基于Skyline的三维GIS构建与研究

三维GIS是指能对区域空间内的对象进行三维描述和分析的GIS系统。随着三维GIS的发展与应用,二维GIS数据难以满足信息化的发展以及对信息数据的客观需求,三维GIS的出现弥补了二维GIS在空间上表达的不足。与二维GIS相比较,三维GIS可实现对空间信息的直观可视化表达,可进行多维度的空间分析,这使得三维GIS成为信息化建设的重要成分,是目前GIS研究和发展的主要方向。Skyline凭借其国际领先的三维可视化显示技术,可以利用遥感影像数据、数字高程数据搭建一个对现实世界进行模拟的三维大场景。Sketch Up、3DMAX都是很好的三维建模及模型渲染软件。本文总结了国内外三维GIS的研究现状,研究了基于Skyline的三维GIS构建的关键技术,包括三维场景的构建与优化、数据加载以及协同技术,以及基于Sketch Up、3DMAX的精细模型的构建、模型导入Skyline。     

基于Skyline的三维虚拟旅游系统建模方法研究

研究了虚拟现实的相关技术。以Skyline三维GIS软件为手段,以南郭寺为模型,通过资料收集、数据预处理、建立三维虚拟地形、普通建筑建模、特殊地物建模、系统集成等一系列步骤,初步实现了景区场景的实时漫游和部分交互功能。对三维虚拟场景的建模、场景的空间管理与调度以及碰撞监测等关键技术进行了相关实验开发和验证。     

3DGIS与BIM集成在数字城市中的应用研究

BIM和3DGIS的集成融合已经引起广泛关注,BIM是建筑信息模型,Skyline是基于网络的三维地理信息系统平台软件,两者可以应用在很多领域。基于此,利用三维GIS的空间分析方法对BIM的建筑模型进行三维分析,以实现对城市建筑物的管理等,可为相关行业提供参考。     

3DMax模型在Skyline球面空间中的优化展示技术研究

对大范围3DMax三维模型在Skyline球面空间展示时出现位置偏差的原因进行了研究,并提出了基于坐标变换优化展示的技术原理和实施方案,最后对比了中山市原始三维模型和优化后的三维模型在Skyline球面空间中的展示效果。结果表明,这种优化展示技术能有效解决位置偏差问题。     

基于Skyline的城市三维管道自动生成技术

围绕城市地下三维管道的自动生成技术进行了分析与探讨,针对传统管道建模方法中所存在的不足之处,提出了一种基于Skyline的三维管道动态批量生成方法。其主要思想是：通过自动计算二维管线的三维管道建模参数,配合其属性参数的模板化机制,就能实现大规模三维场景中管道模型的快速创建。实践应用表明,该方法较传统建模方法更为快速和实用。     

基于Skyline的三维规划辅助决策系统的设计与实现

Skyline是一款世界领先的三维地球可视化软件,不仅支持快速的数据融合,还为用户提供了实时高效的3D空间影像和丰富便捷的API。本文分享了基于Skyline的三维规划辅助决策系统的设计与实现,及在其过程中遇到的关键问题与解决方法。     

地震灾害应急三维GIS系统开发及应用

三维GIS能够将专业模型和计算结果在平台上进行直观地展示,实现三维可视化和仿真。本文基于Skyline,针对地震灾害应急管理特点开发实现了地震灾害应急三维GIS系统,利用地理信息系统强大的空间分析能力,为灾害应急抢险提供辅助决策。     

基于Skyline煤层气管网三维可视化管理系统探讨

在中石油煤层气公司韩城分公司煤层气管网精确测量数据基础上,以Skyline作为软件开发平台,以3Dmax作为三维建模软件,对地下管网、附属设施和地表三维场景进行真实建模,采用Geodatabase空间数据库管理方式,通过Arc SDE空间数据引擎对煤层气管线二三维数据进行调用,实现二三维联动的设计,采用C#面向对象编程技术,开发了煤层气管网三维可视化管理系统。该系统实现了三维视图的浏览控制、管网的统计查询以及管线分析等功能,对煤层气安全生产提供分析和决策支持。     

