Optimizing Remote Sensing and GIS Tools for Mapping and Managing the Distribution of an Invasive Mangrove (Rhizophora mangle) on South Molokai,Hawaii

In 1902, the Florida red mangrove, Rhizophora mangle L., was introduced to the island of Molokai, Hawaii, and has since colonized nearly 25% of the south coast shoreline. By classifying three kinds of remote sensing imagery, we compared abilities to detect invasive mangrove distributions and to discriminate mangroves from surrounding terrestrial vegetation. Using three analytical techniques, we compared mangrove mapping accuracy for various sensor-technique combinations. ANOVA of accuracy assessments demonstrated significant differences among techniques, but no significant differences among the three sensors. We summarize advantages and disadvantages of each sensor and technique for mapping mangrove distributions in tropical coastal environments.     

中国热带6种海参骨片的种类和形态研究

利用扫描电子显微镜对我国6种热带海参(蛇目白尼参、玉足海参、花刺参、梅花参、子安辐肛参和巨梅花参)背脊部骨片的组成和结构进行了观察。结果表明,蛇目白尼参骨片有3种类型的花纹样体和1种杆状体;玉足海参骨片有2种类型的桌形体和1种扣形体;花刺参骨片有2种类型的桌形体、1种C形体和1种花纹样体;梅花参骨片有4种类型的杆状体;子安辐肛参骨片有1种花纹样体;巨梅花参骨片有1种杆状体、1种拟桌形体和1种颗粒体。结果显示,与之前学者光镜下观察的结果存在差异,玉足海参桌形体底盘中央是单孔而非4孔;花刺参桌形体立柱之间没有横梁连接。并发现了新的骨片类型:蛇目白尼参Ⅲ型花纹样体和杆状体;玉足海参顶部周缘小孔桌形体;花刺参顶部5簇小齿桌形体;梅花参Ⅱ型、Ⅲ型、Ⅳ型杆状体。     

基于国产高分遥感的人工种植红树林种间分类方法研究——以广西茅尾海红树林为例

红树林的种间结构组成对红树林生态系统的健康和发展至关重要,而红树林种间分类问题一直以来都是基于遥感手段的红树林监测中的难点。针对该问题,以人工种植为特点的广西茅尾海红树林遥感种间分类为例,基于面向对象的分类思想,提出了一种现场样本与分割对象相结合的红树林种间分类方法。利用GF-2 PMS1高分辨率卫星遥感影像数据,开展了广西茅尾海红树林湿地典型植被精细分类和空间分布研究,并将分类结果与基于像素和传统面向对象SVM分类方法进行了对比。结果显示:总体上,面向对象分类方法更适合用于茅尾海红树林湿地典型植被分类;对于局部混生明显的区域使用基于像素SVM分类方法效果会更好;传统面向对象分类方法中将整个影像分割对象单元作为训练样本可能会在某种程度上造成负面影响。因此,使用文中提出的样本选择新方法进行面向对象分类精度最高,总体精度达到了93.13%,Kappa为0.89。     

利用极化SAR数据进行土地覆盖分类研究

基于相关矩阵特征向量的目标分解将地物回波复杂的散射过程分解成相互独立的三种单一散射分量:单向散射、双向散射和交叉散射,分别对应各自的目标相关矩阵。目标分解技术降低了散射回波之间的相关性,有利于分析地物散射机理,有助于提高分类精度。对荷兰F levoland地区全极化数据进行分解,经过试验和相关性分析,选用7种数据形成多参数数据组合,对其进行最大似然监督分类,同时进行常规三种极化加相位差的分类和基于复W ishart分布的最大似然分类,逐像元计算混淆矩阵,分析对比三种分类结果的精度,试验表明:相对于常规数据组合分类,基于复W ishart分布的监督分类可以小幅度提高分类精度,而利用目标分解得到多参数组合数据进行分类则有大幅度的提高。     

New Zealand estuary benthic health indicators summarised nationally and by estuary type

ABSTRACT

Ecological data are often collected at small geographic scales. However, analysing data collectively over wider scales can reveal results and patterns not shown in the smaller-scale data. We summarised data for intertidal benthic ecological (physico-chemical and biological) health indicators from New Zealand estuaries and compared the results against thresholds above which ecological impacts are expected to occur. Values for the sediment physico-chemical indicators mud and nutrients (nitrogen and phosphorus collectively) were above thresholds for at least 50% and at 31% of sites measured respectively. Sediment organic content and metal concentrations were generally low, with only maximum values exceeding the thresholds. Scores for biological indicators were within (or better than) the moderate health category for either at least 50%, or at least 75%, of sites. When compared across estuary types (based on a geomorphic classification system) we found statistically significant differences for thirteen of the sixteen indicators. Differences (among mean values) for highly significant results were relatively large compared to the range of values observed nationally for those indicators. However, the differences, except those for mud, were smaller than their respective ecological health threshold values. Our summary provides a reference for future comparisons of estuary indicators nationally and across estuary types.     

