冬小麦种植面积空间抽样单元尺寸优化设计

抽样单元尺寸是空间抽样调查方案设计过程中的关键要素。合理的抽样单元尺寸对降低抽样调查费用、改善抽样外推总体精度具有重要意义。为实现农作物种植面积空间抽样调查中抽样单元尺寸的优化设计,论文以河南省为研究区,以冬小麦种植面积为研究对象,选取正方形网格作为抽样单元,遵循传统抽样理论中抽样单元间相互独立原则,通过分析不同抽样单元尺寸与对应该尺寸下的抽样框内总体单元间全局空间自相关指数(Moran’s I)的相关关系,进行了抽样单元尺寸的初选。为最终实现抽样单元尺寸优选,基于初选的8种抽样单元尺寸,分别构建抽样框,采用以抽样单元内冬小麦面积占单元面积比例(麦土比)为分层标志的分层抽样方法进行了研究区冬小麦种植面积空间抽样样本抽选、总体外推及误差估计试验研究,结果表明:抽样比随抽样单元尺寸的增大而增大;8种抽样单元尺寸下的外推总体相对误差和变异系数均较小,变化范围分别为3.82%～5.75%和3.76%～4.69%;以抽样调查费用和抽样误差为抽样效率评价指标,优选出抽样单元尺寸为20 000 m×20 000 m时,进行研究区冬小麦种植面积抽样调查的效率最高。

Effect of Sample Unit Size on Visually Examining Pavement Condition for Asphalt-Surfaced Roads

Road surface condition evaluation involves the collection of data over pavement surface for different types of distresses. The exercise consumes a lot of resources if the whole road section length is surveyed and may be prone to errors as a result of surveyors＇ fatigue. It is therefore important to develop a representative sample to be used when evaluating road condition manually. This study aimed at determining an adequate sample size for section level as well as a way forward for network level condition evaluation of highways in Nepal. Again the study was conducted to quantify the effects of altering the sample unit size for performing a distress survey according to the PCI （pavement condition index） and SDI （surface distress index） method separately for asphalt surfaced roads. The effect of reducing/increasing sample unit size was investigated adopting visual examination through field survey by eight teams in July, 2015, along the section of Banepa-Bardibas highway. The PCI was then calculated for each sample unit using standard deduct curves and PCI calculation methodology as per SHRP （Strategic Highway Research Program） recommendations and the computation of SDI was done as per DoR （Department of Roads） guidelines. The results show that 13% sample unit are needed for SDI and 21% for PCI computation, however, the results are out of the significant level. This is higher than DoR and SHRP guidelines. Again no strong relationship is observed between SDI and PCI values.

基于空间自相关的县域尺度土壤侵蚀抽样方法研究

【目的】探讨不同空间抽样方法和插值方法对土壤侵蚀预测精度的影响,为县域土壤侵蚀快速评价提供技术支撑。【方法】以沂蒙山区蒙阴县为研究区,GF⁃1号和GF⁃6号卫星影像、1∶1万地形图等数据为基础数据源,基于空间自相关理论,采用不同的空间抽样方法和空间插值方法,对研究区土壤侵蚀状况进行快速抽样预测研究。【结果】①根据全局Moran'sI指数、Zscore和平均图斑类型,选取400 m×400 m方格为抽样单元最优尺寸。②与空间随机抽样、空间系统抽样方法相比,空间分层抽样样本容量最少(150个,其他样本容量最均超过250个),抽样精度最高(96.69%),抽样效率最高。③空间分层抽样下,普通克里金法和协同克里金法的计算结果与栅格计算法差异较小,相对差异分别为8.25%和9.85%(另两种方法的相对误差大于25%),但在空间分布上,受坡度影响的协同克里金法土壤侵蚀强度分布更为详细。【结论】综合考虑抽样调查的精度和工作量,在沂蒙山区县域尺度开展土壤侵蚀调查工作时,可采用空间分层抽样和协同克里金法或普通克里金法的方法进行土壤侵蚀快速预测,从而为区域生态环境保护提供重要数据支撑。