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 28º 14′ 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.

IntroductionResearch and development in high-potential agricultural areas are of great importance for ensuring the food needs of the population and livestock. Neglecting these regions can lead to increased food prices and food shortages, which can have a negative impact on the economy and public health. Land suitability maps provide essential information for agricultural planning and are vital for reducing land degradation and evaluating sustainable land use. The utilization of modern mapping techniques such as digital soil mapping and machine learning algorithms can significantly improve the accuracy of land suitability assessment and crop performance prediction. These methods have been widely employed as primary tools for mapping and evaluating land suitability in various regions worldwide. Materials and MethodsIn this study, a total of 288 soil profiles were utilized to compute the land suitability index for wheat, barley, and alfalfa crops. Various environmental variables were included, such as topographic factors derived from the digital elevation model and spectral indices obtained from Landsat 8 satellite imagery. Eight key factors, namely slope percentage, climate, texture, gypsum content, equivalent calcium carbonate, electrical conductivity (EC), and sodium absorption ratio (SAR), were identified as influential in the assessment of land suitability. To quantify the degrees of land suitability for the target crops, a parametric approach based on the square root method was employed. Moreover, the random forest machine learning model was utilized for spatial modeling, zoning mapping, and determining the significance of environmental variables in the land suitability evaluation process. By incorporating these comprehensive methodologies, a more detailed and accurate understanding of the land suitability for wheat, barley, and alfalfa cultivation can be achieved, facilitating informed decision-making in agricultural planning and land management strategies. Results and DiscussionThe spatial prediction results demonstrated the effectiveness of the random forest model in classifying land suitability for wheat, barley, and alfalfa. The model achieved high accuracy, with Kappa coefficients of 81%, 84%, and 85% for wheat, barley, and alfalfa, respectively. The overall accuracies were also impressive, reaching 86% for wheat, 88% for barley, and 89% for alfalfa. Analyzing the land suitability assessment results, it was found that barley had the highest land suitability class, covering a significant portion of 40% in class S1. Alfalfa followed closely with 35.5% of the total area, and wheat occupied 32% in the same class. Delving into the predictive environmental variables for barley, Diffuse, SHt, and MrVBF emerged as the most influential factors. These variables played a crucial role in assessing the suitability of land for barley cultivation. Similarly, for wheat, the variables Diffuse, MrVBF, and TWI were identified as significant indicators, contributing to the accurate prediction of wheat performance. Regarding alfalfa, the variables MrVBF, Diffuse, and Valley_depth stood out as the most important variables, providing valuable insights into land suitability for alfalfa cultivation. In general, the limiting factors for irrigated cultivation of these crops were primarily associated with soil properties. In the northern regions, soil texture was identified as a significant limiting factor, impacting the suitability of the land for crop cultivation. On the other hand, in the southern regions, soil characteristics such as the percentage of lime, gypsum, salinity, and alkalinity were recognized as the most influential limiting factors, affecting the suitability of the land for successful crop production. These findings provide valuable information for land planners, farmers, and decision-makers in determining suitable areas for wheat, barley, and alfalfa cultivation. By considering the identified influential factors and addressing the limiting soil properties, agricultural practices can be optimized to maximize crop productivity and ensure sustainable land use. ConclusionThe research aimed to evaluate land suitability for wheat, barley, and alfalfa crops under irrigation. Data selection focused on the most limiting factors for these crops. The model achieved acceptable predictions for wheat, barley, and alfalfa, with Kappa coefficients of 0.81, 0.85, and 0.84, and overall accuracies of 0.86, 0.89, and 0.88, respectively. Barley had the highest percentage of suitable land (40%), followed by alfalfa (39.5%) and wheat (32%). Soil constraints varied across the study area, including texture, stoniness, lime, gypsum, salinity, and alkalinity. The analysis identified 31 soil types, and the random forest model yielded a digital soil map with a Kappa coefficient of 0.76 and overall accuracy of 0.81. The findings support effective land management and agricultural planning.

