Shifts in the timing of spring phenology are a central feature of global change research. Long-term observations of plant phenology have been used to track vegetation responses to climate variability but are often limited to particular species and locations and may not represent synoptic patterns. Satellite remote sensing is instead used for continental to global monitoring. Although numerous methods exist to extract phenological timing, in particular start-of-spring (SOS), from time series of reflectance data, a comprehensive intercomparison and interpretation of SOS methods has not been conducted. Here, we assess 10 SOS methods for North America between 1982 and 2006. The techniques include consistent inputs from the 8km Global Inventory Modeling and Mapping Studies Advanced Very High Resolution Radiometer NDVIg dataset, independent data for snow cover, soil thaw, lake ice dynamics, spring streamflow timing, over 16000 individual measurements of ground-based phenology, and two temperature-driven models of spring phenology. Compared with an ensemble of the 10 SOS methods, we found that individual methods differed in average day-of-year estimates by ! 60 days and in standard deviation by ! 20 days. The ability of the satellite methods to retrieve SOS estimates was highest in northern latitudes and lowest in arid, tropical, and Mediterranean ecoregions. The ordinal rank of SOS methods varied geographically, as did the relationships between SOS estimates and the cryospheric/hydrologic metrics. Compared with ground observations, SOS estimates were more related to the first leaf and first flowers expanding phenological stages. We found no evidence for time trends in spring arrival from ground- or model-based data; using an ensemble estimate from two methods that were more closely related to ground observations than other methods, SOS

Intercomparison, interpretation, and assessment of spring phenology in North America estimated from remote sensing for 1982-2006 M I C H A E L A. W H I T E*, K I R S T E N M. DE BEURS w , K A M E L D I D A Nz, D AV I D W. I N O U Y E § ,

The annual timing of temperate forest leaf colouring is affected by climate change; to date, its modelling remainsa challenge. Wetake advantage of aca.400leaf colouring observations database acquired in France during the period of 1997‐2006 in order to develop a new modelling framework aimed at predicting the spatial and year-to-year variability of leaf colouring in European beech and oak (Fagus sylvatica L., Quercus petraea (Matt.) Liebl. and Quercus robur L.). We postulate colouring to be the outcome of a one-way process triggered by photoperiod and progressing through a photoperiod-sensitive cold-degree day summation procedure. Observations were pooled according to genus for the fitting and ensuing validation procedures. Parameters of the model suggest that colouring processes start earlier, and are sensitive to higher temperatures for Quercus than for Fagus. Errors associated with the modelled predictions are up to 13.0 days in Fagus and 10.3 days in Quercus, which are significantly lower than errors associated with (1) the overall mean (null model) or (2) previously published modelling frameworks. When averaged on a site basis, model predictions reproduced spatial variability of leaf colouring over the French territory with good efficiency (modelling efficiencies: 0.44 forFagus, 0.45 forQuercus). The interannual variability of leaf colouring over France was fairly reproduced (r 2 = 0.74 for Fagus, 0.83 for Quercus). On that basis, we claim that the modelling framework developed herein can be efficiently integrated into more general schemes aimed at simulating matter and energy fluxes on a regional scale, and we provide a generic parameterisation of the model to be integrated into such schemes. When used in a prospective analysis, the model predicts a trend towards delay in leaf colouring of 1.4 and 1.7 days per decade in Fagus and Quercus, respectively, over the period of 1951‐2099 in France.

