利用GIS和多变量分析估算青藏高原月降水

随着空间降水信息需求的日益增加,空间降水插值已被广泛应用。降水区域不同,插值方法不同;时间尺度不同,插值方法也不相同。适合于所有地区的通用降水插值模型是不存在的。青藏高原自然地理特征独特,分析高原降水的时空格局意义重要。以青藏高原及其周边地区140个气象站点的月降水信息及其该地区的数字高程数据(DEM)为基础,利用G IS工具,对比分析了五种插值方法在青藏高原不同降水年份(以1998年、1997年分别代表丰水及欠水年份)的干湿季(1998年的干湿季分别以12月份和8月份为代表,1997年的干湿季分别以1月份和7月份为代表)月降水插值中的应用,并对整个高原地区的干季和湿季的月降水进行制图。这5种插值方法分别是:克里金插值法、反距离加权法、样条法、混合插值法Ⅰ和混合插值法Ⅱ,前3种插值方法未考虑海拔高度对降水的影响,而混合插值法则将高程作为降水的重要影响因子。结果表明:①在干季,无论是丰水还是欠水年份,月降水量都比较少,高程对降水量的影响较小,在月降水插值时可不考虑高程的影响,克里金法的月降水插值精度最高。②在湿季,月降水量较多,高程的影响较大,混合插值法比局部插值法及克里金插值法的精度高,尤以混合插值法Ⅱ(多元回归和样条法的综合)的精度最高。③干季,整个高原的月降水很少,西部和北部降水最少,东部和南部相对较多;湿季,高原的月降水较多,空间格局表现为由东南向西北递减。     

农田表层土壤镉的典型空间插值方法对比研究

空间插值对于土壤重金属空间分布和污染评价具有重要意义,以广东省广州市某地区农田表层土壤重金属镉的调查监测结果为例,选取具有代表性的反距离加权、径向基函数、普通克里金、简单克里金、泛克里金5种空间插值方法,进行空间插值及土壤等级划分,比较不同插值方法结果精度,分析不同插值方法结果差异。研究结果表明:不同插值方法识别的土壤镉浓度空间分布特征和土壤等级划分存在差异,主要体现在局部极值向外过渡区域存在较大不确定性,其中径向基函数精度最高,克里金插值法对数据存在"压缩"效应较强。因此,在开展土壤重金属污染调查时应考虑土壤重金属样本数据特征和空间结构特征,选择合适的插值模型,并适当加大土壤重金属浓度过渡区域采样密度。     

杉木种子涩籽地理分布的空间插值法研究

应用距离反比插值法进行杉木种子涩籽率的空间插值,并提出一种改进的距离反比插值法(MID),其包含了距离反比法(ID)和距离平方反比法(IDS)。根据福建省25个杉木种子产地的涩籽率观察值,建立了杉木种子涩籽率的改进距离反比插值模型。改进距离反比插值法经交叉验证,其平均误差及平均误差平方的平方根均较距离反比法和距离平方反比法更小,具有较高的精度,平均精度达88．91％,可应用于杉木种子涩籽的空间内插。这不仅为杉木种子涩籽的空间插值提供了一种新方法,而且为地理信息系统(Geographic information system,GIS)辅助的杉木种子涩籽区域分布与流行趋势研究提供了理论依据,为福建省杉木种子园的合理布局与优化奠定基础。     

河北省年均降水量插值方法比较

以河北省及临近区域120个气象观测站点1971～2000年均降水量数据为基础,选择其中的40个作为检验站点,其余站点分别取80、40、20个作为插值站点,采用局部插值、整体插值、多元线性回归、综合模拟等多种插值模型讨论了降水空间插值问题,主要结论如下:插值站点数、模型类型、模型参数都会影响插值精度.局部插值模型相对误差最小值出现在Spline、IDW模型中,其次为Kridging模型,而整体模型Trend、多元线性回归模型误差均较大,但综合了局部插值模型和统计模型的综合模型一定程度上能改善插值精度及误差分布.河北省80和40个站点的最优插值模型为综合模型,20个站点的最优插值模型为IDW2.     

