A simultaneous sample-and-filter strategy for robust multi-structure model fitting

In many robust model fitting methods, obtaining promising hypotheses is critical to the fitting process. However the sampling process unavoidably generates many irrelevant hypotheses, which can be an obstacle for accurate model fitting. In particular, the mode seeking based fitting methods are very sensitive to the proportion of good/bad hypotheses for fitting multi-structure data. To improve hypothesis generation for the mode seeking based fitting methods, we propose a novel sample-and-filter strategy to (1) identify and filter out bad hypotheses on-the-fly, and (2) use the remaining good hypotheses to guide the sampling to further expand the set of good hypotheses. The outcome is a small set of hypotheses with a high concentration of good hypotheses. Compared to other sampling methods, our method yields a significantly large proportion of good hypotheses, which greatly improves the accuracy of the mode seeking-based fitting methods.     

Detecting the effects of climate change on canopy phenology in coniferous forests in semi-arid mountain regions of China

An increased understanding of the responses of forest phenology to climate on regional scales is critical to the evaluation of biochemical cycles (i.e. carbon, water, heat, and nutrient) under environmental changes. In this study, we aimed to identify climatic constraints on phenological events in an evergreen coniferous forest in semi-arid mountain regions of northern China. We quantified the start of season (SOS), end of season (EOS), and growing season length (GSL) based on satellite-derived data sets (normalized difference vegetation index (NDVI)) and investigated the relationships between these phenological events and climate factors. The results revealed discontinuous trends in phenological events throughout the study period, with neither an obvious extension nor decrement in GSL. We demonstrated that minimum temperatures controlled the dynamics of SOS and EOS, thus providing strong evidence for the need to include minimum temperature as a control on phenology in simulation models. Additionally, precipitation was coupled to the shift in maximum NDVI, as rainfall is a major climatic limitation to vegetation growth in semi-arid regions. It appears that selecting appropriate timescales to analyse the relationships between phenology and climate is critical. We illustrated that NDVI was an effective tool in an effort to gain greater understanding of the effects of environmental change on ecosystem functioning in this forest. Our results may be used as reference to track local changes in the evergreen coniferous forest dynamics under different climate change scenarios for semi-arid mountain regions.     

The persistence of shocks in GDP and the estimation of the potential economic costs of climate change

Integrated assessment models (IAMs) typically ignore the impact climate change could have on economic growth. The damage functions of these models assume that climate change impacts have no persistence at all, affecting only the period when they occur. Persistence of shocks is a stylized fact of macroeconomic time series and it provides a mechanism that could justify larger losses from climate change than previously estimated. Given that the degree of persistence of climate impacts is unknown, we analyze the persistence of generic shocks in observed GDP series for different world regions and compare it to that of the leading IAMs. Under the working hypothesis of interpreting the direct impact of climate change as such shocks, the implications for growth are investigated for two RCP scenarios. The way of introducing climate shocks to GDP in most IAMs can be interpreted as assuming an autonomous, costless, large and effective reactive adaptation capacity.     

Data mining for large scale 3D seismic data analysis

The automation of the analysis of large volumes of seismic data is a data mining problem, where a large database of 3D images is searched by content, for the identification of the regions that are of most interest to the oil industry. In this paper we perform this search using the 3D orientation histogram as a texture analysis tool to represent and identify regions within the data, which are compatible with a query texture.     

A rule-based event detection system for real-life underwater domain

Understanding and analyzing fish behaviour is a fundamental task for biologists that study marine ecosystems because the changes in animal behaviour reflect environmental conditions such as pollution and climate change. To support investigators in addressing these complex questions, underwater cameras have been recently used. They can continuously monitor marine life while having almost no influence on the environment under observation, which is not the case with observations made by divers for instance. However, the huge quantity of recorded data make the manual video analysis practically impossible. Thus machine vision approaches are needed to distill the information to be investigated. In this paper, we propose an automatic event detection system able to identify solitary and pairing behaviours of the most common fish species of the Taiwanese coral reef. More specifically, the proposed system employs robust low-level processing modules for fish detection, tracking and recognition that extract the raw data used in the event detection process. Then each fish trajectory is modeled and classified using hidden Markov models. The events of interest are detected by integrating end-user rules, specified through an ad hoc user interface, and the analysis of fish trajectories. The system was tested on 499 events of interest, divided into solitary and pairing events for each fish species. It achieved an average accuracy of 0.105, expressed in terms of normalized detection cost. The obtained results are promising, especially given the difficulties occurring in underwater environments. And moreover, it allows marine biologists to speed up the behaviour analysis process, and to reliably carry on their investigations.     

