典型地物波谱库的数据体系与波谱模拟

本文讨论典型地物波谱知识库的数据体系与地物波谱模拟的相关问题 ,并给出波谱知识库支持的农业定量遥感应用示例。波谱知识库的数据体系强调波谱参数与环境参数的配套 ,波谱数据测量是在相关规范的支持下完成的 ,质量控制贯穿于数据采集的全过程。波谱模拟通过遥感物理模型完成 ,模拟波谱计算包括地表参数的时间、空间扩展与遥感物理模型运算。最后以作物生长模型结合植被组分光谱模型和冠层遥感模型为核心 ,构造了定量遥感的农业示例。     

云南省腾冲油菜反射波谱特征与应用分析

以云南省腾冲县的油菜为例,探讨同种作物在不同生长阶段的光谱特点及其关系,探讨不同盖度情况下的同种作物的光谱特点及其关系。通过野外典型选样测定,并对测定数据进行处理和分析。研究初步表明,在375-670nm的范围内,其反射率随油菜开花量的增加而增加。在800-1100nm的范围内,其反射率随油菜开花量的增加而减少。在800-1100nm范围内,未开花油菜与全开花油菜的组合比例与其光谱组合比例比较接近。在368.4-738.2nm的范围内,盖度越高的菜地,其反射率越低。在741.2-1110.6nm的范围内,盖度越高的菜地,其反射率越高。在500-700nm范围内,土壤与覆盖度为100%的油菜的组合比例与其光谱组合比例比较一致。该研究对不同生长阶段油菜的识别以及不同盖度油菜的识别具有积极的应用意义。     

应用GIS技术构建地学模拟模型

地理信息系统（简称GIS）是由计算机硬件、软件和不同的方法组成的系统。该系统设计支持空间数据的采集、管理、处理、分析、建模和显示,以便解决复杂的规划和管理问题。GIS的应用模型,就是根据具体的应用目标和问题,借助于GIS自身的技术优势,使观念世界中形成的概念模型,具体化为信息世界中可操作的机理和过程。     

遥感图像模拟技术方法与应用分析

近年来,遥感图像模拟的研究广泛地展开,利用图像模拟技术获取的大量仿真遥感图像,为其他遥感信息的论证与评价提供了有效的参考。本文介绍了图像模拟技术的发展现状,及SAR、光学遥感图像的模拟方法,分析了各种模拟方法的特点与差异,指出各种方法在生成模拟图像时的局限性,最后提出了基于宽光谱遥感数据的细分光谱遥感图像模拟的构想。     

土地利用数据库在地籍管理中的应用

笔者在进行土地利用数据库应用调查中发现,目前很多的县(市)土地利用数据库建设完成后,由于经费或人才等方面原因,数据库原封不动地放在电脑光盘里或是仅仅用来打印土地利用现状图,土地利用数据库的应用程度还远远不够.     

川中紫色丘陵区径流泥沙SWAT模型的模拟应用分析

针对川中丘陵区紫色土坡耕地严重水土流失,选取典型代表李子溪流域为研究区,构建了其SWAT的模型数据库,包括地形、土壤、气象和土地利用数据库。并利用赵家祠水文站1970-1979年的实测径流和泥沙资料,对该流域的SWAT模型参数进行率定再采用1980-1986年的实测资料,对模型的适用性进行验证,同时用相对误差Re和Nash确定性系数Ens评价模拟效果。结果显示,径流和泥沙模拟相对误差均在±15%范围以内,Nash确定性系数均大于等于0.70,说明SWAT模型对李子溪流域年、月径流和年泥沙量的模拟精度较高。同时模拟值与实测值和降雨量的变化趋于一致。可见,用SWAT模型模拟和预测雨量较为丰沛、土壤侵蚀较严重的紫色丘陵地区的产流产沙是实用、可行的。     

