基于ArcGIS建模的基本农田数据库建库方法探讨

通过图形化建模工具Model Builder对基本农田数据进行建库,是通过构造地理工作流和脚本将数据和空间处理工具连接起来处理的复杂的GIS任务。本文主要对基本农田数据库的建库采用Arc GIS建模的方法进行了探讨。     

地形三维建模中特殊地物的处理方法

在三维建模领域,空中三角测量通过少量的控制点,按一定的数学模型加密解算出所需要的全部控制点及每张像片的外方位元素并由此进行三维重建工作;若遇到一些特殊地物(如桥梁、铁塔、水域等)时,往往会出现模型错乱、模糊不清、有漏洞或无法正确显示等一系列问题。本文采用倾斜摄影测量建模的方式,对一些特殊地物进行信息采集,再利用专业软件进行加工处理,用3dsmax类建模软件对采集的信息进行精细建模,用skyline类软件进行三维场景编辑及漫游,从而能够实现基于原本样貌重建特殊地物的三维模型。     

地面三维激光扫描技术在变电站精细测量中的应用

变电站是重要的电力设施,对其进行测绘采集是三维建模与可视化、运行维护、管理、异常监控等工作的基础,但其中构筑物具有复杂、结构异形等特点。本文利用三维激光扫描技术获取点云数据,对变电站进行精细测量。提出了基于空间区域复杂度的划分（如将测区划分为均匀分布区、紧密排列区、气体绝缘封闭组合电器（GIS）区、过渡区）方法,并以新疆亚中变电站为例展示了该方法的应用,最终实现了变电站的三维建模与可视化。     

CityEngine CGA支持下的苗族特色民居三维建模

少数民族民居是中华民族文化的重要组成部分,而在现今民族大融合的背景下,传统少数民族建筑的传承和发展正呈现衰退之势,采用三维建模技术对少数民族民居进行数字化保存意义重大。苗族民居造型复杂,具有独特的吊脚楼结构,给规则建模带来困难。为此,本文以CityEngine软件为建模平台,巧妙地组合与运用挤出、拆分、平移等基础函数,编写了苗族民居底层支柱、二层主屋和歇山型屋顶的规则,完整地实现了苗族典型民居的规则建模,并实现了民居样式的动态调整与民族村寨的快速生成。研究结果表明,基于CGA规则对复杂的民族民居三维建模方法可行,为数字化保存少数民族民居提供了便捷的方式。     

Monitoring canopy growth and grain yield of paddy rice in South Korea by using the GRAMI model and high spatial resolution imagery

Monitoring crop conditions and forecasting crop yields are both important for assessing crop production and for determining appropriate agricultural management practices; however, remote sensing is limited by the resolution, timing, and coverage of satellite images, and crop modeling is limited in its application at regional scales. To resolve these issues, the Gramineae (GRAMI)-rice model, which utilizes remote sensing data, was used in an effort to combine the complementary techniques of remote sensing and crop modeling. The model was then investigated for its capability to monitor canopy growth and estimate the grain yield of rice (Oryza sativa), at both the field and the regional scales, by using remote sensing images with high spatial resolution. The field scale investigation was performed using unmanned aerial vehicle (UAV) images, and the regional-scale investigation was performed using RapidEye satellite images. Simulated grain yields at the field scale were not significantly different (p = 0.45, p = 0.27, and p = 0.52) from the corresponding measured grain yields according to paired t-tests (α = 0.05). The model’s projections of grain yield at the regional scale represented the spatial grain yield variation of the corresponding field conditions to within ±1 standard deviation. Therefore, based on mapping the growth and grain yield of rice at both field and regional scales of interest within coverages of a UAV or the RapidEye satellite, our results demonstrate the applicability of the GRAMI-rice model to the monitoring and prediction of rice growth and grain yield at different spatial scales. In addition, the GRAMI-rice model is capable of reproducing seasonal variations in rice growth and grain yield at different spatial scales.     

Modeling the environmental susceptibility of landfill sites in California

??The proper management of solid waste (SW) is a global environmental challenge. A major issue is the proper disposal of SW while balancing a wide range of criteria and working with different spatial data. In this study, we used geographic information system as a tool to perform multi-criteria decision analysis with an analytical hierarchy process to develop an environmental impact susceptibility model (EISM) for landfills. The model was applied to the state of California, USA and results are presented herein. In particular, the EISM considers factors such as geology, pedology, geomorphology, water resources, and climate as represented by 13 associated environmental indicators. The results of the EISM indicate that more than 75% of California’s territory is situated in areas with very low, low, and medium environmental impact susceptibility categories. However, in the remaining 25% of the state’s land, 61 landfills are located in the high and very high categories. These results are alarming because during the period from 2000 to 2015, these 61 landfills received approximately 308 million tons of SW, which corresponds to more than 57% of all SW disposed in California. The model results can be used toward mitigating the environmental impacts of these facilities.     

