegetation indices in Dehdez watershed located in the southeast of Khuzestan Province, Iran country; the images of Landsat 8 with pass number 164 and the row of 38 provided from the Earth Explorer website with GeoTIFF format. The radiometric correction performed using ENVI 5.1 software. The evaluated vegetation indices including, RVI, VIN, TVI, YVI, SBL, DVI, NDGI, RI, NDI, NDVI, PVI, SAVI, TSAVI, ARVI ,and MSAVI. The Pearson correlation between the above vegetation indices and organic carbon, sand, silt, clay ,and mean weight diameter evaluated for 50 soil samples collected from two land uses. In this study, two type of land use, including forest and rangeland assessed. Pearson ´s correlation analysis and multivariate adaptive regression spline (MARS) applied to investigate the relation between vegetation indices and MWD.

The results illustrated that in the rangeland, MWD had a significant correlation with VIN, TVI, MSAVI, RVI, SAVI, TSAVI, NDVI, and PVI indices at the level of 0.01. However, in the forest, there was no significant relation between the studied indices. Also, MARS modeling showed that the fitted model based on vegetation indices in rangeland (Adjusted R-Sq. = 0.55, RMSE = 0.16) has much higher predictive power and accuracy than the fitted model for forest (Adjusted R-Sq. = 0.35, RMSE= 0.36).

In general, the results of this study revealed that the controlling factors of soil aggregate stability are different for rangeland and forest land uses. This has led to differences in correlation coefficient between vegetation indices and MWD, and consequently modeling results. Also, remote sensing can provide convenient technology for providing the sustainable management purposes of soil and water resources. To cover the sustainability purposes in the watershed scale, it is necessary to utilize different technologies such as remote sensing. Indeed application of various techniques helps to simplify the processes and finally preserve the soil and water resources against degradable factors.

The most important factors which are effective for landscape analysis is spatial resolution of digital elevation model (DEM).In this study, the effect of spatial resolution on soil parameters and modeling of soil properties have studied. For this research 6 parameters including (height, slope gradient, slope aspect, minimal curvature, upland area and sediment transportation index) from 5 different spatial resolutions (10, 30 (base), 60,90 and 120) have originated and for modeling of soil properties (soil texture, K, P,EC,pH and soil depth) have used. The differences between mean of each parameter of spatial resolutions accomplished using Kruskal-Wallis test and multi linear regression, then the best model in each spatial resolution was selected based on AICC index. Our results depicted that with coarser DEM, the mean of slope gradient (G), sediment transportation index (STI) and the minimum curvature (Cmin) were decreased whereas the mean and minimum of upslope area (UP) was enhanced. Statistical indices of height showed the low sensivity to spatial resolution variations. Changes of mean and maximum slope aspect in different spatial resolutions have no regular trend. Only minimum curvature and upland area have significant difference relating to different spatial resolutions. With changes of DEM spatial resolution, the best combination of parameters for modeling of soil properties and AICc and R2adj will be change. Finally, our results illustrated that for an area with high variability of geomorphic conditions, there is no capability to use only one specific resolution for all soil properties.

In recent decades, the potential of soil erosion as serious threat against sustainable land management (SLM) and soil sustainability has been recognized. Furthermore, human-induced soil erosion lead to harmful environmental effects and transportation of sediment to water bodies is accompanied by loss of nutrients and eutrophication. Therefore, there is a need to focus on soil erosion outcomes to prevent its environmental impacts and mitigate the negative feedbacks of soil erosion. One of the most important factors that affecting the amount of erosion and sediment yield is soil erodibility (K-factor in USLE), which is an index of soil susceptibility. Moreover, K-factor is one of the 6 effective factors on soil erosion in universal soil loss equation and mainly is representative of soil properties. Regarding the difficulty of soil erodibility ´s measurement, therefore we need to an easily measurable parameter. In this study soil aggregate is used as an index of soil erodibility factor. Generally, topography alters from one slope to another one, which conduces to dramatically changes of soil erosion intensity, therefore topography in terms of geomorphometric parameters is effective on soil erosion processes; morphometric analysis and soil erosion modeling are robustly interconnected. Also, digital elevation model (DEM) is a base to extract geomorphometric parameters and the quality of DEM is effective on geomorphometric characteristics.

This study conducted with the aim of modelling the aggregate stability using multivariate linear regression and K-means clustering algorithm as an index of soil erodibility factor. For this purpose a study area with an approximate area of 20000 hectares selected. This area is located in Dehdez which is in the north east of Khuzestan province. Based on the climatologic data this area has xeric and thermic regimes. The main land uses in this area including rangeland and forest and in the rangeland the crop cover is mostly grasses. According to the purpose of this study and degree of homogeneity the soil sampling accomplished using completely randomized method. From each land use 25 soil samples collected (the depth of soil sampling was 0-30 cm). Then the soil samples prepared and the mean weight diameter (MWD) as an index of soil aggregate stability using wet sieving, soil texture with hydrometer method and organic carbon content measured in at least 3 replications. In another part of this project the digital elevation model (DEM) of study area extracted from SRTM (Shuttle radar topographic mission) with the spatial resolution of30 m. After DEM preparation all preprocessing operations performed on DEM using Arc Hydro extension in ArcGIS 10.3 software in order to remove sinks and enhance the quality of DEM. In the next step the primary and secondary geomorphometric parameters extracted from this DEM using ArcGIS 10.3 and SAGA 6.3. software. Finally, multivariate linear regression and K-means clustering models developed between geomorphometric parameters and soil properties as dependent variables and aggregate stability as independent variable to achieve statistical indices in order to evaluate developed models.

Our results showed that there was a significant correlation between some geomorphometric parameters and soil aggregate stability; therefore there is a possibility to apply these parameters in order to parameterize aggregate stability. Moreover, the results of modelling indicate that regression models using geomorphometric parameters and soil properties was able to cover approximately 75 and 70 percentage of spatial variation of aggregate stability in the rangeland and forest land use of the study area, respectively. While clustering-regression models were able to explain 77 and 82 percentage of the spatial variation of the aggregates stability in the first and second cluster respectively. Also, the results of validation of developed models in this study showed that the root mean square error (RMSE) of regression models for rangeland and forest land use was 0.33 and 0.26 respectively and RMSE of clustering-regression models for first and second clusters was 0.93 and 0.62 respectively. According to importance of soil aggregate stability on soil erodibility factor (K-factor) and the difficulty of soil erodibility ´s measurement therefore these developed models are useful tools in order to predict soil erosion and based on the distribution of soil erosion and deposition using STI (Sediment transport index) and TWI (Topographical wetness index) could be able to select and apply the best management practices in the critical areas. Indeed STI map and TWI map are indices of spatial distribution of soil erosion and deposition in the studied area therefore using these geomorphometric indices we able to control soil erosion and its negative feedbacks.

Regarding the difficulty of soil erodibility ´s measurement therefore we used some indices to simplify this process and our results illustrated that it is possible to develop some regression models in order to estimate aggregate stability as an index of soil erodibility. In summary our results confirm that geomorphometric parameters are easily available parameters based on the DEM to predict soil erodibility.