基于遥感的福建闽侯丘陵区农作物种植面积空间抽样方法

以福建闽侯县作为研究区,采用传统抽样(简单随机抽样、系统抽样、分层抽样)、空间抽样(空间随机抽样、空间系统抽样、空间分层抽样)等方法对研究区农作物种植面积进行样本抽选、总体推算及误差估计,结果表明:1 500 m×1 500 m格网为最优抽样单元尺寸,空间分层抽样方法的相对误差为3.86%,变异系数为6.03%,抽样成本为6.03,抽样效率高.与传统抽样方法相比,空间抽样方法显著减少样本容量,节约调查成本.     

基于总体表面属性特征的森林资源抽样调查方法比较

以南京紫金山国家森林公园的6个景区为抽样总体,以GIS为分析平台,以2002年数字化的森林资源二类调查数据为主要信息源,在分析各个总体相关性、异质性、聚集性3个表面属性特征基础上,分别采用简单随机抽样、系统抽样、空间简单随机抽样、空间分层抽样、空间平衡抽样方法对单位面积蓄积量进行了抽样估计,并从抽样平均误差、抽样效率2...     

福建省闽侯县区域农作物种植面积的空间抽样方案

基于国产GF-1号卫星影像农作物空间分布数据,以正方形网格作为抽样单元形状,综合应用遥感技术、空间抽样方法、模拟退火算法及空间自相关理论,对福建省闽侯县农作物种植面积进行估算.结果表明:根据全局自相关指数Moran's I及其显著性指标Z Score,选取1 500 m×1 500 m作为最优单元尺寸;对研究区进行剖分,构建抽样框,对比空间随机抽样、空间系统抽样及空间分层抽样的抽样结果,空间分层抽样方法的样本容量为37,相对误差为3.86%,抽样效率最高;运用模拟退火优化算法规划野外调查的最优路径,确定空间抽样调查方案.     

冬小麦种植面积空间抽样样本布局的优化设计

【目的】样本布局是空间抽样调查方案设计中的关键要素。优化设计样本布局对于提高抽样样本对总体的代表性、降低抽样调查成本、改善抽样外推总体精度具有重要意义。论文针对现有农作物种植面积空间抽样调查技术体系中存在样本布局设计合理性不足的问题（如采用简单随机抽样进行样本布局设计时,无法保证各样本单元间相互独立、彼此间不存在空间相关性;而以往系统等距布样方式又存在样本间隔的制定缺乏科学依据）,进一步提高现行农作物种植面积空间抽样调查效率。【方法】选取安徽省蒙城县和冬小麦种植面积2009年和2010年的冬小麦空间分布数据（分别源自ALOS AVNIR-2 和Landsat5 TM影像提取结果）,通过地统计学理论与“3S”技术（遥感、地理信息系统和全球定位技术）及传统抽样方法相结合,首先,设计8种抽样单元尺寸水平,利用不同种尺寸水平的抽样单元离散抽样区、构建抽样框;其次,选取简单随机抽样方法初选样本单元,利用初选样本构建抽样单元内冬小麦种植面积比例的变异函数理论模型,基于该模型分析抽样单元间空间关联性和异质性,定量确定抽样单元空间关联阈值;然后,遵循传统抽样理论要求样本间相互独立原则,以抽样单元空间关联阈值为抽样间隔,采用空间系统等距的布局方式对冬小麦种植面积空间抽样样本布局进行了优化设计;最后,以简单随机抽样方法为对照,选取抽样外推总体相对误差、总体总值估计量的变异系数（CV）及样本容量为评价指标,对布局优化设计后的抽样样本外推总体精度、稳定性（通过变异系数反映）及抽样成本（通过样本容量反映）进行定量评价试验研究。【结果】抽样单元内冬小麦种植面积比例的变异性随单元尺度的增大而增大,8种单元尺度下的抽样单元内冬小麦种植面积比例的变异系数变化范围为32.75%—43.46%,属中等变异;抽样单元内冬小麦种植面积比例在一定范围内存在强烈的空间相关性,该空间相关性主要由结构性因素（如气候、地形、土壤类型等自然因素）决定。抽样单元内冬小麦种植面积比例的空间关联阈值随抽样单元尺寸的增大而增大;当抽样单元尺寸较小时（500 m×500 m—2 000 m×2 000 m）,在样本容量相同条件下,经布局优化设计后的抽样样本外推总体的相对误差和变异系数明显低于简单随机抽样;当抽样单元尺寸较大时（2 500 m×2 500 m—4 000 m×4 000 m）,布局优化设计后的抽样样本外推总体的相对误差和变异系数虽未明显降低,但样本容量却显著减小。【结论】该文可为改善农作物种植面积空间抽样调查效率提供试验依据。为研究区和研究对象,以正方形网格作为抽样基础单元,基于蒙城县     

