Bibliometric analysis of ecosystem monitoring-related research in Africa: implications for ecological stewardship and scientific collaboration

Regional ecosystem monitoring is a central form of knowledge sharing and collaboration amongst scientists and decision makers on environmental health, land use change, and science-policy development. Despite the proliferation of such research networks on long-term monitoring on many continents, little has been achieved in Africa. This study aims to assess and examine the spatiotemporal trend and categorical patterns in ecosystem monitoring-related research in Africa for the benefits of conserving biodiversity and sustaining natural resource sectors for well-being and livelihood security, environmental planning, and ecological stewardship. A systematic review was conducted using bibliometric tools. Based on a set of search terms and peer-reviewed publications retrieved from various ecosystem monitoring networks and journal databases, further analysis was conducted using social network approaches, mapping tools, and content analysis. About 1442 scientific publications on ecosystem monitoring and related research were documented from 1987 to 2014 mostly published in English. The number of publication increased progressively since 1992 after the Convention on Biodiversity was signed and this trend peaked till 2008. South African Journal of Science was the most leading journal and Nature the most cited. Internationally coauthored and collaborative articles represented majority of the findings with the United Kingdom at the central position in the research network due to colonial relationships. Regional collaboration amongst countries is limited owing to language barriers and other institutional constraints such as funding and short-term projects. These findings have implication for prioritizing national and regional policies toward biodiversity science and its contribution to human well-being, food security, and global change responses.     

Use of linkage mapping and centrality analysis across habitat gradients to conserve connectivity of gray wolf populations in western North America

Centrality metrics evaluate paths between all possible pairwise combinations of sites on a landscape to rank the contribution of each site to facilitating ecological flows across the network of sites. Computational advances now allow application of centrality metrics to landscapes represented as continuous gradients of habitat quality. This avoids the binary classification of landscapes into patch and matrix required by patch-based graph analyses of connectivity. It also avoids the focus on delineating paths between individual pairs of core areas characteristic of most corridor- or linkage-mapping methods of connectivity analysis. Conservation of regional habitat connectivity has the potential to facilitate recovery of the gray wolf (Canis lupus), a species currently recolonizing portions of its historic range in the western United States. We applied 3 contrasting linkage-mapping methods (shortest path, current flow, and minimum-cost-maximum-flow) to spatial data representing wolf habitat to analyze connectivity between wolf populations in central Idaho and Yellowstone National Park (Wyoming). We then applied 3 analogous betweenness centrality metrics to analyze connectivity of wolf habitat throughout the northwestern United States and southwestern Canada to determine where it might be possible to facilitate range expansion and interpopulation dispersal. We developed software to facilitate application of centrality metrics. Shortest-path betweenness centrality identified a minimal network of linkages analogous to those identified by least-cost-path corridor mapping. Current flow and minimum-cost-maximum-flow betweenness centrality identified diffuse networks that included alternative linkages, which will allow greater flexibility in planning. Minimum-cost-maximum-flow betweenness centrality, by integrating both land cost and habitat capacity, allows connectivity to be considered within planning processes that seek to maximize species protection at minimum cost. Centrality analysis is relevant to conservation and landscape genetics at a range of spatial extents, but it may be most broadly applicable within single- and multispecies planning efforts to conserve regional habitat connectivity.     

珠三角工业城市污染物入河量空间特征分析

以东莞市第一次全国污染源普查成果为数据基础,调查估算2010年东莞市33个镇街和25个水功能区的COD、氨氮入河量,并应用GIS技术及相关分析法分析污染物入河量空间分布特征及其影响因素。研究发现：①东莞市各镇街及水功能区的污染物入河量呈明显不均匀分布,人口、GDP是影响污染物入河量空间分布的主要因素。②河网区、非河网区镇街的工业污染物入河量与GDP均呈较好的正相关关系,但非河网区单位污染物入河量GDP产出拟合曲线斜率是河网区的31倍,显示河网区镇街每增长单位污染物入河量所产生的GDP远远小于非河网区,河网区镇街往往付出巨大的污染代价而经济增长却非常有限。     

时延神经网络对宝兴树蛙异质种群鸣叫的聚类分析

对瓦屋山宝兴树蛙3个不同海拔分布(A:山下;B:山腰;C:山上)的异质种群鸣叫的时频参数进行分析,发现三地鸣叫各参数互相重叠,无明显界线.常规统计分析不能使三地的鸣叫聚类为各自的组.使用时延神经网络对三地宝兴树蛙鸣叫的时频参数进行有指导聚类,发现三种群鸣叫有地区性差异,并表现出由个体交流引起的山腰种群群体特征的不显著.推测差异可能是因为长期地理隔离所导致.基于神经网络的分析结果,对各种群间的个体交流情况进行了初步估计.图6表1参20     

江苏规划高速公路网生态环境影响研究

以RS和GIS为基础，运用遥感信息提取、编制自然保护专题系列图、系列图与规划高速公路网叠加、缓冲分析、统计分析等方法，对江苏省规划高速公路网生态环境影响进行分析。规划的5纵9横6联线路中的9条，对127个不同类型保护区中的9个有影响。11个过江通道中的5个对沿江饮用水源保护地有影响。规划高速公路占用的土地中76．74％是基本农田。跨越太湖的沪宁复线将加重梅梁湾、五里湖的水质富营养化。针对上述问题，从保护生态环境的角度提出6大对策：避绕生态保护对象，修改跨太湖线路方案，合理规划穿越山地丘陵区的线路，减少平原区生态破坏，调整过江通道与饮用水源地的交叉关系，保护白鳍豚生存环境安全。     