黄土高原年均降水量空间插值及其方法比较

采用不同空间插值方法(普通克里格法OK、反距离权重法IDW和径向基函数法RBF),对黄土高原地区85个气象站点1957—2013年逐日降水数据进行年均降水量空间插值分析,采用平均绝对误差(MAE)、均方根误差(RMSE)、Pearson相关系数(R)以及准确度(AC)对插值结果进行对比,研究不同方法对该地区年均降水量插值结果的影响.结果表明:研究期间,黄土高原地区年均降水量的Moran I值为0.67,各站点年均降水量存在较强的空间自相关性.63个预测站点和22个检验站点的验证结果显示,3种方法的实测值和预测值均显著相关,但反距离权重法和径向基函数法的平均绝对误差(MAEIDW=38.98,MAERBF=34.61)和均方根误差(RMSEIDW=51.49,RMSERBF=43.79)均高于普通克里格法.通过普通克里格法的不同半变异函数模型(环形模型、球体模型、指数模型、高斯模型)对比发现,环形模型的平均绝对误差最小(MAE=32.34),准确度最高(AC=0.976),指数模型平均绝对误差最大(MAE=33.24).综合对比不同插值方法实测值与估算值的验证结果表明,采用半变异函数为环形模型的普通克里格法是进行黄土高原年均降水量插值较好的方法.     

人类群体遗传空间结构的"克立格"模型

通过将“克立格”技术应用于人类群体遗传学领域,构建了人类群体遗传空间结构的“克立格”模型,并论述了其原理和计算方法。以HLA-A基因座为例,应用“克立格”模型,定量分析了中国人群HLA-A基因座的空间遗传异质性;对HLA-A基因频率的空间数据矩阵进行了主成分分析,进而定义了人类群体遗传结构的综合遗传测度(SPC),绘制了综合遗传测度和主成分(PC)的“克立格”地图,分析了其群体遗传空间结构特性。与其他空间插值或平滑方法相比,人类群体遗传空间结构的“克立格”模型具有明显优点：1)“克立格”估计以空间遗传变异函数模型为基础,在绘制空间遗传结构地图之前,可利用变异函数模型定量分析所研究基因座(或多基因座)的空间遗传异质性;2)“克立格”插值方法是真正意义上的无偏估计模型,它利用待估区域周围的已知群体遗传调查点数据,并充分考虑调查点的空间影响范围,给出待估区域的最优估计值;3)“克立格”模型允许估计插值误差,这种插值误差既可用于评价空间估计效果,又可通过绘制误差地图指导在误差过高的地点增加新的群体遗传调查样本点,以优化估计效果。然而,人类群体遗传空间结构的“克立格”模型也存在一定缺点：1)若不能用任何理论遗传变异函数模型拟合观察遗传变异函数值,则不能建立“克立格”模型;2)若理论遗传变异函数的拟合优度很低,则据此建立的“克立格”模型的估计标准差在整个空间范围内会很大,此时“克立格”模型不适用于估计群体遗传空间结构。出现上述两种情形时,应选用不考虑空间相关性的空间随机插值方法绘制群体遗传结构地图,如基因绘图软件中的Cavalli-Sforza方法,反向距离加权法和条样函数插值法等。     

基于辅助变量的森林半腐层厚度空间插值精度

基于地统计学方法,利用3种以海拔作为辅助变量的空间插值算法［局部平均的简单kriging法(simple kriging with locally varying mean,SKlm)、带有外部漂移的kriging法(kriging with an external drift,KED)和协kriging法(cokrging,COK)］计算了森林半腐层厚度的空间插值精度,并进行了交叉验证.结果表明：KED法既考虑了变量之间的空间变异,又考虑到影响局部空间变化的因素,与其他插值方法相比,其精度有很大提高;由于海拔与半腐层厚度之间的相关关系较弱,导致SKlm法的插值精度没有达到预期效果;COK法直接将海拔用于估计半腐层厚度,由于在边界地区缺乏采样点数据,因此边界地区的插值出现了多处突变区域.对比地统计学方法与距离反比权重法(inverse distance weighting, IDW)在本研究中的插值精度,除了KED方法的插值精度较高外,其余方法的插值精度均不及IDW,原因可能是利用辅助变量辅助地统计学插值时,主、辅助变量之间的相关关系在插值中起着重要作用.     