The uncertain impact of climate change on forest ecosystems – How qualitative modelling can guide future research for quantitative model development

Climate change could significantly alter forest productivity and climax states. Hence modelling productivity under climate change will need to account for many alternative ecosystem states. We apply qualitative modelling to identify the most likely ecosystem representations for a well-researched Tasmanian forest. Its main ecosystem is a tiered forest with rainforest, wet sclerophyll and myrtaceae components. Interactions between these components are uncertain, especially under additional pressures from climate change. Qualitative modelling is a structured method to analyse these uncertainties. We identify the most appropriate models and research efforts for model development. Further, we identify research needs for interactions between root pathogens and forest components, with research on some impacts of system components on fire being ruled out. The qualitative modelling approach applied here was useful in identifying research priorities for modelling complex ecosystems, even under uncertain system understanding or deficiencies in quantitative data.     

Heat waves measured with MODIS land surface temperature data predict changes in avian community structure

Heat waves are expected to become more frequent and severe as climate changes, with unknown consequences for biodiversity. We sought to identify ecologically-relevant broad-scale indicators of heat waves based on MODIS land surface temperature (LST) and interpolated air temperature data and assess their associations with avian community structure. Specifically, we asked which data source, time periods, and heat wave indices best predicted changes in avian abundance and species richness. Using mixed effects models, we analyzed associations between these indices and data from the North American Breeding Bird Survey in the central United States between 2000 and 2007 in four ecoregions and five migratory and nesting species groups. We then quantified avian responses to scenarios of severe, but commonly-occurring early, late, and summer-long heat waves. Indices based on MODIS LST data, rather than interpolated air temperatures, were more predictive of avian community structure. Avian communities were more related to 8-day LST exceedances (positive anomalies only); and were generally more sensitive to summer-long heat waves. Across the region, abundance, and to a lesser extent, species richness, declined following heat waves. Among the ecoregions, relationships were most consistently negative in the southern and montane ecoregions, but were positive in a more humid northern ecoregion. Among migratory groups, permanent resident species were the most sensitive, declining in abundance following a summer-long heat wave by 19% and 13% in the montane and southern ecoregions, respectively. Ground-nesting species, which declined in the south by 12% following a late summer heat wave, were more sensitive than avifauna overall. These results demonstrate the value of MODIS LST data for measuring ecologically-relevant heat waves across large regions. Ecologically, these findings highlight the importance of extreme events for avian biodiversity and the considerable variation in response to environmental change associated with different functional groups and geographic regions. The magnitude of the relationships between avian abundance and heat waves reported here raises concerns about the impacts of more frequent and severe heat waves in a warming climate.     

North American vegetation dynamics observed with multi-resolution satellite data

We investigated normalized difference vegetation index data from the NOAA series of Advanced Very High Resolution Radiometers and found regions in North America that experienced marked increases in annual photosynthetic capacity at various times from 1982 to 2005. Inspection of these anomalous areas with multi-resolution data from Landsat, Ikonos, aerial photography, and ancillary data revealed a range of causes for the NDVI increases: climatic influences; severe drought and subsequent recovery; irrigated agriculture expansion; insect outbreaks followed by logging and subsequent regeneration; and forest fires with subsequent regeneration. Vegetation in areas in the high Northern Latitudes appear to be solely impacted by climatic influences. In other areas examined, the impact of anthropogenic effects is more direct. The pattern of NDVI anomalies over longer time periods appear to be driven by long-term climate change but most appear to be associated with climate variability on decadal and shorter time scales along with direct anthropogenic land cover conversions. The local variability of drivers of change demonstrates the difficulty in interpreting changes in NDVI and indicates the complex nature of changes in the carbon cycle within North America. Coarse scale analysis of changes could well fail to identify the important local scale drivers controlling the carbon cycle and to identify the relative roles of disturbance and climate change. Our results document regional land cover land use change and climatic influences that have altered continental scale vegetation dynamics in North America.     

Determinants of information systems outsourcing: an empirical investigation in Japan

Two typical but different patterns of information system (IS) outsourcing are considered to be the most effective approaches in supply-chain management. These are conventional outsourcing and quasi-outsourcing. The latter is more generally adopted in large-scale organizations in Japan. In order to design an effective strategy, we will identify the factors which will have an important impact on the performance of IS outsourcing. In this article, we review the relevant literature on IS outsourcing, and propose four hypotheses. We test these four hypotheses by logistic regression analysis based on our original questionnaire survey of Japanese companies in order to find the relationship between IS outsourcing and its determinants. Finally, the implications of this study are discussed. Therefore, this article provides an empirical perspective to identify the determinants of conventional outsourcing versus quasi-outsourcing in Japan.     

