Adaptive processing for LANDSAT data

Analytical and test results on the use of adaptive processing on LANDSAT data are presented. The Kalman filter was used as a framework to contain different adapting techniques. When LANDSAT MSS data were used all of the modifications made to the Kalman filter performed the functions for which they were designed. It was found that adaptive processing could provide compensation for incorrect signature means, within limits. However, if the data were such that poor classification accuracy would be obtained when the correct means were used, then adaptive processing would not improve the accuracy and might well lower it even further

Feature Extraction of Multispectral Data

A method is presented for feature extraction of multispectral scanner data. Non-training data is used to demonstrate the reduction in processing time that can be obtained by using feature extraction rather than feature selection

Embedding Principal Component Analysis for Data Reductionin Structural Health Monitoring on Low-Cost IoT Gateways

Principal component analysis (PCA) is a powerful data reductionmethod for Structural Health Monitoring. However, its computa-tional cost and data memory footprint pose a significant challengewhen PCA has to run on limited capability embedded platformsin low-cost IoT gateways. This paper presents a memory-efficientparallel implementation of the streaming History PCA algorithm.On our dataset, it achieves 10x compression factor and 59x memoryreduction with less than 0.15 dB degradation in the reconstructedsignal-to-noise ratio (RSNR) compared to standard PCA. More-over, the algorithm benefits from parallelization on multiple cores,achieving a maximum speedup of 4.8x on Samsung ARTIK 710

Efficiency comparison of three attractant products against webbing clothes moth Tineola bisselliella (Hummel) (Lepidoptera: Tineidae) using an adapted four arms olfactometer

Contribution to section 2: Biology, behaviour and detectio

Elk and Fire Impacts on Mountain Big Sagebrush Range in Yellowstone

Range recovery after fire has many implications for wildlife habitat. It was our objective to determine the effect of elk herbivory on recovery of mountain big sagebrush (Artemisia tridentata vaseyana) – bluebunch wheatgrass (Agropyron spicatum) – Idaho fescue (Festuca idahoensis) winter range a decade after a 1988 wildfire on the Black-tailed Deer Plateau of the Northern Yellowstone Winter Range. Our hypotheses were that shrubs and herbaceous plants will recover from fire equally with or without elk herbivory. Measurements were taken in and out of exclosures established in 1957 and 1962 on environmentally paired, protected and browsed-grazed sites (n = 12). Mountain big sagebrush cover (n = 5) averaged 20 percent with protection and 9.7 percent where browsed (P ≤ 0.01). Mountain big sagebrush densities were not different (P ≤ 0.01). The sprouting shrubs, rubber rabbitbrush (Chrysothamnus nauseosus), green rabbitbrush (C. viscidiflorus), and gray horsebrush (Tetradymia canescens), (n = 4) responded the same as mountain big sagebrush with cover of 5.8 percent and two percent where protected and browsed, respectively (P ≤ 0.001), with no overall difference in density (P ≤ 0.47). Few cover differences existed between individual pairs of protected and unprotected sites for herbaceous plants (n = 12). Similarly no cover differences were found over all sites for total herbaceous species, grasses, or forbs. We rejected our hypotheses for shrubs as elk herbivory did negatively impact shrub recovery after wildfire. Reductions of shrub cover and productivity in mountain big sagebrush-grass communities from wildfire and intense herbivory have implications for many organisms. Keywords: Artemisia tridentata, elk, fire, sagebrush, Yellowston

Reproductive ecology of Bombina variegata: aspects of life history

Individual reproductive behaviour and survival of Bombina variegata was studied in a dynamic habitat with a variety of rain-filled ponds from 1990 to 1992. Most animals reached sexual maturity at the age of two years. Annual adult survival was at least 62%. Individual females, on average, laid between 40 and 70 eggs per "clutch". While about 12% of the breeding females spawned a second time within the season, a similar proportion did not seem to spawn every possible year, probably depending on climatic conditions. The results are consistent with ultimate predictions from life history theory, but the proximate mechanisms of ovulation and spawning in response to environmental conditions remain to be investigated

Climate change reduces winter overland travel across the Pan-Arctic even under low-end global warming scenarios

