Development and analysis of a 12-year daily 1-km forest fire dataset across North America from NOAA/AVHRR data

Fires in boreal and temperate forests play a significant role in the global carbon cycle. While forest fires in North America (NA) have been surveyed extensively by U.S. and Canadian forest services, most fire records are limited to seasonal statistics without information on temporal evolution and spatial expansion. Such dynamic information is crucial for modeling fire emissions. Using the daily Advanced Very High Resolution Radiometer (AVHRR) data archived from 1989 to 2000, an extensive and consistent fire product was developed across the entire NA forest regions on a daily basis at 1-km resolution. The product was generated following data calibration, geo-referencing, and the application of an active fire detection algorithm and a burned area mapping algorithm. The spatial-temporal variation of forest fire in NA is analyzed in terms of (1) annual and monthly patterns of fire occurrences in different eco-domains, (2) the influence of topographic factors (elevation zones, aspect classes, and slope classes), and (3) major forest types and eco-regions in NA. It was found that 1) among the 12 years analyzed, 1989 and 1995 were the most severe fire years in NA; 2) the majority of burning occurred during June-July and in low elevation zones (< 500 m) with gentle slopes (< 10°), except in the dry eco-domain where more fires occurred in higher elevation zones (> 2000 m); 3) most fires occurred in the polar eco-domain, sub-arctic eco-division, and in the taiga ( boreal forests), forest-tundras and open woodlands eco-provinces in the boreal forests of Canada. The tendency for multiple burns to occur increases with elevation and slope until about 2500 m elevation and 24° slope, and decreases therefore. In comparison with ground observations, the omission and commission errors are on the order of 20%.     

On simultaneous on-line state and parameter estimation in non-linear state-space models

On-line estimation plays an important role in process control and monitoring. Obtaining a theoretical solution to the simultaneous state-parameter estimation problem for non-linear stochastic systems involves solving complex multi-dimensional integrals that are not amenable to analytical solution. While basic sequential Monte-Carlo (SMC) or particle filtering (PF) algorithms for simultaneous estimation exist, it is well recognized that there is a need for making these on-line algorithms non-degenerate, fast and applicable to processes with missing measurements. To overcome the deficiencies in traditional algorithms, this work proposes a Bayesian approach to on-line state and parameter estimation. Its extension to handle missing data in real-time is also provided. The simultaneous estimation is performed by filtering an extended vector of states and parameters using an adaptive sequential-importance-resampling (SIR) filter with a kernel density estimation method. The approach uses an on-line optimization algorithm based on Kullback–Leibler (KL) divergence to allow adaptation of the SIR filter for combined state-parameter estimation. An optimal tuning rule to control the width of the kernel and the variance of the artificial noise added to the parameters is also proposed. The approach is illustrated through numerical examples.     

SPOT VEGETATION for characterizing boreal forest fires

The potential of the recent SPOT VEGETATION (VGT) sensor for characterizing boreal forest fires was investigated. Its capability for hotspot detection and burned area mapping was assessed by analysing a series of VGT, NOAA/AVHRR, and Landsat TM images over a 1541 km²     

A biophysically based and objective satellite seasonality observation method for applications over the Arctic

Despite wide applications of remote-sensing data with high temporal resolution for monitoring phenology, two persistent problems have prevented the realization of their full potential. The first is the subjectivity in defining thresholds for a phenological event (e.g. the start or end of growing season ? SOS or EOS). The second is the use of various arbitrarily selected filtering and smoothing algorithms for constructing vegetation index seasonal profiles in order to reduce the noise caused by residue cloud contamination and aerosol variations. In this study, we addressed both problems by developing a biophysically based and objective satellite seasonality observation method (BLOSSOM) for application over Canada’s Arctic. Application of the BLOSSOM method to three northern Canadian national parks (Ivvavik, Wapusk, and Sirmilik) proved that the method is operational. Using the uncertainties in the vegetation index and its threshold, we estimated the overall mean uncertainties as being ?5.3 to 3.4 days, ?4.2 to 5.2 days, and ?6.2 to 8.4 days, respectively, for SOS, EOS, and growing season length (GSL). Further independent tests against SOS, determined using records of snow cover at nearby climate stations (as ‘truth’), indicate that the mean absolute error is less than 3.6 ± 0.2 days.     

Informal and formal requirements specification languages: bridgingthe gap

The differences between informal and formal requirements specification languages are noted, and the issue of bridging the gap between them is discussed. Using structured analysis (SA) and the Vienna development method (VDM) as surrogates for informal and formal languages, respectively, two approaches are presented for integrating the two. The first approach uses the SA model of a system to guide the analyst's understanding of the system and the development of the VDM specifications. The second approach proposes a rule-based method for generating VDM specifications from a set of corresponding SA specifications. The two approaches are illustrated through a simplified payroll system case. The issues that emerge from the use of the two approaches are reported     

Processes of overall similarity sorting in free classification.