梨瘿蚊幼虫的空间分布型及序贯抽样技术

在对梨园梨瘿蚊幼虫调查分析的基础上,应用聚集度指标I、wao方程和Taylor幂法则模型测定方法分析了梨瘿蚊幼虫在梨树上的空间分布格局,并研究了梨瘿蚊幼虫在梨树上的序贯抽样技术。结果表明：梨瘿蚊幼虫在梨树内空间分布属聚集分布,个体间相互吸引,分布的基本成分是个体群。平均拥挤度m＊与平均密度m的回归方程为m＊=1.953 68＋1.432 29m（R=0.697 8＊＊）,相关性极显著。以Iwao的序贯抽样为基础,结合Kuno的序贯抽样提出复序贯抽样技术,防治指标上限T0＇（n）=3n＋1.96 12.75n,下限T0＂（n）=3n-1.96 12.75n,截止线T（n）=1.953 68＋1d02-0.432 29/n（t=1.96,d=0.2）,最大样本数为n=46。     

灰飞虱种群空间分布型及抽样技术研究

根据济宁市稻田及稻茬麦田的灰飞虱调查资料,通过David＆Moore聚集指标I、Cassie扩散系数C、Kuno指标CA、平均拥挤度M＊等指标判断,该地区稻田及稻茬麦田的灰飞虱为聚集分布;进一步采用随机（Poisson）分布、奈曼（Neyman）分布和负二项（Mosaic）分布的理论频次分布公式,计算出其理论频次分布并经χ2检验,确定灰飞虱的空间分布型为负二项分布;Blackith聚集均数结果表明,灰飞虱的聚集分布主要是由其聚集习性和环境因素共同作用引起的。同时明确了灰飞虱的抽样方式以＂Z＂字形取样最好,确定了稻田和稻茬麦田序贯抽样的最大抽样量,即稻田每样点实际有虫10头时需调查6个样点,11～12头时需调查9个样点;稻茬麦田每样点实际有虫3头时需调查33～42个样点,4头时需调查47个样点。     

东北漫岗黑土区土壤表层有机质Kriging插值精度及坡度影响研究

东北农垦地区是我国农业现代化程度较高的地区,为研究该区土壤表层有机质空间插值精度及其影响因素,以农垦九三鹤山农场坡耕地为研究对象,按照30 m×30 m的网格采集表层土样,测定土壤有机质含量。采用Arcgis9.3进行空间Kriging插值。结果表明:(1)土壤有机质的空间插值随着参入插值样点距离的增加,其空间分布误差逐渐增大;(2)60 m×60 m、90 m×90 m、120 m×120 m三种样点分布的空间插值,平均相对误差分别为5.91%,9.35%,12.04%;(3)该区坡度低于3.5°时,样点分布可为120 m×120 m,坡度处于3.5~4.5°,样点分布可为90 m×90 m,坡度大于4.5°,采样密度应低于60 m×60 m,并需要进行验证。     

红松母树林下刺五加资源抽样调查方法

针对红松母树林下刺五加资源,采用3种不同抽样方法——随机抽样、系统抽样和典型抽样,从数量估计、分布规律、地径结构和树高结构,以及地形因子对地径结构和树高结构的影响等几个方面进行调查分析。结果表明:不同抽样方法对刺五加数量有显著的影响,随机抽样精度最高;3种方法从样地尺度和单株尺度上证实刺五加是聚集分布;地径结构和树高结构规律相同,3种抽样方法均遵从韦布分布,虽然就坡位对地径和树高的影响,3种抽样方法并不一致,但地径和树高大小总体趋势仍为上部中部下部。     

