加速广义Laguerre多项式级数的Laplace变换的数值反演方法

本文利用Levin t-变换迭代法，对加速广义Laguerre多项式级数，提出了一种新的Laplace变换的数值反演方法，这种方法在精度上和数值稳定性上的效果都较好。     

炭疽病胁迫下的茶树叶片高光谱特征分析

试验以茶树不同炭疽病受害程度的叶片及健康叶片为材料, 室内测定其光谱反射率。结果表明, 炭疽病危害后, 茶树叶片的光谱值随发病程度的增加表现出有规律的变化, 740～1 000 nm波段的光谱反射率随病情加重呈现下降趋势; 而1 370～2 500 nm波段却表现出相反趋势。在742～974 nm和1 374～2 500 nm, 炭疽病受害程度与光谱反射率呈极显著相关。对光谱一阶微分特征分析表明, 在680～780 nm范围内处理间变幅最大, 有2个波段的一阶微分值与受害程度表现出极显著相关性, 分别为715～763 nm和776～778 nm波段。建立的炭疽病严重度诊断模型, 均达到极显著水平, 其中利用植被指数(Rg - Rr)/( Rg+ Rr)建立的模型精确度最高。     

基于地统计与遥感反演相结合的有机质预测制图研究

土壤水分对土壤光谱反射率有显著影响,而以往有机质遥感反演制图中却很少将水分作为预测建模的变量。为了使遥感制图更加符合野外实际环境,提高有机质预测制图精度,在充分考虑土壤样点空间自相关、异相关与野外复杂环境特点的基础上,通过地统计获得研究区水分的空间分布数据,结合遥感反射率,建立多因子预测模型,得到了吉林省黑土区土壤有机质空间分布图。结果表明,有机质遥感制图中,水分因素的加入,使模型的建立更加符合野外实际情况,显著提高了有机质预测制图的精度。     

基于无人机多光谱遥感的冬小麦叶绿素含量反演及监测

旨在实现冬小麦各生育期叶绿素含量的准确估测,探究其时空变化规律。利用无人机获取冬小麦越冬期、返青期、拔节期、孕穗期和灌浆期的高分辨率多光谱图像,同时采集地面SPAD数据。选取三类光谱参数建立反演模型,优选出各生育期的最佳预测模型,并定量监测试验区冬小麦叶绿素含量时间变化和空间分布。结果表明：原始波段模型和波段倒数对数模型分别为越冬期及其他生育期叶绿素含量预测的最佳模型,拟合精度R²>0.59;时空分布上,灌浆期前试验区冬小麦叶绿素含量呈南北高、中部低特点,灌浆期则呈北高南低的趋势,叶绿素含量从越冬期到拔节期逐步增加,拔节期到孕穗期开始降低,孕穗期到灌浆期则大幅度降低。本研究建立的倒数对数预测模型,精度较高,且适用于返青到灌浆的4个生育期,对于试验区冬小麦叶绿素含量有较好的时空监测效果。     

同源重组探针在暂时转化的烟草原生质体内的重组和缺失

通过电激法(electroporation)将同源重组探针pBC 7导入烟草原生质体,再从转化的烟草原生质体中提取DNA,并将其转化到E.coli DH 1细胞.结果在E.codi DH 1细胞中获得了一种新的质粒分子,该质粒含有功能的neo基因,与重组探针pBC 7相比有较大的缺失、倒位,并出现了新的限制酶切部位,重组探针pBC 7的主要特征为含有Tn5的neo基因两个截短的不等部份,只有通过重排才可能产生完整的neo基因,这表明了暂时转化的原生质体在外源DNA整合以前具有这样的基因重排功能。     

基于TM影像数据的林分冠层郁闭度反演技术研究

本文以崂山林场为研究区域,利用森林资源二类调查数据和TM影像数据,分析了林分郁闭度与遥感因子之间的定量关系,在此基础上利用多元回归分析法结合实测数据构建郁闭度估测模型,并对模型精度进行检验,结果表明,预估精度达到81.6%,估测效果较好。利用该模型,反演了研究区的林分冠层郁闭度,将崂山林场的林分冠层郁闭度分为四个等级,即非林地区,低郁闭度区,中郁闭度区和高郁闭度区,研究区的森林郁闭度分布呈现西北部和东南部较低,而中部和南部相对较高。     

基于MODIS的沙漠化地区植被覆盖度提取模型的研究

目前常用的植被覆盖度遥感反演模型,受限条件较多,所用系数精度不高。针对上述问题,提出了利用研究区域的实测最大和最小植被覆盖度确定反演模型系数的方法和基于MODIS影像数据的地面实测验证方法。经用该方法对毛乌素沙地沙漠化地区的植被覆盖度进行反演,并同步监测该区域的地表覆盖度对反演结果进行验证,结果表明地面同步实测植被覆盖度与反演的误差绝对值平均值为0.04,反演方法精度较高。研究表明本文提出的利用MODIS影像数据反演沙漠化地区地表覆盖度的模型、地面验证方法切实可行,适合对沙漠化地区地表植被覆盖度大范围且快速的遥感监测。     

