Soil science
A. Sarabchi; H. Rezaei; F. Shahbazi
Abstract
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 ...
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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.
A. Mousavi; F. Shahabzi; Sh. Oustan; A.A. Jafarzadeh; B. Minasny
Abstract
Introduction: Soils are considered as one of the most important parameters to be achieved the sustainable agriculture at any place in the world. Additionally, the digital environment needs to have a soil continuous maps at local and regional scales. However, such information are always not available ...
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Introduction: Soils are considered as one of the most important parameters to be achieved the sustainable agriculture at any place in the world. Additionally, the digital environment needs to have a soil continuous maps at local and regional scales. However, such information are always not available at the required scale and mapping with high accuracy. Digital soil mapping (DSM) is a key for quantifying and assessing the variation of soil properties such as organic carbon (OC) especially in un-sampled and scarcely sampled areas. Using remotely sensed indices as an important auxiliary information relevant to the study area and data mining techniques were the pathway to create digital maps. Previous studies showed that digital elevation model (DEM) and remotely sensed data are the most commonly useful ancillary data for soil organic carbon prediction. the importance of DEM and derivative data in soil spatial modelling, it was not carried out in our research because there were no sharp differences in relief, and climate for that matter, across the study area. This research aims to investigate the spatial distribution of soil organic carbon (SOC) in a study area in north-western Iran using 21 remotely sensed indices as well as two data mining techniques namely Random Forests (RF) and Cubist.
Materials and Methods: This study was performed on the east shore of Urmia Lake located in the east Azerbaijan province, Iran. The area extension is about 500 km2. Based on the synoptic meteorological station report, the average annual precipitation and temperature of the study area is 345.37 mm and 10.83°C, respectively. Soil moisture and temperature regimes are Xeric and Mesic, respectively. Using stratified random soil sampling method, 131 soil samples (for the depth of 0-10 cm) were collected. Soil organic carbon (SOC) were then measured. The next step was to gather a suite of auxiliary data or environmental covariates thought to be useful (and available) for predicting SOC within a DSM framework for the region studied. Then, a number of remotely sensed imagery scenes from the Landsat 8-OLI acquired were collected in July 2017. The RF and Cubist models were applied to establish a relationship between soil organic carbon and auxiliary data. Both reflectance of the individual bands and indices derived from combinations of the individual bands were used. Fourteen spectral indices relevant to four types of data including: i) vegetation and soil; ii) water; iii) landscape; and iv) geology were gathered. Three different statistics was used for evaluating the performance of model in predicting SOC, namely the coefficient of determination (R2), mean error or bias (ME) and root mean square error (RMSE).
Results and Discussion: The results of the descriptive statistics of determined and calculated SOC for 131 soil samples showed that the mean and median values for SOC were 2.52% and 2.11%, respectively. Also, the CVs was recorded 57.94%. Minimum and maximum recorded values for SOC were 0.83% and 5.22%, respectively. The contents of SOC was left-skewed in the data set. The RF model prediction was quite good with calibration (R2= 0.89, MSE = 0.16 and ME = 0.01). While, in the Cubist calibration data set, the Valve of RMSE and ME were increased (R2= 0.85, MSE = 0.21 and ME = 0.03). In terms of R2, The RF model showed the higher value (0.89) compared with the Cubist model (0.85) for the validation dataset. Generally, the remote sensing (RS) spectral indices can successfully predict various SOC across the study area. The covariate importance rankings showed that VARI, NDVI, CRI2 and CRI1 were the four important covariates to predict SOC in the study area. Accordingly, the changes in SOC over space were not uniform across the study area and also it means that the study area is very dynamic and evolved over time.