Introduction High-resolution satellite imagery data is widely utilized for Land Use/Land Cover (LULC) mapping. Analyzing the patterns of LULC and the data derived from changes in land use caters to the increasing societal demands, improving convenience, and fostering a deeper comprehension of the interaction between human activities and environmental factors. Although numerous studies have focused on remote sensing for LULC‎ mapping, there is a pressing need to improve the quality of LULC maps to achieve sustainable land management, especially in light of recent advancements made. This study was carried out in an area covering approximately 8000 hectares, characterized by diverse conditions in LULC, geomorphology and pedology. The objective was to investigate the potential for achieving maximum differentiation and accurate mapping of land features related to LULC. Additionally, the study assessed the impact of various spectral indices on enhancing the results from the classification of Landsat 8 imagery, while also evaluating the efficacy of support vector machine (SVM) and maximum likelihood algorithms in producing maps with satisfactory accuracy and precision.   Materials and Methods As an initial step, LULC features were identified through fieldwork, and their geographic coordinates were recorded using GPS. These features included various types of LULC, soil surface characteristics, and landform types. Following the fieldwork, 12 types of LULC units were identified. Subsequently, the LULC pattern in the study area was classified using the RGB+NIR+SWIR1 bands of Landsat 8, employing both SVM and maximum likelihood classifiers. To assess the impact of various spectral indices on improving the accuracy of the LULC maps, a set of vegetation indices (NDVI, SAVI, LAI, EVI, and EVI2), bare soil indices (BSI, BSI3, MNDSI, NBLI, DBSI, and MBI), and integrated indices (TLIVI, ATLIVI, and LST), and digital elevation model of study area were successively incorporated into the classification algorithms. Finally, the outcomes from the two classification algorithms were compared, taking into account the influence of the applied indexes. The classification process continued with the selected classifier and indices until reaching the maximum overall accuracy and kappa coefficient.   Results and Discussion Field observations revealed that the study area could be categorized into 12 primary LULC units, including irrigated farms, flow farming, dry farming, traditional gardens (with no evident order observed among planted trees), modern gardens (featuring regular rows where soil reflectance is visible between tree rows), grasslands, degraded grasslands, highland pastures (covered by Astragalus spp., dominantly), lowland pastures (covered by halophyte plants), salt domes (with no or very poor vegetation), outwash areas (River channel with many waterways), and resistant areas. The results of image classification indicated that the performance of the SVM algorithm across different band combinations is superior to that of the maximum likelihood method. Using SVM resulted in an increase in overall accuracy and Kappa coefficient by 3-8% and 0.03-0.08, respectively. For the map generated using RGB+NIR+SWIR1 bands and employing SVM, overall accuracy and Kappa coefficient were determined to be 76.6% and 0.72, respectively. Among the vegetation indices used in the SVM algorithm, LAI had the most significant impact, increasing the classification accuracy by 2.64%. Among the soil indices, BSI and MBI indices demonstrated the best performance; with BSI increasing the classification accuracy by 1.95% and MBI by 1.64%. Among the integrated indices, LST and ALTIVI enhanced the classification accuracy by 2.75% and 2.35%, respectively. It should be noted that the inclusion of the digital elevation model did not significantly improve the classification accuracy when using the support vector machine algorithm; in fact, it led to a decrease in accuracy when applied to the maximum likelihood classification. The probable reason for this issue is the different nature of DEM data compared to the other input data, as well as the limitations of parametric statistical approaches to effectively integrating data from diverse sources. Finally, the classification process was executed using the three visible bands, NIR, and SWIR1, in conjunction with selected indices (LAI, BSI, MBI, LST, and ALTIVI). Results indicated that using these spectral indices significantly improved classification accuracy, particularly for the DF, DGL, MG, O, and IF land cover/use classes. The calculated accuracies for these classes increased by 11.62%, 18.57%, 20.06%, 29.39%, and 33.19% respectively. Consequently, the accuracy of the classification and the Kappa coefficient (using support vector machine algorithm) increased to 85.24% and 0.82, respectively.   Conclusion In this research, we aimed to accurately map various land use/land covers by utilizing Landsat 8 imagery and incorporating three group of spectral indexes. Despite spectral interferences and overlaps among various phenomena related to LULC, the utilization of different spectral indices resulted in significant differentiation among LULC classes. Finally, considering the limitations of modelling in ENVI software, it is recommended to investigate the effectiveness of other models for classification in more specialized software, such as R.

