Remote sensing and GIS application in assessing the adaptability of some key crops in Dak Nong province

Remote sensing (RS) and Geographic information system (GIS) is widely applied in the world and gradually affirms its role in Vietnam in managing agricultural and forest resources. This application is highly effective, providing information timely for managers to make decisions and build development strategies. In this study, RS and GIS were integrated to assess suitability for key crop species in Dak Nong province including coffee, rubber, cashew, and durian based on their suitability to site conditions such as soil (soil type, soil texture, soil thickness), topography (elevation, slope) and climate (temperature, precipitation). Using the restrictive method and overlapping map layers of natural factors, classified into adaptive levels according to FAO (1976). Results show that most land areas in Dak Nong province have different levels of potential suitability for key crop species ranging from non-adaptive to lesst-adaptive and moderately adaptive. However, most suitable areas for key crops are only at low (accounting for a large proportion) and the average adaptation level. The findings from the study are the scientific information for managers to make decisions regarding the structure of major crops in the province.

The Influence of Mineralogy and Cation Exchange Capacity toward Electrical Resistivity Value

Electrical Resistivity Tomography (ERT) is a method used for subsurface profiling in soil to characterize soil thickness, fracture zones, soil saturation, salinity and groundwater based on the electrical resistivity value (ERV). There are multiple factors that influence the electrical resistivity value, such as the porosity, degree of saturation, mineralogy, density, cation exchange capacity (CEC), and water resistivity. For this study, the effect of CEC towards resistivity value is studied via controlling the mineralogy factor, saturation, porosity and water resistivity. Thus, via understanding the CEC factor able to relate the resistivity and mineralogy of soil. This study is using a few common minerals in soil and rock, such as kaolinite, montmorillonite, illite, quartz, mica, and feldspar. The particle sizes of all tested minerals were passing 0.063mm sieve. The basic index properties of minerals such as particle size distribution, specific gravity, and Atterberg limit were tested. The instruments of Terrameter LS2 and resistivity box were used to determine the resistivity value of minerals. The Atomic Absorption Spectroscopy (AAS) machine was used to analyze the CEC of minerals via dilute with the ammonium acetate solution. The porosity and degree of saturation of minerals mixed with distill water were controlled between the range of 0.5 to 0.6 and 20% to 100%. The CEC of each mineral has different value, where the lowest and the highest minerals CEC in this study were Kaolinite and Montmorillonite at 1 and 70, respectively. The electrical resistivity values decrease with the increasing of CEC value and degree of saturation. The mineral that has higher CEC indicates lower resistivity value. Meanwhile, via increasing the degree of saturation of minerals were decrease its resistivity values.

Soil and Vegetation Patterns Along a Toposequence in a Dolomite Peak-cluster Depression Catchment

AimA deeper understanding of relationships between soil and vegetation is a prerequisite for accelerating karst area vegetation restoration. Remarkable achievements have been made at regional and individual plant scales, but research on the relationship between soil and vegetation is insufficient at the hillslope catena scale in karst areas.MethodsSoils and vegetation were investigated along a toposequence (upper-, middle-, lower-slope, and depression) of a dolomite peak-cluster depression catchment.ResultsA continuous soil catena pattern was developed along the toposequence. From the top to bottom of soil catena, soil thickness, fine soil mass ratio, nutrient stocks, and epikarst thickness gradually increased, while gravel mass ratio, pH, and saturated hydraulic conductivity gradually decreased. However, nutrient contents showed no significant change trends along the soil catena. There was a strong spatial association between soil types and dominant vegetation communities. The associations were as follows: herbs associated with entisols in the upper-slope; herbs and shrubs with inceptisols in the middle-slope; shrubs with semi-alfisols in the lower-slope; and trees with alfisols in the depression. ConclusionsThe dolomite rocks displayed an evenly progressive karstification process. This led to an undeveloped underground karstic network incapable of transporting soil materials into underground. Soil materials still accumulated at different topographic positions surface and formed a continuous catena. Parameters for nutrient stock may be more suitable for assessing soil productivity and to guide vegetation restoration key factors in karst regions than nutrient content parameters.

Application of Deterministic Method for Landslide Susceptibility with Deep Learning

A method for estimating landslide susceptibility based on the analytic hierarchy process (AHP) was developed in 2017 as a deterministic method. The objective of this study is to verify the reliability of the proposed method by applying deep learning to improve the applicability of the method. The AHP-based deterministic method comprises eight factors: fines content, soil thickness, porosity, elastic modulus, shear strength, hydraulic conductivity, saturation, and water content. After dividing the testing area into 1 m square grids, eight factors were derived through field and laboratory experiments. The factor of safety was calculated based on the Mohr-Coulomb failure theory. Finally, the input and output values of deep learning were obtained. Bayesian regularization was applied among gradient descents to improve the learning efficiency when applying machine learning. The actual and predicted factors of safety were compared, and they showed excellent reliability in both the training and test phases. This study demonstrates that the AHP-based deterministic method with deep learning is valuable for determining landslide risk areas.

