Autumn olive (<Emphasis Type="Italic">Elaeagnus umbellata</Emphasis>) presence and proliferation on former surface coal mines in Eastern USA

Invasive plants threaten native plant communities. Surface coal mines in the Appalachian Mountains are among the most disturbed landscapes in North America, but information about land cover characteristics of Appalachian mined lands is lacking. The invasive shrub autumn olive (Elaeagnus umbellata) occurs on these sites and interferes with ecosystem recovery by outcompeting native trees, thus inhibiting re-establishment of the native woody-plant community. We analyzed Landsat 8 satellite imagery to describe autumn olive’s distribution on post-mined lands in southwestern Virginia within the Appalachian coalfield. Eight images from April 2013 through January 2015 served as input data. Calibration and validation data obtained from high-resolution aerial imagery were used to develop a land cover classification model that identified areas where autumn olive was a primary component of land cover. Results indicate that autumn olive cover was sufficiently dense to enable detection on approximately 12.6 % of post-mined lands within the study area. The classified map had user’s and producer’s accuracies of 85.3 and 78.6 %, respectively, for the autumn olive coverage class. Overall accuracy was assessed in reference to an independent validation dataset at 96.8 %. Autumn olive was detected more frequently on mines disturbed prior to 2003, the last year of known plantings, than on lands disturbed by more recent mining. These results indicate that autumn olive growing on reclaimed coal mines in Virginia and elsewhere in eastern USA can be mapped using Landsat 8 Operational Land Imager imagery; and that autumn olive occurrence is a significant landscape vegetation feature on former surface coal mines in the southwestern Virginia segment of the Appalachian coalfield.     

Athletes' use of exercise imagery during weight training

Imagery is a cognitive process during which people use their minds to create (or recreate) experiences that are similar to real-life situations. This study examined how college athletes used imagery during weight training. Subjects were 295 Division I (n = 163) and Division II (n = 132) college student athletes (men: n = 138, women: n = 157) who participated in a weight training program as a requirement of their sport. They completed a slightly modified version of the "Weight Lifting Imagery Questionnaire." Results showed that appearance imagery (i.e., images related to the attainment of a fit-looking body) was used and considered the most effective followed by technique imagery (i.e., images related to performing the skill and techniques correctly with good form) and energy imagery (i.e., images related to getting "psyched up" or feeling energized). Other variables that effected imagery use were gender, age, time of season, and levels of motivation. In addition, gender, previous imagery training, and level of motivation had an effect on the perceptions of imagery effectiveness. Confidence in the ability to image was associated with both imagery use and effectiveness, and imagery use and effectiveness were associated with confidence in the weight room. The findings support previous research in exercise imagery that appearance imagery is most used followed by technique and energy imagery and extend them in such a way that strength coaches have practical advice on how to use imagery in a positive way with their athletes. Suggestions about how strength coaches can use imagery with their clients are provided.     

Main Road Extraction from ZY-3 Grayscale Imagery Based on Directional Mathematical Morphology and VGI Prior Knowledge in Urban Areas

Main road features extracted from remotely sensed imagery play an important role in many civilian and military applications, such as updating Geographic Information System (GIS) databases, urban structure analysis, spatial data matching and road navigation. Current methods for road feature extraction from high-resolution imagery are typically based on threshold value segmentation. It is difficult however, to completely separate road features from the background. We present a new method for extracting main roads from high-resolution grayscale imagery based on directional mathematical morphology and prior knowledge obtained from the Volunteered Geographic Information found in the OpenStreetMap. The two salient steps in this strategy are: (1) using directional mathematical morphology to enhance the contrast between roads and non-roads; (2) using OpenStreetMap roads as prior knowledge to segment the remotely sensed imagery. Experiments were conducted on two ZiYuan-3 images and one QuickBird high-resolution grayscale image to compare our proposed method to other commonly used techniques for road feature extraction. The results demonstrated the validity and better performance of the proposed method for urban main road feature extraction.     

