Separating near surface thermal inertia signals from a thermal time series by standardized principal component analysis

Principal component analysis has been applied to remote sensing data to identify spatiotemporal patterns in a time series of images. Thermal inertia is a surface property that relates well to shallow surface thermal and physical properties. Mapping thermal inertia requires quantifying surface energy balance components and soil heat flux, both of which are difficult to measure remotely. This article describes a method to map soil thermal inertia using principal component analysis applied to a time series of thermal infrared images and it also assesses how sensitive this method is to the time intervals between images. Standardized principal component analysis (SPCA) was applied to thermal infrared images captured at half-hour intervals during a complete diurnal cycle. Shallow surface thermal properties accounted for 45%, 82% and 66% of the spatiotemporal variation in surface temperature observed during the heating phase, cooling phase and over the total diurnal cycle respectively. The remaining 55%, 18% and 34% of the variation was attributed to transient effects such as shadows, surface roughness and background noise. Signals related to thermal inertia explained 18% of total variation observed in a complete diurnal cycle and 7% of variation in the cooling series. The SPCA method was found useful to separate critical information such as timing and amplitude of maximum surface temperature variation from delays related to differential heating induced by micro-topography. For the field conditions experienced in this study, decreased temporal resolution when sampling intervals were greater than an hour significantly reduced the quality of results.     

Seasonal and spatial variability of water quality parameters in the Port of Genoa, Italy, from 2000 to 2007

This paper presents the results of the statistical analysis of a set of physico-chemical and biological water quality parameters, monthly collected from 2000 to 2007 in the Genoa Harbour area (Ligurian Sea). We applied multivariate methods, such as principal component analysis (PCA) and dynamic factor analysis (DFA) for investigating the spatial and temporal variability and for providing important background information on pollution problems in the region. PCA evidenced the role of the sewage and river discharges and of the exchanges with the open sea in determining the harbour water quality. DFA was used to estimate underlying common trends in the time series. The DFA results partly show a general improvement of water quality over the 8-years period. However, in other areas, we found inter-annual variations but no significant multi-annual trend. Furthermore, we included meteorological variables in our statistical analyses because of their potential influence on the water quality parameters. These natural forcings explain part of the variability in water quality parameters that are superimposed on the dominating anthropogenic pollution factors.     

2012年春季渤海中部及邻近海域叶绿素a与环境因子的分布特征

分析了2012年春季渤海中部及其邻近海域32个站点叶绿素a和环境因子的空间分布特征及其相互关系。结果发现:渤海中部靠近黄河口邻近水域相对于其他水域,呈现出相对较高的水温和较低的盐度,这与黄河淡水输入以及近岸水深相对较浅有密切关系。营养盐浓度在空间分布上表现为黄河口附近海域较高,在垂直分布上表现为中、底层高于表层,显示出黄河水输入与沉积物营养盐再释放的影响;此外,营养盐浓度与结构显示,渤海海域存在明显的磷和硅限制,磷限制尤其严重。叶绿素a浓度的空间分布显示,表层叶绿素a浓度的高值区出现在渤海湾湾口处,而中层与底层的叶绿素a浓度高值区出现在渤海中部。主成分分析结果表明,磷酸盐和温度是影响表层叶绿素a浓度的重要因素,而中、底层叶绿素a浓度主要受磷酸盐的影响。     

Drought Forecasting in a Semi-arid Watershed Using Climate Signals:a Neuro-fuzzy Modeling Approach

Large-scale annual climate indices were used to forecast annual drought conditions in the Maharlu-Bakhtegan watershed,located in Iran,using a neuro-fuzzy model.The Standardized Precipitation Index（SPI） was used as a proxy for drought conditions.Among the 45 climate indices considered,eight identified as most relevant were the Atlantic Multidecadal Oscillation（AMO）,Atlantic Meridional Mode（AMM）,the Bivariate ENSO Time series（BEST）,the East Central Tropical Pacific Surface Temperature（NINO 3.4）,the Central Tropical Pacific Surface Temperature（NINO 4）,the North Tropical Atlantic Index（NTA）,the Southern Oscillation Index（SOI）,and the Tropical Northern Atlantic Index（TNA）.These indices accounted for 81% of the variance in the Principal Components Analysis（PCA） method.The Atlantic surface temperature（SST：Atlantic） had an inverse relationship with SPI,and the AMM index had the highest correlation.Drought forecasts of neuro-fuzzy model demonstrate better prediction at a two-year lag compared to a stepwise regression model.     