三维房地产虚拟系统在Sky line下的实现

运用GIS软件创建DEM和处理遥感影像,采用3DS MAX和Sketchup进行建筑三维建模,并叠加遥感影像,在Skyline创建三维虚拟场景。根据房地产客户需求,设计满足客户需求的三维分析功能,实现虚拟系统在房地产设计中的运用。     

兰州新区地理国情监测三维展示系统的设计与实现

兰州新区地理国情监测三维展示系统以兰州新区地理国情普查综合统计成果为基础,基于Skyline软件建立三维场景。在所建成的三维场景中叠加地理国情监测各类专题信息并设计展示效果,最后将三维场景封装入所开发的展示系统中,用以展示兰州新区的区位优势、地形地貌、总体规划、地理国情监测成果等内容。     

Estimate of heavy metals in soil and streams using combined geochemistry and field spectroscopy in Wan-sheng mining area,Chongqing, China

Heavy metals contaminated soils and water will become a major environmental issue in the mining areas. This paper intends to use field hyper-spectra to estimate the heavy metals in the soil and water in Wan-sheng mining area in Chongqing. With analyzing the spectra of soil and water, the spectral features deriving from the spectral of the soils and water can be found to build the models between these features and the contents of Al, Cu and Cr in the soil and water by using the Stepwise Multiple Linear Regression (SMLR). The spectral features of Al are: 480 nm, 500 nm, 565 nm, 610 nm, 680 nm, 750 nm, 1000 nm, 1430 nm, 1755 nm, 1887 nm, 1920 nm, 1950 nm, 2210 nm, 2260 nm; The spectral features of Cu are: 480 nm, 500 nm, 610 nm, 750 nm, 860 nm, 1300 nm, 1430 nm, 1920 nm, 2150 nm, 2260 nm; And the spectral features of Cr are: 480 nm, 500 nm, 610 nm, 715 nm, 750 nm, 860 nm, 1300 nm, 1430 nm, 1755 nm, 1920 nm, 1950 nm. With these features, the best models to estimate the heavy metals in the study area were built according to the maximal R². The R² of the models of estimating Al, Cu and Cr in the soil and water are 0.813, 0.638, 0.604 and 0.742, 0.584, 0.513 respectively. And the gradient maps of these three types of heavy metals’ concentrations can be created by using the Inverse distance weighted (IDW).The gradient maps indicate that the heavy metals in the soil have similar patterns, but in the North-west of the streams in the study area, the contents are of great differences. These results show that it is feasible to predict contaminated heavy metals in the soils and streams due to mining activities by using the rapid and cost-effective field spectroscopy.     

Determination of the factors governing soil erodibility using hyperspectral visible and near-infrared reflectance spectroscopy

Soil erodibility, which is difficult to estimate and upscaling, was determined in this study using multiple spectral models of soil properties (soil organic matter (SOM), water-stable aggregates (WSA) > 0.25 mm, the geometric mean radius (D_g)). Herein, the soil erodibility indicators were calculated, and soil properties were quantitatively analyzed based on laboratory simulation experiments involving two selected contrasting soils. In addition, continuous wavelet transformation was applied to the reflectance spectra (350–2500 nm) of 65 soil samples from the study area. To build the relationship, the soil properties that control erodibility were identified prior to the spectral analysis. In this study, the SOM, D_g and WSA >0.25 mm were selected to represent the most significant soil properties controlling erodibility and describe the erodibility indicator based on a logarithmic regression model as a function of SOM or WSA > 0.25 mm. Five, six and three wavelet features were observed to calibrate the estimated soil properties model, and the best performance was obtained with a combination feature regression model for SOM (R² = 0.86, p < 0.01), D_g (R² = 0.79, p < 0.01) and WSA >0.25 mm (R² = 0.61, p < 0.01), respectively. One part of the wavelet features captured amplitude variations in the broad shape of the reflectance spectra, and another part captured variations in the shape and depth of the soil dry substances. The wavelet features for the validated dataset used to predict the SOM, WSA >0.25 mm and D_g were not significantly different compared with the calibrated dataset. The synthesized spectral models of soil properties, and the formation of a new equation for soil erodibility transformed from the spectral models of soil properties are presented in this study. These results show that a spectral analytical approach can be applied to complex datasets and provide new insights into emerging dynamic variation with erodibility estimation.     

