The role of land cover change in Arctic-Boreal greening and browning trends

Many studies have used time series of satellite-derived vegetation indices to identify so-called greening and browning trends across the northern high-latitudes and to suggest that the productivity of Arctic-Boreal ecosystems is changing in response to climate forcing at local and continental scales. However, disturbances that alter land cover are prevalent in Arctic-Boreal ecosystems, and changes in Arctic-Boreal land cover, which complicate interpretation of trends in vegetation indices, have mostly been ignored in previous studies. Here we use a new land cover change dataset derived from Landsat imagery to explore the extent to which land cover and land cover change influence trends in the normalized difference vegetation index (NDVI) over a large (3.76 M km2) area of NASA's Arctic Boreal Vulnerability Experiment, which spans much of northwestern Canada and Alaska. Between 1984 and 2012, 21.2% of the study domain experienced land cover change and 42.7% had significant NDVI trends. Land cover change occurred in 27.6% of locations with significant NDVI trends during this period and resulted in greening and browning rates 48%–128% higher than in areas of stable land cover. While the majority of land cover change areas experienced significant NDVI trends, more than half of areas with stable land cover did not. Further, the extent and magnitude of browning and greening trends varied substantially as a function of land cover class and land cover change type. Forest disturbance from fire and timber harvest drove over one third of statistically significant NDVI trends and created complex mosaics of recent forest loss (as browning) and post-disturbance recovery (as greening) at both landscape and continental scale. Our results demonstrate the importance of land cover changes in highly disturbed high-latitude ecosystems for interpreting trends of NDVI and productivity across multiple spatial scales.Published versio

Identifying Mislabeled Training Data

This paper presents a new approach to identifying and eliminating mislabeled training instances for supervised learning. The goal of this approach is to improve classification accuracies produced by learning algorithms by improving the quality of the training data. Our approach uses a set of learning algorithms to create classifiers that serve as noise filters for the training data. We evaluate single algorithm, majority vote and consensus filters on five datasets that are prone to labeling errors. Our experiments illustrate that filtering significantly improves classification accuracy for noise levels up to 30 percent. An analytical and empirical evaluation of the precision of our approach shows that consensus filters are conservative at throwing away good data at the expense of retaining bad data and that majority filters are better at detecting bad data at the expense of throwing away good data. This suggests that for situations in which there is a paucity of data, consensus filters are preferable, whereas majority vote filters are preferable for situations with an abundance of data

Congruence of chloroplast- and nuclear-encoded DNA sequence variations used to assess species boundaries in the soil microalga Heterococcus (Stramenopiles, Xanthophyceae).

BackgroundHeterococcus is a microalgal genus of Xanthophyceae (Stramenopiles) that is common and widespread in soils, especially from cold regions. Species are characterized by extensively branched filaments produced when grown on agarized culture medium. Despite the large number of species described exclusively using light microscopic morphology, the assessment of species diversity is hampered by extensive morphological plasticity.ResultsTwo independent types of molecular data, the chloroplast-encoded psbA/rbcL spacer complemented by rbcL gene and the internal transcribed spacer 2 of the nuclear rDNA cistron (ITS2), congruently recovered a robust phylogenetic structure. With ITS2 considerable sequence and secondary structure divergence existed among the eight species, but a combined sequence and secondary structure phylogenetic analysis confined to helix II of ITS2 corroborated relationships as inferred from the rbcL gene phylogeny. Intra-genomic divergence of ITS2 sequences was revealed in many strains. The 'monophyletic species concept', appropriate for microalgae without known sexual reproduction, revealed eight different species. Species boundaries established using the molecular-based monophyletic species concept were more conservative than the traditional morphological species concept. Within a species, almost identical chloroplast marker sequences (genotypes) were repeatedly recovered from strains of different origins. At least two species had widespread geographical distributions; however, within a given species, genotypes recovered from Antarctic strains were distinct from those in temperate habitats. Furthermore, the sequence diversity may correspond to adaptation to different types of habitats or climates.ConclusionsWe established a method and a reference data base for the unambiguous identification of species of the common soil microalgal genus Heterococcus which uses DNA sequence variation in markers from plastid and nuclear genomes. The molecular data were more reliable and more conservative than morphological data

The reception of the Deuteronomic social law in the primitive church of Jerusalem according to the Book of Acts

The Book of Deuteronomy was extant in the Jewish cultural memory and played an important role in shaping Jewish identity. Its concept of the holy people of God, who live according to the social order given by YHWH and who stand in contrast to the pagan world, forms the social model for the Primitive Church in Jerusalem. Since New Testament exegesis has, to a large extent, neglected the role of this book of the Torah in understanding the Primitive Church, this study investigates the reception of Deuteronomy’s social law in Acts 2:42-47, 4:32-35, and 6:1-7, in terms of its theological or ecclesiological importance

