Changing land cover and climate regimes modify water quantity and quality in natural stream systems. In regions undergoing rapid change, it is difficult to effectively monitor and quantify these ...impacts at local to regional scales. In Vancouver, British Columbia, one of the most rapidly urbanizing areas in Canada, 750 measurements were taken from a total of 81 unique sampling sites representing 49 streams located in urban, forest, and agricultural-dominant watersheds at a frequency of up to 12 times per year between 2013 and 2016. Dissolved nitrate (NO3-N) and phosphate (PO4-P) concentrations, turbidity, water temperature, pH and conductivity were measured by citizen scientists in addition to observations of hydrology, vegetation, land use, and visible stream impacts. Land cover was mapped at a 15-m resolution using Landsat 8 OLI imagery and used to determine dominant land cover for each watershed in which a sample was recorded. Regional, seasonal, and catchment-type trends in measurements were determined using statistical analyses. The relationships of nutrients to land cover varied seasonally and on a catchment-type basis. Nitrate showed seasonal highs in winter and lows in summer, though phosphate had less seasonal variation. Overall, nitrate concentrations were positively associated to agriculture and deciduous forest and negatively associated with coniferous forest. In contrast, phosphate concentrations were positively associated with agricultural, deciduous forest, and disturbed land cover and negatively associated with urban land cover. Both urban and agricultural land cover were significantly associated with an increase in water conductivity. Increased forest land cover was associated with better water quality, including lower turbidity, conductivity, and water temperature. This study showed the importance of high resolution sampling in understanding seasonal and spatial dynamics of stream water quality, made possible with the large number of measurements taken with the help of trained volunteers. The results underscore the value of citizen science in freshwater research.
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•Volunteers collected data for seasonal and spatial water quality analysis.•Agriculture and deciduous forest main drivers of nitrate and phosphate concentrations•Disturbed land associated with increased phosphate concentrations•Distinct spatial (catchment land cover type) and seasonal nutrient variations•Conductivity more closely tied to land cover than turbidity.
Global metrics of land cover and land use provide a fundamental basis to examine the spatial variability of human-induced impacts on freshwater ecosystems. However, microscale processes and site ...specific conditions related to bank vegetation, pollution sources, adjacent land use and water uses can have important influences on ecosystem conditions, in particular in smaller tributary rivers. Compared to larger order rivers, these low-order streams and rivers are more numerous, yet often under-monitored. The present study explored the relationship of nutrient concentrations in 150 streams in 57 hydrological basins in South, Central and North America (Buenos Aires, Curitiba, São Paulo, Rio de Janeiro, Mexico City and Vancouver) with macroscale information available from global datasets and microscale data acquired by trained citizen scientists. Average sub-basin phosphate (P-PO4) concentrations were found to be well correlated with sub-basin attributes on both macro and microscales, while the relationships between sub-basin attributes and nitrate (N-NO3) concentrations were limited. A phosphate threshold for eutrophic conditions (>0.1 mg L-1 P-PO4) was exceeded in basins where microscale point source discharge points (eg. residential, industrial, urban/road) were identified in more than 86% of stream reaches monitored by citizen scientists. The presence of bankside vegetation covaried (rho = -0.53) with lower phosphate concentrations in the ecosystems studied. Macroscale information on nutrient loading allowed for a strong separation between basins with and without eutrophic conditions. Most importantly, the combination of macroscale and microscale information acquired increased our ability to explain sub-basin variability of P-PO4 concentrations. The identification of microscale point sources and bank vegetation conditions by citizen scientists provided important information that local authorities could use to improve their management of lower order river ecosystems.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Arid lands are distinctive ecological zones that require vegetation maps for management and monitoring. The use of remote sensing for mapping desert vegetation is made difficult by the mixing of ...reflectance spectra of bright desert soils with the relatively weak spectral response of sparse vegetation. To investigate ways to improve desert vegetation mapping, a comparison of the effect of supervised classification using two contrasting measures of field vegetation data as reference data was performed. We took cover- and density-based field vegetation data that had been collected by the US Army on the US Yuma Proving Ground (USYPG) in southwest Arizona, converted them into cover- and density-based reference classification schemes and used them to train both maximum likelihood (ML) and artificial neural net (ANN) classifiers. The impact on the accuracy of cover- and density-based vegetation maps were further analyzed using different combinations of input data (i.e., Landsat Thematic Mapper (TM) imagery, ERS-1 C-band synthetic aperture radar (SAR) imagery, and elevation data). In spite of the fact that a cover-based plot classification is the logical training data for remote sensing classification, both cover- and density-based classified maps had similar accuracies for each data combination. The use of all data combinations gave the highest map classification accuracies, with the radar data improving the accuracy the most where the vegetation is dense. Classification accuracies of maps using the ML classifier were generally higher than those using the ANN classifier. ANN map classification accuracies improved significantly when the sigmoid transfer function was replaced with the hyperbolic tangent transfer function. Using the two contrasting measures for mapping proved complementary: the cover-based map located areas of significant tree presence that were not mapped on the density-based map and the density-based map located areas of significant cacti presence that were not mapped on the cover-based map. Creating both cover- and density-based vegetation maps may therefore better assist arid land management than creating only a cover-based vegetation map.