Numeric classification as an aid to spectral mapping of vegetation communities

This study demonstrates a vegetation mapping methodology that relates the reflectance information contained in multispectral imagery to traditionally accepted ecological classifications. Key elements of the approach used are (a) the use of cover rather than density or presence/absence to quantify the vegetation, (b) the inclusion of physical components as well as vegetation cover to describe and classify field sites, (c) development of an objective land cover classification from this quantitative data, (d) use of the field sample sites as training areas for the spectral classification, and (e) the use of a discriminant function to effectively tie the two classifications together. Land cover over 39000 ha of Australian chenopod shrubland was classified into nine groups using agglomerative hierarchical clustering, a discriminant function developed to relate cover and spectral classes, and the vegetation mapped using a maximum likelihood classification of multi-date Landsat TM imagery. The accuracy of the mapping was assessed with an independent set of field samples and by comparison with a map of land systems previously interpreted from aerial photography. Overall agreement between the digital classification and the land system map was good. The units that have been mapped are those derived from numeric vegetation classification, demonstrating that accepted ecological methods and sound image analysis can be successfully combined.     

xtalPiMS: a PiMS-based web application for the management and monitoring of crystallization trials

A major advance in protein structure determination has been the advent of nanolitre-scale crystallization and (in a high-throughput environment) the development of robotic systems for storing and imaging crystallization trials. Most of these trials are carried out in 96-well (or higher density) plates and managing them is a significant information management challenge. We describe xtalPiMS, a web-based application for the management and monitoring of crystallization trials. xtalPiMS has a user-interface layer based on the standards of the Protein Information Management System (PiMS) and a database layer which links the crystallization trial images to the meta-data associated with a particular crystallization trial. The user interface has been optimized for the efficient monitoring of high-throughput environments with three different automated imagers and work to support a fourth imager is in progress, but it can even be of use without robotics. The database can either be a PiMS database or a legacy database for which a suitable mapping layer has been developed.     

黄土高原地区植被与气候的关系

利用地理信息系统技术结合典范对应分析和数量区划的方法。研究了黄土高原地区植被与气候之间的关系。排序结果表明：CCA的第一轴代表黄土高原植被和气候梯度的纬向变化,水分梯度是决定植被分布的最主要气候因子,热量梯度中的全年月平均最低气温、月平均最高气温、年均温也对植被的纬向性分布有较大的影响,黄土高原植被与气候梯度表现出明显的纬向性;CCA的第二轴代表黄土高原植被和气候梯度的经向性变化,热量梯度是决定植被经向性分布的最主要气候因子,水分梯度中的全年最大蒸散量对植被的经向性分布有较大的影响。黄土高原植被与气候梯度表现出明显的经向分布规律性。     

Desertification in the Sahel: a reinterpretation

The impact of human management, in particular livestock grazing, on the vegetation cover of the Sahel is still debated. In a range of studies, satellite images have been used to analyze the development of the Sahelian vegetation cover over time. These studies did not reveal any significant degradation of the Sahel in the last two decades. In this paper, we examine the ecological assumptions underlying the use of satellite imagery to analyze degradation of the Sahel. Specifically, we analyze the variability of the rain‐use efficiency (RUE), which is often used as an indicator for the state of the vegetation cover. We detect a fundamental flaw in the way RUE has been handled in most remote sensing studies; they ignored the relation between annual rainfall variation and RUE. Because of the upward trend in annual rainfall that occurred during the 1980s and 1990s, this leads to a bias in the interpretation of the satellite images. In this paper, we show the importance of the variability in RUE for the analysis of remote sensing imagery of semiarid rangelands. Our analysis also shows that it is likely that there has been anthropogenic degradation of the Sahelian vegetation cover in the last two decades. This has important consequences for the debate on the impacts of grazing on semiarid rangelands. Furthermore, the occurrence of anthropogenic degradation is relevant to explain the magnitude of 20th century Sahelian droughts. The analyses also indicate that the population of the Sahel may be more vulnerable for droughts than currently assumed.     

