免疫算法在土壤资源质量评价中的应用

Based on the geographic information system （GIS） technology, ArcInfo software was adopted to collect, process and analyze spatial data of Guangdong Province for an evaluation of soil resource quality. The overlay analysis method was used in combining evaluation factors of Guangdong soil resource quality to determine the evaluation units. Because of its favorable convergent speed and its ability to search solutions, the immune algorithm was applied to the soil resource quality evaluation model. At the same time, the evaluation results of this newly proposed method were compared to two other methods： sum of index and fuzzy synthetic. The results indicated that the immune algorithm reflected the actual condition of soil resource quality more exactly.     

结合多等级代表性采样和模糊隶属度的区域土壤制图方法

High-resolution and detailed regional soil spatial distribution information is increasingly needed for ecological modeling and land resource management. For areas with no point data, regional soil mapping includes two steps: soil sampling and soil mapping. Because sampling over a large area is costly, efficient sampling strategies are required. A multi-grade representative sampling strategy, which designs a small number of representative samples with different representative grades to depict soil spatial variations at different scales,could be a potentially efficient sampling strategy for regional soil mapping. Additionally, a suitable soil mapping approach is needed to map regional soil variations based on a small number of samples. In this study, the multi-grade representative sampling strategy was applied and a fuzzy membership-weighted soil mapping approach was developed to map soil sand percentage and soil organic carbon(SOC) at 0–20 and 20–40 cm depths in a study area of 5 900 km2 in Anhui Province of China. First, geographical sub-areas were delineated using a parent lithology data layer. Next, fuzzy c-means clustering was applied to two climate and four terrain variables in each stratum. The clustering results(environmental cluster chains) were used to locate representative samples. Evaluations based on an independent validation sample set showed that the addition of samples with lower representativeness generally led to a decrease of root mean square error(RMSE). The declining rates of RMSE with the addition of samples slowed down for 20–40 cm depth, but fluctuated for 0–20 cm depth. The predicted SOC maps based on the representative samples exhibited higher accuracy, especially for soil depth 20–40 cm, as compared to those based on legacy soil data. Multi-grade representative sampling could be an effective sampling strategy at a regional scale. This sampling strategy, combined with the fuzzy membership-based mapping approach, could be an optional effective framework for regional soil property mapping. A more detailed and accurate soil parent material map and the addition of environmental variables representing human activities would improve mapping accuracy.     

黄土高原沟壑区土壤侵蚀评价指标体系——以中国王东沟流域为例

The Wangdonggou Watershed on the Loess Plateau in China was selected as the study area to develop a model for soil erosion assessments. Using the data collected at 20 sampling sites all tentatively selected indicators were assessed against their corresponding erosion intensity through a correlation analysis. Eight highly correlated indicators were then chosen for the soil erosion assessment. In addition, threshold limits to delineate the class size for these indicators and weights to rank them were determined. Next, a grading model incorporating the selected indicators class rating and their associated weights was developed and verified by an on site evaluation of the soil erosion intensity in the study area. Results of the verification showed that the overall accuracy of the indicator system for assessing soil erosion in the Loess Plateau gully regions could reach 85%.     

天然载体控释氮肥研究初探

The Wangdonggou Watershed on the Loess Plateau in China was selected as the study area to develop a model for soil erosion assessments. Using the data collected at 20 sampling sites all tentatively selected indicators were assessed against their corresponding erosion intensity through a correlation analysis. Eight highly correlated indicators were then chosen for the soil erosion assessment. In addition, threshold limits to delineate the class size for these indicators and weights to rank them were determined. Next, a grading model incorporating the selected indicators class rating and their associated weights was developed and verified by an on site evaluation of the soil erosion intensity in the study area. Results of the verification showed that the overall accuracy of the indicator system for assessing soil erosion in the Loess Plateau gully regions could reach 85%.     

