浑善达克沙地与河北沽源地区土地沙漠化研究

The Otindag sandy land and the Guyuan region of Hebei Province lie in the agro-pastoral zone, where sandy desertification is serious. So they are typical for us to study on. In this paper, detail investigation were made on the Remote Sensing, Hydrochemistry, Chronology, grain size analyzing of research region to monitor sandy desertification and environmental background. The main conclusions are presented as following: 1. According to the diverse natural condition, the research area is divided into three types as sandy land desertification, cultivated land desertification and desertification reflected by lake change. The monitoring result of the first type shows that the main performance way of the sandy desertification in Otindag sandy land is that (1) the expansion of both the shifting dune and the half fixed sandy dune, (2) the reduce of the fixed sandy dune. While the result of the second type shows (1) the desertification land in the Guyuan region has first increasing then reducing change for about 30 years. (2) The sand mainly concentrates west of the research area and small part of wind-drift sand distributes northeast the research area with the spot shape. (3) The meadow area increases obviously. As far as the third type, the Dalai Nur lake area occurs first expanding then reducing change and the wind-drift sand around the lake first reduces then increases. 2. The land cover of the different types change with the same law. It is worth notice that the lake area changes oppositely with that of the wind-drift sand. 3. For about 5,000 a B.P. -2800 a B.P., the well developed palaeosols emerged. After that, three layer palaeosols were founded in the profile of Otindag sandy land. The analyses of grain size show that the sand grains of the south were coarser than that of the north. The sand in the north and middle were well sorted, while the south poor sorted. 4. Both the natural and human impact on the process of sandy desertification. On this research result, different regions have different influences. So the measures to improve sandy desertification should be choosed respectively.

乡镇级人口统计数据空间化研究

Population data which collected and saved according to administrative region is a kind of statistical data. As a traditional method of spatial data expression, average distribution in every administrative region brings population data on a low spatial and temporal precision. Now, an accurate population data with high spatial resolution is becoming more and more important in regional planning, environment protection, policy making and rural-urban development. Spatial distribution of population data is becoming more important in GIS study area. In this article, the author reviewed the progress of research on spatial distribution of population. Under the support of GIS, correlative geographical theories and Grid data model, Remote Sensing data, terrain data, traffic data, river data, resident data, and social economic statistic were applied to calculate the spatial distribution of population in Fujian province, which includes following parts: (1) Simulating of boundary at township level. Based on access cost index, land use data, traffic data, river data, DEM, and correlative social economic statistic data, the access cost surface in study area was constructed. Supported by the lowest cost path query and weighted Voronoi diagram, DVT model (Demarcation of Villages and Towns) was established to simulate the boundary at township level in Fujian province. (2) Modeling of population spatial distribution. Based on the knowledge in geography, seven impact factors, such as land use, altitude, slope, residential area, railway, road, and river were chosen as the parameters in this study. Under the support of GIS, the relations of population distribution to these impact factors were analyzed quantificationally, and the coefficients of population density on pixel scale were calculated. Last, the model of population spatial distribution at township level was established through multiplicative fusion of population density coefficients and simulated boundary of towns. (3) Error test and analysis of population spatial distribution base on modeling. The author not only analyzed the numerical character of modeling error, but also its spatial distribution. The reasons of error were discussed.