The processes of overall similarity sorting were investigated in 5 free classification experiments. Experiments 1 and 2 demonstrated that increasing time pressure can reduce the likelihood of overall similarity categorization. Experiment 3 showed that a concurrent load also reduced overall similarity sorting. These findings suggest that overall similarity sorting can be a time-consuming analytic process. Such results appear contrary to the idea that overall similarity is a nonanalytic process (e.g., T. B. Ward, 1983) but are in line with F. N. Milton and A. J. Wills's (2004) dimensional summation hypothesis and with the stochastic sampling assumptions of the extended generalized context model (K. Lamberts, 2000). Experiments 4 and 5 demonstrated that the relationship between stimulus presentation time and overall similarity sorting is nonmonotonic, and the shape of the function is consistent with the idea that the three aforementioned processes operate over different parts of the time course. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

MobGeoSen: facilitating personal geosensor data collection and visualization using mobile phones

Mobile sensing and mapping applications are becoming more prevalent because sensing hardware is becoming more portable and more affordable. However, most of the hardware uses small numbers of fixed sensors that report and share multiple sets of environmental data which raises privacy concerns. Instead, these systems can be decentralized and managed by individuals in their public and private spaces. This paper describes a robust system called MobGeoSens which enables individuals to monitor their local environment (e.g. pollution and temperature) and their private spaces (e.g. activities and health) by using mobile phones in their day to day life. The MobGeoSen is a combination of software components that facilitates the phone’s internal sensing devices (e.g. microphone and camera) and external wireless sensors (e.g. data loggers and GPS receivers) for data collection. It also adds a new dimension of spatial localization to the data collection process and provides the user with both textual and spatial cartographic displays. While collecting the data, individuals can interactively add annotations and photos which are automatically added and integrated in the visualization file/log. This makes it easy to visualize the data, photos and annotations on a spatial and temporal visualization tool. In addition, the paper will present ways in which mobile phones can be used as noise sensors using an on-device microphone. Finally, we present our experiences with school children using the above mentioned system to measure their exposure to environmental pollution.

The role of oncostatin M in animal and human connective tissue collagen turnover and its localization within the rheumatoid joint

OBJECTIVE: To study the interaction of interleukin-1alpha (IL-1alpha) and oncostatin M (OSM) in promoting cartilage collagen destruction. METHODS: Bovine, porcine, and human cartilage and human chondrocytes were studied in culture. The levels of collagenase (matrix metalloproteinase 1 [MMP-1]) and tissue inhibitor of metalloproteinases 1 (TIMP-1) were measured by bioassay and enzyme-linked immunosorbent assay (ELISA). The levels of OSM in rheumatoid synovial fluid were measured by ELISA. RESULTS: When combined with OSM, IL-1alpha, IL-1beta, and tumor necrosis factor alpha released proteoglycan and collagen from cartilage. OSM was the only member of the IL-6 family to have this effect. Human tendon also responded to IL-1alpha and OSM. OSM increased the production of MMP-1 and TIMP-1 but when combined with IL-1alpha, synergistically promoted MMP-1 production in human chondrocytes and synovial fibroblasts. High levels of OSM were found in human rheumatoid synovial fluids, and confocal microscopy showed that OSM was produced by macrophages in rheumatoid synovial tissue. CONCLUSION: These results highlight an important new mechanism by which there is irreversible loss of collagen from cartilage.     

Matrilineal inheritance of complex I dysfunction in a multigenerational Parkinson's disease family

Recent data suggesting complex I dysfunction in Parkinson's disease (PD) arises from mitochondrial DNA (mtDNA) mutation does not conclusively answer whether the responsible genetic lesion is inherited (primary) or somatic (secondary). To address this question, we identified a family in which multiple members over three generations are affected with PD through exclusively maternal lines. Cytoplasmic hybrids (cybrids) were created for 15 family members over two generations by transferring each individual's mtDNA to mtDNA-depleted human neuroblastoma cells. Eight of the 15 cybrid lines contained mtDNA obtained from maternally descended family members and seven contained mtDNA from paternally descended family members. After 6 weeks of culture, cybrid cell lines were assayed for complex I activity and oxidative stress, and mitochondrial morphology was analyzed by electron microscopy. Compared with the cybrid lines containing mtDNA from paternal descendants, cybrid lines containing mtDNA from maternal descendants had lower complex I activity, increased reactive oxygen species production, increased radical scavenging enzyme activities, and more abnormal mitochondrial morphologic features. These findings were present in cybrid lines containing mtDNA from maternal descendants with PD as well as in currently asymptomatic young maternal descendants, and support a precedent for inherited mtDNA mutation in some persons with PD.