多时段泛克立格模型在降水量数据插补中的应用

多时段泛克立格模型是研究水文随机函数最优估计和空间预报的比较优越的方法。针对水文数据在时间上缺乏而空间上富足的特点,为了较好地解决小流域或局部区域内少站点情况下水文变量的最优估计问题,本文用多时段泛克立格模型对滦河内蒙古段闪电河流域各测站年降水量缺测资料进行了插补并在流域内进行了空间插值预报,结果表明插值预报精度较高,所得结论比较可信。     

Modeling spatial variability and pattern of rust and brown eye spot in coffee agroecosystem

The advent of Precision Agriculture has made possible the analysis of complex spatial patterns of plant disease epidemiology considering statements of integrated disease management. The objective of this work was to use geostatistics, statistics and geographical information systems to characterize the structure and magnitude of spatial dependency of rust (Hemileia vastatrix) and brown eye spot (Cercospora coffeicola) incidence and severity in coffee agroecosystem cultivated with Catuai Vermelho IAC-99 (Coffea arabica L.). Evaluations of incidence and severity of rust and brown eye spot were accomplished at 67 georeferenced points arranged in 6.6202 ha of coffee crop, in the years of 2005, 2006 and 2007. Exponential models of covariance enabled the characterization of the magnitude and structure of rust and brown eye spot spatial variability in the evaluated dates. Ordinary block kriging presented satisfactory performance to map rust and brown eye spot outbreaks based on kriging error coefficients. Kriged maps enabled the visualization of intensity of rust and brown eye spot in each evaluation date. Assessments of incidence and severity presented highly statistical correlation based on linear regression models, also confirmed by the spatial variability of kriging maps. Kriging maps of rust and brown eye spot enabled to observe that intensity of disease was dispersed in foci patterns along the coffee plantation, indicating that the current strategy of disease control based in total area may be replaced by site specific disease management, with less environmental impact and sustainability of coffee crop, according to statements of integrated disease management and precision agriculture.     

普通克里格评估土壤污染物空间分布精度控制研究

从数据的统计特征分析、异常值筛选及处理、实验半变异函数计算及拟合等方面对普通克里格计算过程中的精度控制问题进行了初步研究,并以天津市东南郊区土壤砷(As)空间分布评估为例,分析了在多步骤精度控制下普通克里格法估计土壤污染物空间分布的可靠性.结果表明,研究区土壤As表现出了明显的累积特征,且监测值为正态分布,异常值较少且为局部异常值,实验半变异函数表现出明显的各向异性.在较严格的精度控制下,普通克里格估值的最优无偏特性可以得到较好的体现,其对土壤As的空间分布的估计精度可以达到较高的水平.     

江西多年平均降水量空间插值模型的选取与比较

为了探究江西省多年平均降水量的空间分布格局,本文选取81个时间序列完整的气象站30 a(1976-2005年)降水数据,运用泛克里格中不同半变异模型对降水数据进行拟合.分别采用65个建模站点交叉验证和16个验证站点的检验,证实了该研究区域多年平均降水量存在较强的空间相关性.通过不同半变异函数模型的对比发现,球面和指数模型在建模站点交叉验证结果中的标准均方根预测误差分别为1.024和1.023,较为接近1,表明其误差较小;其在验证站点检验结果中的标准均方根预测误差分别为1.105和1.104,表明这两种模型的拟合效果较优,能较为真实地反映江西省多年平均降水量的空间分布情况,其中以指数模型拟合效果最优.     

普通克里格评估土壤污染物空间分布精度控制研究

从数据的统计特征分析、异常值筛选及处理、实验半变异函数计算及拟合等方面对普通克里格计算过程中的精度控制问题进行了初步研究,并以天津市东南郊区土壤砷（As）空间分布评估为例,分析了在多步骤精度控制下普通克里格法估计土壤污染物空间分布的可靠性。结果表明,研究区土壤As表现出了明显的累积特征,且监测值为正态分布,异常值较少且为局部异常值,实验半变异函数表现出明显的各向异性。在较严格的精度控制下,普通克里格估值的最优无偏特性可以得到较好的体现,其对土壤As的空间分布的估计精度可以达到较高的水平。     

典型潮土区土壤耕层盐分含量的趋势效应研究

通过分析典型潮土区山东省禹城市100个土壤采样点2003年耕层土壤的盐分含量,研究得出了潮土区土壤耕层盐分含量的空间分布图,在地统计学和地理信息系统的支持下,分析了区域尺度土壤耕层含盐量的趋势效应,探讨了研究区土壤盐分含量的空间变异性特征。在对比分析研究区2003年和1980年第二次土壤普查土壤耕层盐分含量的空间变异性基础上,基于研究区近50年的气温和降水资料,分析了研究区盐分含量变化的原因,并总结了引起研究区土壤盐碱化的人为原因。     