一种基于结构因果关系和日志变化挖掘的BPMSs故障诊断方法

业务流程管理系统存在可以改变系统行为的潜在故障, 因此研究定位系统中故障发生的最小结构变化区域是 十分必要的, 它对提高业务系统的鲁棒性具有重要意义. 本文提出了一种日志诱导下的变化挖掘方法, 即最小结构故障 域识别方法(minimal structure fault region identification, MSFRI), 该方法通过系统的行为变化来定位故障发生的结构因 果关系. 进一步, 针对合理的自由选择业务流程Petri网系统, 形式化定义了6种典型变化模式, 这些变化模式为故障的结 构因果关系变化挖掘提供理论基础. 本文所提出的故障定位方法通过识别业务流程Petri网系统的行为变化, 实现具有最 少库所和变迁数目的故障区域定位, 有助于实现系统更加复杂的变化挖掘. 本文工作的主要创新之处在于从结构因果关 系的角度出发, 借助系统行为变化挖掘实现定位业务系统中的潜在故障.     

Phenological response of different vegetation types to temperature and precipitation variations in northern China during 1982–2012

Plant phenology is influenced by various climatic factors such as temperature, precipitation, insolation, and humidity, etc. Among these factors, temperature and precipitation are proved to be the most important. However, the relative importance of these two factors is different among various phenophases and regions and is seldom discussed along environmental gradients. Based on normalized difference vegetation index (NDVI) data from the NDVI3g dataset and using the mid-point method, we extracted the start date of the growing season (SOG) and the end date of the growing season (EOG) in northern China during 1982–2012. To determine which climate factor was more influential on plant phenology, partial correlation analysis was applied to analyse the spatial difference between the response of SOG and EOG to temperature and precipitation. Finally, we calculated the temperature and precipitation sensitivities of the SOG and EOG. The results showed that: (1) SOG displayed an advancing trend in most regions, while EOG was delayed for all the vegetation types during 1982–2012. (2) SOG was mainly triggered by preseason temperature. The increase in temperature caused an overall advance in SOG. However, the relationship between SOG and precipitation varied among different vegetation types. Regarding EOG, precipitation had greater impacts than temperature in relatively arid environments, such as deserts, steppes and meadow biomes. (3) The response of vegetation phenology (both SOG and EOG) to temperature became stronger with increasing preseason precipitation across space. The response of EOG to precipitation became weaker from arid regions to relatively humid regions. These results provide a better understanding of the spatial pattern of the phenological response along the precipitation gradient and a reference for assessing impacts of future climate change on vegetation phenology, especially in transitional and fragile zones.     

Analysis of the Forest Dynamic Changes in Southeast Asia based on Remote Sensing and Meteorological Data

To further understand the relationship between dynamic changes of tropical forest and human activities as well as climate changes，we use methods of time series analysis and correlation analysis to study the temporal and spatial changes of forest net primary productivity(NPP) and their correlation with tree coverage(VCF)，temperature，precipitation and photosynthetically active radiation(PAR) in 11 countries in Southeast Asia from 2001 to 2013 based on MODIS remote sensing data and ERA-Interim reanalysis of meteorological data.The main conclusions are as follows：①the NPP in Southeast Asia is increasing from the equator to the north and the south;②NPP in most areas of the study area show a decreasing trend，and regions where have a more dramatic change of NPP usually have a higher coefficient of variation which showsa more unstable carbon sequestration capacity of forest ecosystem;③the tree cover in study areais generally high(60%~80%) and most of thearea have an increasing trend，in addition，the partial correlation coefficient between VCF and NPP was higher than correlation coefficient，indicating that human activities have a greater impacton forest NPP;④the temperature，precipitation and PAR in study area are relatively high，and as for the correlation between NPP and meteorological factors，countries with tropical forest climate have a better correlation than countries with tropical monsoon climate，whose NPP is generally negatively correlated with the temperature and positively correlated with precipitation and PAR.     

