杂交水稻株型因素的相关性及其规律

根据我国超级杂交稻育种研究的技术思路 ,以两优培九 (培矮 6 4S/931 1 )、6 5396、931 1、E32、汕优 6 3、培矮 6 4S、399等 7个株型特性不同的品种 (组合 )为材料 ,测定了 4类 2 0项株型因素并作相关分析。认为株高和叶角是超高产育种和栽培的两项最主要的株型因素。通过对生态条件和两优培九株高变化关系的分析 ,有平均株高的 1 2 .6 %是由温度引起的 ,并建立了株高的生态预测模型。通过对抽穗后顶部三叶的 8种叶角度配置的单位受光量的理论模拟计算证明 ,理想的株型应以剑叶与倒二叶距离 ( d1 2 )为 1 5cm,倒二叶与倒三叶距离 ( d2 3)为 2 5cm,叶角配置应以 4°～ 6°(剑叶 ) ,9°～ 1 1°(倒二叶 ) ,1 4°～ 1 6°(倒三 )为宜。这些结果可以为超级杂交稻株型育种提供依据。     

植被单叶光谱特性的理论模拟

利用PROSPECT模型和实测的玉米叶片光谱数据，得出了叶肉结构参数N，从而模拟了在不同的叶绿不比和水分含量下玉米叶片的光学特性；发现叶绿素对叶片吸收率的影响在可见光部分占了主导作用，随着叶绿素含量的增加，叶片的吸收率增加；从近红外到2400nm，水分对吸收率的影响占据了主导地位，此时，随着水分含量的增加，叶片的吸收率也开始增加。同时，给定叶绿素和水分含量，还模拟了当叶肉结构参数变化时的叶片的光学特性，随着N的增大，叶片的反射率逐渐增大，并渐趋一致，而透过率随着N的增加而渐减小，最后也逐渐靠近。     

Simulation of leaf area index and biomass at landscape scale

The method for simulating the temporal and spatial distribution patterns of leaf area index (LAI) and biomass at landscape scale using remote sensing images and surface data was discussed in this paper. The procedure was: (1) annual maximum normalized difference vegetation index (NDVI) over the landscape was calculated from TM images; (2) the relationship model between NDVI and LAI was built and annual maximum LAI over the landscape was simulated; (3) the relationship models between LAI and biomass were built and annual branch, stem, root and maximum leaf biomass over the landscape were simulated; (4) spatial distribution patterns of leaf biomass and LAI in different periods all the year round were obtained. The simulation was based on spatial analysis module GRID in ArcInfo software. The method is also a kind of scaling method from patch scale to landscape scale. A case study of Changbai Mountain Nature Reserve was dissertated. Analysis and primary validation were carried out to the simulated LAI and biomass for the major vegetation types in the Changbai Mountain in 1995.     

用LAI监测植被信息分析飞蝗生境

东亚飞蝗自20世纪80年代以来在我国再度猖獗危害。本文选择国家一类蝗区河北省黄骅市为实验区,用植被冠层孔隙度反演了该地区不同植被的LAI。从光学模型建立机理及数量分析的角度,分析和对比了四种由植被冠层孔隙度反演LAI的算法。结果表明,在四种估算方法中LAI-2000算法最适用于研究区植被LAI的估算。为了验证分析结果,用实测的植被盖度与四种算法反演的LAI进行了拟合。发现LAI与植被盖度之间呈明显的正相关关系,且LAI-2000算法最能反映研究区的植被特征。在此基础上,建立了LAI与飞蝗发生面积的关系模型,发现两者之间呈负线性相关,即随着LAI的减小,飞蝗的发生面积呈线性增大。研究结果为实时、快速、大面积监测蝗虫种群动态奠定了基础,并为合理、经济地防治蝗灾提供了科学依据。     

2000—2016年广西防城金花茶自然保护区关键生态环境参数时空变化

植被覆盖度(FVC)和叶面积指数(LAI)作为关键生态环境参数,其时空动态变化的遥感监测对揭示陆地生态环境演变规律与科学监管生态环境具有重要意义.本文基于2000—2016年GLASS产品的植被覆盖度和叶面积指数数据,对广西防城金花茶国家级自然保护区FVC和LAI的时空动态变化进行监测.研究结果表明:①2000—201...     

