基于冠层尺度的枣树色素含量的高光谱估算模型

植物冠层色素含量与氮素含量具有高度的相关性,是农业遥感中的关键研究因素。本研究的主要目的是：(1)对比偏最小二乘回归和支持向量机两种建模方法对枣树冠层色素的预测精度;(2)构建基于高光谱数据的枣树冠层色素含量定量反演模型,为枣树冠层色素含量的快速、无损、廉价、环保的测定提供一定的理论依据和技术支持。相关性分析结果表明,枣树冠层色素与高光谱数据之间具有较好的相关性,但叶绿素、叶绿素a要优于叶绿素b和类胡萝卜素。独立样本对模型的预测性能检验结果表明,偏最小二乘回归和支持向量机均能有效的估算枣树色素含量,但不同色素的偏最小二乘回归模型和支持向量机模型的预测精度存在一定的差异,叶绿素和类胡萝卜素的支持向量机模型的预测精度要高于偏最小二乘回归模型,而叶绿素a和叶绿素b则相反。比较不同色素的最佳反演模型的预测精度表明,叶绿素、叶绿素a和类胡萝卜素的预测精度要优于叶绿素b,前三者的决定系数大于0.8,残余预测误差高于2.0,平均相对误差低于13%,而叶绿素b的对应值分别为0.60%,20.79%和1.79%。     

红枣中微量元素含量的测定

用火焰原子吸收光谱法(FAAS)测定了红枣中K、Ca、Mg、Cu、Mn、Zn、Fe等7种微量元素的含量.实验方法加入标准样回收率在99.4%-105.2%之间,RSD在0.4%-1.3%之间,分析结果令人满意.红枣中含有丰富的微量元素,其含量由高到低的排列顺序为:Fe、K、Ca、Mg、Zn、Mn、Cu,说明红枣具有很高的营养价值.     

Dissolved oxygen in the bottom water of the Sea of Japan as a sensitive alarm for global climate change

The Sea of Japan, a semi-closed marginal sea (greatest depth ∼3700 m) in the northwestern-most Pacific Ocean, has an independent, deep convection system, which is driven by the formation and the sinking of cool, saline surface water towards the bottom in severe winters. Continuous measurement of dissolved oxygen using highly precise versions of the Winkler titration method has revealed 8-10% decreases in the bottom concentration of oxygen (O₂) over the past 30 years. The temporal decrease in O₂ means an imbalance between the supply of O₂ from the surface and the in situ consumption of O₂ in decomposing organic matter, suggesting that the change in the deep convection pattern of the Sea of Japan is probably caused by global climate change to reduce winter cooling of surface seawater.     

Maneuver analysis methodology to predict vehicle impacts on training lands

Tactical mobility analysis techniques were merged with land management strategies to assess potential impacts of vehicle operations on training areas for rangeland planning and management. A vehicle mobility analysis was performed for a suite of vehicle types using the NATO Reference Mobility Model (NRMM II). Input parameters include terrain information (soil type, slope, vegetation, surface roughness, soil strength), terrain surface condition based on climate (terrain strength, freeze–thaw, moisture content, snow cover), and vehicle specifications (tire, power train, weight on each axle, ground clearance, dimensions, ride). The vehicle performance was spatially mapped over the terrain for different seasons of the year and used to calculate the maneuverable acreage, which was compared to acreage needed for training requirements. This can be related to land capability based on expected training impact (Maneuver Impact Miles, MIM) and Land Condition Curves which link training density to land condition. This methodology can be used to determine the suitability of training lands and the degree of land management or rehabilitation expected. The methodology was applied to the transformation of the Alaska training lands to support a new brigade unit called the Stryker Brigade Combat Team (SBCT3), but is equally useful for other training areas and military units. For summer use, Alaska training lands are capable of supporting four times the projected training requirements. For winter, when the ground is frozen, more than 10 times the area needed was available.     

基于遥感和作物生长模型的多尺度冬小麦估产研究

粮食安全是社会和谐、政治稳定和经济可持续发展的重要保障。准确预测区域乃至全球的农作物产量能够为各级政府、相关部门制定农业农村政策提供技术支持,保障粮食安全。目前关于农作物估产的研究大多具有地域性、经验性,过分依赖地面实测数据,一种基于多光谱卫星遥感数据和作物生长模型估算农作物产量的模型框架SCYM（Scalable Crop Yield Mapper）能够极大地减少模型对实测数据的依赖,快速应用于不同空间尺度、不同种类作物的估产,为多尺度农作物估产研究提供了一条有效的途径。以安徽省2012年-2018年冬小麦为研究对象,通过总结前人研究确定的敏感参数及其在研究区内的波动范围,结合大量实割实测数据优化WOFOST（WOrld FOod STudies）模型参数;将模拟产量、不同时段的模拟叶面积指数(LAI)同遴选出的天气变量训练随机森林模型,并以最佳观测日期组合下的MODIS-LAI代替对应时段的模拟LAI进行产量估算。结果表明：（1）模型产量估算值与站点实测值的总体相关性为0.758（R2为0.575）,RMSE为790.92 kg·ha-1。精度较高的站点主要分布在淮北平原（<1%）而高误差区域集中于皖南丘陵地带（>40%）;（2）对2012年-2018年全省范围进行冬小麦估产,根据7年平均估产结果的空间分布,小麦单产由北向南逐渐减少,高值区出现在皖北的淮北平原,低值区主要分布于皖中、皖南地区;（3）2012年-2018年实测单产平均值为6 058.00 kg·ha-1,SCYM估算单产平均值为5 984.95 kg·ha-1,且估算产量与实测产量的年际时间序列的相关性为0.822,RMSE为189.96 kg·ha-1,每年估产的相对误差均不超过6%。研究表明SCYM估产框架对安徽省冬小麦产量估算具有一定的可行性,在产量预报方面效果良好。该方法能够在一定程度上改善以往估产模型存在的地域性、经验性问题,在区域尺度的应用方面具有极大的潜力,未来可为农业估产提供极其重要的理论依据和实用价值。     

