Imaging spectroscopy in soil-water based site suitability assessment for artificial regeneration to Scots pine

In a humid northern boreal climate, the success rate of artificial regeneration to Scots pine (Pinus sylvestris L.) can be improved by including a soil water content (SWC) based assessment of site suitability in the reforestation planning process. This paper introduces an application of airborne visible-near-infrared imaging spectroscopic data to identify suitable subregions of forest compartments for the low SWC-tolerant Scots pine. The spatial patterns of understorey plant species communities, recorded by the AISA (Airborne Imaging Spectrometer for Applications) sensor, were demonstrated to be dependant on the underlying SWC. According to the nonmetric multidimensional scaling and correlation results twelve understorey species were found to be most abundant on sites with high soil SWCs. The abundance of bare soil, rocks and abundance of more than ten species indicated low soil SWCs. The spatial patterns of understorey are attributed to time-stability of the underlying SWC patterns. A supervised artificial neural network (radial basis functional link network, probabilistic neural network) approach was taken to classify AISA imaging spectrometer data with dielectric (as a measure volumetric SWC) ground referencing into regimes suitable and unsuitable for Scots pine. The accuracy assessment with receiver operating characteristics curves demonstrated a maximum of 74.1% area under the curve values which indicated moderate success of the NN modelling. The results signified the importance of the training set’s quality, adequate quantity (>2.43 points/ha) and NN algorithm selection over the NN algorithm training parameter optimization to perfection. This methodology for the analysis of site suitability of Scots pine can be recommended, especially when artificial regeneration of former mixed wood Norway spruce (Picea abies L. Karst) - downy birch (Betula pubenscens Ehrh.) stands is being considered, so that artificially regenerated areas to Scots pine can be optimized for forestry purposes.     

极化散射特性保持的改进迭代Wishart分类算法

为克服基于极化散射特性保持的迭代Wishart分类算法不适用于城区及对混合散射像素分类欠理想等不足,本文提出一种改进方法.其基本思想是先应用四分量分解算法将像素分成4种基本散射类型和混合散射类型,接着以平均合并度为指导对基本散射类型中的像素自适应聚类,最后对所有像素进行散射特性保持的迭代Wishart分类.试验结果表明...     

基于ETOPO1数据的全球及重点海域水深统计与分析

由于不同的水深探测手段及海深反演方法适用于不同深度的海域,且全球海域水深分布不均,因此在探测及反演海深之前,需对目标海域的水深分布有一定认识。本文采用ETOPO1模型对全球及部分海域进行统计分析,结果表明,全球及大西洋、印度洋、太平洋区域的水深大多处于3 000—10 000 m,其中3 000—5 000 m水深海域占比较大,因此,在这些海域主要采用重力数据结合船测数据进行反演,同时应注意避开海岸带、岛屿较多、海脊存在的区域;对于水深小于1 000 m的海域,可以根据外部环境,采用多波束船测、水深遥感、机载激光测深等方式进行探测。通过分析及统计全球和重点海域的水深可以为海深探测及海底地形反演方法的选取提供参考。     

松毛虫危害的光谱特征与虫害早期探测模式

根据生态学特征，本文将松毛虫危害的针叶样品分为５个等级，对其反射光谱和叶绿素含量进行了测量分析。结果表明，随受害程度加重，叶绿素含量降低，５５０ｎｍ处的反射率、近红外肩反射率与红光最低反射率之差及红界一阶导数谱最大值均呈下降趋势，６３０ｎｍ处反射率呈上升趋势，红界光谱蓝移、叶绿素反射峰红移明显。应用逐步判别分析法对比分析证实了细分光谱特征参量比绿、红、近红外三波段反射率参量有更强的判别分类能力，这就为用细分光谱特征参量早期遥感探测松毛虫害提供了判别模式。     

Multiple statistical approaches for the discrimination of mangrove species of Rhizophoraceae using transformed field and laboratory hyperspectral data

This study aims at discriminating eight mangrove species of Rhizophoraceae family of Indian east coast using field and laboratory spectra in spectral range (350–2500 nm). Parametric and non-parametric statistical analyses were applied on spectral data in four spectral modes: (i) reflectance (ii) continuum removed, (iii) additive inverse and (iv) continuum removed additive inverse. We introduced continuum removal of inverse spectra to utilize the advantage of continuum removal in reflectance region. Non-parametric test gave better separability than parametric test. Principal component analysis and stepwise discriminant analysis were applied for feature reduction and to identify optimal wavelengths for species discrimination. To quantify the separability, Jeffries–Matusita distance measure was derived. Green (550 nm), red edge (680–720 nm) and water absorption region (1470 and 1850 nm) were found to be optimal wavelengths for species discrimination. The continuum removal of additive inverse spectra gave better separability than the continuum removed spectra.     