Introduction Rapid population growth in developing countries implies that more food will be required to meet the demands of this population. Wheat as one of the most important grain crops in the world is a great source of food for human which is planted under a wide range of environments and its production influences on local food security. The production of wheat per unit area in Iran is low compared to developed countries in the world. One of the main causes for this low yield is that the suitable land for planting has not been recognized. Therefore, to overcome this problem, land suitability assessment is needed, which can help to increase crop yield. The first step in agricultural land use planning is land-suitability assessment which is often conducted to determine which type of land use is suitable for a particular location Digital mapping approach have been applied to link between soil observations and auxiliary variables to understand spatial and temporal variation in soil class and other soil properties. Little attempt has been made for using Digital mapping approach to digitally map land suitability classes Therefore, this paper applied land suitability assessment framework and digital soil mapping approach to map land suitability for rain-fed wheat in Kurdistan province. Materials and Methods The study area is located in Kurdistan Province, western Iran. It surrounds the city of Ghorveh and covers a region of 6500 ha. The climate is semi-arid whose features can be performed using a cold and rainy winter and a moderate and dry summer. The mean yearly rainfall is 369. 8 mm and over 90% of the rain falls between November and March. The mean temperature (10. 8℃ ) is relatively cool. Soil moisture and temperature regimes are Xeric and Mesic, respectively. The physiography units include piedmont, fan, hills, and mountain and slope varies from gentle to very steep. At first land unit component map was prepared by Mahler physiography method, then, 17 representative profiles in each land unit component were dug and described. 105 auger samples also were taken at three depths (0-20, 20-50 and 50-100 cm). Soil texture, acidity, organic carbon, CaCO3, gypsum, ESP, electrical conductivity and gravel were measured in all soil samples. Topography and climate data were also recorded. Numeric ratings of soil, topography and climate parameters based on land requirements of wheat were determined and land suitability index using parametric method were calculated. Then land suitability classes of wheat were determined. A set of auxiliary variables (i. e. land unit component, terrain attributes and remotely sensed data) to predict land suitability classes of rain-fed wheat. In order to generate land suitability class map, artificial neural network were applied to make relation between auxiliary variables and land suitability classes. Results and Discussion The results showed that the area has about 36. 61% N2 class, 40. 32% N1 class and 22. 53% S3 class. The validation results of the model based on the statistical indices including root mean square error, mean error, and determination coefficient (6. 56, 4. 81, 0. 68, respectively), indicates that the artificial neural network model has suitable accuracy. Auxiliary data including MrVBF index, LS factor, MRRTF index, slope, Land unit component, VDCN and band 2 were the most important for prediction of wheat land suitability index in digital method. The major limitation of the study area to plant rain-fed wheat were rainfall in the flowering stage, sever slope, shallow soil depth, high pH and gravel. Therefore, to increase production and sustainable agricultural system it is suggested land improvement operations such as terracing, decreasing pH, supplementary irrigation and gathering gravel. The highest values of rain-fed land suitability index were observed in the units physiographic of river plain and plateau, while the lowest value were observed in the units physiography of mountain and hill which had high slope, shallow soil and high gravel. These results were confirmed by one-way ANOVA and Duncan tests. Conclusion Based on the results of statistics indices artificial neural network had suitable accuracy for predicting land suitability index of wheat. In general, the study area, because of limitation of sever slope, shallow soil, high pH, and gravel, has low land suitability index for rain-fed wheat. Hence, to improve land suitability of the study area and increasing its production, suitable land improvement operations is required.

Assessment of the ability of employees to work without risk to health and safety for themselves and others is the concept of job fitness. The five main criteria, i.e. work capacity, risk of health and safety, ethical indices, economic indicators and indicators of legal, occupational health physician must be considered in assessing fitness for work.This review article consists of different parts including: occupational fitness evaluation indices, safety and health risk, workers' capacity, legal and moral indices, economical index, evaluation tools, decision making process, consequences and challenges. Naturally, for each of the mentioned indicators there is a tool to assess the sensitivity, specificity and cost-effectiveness.Consequences of job fitness assessment can range from suitable to unsuitable. Changes in maintenance and adjustment of work or working conditions should be considered in assessing job fitness. But with all the explanations, in many cases an occupational health physician may face problems in decision making for job fitness. He/She may not be able to make a precise decision which is probably due to different causes such as lack of appropriate tools and atandards, workers' malingering, social laws and etc.