插值方法与样点数对县域土壤有效磷空间变异特征评价的影响

与欧美大规模农场经营不同,土地分散经营使我国县域土壤养分空间变异特征评价更加困难.本研究以安徽芜湖土壤有效磷为例,系统地评价插值方法与样点数对县域土壤养分空间变异特征评价准确性的影响.结果表明：局部多项式、普通克里格、简单克里格和析取克里格插值方法的评价效果优于反距离加权法、全局多项式、径向基插值和泛克里格等插值方法,考虑到实际操作简单,推荐用普通克里格方法进行县域土壤有效磷空间变异特征评价.随着参与空间插值样点数的增加,县域土壤有效磷空间变异特征预测的准确性增加,充分考虑评价的准确性和田间取样费用,建议县域土壤有效磷空间变异特征评价的适宜样点数应介于500～1000个.     

某石油泄漏场地土壤二甲苯空间分布的三维插值

为精确界定污染场地中污染物三维空间分布范围,本文以中国某石油储罐泄露污染场地为研究对象,采用最近邻点法、反距离加权法及克里金法3种三维空间插值模型,对场地中特征污染物二甲苯进行三维空间插值计算及污染分布表征,比较不同插值模型的计算精度,揭示场地中实际受污染土方量。结果表明,反距离加权法预测平均误差为4.94,均方根误差为14.11,其精度明显高于另外2种模型,界定的污染分布范围较符合实际污染情况,通过该方法进行插值后计算受污染土方量为666.2 m~3。研究结果对该类型污染场地污染物空间分布表征和修复治理决策制定提供科学依据。     

基于序贯高斯模拟的日最低气温空间不确定性评估

明确日最低气温对于评估低温对作物的危害、指导人们及时采取补救措施、保障粮食安全具有重要意义.克里格是近地面温度场插值的主流方法,但其平滑效应会导致低值区域被过高估计而高值区域被过低估计.对2011年12月12日冷空气影响下的海南岛日最低气温,采用交叉验证法评估了普通克里格和带漂移的克里格两种插值法的预测精度;并对克里格插值法和序贯高斯模拟法产生的当日海南岛最低气温的空间分布进行对比分析.结果表明:带漂移克里格法的预测精度(r=0.86)并不显著优于普通克里格法(r=0.86);序贯高斯模拟能产生多个等概率的符合数据整体分布和方差函数的模拟结果,模拟结果克服了克里格插值的平滑效应,能够比克里格插值更加真实地反映当日最低气温的空间分布;在低温区域,气温变化小,序贯高斯模拟结果的条件方差小于普通克里格方差;潜在寒害区的空间不确定性能够通过多个序贯高斯模拟实现并加以量化.序贯高斯模拟在低温导致农业气象灾害的评估中具有较高的应用价值.     

不同气象数据空间内插对区域玉米生长模拟结果的影响

作物模型区域模拟已成为作物模型应用的一个新方向。运用作物模型进行区域研究时,遇到的问题之一就是输入模型的空间数据质量问题,研究不同空间内插法获得的气象数据对作物模型区域模拟结果的影响,可以为区域模拟对输入数据的敏感性研究提供一定的参考。利用区域校准的CERES-Maize模型,将3类内插方法(几何内插、统计内插、动力模型内插)产生的网格化天气数据分别输入到CERES-Maize模型中,模拟了50km×50km网格水平下1961—1990年我国玉米生产状况,并选取1980—1990年模拟的平均产量与同期农调队调查产量进行比较,以了解区域模拟中,不同空间内插方法所得的逐日气象数据对区域模拟结果的影响。结果表明:(1)作物模型区域应用时,所采用的3种内插方法都能满足作物模型区域模拟对网格化天气数据的要求,采用3种天气数据的区域模拟结果都能反映出玉米平均产量的空间变化特征,与网格调查平均产量之间具有极显著的相关关系,但采用不同内插天气数据对模拟结果造成了8%以内的偏差。(2)采用不同内插天气数据,在进行作物区域模拟时,各方法的模拟结果之间呈极显著的相关关系,但这些模拟结果之间,在全国大部分地区是差异显著。     