基于EnKF-3DVar模型的海淀区地表温度模拟

以城市化程度较深的北京市海淀区为研究区,基于2005年、2010年和2015年的遥感影像数据,利用基于影像(IB)的算法反演城市地表温度空间分布。将数据同化算法EnKF-3DVar与CA/Markov模型集成,将海淀区的多年平均臭氧浓度空间分布数据同化进行城市地表温度的模拟预测。结果表明海淀区的城市地表温度10年间呈现先下降后上升的趋势,但总体呈现下降趋势,其中2015年的平均温度为31.139 3℃。引入EnKF-3DVar的预测模型能够显著提升模型的模拟精度,所预测的2015年的数据结果 Kappa系数达到0.821 6。在有城市公园绿地的模式下,地表温度高值区呈现缓解趋势,在无城市绿地公园的模式下,城市地表温度高值区呈现出明显的扩张趋势,最高温度达到了56.142 3℃,城市生态绿地对于城市地表温度的空间分布影响巨大,合理布局城市绿地公园意义重大。     

Time‐lapse monitoring of soil water content using electromagnetic conductivity imaging

The volumetric soil water content (θ) is fundamental to agriculture because its spatiotemporal variation in soil affects the growth of plants. Unfortunately, the universally accepted thermogravimetric method for estimating volumetric soil water content is very labour intensive and time‐consuming for use in field‐scale monitoring. Electromagnetic (EM) induction instruments have proven to be useful in mapping the spatiotemporal variation of θ. However, depth‐specific variation in θ, which is important for irrigation management, has been little explored. The objective of this study was to develop a relationship between θ and estimates of true electrical conductivity (σ) and to use this relationship to develop time‐lapse images of soil θ beneath a centre‐pivot irrigated alfalfa (Medicago sativa L.) crop in San Jacinto, California, USA. We first measured the bulk apparent electrical conductivity (EC_a – mS/m) using a DUALEM‐421 over a period of 12 days after an irrigation event (i.e. days 1, 2, 3, 4, 6, 8 and 12). We used EM4Soil to generate EM conductivity images (EMCIs). We used a physical model to estimate θ from σ, accounting for soil tortuosity and pore water salinity, with a cross‐validation RMSE of 0.04 cm³/cm³. Testing the scenario where no soil information is available, we used a three‐parameter exponential model to relate θ to σ and then to map θ along the transect on different days. The results allowed us to monitor the spatiotemporal variations of θ across the surveyed area, over the 12‐day period. In this regard, we were able to map the soil close to field capacity (0.27 cm³/cm³) and approaching permanent wilting point (0.03 cm³/cm³). The time‐lapse θ monitoring approach, developed using EMCI, has implications for soil and water use and management and will potentially allow farmers and consultants to identify inefficiencies in water application rates and use. It can also be used as a research tool to potentially assist precision irrigation practices and to test the efficacy of different methods of irrigation in terms of water delivery and efficiency in water use in near real time.     

COMPARISON OF FLUID-ATTENUATED INVERSION RECOVERY AND T2-WEIGHTED MAGNETIC RESONANCE IMAGES IN DOGS AND CATS WITH SUSPECTED BRAIN DISEASE

To compare fluid-attenuated inversion recovery (FLAIR) and T2-weighted magnetic resonance (MR) imaging in small animal patients with suspected brain disease, paired sets of FLAIR and T2-weighted MR images of 116 dogs and cats were reviewed separately without any patient information. Images were rated as normal or abnormal using a five-point scale, and the distribution, signal intensity, and anatomic location of abnormalities were recorded. In 60 animals, both FLAIR and T2-weighted images were normal. In 50 animals, the same abnormalities were identified in both FLAIR and T2-weighted images. Overall, very good agreement was found between FLAIR and T2-weighted MR images (kappa = 0.88). FLAIR images had abnormalities that were not recognized in the corresponding T2-weighted images in six of 116 examinations (5%). In four of these, the abnormalities in FLAIR images were thought to represent pathology, including granulomatous meningoencephalitis in one dog, postictal edema in one dog, and undiagnosed lesions in two dogs. In the remaining two examinations, the abnormalities in FLAIR images were probably artifacts. No examples were found of intracranial abnormalities in T2-weighted images that were not visible in FLAIR images. In this study, acquiring FLAIR images in addition to T2-weighted images resulted in detection of otherwise occult abnormalities in relatively few patients.