Coastal and estuarine habitat mapping, using LIDAR height and intensity and multi-spectral imagery

The airborne laser scanning LIDAR (LIght Detection And Ranging) provides high-resolution Digital Terrain Models (DTM) that have been applied recently to the characterization, quantification and monitoring of coastal environments. This study assesses the contribution of LIDAR altimetry and intensity data, topographically-derived features (slope and aspect), and multi-spectral imagery (three visible and a near-infrared band), to map coastal habitats in the Bidasoa estuary and its adjacent coastal area (Basque Country, northern Spain). The performance of high-resolution data sources was individually and jointly tested, with the maximum likelihood algorithm classifier in a rocky shore and a wetland zone; thus, including some of the most extended Cantabrian Sea littoral habitats, within the Bay of Biscay. The results show that reliability of coastal habitat classification was more enhanced with LIDAR-based DTM, compared with the other data sources: slope, aspect, intensity or near-infrared band. The addition of the DTM, to the three visible bands, produced gains of between 10% and 27% in the agreement measures, between the mapped and validation data (i.e. mean producer's and user's accuracy) for the two test sites. Raw LIDAR intensity images are only of limited value here, since they appeared heterogeneous and speckled. However, the enhanced Lee smoothing filter, applied to the LIDAR intensity, improved the overall accuracy measurements of the habitat classification, especially in the wetland zone; here, there were gains up to 7.9% in mean producer's and 11.6% in mean user's accuracy. This suggests that LIDAR can be useful for habitat mapping, when few data sources are available. The synergy between the LIDAR data, with multi-spectral bands, produced high accurate classifications (mean producer's accuracy: 92% for the 16 rocky habitats and 88% for the 11 wetland habitats). Fusion of the data enabled discrimination of intertidal communities, such as Corallina elongata, barnacles (Chthamalus spp.), and stands of Spartina alterniflora and Phragmites australis, which presented misclassification when conventional visible bands were used alone. All of these results were corroborated by the kappa coefficient of agreement. The high classification accuracy found here, selecting data sources, highlights the value of integrating LIDAR data with multi-spectral imagery for habitat mapping in the intertidal complex fringe.     

Deltaic soil behavior classification using CPTu records—Proposed approach and applied to fifty-four case histories

For the past four decades, the CPT has played a key role in onshore and offshore soil investigations. One of the main applications of cone penetration test (CPT) is the soil behavioral classification. Most of the developed methods for soil identification using CPT and CPTu (piezocone) data are well categorized for common soils, such as clays, silts, and sands. Soils with low resistance or more compressibility generally involve problems in geotechnical engineering practice and construction projects. Consequently, these unusual deposits require further evaluation and more detailed data. Five major groups of problematic soils including: liquefiable, sensitive, peaty, collapsible, and expansive soils have been considered in this study. One hundred and forty CPT and CPTu test records were collected from fifteen countries. Sixty-one of the records are related to difficult soils. A brief comparison is performed for currently used soil behavioral classification charts, such as by Campanella et al. (1985 Campanella, R. G., P. K. Robertson, D. Gillespie, and J. Greig. 1985. Recent developments in in-situ testing of soils. Proceedings of 11th International Conference on Soil Mechanics and Foundation Engineering, ICSMFE, San Francisco, Vol. 2, 849–54. [Google Scholar]), Robertson (1990 Robertson, P. K. 1990. Soil classification using the cone penetration test. Canadian Geotechnical Journal 27 (1):151–58. doi:10.1139/t90–014[Crossref], [Web of Science ®] , [Google Scholar]), Jefferies and Davies (1991 Jefferies, M. G., and M. P. Davies. 1991. Soil classification using the cone penetration test: Discussion. Canadian Geotechnical Journal 28 (1):173–76. doi:10.1139/t91–023[Crossref], [Web of Science ®] , [Google Scholar]) and Eslami and Fellenius (1997 Eslami, A., and B. H. Fellenius. 1997. Pile capacity by direct CPT and CPTu methods applied to 102 case histories. Canadian Geotechnical Journal 34 (6):886–904. doi:10.1139/cgj-34–6-886[Crossref], [Web of Science ®] , [Google Scholar]). Analysis based on CPT data indicates that a few commonly used charts recognize relatively well problematic deposits. However, further studies are needed to increase the accuracy and capability of methods. Existing charts have some problems due to the limitations of the nature of rectangular charts based on two axes. A new format of classification chart, i.e., triangular form containing cone tip resistance (q_c), sleeve friction (f_s), and pore pressure (u₂) is proposed for soil identification which can be realized in practice. The proposed chart with more accuracy and less scattering of data than the previous charts is able to identify soil types particularly for deltaic soils.     

基于主成分分析和决策级融合的高光谱图像分类方法研究

为了评价主成分分析方法和决策级数据融合方法对高光谱图像地物分类结果的影响,作者利用各分类器和主成分分量的优势,提出了两组地物分类结果的决策级融合策略,利用8种常用监督分类方法对高光谱原始图像和PCA变换后不同分量组合图分类得到的结果进行决策级融合,并应用覆盖黄河入海口新老河道交界处的CHRIS/PROBA高光谱图像开展实验研究。结果表明:直接采用每类地物分类精度最高的、空穴和缝隙采用总体分类精度最高的融合策略,在综合考虑生产者精度和用户精度的情形下,仅使用最大似然法、支持向量机和人工神经网络3种分类方法,按照分类精度从高到低的顺序进行的融合分类效果最好,总体分类精度为87.82%。与8种监督分类方法中效果最好的最大似然法相比,精度提高了2.7个百分点,同时明显减少了错分现象,尤其是对于分布面积较小的翅碱蓬和柽柳,滩涂被误分为翅碱蓬、芦苇被误分为柽柳的现象大大降低。     

基于遗传小波神经网络的海底声学底质识别分类

分割海底声纳探测图像,提取单元特征向量进行主成份分析,选取均值、标准差、对比度、相关系数、能量及同质性作为训练特征向量,构建小波神经网络。利用遗传算法优化小波神经网络的初始权值及小波参数,对砂、礁石、泥3种底质类型分别进行训练,并得到3种底质的测试精度都在90%以上,优于单独利用小波神经网络进行训练时的测试精度,克服了小波神经网络训练时易陷入局部极小的固有缺陷,表明基于遗传算法的小波神经网络可有效用于海底底质声纳图像的识别和分类。