ArcGIS与AutoCAD结合在1:50 000数据库更新工程中的应用

由于GIS软件平台与CAD软件平台各自具有的优势不一样,如何把ArcGIS和AutoCAD相结合,利用各自优势,快速有效地进行地形图数据库内业编辑、数据分析;本文重点讨论了GIS和CAD数据交换的问题.     

发扬夏坚白院士精神,倡导尊重知识、重视知识产权的行业之风

前不久,报章上出现了这样一条消息:国家信息产业部、国家版权局、商务部、财政部四部委联合下发了<关于计算机预装正版操作系统软件有关问题的通知>.根据通知精神,从现在开始没有预装正版操作系统的PC产品将无法上市销售.     

搜酷

强悍的摄像机作为索尼XR系列摄像机的开篇之作,HDRXR520E集众多项级配置于一身,采用了在低光照环境下有着出色表现的Exmor R CMOS感光元件,     

电脑族每天必喝的四杯茶

1.上午一杯绿茶绿茶中含强效的抗氧化剂以及维生素C,不但可以清除体内的自由基,还能分泌出对抗紧张压力的荷尔蒙。绿茶中所含的少量咖啡因可以刺激中枢神经,振奋精神。不过最好在白天饮用,以免影响睡眠。2.下午一杯菊花茶菊花有明目清肝的作用,有些人就干脆用菊花加上枸杞一起泡来喝,或     

中国最美的古村落(十) 浙中奇绝 乌石古村

最吸引人的是这里的建筑,古民居外墙都由一小块一小块的乌石垒成,粗犷的外形与凝重的黑色相结合,结构精巧独特,给人以"鬼斧神工"的感觉,据说最早的乌石屋已有近三百年的历史.     

同江:一曲赫哲族船歌

同江位于三江平原北端,黑龙江与松花江汇流处南岸的三角地带,是有百年历史的国际口岸城市,由佳木斯市代管。清末设立临江州,1905年辟为通商口岸,1914年以黑龙江、松花江在此合一而改名同江。东接抚远,南邻饶河,北与俄列宁斯科耶隔水相望,边境线长166公里,设有国家一类口岸。同江有八岔、街津口两个赫哲族乡,这是一个只有四千人口,却在漫     

国家测绘局跨世纪青年学术带头人李清泉教授

李清泉教授生于1965年1月,安徽天长人,中共党员,工学博士,武汉大学副校长,湖北省第八、九届政协委员,第二届"湖北省五四青年奖章"获得者,湖北省青年联合会第十届委员会副主席,武汉市青年联合会第十届委员会副主席,李清泉教授先后承担了教学、科研、产业和财务管理以及大量社会工作.     

万维网GIS的若干关键技术及其实现

提出基于J2EE多层体系结构的WebGIS系统,能够平衡好服务器、应用服务器、客户端负载,极大地提高系统的响应速度;对海量的矢量数据与影像数据采用不同的压缩方式,在数据不失真的情况下尽量减少数据的传输量,减轻网络的负载;栅格数据与矢量数据的"无缝"叠加,更加形象清楚地发布空间数据;基于0racle Spatial关系数据库的WebGIS,使系统更加安全可靠.     

中国最美的古村落(一)千古太极奇村——俞源探秘

俞源的经典在于村落结构的设计:村中有大小七个水塘组成北斗七星的格局,村内的28幢主要建筑则按照天上的28星宿排布。     

一种基于先验知识的矢量边界更新方法

本文针对GIS中地物矢量边界更新繁琐、低效的问题, 提出一种以GIS数据作为先验知识的简便矢量边界更新方法.该方法针对GIS线性矢量边界只需有限个拐点描述的特性, 将对边界寻找转化为直接对矢量边界上拐点位置的确定, 简化了变化检测与更新过程.以TM遥感影像为数据源,矢量shape文件为先验知识, 分别利用该方法检测、更新扎龙湿地内克钦湖、东升水库以及一块沼泽地边界.并运用简化的缓冲区检测算法计算拐点更新准确率, 同时对本文结果与手动更新结果的相似性进行计算与分析.实验结果表明, 本方法不但简化了变化检测和更新过程, 而且可以得到接近手动更新方法的准确率, 并且更新后矢量多边形与手动更新所得到的矢量多边形保持了较高相似性, 达到了较好的效果.     

