Environmental factors influencing aggregation of manta rays (Manta birostris) off the northeastern coast of the Yucatan Peninsula

Manta rays inhabit tropical, subtropical and temperate waters. Aggregation sites of manta rays have been recognized worldwide, but the reasons for this behavior are still poorly understood. This study describes environmental factors influencing aggregation sites of the giant manta ray (Manta birostris) off the northeastern coast of the Yucatan Peninsula. Observations of manta rays were obtained from scientific surveys conducted during 2006–2011. Environmental data were obtained from satellite imagery. The maximum entropy (Maxent) method for habitat modeling was used to determine the effects of environmental conditions on the species and predict suitable habitat for manta rays in this region. Primary productivity and distance to the coast were the most influential variables, suggesting that aggregation occurs in highly productive coastal waters. The distribution of manta rays predicted by the Maxent model showed that the most suitable habitat within the study area is located off the northeastern coast of the Yucatán Peninsula, more precisely, northeast of Isla Holbox and northwest of Isla Contoy. Seasonal patterns of distribution suggest that the most suitable conditions are present from July through September.     

面向对象的航空高光谱图像混合分类方法

传统的高光谱分类通常仅考虑单一像元的光谱或纹理特征,分类后容易出现地物破碎的现象。鉴于此,本文提出了一种面向对象的混合分类方法,将面向对象的分割结果与传统的像元级分类结果进行有机融合,充分利用对象的光谱特征和空间结构特征。在此基础上,引入了2种具体的混合分类方法,即多尺度分割的SVM分类和多波段分水岭分割的SVM分类。前者将地物光谱的可变性进行弱化处理,转化为多尺度均质对象单元进行分类;后者融入了地物的空间信息和形态学特征,对分割得到的同质区域进行分类。将这2种分类方法应用于航空高光谱数据,实验结果表明：面向对象的混合分类方法的总体精度分别为92.63%和96.13%,与传统的像元级分类法相比,分别提高了10.14%和13.64%,有效地解决了分类后地物的破碎现象。     

The response of braided planform configuration to flow variations,bed reworking and vegetation: the case of the Tagliamento River,Italy

Morphological features of braided rivers (bars, channels and pools) experience major changes in area, shape and spatial distribution as a response to (i) the pulsation of discharge during a flood and (ii) the bed evolution induced by floods. In this work, at‐a‐station relationships between water level and planform configuration were investigated on the Tagliamento River, a large gravel‐bed braided river in northeast Italy, over a 2‐year study period comprising three bankfull events and several small‐to‐medium floods. The analysis was performed on two 1‐km‐long reaches, characterized by different riparian vegetation cover. Ground‐based images with an hourly temporal resolution were acquired using software‐controlled, digital cameras. Bars, channels, pools and vegetated patches were manually digitized on more than 100 rectified images. Sequences of constant‐level images spanning the study period were used to quantify the impact of floods on the stability of at‐a‐station relationships and on the turnover rate of water bodies. The analysis shows that wetted area increased almost linearly with water level in both reaches. The average number of branches per cross‐section peaked at intermediate flow levels, increasing from 2 at low flow up to 6–7. The number of branches displayed the largest fluctuations over time, with significant changes produced also by moderate floods. Turnover rates were high in both reaches, with more than 30% of wetted areas at low flow converting into bare gravel in less than 2 months. Vegetation colonization was found to limit the mobility of the low flow channels over time by concentrating the flow in fewer, deeper anabranches. The number of channels per cross‐section was 30–40% less in the vegetated reach and the proportion of low flow water bodies in the same position after 12 months increased from 3% to 14%. Copyright © 2012 John Wiley & Sons, Ltd.     

2008年“威马逊”台风期间海上大气波导时空特征

结合 MM5数值模拟结果、天气图和卫星云图,系统分析了2008年“威马逊”台风引起的大气波导特征.本次台风过程中发生的大都为悬空大气波导,位于台风涡旋之外的西北部,台风越近波导高度、强度、厚度越大;同时数值模拟表明陆地地形对海上本次大气波导形成具有一定影响.利用大气波导这一特殊大气层结可以很好的评估和预测电磁波传播和海上探测通信系统     

三维成矿预测多源地学空间数据库建设——以宁芜盆地钟姑矿田为例

大数据是海量、高增长率和多样化的信息资产,是未来找矿靶区预测的不可或缺的技术。大数据-三维成矿预测研究面临机遇与挑战,其涉及的地学大数据除了数据来源众多、比例尺不同、数据量大、非结构化管理、时效性强、空间数据与非空间数据协同管理等复杂特点外,还必须具有适应进行三维建模及空间分析的数据结构。本文分析了地学空间大数据的特点,对多源地学综合信息的管理需求进行研究,参考国家及行业标准,建立了可满足三维成矿预测需求的多源地学空间数据库模型,并依据实际划分为勘查控制钻孔地质数据库、空间属性数据库和地球物理数据库,各数据库可在多源地学空间索引库的支持下协同工作。本文以大数据应用的典型实例--钟姑矿田作为研究对象,系统收集了矿田内勘查成果资料,建立了钟姑矿田多源地学空间数据库,并在此基础上进行了控矿要素的有效提取,可进一步支持三维成矿预测。研究结果表明,本文提出的多源地学空间数据库可有效管理地学空间大数据,是大数据-三维成矿预测的重要解决方案,是进行三维成矿预测的重要数据支持。     