Determination of the crop row orientations from Formosat-2 multi-temporal and panchromatic images

This paper presents a technique developed for the retrieval of the orientation of crop rows, over anthropic lands dedicated to agriculture in order to further improve estimate of crop production and soil erosion management. Five crop types are considered: wheat, barley, rapeseed, sunflower, corn and hemp. The study is part of the multi-sensor crop-monitoring experiment, conducted in 2010 throughout the agricultural season (MCM’10) over an area located in southwestern France, near Toulouse. The proposed methodology is based on the use of satellite images acquired by Formosat-2, at high spatial resolution in panchromatic and multispectral modes (with spatial resolution of 2 and 8 m, respectively). Orientations are derived and evaluated for each image and for each plot, using directional spatial filters (45° and 135°) and mathematical morphology algorithms. “Single-date” and “multi-temporal” approaches are considered. The single-date analyses confirm the good performances of the proposed method, but emphasize the limitation of the approach for estimating the crop row orientation over the whole landscape with only one date. The multi-date analyses allow (1) determining the most suitable agricultural period for the detection of the row orientations, and (2) extending the estimation to the entire footprint of the study area. For the winter crops (wheat, barley and rapeseed), best results are obtained with images acquired just after harvest, when surfaces are covered by stubbles or during the period of deep tillage (0.27 > R² > 0.99 and 7.15° > RMSE > 43.02°). For the summer crops (sunflower, corn and hemp), results are strongly crop and date dependents (0 > R² > 0.96, 10.22° > RMSE > 80°), with a well-marked impact of flowering, irrigation equipment and/or maximum crop development. Last, the extent of the method to the whole studied zone allows mapping 90% of the crop row orientations (more than 45,000 ha) with an error inferior to 40°, associated to a confidence index ranging from 1 to 5 for each agricultural plot.     

Plant phenolics and absorption features in vegetation reflectance spectra near 1.66 μm

Past laboratory and field studies have quantified phenolic substances in vegetative matter from reflectance measurements for understanding plant response to herbivores and insect predation. Past remote sensing studies on phenolics have evaluated crop quality and vegetation patterns caused by bedrock geology and associated variations in soil geochemistry. We examined spectra of pure phenolic compounds, common plant biochemical constituents, dry leaves, fresh leaves, and plant canopies for direct evidence of absorption features attributable to plant phenolics. Using spectral feature analysis with continuum removal, we observed that a narrow feature at 1.66 μm is persistent in spectra of manzanita, sumac, red maple, sugar maple, tea, and other species. This feature was consistent with absorption caused by aromatic CH bonds in the chemical structure of phenolic compounds and non-hydroxylated aromatics. Because of overlapping absorption by water, the feature was weaker in fresh leaf and canopy spectra compared to dry leaf measurements. Simple linear regressions of feature depth and feature area with polyphenol concentration in tea resulted in high correlations and low errors (% phenol by dry weight) at the dry leaf (r² = 0.95, RMSE = 1.0%, n = 56), fresh leaf (r² = 0.79, RMSE = 2.1%, n = 56), and canopy (r² = 0.78, RMSE = 1.0%, n = 13) levels of measurement. Spectra of leaves, needles, and canopies of big sagebrush and evergreens exhibited a weak absorption feature centered near 1.63 μm, short ward of the phenolic compounds, possibly consistent with terpenes. This study demonstrates that subtle variation in vegetation spectra in the shortwave infrared can directly indicate biochemical constituents and be used to quantify them. Phenolics are of lesser abundance compared to the major plant constituents but, nonetheless, have important plant functions and ecological significance. Additional research is needed to advance our understanding of the spectral influences of plant phenolics and terpenes relative to dominant leaf biochemistry (water, chlorophyll, protein/nitrogen, cellulose, and lignin).     