加权人口密度连续分布模拟模型

人口分布是人文科学的重要研究对象。如何在数据不足的情况下获得较为符合实际的人口分布数据是业界的一大难题。为了解决这个问题,本文依照人口分布的规律,以人口统计数据、高程、海岸线、城市中心、河流和行政区划为影响因子,通过距离计算、重分类、加权计算等方法,建立了加权人口密度连续分布模拟模型,并进行了改进。基于2011年的福建省县级人口统计数据,本文在ArcGIS平台上模拟了该省的人口密度空间分布,模拟结果表明：各县人口总数误差与平均人口误差在40%以下的百分比分别为89%与84%,突出了福建省在城市中心、沿海、沿江,以及平原地区的人口聚集规律;各个县级行政区域内的数据集中,相对独立;县与县之间的区分边界数据又相互联系,变化连续,符合人口分布的实际情况。该模型相比于其他的人口模型易于理解,结构简单,可应用于各省的人口密度分布模拟,能有效地解决人口数据不足的问题。     

纳入空间自相关的FLUS模型在土地利用变化多情景模拟中的应用

FLUS模型是一种新型的土地利用变化模拟模型,应用前景广阔。本文通过在FLUS模型的人工神经网络(Artificial Neural Network,ANN)训练模块中引入空间自相关因子来改进模型,以珠江三角洲地区为例,基于2009年、2015年土地利用数据和一系列驱动因子对改进的模型进行了验证,并利用该改进的FLUS模型模拟了2035年研究区在3种情景下土地利用变化格局。结果表明:①引入空间自相关因子后各地类发生概率分布的预测精度更高,耕地、林地、建设用地、水体和未利用土地的拟合优度ROC值分别从0.819、0.928、0.885、0.855和0.861提高到0.857、0.934、0.890、0.863和0.978;②改进的FLUS模型的模拟精度有一定的提高,Kappa系数从0.732提高到0.744,FOM系数从0.077升到0.106;③情景模拟表明,3种情景下珠江三角洲建设用地和林地均将增加、而耕地均呈减少趋势。但不同情景下模拟的土地利用格局也存在显著差异:基准情景下,建设用地明显扩张且大幅侵占耕地。耕地保护情景下,耕地面积保持在合理水平,建设用地蔓延扩张趋势得到遏制,土地利用布局总体趋向合理。生态保护情景下,耕地、林地和水体得到较好保护,建设用地布局更为合理,土地利用可持续性明显提高。     

林木多样性模型及生长模拟

林木生存环境复杂多变,导致林木形态千差万别,为了准确描述林木的形态多样性特征,提出了一种基于模型分解的林木生长模拟方法。首先构建多样性林木三维模型,使用林木形态的实际测量数据,基于B样条函数约束树干、树冠形态,依据削度方程模拟树干直径的变化趋势,利用Direct3D实现林木形态的三维建模;其次,按林木形态特征进行林木模型分解,将林木模型分为9个子模型;最后,使用场景节点按照拓扑结构关系对子模型进行动态组织,基于胸径、树高、枝下高、冠高和冠幅生长模型,使用多核CPU多线程并行的计算方式,获得各年龄阶段林木东、南、西、北方向形态参数,从而对子模型生长状态进行控制,实现林木不同方向生长变化的可视化模拟。结果表明：该方法可将林木三维模型与生长模型紧密结合起来,林木各方向的枝下高、冠高和冠幅严格按照生长模型进行生长,可有效模拟出林木各方向生长状态的多样性;三维场景渲染帧率（FPS）保持在25以上,平均FPS可以达到50,实现林木生长的方向异质性模拟。     

基于Rothermel模型的森林火灾模拟算法的改进

我国是一个森林火灾多发的国家,森林火灾不仅破坏森林生态系统、造成林木资源损失,还会威胁到人民的生命财产安全。因此如何快速精确地模拟森林火灾的蔓延过程对于防灾减灾有重大的意义和作用,本文在深入对比了国内外各种森林火灾蔓延模型的基础上,选择了抽象程度较高、顾及因素全面的Rothermel火灾蔓延模型作为本文的应用模型,在模型的应用中改正了森林火灾范围割的缺陷,优化了多着火点同时蔓延时火灾边界计算方法,最终实现森林火灾模拟的功能,满足了森林火灾蔓延模拟实时高效的要求,为管理部门提供可靠的决策支持。     