土壤有机质光谱特征研究

对在宜兴市和横山县采集的174个土样400nm～2500nm波段的光谱曲线进行了研究。为了有效去除背景噪声对目标光谱的影响，并将非线性关系线性化，首先对土壤光谱进行了14种变换，然后运用光谱微分技术、逐步回归分析等方法研究了土壤光谱反射特性与土壤有机质之间的关系。结果表明，反射率对数的一阶微分这一变换形式对土壤有机质含量最为敏感。建立了相应的回归预测模型，模型方程判定系数达到0．885，较好地利用土壤光谱反射特性预测了土壤有机质的含量。     

应用高光谱遥感数据估算土壤表层水分的研究

土壤水分是土壤的重要组成部分，它在陆地表层和大气之间的物质和能量交换方面扮演着重要角色，寻求快速而准确的方法估算土壤水分具有重要意义。通常，从可见光一近红外对土壤表层水分的估计多是建立在土壤水分与反射率的关系之上的。而在土壤水分含量不高时，土壤水分的增加使土壤光谱反射率在整个波长范围内降低，尤其在760nm，970nm，1190nm，1450nm，1940nm和2950nm等水分吸收波段，而在土壤水分含量较高时，土壤水分的增加会使土壤光谱反射率在某些光谱波段升高。而土壤水分的估计往往是基于土壤水分与土壤水分吸收波段的吸收强度之间的线性关系上，虽然这些经验的方法对于估算某些土壤的表层水分含量是有效的，但这些关系应用于其它条件(如不同种类土壤、土壤湿度变化范围很大的情况)时却面临很多困难，这与土壤的光谱反射率是由土壤的组成成分(土壤水分、有机质、氧化铁和粘土矿物等)的含量和它们在土壤中的分布密切相关。微分技术处理“连续”的光谱是遥感中常用的数学方法，微分技术能部分消除低频光谱成分的影响。现在微分光谱已广泛地应用于研究植被的生物物理参数、矿物和有机质等。然而利用微分光谱对土壤水分反演的研究却鲜见报道。本文通过对实验室中多种不同类型的土壤进行光谱与土壤表层水分含量进行观测，探讨了通过土壤反射率与微分光谱对土壤表层水分的反演方法。4种类型的土壤光谱数据(反射率(R)，反射率倒数的对数(log(1／R))，反射率的一阶微分光谱(dR／dλ)，反射率倒数的对数的一阶微分光谱(d(log(1／R))／dλ))与土壤表层水分之间的关系在本文中得到分析，R与log(1／R)对于不同土壤类型与土壤表层水分都很敏感，说明通过R与log(1／R)反演土壤表层水分受土壤类型的影响很大，而dR／dλ，d(log(1／R))／dλ)对土壤类型却不敏感，对土壤表层水分较为敏感，说明dR／dλ和d(log(1／R))／dλ)对于反演不同类型土壤具有很大的潜力，微分光谱与土壤水分在某些波段具有显著的相关性。通过随机对9种土壤(各具有4个土壤水分)的数据建立反演土壤水分的模型，并其他9种土壤(各具有4个土壤水分)的数据进行验证模型，结果表明，dR／dλ和d(log(1／R))／dλ)能够显著提高R与log(1／R)对于不同土壤类型土壤表层水分的反演精度，由于吸收过程是非线性的，在四种类型的土壤光谱数据中，总体来说，d(log(1／R))／dλ)具有最好的能力预测不同类型土壤的表层水分含量。     

基于反射光谱预测土壤重金属元素含量的研究

本文利用实验室实测的土壤反射光谱以及铅、镉、汞等重金属元素数据,进行土壤重金属元素含量快速预测的可行性研究。本文利用偏最小二乘回归方法,研究了反射率(R)、一阶微分(FDR)、反射率倒数的对数(lg(1/R))和波段深度(BD)等对预测精度的影响,对这几种光谱指标预测土壤重金属含量的能力进行了分析和评价,同时分析了多光谱数据估算土壤重金属元素含量的可行性。结果表明,反射率倒数的对数lg(1/R)是估算土壤重金属元素含量最好的光谱指标,尤其是Cd和Pb,检验精度R超过0.82。有机质、铁锰氧化物和黏土矿物对土壤重金属元素的吸附是可见光—近红外—短波红外光谱估算其含量的机理。多光谱数据同样具有估算土壤重金属元素含量的能力,但实际数据则要考虑多种因素的影响。     

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.     

Analysis and classification of hyperspectral data for mapping land degradation: An application in southern Spain

Desertification is a severe stage of land degradation, manifested by “desert-like” conditions in dryland areas. Climatic conditions together with geomorphologic processes help to mould desert-like soil surface features in arid zones. The identification of these soil features serves as a useful input for understanding the desertification process and land degradation as a whole. In the present study, imaging spectrometer data were used to detect and map desert-like surface features. Absorption feature parameters in the spectral region between 0.4 and 2.5 μm wavelengths were analysed and correlated with soil properties, such as soil colour, soil salinity, gypsum content, etc. Soil groupings were made based on their similarities and their spectral reflectance curves were studied. Distinct differences in the reflectance curves throughout the spectrum were exhibited between groups. Although the samples belonging to the same group shared common properties, the curves still showed differences within the same group.Characteristic reflectance curves of soil surface features were derived from spectral measurements both in the field and in the laboratory, and mean reflectance values derived from image pixels representing known features. Linear unmixing and spectral angle matching techniques were applied to assess their suitability in mapping surface features for land degradation studies. The study showed that linear unmixing provided more realistic results for mapping “desert-like” surface features than the spectral angle matching technique.     