An adaptive approach for the segmentation and extraction of planar and linear/cylindrical features from laser scanning data

Laser scanning systems have been established as leading tools for the collection of high density three-dimensional data over physical surfaces. The collected point cloud does not provide semantic information about the characteristics of the scanned surfaces. Therefore, different processing techniques have been developed for the extraction of useful information from this data which could be applied for diverse civil, industrial, and military applications. Planar and linear/cylindrical features are among the most important primitive information to be extracted from laser scanning data, especially those collected in urban areas. This paper introduces a new approach for the identification, parameterization, and segmentation of these features from laser scanning data while considering the internal characteristics of the utilized point cloud – i.e., local point density variation and noise level in the dataset. In the first step of this approach, a Principal Component Analysis of the local neighborhood of individual points is implemented to identify the points that belong to planar and linear/cylindrical features and select their appropriate representation model. For the detected planar features, the segmentation attributes are then computed through an adaptive cylinder neighborhood definition. Two clustering approaches are then introduced to segment and extract individual planar features in the reconstructed parameter domain. For the linear/cylindrical features, their directional and positional parameters are utilized as the segmentation attributes. A sequential clustering technique is proposed to isolate the points which belong to individual linear/cylindrical features through directional and positional attribute subspaces. Experimental results from simulated and real datasets demonstrate the feasibility of the proposed approach for the extraction of planar and linear/cylindrical features from laser scanning data.     

主成分分析在遥感影像数据中的实例应用

主成分分析(Principal Component Analysis)是根据变量之间的相互关系,尽可能不丢失信息地用几个综合性指标表示多个变量的方法。在多(高)光谱图像中,由于各波段的数据间具有相关性,因此包含许多冗余信息。通过主成分分析法可以把遥感图像中所含的大部分信息用少数波段表示出来,这样就可以几乎不丢失数据但可以减少数据量,消除冗余信息。在遥感数据处理时用主成分分析法作数据分析前的预处理,以达到数据压缩和图像增强的效果,更加有利于影像信息提取。文章对主成分分析在遥感图像处理中的实际应用进行了实例示范应用研究。     

A statistical approach to disentangle environmental forcings in a lacustrine record: the Lago Chungará case (Chilean Altiplano)

A high resolution multiproxy study (magnetic susceptibility, X-ray diffraction, XRF scanner, gray-colour values, Total Organic Carbon, Total Inorganic Carbon, Total Carbon and Total Biogenic Silica) of the sedimentary infill of Lago Chungará (northern Chilean Altiplano) was undertaken to unravel the environmental forcings controlling its evolution using a number of different multivariate statistical techniques. Redundancy analyses enabled us to identify the main provenance of the studied proxies whereas stratigraphically unconstrained cluster analyses allowed us to distinguish the “outsiders” as result of anomalous XRF scanner acquisitions. Principal Component Analysis (PCA) was employed to identify and isolate the main underlying environmental gradients that characterize the sedimentary infill of Lago Chungará. The first eigenvector of the PCA could be interpreted as an indicator of changes in the input of volcaniclastic material, whereas the second one would indicate changes in water availability. The chronological model of this sedimentary sequence was constructed using 17 AMS ¹⁴C and 1 ²³⁸U/²³⁰Th dates in order to characterize the volcaniclastic input and the changes in water availability in the last 12,300 cal years BP. Comparison of the reconstructed volcaniclastic input of Lago Chungará with the dust particle record from the Nevado Sajama ice core suggested that the Parinacota volcano eruptions were the main source of dust during the mid and Late Holocene rather than the dry out lakes as has previously been pointed out. The comparison of the water availability reconstruction of Lago Chungará with three of the most detailed paleoenvironmental records of the region (Paco Cocha, Lake Titicaca and Salar Uyuni) showed an heterogeneous (and sometimes contradictory) temporal and spatial pattern distribution of moisture. Although the four reconstructions showed a good correlation, each lacustrine ecosystem responded differently to the moisture oscillations that affected this region. The variations in the paleoenvironmental records could be attributed to the dating uncertainities, lake size, lake morphology, catchment size and lacustrine ecosystem responses to the abrupt arid events.     