Estimating the parameters of forest inventory using machine learning and the reduction of remote sensing features

Locally computed statistics of image texture and a case-based reasoning (CBR) system were evaluated for mapping of forest attributes. Cluster analysis was preferred to regression models, as a pre-selection method of features. The best stand-based accuracy using satellite sensor images was 74.64 m⁻³ ha⁻¹ (36%) RMSE for stand volume, 1.98 m⁻³ ha⁻¹ a⁻¹ (49%) for annual increase in stand volume, where κ = 0.23 for stand growth classes and κ = 0.41 for dominant tree species in stands. The top pixel-based accuracy using orthophotos was 76.54 m⁻³ ha⁻¹ (41%) RMSE for stand volume, 1.87 m⁻³ ha⁻¹ a⁻¹ (44%) for annual increase in stand volume, where κ = 0.24 for stand growth classes and κ = 0.38 for dominant tree species in stands. Mean saturation in 30 m radius was the most useful feature when orthophotos were used, and standard deviation of Landsat ETM 6.2 values in 80 m radius was the best when satellite sensor images were used. The most valuable feature components (radii, channels and local statistics) for orthophotos were: 30 m kernel radius, lightness and the mean of pixel values; for satellite sensor images: 80 m kernel radius, near-infrared channel (ETM 4) and the mean of pixel values. Locally computed statistics.     

Construction of anisotropic covariance functions using Riesz-representers

A reproducing-kernel Hilbert space (RKHS) of functions harmonic in the set outside a sphere with radius R ₀, having a reproducing kernel K ₀(P,Q) is considered (P, Q, and later P _n being points in the set of harmonicity). The degree variances of this kernel will be denoted σ_{0 n} . The set of Riesz representers associated with the evaluation functionals (or gravity functionals) related to distinct points P _n ,n = 1,…,N, on a two-dimensional surface surrounding the bounding sphere, will be linearly independent. These functions are used to define a new N-dimensional RKHS with kernel (a _n >0)

An historical empirical line method for the retrieval of surface reflectance factor from multi-temporal SPOT HRV,HRVIR and HRG multispectral satellite imagery

SPOT satellites have been imaging Earth's surface since SPOT 1 was launched in 1986. It is argued that absolute atmospheric correction is a prerequisite for quantitative remote sensing. Areas where land cover changes are occurring rapidly are also often areas most lacking in situ data which would allow full use of radiative transfer models for reflectance factor retrieval (RFR). Consequently, this study details the proposed historical empirical line method (HELM) for RFR from multi-temporal SPOT imagery. HELM is designed for use in landscape level studies in circumstances where no detailed overpass concurrent atmospheric or meteorological data are available, but where there is field access to the research site(s) and a goniometer or spectrometer is available. SPOT data are complicated by the ±27° off-nadir cross track viewing. Calibration to nadir only surface reflectance factor (ρ_s) is denoted as HELM-1, whilst calibration to ρ_s modelling imagery illumination and view geometries is termed HELM-2. Comparisons of field measured ρ_s with those derived from HELM corrected SPOT imagery, covering Helsinki, Finland, and Taita Hills, Kenya, indicated HELM-1 RFR absolute accuracy was ±0.02ρ_s in the visible and near infrared (VIS/NIR) bands and ±0.03ρ_s in the shortwave infrared (SWIR), whilst HELM-2 performance was ±0.03ρ_s in the VIS/NIR and ±0.04ρ_s in the SWIR. This represented band specific relative errors of 10–15%. HELM-1 and HELM-2 RFR were significantly better than at-satellite reflectance (ρ_SAT), indicating HELM was effective in reducing atmospheric effects. However, neither HELM approach reduced variability in mean ρ_s between multi-temporal images, compared to ρ_SAT. HELM-1 calibration error is dependent on surface characteristics and scene illumination and view geometry. Based on multiangular ρ_s measurements of vegetation-free ground targets, calibration error was negligible in the forward scattering direction, even at maximum off-nadir view. However, error exceeds 0.02ρ_s where off-nadir viewing was ≥20° in the backscattering direction within ±55° azimuth of the principal plane. Overall, HELM-1 results were commensurate with an identified VIS/NIR 0.02ρ_s accuracy benchmark. HELM thus increases applicability of SPOT data to quantitative remote sensing studies.     

