中国植被分类系统修订方案

为了推动《中国植被志》研编工作, 该文回顾了中国植被分类系统的发展过程和主要阶段性成果, 提出了作为《中国植被志》研编技术框架组成部分的中国植被分类系统修订方案, 对各植被型组及各植被型进行了简单定义和描述, 并针对中国植被分类系统若干问题, 特别就中国植被分类系统总体框架、混交林的界定以及土壤在植被分类中的重要性等问题进行了讨论。1960年侯学煜在《中国的植被》中首次提出了中国植被分类的原则和系统, 1980年出版的《中国植被》制定了分类等级和划分依据等更加完善的系统, 之后《中国植被及其地理格局——中华人民共和国1:1 000 000植被图说明书》和《中国植物区系与植被地理》以及很多省区的植被专著对该系统进行过修订。2017年宋永昌在《植被生态学》中提出了一个分类等级单位调整的方案。本次提出的中国植被分类系统修订方案基本沿用《中国植被》的植被分类原则、分类单位及系统, 采用“植物群落学-生态学”分类原则, 主要以植物群落特征及其与环境的关系作为分类依据, 包含三级主要分类单位, 即植被型(高级单位)、群系(中级单位)和群丛(低级单位); 在三个主要分类单位之上分别增加辅助单位植被型组、群系组和群丛组, 在植被型和群系之下主要根据群落的生态差异和实际需要可再增加植被亚型或亚群系。修订方案包含了森林、灌丛、草本植被(草地)、荒漠、高山冻原与稀疏植被、沼泽与水生植被(湿地)、农业植被、城市植被和无植被地段9个植被型组, 划分为48个植被型(含30个自然植被型、12个农业植被型、5个城市植被型和无植被地段)。自然植被中有23个植被型进一步划分出了81个植被亚型。     

新疆博格达山地面生地衣群落数量分类及物种多样性

根据对新疆博格达山地面生地衣群落27个样点(20 m×20 m)调查的数据,以各地衣种的盖度为指标结合双向指示种分析方法(TWINSPAN)和除趋势对应分析法(DCA)对博格达山地面生地衣群落进行数量分类并分析了群落结构特征及其多样性和相似性。采用典范对应分析法(CCA)对各群落的物种分布格局与环境因子的关系进行了探讨。结果表明,TWINSPAN分析和DCA排序将分布在博格达山的37种地面生地衣分为以下5个群丛。群丛1:膜地卷(Peltigera membranacea)+伴藓大孢蜈蚣衣(Physconia muscigena)群丛;群丛2:地卷(Peltigera rufescens)+伴藓大孢蜈蚣衣(Physconia muscigena)群丛;群丛3:裂边地卷(Peltigera degenii)+平盘软地卷(Peltigera elisabethae)群丛;群丛4:喇叭石蕊(Cladonia pyxidata)+犬地卷(Peltigera canina)群丛;群丛5:盾鳞衣(Placidium squamulosum)+缠结茸枝衣(Seirophora contortuplicata)群丛。物种多样性以群丛4最大为3.427;群丛1最小为0.672;群丛1和群丛4间的相似性最高为0.889,群丛3和群丛4相似性系数最低为0.607。CCA排序结果反映,该地区地面生地衣的分布受到森林植被郁闭度、人为干扰、光照强度的影响,而地表植被盖度和土壤pH等因素的影响不大。     

Three critical factors affecting automated image species recognition performance for camera traps

Ecological camera traps are increasingly used by wildlife biologists to unobtrusively monitor an ecosystems animal population. However, manual inspection of the images produced is expensive, laborious, and time‐consuming. The success of deep learning systems using camera trap images has been previously explored in preliminary stages. These studies, however, are lacking in their practicality. They are primarily focused on extremely large datasets, often millions of images, and there is little to no focus on performance when tasked with species identification in new locations not seen during training. Our goal was to test the capabilities of deep learning systems trained on camera trap images using modestly sized training data, compare performance when considering unseen background locations, and quantify the gradient of lower bound performance to provide a guideline of data requirements in correspondence to performance expectations. We use a dataset provided by Parks Canada containing 47,279 images collected from 36 unique geographic locations across multiple environments. Images represent 55 animal species and human activity with high‐class imbalance. We trained, tested, and compared the capabilities of six deep learning computer vision networks using transfer learning and image augmentation: DenseNet201, Inception‐ResNet‐V3, InceptionV3, NASNetMobile, MobileNetV2, and Xception. We compare overall performance on “trained” locations where DenseNet201 performed best with 95.6% top‐1 accuracy showing promise for deep learning methods for smaller scale research efforts. Using trained locations, classifications with <500 images had low and highly variable recall of 0.750 ± 0.329, while classifications with over 1,000 images had a high and stable recall of 0.971 ± 0.0137. Models tasked with classifying species from untrained locations were less accurate, with DenseNet201 performing best with 68.7% top‐1 accuracy. Finally, we provide an open repository where ecologists can insert their image data to train and test custom species detection models for their desired ecological domain.     