A new approach for computing a flood vulnerability index using cluster analysis

A Flood Vulnerability Index (FloodVI) was developed using Principal Component Analysis (PCA) and a new aggregation method based on Cluster Analysis (CA). PCA simplifies a large number of variables into a few uncorrelated factors representing the social, economic, physical and environmental dimensions of vulnerability. CA groups areas that have the same characteristics in terms of vulnerability into vulnerability classes. The grouping of the areas determines their classification contrary to other aggregation methods in which the areas' classification determines their grouping. While other aggregation methods distribute the areas into classes, in an artificial manner, by imposing a certain probability for an area to belong to a certain class, as determined by the assumption that the aggregation measure used is normally distributed, CA does not constrain the distribution of the areas by the classes.FloodVI was designed at the neighbourhood level and was applied to the Portuguese municipality of Vila Nova de Gaia where several flood events have taken place in the recent past. The FloodVI sensitivity was assessed using three different aggregation methods: the sum of component scores, the first component score and the weighted sum of component scores.The results highlight the sensitivity of the FloodVI to different aggregation methods. Both sum of component scores and weighted sum of component scores have shown similar results. The first component score aggregation method classifies almost all areas as having medium vulnerability and finally the results obtained using the CA show a distinct differentiation of the vulnerability where hot spots can be clearly identified.The information provided by records of previous flood events corroborate the results obtained with CA, because the inundated areas with greater damages are those that are identified as high and very high vulnerability areas by CA. This supports the fact that CA provides a reliable FloodVI.     

Geoaccumulation and distribution of heavy metals in the urban river sediment

Current study presents the application of chemometric techniques to comprehend the interrelations among sediment variables whilst identifying the possible pollution source at Langat River,Malaysia.Surface sediment samples（0-10 cm）were collected at 22 sampling stations and analyzed for total metals（~（48）Cd,~（29）Cu,~（30）Zn,~（82）Pb）,pH,redox potential（Eh）,salinity,electrical conductivity（EC）,loss on ignition（LOI）and cation exchange capacity（CEC）.The principal component analysis（PCA）scrutinized the origin of environmental pollution by various anthropogenic and natural activities：four principal components were obtained with 86.34%（5 cm）and88.34%（10 cm）.Standard,forward and backward stepwise discriminant analysis effectively discriminate 2variables（84.06%）indicating high variation of heavy metals accumulation at both depth.The cluster analysis accounted for high input of Zn and Pb at LA8,LA 10,LA 11 and LA 12 that mergers three（5 cm）and four（10cm）into clusters.This is consistent with the contamination factor（C_1）that shows high Cd（LA 1）and Pb（LA 7,LA 8,LA 10,LA 11 and LA 12）contaminations at 5cm.These indicate that Pb and Zn are the most bioavailable metals in the sediment with significant positive linear relationship at both sediment depths.Therefore,this approach is a good indication of environmental pollution status that transfers new findings on the assessment of heavy metals by interpreting large complex datasets and predicting the fate of heavy metals in the sediment.     

新疆4次中强地震前小震震源机制变化特征

采用点源位错模型、层状介质速度结构,利用地震波垂直向记录的直达 、 波最大振幅,计算小地震震源机制。通过系统聚类,利用矢量合成方法,计算得到各类解的平均震源机制解。采用上述方法,针对2003年以来新疆北天山西段和中天山地区4次中强地震前,震源区周围中小地震震源断错性质和P轴方位的变化进行分析。结果显示,中强地震前2～3年中小地震震源机制解类型随机分布,震前1年表现出明显的优势分布特点,主压应力P轴方位发生较明显的偏转变化。