An artificial neural network approach to identify fungal diseases of cucumber (Cucumis sativus L.) plants using digital image processing

Nowadays, artificial intelligence solutions such as digital image processing and artificial neural networks (ANN) have become important applicable techniques in phytomonitoring and plant health detection systems. In this research, an autonomous device was designed and developed for detecting two types of fungi (Pseudoperonospora cubensis, Sphaerotheca fuliginea) that infect the cucumber (Cucumis sativus L.) plant leaves. This device was able to recognise the fungal diseases of plants by detecting their symptoms on plant leaves (downy mildew and powdery mildew). For leaves of cucumber inoculated with different spores of the fungi, it was possible to estimate the amount of hour post inoculation (HPI) by extracting leaves’ image parameters. Device included a dark chamber, a CCD digital camera, a thermal camera, a light dependent resistor lightening module and a personal computer. The proposed programme for precise disease detection was based on an image processing algorithm and ANN. Three textural features and two thermal parameters from the obtained images were measured and normalised. Performance of ANN model was tested successfully for disease recognition and detecting HPI in images using back-propagation supervised learning method and inspection data. Such this machine vision system can be used in robotic intelligent systems to achieve a modern farmer’s assistant in agricultural crop fields.     

Use of artificial neural networks to accurately identify Cryptosporidium oocyst and Giardia cyst images

Cryptosporidium parvum and Giardia lamblia are protozoa capable of causing gastrointestinal diseases. Currently, these organisms are identified using immunofluorescent antibody (IFA)-based microscopy, and identification requires trained individuals for final confirmation. Since artificial neural networks (ANN) can provide an automated means of identification, thereby reducing human errors related to misidentification, ANN were developed to identify Cryptosporidium oocyst and Giardia cyst images. Digitized images of C. parvum oocysts and G. lamblia cysts stained with various commercial IFA reagents were used as positive controls. The images were captured using a color digital camera at 400 x (total magnification), processed, and converted into a binary numerical array. A variety of "negative" images were also captured and processed. The ANN were developed using these images and a rigorous training and testing protocol. The Cryptosporidium oocyst ANN were trained with 1,586 images, while Giardia cyst ANN were trained with 2,431 images. After training, the best-performing ANN were selected based on an initial testing performance against 100 images (50 positive and 50 negative images). The networks were validated against previously "unseen" images of 500 Cryptosporidium oocysts (250 positive, 250 negative) and 282 Giardia cysts (232 positive, 50 negative). The selected ANNs correctly identified 91.8 and 99.6% of the Cryptosporidium oocyst and Giardia cyst images, respectively. These results indicate that ANN technology can be an alternate to having trained personnel for detecting these pathogens and can be a boon to underdeveloped regions of the world where there is a chronic shortage of adequately skilled individuals to detect these pathogens.     

Biogeographical variation in the diet of Holarctic martens (genus Martes,Mammalia: Carnivora: Mustelidae): adaptive foraging in generalists

