Detection and mapping of bracken fern weeds using multispectral remotely sensed data: a review of progress and challenges

Bracken fern is one of the major invasive plants distributed all over the world currently threatening socio-economic and ecological systems due to its ability to swiftly colonize landscapes. The study aimed at reviewing the progress and challenges in detecting and mapping of bracken fern weeds using different remote sensing techniques. Evidence from literature have revealed that traditional methods such as field surveys and modelling have been insufficient in detecting and mapping the spatial distribution of bracken fern at a regional scale. The applications of medium spatial resolution sensors have been constrained by their limited spatial, spectral and radiometric capabilities in detecting and mapping bracken fern. On the other hand, the availability of most of these data-sets free of charge, large swath width and their high temporal resolution have significantly improved remote sensing of bracken fern. The use of commercial satellite data with high resolution have also proven useful in providing fine spectral and spatial resolution capabilities that are primarily essential to offer precise and reliable data on the spatial distribution of invasive species. However, the application of these data-sets is largely restricted to smaller areas, due to high costs and huge data volumes. Studies on bracken fern classification have extensively adopted traditional classification methods such as supervised maximum likelihood classifier. In studies where traditional methods performed poorly, the combination of soft classifiers such as super resolution analysis and traditional methods of classification have shown an improvement in bracken fern classification. Finally, since high spatial resolution sensors are expensive to acquire and have small swath width, the current study recommends that future research can also consider investigating the utility of the freely available recently launched sensors with a global footprint that has the potential to provide invaluable information for repeated measurement of invasive species over time and space.     

A cross-ecosystem comparison of spatial and temporal patterns of covariation in the recruitment of functionally analogous fish stocks

Temporal and spatial patterns of recruitment (R) and spawning stock biomass (S) variability were compared among functionally analogous species and similar feeding guilds from six marine ecosystems. Data were aggregated into four regions including the Gulf of Maine/Georges Bank, the Norwegian/Barents Seas, the eastern Bering Sea, and the Gulf of Alaska. Variability was characterized by calculating coefficients of variation and anomalies for three response variables: ln(R), ln(R/S), and stock–recruit model residuals. Patterns of synchrony and asynchrony in the response variables were examined among and between ecosystems, between- and within-ocean basins and among functionally analogous species groups using pair-wise correlation analysis corrected for within-time series autocorrelation, multivariate cross-correlation analyses and regime shift detectors. Time series trends in response variables showed consistent within basin similarities and consistent and coherent differences between the Atlantic and Pacific basin ecosystems. Regime shift detection algorithms identified two broad-scale regime shift time periods for the pelagic feeding guild (1972–1976 and 1999–2002) and possibly one for the benthic feeding guild (1999–2002). No spatial patterns in response variable coefficients of variation were observed. Results from multivariate cross-correlation analysis showed similar trends. The data suggest common external factors act in synchrony on stocks within ocean basins but temporal stock patterns, often of the same species or functional group, between basins change in opposition to each other. Basin-scale results (similar within but different between) suggest that the two geographically broad areas are connected by unknown mechanisms that, depending on the year, may influence the two basins in opposite ways. This work demonstrates that commonalities and synchronies in recruitment fluctuations can be found across geographically distant ecosystems but biophysical causes of the fluctuations remain difficult to identify.     

Comparative growth of juvenile white sturgeon and striped bass: Effects of temperature and hypoxia

The influences of temperature and environmental hypoxia on the growth rates of two California anadromous fishes, white sturgeon (Acipenser transmontanus) and striped bass (Morone saxatilis) were examined. Fish (0.5–0.6 g initial weight) were fedad libitum rations ofArtemia in flow-through aquaria regulated for temperature (15, 20, and 25°C) and oxygen tension (130 and 90 torr P_{o 2}). Growth of sturgeon was significantly greater at 20 °C compared with 15 °C, but there was no difference between 20 and 25 °C. Striped bass growth increased with each 5° increment of temeprature elevation to 3.2% body weight per d at 25 °C, the fastest growth rate measured. The temperature of maximum growth reflected the temperature of the native estuarine rearing area. Environmental hypoxia (90 torr P_{o 2}) reduced growth of sturgeon within each temperature level, whereas striped bass growth was reduced by hypoxia only at the upper two temperatures. Sturgeon were much more active in the growth chambers than striped bass. Sturgeon activity increased with each 5 °C temperature increase under normoxia and hypoxia, except at 25 °C (hypoxia) where activity was insignificantly different from that at 20 °C (hypoxia).     