月表热参数模型与月壤厚度的被动微波遥感理论分析

利用被动微波遥感亮度温度数据反演月壤厚度是“嫦娥”探月工程的科学目标之一，也是人类探测月壤厚度的一种新的尝试。深入研究月表太阳辐射、月球内部热流以及月表温度的分布和变化规律，是解译遥感数据，反演月壤厚度的前提条件，也为进一步开展月球探测、开发利用月球资源乃至建立月球基地相关研究工作提供必要的参考。 本文根据月表有效太阳辐照度与太阳常数、日月距离和太阳辐射入射角的关系，建立了月表有效太阳辐照度的实时模型如下： (1) 其中， (2) (3) 通过对月表有效太阳辐照度实时模型的各个参数分析发现，影响月表有效太阳辐照度变化的主要因素是日地距离和太阳辐射入射角的变化。对模型的误差分析表明，从1950年到2050年的100年内，月表有效太阳辐照度计算结果的误差百分比小于0.28％，能更准确地反映月表有效太阳辐照度的变化情况。从2007年月表有效太阳辐照度的计算结果发现，该年内的月表有效太阳辐照度变化在1321.5～1416.6 W•m-2之间，平均为1368.0 W•m-2，一个月内的变化最小幅度为6.0 W•m-2，最大幅度为23.6 W•m-2。 在月表有效太阳辐照度的实时模型基础上，根据能量守恒和Stefan-Boltzmann定律，本文还得出了月表温度分布模型如下： (4) 其中，初始条件由下式决定， (5) 通过与月表温度实际观测结果的比较发现，当月表反射率、热发射率和热惯量分别取0.127、0.94和125 J•m-2•s-1/2•K-1时，模型的计算结果与实际观测值比较符合，能较好地预测理想条件下的月表温度。 月表热参数研究的一个重要应用就是解译对月被动微波遥感的亮度温度数据。在对月被动微波遥感探测中，辐射计获得的亮度温度反映了月球表层的热辐射特性。月球表层的热辐射与其自身的热状况紧密相关，结合文中建立的月表热参数模型，根据辐射传播理论进一步分析了对月微波遥感探测中，月球表层在不同情况下对亮度温度的贡献，确定了亮度温度随月表温度和月壤厚度的变化关系，对被动微波遥感探测月壤厚度的可能性和可能达到的精度进行了估算。 对月球表层的热辐射传播的分析发现，对月被动微波遥感探测获得的亮度温度受月球表层热辐射的控制，与月壤厚度具有指数相关性，并受到月表温度的影响。当月壤和月岩的复介电常数分别为2 + 0.005 j和9 + 1 j、相对磁导率均为1时，对应3.0GHz、7.8GHz、19.35GHz和37.0GHz四个频率的亮度温度与月壤厚度及月表温度的关系可分别近似表示为， 3.0GHz亮度温度： (6) 7.8GHz亮度温度： (7) 19.35GHz亮度温度： (8) 37.0GHz亮度温度： (9) 当月壤厚度和月表温度分别在0.5m~30m和100K~400K之间变化时，上述四个频率的亮度温度变化范围分别在212.5K～252.8K、207.4 K～266.7K、193.8 K～288.6K和174.0 K～310.9K之间。对于较低频率的被动微波遥感，亮度温度随月壤厚度的增大逐渐增大并趋于稳定；对较高频率的被动微波遥感，亮度温度随月壤厚度的增大会产生起伏波动，不利于用单波段反演月壤厚度。亮度温度梯度在频率较高时梯度较大，在很小的月壤厚度范围内很快就趋于0，不利于厚度较大时的月壤厚度反演，但对于厚度较小时的月壤厚度反演精度较高；同时，除3.0GHz外，7.8GHz、19.35GHz和37.0GHz三个频率的亮度温度梯度随月表温度的升高降幅较大，尤其是19.35GHz，适合在夜间对月壤厚度较小的地区进行更精确的探测。对于3.0GHz，其亮度温度梯度受月表温度变化的影响很小，能反映出较深层月壤厚度的信息，可以对月球进行全球全天时探测。若辐射计的分辨率为0.02K，3.0GHz频率对10m厚月壤的判别精度达到0.07m；对于20m厚月壤的精度为1.4m。当月壤厚度小于0.5m时，随着月壤厚度从0到0.5m增加，月球表层的亮度温度贡献呈先减小后增大的趋势，从而使某一亮度温度值可能对应存在两种不同的月壤厚度。因此，对于月壤厚度小于0.5m的区域，利用单波段被动微波遥感亮度温度反演月壤厚度是比较困难的。 在对月被动微波遥感探测中，可以利用月球夜晚时段的亮度温度数据判别月壤厚度是否小于0.5m。当月表温度为100K时，3.0GHz、7.8GHz、19.35GHz和37.0GHz四个频率的亮度温度判别参考值分别为212.9K、207.4K、193.5K和174.1K；月表温度为240K时，上述四个频率的亮度温度判别参考值分别为220.8K、226.8K、234.1K和237.2K。当亮度温度小于参考值时表示月壤厚度小于0.5m，反之，表示月壤厚度大于0.5m。更进一步地，可以根据月表温度的影响系数对月岩是否裸露于月表进行判断。当3.0GHz、7.8GHz、19.35GHz和37.0GHz四个频率的月表温度影响系数接近0.77、0.82、0.84和0.85时，可以认为月岩直接暴露于月表。