The Greater Vancouver area has undergone significant land use and land cover (LULC) change over the past several decades, often adversely affecting stream health and water quality, particularly in ...those areas that have undergone the most urbanization. In this study 30 years of historical LULC and water quality data were examined using GIS and statistical analysis to better understand these impacts and to help build a broader understanding of cause and effect relationships of changing LULC, especially since urbanization is increasingly occurring within sensitive watersheds at greater distances from the City of Vancouver. Urban, agriculture, and disturbed LULC data from 1976, 1986, and 2000 were examined within a number of watersheds and related to historical water quality data sampled from streams during similar time frames. Additional higher resolution 2006 LULC data from a smaller number of watersheds were then examined and compared to stream health data to investigate the sensitivity of LULC data resolution on monitoring watershed impact. While LULC impact can be clearly seen at both high and lower resolutions, issues of ambiguous land cover and land use designations can potentially affect the magnitude of the relationship.
This paper describes the classification and ordination of Sonoran Desert vegetation using systematically collected data from the US Army Yuma Proving Ground (USYPG). Two classifications were created, ...one based upon relative plant cover derived from 100 m line transect data and one based upon relative plant density derived from 6 m . x 100 m belt transect data, with the belt transect being a lateral extension of the line transect. Both cluster analysis using Ward's Method and TWINSPAN were used for classifying the data while Principal Component Analysis, Correspondence Analysis, Detrended Correspondence Analysis, and Non-Metric Multidimensional Scaling were used as ordination methods. Cluster analysis was superior to TWINSPAN in creating logical classifications comparable to published descriptions of vegetation communities found in the Lower Colorado Subdivision of the Sonoran Desert. Together, the ordination methods served to accentuate different aspects of the data including main gradients of species composition, in particular a gradient separating plots with riparian-af-finities from the main data set, a Larrea tridentata-Ambrosia dumosa gradient, and a gradient separating Encelia farinosa from the main data set. The main difference between the relative cover and relative density classifications was that the former under-represented cacti such as Opuntia bigelovii and the latter under-represented such as Parkinsonia microphylla and Olneya tesota. The classification methodology used in this study is useful for evaluating resource sampling strategies on U.S. Army bases in sparsely vegetated areas and the classifications could be used as a baseline for monitoring changes in vegetation communities.
Vegetation mapping in and regions facilitates ecological studies, land management, and provides a record to which future land changes can be compared. Accurate and representative mapping of desert ...vegetation requires a sound field sampling program and a methodology to transform the data collected into a representative classification system. Time and cost constraints require that a remote sensing approach be used if such a classification system is to be applied on a regional scale. However, desert vegetation may be sparse and thus difficult to sense at typical satellite resolutions, especially given the problem of soil reflectance. This study was designed to address these concerns by conducting vegetation mapping research using field and satellite data from the US Army Yuma Proving Ground (USYPG) in Southwest Arizona. Line and belt transect data from the Army's Land Condition Trend Analysis (LCTA) Program were transformed into relative cover and relative density classification schemes using cluster analysis. Ordination analysis of the same data produced two and three-dimensional graphs on which the homogeneity of each vegetation class could be examined. It was found that the use of correspondence analysis (CA), detrended correspondence analysis (DCA), and non-metric multidimensional scaling (NMS) ordination methods was superior to the use of any single ordination method for helping to clarify between-class and within-class relationships in vegetation composition. Analysis of these between-class and within-class relationships were of key importance in examining how well relative cover and relative density schemes characterize the USYPG vegetation. Using these two classification schemes as reference data, maximum likelihood and artificial neural net classifications were then performed on a coregistered dataset consisting of a summer Landsat Thematic Mapper (TM) image, one spring and one summer ERS-1 microwave image, and elevation, slope, and aspect layers. Classifications using a combination of ERS-1 imagery and elevation, slope, and aspect data were superior to classifications carried out using Landsat TM data alone. In all classification iterations it was consistently found that the highest classification accuracy was obtained by using a combination of Landsat TM, ERS-1, and elevation, slope, and aspect data. Maximum likelihood classification accuracy was found to be higher than artificial neural net classification in all cases.
