基于BP神经网络自动提取沟谷研究

 提出将提取沟谷的过程转化为根据地形因子综合判定地貌类型的思路。以陕西绥德黄土丘陵沟壑区域1∶1万地形图制作的分辨率为5 m的DEM为研究对象,运用BP神经网络分析6种地形因子与沟谷地形的相互关联关系,认为降水累积量是判定沟谷地形的最重要因子。在试验样区建立BP神经网络,利用4种地形因子自动提取沟谷,并在检验样区通过了检验。     

基于数字高程模型和数学规划的土地平整工程设计优化

应用数字高程模型（DEM）与数学规划（MP）方法,研究土地平整工程设计中田块的划分、土方量计算和土方量调配的优化方法,为土地平整工程设计提供综合、系统和精确的方法,节约投资费用。该文以南京市江宁区湖熟镇土地平整项目工程为例,采用ArcGIS9.3软件建立大比例尺DEM,划分了土地平整区域和耕作田块,基于DEM计算了现状高程、土方量、土方运输重心,采用数学规划求解了设计高程和土方调配的最优方案。研究表明：该方法合理确定了田块的规模、定量计算了优化设计高程、总土方量比原设计方案减少了9.37%,且制作了最优土方调配路径图。     

面向土地精细平整的车载三维地形测量系统设计与实现

设计开发了一种基于全地形车（ATV）的农田三维地形快速采集系统,系统由ATV车辆、RTK-GPS、田间作业辅助导航装置、车载计算机和数据采集软件等部分组成。采用高精度RTK-GPS自动测量三维地形数据,车载计算机能实时记录三维地形数据,辅助平行作业导航装置能指引数据采集车辆在测区进行全区域覆盖测量,以提高农田三维地形数据采集质量和效率。田间实测试验表明,基于ATV的三维地形数据自动采集系统测量的三维地形与人工RTK-GPS测量的三维地形与具有很好的空间一致性,最大平均偏差3.54 cm,最大标准偏差2.48 cm,能够满足农田土地精细平整三维地形数据采集工作的需要。     

Identification of key soil and terrain properties that influence the spatial variability of soil moisture throughout the growing season

Combining soils and terrain information is the key to understanding hydrological processes at a landscape scale. Increasing the scale of soil maps has been shown to allow the spatial patterns of soil moisture to be more fully represented in the landscape, but soil data are often only available at reconnaissance scales (e.g. 1:250 000). It is widely acknowledged that soil hydrological properties vary within the landscape and there are widely available digital terrain models at a 10-m grid resolution in the UK. The aim of this study was to investigate soil moisture variations and how soil and terrain data can be used in combination to explain the spatial variation in soil moisture contents. Field monitoring of surface soil moisture content on eight occasions in three different fields in Bedfordshire (UK) was undertaken between April and July in 2004 and 2005. Between 100 and 120 points were sampled in each survey using a Delta-T ML2x™ Theta Probe. The results from regression models show that up to 80% of the variation in surface soil moisture can be explained using 1:10 000 soil series maps and terrain variables. Short-wave radiation on a sloping surface (SWRSS), calculated by SRAD, and a topographic wetness index combined explained a maximum of 44% of the variation. The results show that the terrain effect on soil moisture is modified by soils. The additional variation explained by adding 1:10 000 soil series information to terrain variables was up to 50% and adding 1:25 000 soil series information increased the variation explained by up to 29%. The interactions in the variation explained by soils and landscape indices at different scales tie in with the concept of hydropedology. It also has implications for data requirements for modelling soil hydrological response and associated soil functions.     

起伏地形下地理可照时数空间分异分析——以重庆市为例

基于重庆市100 m×100 m分辨率的数字高程模型,利用Solar Analyst模型对各月份地理可照时数进行了模拟,并通过空间叠加运算进一步求得了季、年地理可照时数。季节和月地理可照时数变化表明重庆市夏季地理可照时数最长,以6月最高;冬季地理可照时数最小,以12月最低,地形遮蔽对可照时数的影响显著,可明显影响可照时数的局地空间分布。结合坡度、坡向因子,对起伏地形条件下地理可照时数模拟结果进行了时空分布特征分析。分析结果表明:同坡度不同坡向的地理可照时数都随着太阳高度角的增大而增加;同坡向的地理可照时数随着坡度的增加而减小;坡度越高地理可照时数受坡向影响程度越大。     

