城郊"水土流失带"的结构、特征与治理模式研究

从生态学和地理学的角度提出“水土流失带”的概念,及其在城市边缘地区的分布,剖析了它的空间结构,物质组成结构,指出其域界范围,宽度、“环状”;分布,垂直结构特征及物质组成结构的多元性。     

多尺度特征融合深度学习建筑物的提取方法

提出一种基于多尺度特征融合的建筑物提取方法,结合新的网络DenseASPP-UNet,以实现影像多尺度特征的融合,进而高精度提取建筑物.通过Inria开源建筑物航空影像数据集进行验证,表明DenseASPP-UNet相比其他深度学习方法建筑物提取精度有很大的提升.     

玻璃海鞘脂肪酸提取工艺的研究

用乙酸乙酯从玻璃海鞘中提取多不饱和脂肪酸,通过正交实验对提取工艺进行研究,得其最佳提取条件为：温度50℃,提取时间240min,料液比1：3,提取次数2次。按此工艺提取,得脂肪酸产率为1．22％,利用气相色谱进行分析,得其中多不饱和脂肪酸含量为66．40％,EPA和DHA含量之和为15．20％。     

“祝融号”火星车多光谱相机岩矿类型识别的地面验证研究

中国首个自主火星探测任务“天问一号”探测器于2020-07成功发射,其搭载的中国首台火星车“祝融号”将在着陆火星后开展巡视探测,获取中国火星探测的原始科学数据。识别火星表面岩矿类型对于了解火星的大气变化、环境状态、地质演化以及生物宜居性等具有重要意义。本研究利用“祝融号”火星车搭载的多光谱相机载荷鉴定件（8个波段）在地球环境下拍摄并获取了18种火星表面岩矿的多光谱影像数据。基于该数据所开展的火星岩矿类型的识别研究可为未来基于“祝融号”多光谱相机数据进行火星表面岩矿的原位识别提供指导。本文所采用的基于多光谱影像波段运算和彩色图像HSV颜色特征直方图的岩矿识别方法,可从多光谱特征和颜色特征两个层面来识别不同类型的岩矿。所使用的多光谱影像数据是在太阳高度角约60°、拍摄高度1.8 m、拍摄角度约37°的条件下采集所得。在该实验条件下,两种识别方法共提取出12种岩矿的识别指标。基于多光谱影像波段运算可以区分出在8个特定波段下具有突出光谱特征的岩矿类型,而基于图像HSV颜色特征可区分出颜色特征分布较为集中的岩矿类型。同时,研究发现拍摄角度不同会对岩矿的识别效果产生不可忽略的影响。     

近30年青藏高原年平均0cm地温的分布和变化特征

选取青藏高原40个测站1970～2002年各月平均0cm地温资料，通过EOF、二阶多项式函数和小波分析等方法，对青藏高原年平均0cm地温的时空分布特征进行了研究。结果表明，青藏高原年平均0cm地温EOF展开的第一特征向量反映了高原地温分布的一致性，而第二、三特征向量分别揭示了高原地温分布受到各种中、小尺度天气系统和海拔高度制约的事实。高原地温空间异常可分为4个气候区，即东北部、南部、主体和西部。高原地温各分区代表站的二阶多项式反映出近30年高原东北部地温呈降温趋势；南部呈增温趋势；高原主体和西部具有高一低一高的抛物线型变化趋势。高原地温各分区皆有3a和准7a的振荡周期。     

混沌理论在桥梁变形监测中的应用

针对桥梁的非线性下沉问题,引用了混沌理论,首先求取时间序列的两重构参数时间延迟τ和嵌入维数m进行相空间重构;随后进行混沌特性判别,确定该时间序列存在混沌迹象;最后根据所求参数建立加权零阶局域预计模型和RBF神经网络混沌预计模型对观测数据进行预计分析,并与系数为0.9的指数平滑预测模型进行比较,结果显示混沌预计模型值更接...     

Evidence for variations in cryogenic extraction deuterium biases of plant xylem water across foundational northeastern US trees

Measurements of oxygen and hydrogen isotopes in plant xylem water (²H, ¹⁸O) have helped to redefine conceptual and numerical models of the hydrological cycle and understand how plants compete for subsurface water. Recent experiments have shown that Cryogenic Vacuum Extraction (CVE) of plant xylem water can result in a δ²H bias. We tested if CVE δ²H-biases varied significantly across seven foundational northeastern US forest trees with a series of tree core rehydration experiments. Our analysis demonstrated that CVE δ²H-biases were well predicted by sample gravimetric water content and varied significantly with tree species identity. We show that species-level δ²H-bias corrections can result in substantially different understandings of plant water uptake and transpiration versus uncorrected data or generic bias corrections. This research demonstrates an urgent need for the critical evaluation of CVE for plant water extraction. In the absence of a stronger understanding of CVE δ²H-biases, we recommend that xylem water δ²H observations should not be used in plant water uptake studies.     