Amplified climate warming has led to permafrost degradation and a shortening of the winter season, both impacting cost-effective overland travel across the Arctic. Here we use, for the first time, four state-of-the-art Land Surface Models that explicitly consider ground freezing states, forced by a subset of bias-adjusted CMIP5 General Circulation Models to estimate the impact of different global warming scenarios (RCP2.6, 6.0, 8.5) on two modes of winter travel: overland travel days (OTDs) and ice road construction days (IRCDs). We show that OTDs decrease by on average −13% in the near future (2021–2050) and between −15% (RCP2.6) and −40% (RCP8.5) in the far future (2070–2099) compared to the reference period (1971–2000) when 173 d yr−1 are simulated across the Pan-Arctic. Regionally, we identified Eastern Siberia (Sakha (Yakutia), Khabarovsk Krai, Magadan Oblast) to be most resilient to climate change, while Alaska (USA), the Northwestern Russian regions (Yamalo, Arkhangelsk Oblast, Nenets, Komi, Khanty-Mansiy), Northern Europe and Chukotka are highly vulnerable. The change in OTDs is most pronounced during the shoulder season, particularly in autumn. The IRCDs reduce on average twice as much as the OTDs under all climate scenarios resulting in shorter operational duration. The results of the low-end global warming scenario (RCP2.6) emphasize that stringent climate mitigation policies have the potential to reduce the impact of climate change on winter mobility in the second half of the 21st century. Nevertheless, even under RCP2.6, our results suggest substantially reduced winter overland travel implying a severe threat to livelihoods of remote communities and increasing costs for resource exploration and transport across the Arctic

Understanding the Polymerization of Polyfurfuryl Alcohol: Ring Opening and Diels-Alder Reactions

Polyfurfuryl alcohol (PFA) is one of the most intriguing polymers because, despite its easy polymerization in acid environment, its molecular structure is definitely not obvious. Many studies have been performed in recent decades, and every time, surprising aspects came out. With the present study, we aim to take advantage of all of the findings of previous investigations and exploit them for the interpretation of the completely cured PFA spectra registered with three of the most powerful techniques for the characterization of solid, insoluble polymers: Solid-State 13C-NMR, Attenuated Total Reflectance (ATR), Fourier Transform Infrared (FTIR) spectroscopy, and UV-resonant Raman spectroscopy at different excitation wavelengths, using both an UV laser source and UV synchrotron radiation. In addition, the foreseen structures were modeled and the corresponding 13C-NMR and FTIR spectra were simulated with first-principles and semi-empiric methods to evaluate their matching with experimental ones. Thanks to this multi-technique approach, based on complementary analytical tools and computational support, it was possible to conclude that, in addition to the major linear unconjugated polymerization, the PFA structure consists of Diels-Alder rearrangements occurring after the opening of some furanic units, while the terminal moieties of the chain involves \u3b3-lactone arrangements. The occurrence of head-head methylene ether bridges and free hydroxyl groups (from unreacted furfuryl alcohol, FA, or terminal chains) could be excluded, while the conjugated systems could be considered rather limited

The role of beliefs, expectations and values in decision-making favoring climate change adaptation—implications for communications with European forest professionals

Beliefs, expectations and values are often assumed to drive decisions about climate change adaptation. We tested hypotheses based on this assumption using survey responses from 508 European forest professionals in ten countries. We used the survey results to identify communication needs and the decision strategies at play, and to develop guidelines on adequate communications about climate change adaptation. We observed polarization in the positive and negative values associated with climate change impacts accepted by survey respondents. We identified a mechanism creating the polarization that we call the 'blocked belief' effect. We found that polarized values did not correlate with decisions about climate change adaptation. Strong belief in the local impacts of climate change on the forest was, however, a prerequisite of decision-making favoring adaptation. Decision-making in favor of adaptation to climate change also correlated with net values of expected specific impacts on the forest and generally increased with the absolute value of these in the absence of 'tipping point' behavior. Tipping point behavior occurs when adaptation is not pursued in spite of the strongly negative or positive net value of expected climate change impacts. We observed negative and positive tipping point behavior, mainly in SW Europe and N-NE Europe, respectively. In addition we found that advice on effective adaptation may inhibit adaptation when the receiver is aware of effective adaptation measures unless it is balanced with information explaining how climate change leads to negative impacts. Forest professionals with weak expectations of impacts require communications on climate change and its impacts on forests before any advice on adaptation measures can be effective. We develop evidence-based guidelines on communications using a new methodology which includes Bayesian machine learning modeling of the equivalent of an expected utility function for the adaptation decision problem