“3S”技术系统集成研究及其在县级土地利用动态监测中的应用

用遥感（Remote Sensing, RS）进行小比例尺的土地动态监测已是比较成熟的技术，随着“3S”技术的发展，如何用“3S”技术进行县级1：10000大比例尺土地利用动态监测是一个新课题，本文以国家“九五”重点科技攻关课题为基础，提出用“3S”技术进行县级土地利用动态监测的技术路线：用RS发现靶区；GPS实测变化地块数据，保证精度；GIS做为集成平台，进行土地数据的日常管理。在包头示范区，用“3S”技术进行县级1：1万大比例尺土地利用动态监测取得了圆满成功：RS“靶区”发现率在92%以上；GPS实测精度在2M以内；选国产GIS软件MAPGIS，进行二次开发而成的县级土地利用动态监测系统界面友好，功能强大。实现证明用“3S”技术进行县级土地利用动态监测方法具有明显的优势。本文对用“3S”技术进行县级土地利用动态监测的技术路线，和实施过程作了较详细的说明。

Monitoramento espaço-temporal da cana-de-açúcar a partir de índices espectrais de vegetação.

Para culturas agrícolas que abrangem grandes áreas, como é o caso da cana-de-açúcar no Brasil, técnicas de geoprocessamento aplicadas a imagens orbitais de alta resolução temporal apresentam grande potencial de mapear e monitorar os ciclos fenológicos/agronômicos das lavouras. Para essa finalidade, destaca-se o uso de séries temporais de índices espectrais de vegetação (IV) como NDVI (Normalized Difference Vegetation Index) e EVI (Enhanced Vegetation Index) calculados a partir das imagens orbitais de reflectância. Este documento apresenta resultados de uma pesquisa que avaliou a utilização de um método de suavização de perfis temporais de IV e a posterior derivação de parâmetros do ciclo fenológico/agrícola de talhões de cana-de-açúcar, com o objetivo de monitorar e mapear áreas ocupadas por cana-de-açúcar e de distinguir áreas de cana-planta e cana-soca. Foram utilizadas séries temporais de NDVI e EVI do sensor MODIS (Moderate Resolution Imaging Spectroradiometer) a bordo do satélite Terra referentes a uma região do Nordeste do Estado de São Paulo, densamente ocupada por cana-de-açúcar. Os resultados obtidos mostraram grande utilidade das séries temporais de IV do MODIS para monitorar o ciclo agronômico/fenológico de talhões de cana-de-açúcar. Foi possível acompanhar o desenvolvimento da cana-de-açúcar e identificar a ocorrência de cana-planta ou cana-soca para um determinado talhão. O cultivo de uma cultura de ciclo mais curto, ao fazer a reforma do talhão de cana-de-açúcar, também foi identificado nos perfis temporais. A metodologia desenvolvida para classificação de áreas de cana-de-açúcar obteve erro de comissão relativamente pequeno (<10%), mas ao custo de erro de emissão mais elevado (>40%). A classificação realizada para distinção entre áreas de cana-planta e cana-soca também apresentou resultados interessantes, com erro de emissão em torno de 4% e erro de comissão >30%.

Identificação e quantificação de impurezas e misturas em café torrado e moído por análise de imagens.

2001

Uso de imagens digitais na avaliação do crescimento da taxa de cobertura do solo.

RESUMO: Existem vários métodos para avaliar o crescimento da vegetação e a taxa de cobertura do solo. Medidas precisas e rápidas podem ser obtidas do tratamento digital de imagens geradas de câmeras fotográficas ou de vídeo. Há disponível, no mercado, diversos processadores de imagens que apresentam funções básicas semelhantes, mas com certas particularidades que poderão trazer maior benefício para o usuário, dependendo da aplicação. O SPRING, desenvolvido pelo INPE, é de domínio público, sendo mais abrangente do que um processador de imagens, incluindo funções de geoprocessamento. O ENVI foi desenvolvido para a análise de imagens multiespectrais e hiperespectrais, podendo também ser utilizado para o processamento de imagens obtidas de câmeras de vídeo, por exemplo. O KS-300 é um conjunto de hardware e de software destinado ao processamento e à quantificação de imagens microscópicas, permitindo a captação direta das imagens geradas por meio de lupas, microscópios eletrônicos ou câmeras de vídeo. O SIARCS foi desenvolvido pela Embrapa Instrumentação Agropecuária para tornar mais ágil o processo de captação de dados de um sistema. Este trabalho apresenta os fundamentos teóricos básicos envolvidos na técnica de análise de imagens, com as principais características dos softwares citados acima e sua aplicação na quantificação da taxa de crescimento e da cobertura do solo por espécies vegetais. ABSTRACT: Several methods exist to evaluate the growth of the vegetation and the tax of covering of the soil. Necessary and fast measures can be obtained of the digital treatment of generated images of photographic cameras or of video. There is available, in the market, several processors of images that you/they present similar basic functions, but with certain particularities that can bring larger benefit for the user, depending on the application. SPRING, developed by INPE, it is public domain, being including than a processor of images, including functions. ENVI was developed for the analysis of images multiespectrais and hiperespectrais, could also be used for the processing of obtained images of video cameras, for instance. The KS-300 it is a hardware group and software destined to the processing and quantification of microscopic images, allowing the direct reception of the images generated through magnifying glasses, eletronic microscopes or video cameras. SIARCS was developed by Embrapa Agricultural Instrumentation to turn more agile the process of reception of data of a system. This work presents the basic theoretical foundations involved in the technique of analysis of images, with the main characteristics of the softwares mentioned above and his application in the quantification of the growth tax and of the covering of the soil for vegetable species.