Development of an expert system for tackling the public''s perception to climate-change impacts on petroleum industry

Climate change could cause significant impacts on human activities, which is especially true for regions that are of high latitude such as Canada. Petroleum industry is a main economic sector in Canada's prairie, where a number of its production and processing practices are vulnerable to the fluctuations of climatic conditions. In this study, an expert system (ES) for integrated climate-change impact assessment within the prairie's petroleum sector was developed. Interactive relationships among climate change, natural-condition variations, industrial activities, environmental concerns and economic objectives, as well as the related policy implications, were comprehensively examined and incorporated within the ES. A series of questionnaire surveys were conducted for acquiring knowledge about the interrelationships between the climate change and the petroleum-related activities. Processes that were vulnerable to climate change were analyzed, followed by an integrated impact assessment. The results indicated that the impacts of increased temperature and natural hazards would be very significant on most of the petroleum-related processes. Also, the petroleum industry would be quite sensitive to changed precipitation patterns. The developed ES can be used for both acquiring knowledge of climate-change impacts on the petroleum industry and supporting formulation of the relevant adaptation policies.     

Fractional vegetation coverage response to climatic factors based on grey relational analysis during the 2000-2017 growing season in Sichuan Province,China

ABSTRACT

Sichuan Province, China, is a typical ecologically fragile area that is sensitive to global climate change. Studies regarding the spatial-temporal variations and driving factors of FVC (Fractional Vegetation Coverage) in Sichuan Province’s vegetation ecosystem are of important theoretical and practical significance for revealing the relationship between global climate change and vegetation ecosystems. These studies are also important theoretical and practical significance for the evaluation of environmental quality and service function adjustment of terrestrial ecosystems. In existing studies, there is a lack of detailed depictions of the FVC response to climatic factors in the context of different vegetation types and different landform features in Sichuan Province. In this study, the spatial-temporal patterns and change trends for the FVC of the growing seasons during the 2000–2017 period for Sichuan Province were analysed based on the FVCs that were inversely determined from MODIS (MODerate-resolution Imaging Spectroradiometer) NDVI (Normalized Difference Vegetation Index) remote sensing data, and they were combined with air temperature, relative humidity and precipitation data. Moreover, the GRA (Grey Relational Analysis) method was used to study the response of the FVC to climate changes. Based on the results of the GRA, the zoning of climatic factors as driving forces for the FVC was performed, and the differences in the spatial-temporal characteristics of the FVC response to different climatic factors were presented in quantitative form. Here, we found that the vegetation coverage in Sichuan province showed a slight degradation trend, and that the medium to low altitude woody plants were significantly degraded. The proportion of regions in which relative humidity (17.3%) and precipitation (17.4) were strong drivers for FVC changes, was much greater than the regions in which air temperature (1.8%) and other co-drivers were the force.     

Rapid hypothesis generation by combining residual sorting with local constraints

Efficient hypothesis generation plays an important role in robust model fitting. In this study, based on the combination of residual sorting and local constraints, we propose an efficient guided hypothesis generation method, called Rapid Hypothesis Generation (RHG). By exploiting the local constraints to guide the hypothesis generation process, RHG raises the probability of generating promising hypotheses and reduces the computational cost during hypotheses generation. Experimental results on homography and fundamental matrix estimation show that RHG can effectively guide hypothesis generation process and rapidly generate promising hypotheses for heavily contaminated multi-structure data.     

Effect of climate change on vegetation phenology of different land-cover types on the Tibetan Plateau

Research in vegetation phenology change has been one heated topic of current ecological and climate change study. The Tibetan Plateau, as the highest plateau of the earth, is more vulnerable and sensitive to climate change than many other regions. In this region, shifts in vegetation phenology have been intensively studied during recent decades, primarily based on satellite-retrieved data. In this study, we explored the spatiotemporal changes of vegetation phenology for different land-cover types in the Tibetan Plateau and characterized their relationship with temperature and precipitation by using long-term time-series datasets of normalized difference vegetation index (NDVI) from 1982 to 2014. Diverse phenological changes were observed for different land-cover types, with an advancing start of growing season (SOS), delaying end of growing season (EOS) and increasing length of growing season (LOS) in the eastern Tibetan Plateau where meadow was the dominant vegetation type, but with the opposite changes in the steppe and sparse herbaceous or sparse shrub regions which are mostly located in the northwestern and western edges of the Tibetan Plateau. Correlation analysis indicated that sufficient preseason precipitation may delay the SOS of evergreen forests in the southeastern Plateau and advance the SOS of steppe and sparse herbaceous or sparse shrub in relatively arid areas, while the advance of SOS in meadow areas could be related to higher preseason temperature. For EOS, because it is less sensitive to climate change than SOS, the response of EOS for different land-cover types to precipitation and temperature were more complicated across the Tibetan Plateau.     