小流域数字地面模型(DTM)的建立及其在水土保持遥感动态监测中的应用

以陕北安塞纸坊沟流域为研究区,采用航测精密立体测法建立了全流域1975年和1987年两期20m×20m格网数字高程模型和6条5m×5m断面的数字高程模型,利用Foxpro数据库系统建立了相应的DTM数据库,基于DPP测绘软件包和ARC/INFO、APSIS地理信息系统软件平台,分别绘制了多幅两期全流域和三条典型小沟的监测专题图件。通过对产生的图件和DTM数据进行定量和定性分析,对沟谷的扩张、下切及溯源侵蚀进行了较精确的动态监测。初步建立了地面三维数据采集→DTM数据库→监测专题图件→动态监测先进而又实用的科研技术系统,为较高精度和信息化小流域动态监测,提供了比较理想的技术方法和工作基础。     

烟台市人口分析及预测

利用时间序列分析模型和GM(1,1)模型拟合了烟台市人口数量,并对未来4年烟台市人口进行了预测.结果显示,二次滑动平均模型的相对误差波动范围为-0.731%～0.70%,均接近于0,预测精度最高.利用二次滑动平均模型预测未来4年烟台市人口分别为646.395万人、646.445万人、646.495万人和646.545万人,人口有逐年缓慢上升的趋势.     

面向农田信息采集的数字照片空间标识索引方法

针对精准农业多源信息采集管理应用的实际需求,提出一种基于时间阈值的数字照片自动空间位置标识方法和基于空间位置的数字照片索引及热链接方法。利用该方法开发了面向精准农业的农田空间多媒体信息采集管理软件,软件将数字照片采集、空间标识、快速索引、热链接表达和数据转换等环节作为一个完整的流程处理,支持普通数码照相机配合手持GPS设备、GPS数码相机和具有定位拍照功能手持设备等多种数据采集方式下的空间多媒体数据采集、空间位置标识索引和数据交换,实现了地理信息系统环境下的数字照片动态索引和关联,并给出了匹配照片时间阈值ε的取值范围。通过精准农业农田基础设施空间多媒体数据采集融合实际应用,验证了该方法的有效性和实用性。     

Determining minimum sample size for the conditioned Latin hypercube sampling algorithm

In digital soil mapping (DSM), a fundamental assumption is that the spatial variability of the target variable can be explained by the predictors or environmental covariates. Strategies to adequately sample the predictors have been well documented, with the conditioned Latin hypercube sampling (cLHS) algorithm receiving the most attention in the DSM community. Despite advances in sampling design, a critical gap remains in determining the number of samples required for DSM projects. We propose a simple workflow and function coded in R language to determine the minimum sample size for the cLHS algorithm based on histograms of the predictor variables using the Freedman-Diaconis rule for determining optimal bin width. Data preprocessing was included to correct for multimodal and non-normally distributed data, as these can affect sample size determination from the histogram. Based on a user-selected quantile range (QR) for the sample plan, the densities of the histogram bins at the upper and lower bounds of the QR were used as a scaling factor to determine minimum sample size. This technique was applied to a field-scale set of environmental covariates for a well-sampled agricultural study site near Guelph, Ontario, Canada, and tested across a range of QRs. The results showed increasing minimum sample size with an increase in the QR selected. Minimum sample size increased from 44 to 83 when the QR increased from 50% to 95% and then increased exponentially to 194 for the 99% QR. This technique provides an estimate of minimum sample size that can be used as an input to the cLHS algorithm.     

基于无人机数码影像的冬小麦株高和生物量估算

高效、快速地获取作物的株高和生物量信息,对农业生产有重要意义。该文利用2015年4月-6月获得了冬小麦拔节期、挑旗期和开花期的高清数码影像。首先基于无人机高清数码影像生成冬小麦的作物表面模型(crop surface model,CSM),利用CSM提取出冬小麦的株高(Hcsm),然后利用提取的21种数码影像图像指数,构建了拔节期、挑旗期和开花期混合的多生育期生物量估算模型,并进行单生育期和多生育期模型对比分析;最后选择逐步回归(stepwise regression,SWR)、偏最小二乘(partial least square,PLSR)、随机森林(random forest,RF)3种建模方法对多生育期估算模型进行对比,挑选出冬小麦生物量估算的最优模型。结果表明,提取的Hcsm和实测株高(H)具有高度拟合性(R2=0.87,RMSE=6.45 cm,NRMSE=11.48%);与仅用数码影像图像指数构建的生物量估算模型相比(R2=0.721 2,RMSE=0.137 2 kg/m2,NRMSE=26.25%),数码影像图像指数融入H和Hcsm所得模型效果更佳,其中融入Hcsm的模型精度和稳定性(R2=0.819 1,RMSE=0.110 6 kg/m2,NRMSE=21.15%)要优于加入株高H所构建的估算模型(R2=0.794 1,RMSE=0.117 9 kg/m2,NRMSE=22.56%);SWR生物量估算模型(R2=0.7212)效果优于PLSR(R2=0.677 4)和RF(R2=0.657 1)生物量估算模型。该研究为冬小麦生长状况高效、快速监测提供参考。