基于GF-1 WFV影像和BP神经网络的太湖叶绿素a反演

叶绿素a浓度是可直接遥感反演的重要水质参数之一,常用来评价湖泊水体的富营养化程度.太湖是典型的二类水体,光学性质复杂,应用一类水体线性反演模式拟合较为片面且难以找到最佳拟合模型.BP神经网络模型具有模拟复杂非线性问题的功能.为研究高分一号卫星16m多光谱相机WFV4结合BP神经网络进行太湖叶绿素a浓度监测的可行性,实验利用GF-1 WFV4影像和实时的地面采样数据,建立了BP神经网络模型,同时采用波段比值经验模型进行对比.经精度检验,BP神经网络模型预测值与实测值之间的可决系数R2高达0.9680,而波段比值模型的R2为0.9541,且均方根误差RMSE由波段比值模型的18.7915降低为BP神经网络模型的7.6068,平均相对误差e也由波段比值模型的19.16%降低为BP神经网络模型的6.75%.结果证明,GF-1 WFV4影像应用BP神经网络模型反演太湖叶绿素a浓度较波段比值模型精度有所提高.将经过水体掩膜的GF-1 WFV4影像用于训练好的BP神经网络反演太湖叶绿素a浓度分布,结果显示,叶绿素a高浓度区集中分布在湖心区北部、竺山湾、梅梁湾区域,与之前的研究一致.本文研究结果验证了采用BP神经网络模型对GF-1 WFV4影像进行太湖叶绿素a浓度反演的可行性.     

我国汽车报废回收利用体系框架初探

主要介绍了国内外废旧汽车回收处理现状,并从法制建设、回收网络构建、回收技术研究、宣传教育等方面提出了我国应对废旧汽车回收利用的对策建议。     

飞机飞行振动预计技术研究

介绍了飞机飞行振动的主要来源和特征,分析了传统预计方法的不足,针对飞机飞行振动与高度、马赫数和攻角等飞行参数呈非线性关系的特点,提出了基于BP神经网络的飞行振动预计新技术,建立了预计模型,通过8组建模样本训练网络,确定了预计模型中的各个参数值。预计结果和实测飞机飞行振动信号比较分析表明,该方法预计精度高,验证了BP神经网络预计飞机飞行振动的可行性。     

近20年来中国典型区域PM2.5时空演变过程

近20年来PM_(2.5)污染严重制约了中国可持续发展.长时间序列历史监测数据的缺失阻碍了相关研究.为此,本文以四大典型区域2013~2016年的PM_(2.5)浓度监测值和2000~2016年MODIS AOD数据、边界层高度、温度等气象数据作为基础数据,将反向人工神经网络和支持向量回归机两种算法相结合,构建组合模拟模型,并利用地理空间分析技术实现近20年来PM_(2.5)浓度历史变化过程的情景再现.研究结果表明,组合模型具有较低的误差和更高的泛化能力;时空分析结果表明,2000~2010年京津冀和东三省PM_(2.5)浓度持续增长,珠三角PM_(2.5)浓度缓慢下降,3个研究区PM_(2.5)污染范围呈扩大趋势,长三角PM_(2.5)浓度值及污染范围基本保持稳定.2012年4个研究区PM_(2.5)浓度值降低且污染范围缩小,但2013~2016年PM_(2.5)浓度略微上升后又下降,高污染范围缩小,这与国家采取PM_(2.5)区域联防等治理措施有关.     

三峡库区社区水平乡村路网对森林景观变化的影响

人为扰动是短时空尺度内森林景观变化或用途转换的主要动力,而乡村路网则成为人为扰动拓展的主要通道。基于1992、2002、2014年三峡库区重庆市石柱县西沱镇3期遥感影像和实地调研数据,在ArcGIS平台支持下,研究社区水平乡村路网对森林景观变化的影响。结果表明：1）1992—2014年研究区乡村路网影响域内次生林、退化林地和耕地的动态变化较显著,集中分布在中西部平坦区。1992—2002年乡村路网缓冲区内森林景观整体呈退化趋势,退化类型以次生林→退化林地、次生林→耕地、退化林地→耕地为主,演化量达1 705.91 hm²;2002—2014年乡村路网缓冲区内森林景观主要呈恢复趋势,恢复类型以耕地→退化林地、耕地→次生林、退化林地→次生林和原始林→退化原始林为主,演化量达1 674.52 hm²。2）相比发现,前一时期研究区乡村路网长度的增加和路网材质的提升很大程度上驱动森林景观的退化,而后一时期低等级路段的消失、废弃或利用率的降低则有助于森林景观的恢复,消失路段恢复率近100%。3）研究有助于丰富人们对社区水平乡村路网影响森林景观变化的理解与认识,为生态文明建设中合理规划乡村路网提供科学依据。