Reconstruction of MODIS land-surface temperature in a flat terrain and fragmented landscape

Moderate Resolution Imaging Spectroradiometer (MODIS) land-surface temperature (LST) products provide important and reliable time-series data for the examination of global climate change, water cycling, and ecological evolution. In particular, in recently developed remote-sensing evapotranspiration models, such as the Surface Energy Balance Algorithm for Land and the Surface Energy Balance System, LST is a critical parameter that can directly influence the accuracy and integrity of final results. However, clouds and other atmospheric disturbances, which cover a large area throughout most of the year, are read as blank values by these programs, creating a problem. To solve this, a number of algorithms have been proposed to reconstruct LST data, but few can be used to evaluate flat and relatively fragmented landscape regions, such as the Yellow River Delta in China. Here, we conducted an analysis where we considered the LST of a flat area to be mainly influenced by land cover and other environmental elements (e.g. soil moisture). We used maps such as land cover, normalized difference vegetation index, and MODIS band 7 as additional data in the reconstruction model. All of the LST pixels we used were randomly divided into two parts: one part was used to train the model, and the other part was used to validate the calculated results. Three different methods have been developed to reconstruct LST data – linear regression, regression tree (RT) analysis, and artificial neural networks. In comparing these methods, we found that the RT method is able to estimate the LST of MODIS pixels with the greatest accuracy, and that it is both convenient and useful for reconstructing the LST map in flat and fragmented regions.     

Detecting local regions of change in high-dimensional criminal or terrorist point processes

A method is presented for detecting changes to the distribution of a criminal or terrorist point process between two time periods using a non-model-based approach. By treating the criminal/terrorist point process as an intelligent site selection problem, changes to the process can signify changes in the behavior or activity level of the criminals/terrorists. The locations of past events and an associated vector of geographic, environmental, and socio-economic feature values are employed in the analysis. By modeling the locations of events in each time period as a marked point process, we can then detect differences in the intensity of each component process. A modified PRIM (patient rule induction method) is implemented to partition the high-dimensional feature space, which can include mixed variables, into the most likely change regions. Monte Carlo simulations are easily and quickly generated under random relabeling to test a scan statistic for significance. By detecting local regions of change, not only can it be determined if change has occurred in the study area, but the specific spatial regions where change occurs is also identified. An example is provided of breaking and entering crimes over two-time periods to demonstrate the use of this technique for detecting local regions of change. This methodology also applies to detecting regions of differences between two types of events such as in case-control data.     

Econometric analysis of global climate change

This paper reports on research that applies econometric time series methods to the analysis of global climate change. The aim of this research was to test hypotheses concerning the causes of the historically observed rise in global temperatures. Longer term applications include quantification of the contribution of different forcing variables to historic warming and use of the model as a module in integrated assessment. Research to date has comprised three stages. In the first stage we used the concept of Granger causality and differences between the temperature record in the northern and southern hemispheres to investigate the causes of temperature increase. In the second stage we tested various global change time series for the presence of stochastic trends. We found that most series contain a stochastic trend with the greenhouse gas series containing I(2) stochastic trends. In the third stage we developed a structural time series to investigate some of the hypotheses suggested by the earlier stages and further tested for the presence of an I(2) trend in hemispheric temperature series. We found that the two temperature series share a common I(2) stochastic trend that may have its source in radiative forcing due to greenhouse gases. There is a second non-stationary component that appears only in the northern hemisphere and appears to be related to radiative forcing due to anthropogenic sulphur emissions.     

A Global Parity Measure for Incomplete Point Cloud Data

Shapes with complex geometric and topological features such as tunnels, neighboring sheets, and cavities are susceptible to undersampling and continue to challenge existing reconstruction techniques. In this work we introduce a new measure for point clouds to determine the likely interior and exterior regions of an object. Specifically, we adapt the concept of parity to point clouds with missing data and introduce the parity map, a global measure of parity over the volume. We first examine how parity changes over the volume with respect to missing data and develop a method for extracting topologically correct interior and exterior crusts for estimating a signed distance field and performing surface reconstruction. We evaluate our approach on real scan data representing complex shapes with missing data. Our parity measure is not only able to identify highly confident interior and exterior regions but also localizes regions of missing data. Our reconstruction results are compared to existing methods and we show that our method faithfully captures the topology and geometry of complex shapes in the presence of missing data.