山区降水空间分布的插值分析

降水空间分布信息是进行区域水资源管理的基础,同时也是区域防洪减灾中所需的重要信息。采用地质统计学方法克里金方法,建立了区域降水插值模型。并以黄河故县以上集水区域为例,进行多年平均月降水插值实验。由于研究区域为地形起伏的山区,因此降水空间分析中又建立了融合地形信息的人工神经网络模型。此外还选取泰森多边形法、距离平方反比法、线性回归法,采用交叉验证法对各方法的插值精度进行对比分析。研究显示,克里金方法和融合地形信息的人工神经网络模型插值精度较高。。     

海伦市耕层土壤有机质含量空间预测方法研究

有机质含量是表征土壤肥力质量的重要属性,其空间分布模式对于施肥等耕作管理措施的推荐具有重要的指导意义。本文以我国黑土区黑龙江省海伦市为研究区域,在土壤采样点数量较有限的情况下,分别采用普通克里格、反距离权重、遥感反演和基于土壤学专业知识四种方法对耕层土壤有机质含量进行了空间预测。结果表明:四种方法表征的海伦耕地土壤有机质含量空间分布特征具有相似性,即由东北向西南方向递减。空间预测精度从高到低依次为反距离权重、普通克里格、基于土壤学专业知识和遥感反演法;而在有机质的局部变异细节表达方面,从高到低为遥感反演、基于土壤学专业知识、反距离权重和普通克里格法。四种方法中仅遥感反演法预测结果的极差范围较宽,普通克里格法则存在明显的平滑效应,而综合比较结果则表明,最合适的方法是基于土壤学专业知识的方法。     

Effect of sampling density on regional soil organic carbon estimation for cultivated soils

An extensive knowledge of how sampling density affects soil organic C (SOC) estimation at regional scale is imperative to reduce uncertainty to a meaningful confidence level and aid in the development of sampling schemes that are both rational and economical. Using kriging prediction, this paper examined the effect of sampling density on regional SOC‐concentration estimations in cultivated topsoils at six scales in a 990 km² area of Yucheng County, a typical region in the N China Plain. Except the original data set (n = 394), five other sampling densities were recalculated using grids of 8 km × 8 km (n = 28), 8 km × 4 km (n = 44), 4 km × 4 km (n = 82), 4 km × 2 km (n = 142), and 2 km × 2 km (n = 257), respectively. Experimental SOC semivariances and kriging interpolations at six sampling density scales were calculated and modeled to estimate regional SOC variability. Accuracy of the effects of the five sampling densities on regional SOC estimations was assessed using the indices of mean error (ME) and root mean square error (RMSE) with 100 independent validation samples. By comparison with the kriged grid map derived from the 394 samples data set, the relative error (RE,%) was spatially calculated to highlight the spatial variability of prediction errors at five sampling‐density scales due to the intrinsic limitations of ME and RMSE in accuracy assessment. The results indicated that sampling density significantly affected the estimation of regional SOC concentration. Particularly when the sampling density was < 4 km × 4 km, the large spatial variation of SOC was concealed. Semivariance analysis indicated that different sampling density had significant effect on reasonable detection of the dominant factors which influenced SOC spatial variation. Greater sampling density could more exactly reveal regional SOC variation caused by human management. The prediction accuracy for regional SOC estimation increased with the increasing of sampling density. The critical areas with larger RE values should be intensified in the future sampling scheme, and the areas of lower RE values should be decreased relatively. A specific sampling scheme should be considered in accordance with the demand to the estimation accuracy of regional SOC stock at a certain confidence level. Our results will facilitate a better understanding of the effect of sampling density on regional SOC estimation for future sampling schemes by providing meaningful confidence levels.     

杞麓湖盆地土壤有机质多时相空间分布与演变

基于四期土壤采样点数据,结合样点调查和统计年鉴数据,采用普通克里格、多元回归和探索性回归方法预测杞麓湖盆地土壤有机质（SOM）空间分布并分析其影响因素。结果表明指数和椭球模型分别较好地拟合了2008和2011年、2013和2015年的SOM的空间变异性。四个时相的SOM均由西南到中部再到东北部呈现出低-高-低的分布特征,并且高值核心区的位置不变,但高值区范围逐年扩大。前三个时间段内SOM连续增加的区域面积是连续减小的区域面积的两倍多。土壤亚类类型、农业设施和土壤质地各解释SOM变异的14.3%、2.6%和1.3%。SOM的降低主要由粮食和蔬菜的高产出所导致,适当地减少氮肥施用量并增加复合肥施用量能在一定程度上维持甚至提升SOM含量。