The D-CIXS X-ray spectrometer on the SMART-1 mission to the Moon - First Results

Grande, Manuel; Kellett, B.; Howe, C.; Perry, C.H., 'The D-CIXS X-ray spectrometer on the SMART-1 mission to the Moon - First Results', Planetary And Space Science (2007) 55(4) pp.494-502 RAE2008

Hybridization between Brassica napus and B-rapa on a national scale in the United Kingdom

Mike J. Wilkinson, Luisa J. Elliott, Jo?l Allainguillaume, Michael W. Shaw, Carol Norris, Ruth Welters, Matthew Alexander, Jeremy Sweet, David C. Mason (2003). Hybridization between Brassica napus and B-rapa on a national scale in the United Kingdom, Science, 302 (5644), 457-459. RAE2008

Czynniki glebowe w infekcji trawy mannicy odstającej (Puccinellia distans) endofitycznym grzybem Epichloё typhina: określenie ich roli z zastosowaniem analiz przestrzennych

Wydział Biologii

Próba zastosowania lotniczych termowizyjnych obrazów wideo do wykrywania miejsc nielegalnego składowania odpadów zwierzęcych

Access to the remote sensing data was increasing in Poland since 1989. This procccess had stimulating impact on scientific research in the scope of changes in the environment. Special attention should be given to the thermal imagery methods because of its information potential. Presented paper discusses the possibilities of using information from thermal images for detecting of places of illegal dumping of animal waste in the ground. On the basis of earlier survey and gathered data draft fl ight plan was created, covering the sorroundings of Śmiłowo (around 30 sq km). Theoretical thesis for the subject was an assumption that all disturbances of the ground and soil structure should give visible representation in both thermal and visible images. Moreover the process of decay of animal tissues should be the source of heat, which can be observed through thermal sensor. Several places of potential dumping of animal waste were selected. For detailed ground verifi cation eight of them were chosen. In these location geological drillings were performed and than analysis of the samples. Thermovision is a method with great potential for the monitoing of the environment, but its effectiveness depends on the access to another sources of geoinformation.

Information Fusion and Hierarchical Knowledge Discovery by ARTMAP Neural Networks

Mapping novel terrain from sparse, complex data often requires the resolution of conflicting information from sensors working at different times, locations, and scales, and from experts with different goals and situations. Information fusion methods help resolve inconsistencies in order to distinguish correct from incorrect answers, as when evidence variously suggests that an object's class is car, truck, or airplane. The methods developed here consider a complementary problem, supposing that information from sensors and experts is reliable though inconsistent, as when evidence suggests that an objects class is car, vehicle, or man-made. Underlying relationships among objects are assumed to be unknown to the automated system of the human user. The ARTMAP information fusion system uses distributed code representations that exploit the neural network's capacity for one-to-many learning in order to produce self-organizing expert systems that discover hierarchial knowledge structures. The system infers multi-level relationships among groups of output classes, without any supervised labeling of these relationships. The procedure is illustrated with two image examples.

Self-Organizing Hierarchical Knowledge Discovery by an ARTMAP Image Fusion System

Classifying novel terrain or objects front sparse, complex data may require the resolution of conflicting information from sensors working at different times, locations, and scales, and from sources with different goals and situations. Information fusion methods can help resolve inconsistencies, as when evidence variously suggests that an object's class is car, truck, or airplane. The methods described here consider a complementary problem, supposing that information from sensors and experts is reliable though inconsistent, as when evidence suggests that an object's class is car, vehicle, and man-made. Underlying relationships among objects are assumed to be unknown to the automated system or the human user. The ARTMAP information fusion system used distributed code representations that exploit the neural network's capacity for one-to-many learning in order to produce self-organizing expert systems that discover hierarchical knowledge structures. The system infers multi-level relationships among groups of output classes, without any supervised labeling of these relationships.