Soil is known as a dynamic media so it easily degrade with inapplicable usage so with increasing in degradation of this limited source, the world’ s food safety would be in danger. Thus, applicable and sustainable usage of agricultural lands are become an essential and inevitable agenda. Therefore, the aim of this study is to Digital soil mapping using decision tree for agricultural land suitability, In order to constitute management programs for sustainable use of agricultural lands. For this aim, samples were collected based on cLHS and after some laboratory experiments, modeling and digital soil mapping were created by Random Forest Model. Also, agricultural land suitability for dominant crops were investigated by parametric method. The results showed that the land evaluation for irrigated wheat with surface irrigation 75. 27% of the total area S2 class and 24. 73% of the land in the class S3, respectively. In assessing the suitability of land for Maize irrigation, 14. 78% of the land in classes S1, S2 84. 82 of class and 0. 39% of the land in the class S3, respectively. Results for alfalfa irrigation land evaluation showed that 11. 10 percent of the land in classes S1, 88. 49% in the S2 class and 0. 4% of the class S3, respectively

Introduction: Topographic maps show natural and artificial features. natural features such as rivers, lakes, mountains, etc., Man-made features such as cities, roads and bridges. Using the satellite images is a way to extract digital elevation models. In general, there are two types of resolution in digital ground elevation models. ü Area resolution: The dimensions of the length and width of each cell in the pixel grid is a digital elevation model that shows the minimum dimensions of the topographic features taken on the ground. ü Height resolution: represents the minimum elevation dimensions that the digital elevation model is able to display. For example, in the digital model of ground elevation with a resolution of 30 meters, elevation features less than 30 meters are not visible. The digital elevation model can be prepared for a region with different accuracy. The high accuracy of the digital elevation map provides more accurate estimates of the physiographic characteristics of the basin, but the preparation of such maps is very costly. PRISM sensor from ALOS satellite with three cameras: 1-Forward 2-Vertical 3-Forward, which is captured earth surface with the characteristics of the earth (low and high). Therefore, an object that is high above the ground is shown with other points on a flat surface. As a result, by imaging points from different angles, the elevation of those points can be obtained through adaptive mathematical calculations. The purpose of this study is to evaluate the accuracy of the digital elevation model generated by the PRISM sensor of ALOS satellite in comparison with the digital elevation model of ASTER and SRTM for Sarakhs border region (between Iran and Turkmenistan). Method: The study area is located in north-eastern Iran in the range of 35 to 38 degrees north latitude and 56 to 60 degrees east longitude and on the border between Iran and Turkmenistan in the border region of Sarakhs. The research method in this research has an exploratory aspect that the production and extraction of digital elevation model from PRISM sensor stereo images from Alves satellite and its evaluation is with digital model extracted from ASTER image. The digital SRTM model has a spatial resolution of 90meters, the digital ASTER model has a spatial resolution of 15 meters and the digital elevation model obtained from the PRISM sensor from the ALOS satellite is 5 meters. In this study, elevation control points using Google Earth and GPS have been examined. The algorithms used in this method to extract elevation information are the same as the algorithms used in the photogrammetric method. Elevation digital models are made from satellite images taken in pairs. The accuracy of digital elevation models of this method is perfectly proportional to the scale or resolution of satellite images. Results & Discussion: In this study, we evaluated the digital elevation model from stereo satellite images of ALOS/PRISM satellite and compared it with the digital model of ASTER elevation and ground observations in the Sarakhs border region located on the border between Iran and Turkmenistan. In this study, the ability to generate a digital elevation model prepared from stereo images extracted from a PRISM sensor with a file of rational polynomial coefficients has been investigated, and we compared it with digital models extracted from stereo ASTER satellite and digital models extracted from SRTM. The results obtained from the digital elevation model are the accuracy of the digital elevation model produced by the pair of ASTER satellite images using a correlation between the two images of 0. 47 pixels. Due to the spatial accuracy of the image pixels, which is about 15 meters, the accuracy of the digital model is less than the size of pixels, i. e. less than 15 meters, 6 meters horizontally and 7 meters vertically, which is a total of 13 meters. The results show that RMSE as error index for digital model of elevation extracted from ASTER and PRISM and ground observations are 7. 46, 8. 77, 3. 66 and 6. 8 meters, respectively. The results obtained from the stereo images of the PRISM sensor are the standard deviation of the pixels in the longitudinal direction of 1. 9 meters and in the transverse direction of 2. 3 meters and the distance between the pixels of the digital model is 3 meters high. Therefore, the accuracy of the digital model extracted from PRISM sensor images is higher than SRTM and ASTER. It is recommended to use a high-precision digital elevation model in all borders of the country, which uses a digital elevation model produced from stereo PRISM images from ALOS satellite, which is accompanied by polynomial logical coefficient (RPC) files for geometric correction of images. Conclusion: The higher the accuracy of the DEM, the more efficient it will be and give border commanders the ability to make better decisions in different situations. The elevation accuracy obtained from the stereo images of the PRISM sensor is 3 meters. The accuracy of the digital model of SRTM elevation in the plains is about 30 meters, which can be used for studies of phase zero and one of the projects, as well as reducing the huge costs of studies. The results of this paper, shows that the accuracy of the digital elevation model produced from the stereo images of the PRISM sensor is higher than the digital elevation and SRTM digital models, i. e. the RMSE error and standard deviation are relatively lower. As a result, it is recommended for border studies that require higher accuracy, and the entire borders of the country, to use the digital elevation model with accuracy.