Atmospheric radiative transfer simulation for atmospheric correction of remote sensing data

The radiance leaving the earth-atmosphere system which can be sensed by a satellite borne radiometer is the sum of radiation emission from the earth surface and each atmospheric level that are transmitted to the top of the atmosphere. The radiation emission from the earth surface and the radiance of each atmospheric level can be separated from the radiance at the top the atmospheric level measured by a satellite borne radiometer. However, it is very difficult to measure the atmospheric radiance, especially the synchronous measurement with the satellite. Thus some atmospheric radiative transfer models have been developed to provide many options for modeling atmospheric radiation transport, such as LOWTRAN, MODTRAN, 6S, FASCODE, LBLRTM, SHARC, and SAMM. Meanwhile, these models can support the detailed detector system design, the optimization and evaluation of satellite mission parameters, and the data processing procedures. As an example, the newly atmospheric radiative transfer models, MODTRAN will be compared with other models after the atmospheric radiative transfer is described. And the atmospheric radiative transfer simulation procedures and their applications to atmospheric transmittance, retrieval of atmospheric elements, and surface parameters, will also be presented.     

Atmospheric radiative transfer simulation for atmospheric correction of remote sensing data

IntroductionThe radiance leaving the earth-atmosphere sys-tem which can be sensed by a satellite borne ra-diometer is the sum of radiation emission fromtheearth surface and each atmospheric level that aretransmittedtothe top of the atmosphere.The radia-tion emissionfromthe earthsurface andthe radianceof each atmospheric level can be separated fromtheradiance at the top the atmospheric level a satellitemeasured.Thus,the earth surface parameters willbe retrieved from the surface radiance after a…     

APPLICATION OF REMOTE SENSING TECHNOLOGY TO POPULATION ESTIMATION

This paper attempts to explore a new avenue of urban small-regional population estimation by remote sensing technology, creatively and comprehensively for the first time using a residence count method, area (density)method and model method, incorporating the application experienqe of American scholars in the light of the state of our country. Firstly, the author proposes theoretical basis for population estimation by remote sensing, on the basis of analysing and evaluating the history and state quo of application of methods of population estimation by remote sens-ing. Secondly, two original types of mathematical models of population estimation are developed on the basis of remote sensing data, taking Tianjin City as an example. By both of the mathematical models the regional population may be estimated from remote sensing variable values with high accuracy. The number of the independent variables in the lat-ter model is somewhat smaller and the collection of remote sensing data is somewhat easier, but the deviation is a little larger. Finally, some viewpoints on the principled problems about the practical application of remote sensing to popu-lation estimation are out forward.     

基于光谱时间序列拟合的中国南方水稻遥感识别方法研究

多云多雾现象是农作物遥感分类经常遇到的问题,影响分类精度。为解决此类问题,本文提出一种基于时间序列GF-1号遥感影像识别水稻方法。利用多时相时间序列的GF-1号遥感影像提取中稻、晚稻的近红外波段（NIR）反射率、红光（R）波段反射率、归一化植被指数（NDVI）特征;拟合光谱和植被指数时间序列特征曲线;分析多时相影像离散近红外波段 、红光波段、NDVI值落在拟合中稻、晚稻近红外波段、红光波段、NDVI时间序列曲线两侧的敏感性区域的比例,该区域也可以视为水稻作物识别特征的目标特征区域,只有达到一定的比例才能视为某类水稻作物。在此情形下,需要综合3种情况进行集中投票决定其最终分类结果。研究表明：该方法可以在多云雾地区对中稻和晚稻精确识别,中稻和晚稻用户精度可达95.97%和95.95%,总体精度为95.76%,kappa系数为0.9335。实验结果表明了NIR、R、NDVI时间序列曲线拟合的有效性,以及拟合曲线目标特征区域设置的合理性。     

Study on model for remote sensing estimation of maize yield

Through analysis of perpendicular vegetation index (PVI) from combination of visible and near-infrared spectrums reflecting the feature of crop reflectance, we come to the conclusion that the index can better indicate crop instantaneous photosynthesis whereas people generally regard it as the representation of crop leaf area index (LAI). Exploration of crop photosynthesis within a day and its period of duration leads to production of photosynthetic vegetation index (PST) that can reflect the whole crop accumulated photosynthesis, which means the total biomass produced by crop, moreover the method simulating PST is put forward by employment of multi-temporal spectrum parameters. On the basis of the achievements mentioned above, a new comprehensive model for remote sensing estimation of maize yield is established, which can comprehensively show major physiological actions of maize and the course of its yield formation, organically integrate various effective ways of crop yield estimation. It lays a solid foundation for carrying out remote sensing estimation of maize yield on a large scale.     