Voice Pathologies Classification and Detection Using EMD-DWT Analysis Based on Higher Order Statistic Features

BackgroundThe voice is a prominent tool allowing people to communicate and to change information in their daily activities. However, any slight alteration in the voice production system may affect the voice quality. Over the last years, researchers in biomedical engineering field worked to develop a robust automatic system that may help clinicians to perform a preventive diagnosis in order to detect the voice pathologies in an early stage.MethodIn this context, pathological voice detection and classification method based on EMD-DWT analysis and Higher Order Statistics (HOS) features, is proposed. Also DWT coefficients features are extracted and tested. To carry out our experiments a wide subset of voice signal from normal subjects and subjects which suffer from the five most frequent pathologies in the Saarbrücken Voice Database (SVD), is selected. In The first step, we applied the Empirical Mode Decomposition (EMD) to the voice signal. Afterwards, among the obtained candidates of Intrinsic Mode Functions (IMFs), we choose the robust one based on temporal energy criterion. In the second step, the selected IMF was decomposed via the Discrete Wavelet Transform (DWT). As a result, two features vector includes six HOSs parameters, and a features vector includes six DWT features were formed from both approximation and detail coefficients. In order to classify the obtained data a support vector machine (SVM) is employed. After having trained the proposed system using the SVD database, the system was evaluated using voice signals of volunteer's subjects from the Neurological department of RABTA Hospital of Tunis.ResultsThe proposed method gives promising results in pathological voices detection. The accuracies reached 99.26% using HOS features and 93.1% using DWT features for SVD database. In the classification, an accuracy of 100% was reached for “Funktionelle Dysphonia vs. Rekrrensparese” based on HOS features. Nevertheless, using DWT features the accuracy achieved was 90.32% for “Hyperfunktionelle Dysphonia vs. Rekurrensparse”. Furthermore, in the validation the accuracies reached were 94.82%, 91.37% for HOS and DWT features, respectively. In the classification the highest accuracies reached were for classifying “Parkinson versus Paralysis” 94.44% and 88.87% based on HOS and DWT features, respectively.ConclusionHOS features show promising results in the automatic voice pathology detection and classification compared to DWT features. Thus, it can reliably be used as noninvasive tool to assist clinical evaluation for pathological voices identification.     

广州市主城区城市地形类型与风环境评价

对城市地形组合类型及其地形对风场影响的研究,有助于城市风场机理的认识与生态环境的优化。以高层建筑密集的广州市主城区为例,在确定城市宏观地形类型的基础上,基于最小成本路径（LCP）辨识了风道,并对风环境质量进行评价。主要结论有：（1）将城市地形简要概括划分为4个一级地形、11个二级地形类型。（2）以LCP路径与盛行风交角不超过22.5°的标准,筛选确定通风路径作为风道。根据LCP格网的密度与频次,结合不同风向下风速与风频,评价分析了风环境类型与空间分布。（3）根据自然与城市地形的配置,北风风环境质量远高于西北风和东风;东风由于风频与风速最低,因此通风条件较差。（4）珠江航道在三种风向下都是尺度最宽贯穿城市最好的风道;区域性的风道与风环境较好的地段集中在与主风向平行的主干道上,但以近似南北走向的居多;由于广州城市地形高度以珠江新城峰林为中心向外递减,以低地地形为主的主城区外围通风优于中心区域,特别是珠江新城峰林与网络状台地为主的老城区,通风环境较差。基于LCP的评价结果需要同其他方法相互验证,才能使其不断完善优化。     

大比例尺土壤保持服务制图分级方法研究

科学地编制生态系统服务空间分布图,有助于识别生态系统优先保护热点及空缺,能够为国土安全规划提供基础信息。但由于缺乏统一科学的生态系统服务分级方法,生态系统服务分布图的展示效果和制图效率都受到限制。不恰当的分级制图会误导决策者对生态系统服务空间分布特征的判断,增加生态保护决策的不确定性。以构建生态服务制图分级标准为目标,对我国6个典型县区的土壤保持服务数据展开1：25万比例尺制图分级案例研究。采用分级精度、面积均衡性、极差一致性、空间自相关一致性4个分级质量评价指标评价并比较了目前地图编制中常用的自然断点法、几何间隔法、累积比例法、等差法、分位数法5种分级算法的分级制图效果。结果表明,6个县区有相似的土壤保持服务分布特征：低值区覆盖面积大,高值区覆盖面积小。各县区土壤保持服务制图最优分级方法有差异：延庆区、丰满区最优分级方法为自然断点法;永靖县、天山区、安宁市最优分级方法为累积比例法;富阳区最优分级方法为几何间隔法。各分级方法中,累积比例法在6个县区综合制图效果最优或接近最优,适用性最好,能够较好的刻画各县区的土壤保持服务空间分布特征。本文提出的分级评价方法及其结果,可为生态系统土壤保持服务制图规范化和自动化提供科学依据。