Harmonization of the Land Cover Classification System (LCCS) with the General Habitat Categories (GHC) classification system

Monitoring land cover and habitat change is a key issue for conservation managers because of its potential negative impact on biodiversity. The Land Cover Classification System (LCCS) and the General Habitat Categories (GHC) System have been proposed by the remote sensing and ecological research community, respectively, for the classification of land covers and habitats across various scales. Linking the two systems can be a major step forward towards biodiversity monitoring using remote sensing. The translation between the two systems has proved to be challenging, largely because of differences in definitions and related difficulties in creating one-to-one relationships between the two systems. This paper proposes a system of rules for linking the two systems and additionally identifies requirements for site-specific contextual and environmental information to enable the translation. As an illustration, the LCCS classification of the Le Cesine protected area in Italy is used to show rules for translating the LCCS classes to GHCs. This study demonstrates the benefits of a translation system for biodiversity monitoring using remote sensing data but also shows that a successful translation is often depending on the degree of ecological knowledge of the habitats and its relationship with land cover and contextual information.     

Predicting and mapping the spatial distribution of Chamaecyparis formosensis in central Taiwan in a GIS with species distribution models

Analyzing the relationship between species and environment is always a focal question of ecological research. In recent years species distribution models (SDMs) has been widely used to predict the spatial distribution of species. SDMs are numerical tools that combine observations and species occurrence or abundance with environmental variables to predict the spatial distribution of species across landscapes, sometimes requiring extrapolation in space and time. Chamaecyparis formosensis (Taiwan red cypress, TRCs) is a coniferous species endemic to Taiwan, where it natural grows in the central mountains at moderate to high altitudes of 800–2800 m, and most stands in the range of 1500–2150 m. It is threatened by habitat loss and over-cutting for its valuable timber. To preserve TRCs species and achieve sustainable use of biological resources, we choose TRCs as a target for the study to predict its distribution in central Taiwan.The pure forests of TRCs in the study area were mainly located in Pachsienshan (P), Shouchentashan (S) and Baigou Mountain (B) in central Taiwan, and the distribution data were originally obtained by The Third Survey of Forest Resources and Land Use in Taiwan. Elevation, slope, aspect, and three vegetation indices were derived from both SPOT-5 satellite images and DEM. GIS technique was used to overlay those factors. Discriminant analysis (DA), decision tree (DT) and maximum entropy (MAXENT), three commonly used SDMs, were applied based on above-mentioned six variables to predict the suitable habitat of TRCs, and to evaluate which the best model is in terms of accuracy and efficiency. Three experiment designs (ED1, ED2 and ED3) with different combinations of samples were used for model building and validation. The 200 target samples were collected from the site P–B, B–S and P–S for model building under ED1, ED2 and ED3 respectively, while the 100 samples were collected from the site S, P and B for model validation. All experiment designs had same 1350 background samples. The results showed that the overall accuracy and kappa coefficient of DT (96%, 0.88) was higher than that of MAXENT (91%, 0.70), and their accuracies were better than that of DA (84%, 0.58). All the three models were highly efficient in implementation of model construction and evaluation, while the DT model was difficult for generating the entire predicted map of potential habitat due to its complex conditional sentence. Vegetation indices derived from SPOT-5 satellite images could not improve model accuracy because of its insufficiency of spectral resolution and spatial resolution. High spatial resolution and spectral resolution remotely sensed imagery should be used in our future research to improve model performance and reliability.     

基于知识库的像斑光谱向量相似度土地覆盖变化检测方法

土地利用/覆盖变化检测是国内外全球化进程研究的重要内容,选择适当的变化检测方法对西北地区土地利用/覆盖变化进行研究在"生态十年项目"中具有重要的意义。选择西北地区具有典型代表性的TM轨道号134033区域作为变化检测方法验证的试验区,采用2005和2010年两期Landsat TM影像,在e Cognition Developer 8.64软件支持下,采用基于像斑的光谱特征特征向量相似度方法进行变化检测,并利用2010年土地覆盖数据作为先验知识库对变化区域分类,提取土地利用/覆盖变化信息,并对变化结果进行定量分析。结果表明,采用基于像斑的光谱特征特征向量相似度方法对于试验区的土地利用/覆盖变化制图具有检测快速、检测精度高等优点,适合试验区以及整个西北地区的土地利用/覆盖变化的检测。最终采用该方法以及分类后比较法获得了西北地区2000—2010年近10年的土地利用/覆盖分类图。     

Critical evaluation of the JDO API for the persistence and portability requirements of complex biological databases

Background  

Complex biological database systems have become key computational tools used daily by scientists and researchers. Many of these systems must be capable of executing on multiple different hardware and software configurations and are also often made available to users via the Internet. We have used the Java Data Object (JDO) persistence technology to develop the database layer of such a system known as the SigPath information management system. SigPath is an example of a complex biological database that needs to store various types of information connected by many relationships.     