利用地统计学研究土壤有机质空间分布：评估意大利中部地区土地退化的重要指标

Soil organic matter (SOM) content is one of the main factors to be considered in the evaluation of soil health and fertility. As timing, human and monetary resources often limit the amount of available data, geostatistical techniques provide a valid scientific approach to cope with spatial variability, to interpolate existing data and to predict values at unsampled locations for accurate SOM status survey. Using geostatistical and geographic information system (GIS) approaches, the spatial variability of some physical and chemical soil parameters was investigated under Mediterranean climatic condition in the Abruzzo region of central Italy, where soil erosion processes accelerated by human induced factors are the main causes of soil degradation associated with low SOM content. Experimental semivariograms were established to determine the spatial dependence of the soil variables under investigation. The results of 250 soil sampling point data were interpolated by means of ordinary kriging coupled with a GIS to produce contour maps distribution of soil texture, SOM content related to texture, and C/N ratio. The resulting spatial interpolation of the dataset highlighted a low content of SOM in relation with soil texture in most of the surveyed area (87%) and an optimal C/N ratio for only half of the investigated surface area. Spatial location of degraded area and the assessment of its magnitude can provide decision makers with an accurate support to design appropriate soil conservation strategies and then facilitate a regional planning of agri-environmental measures in the framework of the European Common Agricultural Policy.     

辣椒丛枝菌根真菌吸磷机制研究

A pot experiment was conducted to investigate the action mechanisms of arbuscular mycorrhizal (AM) fungi in phosphorus (P) uptake of Capsicum annuum L.in a sterilized fossil Oxisol.Three P levels of 0,10 and 200 mg kg-1 soil (P0,P10 and P200,respectively) without and with AM fungal inoculation were applied as Ca(H2PO4)2·H2O.Shoot dry matter yields and shoot P uptake increased significantly (P > 0.05) by the inoculation of AM fungi at P0 and P10.Root length and P concentration in soil solution increased with the inoculation of AM fungi but the root:shoot ratio decreased or remained constant.Around 50% roots of inoculated plants were infected by AM and the external hyphae amounted to 20 m g-1 soil at P10 and P200.The hyphae surface area of the infected root cylinder amounted to 11 and 2 cm-2 cm-2 root at P0 and P10,respectively.The increased P uptake of inoculated plants was mainly because of an up to 5 times higher P influx of the infected root.Model calculations showed that the root alone could not have achieved the measured P influx in both infected and non-infected roots.But the P influx for hyphae calculated by the model was even much higher than the measured one.The P uptake capacity of hyphae introduced in the model was too high.Model calculations further showed that the depletion zone around roots or hyphae was very narrow.In the case of the root only 7% of the soil volume would contribute P to the plant,while in the case of hyphae it would be 100%.The results together with the model calculations showed that the increased P uptake of AM inoculated plants could be explained partly by the increased P concentration in the soil solution and by the increased P absorbing surface area coming from the external hyphae.     

基于模糊逻辑的北京城市边缘区土壤重金属污染空间预测

Fuzzy classification combined with spatial prediction was used to assess the state of soil pollution in the peri-urban Beijing area. Total concentrations of As, Cr, Cd, Hg, and Pb were determined in 220 topsoil samples （0-20 cm） collected using a grid design in a study area of 2 600 kin2. Heavy metal concentrations were grouped into three classes according to the optimum number of classes and fuzziness exponent using the fuzzy comean （FCM） algorithm. Membership values were interpolated using ordinary kriging. The polluted soils of the study area induced by the measured heavy metals were concentrated in the northwest corner and eastern part, especially the southeastern part close to the urban zone, whereas the soils free of pollution were mainly distributed in the southwestern part. The soils with potential risk of heavy metal pollution were located in isolated spots mainly in the northern part and southeastern corner of the study region. The FCM algorithm combined with geostatistical techniques, as compared to conventional single geostatistical kriging methods, could produce a prediction with a quantitative uncertainty evaluation and higher reliability. Successful prediction of soil pollution achieved with FCM algorithm in this study indicated that fuzzy set theory had great potential for use in other areas of soil science.     

地膜覆盖滴灌棉田土壤上水分动态的数值模拟

Drip irrigation under plastic mulch has been widely applied in arid Northwest China as a water-saving irrigation technology.A comprehensive knowledge of the distribution and movement of soil water in root zone is essential for the design and management of irrigation regimes.Simulation models have been proved to be efcient methods for this purpose.In this study,the numerical model Hydrus -2D was used to simulate the temporal variations of soil water content in a drip irrigated cotton field under mulching.A concept of partitioning coefcient,calibrated to be 0.07,was introduced to describe the efect of plastic mulch on prevention of evaporation.The soil hydraulic parameters were optimized by inverse solution using the field data.At the optimized conditions,the model was used to predict soil water content for four field treatments.The agreements between the predictions and observations were evaluated using coefcient of determination (R2) and root mean square error (RMSE).The results suggested that the model fairly reproduced the variations in soil water content at all locations in four treatments,with R2 ranging from 0.582 to 0.826 and RMSE from 0.029 to 0.050 cm3 cm-3,indicating that the simulations agreed well with the observations.     