CONTACT PROBLEM FOR ORTHOTROPIC PLATES OF VARIABLE THICKNESS WITH A SPATIALLY INHOMOGENEOUS BASE

В работе приведено численное моделирование пространственной задачи контактного взаимодействия ортотропных плит переменной жесткости с упругими основаниями. Используемая методика расчета пригодна в случае любых известных контактных моделей упругих оснований. В качестве примера приведены численные результаты для пространственно-неоднородных оснований типа упругих слоев постоянной и переменной толщины. Система интегро-дифференциальных уравнений, к которой сводится задача, решается численно, сочетанием методов конечных разностей типа сквозного счета и граничных элементов. Найдены прогибы, изгибающие моменты и распределения контактных давлений прямоугольной плиты переменной жесткости, полностью примыкающей к основанию. Приводится анализ влияния на напряженно-деформированное состояние плиты, изменения ортотропных свойств ее материала и степень неравномерной сжимаемости толщи грунта. Разработанная методика позволяет эффективно моделировать работу плитных фундаментных конструкций, когда необходим учет неоднородности грунтов сжимаемой толщи в пределах габарита зданий или сооружений. The paper presents a numerical simulation of the spatial problem of contact interaction of orthotropic slabs of variable stiffness with elastic foundations. The calculation technique used is suitable for any known contact models of elastic foundations. As an example, numerical results are given for spatially inhomogeneous foundations such as elastic layers of constant and variable thickness. The system of integrodifferential equations, to which the problem is reduced, is solved numerically by a combination of finite difference methods such as end-to-end counting and boundary elements. Deflections, bending moments and contact pressure distributions of a rectangular slab of variable stiffness, completely adjacent to the base, are found. An analysis of the influence on the stress-strain state of the slab of changes in the orthotropic properties of its material and the degree of uneven compressibility of the soil thickness is given. The developed technique makes it possible to effectively simulate the operation of slab foundation structures when it is necessary to take into account the heterogeneity of the soil of the compressible strata within the dimensions of buildings or structures.

A first investigation of hydrogeology and hydrogeophysics of the Maqu catchment in the Yellow River source region

The Tibetan Plateau is the source of most of Asia's major rivers and has been called the Asian Water Tower. Detailed knowledge of its hydrogeology is paramount to enable the understanding of groundwater dynamics, which plays a vital role in headwater areas like the Tibetan Plateau. Nevertheless, due to its remoteness and the harsh environment, there is a lack of field survey data to investigate its hydrogeology. In this study, borehole core lithology analysis, soil thickness measurement, an altitude survey, hydrogeological surveys, and hydrogeophysical surveys (e.g. magnetic resonance sounding – MRS, electrical resistivity tomography – ERT, and transient electromagnetic – TEM) were conducted in the Maqu catchment within the Yellow River source region (YRSR). The hydrogeological surveys reveal that groundwater flows from the west to the east, recharging the Yellow River. The hydraulic conductivity ranges from 0.2 to 12.4 m d−1. The MRS sounding results, i.e. water content and hydraulic conductivity, confirmed the presence of an unconfined aquifer in the flat eastern area. Based on TEM results, the depth of the Yellow River deposits was derived at several places in the flat eastern area, ranging from 50 to 208 m. The soil thickness measurements were done in the western mountainous area of the catchment, where hydrogeophysical and hydrogeological surveys were difficult to be carried out. The results indicate that most soil thicknesses, except on the valley floor, are within 1.2 m in the western mountainous area of the catchment, and the soil thickness decreases as the slope increases. These survey data and results can contribute to integrated hydrological modelling and water cycle analysis to improve a full-picture understanding of the water cycle at the Maqu catchment in the YRSR. The raw dataset is freely available at https://doi.org/10.17026/dans-z6t-zpn7 (Li et al., 2020a), and the dataset containing the processed ERT, MRS, and TEM data is also available at the National Tibetan Plateau Data Center with the link https://doi.org/10.11888/Hydro.tpdc.271221 (Li et al., 2020b).

A hybrid data–model approach to map soil thickness in mountain hillslopes

Soil thickness plays a central role in the interactions between vegetation, soils, and topography, where it controls the retention and release of water, carbon, nitrogen, and metals. However, mapping soil thickness, here defined as the mobile regolith layer, at high spatial resolution remains challenging. Here, we develop a hybrid model that combines a process-based model and empirical relationships to estimate the spatial heterogeneity of soil thickness with fine spatial resolution (0.5 m). We apply this model to two aspects of hillslopes (southwest- and northeast-facing, respectively) in the East River watershed in Colorado. Two independent measurement methods – auger and cone penetrometer – are used to sample soil thickness at 78 locations to calibrate the local value of unconstrained parameters within the hybrid model. Sensitivity analysis using the hybrid model reveals that the diffusion coefficient used in hillslope diffusion modeling has the largest sensitivity among all input parameters. In addition, our results from both sampling and modeling show that, in general, the northeast-facing hillslope has a deeper soil layer than the southwest-facing hillslope. By comparing the soil thickness estimated between a machine-learning approach and this hybrid model, the hybrid model provides higher accuracy and requires less sampling data. Modeling results further reveal that the southwest-facing hillslope has a slightly faster surface soil erosion rate and soil production rate than the northeast-facing hillslope, which suggests that the relatively less dense vegetation cover and drier surface soils on the southwest-facing slopes influence soil properties. With seven parameters in total for calibration, this hybrid model can provide a realistic soil thickness map with a relatively small amount of sampling dataset comparing to machine-learning approach. Integrating process-based modeling and statistical analysis not only provides a thorough understanding of the fundamental mechanisms for soil thickness prediction but also integrates the strengths of both statistical approaches and process-based modeling approaches.