Vegetation mapping in and regions facilitates ecological studies, land management, and provides a record to which future land changes can be compared. Accurate and representative mapping of desert ...vegetation requires a sound field sampling program and a methodology to transform the data collected into a representative classification system. Time and cost constraints require that a remote sensing approach be used if such a classification system is to be applied on a regional scale. However, desert vegetation may be sparse and thus difficult to sense at typical satellite resolutions, especially given the problem of soil reflectance. This study was designed to address these concerns by conducting vegetation mapping research using field and satellite data from the US Army Yuma Proving Ground (USYPG) in Southwest Arizona. Line and belt transect data from the Army’s Land Condition Trend Analysis (LCTA) Program were transformed into relative cover and relative density classification schemes using cluster analysis. Ordination analysis of the same data produced two and three-dimensional graphs on which the homogeneity of each vegetation class could be examined. It was found that the use of correspondence analysis (CA), detrended correspondence analysis (DCA), and non-metric multidimensional scaling (NMS) ordination methods was superior to the use of any single ordination method for helping to clarify between-class and within-class relationships in vegetation composition. Analysis of these between-class and within-class relationships were of key importance in examining how well relative cover and relative density schemes characterize the USYPG vegetation. Using these two classification schemes as reference data, maximum likelihood and artificial neural net classifications were then performed on a coregistered dataset consisting of a summer Landsat Thematic Mapper (TM) image, one spring and one summer ERS-1 microwave image, and elevation, slope, and aspect layers. Classifications using a combination of ERS-1 imagery and elevation, slope, and aspect data were superior to classifications carried out using Landsat TM data alone. In all classification iterations it was consistently found that the highest classification accuracy was obtained by using a combination of Landsat TM, ERS-1, and elevation, slope, and aspect data. Maximum likelihood classification accuracy was found to be higher than artificial neural net classification in all cases.
A BIRD’S-EYE VIEW de Boer, Gijs; Ivey, Mark; Schmid, Beat ...
Bulletin of the American Meteorological Society,
06/2018, Letnik:
99, Številka:
6
Journal Article
Recenzirano
Odprti dostop
Thorough understanding of aerosols, clouds, boundary layer structure, and radiation is required to improve the representation of the Arctic atmosphere in weather forecasting and climate models. To ...develop such understanding, new perspectives are needed to provide details on the vertical structure and spatial variability of key atmospheric properties, along with information over difficult-to-reach surfaces such as newly forming sea ice. Over the last three years, the U.S. Department of Energy (DOE) has supported various flight campaigns using unmanned aircraft systems UASs, also known as unmanned aerial vehicles (UAVs) and drones and tethered balloon systems (TBSs) at Oliktok Point, Alaska. These activities have featured in situ measurements of the thermodynamic state, turbulence, radiation, aerosol properties, cloud microphysics, and turbulent fluxes to provide a detailed characterization of the lower atmosphere. Alongside a suite of active and passive ground-based sensors and radiosondes deployed by the DOE Atmospheric Radiation Measurement (ARM) program through the third ARM Mobile Facility (AMF-3), these flight activities demonstrate the ability of such platforms to provide critically needed information. In addition to providing new and unique datasets, lessons learned during initial campaigns have assisted in the development of an exciting new community resource.
Celotno besedilo
Dostopno za:
BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
The detonation of a helium shell on a white dwarf (WD) has been proposed as a possible explosion triggering mechanism for SNe Ia. Here, we report ZTF 18aaqeasu (SN 2018byg/ATLAS 18pqq), a peculiar ...Type I supernova, consistent with being a helium-shell double-detonation. With a rise time of 18 days from explosion, the transient reached a peak absolute magnitude of MR −18.2 mag, exhibiting a light curve akin to sub-luminous SN 1991bg-like SNe Ia, albeit with an unusually steep increase in brightness within a week from explosion. Spectra taken near peak light exhibit prominent Si absorption features together with an unusually red color (g − r 2 mag) arising from nearly complete line blanketing of flux blueward of 5000 . This behavior is unlike any previously observed thermonuclear transient. Nebular phase spectra taken at and after 30 days from peak light reveal evidence of a thermonuclear detonation event dominated by Fe-group nucleosynthesis. We show that the peculiar properties of ZTF 18aaqeasu are consistent with the detonation of a massive ( 0.15 ) helium shell on a sub-Chandrasekhar mass ( 0.75 ) WD after including mixing of 0.2 of material in the outer ejecta. These observations provide evidence of a likely rare class of thermonuclear supernovae arising from detonations of massive helium shells.
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