A review of diagnostic emission line ratios in the Narrow Line Region of Active Galactic Nuclei

The availability of new observing facilities both from ground and space such as the Keck 10m telescope and the Hubble Space Telescope is casting new light on the spectroscopic investigation of emission line galaxies. In particular, it is now possible to detect spectra with a significantly improved signal to noise ratio in a very wide wavelength range, from the ultraviolet to the near infrared (HST, Keck) and beyond (ISO is unveiling the far infrared domain). As we move to high redshifts, however, it is more likely that the observed spectra are given by the contribution of different components in the galaxy: in particular, this may be the case if an Active Galactic Nucleus (AGN hereafter) emitting a power-law continuum is surrounded by regions with strong star formation activity. The identification of the source which ionizes the line emitting regions is then complicated by the fact that we are observing the integrated spectrum from regions which are ionized by different sources. In this paper we wish to review which line ratios may be used in order to discriminate between photoionization by young stars and power-law continuum in the wavelength range from 1200 Å to 100 μm. To this aim we used the photoionization code Cloudy (Ferland 1996) to present a series of diagnostic diagrams showing the dependence of emission line ratios on the main input parameters of photoionization models in the case of one component models with gaseous clouds ionized by (1) a stellar continuum typical of an HII region and (2) a power-law continuum typical of an AGN: these line ratios are plotted as isoratio maps for different values of the hydrogen density, ionization parameter and slope of the power-law continuum. We then show how the results may be affected by more realistic assumptions about the environment of the ionized gas: to this aim, we show the effects on selected line ratios of (a) the presence of two populations of clouds with different densities and (b) an AGN surrounded by regions with strong star formation activity.     

Automatic urban building boundary extraction from high resolution aerial images using an innovative model of active contours

To present a new method for building boundary detection and extraction based on the active contour model, is the main objective of this research. Classical models of this type are associated with several shortcomings; they require extensive initialization, they are sensitive to noise, and adjustment issues often become problematic with complex images. In this research a new model of active contours has been proposed that is optimized for the automatic building extraction. This new active contour model, in comparison to the classical ones, can detect and extract the building boundaries more accurately, and is capable of avoiding detection of the boundaries of features in the neighborhood of buildings such as streets and trees. Finally, the detected building boundaries are generalized to obtain a regular shape for building boundaries. Tests with our proposed model demonstrate excellent accuracy in terms of building boundary extraction. However, due to the radiometric similarity between building roofs and the image background, our system fails to recognize a few buildings.     

An improved segmentation approach for planar surfaces from unstructured 3D point clouds

The extraction of object features from massive unstructured point clouds with different local densities, especially in the presence of random noisy points, is not a trivial task even if that feature is a planar surface. Segmentation is the most important step in the feature extraction process. In practice, most segmentation approaches use geometrical information to segment the 3D point cloud. The features generally include the position of each point (X, Y and Z), locally estimated surface normals and residuals of best fitting surfaces; however, these features could be affected by noisy points and in consequence directly affect the segmentation results. Therefore, massive unstructured and noisy point clouds also lead to bad segmentation (over-segmentation, under-segmentation or no segmentation). While the RANSAC (random sample consensus) algorithm is effective in the presence of noise and outliers, it has two significant disadvantages, namely, its efficiency and the fact that the plane detected by RANSAC may not necessarily belong to the same object surface; that is, spurious surfaces may appear, especially in the case of parallel-gradual planar surfaces such as stairs. The innovative idea proposed in this paper is a modification for the RANSAC algorithm called Seq-NV-RANSAC. This algorithm checks the normal vector (NV) between the existing point clouds and the hypothesised RANSAC plane, which is created by three random points, under an intuitive threshold value. After extracting the first plane, this process is repeated sequentially (Seq) and automatically, until no planar surfaces can be extracted from the remaining points under the existing threshold value. This prevents the extraction of spurious surfaces, brings an improvement in quality to the computed attributes and increases the degree of automation of surface extraction. Thus the best fit is achieved for the real existing surfaces.