Digital elevation models (DEMs) are one of the most important data for various applications such as hydrological studies, topography mapping, image ortho rectification, 3D images generation, extraction terrain parameters, disaster management, and etc. A digital elevation model can be derived from numerous techniques with different elevation accuracies. In photogrammetric techniques, a DEM can be extracted from stereo satellite images through many processing steps. A satellite imagery based digital elevation model is called ASTER GDEM ver2 was released on 2011 at a spatial resolution of 1 arc-second. This model was evaluated by differencing with other reference DEMs in order to investigate the quality and accuracy parameters over different land cover types. In this paper, an accuracy assessment of ASTER GDEM ver2 dataset with SRTM and local elevation model over the bare area of a port in southwest of IRAN is presented. This study investigates DEM’s characteristics such as systematic error (bias), vertical accuracy and outliers for these three datasets. The accuracy measures for the assessment of the height differences between each DEMs can be calculated based on the usual (Mean error, Root Mean Square Error, Standard Deviation) and the robust (Median, Normalized Median Absolute Deviation, Sample Quantiles) descriptor. The results demonstrated that there is a large negative elevation bias of approximately -4.5 m and -2.8 m of ASTER GDEM ver2 against the SRTM and local DEM. The median of the differences between GDEM and local DEM is about -3.7 m which is a robust measure to prove the existence of systematic shift between the two data. The RMSE measured for elevation differences between GDEM and two other DEMs is same, but the standard deviation GDEM and local DEM differences are higher than the value of this parameter between GDEM and SRTM. The accuracy measures NMAD of GDEM against the local DEM and SRTM are 5.5 m and 5.9 m, respectively. On the other hand, about 68% of the GDEM and local DEMelevation differences are in [0, 7.5] m, while this values are in [0, 9] m for GDEM and SRTM elevation differences.

Introduction: The implementation of the forest restoration plan in the northern region of Iran has led to an increase in efforts to enhance wood production through the cultivation of poplar seedling. Particularly, poplar trees hold a significant position among wood producers due to their distinctive features. The key consideration revolves around identifying suitable and cost-effective lands for planting fast-growing species. Purpose of this study focuses on assessing the land suitability for poplar (Populus nigra L.), a prevalent forest species in the country, through a quantitative land evaluation method based on the FAO approach. Material and Methods: The research gathered and classified the edaphic and climatic requirements of poplar tree by utilizing various library resources, data from the soil laboratory of the Research Institute of Forests and Ranglands, and field survey (Soil Profiles). The climatic and soil characteristics were quantitatively classified into five categories: highly suitable (S1), moderately suitable (S2), low/critically suitable (S3), The Quantitative Classification of soil and climate properties was done in five classes: suitable (S1), moderate suitability (S2), low or critical suitability (S3), currently not suitable (N1) and permanent not suitable (N2). For validation, a quantitative assessment of land suitability for poplar cultivation was performed across six soil types, including Nowshahr forest brown, Nowshahr gray brown pedzolic rich in organic matter, Chamestan alluvial regosol, Chamestan Rendezina, Chalous  Pseudogley , and Karaj alluvial cambisol, using a parametric method (second root). Findings: Results indicate that spruce thrives in wet and semi-humid regions in a dry-type manner, with ideal conditions found in areas receiving 1500-1800 mm of rainfall. In drier, semi-arid zones, irrigation is essential for meeting water requirements. Optimal growth temperatures for spruce range from 11-16 degrees Celsius in moderate climates. Poplar flourishes in soils with a light to medium texture, granular or cubic structure, good to fast drainage, and low underground water levels with adequate calcium and magnesium cations. Conclusion: The study showed that the most suitable soils for poplars have a light to medium texture with a granular or crumb structure, with adequate drainage, the absence of high groundwater and the sufficient amount of calcium and magnesium cations in the soil. Soils with incomplete drainage and heavy texture do not provide suitable conditions for poplar plantations. In terms of quantitative evaluation, Mollic Udifluvents soils with medium texture had the highest score for poplar plantations.