Conclusion: The results of this study showed that although variables and auxiliary data had different importance in predicting the distribution of SOC, in general it can be found by modelling the relationship between them and SOC through the model. The results revealed that the RF model was suitable for the target variable. Accordingly, the auxiliary variables had different importance in predicting the spatial distribution of SOC. Remote sensing imagery, particularly those encompassing the combined indices played an important role in the prediction of SOC. The obtained results also indicated that the Visible Atmospherically Resistant Index (VARI) and Normalized Difference Vegetation Index (NDVI) were important to predict SOC. The current study revealed that DSM using important environmental covariates can be successfully used in Iran which there is no sufficient soil databases. This research also provided a pathway to start further works in the future such as DSM relevant to the soil erosion, soil ripening, trace elements and so on.
H. Rezaei; A.A. Jafarzadeh; A. Alijanpour; F. Shahbazi; Kh. Valizadeh Kamran
Abstract
Introduction: According to important ecological roles of soil organic matter in stabilizing ecosystems, it is essential to consider soil organic carbon condition for managements of worldwide problems such as soil quality, carbon cycle and climate change. Also, organic matter is one of the main ...
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Introduction: According to important ecological roles of soil organic matter in stabilizing ecosystems, it is essential to consider soil organic carbon condition for managements of worldwide problems such as soil quality, carbon cycle and climate change. Also, organic matter is one of the main component of soil which have vital impress on its evolution. Therefore, assessing soil organic matter fate in various environmental conditions and its relation with environmental factors will be useful for management decisions. Determining soil organic carbon content, stocks and forms by the physico-chemical and micromorphological studies may respond to the question about soil organic matter evolution from the different point of views. Based on mentioned reasons, our research work focused on soil organic matter content, stocks and forms under various environmental condition of the forest ecosystem to find new aspects of its relation with environmental factors.
Material and Methods: This research work was carried out in Arasbaran forest, northwest of Iran, which recognized as a part of the international network of biosphere reserves and has unique species of plants with special ecological properties. Sampling was carried out in a Kaleybar Chai Sofla sub-basin as a part of Arasbaran forest with eastern longitude of 46º 39´ to 46º 52´ and northern latitude of 38º 52´ to 39º 04´. Based on the Amberje climate classification, the climate of the region is semi-humid and moderate. The soil moisture and temperature regimes are Xeric and Mesic, respectively. Hornbeam (Carpinus betulus) and Oak (Quercus petraea and Quercus macranthera) were identified as the main woody species in this area and volcano-sedimentary rocks were the geological structure. Primary site surveying showed 5 forest stand types such as Oak (Quercus macranthera), Hornbeam-Oak (Carpinus betulus-Quercus macranthera), Hornbeam (Carpinus betulus), Hornbeam-Oak (Carpinus betulus-Quercus petraea), Oak (Quercus petraea) along altitudinal transects, that used as environmental parts with different conditions. In each environmental part, a soil profile was described and sampling was done for physical, chemical and micromorphological analysis. After preparing soil samples in the laboratory, soil physico-chemical routine analyses were carried out by standard methods and then the studied soils were classified on the basis of 12th edition of soil taxonomy. To achieve the main aim of the study, various aspects of soil organic matter evolution were assessed. Soil organic matter content was determined according to the Walkley–Black wet oxidation method and using alteration factor f = 1.724 recommended by USDA. Variance analysis and means compare of soil organic matter content in surface horizons of different environmental parts were performed by using the SPSS software package and Dunkan's multiple range test, respectively. Soil organic carbon stocks were calculated for each soil horizon and weighted average based on profile depth was used to calculate this index for each soil profile. The prepared thin section for micromorphological study was examined under both plane-polarized light (PPL) and cross-polarized light (XPL) using a polarized microscope and explained based on standard terminology to identify various forms of soil organic matter all over the study area.
