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。     

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.     

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。     

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。     

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。     

中国东部黄河三角洲土壤含盐量估测——一种整合光谱、地形指数与广义相加模型的方法

Soil salinity is one of the most severe environmental problems worldwide. It is necessary to develop a soil-salinity-estimation model to project the spatial distribution of soil salinity. The aims of this study were to use remote sensed images and digital elevation model (DEM) to develop quantitative models for estimating soil salinity and to investigate the influence of vegetation on soil salinity estimation. Digital bands of Landsat Thematic Mapper (TM) images, vegetation indices, and terrain indices were selected as predictive variables for the estimation. The generalized additive model (GAM) was used to analyze the quantitative relationship between soil salt content, spectral properties, and terrain indices. Akaike’s information criterion (AIC) was used to select relevant predictive variables for fitted GAMs. A correlation analysis and root mean square error between predicted and observed soil salt contents were used to validate the fitted GAMs. A high ratio of explained deviance suggests that an integrated approach using spectral and terrain indices with GAM was practical and efficient for estimating soil salinity. The performance of the fitted GAMs varied with changes in vegetation cover. Salinity in sparsely vegetated areas was estimated better than in densely vegetated areas. Red, near-infrared, and mid-infrared bands, and the second and third components of the tasseled cap transformation were the most important spectral variables for the estimation. Variable combinations in the fitted GAMs and their contribution varied with changes in vegetation cover. The contribution of terrain indices was smaller than that of spectral indices, possibly due to the low spatial resolution of DEM. This research may provide some beneficial references for regional soil salinity estimation.     

控制水稻抽穗期和株高的QTL的定位及遗传分析

抽穗期（headingdata,HD）和株高（plantheight,PH）是水稻（Oryza sativaL.）非常重要的农艺性状。本研究利用金23B（Jin23B）和青谷矮1号（QGA-1）构建的BC3F1群体及其衍生的BC3F2群体通过分子标记定位水稻抽穗期和株高的QTL（quantitativetraitlocus）。构建的遗传连锁图包含105对SSR标记和8对InDel标记,图谱较好地覆盖了水稻12条染色体。两年来共定位到了9个抽穗期相关QTLs,6个株高相关的QTLs,其中抽穗期和株高最大效应都来源于第7染色体。抽穗期QTLqHD7-3在2011年LOD为37.07,可以解释的表型贡献率为41.05%,加性效应为11.68;株高QTLqPH7-2在2011年LOD为43.73,可以解释的表型贡献率为54.17%,加性效应为21.60;2012年LOD为42.66,可以解释的表型贡献率为54.39%,加性效应为19.95。qHD7-3和qPH7-2位于同一区域RM214-RM5543之间,Ghd7也位于这一区间,该QTL可能是Ghd7的等位基因。抽穗期QTLqHD2定位于第2染色体上标记ZH282和RM71之间,在两年内都能检测到,其LOD值分别为4.56和4.99,可解释的表型贡献率分别为4.31%和7.99%。株高QTLqPH4定位于第4染色体上标记RM241和RM317之间,其两年内的LOD分别为2.89和2.67,解释的表型贡献率为9.42%和8.78%。抽穗期QTL qHD2和株高QTL qPH4所定位的区间没有相关的基因或QTL报道,这两个QTL可能含有控制抽穗期和株高的新基因。本研究通过遗传定位证明了株高和抽穗期是由主效QTL和微效QTL共同控制的,并发掘了新的抽穗期和株高的QTL,为育种家利用分子标记辅助选择培育新品种提供更多的选择。     

Numerical assessment of the effect of forest structure changes on CO2 flux footprints for the flux tower in Solling, Germany

There are many natural and anthropogenic reasons why a gap can occur inside the forest. When a gap appears within a studied stand (e.g. near a flux tower which operated for some time, providing information about the ecosystem-atmosphere exchange), an assessment of new measurement conditions should be carried out. Using a three-dimensional approach for footprint estimation based on numerical solution of Reynolds-Averaged Navier-Stokes (RANS) equations, we investigated possible changes in air flow and CO₂ flux footprints resulting from two suggested forest management activities - clear-cut and stripe-cut - around the flux tower located in 130-year-old spruce forest in the Solling highland, Germany. The model results show that degree of changes in flux footprints depends on the chosen management strategy. The clear-cut strategy produces the largest changes and the stripe-cut leads to weaker changes of investigated characteristics. The role of remote canopy sources increases, while the contribution of remote soil sources decreases with increased share of removed trees. In general, the investigated characteristics change differently for summer and winter due to the combined effects of phenology and upwind topography.     

泰安市茶树种植气候条件分析

利用泰安市5个气象台站1970-2004年的气候资料,结合茶树的生态学特性,探讨了泰安市气候条件对茶树生长发育和茶叶品质的影响,系统分析了有利和不利的气候因素,由此提出了泰安市茶园栽培管理技术措施和对茶叶产业发展的建议.