土壤性质指标光谱反演数据变换模型研究

本文采用土壤含铁量和有机质含量作为性质指标,对反射率作常规模型变换,变换后的数值与性质指标的相关系数最大值有一定提高。又进行反射率一阶微分乘以平方根一阶微分等几种创新模型变换,以及将变换效果较好的个别波段选出进行交叉组合模型变换,变换后的数值与性质指标的相关系数最大值又有一定提高。实验证明,反射率一阶微分乘以平方根一阶微分模型以及选出波段交叉组合变换模型可以有效提高反射率与土壤性质指标的相关性。     

Spectral Reflectance Characteristics of the Soils on Basaltic Terrain of Central Indian Plateau

In-situ spectral reflectance characteristics of soils were studied under field conditions with Multiband Ground Truth Radiometer covering 0.45?C0.52, 0.52?C0.59, 0.62?C0.68, and 0.77?C0.86 ??m spectral bands. Twenty-two surface soil samples were studied in laboratory for their spectral reflectance characteristics using ISCO Model S.R. Spectroradiometer in visible wavelength (450?C725 nm), with 25 nm bandwidth, and in infrared wavelength (750?C1550 nm), with 50 nm bandwidth. The Bidirectional reflectance factor representative of spectral reflectance varied from 3.78 to 11.3???m in band 1, 6.09 to 15.41???m in band 2, 8.05 to 19.41???m in band 3, and 12.18 to 31.2???m in band 4. In-situ spectral reflectance in general increases with the wavelength from visible to infrared bands for all the soils. Black soils have relatively lower reflectance as compared to red soils, which is attributed to the variation in the physicochemical properties of soils. Spectral reflectance, under laboratory conditions, for all the soils increases with wavelength from visible to infrared region except at 950 nm and 1200 nm, where reflectance decreased in all soils, due to weaker water absorption bands and also at 1350 nm, due to strong water absorption at this band. The spectral reflectance of red soils were higher, in-situ as well as under laboratory conditions, as compared to black soils, which is attributed to variation in soil colour, organic matter and clay content of soils. It is observed that the spectral reflectance decrease due to moisture content in soils in all the spectral bands because of darker appearance of soils at moist conditions. Laboratory reflectance measurements serve to define the extent to which intrinsic spectral information is available from soils as a consequence of their composition.     

Estimating soil zinc concentrations using reflectance spectroscopy

Soil contamination by heavy metals has been an increasingly severe threat to nature environment and human health. Efficiently investigation of contamination status is essential to soil protection and remediation. Visible and near-infrared reflectance spectroscopy (VNIRS) has been regarded as an alternative for monitoring soil contamination by heavy metals. Generally, the entire VNIR spectral bands are employed to estimate heavy metal concentration, which lacks interpretability and requires much calculation. In this study, 74 soil samples were collected from Hunan Province, China and their reflectance spectra were used to estimate zinc (Zn) concentration in soil. Organic matter and clay minerals have strong adsorption for Zn in soil. Spectral bands associated with organic matter and clay minerals were used for estimation with genetic algorithm based partial least square regression (GA-PLSR). The entire VNIR spectral bands, the bands associated with organic matter and the bands associated with clay minerals were incorporated as comparisons. Root mean square error of prediction, residual prediction deviation, and coefficient of determination (R²) for the model developed using combined bands of organic matter and clay minerals were 329.65 mg kg⁻¹, 1.96 and 0.73, which is better than 341.88 mg kg⁻¹, 1.89 and 0.71 for the entire VNIR spectral bands, 492.65 mg kg⁻¹, 1.31 and 0.40 for the organic matter, and 430.26 mg kg⁻¹, 1.50 and 0.54 for the clay minerals. Additionally, in consideration of atmospheric water vapor absorption in field spectra measurement, combined bands of organic matter and absorption around 2200 nm were used for estimation and achieved high prediction accuracy with R² reached 0.640. The results indicate huge potential of soil reflectance spectroscopy in estimating Zn concentrations in soil.     

Hydrocarbon micro-seepage detection from airborne hyper-spectral images by plant stress spectra based on the PROSPECT model

Hydrocarbon micro-seepage can result in vegetation spectral anomalies. Early detection of spectral anomalies in plants stressed by hydrocarbon micro-seepage could help reveal oil and gas resources. In this study, the origin of plant spectral anomalies affected by hydrocarbon micro-seepage was measured using indoor simulation experiments. We analyzed wheat samples grown in a simulated hydrocarbon micro-seepage environment in a laboratory setting. The leaf mesophyll structure (N) values of plants in oil and gas micro-seepage regions were measured according to the content of measured biochemical parameters and spectra simulated by PROSPECT, a model for extracting hydrocarbon micro-seepage information from hyper-spectral images based on plant stress spectra. Spectral reflectance was simulated with N, chlorophyll content (C_ab), water content (C_w) and dry matter content (C_m). Multivariate regression equations were established using varying gasoline volume as the dependent variable and spectral feature parameters exhibiting a high rate of change as the independent variables. We derived a regression equation with the highest correlation coefficient and applied it to airborne hyper-spectral data (CASI/SASI) in Qingyang Oilfield, where extracted information regarding hydrocarbon micro-seepage was matched with known oil-producing wells.