Aim Studies comparing feeding habits across a genus in different geographical regions or habitats can identify factors associated with adaptive feeding behaviour, linking key ecological traits between consumers and their environment. We investigated biogeographical patterns in dietary composition and trophic diversity across the genus Martes in relation to geographical range and environmental variables. We hypothesized that widely distributed opportunistic Martes species should demonstrate adaptive variations in dietary composition and trophic diversity relative to regional geographical location (e.g. latitude, elevation), environmental variation (e.g. temperature, rainfall, snow cover and primary productivity) and concomitant variation in food supply. Location Europe, Asia and North America. Methods We examined the dietary habits of martens (Martes spp.) using original data expressed as relative frequency of occurrence, and using principal components analysis to extract the main gradients in diet composition. These were then used as response variables in regression analyses, predicted from latitude or elevation. Multiple regression analyses were performed to assess the influence of food types and environmental variables on the trophic diversity index. Results A clear latitudinal gradient in dietary composition was observed. Small mammals were the primary food type, but were less abundant in the diet of martens at lower latitude and elevation. Vegetable matter and insects were consumed more frequently in southerly and/or lower‐elevation localities. Trophic diversity was lower at higher elevation, and increased with a decline in consumption of the dominant food types, i.e. rodents, fruits and insects. Trophic diversity also increased with increasing mean temperature. Main conclusions Biogeographical variations in feeding habits across the genus Martes proved to be associated with latitude, local climate (especially temperature regime) and the availability of alternative potential foods. On an extensive geographical scale, martens respond to varying food availability by adjusting their foraging strategy and thus should be considered facultative generalists. At the species level, however, different climatic variables emerged as differentially important, indicative of adaptations to local conditions. Martes species are opportunistic and flexible feeders, and thus their conservation requires informed management, mindful of how changes in environmental conditions might influence their varied food supply.     

A pHMM-ANN based discriminative approach to promoter identification in prokaryote genomic contexts

The computational approach for identifying promoters on increasingly large genomic sequences has led to many false positives. The biological significance of promoter identification lies in the ability to locate true promoters with and without prior sequence contextual knowledge. Prior approaches to promoter modelling have involved artificial neural networks (ANNs) or hidden Markov models (HMMs), each producing adequate results on small scale identification tasks, i.e. narrow upstream regions. In this work, we present an architecture to support prokaryote promoter identification on large scale genomic sequences, i.e. not limited to narrow upstream regions. The significant contribution involved the hybrid formed via aggregation of the profile HMM with the ANN, via Viterbi scoring optimizations. The benefit obtained using this architecture includes the modelling ability of the profile HMM with the ability of the ANN to associate elements composing the promoter. We present the high effectiveness of the hybrid approach in comparison to profile HMMs and ANNs when used separately. The contribution of Viterbi optimizations is also highlighted for supporting the hybrid architecture in which gains in sensitivity (+0.3), specificity (+0.65) and precision (+0.54) are achieved over existing approaches.     

全球变化下的国际水文学研究进展:特点与启示——2011年欧洲地球科学联合会会员大会述评

欧洲地球科学联合会2011年会员大会于4月3-8日在奥地利首都维也纳举行。其中,水文学专场重点讨论了全球变化对陆地表层水文过程的影响。主要包括气候变化下的洪涝灾害与干旱胁迫、冰川融雪、降雨特性、区域水平衡、土壤侵蚀与泥沙搬运、景观进化与水文过程、地下水与溶质迁移、植被动态与水文格局、气候-水-健康、气候和土地利用综合影响下的区域水行为、耦合水与人类经济社会的关键要素、基于公众和利益相关者参与的水资源优化管理等重要科学进展。众多议题凸现了多学科交叉、多区域、跨尺度、系统性以及先进技术设备的广泛应用等几大特点,对于今后应对气候变化和环境变迁所带来的种种挑战具有重要启示。当前和今后的工作中应瞄准与把握以下几个基本原则。(1)进一步加强基础研究、促进学科交融与渗透,以实现方法和理念互补、共同解决复杂生态学问题。(2)重视区域分异、以凸现具体科学问题,实现研究目标、对象、内容和方法的有机统一。(3)重视系统性研究,加强国际合作、共同研发,促进先进技术和研究手段的创新与应用。     

Distinguishing Patients with Parkinson's Disease Subtypes from Normal Controls Based on Functional Network Regional Efficiencies