Study on model for remote sensing estimation of maize yield

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A study on wetland classification model of remote sensing in the Sangjiang plain

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APPLICATION OF REMOTE SENSING TECHNIQUE TO THE INVESTIGATION ON REED RESOURCES OF BOSTEN LAKE

It is efficient and reliable to investigate the reed resources of Bosten Lake in Xinjiang, characterized by complicated types of reed land and very different reed growth using colour infrared aerial photos, Landsat images and multi band imagery. At first, on the basis of Landsat images and relevant data, we analyzed the ecological environment of the studied area, the characteristics of reed land types and imaging mechanism to determine the program for abstracting remote sensing information and aerial photographic conditions:On the processed Landsat images and aerial photos, we considered typically geographic features and reed land types, preselected ground sampling sections and sites, then determined indexes classifying reed land types according to sampling data and image characteristics, established various interpretation keys, drew distribution maps of reed land types and measured the area of various types of reed land following geometric correction. On the other hand, based on sampling values and hab     

Semi-supervised support vector regression model for remote sensing water quality retrieving

This paper proposed a semi-supervised regression model with co-training algorithm based on support vector machine, which was used for retrieving water quality variables from SPOT5 remote sensing data. The model consisted of two support vector regressors (SVRs). Nonlinear relationship between water quality variables and SPOT5 spectrum was described by the two SVRs, and semi-supervised co-training algorithm for the SVRs was established. The model was used for retrieving concentrations of four representative pollution indicators—permanganate index (CODmn), ammonia nitrogen (NH₃-N), chemical oxygen demand (COD) and dissolved oxygen (DO) of the Weihe River in Shaanxi Province, China. The spatial distribution map for those variables over a part of the Weihe River was also produced. SVR can be used to implement any nonlinear mapping readily, and semi-supervised learning can make use of both labeled and unlabeled samples. By integrating the two SVRs and using semi-supervised learning, we provide an operational method when paired samples are limited. The results show that it is much better than the multiple statistical regression method, and can provide the whole water pollution conditions for management fast and can be extended to hyperspectral remote sensing applications.     

A methodology for estimating Leaf Area Index by assimilating remote sensing data into crop model based on temporal and spatial knowledge

In this paper,a methodology for Leaf Area Index(LAI) estimating was proposed by assimilating remote sensed data into crop model based on temporal and spatial knowledge.Firstly,sensitive parameters of crop model were calibrated by Shuffled Complex Evolution method developed at the University of Arizona(SCE-UA) optimization method based on phenological information,which is called temporal knowledge.The calibrated crop model will be used as the forecast operator.Then,the Taylor′s mean value theorem was applied to extracting spatial information from the Moderate Resolution Imaging Spectroradiometer(MODIS) multi-scale data,which was used to calibrate the LAI inversion results by A two-layer Canopy Reflectance Model(ACRM) model.The calibrated LAI result was used as the observation operator.Finally,an Ensemble Kalman Filter(EnKF) was used to assimilate MODIS data into crop model.The results showed that the method could significantly improve the estimation accuracy of LAI and the simulated curves of LAI more conform to the crop growth situation closely comparing with MODIS LAI products.The root mean square error(RMSE) of LAI calculated by assimilation is 0.9185 which is reduced by 58.7% compared with that by simulation(0.3795),and before and after assimilation the mean error is reduced by 92.6% which is from 0.3563 to 0.0265.All these experiments indicated that the methodology proposed in this paper is reasonable and accurate for estimating crop LAI.