农业用地土壤重金属样本点数据精化方法——以北京市顺义区为例

样本点空间分布是样点数据检测评价和挖掘分析的关键因素。以北京市顺义区为例，研发了一种农业用地土壤重金属样本点数据精化方法：首先构建样本点均匀变异指数和均匀因子离散图来共同检测样本点数据均匀性，进一步将样本点类型划分为均匀样本点、聚集样本点和稀疏样本点并确定其数量；其次删除聚集样本点，基于研究区历史数据加密稀疏样本点；最后基于地理空间样本点均匀变异指数、特征空间偏离指数和插值误差共同评价数据精化效果。结果表明，研究区样本点的均匀变异指数为0.429，存在一个聚集样本点和一个稀疏样本点，空间偏离指数为0.327，空间属性插值误差为6.538；冗余数据精化后进行均匀性检测没有发现聚集样本点和稀疏样本点，均匀变异指数下降到0.406，特征空间偏离指数微弱下降，空间属性插值误差下降到6.357。研究表明该方法可以对提高采样数据的均匀性和代表性提供理论指导，可以服务于土壤污染防治行动计划（土十条）、土壤污染状况详查等，为更加精确研究土壤空间信息变化提供一定的基础条件。     

土地更新调查成果在第二次土地调查（农村部分）中转换应用研究

把更新调查与三次调查进行了对比分析,对更新调查成果在二次调查（农村部分）中的坐标转换、地类转换、线状地物转换、新增地物补绘、零星地物转换、权属境界转换等内容方法进行了研究,并得出了结论。     

Simulation of Cotton Production for Precision Farming

Most crop simulation models do not directly consider the spatial variability of inputs nor do they produce outputs that show the expected spatial variability of yield across a field. If such models were available for precision farming, then researchers could much better evaluate the effects of soil sampling densities to determine the number of samples necessary to adequately model a particular field. The objectives of this study were: (1) to design and implement a spatial simulation methodology for examining details of precision farming and (2) use this to evaluate the effects of different soil sampling resolutions on predicted yield and residual nitrates through spatially variable nitrogen applications. The GOSSYM/COMAX cotton growth model/expert system and the GRASS geographic information system were used to develop a spatial simulation that produces spatially variable outputs. Inputs to the model were collected from a 3.9-ha cotton field. Soil nitrate, a primary driver in fertilizer recommendations, was sampled on a 15.2-m regular grid for depths to 15 cm and on a 30.5-m regular grid at six 15-cm depth intervals (down to 90 cm). COMAX was used to determine spatially variable fertilizer recommendations. GOSSYM was used to simulate perfect application of these recommendations and predicted spatially variable yield and residual nitrates. Reductions in sampling density or resolution were simulated by systematically reducing the amount of data available to COMAX for calculating spatially variable fertilizer recommendations. GOSSYM subsequently used these recommendations (based upon less and less knowledge of soil nitrates) to simulate the effects of differing sampling resolutions on predicted yield and residual nitrates. For recommendations based upon a 15.2-m grid of inputs, 41.4 kg/ha of nitrate fertilizer produced 801.7 kg/ha of cotton and left an average of 9.4 ppm of nitrate in the soil profile. For a 30.5-m grid, 42.8 kg/ha of nitrate fertilizer resulted in a yield of 811.2 kg/ha and residual soil nitrate of 8.3 ppm. For 45.7-m and 61.0-m grids, the results were 43.3 kg/ha and 41.2 kg/ha of nitrate fertilizer, 755.3 kg/ha and 794.3 kg/ha of cotton, and 11.5 ppm and 8.1 ppm of residual soil nitrate, respectively. This study concluded that crop simulations and geographic information systems are a valuable combination for modeling the effects of precision farming and planning variable rate treatments. Simulation results indicate that excessive fertilization, while potentially damaging to the environment, may also have a negative impact on yield.     