顾及形态特征和语义信息的双线道路识别方法

双线道路识别与提取是城市路网综合的关键。提出了一种城市双线道路提取的方法。在构建好的道路网眼的基础上,综合考虑双线道路网眼形态特征以及构成网眼路段之间的语义相似度,设计了识别双线道路网眼的综合指标。通过判断网眼综合指标确定最终的双线道路网眼,最后根据识别出的双线道路网眼提取出双线道路。实验表明,该方法能有效提取出城市道路数据中的双线道路。     

一种曲线拟合圆心坐标算法及精度分析

针对在工程应用中,经常需要根据某些圆形物体上的离散点求取其圆心坐标,而采用何种算法简单易行,其精度又与哪些因素有关的相关研究较少的问题,该文提出一种利用曲线拟合求取圆心坐标的简洁算法,并编程实现。然后采用模拟仿真结合统计分析的方法评估了该算法在不同条件下的拟合精度,得出测点误差越大,拟合精度越低;测点个数越多,分布越均匀,拟合精度越高;圆半径对拟合精度影响甚小等结论。在某工程项目中使用该算法得到的结果精度较高,验证了其有效可行,且与采用其他方法得到的坐标值较为接近,说明了该算法的可靠性高。     

GIS在统计行业中的应用

地理信息系统技术在很多各业获得了成功的应用,但在统计行业中的应用却相对滞后.本文综述了地理信息系统在统计行业中的应用现状、优势及存在的问题,并对统计地理信息系统的解决方案作了简要分析,给出了建立统计GIS的技术方案和统计GIS的总体框架.     

基于小波统计特性的遥感图像像素与特征联合最优融合方法

遥感影像的IHS融合方法由于匹配误差导致光谱畸变和退化,而小波变换在变换域具有良好的分频特性,小波系数的统计特性反映了遥感影像的边缘、线和区域等显著特征。提出了基于小波统计特性的遥感影像的像素和特征联合最优融合方法,在IHS空间,对强度分量I的高频部分利用多分辨率小波融合方法进行影像的高频细节特征融合,低频部分选取光谱信息和空间分辨率评价指标作为融合权系数求优指标,进行像素级最优融合,实验结果证明了该方法的有效性。     

柴北缘鱼卡地区中侏罗统石门沟组含煤层段沉积有机相分析

柴达木盆地北缘鱼卡地区中侏罗统石门沟组含煤段中发育有油页岩、煤、碳质泥岩和泥岩等富有机质细粒沉积.为了研究其沉积有机相的类型及煤和油页岩形成的控制因素,本文通过岩心观察、工业分析结合有机地球化学测试分析等方法将含煤段细粒沉积物划分为了类型A(三角洲平原沉积环境)、B(三角洲前缘-浅湖沉积环境)、C1(湖沼非油页岩亚相)及C2(湖沼油页岩亚相)四种类型,其中类型C1沉积物中发育煤,类型C2中发育油页岩,且煤较油页岩具有更高的含油率、水分、挥发分以及发热量值.有机质类型特征方面,沉积有机相类型A、B及C1沉积物有机质类型均为Ⅱ2-Ⅲ型、有机质来源均以陆源和混合来源两种有机质来源为主,而类型C2中主要为Ⅱ2型,以混合有机质来源为主;类型A及C2其沉积物有机质均处于未成熟阶段,而类型B和C1中有机质均处于未成熟-低成熟阶段;有机质保存条件方面,类型A沉积物主要形成于缺氧的淡水环境,类型B主要形成于贫氧-缺氧的淡水-半咸水环境,类型C1形成于贫氧-缺氧的淡水-半咸水环境,而类型C2则主要形成于缺氧的淡水-半咸水环境.其中类型B较类型A,类型C2较类型C1,其沉积物均形成于更为还原且盐度更高的水体环境中.石门沟组含煤段是煤和油页岩的形成层位,稳定的沉积环境、丰富的湖泊有机质来源、良好的保存条件及较少的陆源碎屑的稀释共同促进了类型C2中油页岩的形成,而丰富的陆源植物供给及良好的保存条件则促进了类型C1中煤层的形成.