Many studies have demonstrated that the pathophysiology and clinical symptoms of Parkinson''s disease (PD) are inhomogeneous. However, the symptom-specific intrinsic neural activities underlying the PD subtypes are still not well understood. Here, 15 tremor-dominant PD patients, 10 non-tremor-dominant PD patients, and 20 matched normal controls (NCs) were recruited and underwent resting-state functional magnetic resonance imaging (fMRI). Functional brain networks were constructed based on randomly generated anatomical templates with and without the cerebellum. The regional network efficiencies (i.e., the local and global efficiencies) were further measured and used to distinguish subgroups of PD patients (i.e., with tremor-dominant PD and non-tremor-dominant PD) from the NCs using linear discriminant analysis. The results demonstrate that the subtype-specific functional networks were small-world-organized and that the network regional efficiency could discriminate among the individual PD subgroups and the NCs. Brain regions involved in distinguishing between the study groups included the basal ganglia (i.e., the caudate and putamen), limbic regions (i.e., the hippocampus and thalamus), the cerebellum, and other cerebral regions (e.g., the insula, cingulum, and calcarine sulcus). In particular, the performances of the regional local efficiency in the functional network were better than those of the global efficiency, and the performances of global efficiency were dependent on the inclusion of the cerebellum in the analysis. These findings provide new evidence for the neurological basis of differences between PD subtypes and suggest that the cerebellum may play different roles in the pathologies of different PD subtypes. The present study demonstrated the power of the combination of graph-based network analysis and discrimination analysis in elucidating the neural basis of different PD subtypes.     

Potassium diffusive coupling in neural networks

Conventional neural networks are characterized by many neurons coupled together through synapses. The activity, synchronization, plasticity and excitability of the network are then controlled by its synaptic connectivity. Neurons are surrounded by an extracellular space whereby fluctuations in specific ionic concentration can modulate neuronal excitability. Extracellular concentrations of potassium ([K⁺]_o) can generate neuronal hyperexcitability. Yet, after many years of research, it is still unknown whether an elevation of potassium is the cause or the result of the generation, propagation and synchronization of epileptiform activity. An elevation of potassium in neural tissue can be characterized by dispersion (global elevation of potassium) and lateral diffusion (local spatial gradients). Both experimental and computational studies have shown that lateral diffusion is involved in the generation and the propagation of neural activity in diffusively coupled networks. Therefore, diffusion-based coupling by potassium can play an important role in neural networks and it is reviewed in four sections. Section 2 shows that potassium diffusion is responsible for the synchronization of activity across a mechanical cut in the tissue. A computer model of diffusive coupling shows that potassium diffusion can mediate communication between cells and generate abnormal and/or periodic activity in small (§3) and in large networks of cells (§4). Finally, in §5, a study of the role of extracellular potassium in the propagation of axonal signals shows that elevated potassium concentration can block the propagation of neural activity in axonal pathways. Taken together, these results indicate that potassium accumulation and diffusion can interfere with normal activity and generate abnormal activity in neural networks.     

Application of machine learning approaches for land cover monitoring in northern Cameroon

Machine learning (ML) models are a leading analytical technique used to monitor, map and quantify land use and land cover (LULC) and its change over time. Models such as k-nearest neighbour (kNN), support vector machines (SVM), artificial neural networks (ANN), and random forests (RF) have been used effectively to classify LULC types at a range of geographical scales. However, ML models have not been widely applied in African tropical regions due to methodological challenges that arise from relying on the coarse-resolution satellite images available for these areas. In this study, we compared the performance of four ML algorithms (kNN, SVM, ANN and RF) applied to LULC monitoring within the Mayo Rey department, North Province, Cameroon. We used satellite data from the Landsat 7 Enhanced Thematic Mapper Plus (ETM+) combined with 8 Operational Land Imager (OLI) images of northern Cameroon for November 2000 and November 2020. Our results showed that all four classification algorithms produced relatively high accuracy (overall classification accuracy >80%), with the RF model (> 90% classification accuracy) outperforming the kNN, SVM, and ANN models. We found that approximately 7% of all forested areas (dense forest and woody savanna) were converted to other land cover types between 2000 and 2020; this forest loss is particularly associated with an expansion of both croplands and built-up areas. Our study represents a novel application and comparison of statistical and ML approaches to LULC monitoring using coarse-resolution satellite images in an African tropical forest and savanna setting. The resulting land cover maps serve as an important baseline that will be useful to the Cameroon government for policy development, conservation planning, urban planning, and deforestation and agricultural monitoring.