Modelling interannual and spatial variability of leaf senescence for three deciduous tree species in France

IEEE International Conference on Fuzzy Systems

The two most important issues of expert systems are the acquisition of domain experts' professional knowledge and the representation and reasoning of the knowledge rules that have been identified. First, during expert knowledge acquisition processes, the domain expert panel often demonstrates different experience and knowledge from one another and produces different types of knowledge information such as complete and incomplete, precise and imprecise, and known and unknown because of its cross-functional and multidisciplinary nature. Second, as a promising tool for knowledge representation and reasoning, fuzzy Petri nets (FPNs) still suffer a couple of deficiencies. The parameters in current FPN models could not accurately represent the increasingly complex knowledge-based systems, and the rules in most existing knowledge inference frameworks could not be dynamically adjustable according to propositions' variation as human cognition and thinking. In this paper, we present a knowledge acquisition and representation approach using the fuzzy evidential reasoning approach and dynamic adaptive FPNs to solve the problems mentioned above. As is illustrated by the numerical example, the proposed approach can well capture experts' diversity experience, enhance the knowledge representation power, and reason the rule-based knowledge more intelligently.

Knowledge Acquisition and Representation Using Fuzzy Evidential Reasoning and Dynamic Adaptive Fuzzy Petri Nets

Three equations have been fitted to new data relating the electrical conductivity of seawater to the Practical Salinity Scale 1978. These equations have been designed for the reduction of in-situ measurements of temperature, pressure, and conductivity from anywhere in the world oceans. The standard deviation of the fit is roughly equivalent to \pm0.0015\permil in salinity depending on the pressure at which the data is taken and, as such is commensurate with the best accuracy attainable with modern instruments.

The practical salinity scale 1978: fitting data

Correlated and weakly correlated fault detection based on variable division and ICA

Seawater conductivity is an important indicator of ocean electromagnetic properties and directly impacts the electromagnetic attenuation characteristics and phase distribution features of the ocean. Few studies have considered how the combined effects of salinity, temperature and pressure affect the vertical conductivity distribution and its formation mechanisms. Here, we analyse the vertical distributions of seawater conductivity from the sea surface to a maximum depth of 7062 m at five different locations. Electric conductivity profiles show similar vertical structures at all locations. Electric conductivity decreases with increasing depth first and then slowly increases from approximately 2000 m to the seabed. We observe an exponential relationship between the conductivity minimum and the water depth. At all five measurement locations, seawater conductivity measurements show a stable three-stage vertical distribution on logarithmic scales, with the middle stage satisfying a power law relationship. We analyse the vertical distribution of temperature in the second stage and investigate the relationship between temperature and conductivity. The results show that temperature also exhibits a power-law relationship with depth and a high linear correlation exists between temperature and conductivity. Our findings suggest that the vertical structure of conductivity is largely temperature dependent.

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Three-stage vertical distribution of seawater conductivity.

This paper presents a new monitoring method for multimode processes based on subspace decomposition. In the proposed method, the influence of quality variables and multimode information are considered in multimode processes modeling, which is crucially important to ensure industrial production safety and quality stabilization. Process data are decomposed into the global common subspace and the local specific subspace and monitoring is performed in each subspace to simplify the model structure. Two experiments: penicillin fermentation processes and practical foods industrial production processes, have been used to demonstrate the excellent performance of the proposed method.

Monitoring of Multimode Processes Based on Subspace Decomposition

The reasoning efficiency and reliability of fuzzy Petri nets (FPNs) have been the crucial and intractable issues. This paper proposes improved basic inference models of FPNs. One of the major differences between the proposed models and the conventional ones is that inhibitor arcs are introduced to the former. The improved inference models provide a new mechanism and approach for forward reasoning, enhancing reasoning efficiency, and increasing the response speed of a rule-based system. Especially, for complex or large fuzzy Petri nets, the inference advantages are more evident. An effective concurrent reasoning algorithm is given. An instance is presented to illustrate the feasibility and validity of the proposed inference models.

Haibo Shi

Papers

Correlated and weakly correlated fault detection based on variable division and ICA

Three-stage vertical distribution of seawater conductivity.

Monitoring of Multimode Processes Based on Subspace Decomposition

Improved basic inference models of fuzzy Petri nets

A fault diagnosis method based on attention mechanism with application in Qianlong-2 autonomous underwater vehicle