Observed dependence of the water vapor and clear-sky greenhouse effect on sea surface temperature: comparison with climate warming experiments

This study presents a comparison of the water vapor and clear-sky greenhouse effect dependence on sea surface temperature for climate variations of different types. Firstly, coincident satellite observations and meteorological analyses are used to examine seasonal and interannual variations and to evaluate the performance of a general circulation model. Then, this model is used to compare the results inferred from the analysis of observed climate variability with those derived from global climate warming experiments. One part of the coupling between the surface temperature, the water vapor and the clear-sky greenhouse effect is explained by the dependence of the saturation water vapor pressure on the atmospheric temperature. However, the analysis of observed and simulated fields shows that the coupling is very different according to the type of region under consideration and the type of climate forcing that is applied to the Earth-atmosphere system. This difference, due to the variability of the vertical structure of the atmosphere, is analyzed in detail by considering the temperature lapse rate and the vertical profile of relative humidity. Our results suggest that extrapolating the feedbacks inferred from seasonal and short-term interannual climate variability to longer-term climate changes requires great caution. It is argued that our confidence in climate models' predictions would be increased significantly if the basic physical processes that govern the variability of the vertical structure of the atmosphere, and its relation to the large-scale circulation, were better understood and simulated. For this purpose, combined observational and numerical studies focusing on physical processes are needed.     

Changing poverty distribution in Bolivia: the role of rural–urban migration and urban services

Bolivia is a country with high levels of poverty and inequality among its peoples and regions. For the nation and its urban and rural areas, trends in the social and spatial distribution of poverty (and extreme poverty) are identified from 1976 to 2003 using UBN data with minor support where appropriate from poverty lines. The main survey between 1992 and 2001 uses composite and selected UBN to track detailed poverty change for the country’s nine departments, its ten largest cities and a selection of other smaller urban and rural municipalities. Because of rising background increases in population in the various surveyed administrative units, many instances of relative reductions in poverty are accompanied by rising absolute increases. Marked spatial variations in poverty and development in the country over the last several decades are identified as the main driver for the country’s quickening pace of rural–urban migration. As a result, the paper concludes by assessing two different but closely related views. One investigation tests the notion that because more poor people have been living in Bolivia’s cities than in its rural areas since the mid to late 1990s, rapid rural–urban migration has simply shifted the locus of poverty from the countryside to the cities in a process called, the ‘urbanisation of poverty.’ A second, more challenging, investigation assesses the view that the flow of poor rural people to the better serviced urban areas of Bolivia has actually acted to alleviate national poverty levels.     

Amino acid precursors in lunar fines: limits to the contribution of jet exhaust

A sample of lunar fines collected at a maximum distance, 6.5 km, from the descent engine on Apollo 17 has been analyzed for total amino acids obtainable by hydrolysis of aqueous extracts. The minimum amounts of amino acids, calculated for a disc of 6 km radius are 10⁴–10⁵ times those which could be contributed by the lunar module jet exhaust, on the basis of conservatively limiting assumptions. The amino acids thus obtained are not explainable as due to chemical or biological contamination; their source is accordingly inferred as lunar. Under the conditions of hydrolysis of lunar extracts, cyanide is found to be converted, almost exclusively to glycine, to an extent of 10^?4.     

The application of cluster analysis in geophysical data interpretation

A clustering algorithm that is based on density and is adaptive density-reachable is developed and presented for arbitrary data point distributions in some real-world applications, especially in geophysical data interpretation. Through comparisons of the new algorithm and other algorithms, it is shown that the new algorithm can reduce the dependency of domain knowledge and the sensitivity of abnormal data points, that it can improve the effectiveness of clustering results in which data are distributed in different shapes and different densities, and that it can get a better clustering efficiency. The application of the new clustering algorithm demonstrates that data mining techniques can be used in geophysical data interpretation and can get meaningful and useful results, and that the new clustering algorithm can be used in other real-world applications.