大型水母声学观测与评估技术研究进展

20世纪末以来,世界多个海域频繁出现大型水母暴发现象,对海洋生态系统、海洋渔业、沿海工业和滨海旅游业带来了巨大的灾难。为了研究大型水母生态习性,进而揭示其暴发机理并进行灾害的预警防治,近些年来国内外学者开展了大量的采用网具、目视、水下摄像、声学技术、航空影像等多种手段的大型水母监测调查工作,其中使用声学技术对大型水母进行资源评估和行为跟踪目前在欧美、日本、韩国等渔业发达国家已经开展了相关应用,在资源评估、运动学规律等研究中展现出较好的观测效果和应用潜力。目前我国在大型水母声学观测研究应用领域鲜有文献报道,通过介绍国际上利用声学技术对大型水母进行资源调查与评估、空间分布监测、运动规律等研究成果,为今后我国开展大型水母声学调查研究提供理论基础和科学依据。通过本文的分析,建议可以借鉴国际上采用科学鱼探仪、高分辨率成像声呐、声学信标等方法对大型水母进行监测调查和资源评估的研究成果,结合实际情况将声学技术逐步研究并应用到我国大型水母资源调查与评估、自然生态习性研究、重点水域大型水母动态监测预警中去,完善我国大型水母监测调查体系。     

Monitoring the dynamics of an invasive emergent macrophyte community using operational remote sensing data

Potamogeton crispus L. (curly pondweed) is a cosmopolitan aquatic macrophyte considered invasive in North America and elsewhere. Its range is expanding and, on individual water bodies, its coverage can be dynamic both within and among years. In this study, we evaluate the use of free and low-cost satellite remote sensing data to monitor a problematic emergent macrophyte community dominated by P. crispus. Between 2000 and 2006, we acquired eight satellite images of 24,000-ha Lake Sharpe, South Dakota (USA). During one of the dates for which satellite imagery was acquired, we sampled the lake for P. crispus and other emergent macrophytes using GPS and photography for documentation. We used cluster analysis to assist in classification of the satellite imagery and independently validated results using the field data. Resulting estimates of emergent macrophyte coverage ranged from less than 20 ha in 2002 to 245 ha in 2004. Accuracy assessment indicated 82% of image pixels were correctly classified, with errors being primarily due to failure to identify emergent macrophytes. These results emphasize the dynamic nature of P. crispus-dominated macrophyte communities and show how they can be effectively monitored over large areas using low-cost remote sensing imagery. While results may vary in other systems depending on water quality and local flora, such an approach could be applied elsewhere and for a variety of macrophyte communities.     

Modeling winter wheat phenology and carbon dioxide fluxes at the ecosystem scale based on digital photography and eddy covariance data

Recent studies have shown that the greenness index derived from digital camera imagery has high spatial and temporal resolution. These findings indicate that it can not only provide a reasonable characterization of canopy seasonal variation but also make it possible to optimize ecological models. To examine this possibility, we evaluated the application of digital camera imagery for monitoring winter wheat phenology and modeling gross primary production (GPP).By combining the data for the green cover fraction and for GPP, we first compared 2 different indices (the ratio greenness index (green-to-red ratio, G/R) and the relative greenness index (green to sum value, G%)) extracted from digital images obtained repeatedly over time and confirmed that G/R was best suited for tracking canopy status. Second, the key phenological stages were estimated using a time series of G/R values. The mean difference between the observed phenological dates and the dates determined from field data was 3.3 days in 2011 and 4 days in 2012, suggesting that digital camera imagery can provide high-quality ground phenological data.Furthermore, we attempted to use the data (greenness index and meteorological data in 2011) to optimize a light use efficiency (LUE) model and to use the optimal parameters to simulate the daily GPP in 2012. A high correlation (R² = 0.90) was found between the values of LUE-based GPP and eddy covariance (EC) tower-based GPP, showing that the greenness index and meteorological data can be used to predict the daily GPP. This finding provides a new method for interpolating GPP data and an approach to the estimation of the temporal and spatial distributions of photosynthetic productivity.In this study, we expanded the potential use of the greenness index derived from digital camera imagery by combining it with the LUE model in an analysis of well-managed cropland. The successful application of digital camera imagery will improve our knowledge of ecosystem processes at the temporal and spatial levels.     