Self-Organizing Information Fusion and Hierarchical Knowledge Discovery: A New Framework Using Artmap Neural Networks

Classifying novel terrain or objects from sparse, complex data may require the resolution of conflicting information from sensors woring at different times, locations, and scales, and from sources with different goals and situations. Information fusion methods can help resolve inconsistencies, as when eveidence variously suggests that and object's class is car, truck, or airplane. The methods described her address a complementary problem, supposing that information from sensors and experts is reliable though inconsistent, as when evidence suggests that an object's class is car, vehicle, and man-made. Underlying relationships among classes are assumed to be unknown to the autonomated system or the human user. The ARTMAP information fusion system uses distributed code representations that exploit the neural network's capacity for one-to-many learning in order to produce self-organizing expert systems that discover hierachical knowlege structures. The fusion system infers multi-level relationships among groups of output classes, without any supervised labeling of these relationships. The procedure is illustrated with two image examples, but is not limited to image domain.

Monitoring the vegetation start of season (SOS) across the island of Ireland using the MERIS global vegetation index

The aim of this study was to develop a methodology, based on satellite remote sensing, to estimate the vegetation Start of Season (SOS) across the whole island of Ireland on an annual basis. This growing body of research is known as Land Surface Phenology (LSP) monitoring. The SOS was estimated for each year from a 7-year time series of 10-day composited, 1.2 km reduced resolution MERIS Global Vegetation Index (MGVI) data from 2003 to 2009, using the time series analysis software, TIMESAT. The selection of a 10-day composite period was guided by in-situ observations of leaf unfolding and cloud cover at representative point locations on the island. The MGVI time series was smoothed and the SOS metric extracted at a point corresponding to 20% of the seasonal MGVI amplitude. The SOS metric was extracted on a per pixel basis and gridded for national scale coverage. There were consistent spatial patterns in the SOS grids which were replicated on an annual basis and were qualitatively linked to variation in landcover. Analysis revealed that three statistically separable groups of CORINE Land Cover (CLC) classes could be derived from differences in the SOS, namely agricultural and forest land cover types, peat bogs, and natural and semi-natural vegetation types. These groups demonstrated that managed vegetation, e.g. pastures has a significantly earlier SOS than in unmanaged vegetation e.g. natural grasslands. There was also interannual spatio-temporal variability in the SOS. Such variability was highlighted in a series of anomaly grids showing variation from the 7-year mean SOS. An initial climate analysis indicated that an anomalously cold winter and spring in 2005/2006, linked to a negative North Atlantic Oscillation index value, delayed the 2006 SOS countrywide, while in other years the SOS anomalies showed more complex variation. A correlation study using air temperature as a climate variable revealed the spatial complexity of the air temperature-SOS relationship across the Republic of Ireland as the timing of maximum correlation varied from November to April depending on location. The SOS was found to occur earlier due to warmer winters in the Southeast while it was later with warmer winters in the Northwest. The inverse pattern emerged in the spatial patterns of the spring correlates. This contrasting pattern would appear to be linked to vegetation management as arable cropping is typically practiced in the southeast while there is mixed agriculture and mostly pastures to the west. Therefore, land use as well as air temperature appears to be an important determinant of national scale patterns in the SOS. The TIMESAT tool formed a crucial component of the estimation of SOS across the country in all seven years as it minimised the negative impact of noise and data dropouts in the MGVI time series by applying a smoothing algorithm. The extracted SOS metric was sensitive to temporal and spatial variation in land surface vegetation seasonality while the spatial patterns in the gridded SOS estimates aligned with those in landcover type. The methodology can be extended for a longer time series of FAPAR as MERIS will be replaced by the ESA Sentinel mission in 2013, while the availability of full resolution (300m) MERIS FAPAR and equivalent sensor products holds the possibility of monitoring finer scale seasonality variation. This study has shown the utility of the SOS metric as an indicator of spatiotemporal variability in vegetation phenology, as well as a correlate of other environmental variables such as air temperature. However, the satellite-based method is not seen as a replacement of ground-based observations, but rather as a complementary approach to studying vegetation phenology at the national scale. In future, the method can be extended to extract other metrics of the seasonal cycle in order to gain a more comprehensive view of seasonal vegetation development.