替代还是互补？淀粉糖与食糖在食品饮料业中的应用趋势

【目的】探讨淀粉糖在食品饮料业糖的需求中可能达到的市场份额，并预测淀粉糖的需求增长趋势及其潜在的经济影响。【方法】利用基于创新扩散理论的逻辑斯蒂增长模型研究淀粉糖在食品饮料工业中对食糖的替代能力随时间变化的规律，并运用需求模型预测淀粉糖在食品饮料业中替代食糖的前景。【结果】中国糖的消费市场仍处于扩张期。淀粉糖的市场份额仍有10年左右的快速增长期，然而其增长速度较之前有所减缓。2020年淀粉糖的需求量将比2011年翻一番。淀粉糖消费量的增加会降低食糖需求的增长速度，并改变多年来食糖主导糖的消费市场的局面。研究结果还表明，食品饮料业因用淀粉糖替代食糖而降低的生产成本有利于消费者，但这会增加食糖的需求价格弹性。然而，用于食品饮料加工业的淀粉糖产业的发展对玉米市场的影响有限。【结论】淀粉糖需求增长最终取决于因使用淀粉糖所能带来的成本节约。目前，淀粉糖与食糖在中国糖的消费市场中是一定意义上的互补关系，而不完全是替代关系。     

纳米铜/聚苯乙烯-丁二烯(SBS)杂化材料研究

采用脉冲激光轰击法连续制备了纳米铜,进而采用共混法连续制备了纳米铜/SBS杂化材料,用UV-V is、TEM、荧光光谱、FT-IR和TG-DTA对其结构和性质进行了表征.结果表明因为金属铜纳米颗粒的引入表面原子配位不足,表面能极高,活性高,很容易与SBS链段中的不饱和键相互作用,导致UV-V is吸收峰的移动和消失,SBS分子的荧光部分淬灭,整体荧光强度下降,与C=C双键相连的C-H红外变形振动吸收频率的降低;TEM观察显示由于聚合物链段的空间位阻及聚合物链段与铜纳米颗粒的相互作用对铜纳米颗粒的团聚产生阻碍,使杂化材料中铜纳米颗粒的尺寸受到限制,故铜纳米颗粒在SBS在中有较高的稳定性.TG-DTA结果发现Cu/SBS杂化材料的分解温度比SBS高,高温失重较小,热稳定性较好,这是由于Cu纳米粒子和SBS中的C=C双键之间的相互作用,形成具有桥键的结构,使聚合物SBS大分子间的作用力增大所致.     

基于空间洛伦茨曲线的北京山区土地利用结构变化

基于北京山区各区县1995—2002年土地利用现状数据应用经济学洛伦茨曲线以及基尼系数的概念进行北京山区土地利用结构的定量化研究。结果表明:在空间尺度上,居民点独立工矿用地(2002年空间基尼系数为0.325,下同)和园地(0.276)分布最不均衡(过于集中),耕地(0.258)和未利用地(0.234)次之,林地(0.18)分布相对均衡(比较分散);在时间尺度上,园地、居民点及独立工矿用地和未利用地的不均衡程度有所减弱,而耕地、林地却有增加。这说明北京山区各区县5种主要用地类型的空间分布及其时间变化均具有不均衡性。该结论可为山区土地利用结构优化提供参考。     

基于高SBS阈值的HNLF产生高重复频率超短光脉冲

基于光纤中的四波混频(FWM)产生高重复频率超短光脉冲的原理,并为抑制光纤中的受激Brillouin散射(SBS),采用非均匀掺杂高SBS阈值非线性光纤,通过FWM对双拍频信号进行整形压缩,实验上获得了100GHz的高重复率超短光脉冲序列,进而分析了入纤功率对输出光脉冲的影响。     

增韧剂对竹塑复合材料性能的影响

为研究聚烯烃弹性体(POE)、三元乙丙橡胶(EPDM)、苯乙烯系热塑性弹性体(SBS)3种增韧剂对竹粉/高密度聚乙烯复合材料的增韧机理,制备韧性较好的竹粉/高密度聚乙烯复合材料;分别加入不同质量分数的POE、EPDM、SBS制成竹粉/高密度聚乙烯复合材料试样,并对其进行力学性能和流变性能测试。力学性能测试结果表明:随着3种增韧剂质量分数的增加,复合材料的冲击强度均明显增大,拉伸强度和弯曲强度均减小。当POE、EPDM质量分数为30%,SBS质量分数为40%时,复合材料的冲击强度最好;流变性能测试结果表明,低频区复合材料的储能模量和损耗模量总体随着增韧剂质量分数的增加而增大,说明随着增韧剂质量分数的增加,复合材料的黏弹性增强。     