A simple,fast, and repeatable survey method for underwater visual 3D benthic mapping and monitoring

Visual 3D reconstruction techniques provide rich ecological and habitat structural information from underwater imagery. However, an unaided swimmer or diver struggles to navigate precisely over larger extents with consistent image overlap needed for visual reconstruction. While underwater robots have demonstrated systematic coverage of areas much larger than the footprint of a single image, access to suitable robotic systems is limited and requires specialized operators. Furthermore, robots are poor at navigating hydrodynamic habitats such as shallow coral reefs. We present a simple approach that constrains the motion of a swimmer using a line unwinding from a fixed central drum. The resulting motion is the involute of a circle, a spiral‐like path with constant spacing between revolutions. We test this survey method at a broad range of habitats and hydrodynamic conditions encircling Lizard Island in the Great Barrier Reef, Australia. The approach generates fast, structured, repeatable, and large‐extent surveys (~110 m² in 15 min) that can be performed with two people and are superior to the commonly used “mow the lawn” method. The amount of image overlap is a design parameter, allowing for surveys that can then be reliably used in an automated processing pipeline to generate 3D reconstructions, orthographically projected mosaics, and structural complexity indices. The individual images or full mosaics can also be labeled for benthic diversity and cover estimates. The survey method we present can serve as a standard approach to repeatedly collecting underwater imagery for high‐resolution 2D mosaics and 3D reconstructions covering spatial extents much larger than a single image footprint without requiring sophisticated robotic systems or lengthy deployment of visual guides. As such, it opens up cost‐effective novel observations to inform studies relating habitat structure to ecological processes and biodiversity at scales and spatial resolutions not readily available previously.     

基于医学信息数据仓库模型的数据挖掘

利用数据仓库和数据挖掘技术,以现有医院信息系统HIS及医学信息资源为基础,基于PC和Windows操作系统,利用SQL Server2005及SQL Server 2005 Analysis Services（SSAS）等软件,搭建了医学信息数据仓库模型,并运用数据挖掘技术抽取数据库中数据隐藏的规律,提高医学信息的利用率。为从错综复杂的、庞大的医学信息库中提取有价值的决策支持信息提供有效的途径和方法。     

Differentiating geological fertility derived vegetation zones in Kruger National Park,South Africa,using Landsat and MODIS imagery

Spatial technologies present possibilities for producing frequently updated and accurate habitat maps, which are important in biodiversity conservation. Assemblages of vegetation are equivalent to habitats. This study examined the use of satellite imagery in vegetation differentiation in South Africa's Kruger National Park (KNP). A vegetation classification scheme based on dominant tree species but also related to the park's geology was tested, the geology generally consisting of high and low fertility lithology. Currently available multispectral satellite imagery is broadly either of high spatial but low temporal resolution or low spatial but high temporal resolution. Landsat TM/ETM+ and MODIS images were used to represent these broad categories. Rain season dates were selected as the period when discrimination between key habitats in KNP is most likely to be successful. Principal Component Analysis enhanced vegetated areas on the Landsat images, while NDVI vegetation enhancement was employed on the MODIS image. The images were classified into six field sampling derived classes depicting a vegetation density and phenology gradient, with high (about 89%) indicative classification accuracy. The results indicate that, using image processing procedures that enhance vegetation density, image classification can be used to map the park's vegetation at the high versus low geological fertility zone level, to accuracies above 80% on high spatial resolution imagery and slightly lower accuracy on lower spatial resolution imagery. Rainfall just prior to the image date influences herbaceous vegetation and, therefore, success at image scene vegetation mapping, while cloud cover limits image availability. Small scale habitat differentiation using multispectral satellite imagery for large protected savanna areas appears feasible, indicating the potential for use of remote sensing in savanna habitat monitoring. However, factors affecting successful habitat mapping need to be considered. Therefore, adoption of remote sensing in vegetation mapping and monitoring for large protected savanna areas merits consideration by conservation agencies.     