伊朗西北部半干旱农田径流模型的建立

Transformation of rainfall into runoff over an area is a very complex process which exhibits both temporal and spatial variability;runoff in a defined area can be affected by factors such as topography, vegetation, rainfall characteristics and soil properties. This study was conducted to develop an empirical model using the rainfall characteristics and soil properties for predicting runoff from dry-farming lands in a semi-arid agricultural area in Hashtroud, Northwest Iran. Runoff plots(1.83 m × 22.1 m) in triplicate were installed in thirty-six sloped dry-farming lands in the study area. Runoff under natural rainfalls was measured in each plot during a2-year period. The results showed that runoff for 41 runoff-producing rainstorm events with duration longer than 30 min was largely associated with a rainfall index obtained by multiplying the positive square root of rainfall depth(h0.5) by the logarithm of the maximum 30-minute intensity(LogI30)(R2= 0.81). Runoff significantly varied among the plots(P 0.001), which was considerably related to the effective soil properties(R2= 0.74), i.e., soil permeability(Per) and aggregate stability(AS). A multiple linear regression model was developed between runoff and the rainfall index(h0.5logI30) and the effective soil properties(AS and Per). Evaluation of the model using 34 runoff-producing rainstorm events that occurred during the next two years resulted in high values of the efficiency coefficient and R2(0.88 and 0.91, respectively), which revealed that the model developed in this study could be used in predicting runoff from the dry-farming lands in the semi-arid regions.     

基于模糊神经网络集成的土壤资源评价性能的改进

Land evaluation factors often contain continuous-, discrete- and nominal-valued attributes. In traditional land evaluation, these different attributes are usually graded into categorical indexes by land resource experts, and the evaluation results rely heavily on experts＇ experiences. In order to overcome the shortcoming, we presented a fuzzy neural network ensemble method that did not require grading the evaluation factors into categorical indexes and could evaluate land resources by using the three kinds of attribute values directly. A fuzzy back propagation neural network （BPNN）, a fuzzy radial basis function neural network （RBFNN）, a fuzzy BPNN ensemble, and a fuzzy RBFNN ensemble were used to evaluate the land resources in Guangdong Province. The evaluation results by using the fuzzy BPNN ensemble and the fuzzy RBFNN ensemble were much better than those by using the single fuzzy BPNN and the single fuzzy RBFNN, and the error rate of the single fuzzy RBFNN or fuzzy RBFNN ensemble was lower than that of the single fuzzy BPNN or fuzzy BPNN ensemble, respectively. By using the fuzzy neural network ensembles, the validity of land resource evaluation was improved and reliance on land evaluators＇ experiences was considerably reduced.     

基于县域耕地资源管理信息系统的土壤综合肥力评价

应用ArcGIS 9.3,MapGIS 6.7和县域耕地资源管理信息系统,综合运用层次分析法、模糊数学法,将临泽县土壤图、土地利用现状图、行政区划图叠加求交生成评价单元图,进一步采用层次分析法确定土壤肥力评价因子的权重,对评价指标建立相应的隶属函数,通过计算其隶属度,求得每个评价单元的土壤肥力综合指标值,并依据综合值确定了土壤肥力等级及其面积。结果表明:临泽县土壤综合肥力较高的区域占临泽县耕地总面积的46.51%;土壤综合肥力中等区域占18.54%;土壤综合肥力一般区域占14.17%;土壤综合肥力较差区域占20.68%。利用县域耕地资源管理信息系统与GIS可以快速科学地对土壤肥力进行科学评价,与传统的评价方法相比,可以节约大量的人力、财力和物力。     

大西沟铁矿生态环境模糊综合评价研究

着眼于大西沟矿区的实际情况,提出一套完善的、规范化且能反映矿山生态特性的矿山生态环境质量综合定量的评价模式,实现矿山生态环境质量定量化,克服以前定性评价的模糊性和概念化,基于自然、环境和人文三方面建立一套具有代表性、可操作性的指标体系。对影响露天矿生态环境质量的因素进行分析,选取地貌、气候、植被、大气、土壤、地下水、人口、采矿8个要素及相关20个指标,构建了露天矿生态环境质量评价体系。运用层次分析法(AHP)和模糊数学综合评判法对露天矿生态环境质量进行综合评价。研究结果表明:大西沟铁矿矿山生态环境质量较好,固体废置物堆置率对矿区生态环境质量影响大,与实地调查相符,该方法对矿区生态环境质量评价是有效的,对今后矿区生态环境质量的快速评价具有现实意义。     