不同尺度下低山茶园土壤阳离子交换量空间变异性研究

 【目的】研究茶园土壤阳离子交换量（CEC）的空间变异性特征，可以为茶园生态建设以及茶园土壤肥力管理提供理论依据。【方法】本文利用地统计学方法探讨了两个取样尺度下四川蒙顶山茶园土壤CEC的空间变异性特征。【结果】（1）小尺度下，茶园土壤CEC具有强烈的空间相关性（C0/（C0+C）值为18.84%），相关距离高达1 818 m，结构性因子是影响其空间变异的主要因素；（2）微尺度下，茶园土壤CEC仍具有强烈的空间相关性（C0/（C0+C）值为16.52%），空间相关距离达311 m，结构性因子是影响其空间变异的主要因素。【结论】小尺度下土壤CEC在坡度方向变异明显，横坡方向变异最弱；普通Kriging插值分析表明其在东北至西南方向呈明显的条带状分布，并在垂直方向随海拔高度的上升而增加；微尺度下土壤CEC的变异方向与小尺度下不同，在接近坡度的方向空间变异明显，接近横坡方向变异最弱；土壤CEC沿横坡方向呈条带状分布并伴随部分斑块状分布，从坡面至上而下，呈先增加后减少再增加的趋势。     

Spatial Variability of Soil Cation Exchange Capacity in Hilly Tea Plantation Soils Under Different Sampling Scales

Studies on the spatial variability of the soil cation exchange capacity （CEC） were made to provide a theoretical basis for an ecological tea plantation and management of soil fertilizer in the tea plantation. Geostatistics were used to analyze the spatial variability of soil CEC in the tea plantation site on Mengding Mountain in Sichuan Province of China on two sampling scales. It was found that, （1） on the small scale, the soil CEC was intensively spatially correlative, the rate of nugget to sill was 18.84% and the spatially dependent range was 1 818 m, and structural factors were the main factors that affected the spatial variability of the soil CEC; （2） on the microscale, the soil CEC was also consumingly spatially dependent, and the rate of nugget to sill was 16.52%, the spatially dependent range was 311 m, and the main factors affecting the spatial variability were just the same as mentioned earlier. On the small scale, soil CEC had a stronger anisotropic structure on the slope aspect, and a weaker one on the lateral side. According to the ordinary Kriging method, the equivalence of soil CEC distributed along the lateral aspect of the slope from northeast to southwest, and the soil CEC reduced as the elevation went down. On the microscale, the anisotropic structure was different from that measured on the small scale. It had a stronger anisotropic structure on the aspect that was near the aspect of the slope, and a weaker one near the lateral aspect of the slope. The soil CEC distributed along the lateral aspect of the slope and some distributed in the form of plots. From the top to the bottom of the slope, the soil CEC increased initially, and then reduced, and finally increased.     

Soil Sampling Techniques for Alabama,USA Grain Fields

Characterizing the spatial variability of nutrients facilitates precision soil sampling. Questions exist regarding the best technique for directed soil sampling based on a priori knowledge of soil and crop patterns. The objective of this study was to evaluate zone delineation techniques for Alabama grain fields to determine which method best minimized the soil test variability. Site one (25.8 ha) and site three (20.0 ha) were located in the Tennessee Valley region, and site two (24.2 ha) was located in the Coastal Plain region of Alabama. Tennessee Valley soils ranged from well drained Rhodic and Typic Paleudults to somewhat poorly drained Aquic Paleudults and Fluventic Dystrudepts. Coastal Plain soils ranged from coarse-loamy Rhodic Kandiudults to loamy Arenic Kandiudults. Soils were sampled by grid soil sampling methods (grid sizes of 0.40 ha and 1 ha) consisting of: (1) twenty composited cores collected randomly throughout each grid (grid-cell sampling) and, (2) six composited cores collected randomly from a 3×3m area at the center of each grid (grid-point sampling). Zones were established from (1) an Order 1 Soil Survey, (2) corn (Zea mays L.) yield maps, and (3) airborne remote sensing images. All soil properties were moderately to strongly spatially dependent as per semivariogram analyses. Differences in grid-point and grid-cell soil test values suggested grid-point sampling does not accurately represent grid values. Zones created by soil survey, yield data, and remote sensing images displayed lower coefficient of variations (%CV) for soil test values than overall field values, suggesting these techniques group soil test variability. However, few differences were observed between the three zone delineation techniques. Results suggest directed sampling using zone delineation techniques outlined in this paper would result in more efficient soil sampling for these Alabama grain fields.