Use of principal components of UAV-acquired narrow-band multispectral imagery to map the diverse low stature vegetation fAPAR

The fraction of absorbed photosynthetically active radiation (fAPAR) is an important plant physiological index that is used to assess the ability of vegetation to absorb PAR, which is utilized to sequester carbon in the atmosphere. This index is also important for monitoring plant health and productivity, which has been widely used to monitor low stature crops and is a crucial metric for food security assessment. The fAPAR has been commonly correlated with a greenness index derived from spaceborne optical imagery, but the relatively coarse spatial or temporal resolution may prohibit its application on complex land surfaces. In addition, the relationships between fAPAR and remotely sensed greenness data may be influenced by the heterogeneity of canopies. Multispectral and hyperspectral unmanned aerial vehicle (UAV) imaging systems, conversely, can provide several spectral bands at sub-meter resolutions, permitting precise estimation of fAPAR using chemometrics. However, the data pre-processing procedures are cumbersome, which makes large-scale mapping challenging. In this study, we applied a set of well-verified image processing protocols and a chemometric model to a lightweight, frame-based and narrow-band (10 nm) UAV imaging system to estimate the fAPAR over a relatively large cultivated land area with a variety of low stature vegetation of tropical crops along with native and non-native grasses. A principal component regression was applied to 12 bands of spectral reflectance data to minimize the collinearity issue and compress the data variation. Stepwise regression was employed to reduce the data dimensionality, and the first, third and fifth components were selected to estimate the fAPAR. Our results indicate that 77% of the fAPAR variation was explained by the model. All bands that are sensitive to foliar pigment concentrations, canopy structure and/or leaf water content may contribute to the estimation, especially those located close to (720 nm) or within (750 nm and 780 nm) the near-infrared spectral region. This study demonstrates that this narrow-band frame-based UAV system would be useful for vegetation monitoring. With proper pre-flight planning and hardware improvement, the mapping of a narrow-band multispectral UAV system could be comparable to that of a manned aircraft system.     

船舶减纵摇方法研究

针对现有减小船舶纵摇的方法和装置进行总结和分析,并在此基础之上详细介绍一种新型十字型的被动式抗纵摇舵.通过对比分析,抗纵摇舵对于船舶具有明显地减小纵摇的减摇效果.     

作物LAI的遥感尺度效应与误差分析

以黑河中游盈科绿洲为研究区, 利用Hyperion高光谱数据, 采用双层冠层反射率模型(ACRM)迭代运算反演LAI; 通过LAI的均值化(LAImean)以及Hyperion数据反射率线性累加反演LAI(LAIp), 定量分析LAI反演的尺度效应; 从模型的非线性和地表景观结构的空间异质性2个方面分析引起反演误差的原因, 并在LAI-NDVI回归方程的基础上利用泰勒展开的方法对低分辨率数据反演结果进行了误差纠正。结果表明, 地表景观结构的空间异质性是造成多尺度LAI反演误差的关键因素, 通过泰勒展开式能很好地实现大尺度数据LAI反演结果的误差纠正。     

大米草室内叶片光谱特征参数与叶绿素浓度关系研究

本文通过分析大米草叶片反射光谱特征,并提取红边位置、红边斜率和红边面积三个红边参数以及叶面叶绿素指数(LCI)、水分指数(WI)、三角植被指数(TVI)、结构相关色素指数(SIPI)四个高光谱植被指数,利用线性、对数、倒数、二次函数和三次函数曲线模拟算法得到大米草叶片叶绿素a浓度的高光谱估算模型。研究结果表明:叶绿素a浓度与红边斜率和红边面积在0.01水平上显著负相关,与LCI、WI和TVI在0.01水平上显著正相关。基于红边斜率、红边面积、TVI三个参数,选用倒数法构建叶绿素a浓度的估算模型精度明显高于其他算法。基于LCI和WI参数,应用三次函数法构建的叶绿素a浓度的高光谱检测模型精度明显高于其他算法。比较R2和模型估算误差,利用WI水分指数应用三次函数构建叶绿素a浓度的高光谱检测模型精度在所有模型中最高。因此,利用叶片光谱技术可以较高精度地估算叶绿素a浓度。     

银杏叶有效成分提取工艺研究

用正交实验探讨了银杏叶有效成分的最佳提取工艺。研究结果表明,在乙醇体积浓度为60%,回流温度为80℃,提取时间为3h的条件下,银杏叶提取效果最好;并分别用Al3 和红外光谱法对提取物有效成分进行了定性检测。该实验操作简单,重复性好,可行性强,为后续精制工艺提供前提条件。