A global fingerprint of macro-scale changes in urban structure from 1999 to 2009

Urban population now exceeds rural population globally, and 60–80% of global energy consumption by households, businesses, transportation, and industry occurs in urban areas. There is growing evidence that built-up infrastructure contributes to carbon emissions inertia, and that investments in infrastructure today have delayed climate cost in the future. Although the United Nations statistics include data on urban population by country and select urban agglomerations, there are no empirical data on built-up infrastructure for a large sample of cities. Here we present the first study to examine changes in the structure of the world\u27s largest cities from 1999 to 2009. Combining data from two space-borne sensors—backscatter power (PR) from NASA\u27s SeaWinds microwave scatterometer, and nighttime lights (NL) from NOAA\u27s defense meteorological satellite program/operational linescan system (DMSP/OLS)—we report large increases in built-up infrastructure stock worldwide and show that cities are expanding both outward and upward. Our results reveal previously undocumented recent and rapid changes in urban areas worldwide that reflect pronounced shifts in the form and structure of cities. Increases in built-up infrastructure are highest in East Asian cities, with Chinese cities rapidly expanding their material infrastructure stock in both height and extent. In contrast, Indian cities are primarily building out and not increasing in verticality. This new dataset will help characterize the structure and form of cities, and ultimately improve our understanding of how cities affect regional-to-global energy use and greenhouse gas emissions

Ku and the Stability of the Genome

Ku proteins are associated with a variety of cellular processes such as repair of DNA-double-strand breaks, telomere maintenance and retrotransposition. In recent years, we have learned a lot about their cellular and molecular functions and it has turned out that Ku-dependent processes affect the stability of the genome, both positively and negatively, in several ways. This article gives an overview on the role of Ku in determining the shape of the genome

Mapping Crop Cycles in China Using MODIS-EVI Time Series

As the Earth’s population continues to grow and demand for food increases, the need for improved and timely information related to the properties and dynamics of global agricultural systems is becoming increasingly important. Global land cover maps derived from satellite data provide indispensable information regarding the geographic distribution and areal extent of global croplands. However, land use information, such as cropping intensity (defined here as the number of cropping cycles per year), is not routinely available over large areas because mapping this information from remote sensing is challenging. In this study, we present a simple but efficient algorithm for automated mapping of cropping intensity based on data from NASA’s (NASA: The National Aeronautics and Space Administration) MODerate Resolution Imaging Spectroradiometer (MODIS). The proposed algorithm first applies an adaptive Savitzky-Golay filter to smooth Enhanced Vegetation Index (EVI) time series derived from MODIS surface reflectance data. It then uses an iterative moving-window methodology to identify cropping cycles from the smoothed EVI time series. Comparison of results from our algorithm with national survey data at both the provincial and prefectural level in China show that the algorithm provides estimates of gross sown area that agree well with inventory data. Accuracy assessment comparing visually interpreted time series with algorithm results for a random sample of agricultural areas in China indicates an overall accuracy of 91.0% for three classes defined based on the number of cycles observed in EVI time series. The algorithm therefore appears to provide a straightforward and efficient method for mapping cropping intensity from MODIS time series data

Ultra-low work function of caesiated surfaces and impact of selected gas species

Modern high-power negative hydrogen ion sources rely predominantly on the surface production of negative hydrogen ions. Hence, low work function converter surfaces are mandatory, for which the alkali metal Cs is commonly evaporated into the ion source to lower the work function of refractory metals by surface adsorption. To study the work function behaviour upon caesiation under the typically given non-ultra-high vacuum conditions, investigations are performed at a dedicated laboratory experiment. In a vacuum environment dominated by water vapour, the work function evolution is found to be dependent on the flux ratio of Cs to H2O onto the surface. For sufficiently high flux ratios, ultra-low work functions in the range of 1.25±0.10 eV are generated with excellent reproducibility. In the absence of Cs evaporation, the work function gradually increases under the influence of the residual gases, and re-caesiation processes lead to lower quantum efficiencies and higher work functions of typically 1.9–2.1 eV. While the addition of hydrogen and deuterium gas at several Pa as well as the leakage of inert gases (argon and nitrogen) into the vacuum system have a negligible influence on the caesiation process, small amounts of oxygen with partial pressures of ∼10-2–10-1 Pa lead to an instant reduction of the Cs density in the gas phase by several orders of magnitude and to an increase in the work function of the order of 1 eV. After the oxygen exposure is terminated, however, the Cs density and work function fully recover within several minutes

Computer simulation of pulsed field gel runs allows the quantitation of radiation-induced double-strand breaks in yeast

A procedure for the quantification of double-strand breaks in yeast is presented that utilizes pulsed field gel electrophoresis (PFGE) and a comparison of the observed DNA mass distribution in the gel lanes with calculated distributions. Calculation of profiles is performed as follows. If double-strand breaks are produced by sparsely ionizing radiation, one can assume that they are distributed randomly in the genome, and the resulting DNA mass distribution in molecular length can be predicted by means of a random breakage model. The input data for the computation of molecular length profiles are the breakage frequency per unit length, , as adjustable parameter, and the molecular lengths of the intact chromosomes. The obtained DNA mass distributions in molecular length must then be transformed into distributions of DNA mass in migration distance. This requires a calibration of molecular length vs. migration distance that is specific for the gel lane in question. The computed profiles are then folded with a Lorentz distribution with adjusted spread parameter to account for and broadening. The DNA profiles are calculated for different breakage frequencies and for different values of , and the parameters resulting in the best fit of the calculated to the observed profile are determined