基于GIS和VRML技术的东圳水库3维仿真建模

以福建省莆田市东圳水库为例,讨论了基于VRML的水库及其周围地理环境、流域状况仿真建模过程,以及所采用的技术和方法。通过仿真系统可以直观分析水库的环境情况,水库管理者可在场景中漫游,从不同位置进行观察分析,进而提出相应解决措施。同时该系统作为对水库进行研究的人机交互平台,可进行实时完善和扩展。     

A review on reflective remote sensing and data assimilation techniques for enhanced agroecosystem modeling

During the last 50 years, the management of agroecosystems has been undergoing major changes to meet the growing demand for food, timber, fibre and fuel. As a result of this intensified use, the ecological status of many agroecosystems has been severely deteriorated. Modeling the behavior of agroecosystems is, therefore, of great help since it allows the definition of management strategies that maximize (crop) production while minimizing the environmental impacts. Remote sensing can support such modeling by offering information on the spatial and temporal variation of important canopy state variables which would be very difficult to obtain otherwise.In this paper, we present an overview of different methods that can be used to derive biophysical and biochemical canopy state variables from optical remote sensing data in the VNIR-SWIR regions. The overview is based on an extensive literature review where both statistical–empirical and physically based methods are discussed. Subsequently, the prevailing techniques of assimilating remote sensing data into agroecosystem models are outlined. The increasing complexity of data assimilation methods and of models describing agroecosystem functioning has significantly increased computational demands. For this reason, we include a short section on the potential of parallel processing to deal with the complex and computationally intensive algorithms described in the preceding sections.The studied literature reveals that many valuable techniques have been developed both for the retrieval of canopy state variables from reflective remote sensing data as for assimilating the retrieved variables in agroecosystem models. However, for agroecosystem modeling and remote sensing data assimilation to be commonly employed on a global operational basis, emphasis will have to be put on bridging the mismatch between data availability and accuracy on one hand, and model and user requirements on the other. This could be achieved by integrating imagery with different spatial, temporal, spectral, and angular resolutions, and the fusion of optical data with data of different origin, such as LIDAR and radar/microwave.     

A filtering approach for estimating lake water quality from remote sensing data

In this paper we consider the estimation of lake water quality constituent distributions from hyperspectral remote sensing data. We present a computational approach that can be used to assimilate information from mathematical evolution models into data processing. The method is based on a reduced order iterated extended Kalman filter, and a convection–diffusion model is used to describe the movement of the water quality constituents. The performance of the technique is evaluated in a simulation study. The results show that the filter approach with an appropriate evolution model yields estimates that have better spatial and temporal resolutions than those obtained with conventional methods. Furthermore, the use of a feasible evolution model may make it possible to obtain information also on the concentrations in the lower parts of the lake.     

On the multivariate total least-squares approach to empirical coordinate transformations. Three algorithms

The multivariate total least-squares (MTLS) approach aims at estimating a matrix of parameters, Ξ, from a linear model (Y−E _Y = (X−E _X ) · Ξ) that includes an observation matrix, Y, another observation matrix, X, and matrices of randomly distributed errors, E _Y and E _X . Two special cases of the MTLS approach include the standard multivariate least-squares approach where only the observation matrix, Y, is perturbed by random errors and, on the other hand, the data least-squares approach where only the coefficient matrix X is affected by random errors. In a previous contribution, the authors derived an iterative algorithm to solve the MTLS problem by using the nonlinear Euler–Lagrange conditions. In this contribution, new lemmas are developed to analyze the iterative algorithm, modify it, and compare it with a new ‘closed form’ solution that is based on the singular-value decomposition. For an application, the total least-squares approach is used to estimate the affine transformation parameters that convert cadastral data from the old to the new Israeli datum. Technical aspects of this approach, such as scaling the data and fixing the columns in the coefficient matrix are investigated. This case study illuminates the issue of “symmetry” in the treatment of two sets of coordinates for identical point fields, a topic that had already been emphasized by Teunissen (1989, Festschrift to Torben Krarup, Geodetic Institute Bull no. 58, Copenhagen, Denmark, pp 335–342). The differences between the standard least-squares and the TLS approach are analyzed in terms of the estimated variance component and a first-order approximation of the dispersion matrix of the estimated parameters.