西北地区水库黄土库岸稳定分析

应用土地资源评价理论、GIS和GPS技术,选取地处南方红壤侵蚀区燕沟小流域内某一典型区域作为研究对象,结合模糊数学方法和指数和法对研究区域内的土地资源进行评价。结果显示,研究区55.1%的土地属5等地,土地质量较差,土壤侵蚀是制约该区域的土地资源利用的瓶颈因素。提出采取工程措施与植物措施相结合的方式,尽快恢复与培育土壤肥力是该区域土地合理利用的重要措施与方向。同时,研究表明,采用GIS,GPS技术,结合模糊数学方法进行小流域大比例尺土地资源评价的方法是科学可行的,在此类土地资源评价中具有推广应用前景。     

Integration of ANP and Fuzzy set techniques for land suitability assessment based on remote sensing and GIS for irrigated maize cultivation

Land suitability assessment can inform decisions on land uses suitable for maximizing crop yield while making best use, but not impairing the ability of natural resources such as soil to support growth. We assessed the suitability of maize to be produce in 12,000 ha land of Dasht-e-Moghan region of Ardabil province, northwest of Iran. Suitability criteria included soil depth, gypsum (%), CaCO₃ (%), pH, electrical conductivity (EC), exchangeable sodium percentage (ESP), slope (%) and climate data. We modified and developed a novel set of techniques to assess suitability: fuzzy set theory, analytic network process (ANP), remote sensing and GIS. A map of suitability was compared a map created using a traditional suitability technique, the square root method. The coefficient of determination between the land suitability index and observed maize yield for square root and ANP-fuzzy methods was 0.747 and 0.919, respectively. Owing to greater flexibility to represent different data sources and derive weightings for meaningful land suitability classes, the ANP-fuzzy method was a superior method to represent land suitability classes than the square root method.     

基于正态云模型的湖北省土地资源生态安全评价

针对综合指数法与模糊综合法无法同时兼顾评价指标模糊性与随机性的问题,该研究将正态云模型引入区域土地资源生态安全评价。基于正态云模型,以湖北省为例,对区域土地资源生态安全状况进行了定量测度。研究结果表明：2000-2010年间,湖北省土地资源生态安全状态从敏感级上升到良好级,土地资源生态安全综合值从2.9397上升为3.6033,区域土地资源生态安全整体水平有变好的趋势,但目前仍有部分指标处于恶劣、危险或敏感级,其单因子指标值<2.5,有待于进一步提高与改善;正态云模型使土地资源生态安全的定量评价兼顾随机性和模糊性,该研究可以为区域土地资源可持续发展提供一定借鉴与参考。     

基于变量优选与机器学习的干旱区湿地土壤盐渍化数字制图

土壤盐渍化是导致土壤退化和生态系统恶化的主要原因之一，对干旱区的可持续发展构成主要威胁。为了尽可能精确地监测土壤盐渍化的空间变异性，该研究收集新疆艾比湖湿地78个典型样点，其中选取54个样本作为训练集，24个样本作为独立验证集。基于Sientinel-2 多光谱传感器（Multi-Spectral Instrument，MSI）、数字高程模型（Digital Elevation Model，DEM）数据提取3类指数（红边光谱指数、植被指数和地形指数），经过极端梯度提升（Extreme Gradient Boosting，XGBoost）算法筛选有效特征变量，构建了关于土壤电导率（Electrical Conductivity，EC）的随机森林（Random Forest，RF）、极限学习机（Extra Learning Machine，ELM）和偏最小二乘回归（Partial Least Squares Regression，PLSR）预测模型，并选择最优模型绘制了艾比湖湿地盐渍化分布图。结果表明：优选的红边光谱指数基本能够预测EC的空间变化；红边光谱指数与植被指数组合建模效果总体上优于其与地形指数的组合，3类指数组合的建模取得了较为理想的预测精度，其中RF模型表现最优（验证集R2=0.83，RMSE=4.81 dS/m，RPD=3.11）；在整个研究区内，中部和东部地区土壤盐渍化程度尤为严重。因此，XGBoost所筛选出的环境因子结合机器学习算法可以实现干旱区土壤盐渍化的监测。     