兰坪—思茅盆地钾盐矿床的物质来源探讨

云南兰坪—思茅盆地是我国重要的成钾盆地。综合前人的研究成果,简要介绍了该地区的地质概况。对兰坪—思茅盆地钾盐矿床的物质来源进行了阐述和讨论,认为该盆地钾盐矿床的物质来源以残存古海水为主,并有陆源水体的补给。盆地内的富钾火山凝灰岩、古盐和风化盐也可能为该盆地钾盐矿床提供物源,至于深部地下卤水是否提供物源还需更深入的研究论证。兰坪—思茅盆地钾盐矿床成矿物源具有多源性。     

基于GML的多源异构空间数据集成框架研究

基于GML的多源异构空间数据集成将GML技术与空间数据集成技术相结合,能解决网络环境下地理信息资源相互不兼容的瓶颈问题,有效的实现数据的互操作。本文在分析GML数据格式特征的基础上,基于J2EE网络环境提出一个基于GML的多源异构空间数据应用集成框架。重点介绍了E00格式的空间数据规格定义以及向GML格式转换处理过程,并以福建省主要公路数据集为样本数据验证了集成结果。结果表明,采用GML作为多源异构数据集成的描述格式,可在数据层上有效地实现空间数据的集成和互操作。     

Darkness on the Edge of Town: Mapping Urban and Peri-Urban Australia Using Nighttime Satellite Imagery

This article explores the use of nighttime satellite imagery for mapping urban and peri-urban areas of Australia. A population-weighted measure of urban sprawl is used to characterize relative levels of sprawl for Australia's urban areas. In addition, the expansive areas of low light surrounding most major metropolitan areas are used to map the urban–bush interface of exurban land use. Our findings suggest that 82 percent of the Australian population lives in urban areas, 15 percent live in peri-urban or exurban areas, and 3 percent live in rural areas. This represents a significantly more concentrated human settlement pattern than presently exists in the United States.     

Analyzing fine-scale wetland composition using high resolution imagery and texture features

In order to monitor natural and anthropogenic disturbance effects to wetland ecosystems, it is necessary to employ both accurate and rapid mapping of wet graminoid/sedge communities. Thus, it is desirable to utilize automated classification algorithms so that the monitoring can be done regularly and in an efficient manner. This study developed a classification and accuracy assessment method for wetland mapping of at-risk plant communities in marl prairie and marsh areas of the Everglades National Park. Maximum likelihood (ML) and Support Vector Machine (SVM) classifiers were tested using 30.5 cm aerial imagery, the normalized difference vegetation index (NDVI), first and second order texture features and ancillary data. Additionally, appropriate window sizes for different texture features were estimated using semivariogram analysis. Findings show that the addition of NDVI and texture features increased classification accuracy from 66.2% using the ML classifier (spectral bands only) to 83.71% using the SVM classifier (spectral bands, NDVI and first order texture features).     

Stochastic gradient boosting classification trees for forest fuel types mapping through airborne laser scanning and IRS LISS-III imagery

This paper presents an application of Airborne Laser Scanning (ALS) data in conjunction with an IRS LISS-III image for mapping forest fuel types. For two study areas of 165 km² and 487 km² in Sicily (Italy), 16,761 plots of size 30-m × 30-m were distributed using a tessellation-based stratified sampling scheme. ALS metrics and spectral signatures from IRS extracted for each plot were used as predictors to classify forest fuel types observed and identified by photointerpretation and fieldwork. Following use of traditional parametric methods that produced unsatisfactory results, three non-parametric classification approaches were tested: (i) classification and regression tree (CART), (ii) the CART bagging method called Random Forests, and (iii) the CART bagging/boosting stochastic gradient boosting (SGB) approach. This contribution summarizes previous experiences using ALS data for estimating forest variables useful for fire management in general and for fuel type mapping, in particular. It summarizes characteristics of classification and regression trees, presents the pre-processing operation, the classification algorithms, and the achieved results. The results demonstrated superiority of the SGB method with overall accuracy of 84%. The most relevant ALS metric was canopy cover, defined as the percent of non-ground returns. Other relevant metrics included the spectral information from IRS and several other ALS metrics such as percentiles of the height distribution, the mean height of all returns, and the number of returns.