Results and Discussion: Results revealed increasing of soil evolution with decreasing of elevation. Entisols, Inceptisols, Alfisols and Mollisols with different families were the soil observed along altitudinal transects by decreasing elevation. According to the obtained results, environmental effects caused different soil organic matter content and evolution with various soil organic carbon stocks in each part. Improvement of environmental condition by decreasing elevation resulted in more evolution of soil organic matter, dominant of decomposed forms of organic matter and rise of soil organic carbon stocks from the highest part to the lowest one. Soil organic matter content in soil surface increased by elevation, although the main source of soil organic matter have better condition in lower parts due to ecological reasons. This inverse statue can be explained by special environmental conditions causing limited organic remnants decomposition in the highest parts. In the same trend with soil evolution, soil organic carbon stocks increased by decreasing of elevation. This trend refers to the relation of mentioned index ability with various soil-forming processes. Micromorphological study showed that organic intact remnants were the dominant forms in upper parts which changed to well-decomposed forms in the lowest parts. This observation revealed the occurrence of mechanical decomposition processes of organic remnants in high elevation while biochemical ones happen in the lower parts. Also, this distribution of soil organic matter decomposition processes can explain soil organic carbon content and stocks all over the study area.
Conclusion: Elevation was identified as an important environmental factor controlling soil organic matter in the studied scale. Generally, results confirm the same trend for soil organic matter evolution and soil organic carbon stocks with soil development, especially in pedogenesis processes in relation to organic matter. Thus, it can be recommended to use soil map for management of soil organic matter under various environmental conditions in large-scale studies.
Sheyda Kaboodi; farzin shahbazi; Nasser Aliasgharzad; nosratola najafi; naser davatgar
Abstract
Introduction: Understanding soil biology and ecology is increasingly important for renewing and sustainability of ecosystems. In all ecosystems, soil microbes play an important role in organic matter turnover, nutrient cycling and availability of nutrients for plants. Different scenarios of land use ...
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Introduction: Understanding soil biology and ecology is increasingly important for renewing and sustainability of ecosystems. In all ecosystems, soil microbes play an important role in organic matter turnover, nutrient cycling and availability of nutrients for plants. Different scenarios of land use may affect soil biological properties. Advanced information technologies in modern software tools such as spatial geostatistics and geographical information system (GIS) enable the integration of large and complex databases, models, tools and techniques, and are proposed to improve the process of soil quality and sustainability. Spatial distribution of chemical and biological properties under three scenarios of land use was assessed.
Materials and Methods: This study was carried out in Mirabad area located in the western part of Souldoz plain surrounded by Urmieh, Miandoab, Piranshahr and Naghadeh cities in the west Azerbaijan province with latitude and longitude of 36°59′N and 45°18′E, respectively. The altitude varies from 1310 to 1345 with average of 1325 m above sea level. The monthly average temperature ranges from -1.4 °C in January to 24.6 °C in July and monthly precipitation ranges from 0.9 mm in July to 106.6 mm in March. Apple orchard, crop production field and rich pasture are three selected scenarios in this research work. Soil samples were systematically collected at 65 sampling points (0-30 cm) on mid July 2010. Soil chemical and biological properties i.e. microbial community, organic carbon and calcium carbonate equivalent were determined. The ArcGIS Geostatistical Analyst tool was applied for assessing and mapping the spatial variability of measured properties. The experimental design was randomized complete blocks design (RCBD) with five replications. Two widely applied methods i.e. Kriging and Inverse Distance Weighed (IDW) were employed for interpolation. According to the ratio of nugget variance to sill of the best variogram model three following spatial dependence conditions for the soil properties can be considered: (I) if this ratio is less than 25%, then the variable has strong spatial dependence; (II) if the ratio is between 25% and 75%, the variable has moderate spatial dependence; and (III) otherwise, the variable has weak spatial dependence. Data were also integrated with GIS for creating digital soil biological maps after testing analysis and interpolating the mentioned properties.
Results and Discussion: Spherical model was the best isotropic model fitted to variograms of all examined properties. The value of statistics (R2 and reduced sum of squares (RSS)) revealed that IDW method estimated calcium carbonate equivalent more reliably while organic carbon and microbial community was estimated more accurately by Kriging method. The minimum effective range (6110 m) was found for microbial community which had the strong spatial dependence [(Co/Co+C)