Exploring hyperspectral bands and estimation indices for leaf nitrogen accumulation in wheat

Hyperspectral sensing can provide an effective means for fast and non-destructive estimation of leaf nitrogen (N) status in crop plants. The objectives of this study were to design a new method to extract hyperspectral spectrum information, to explore sensitive spectral bands, suitable bandwidth and best vegetation indices based on precise analysis of ground-based hyperspectral information, and to develop regression models for estimating leaf N accumulation per unit soil area (LNA, g N m⁻²) in winter wheat (Triticum aestivum L.). Three field experiments were conducted with different N rates and cultivar types in three consecutive growing seasons, and time-course measurements were taken on canopy hyperspectral reflectance and LNA under the various treatments. Then, normalized difference spectral indices (NDSI) and ratio spectral indices (RSI) based on the original spectrum and the first derivative spectrum were constructed within the range of 350–2500 nm, and their relationships with LNA were quantified. The results showed that both LNA and canopy hyperspectral reflectance in wheat changed with varied N rates, with consistent patterns across different cultivars and seasons. The sensitive spectral bands for LNA existed mainly within visible and near infrared regions. The best spectral indices for estimating LNA in wheat were found to be NDSI (R₈₆₀, R₇₂₀), RSI (R₉₉₀, R₇₂₀), NDSI (FD₇₃₆, FD₅₂₆) and RSI (FD₇₂₅, FD₅₁₆), and the regression models based on the above four spectral indices were formulated as Y = 26.34x^1.887, Y = 5.095x − 6.040, Y = 0.609 e^3.008x and Y = 0.388x^1.260, respectively, with R² greater than 0.81. Furthermore, expanding the bandwidth of NDSI (R₈₆₀, R₇₂₀) and RSI (R₉₉₀, R₇₂₀) from 1 nm to 100 nm at 1 nm interval produced the LNA monitoring models with similar performance within about 33 nm and 23 nm bandwidth, respectively, over which the statistical parameters of the models became less stable. From testing of the derived equations, the model for LNA estimation on NDSI (R₈₆₀, R₇₂₀), RSI (R₉₉₀, R₇₂₀), NDSI (FD₇₃₆, FD₅₂₆) and RSI (FD₇₂₅, FD₅₁₆) gave R² over 0.79 with more satisfactory performance than previously reported models and physical models in wheat. It can be concluded that the present hyperspectral parameters of NDSI (R₈₆₀, R₇₂₀), RSI (R₉₉₀, R₇₂₀), NDSI (FD₇₃₆, FD₅₂₆) and RSI (FD₇₂₅, FD₅₁₆) can be reliably used for estimating LNA in winter wheat.     

Nonlinear regional economic dynamics: continuous-time specification,estimation and stability analysis

This paper presents an innovative approach to the study of regional economic dynamics within a nonlinear continuous-time econometric framework—a generalized specification of the Lotka–Volterra system of equations. This specification, which accounts for interdependent behavior of three industrial sectors and spillover effects of activities in neighboring regions, is employed in an analysis of five Italian regions between 1980 and 2003. For these regions, we report estimation results, characterize the varying systems dynamics, analyze the models’ local and global stability properties, and determine via sensitivity analyses which structural features appear to exert the greatest influence on these properties.