Modifying the United States National Hydrography Dataset to improve data quality for ecological models

The US National Hydrography Dataset (NHD) is the primary spatial hydrography data source for research and conservation of stream ecosystems in the United States due to its national coverage, high resolution, and delivery of high-volume attribute data. However, despite its wide use, numerous topological errors limit the utility of the NHD for the critical needs of ecological and environmental modeling when network connectivity is of interest. In this paper we demonstrate an automated process that integrates a set of GIS tools to identify, track, and correct four major types of topological errors in the entire NHD network (at the scale of 1:100,000) of the conterminous United States, thereby quantifying and summarizing the type, magnitude, and spatial distribution of topological errors in the NHD. Topological errors occurred on approximately 20% of the flowline features in the NHD dataset, with pseudo nodes being the dominant type of error. In a case study of New River Basin of North Carolina, Virginia, and West Virginia USA, after correcting the topology of the NHD network the number of streams in the database designated as first order decreased by 14%, and the average length of stream between confluences (i.e., inter-confluence stream segments) increased by slightly over half of a kilometer (0.53 km). The topology-corrected NHD dataset should particularly facilitate large-scale (e.g., national or regional) ecological and environmental applications requiring topologically sound stream networks.     

Remote sensing imagery in vegetation mapping: a review

Aims Mapping vegetation through remotely sensed images involves various considerations, processes and techniques. Increasing availability of remotely sensed images due to the rapid advancement of remote sensing technology expands the horizon of our choices of imagery sources. Various sources of imagery are known for their differences in spectral, spatial, radioactive and temporal characteristics and thus are suitable for different purposes of vegetation mapping. Generally, it needs to develop a vegetation classification at first for classifying and mapping vegetation cover from remote sensed images either at a community level or species level. Then, correlations of the vegetation types (communities or species) within this classification system with discernible spectral characteristics of remote sensed imagery have to be identified. These spectral classes of the imagery are finally translated into the vegetation types in the image interpretation process, which is also called image processing. This paper presents an overview of how to use remote sensing imagery to classify and map vegetation cover.Methods Specifically, this paper focuses on the comparisons of popular remote sensing sensors, commonly adopted image processing methods and prevailing classification accuracy assessments.Important findings The basic concepts, available imagery sources and classification techniques of remote sensing imagery related to vegetation mapping were introduced, analyzed and compared. The advantages and limitations of using remote sensing imagery for vegetation cover mapping were provided to iterate the importance of thorough understanding of the related concepts and careful design of the technical procedures, which can be utilized to study vegetation cover from remote sensed images.     

Applications of Low Altitude Remote Sensing in Agriculture upon Farmers' Requests– A Case Study in Northeastern Ontario,Canada

With the growth of the low altitude remote sensing (LARS) industry in recent years, their practical application in precision agriculture seems all the more possible. However, only a few scientists have reported using LARS to monitor crop conditions. Moreover, there have been concerns regarding the feasibility of such systems for producers given the issues related to the post-processing of images, technical expertise, and timely delivery of information. The purpose of this study is to showcase actual requests by farmers to monitor crop conditions in their fields using an unmanned aerial vehicle (UAV). Working in collaboration with farmers in northeastern Ontario, we use optical and near-infrared imagery to monitor fertilizer trials, conduct crop scouting and map field tile drainage. We demonstrate that LARS imagery has many practical applications. However, several obstacles remain, including the costs associated with both the LARS system and the image processing software, the extent of professional training required to operate the LARS and to process the imagery, and the influence from local weather conditions (e.g. clouds, wind) on image acquisition all need to be considered. Consequently, at present a feasible solution for producers might be the use of LARS service provided by private consultants or in collaboration with LARS scientific research teams.