Mapping of powdery mildew using multi-spectral HJ-CCD image in Beijing suburban area

Powdery mildew is one of the most serious diseases, which has a significant impact on the production of winter wheat. As an effective alternative to traditional sampling methods, remote sensing can be a useful tool in disease detection. This study examines the potential of a moderate resolution multispectral satellite image in disease monitoring at regional scale. At the suburban area around Beijing, a large size ground survey sample (n = 90) and the corresponding HJ-CCD image were acquired at the grain filling stage of winter wheat. A number of spectral features were found to be sensitive to powdery mildew through an independent t-test. Based on these spectral features, classification models were established using both spectral information divergence (SID) and spectral angle mapper (SAM), respectively. The results showed that the overall accuracies of disease identification and severity estimation were moderate. The estimation of normal and seriously infected samples yielded higher accuracies than slightly infected samples. The single phase HJ-CCD can only be used for locating the infected areas of powdery mildew, whereas is unable to discriminate the severity levels of disease. The presence of several stressors and disturbances other than disease is a possible reason of the unsatisfactory performance of disease monitoring models. Therefore, the integration of multi-phase onboard data and some relevant ancillary data is necessary to improve the accuracy and reliability of disease monitoring at regional scale.     

Possible effects of climate change of wind on aerosol variation during winter in Shanghai,China

Several data sets were introduced to investigate the possible effects of climate-change-related variation of wind on aerosol concentration during winter in Shanghai, China. These data sets included the daily wind speed, wind direction, visibility, and precipitation from 1956 to 2010, hourly PM₁₀ concentration from 2008 to 2010, and the NCEP/NCAR reanalysis data of global atmospheric circulation from 1956 to 2010. The trend of aerosol concentration and its correlations with wind speed and wind direction in winter were analyzed. Results indicated that there was an increase in the number of haze days in winter of 2.1 days/decade. Aerosol concentration, represented by PM₁₀ in this study, was highly correlated to both wind speed and direction in winter. The PM₁₀ concentration increased as wind speed decreased, reaching maximum values under static wind conditions. The PM₁₀ concentration was relatively lower under easterly winds and higher under westerly winds. The analysis showed that weaker East Asia winter monsoons have resulted in a reduction of wind speed, increase in static wind frequency, and decline in the frequency of northerly winds since the 1980s. Moreover, the rapid expansion of urban construction in Shanghai has changed the underlying surface considerably, which has led to a reduction in wind speed. Finally, a wind factor was defined to estimate the combined effects of wind speed and wind direction on aerosol concentrations in Shanghai. The analysis of this factor indicated that changes in atmosphere circulation and urbanization have had important effects on the number of winter haze days in Shanghai.     

Optimizing winter road maintenance operations under real-time information

This paper introduces a real-time optimization model that can be used by maintenance managers to develop and evaluate alternative resources allocation plans for winter road maintenance operations. The model takes into account a wide range of road and weather condition factors such as road network topology, road class, weather forecasts, and contractual service levels, and produces a vehicle dispatch schedule that is optimal with respect to operating costs and quality of service. The model is then used in an analysis on a realistic case to illustrate the potential impact of improved information on winter maintenance operations.     

最小二乘支持向量机方法对冬小麦叶面积指数反演的普适性研究

冬小麦叶面积指数(leaf area index, LAI)是进行作物长势判断和产量估测的重要农学指标之一,高光谱遥感技术为大面积、快速监测植被LAI提供了有效途径。在探讨利用最小二乘支持向量机(least squares support vector machines, LS-SVM)方法和高光谱数据对不同条件下冬小麦LAI的估算能力。在用主成分分析法(principal component analysis, PCA)对PHI航空数据降维的基础上,利用实测LAI数据和高光谱反射率数据,构建LS-SVM模型,采用独立变量法,分别估算不同株型品种、不同生育时期、不同氮素和水分处理条件下的冬小麦LAI,并与传统NDVI模型反演结果对比。结果显示,每种条件下的LS-SVM 模型都具有比NDVI模型更高的决定系数和更低的均方根误差值, 即反演精度高于相应的NDVI模型。NDVI模型对不同株型品种、不同氮素和水分条件下冬小麦LAI估算精度不稳定,LS-SVM则表现出较好的稳定性。表明LS-SVM 方法利用高光谱反射率数据对于不同条件下的冬小麦LAI反演具有良好的学习能力和普适性。