基于熵值赋权的黑龙港地区水土资源综合质量评价

区域水土资源综合质量评价是开展区域农业种植结构调整、资源优化配置和合理开发利用的基础。为促进黑龙港地区水土资源与区域经济协调发展,基于熵值赋权法,利用ArcGIS 10平台,将区域水资源与土地资源作为统一的系统,开展了单位像元尺度的水土资源综合质量评价。评价结果显示,黑龙港地区水土资源综合质量较低,多数地区综合质量值处于0.4～0.6之间,0.6以上相对优良区面积仅占总面积的2.5%,黑龙港地区总体趋势呈现中间及南部区域水土资源综合质量值较高,而东西两侧的边缘区域质量较低。研究结果还表明,在土壤资源质量相对差的区域,如果水资源质量较好,则土壤资源将被改良,区域水土资源综合质量也得以提升。     

热带地区农场尺度土壤质量现状的系统评价

以我国热带地区海南儋州的农场为典型样区 ,按网格方法系统调查和采集了不同利用条件下的土壤剖面和表层样品。提出了一个系统的评价方法 ,将土壤质量划分为水分有效性、养分有效性和根系适应性这三个功能 ,确定了十项符合热带地区特点的土壤质量指标 ,建立各项功能和各项指标的权重。综合参考国内外研究结果 ,结合热带地区实际状况 ,建立标准评分方程 (SSF)对各项指标进行标准化。研究根据海南儋州样区土壤质量分析数据 ,在GIS支持下 ,采用系统评价模型对样区的土壤质量进行综合评价 ,并对农场尺度土壤质量的变异进行了分析。结果表明 ,系统评价模型可以在对土壤功能进行评价的基础上综合评价土壤质量 ,在GIS支持下可以方便有效的直观反映土壤质量的状况。研究区域土壤具有较好的水分供应性能 ,能够提供根系生长的适宜环境 ,存在的主要问题在于养分供应和保持能力较弱 ,但是在合理的管理措施下 ,土壤养分能够较快增加 ,土壤质量可以改善     

基于GIS的黄河三角洲土壤肥力质量自动化评价

土壤盐碱加重、质量退化是黄河三角洲土地开发利用中面临的重要问题,定量客观评价土壤质量对该区土地资源合理利用与保护有重要意义。本文运用相关分析法筛选土壤质量评价指标,确定其权重,通过G IS叠加分析划分评价单元,以隶属度模型和指数和法计算土壤质量综合指标值,进而评价土壤质量。结果显示:黄河三角洲土壤总体质量较差,适宜种植业的Ⅰ、Ⅱ、Ⅲ等地仅占土地总面积的18.62%,质量最差的Ⅷ等地(光板地)占19.56%,质量中等Ⅳ、Ⅴ、Ⅵ等地占总面积56.78%;评价结果与实际作物产量高度相关。在此基础上,研究提出了该区农业生产和土地资源合理配置的措施建议。     

玉米作物系数无人机遥感协同地面水分监测估算方法研究

该文研究不同水分胁迫条件下无人机遥感与地面传感器协同估算玉米作物系数的可行性。利用自主研发的六旋翼无人机遥感平台搭载多光谱传感器获取内蒙古达拉特旗昭君镇试验站不同水分胁迫下大田玉米冠层光谱影像,计算植被指数,采用经气象因子和作物覆盖度校正后的FAO-56双作物系数法计算玉米的作物系数,研究作物系数与简单比值植被指数(simple ratio index,SR)、叶面积指数(leaf area index,LAI)和表层土壤含水率(surface soil moisture,SM)的相关关系,结果表明,作物系数与SR、LAI和SM的相关程度与水分胁迫程度有关,但均呈现出显著或极显著的线性关系,说明了基于这些指标建立作物系数估算模型的可能性。利用逐步回归分析方法建立了作物系数的估算模型,其估算模型,修正的决定系数、均方根误差和归一化的均方根误差分别为0.63、0.21、25.16%。经验证,模型决定系数、均方根误差和归一化的均方根误差分别为0.60、0.21、23.35%。研究结果可为利用无人机多光谱遥感平台进行作物系数估算提供技术参考。