57 resultados para Aerial photography in soil surveys.


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This data set contains four time series of particulate and dissolved soil nitrogen measurements from the main experiment plots of a large grassland biodiversity experiment (the Jena Experiment; see further details below). In the main experiment, 82 grassland plots of 20 x 20 m were established from a pool of 60 species belonging to four functional groups (grasses, legumes, tall and small herbs). In May 2002, varying numbers of plant species from this species pool were sown into the plots to create a gradient of plant species richness (1, 2, 4, 8, 16 and 60 species) and functional richness (1, 2, 3, 4 functional groups). Plots were maintained by bi-annual weeding and mowing. 1. Total nitrogen from solid phase: Stratified soil sampling was performed every two years since before sowing in April 2002 and was repeated in April 2004, 2006 and 2008 to a depth of 30 cm segmented to a depth resolution of 5 cm giving six depth subsamples per core. In 2002 five samples per plot were taken and analyzed independently. Averaged values per depth layer are reported. In later years, three samples per plot were taken, pooled in the field, and measured as a combined sample. Sampling locations were less than 30 cm apart from sampling locations in other years. All soil samples were passed through a sieve with a mesh size of 2 mm in 2002. In later years samples were further sieved to 1 mm. No additional mineral particles were removed by this procedure. Total nitrogen concentration was analyzed on ball-milled subsamples (time 4 min, frequency 30 s-1) by an elemental analyzer at 1150°C (Elementaranalysator vario Max CN; Elementar Analysensysteme GmbH, Hanau, Germany). 2. Total nitrogen from solid phase (high intensity sampling): In block 2 of the Jena Experiment, soil samples were taken to a depth of 1m (segmented to a depth resolution of 5 cm giving 20 depth subsamples per core) with three replicates per block ever 5 years starting before sowing in April 2002. Samples were processed as for the more frequent sampling but were always analyzed independently and never pooled. 3. Mineral nitrogen from KCl extractions: Five soil cores (diameter 0.01 m) were taken at a depth of 0 to 0.15 m (and between 2002 and 2004 also at a depth of 0.15 to 0.3 m) of the mineral soil from each of the experimental plots at various times over the years. In addition also plots of the management experiment, that altered mowing frequency and fertilized subplots (see further details below) were sampled in some later years. Samples of the soil cores per plot (subplots in case of the management experiment) were pooled during each sampling campaign. NO3-N and NH4-N concentrations were determined by extraction of soil samples with 1 M KCl solution and were measured in the soil extract with a Continuous Flow Analyzer (CFA, 2003-2005: Skalar, Breda, Netherlands; 2006-2007: AutoAnalyzer, Seal, Burgess Hill, United Kingdom). 4. Dissolved nitrogen in soil solution: Glass suction plates with a diameter of 12 cm, 1 cm thickness and a pore size of 1-1.6 µm (UMS GmbH, Munich, Germany) were installed in April 2002 in depths of 10, 20, 30 and 60 cm to collect soil solution. The sampling bottles were continuously evacuated to a negative pressure between 50 and 350 mbar, such that the suction pressure was about 50 mbar above the actual soil water tension. Thus, only the soil leachate was collected. Cumulative soil solution was sampled biweekly and analyzed for nitrate (NO3-), ammonium (NH4+) and total dissolved nitrogen concentrations with a continuous flow analyzer (CFA, Skalar, Breda, The Netherlands). Nitrate was analyzed photometrically after reduction to NO2- and reaction with sulfanilamide and naphthylethylenediamine-dihydrochloride to an azo-dye. Our NO3- concentrations contained an unknown contribution of NO2- that is expected to be small. Simultaneously to the NO3- analysis, NH4+ was determined photometrically as 5-aminosalicylate after a modified Berthelot reaction. The detection limits of NO3- and NH4+ were 0.02 and 0.03 mg N L-1, respectively. Total dissolved N in soil solution was analyzed by oxidation with K2S2O8 followed by reduction to NO2- as described above for NO3-. Dissolved organic N (DON) concentrations in soil solution were calculated as the difference between TDN and the sum of mineral N (NO3- + NH4+).

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There is a paucity of information on abundance, densities, and habitat selection of narwhals Monodon monoceros in the offshore pack ice of Baffin Bay, West Greenland, despite the critical importance of winter foraging regions and considerable sea ice declines in the past decades. We conducted a double-platform visual aerial survey over a narwhal wintering ground to obtain pack ice densities and develop the first fully corrected abundance estimate using point conditional mark-recapture distance sampling. Continuous video recording and digital images taken along the trackline allowed for in situ quantification of winter narwhal habitat and for the estimation of fine-scale narwhal habitat selection and habitat-specific sighting probabilities. Abundance at the surface was estimated at 3484 (coefficient of variation [CV] = 0.46) including whales missed by observers. The fully corrected abundance of narwhals was 18 044 (CV = 0.46), or approximately one-quarter of the entire Baffin Bay population. The narwhal wintering ground surveyed (~9500 km**2) had 2.4 to 3.2% open water based on estimates from satellite imagery (NASA Moderate Resolution Imaging Spectroradiometer) and 1565 digital photographic images collected on the trackline. Thus, the ~18 000 narwhals had access to 233 km**2 of open water, resulting in an average density of ~77 narwhals/km**2 open water. Narwhal sighting probability near habitats with <10% or 10 to 50% open water was significantly higher than sighting probability in habitats with >50% open water, suggesting narwhals select optimal foraging areas in dense pack ice regardless of open water availability. This study provides the first quantitative ecological data on densities and habitat selection of narwhals in pack ice foraging regions that are rapidly being altered with climate change.

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Surveying habitats critical to the survival of grey nurse sharks in South-East Queensland has mapped critical habitats, gathered species inventories and developed protocols for ecological monitoring of critical habitats in southern Queensland. This information has assisted stakeholders with habitat definition and effective management. In 2002 members of UniDive applied successfully for World Wide Fund for Nature, Threatened Species Network funds to map the critical Grey Nurse Shark Habitats in south east Queensland. UniDive members used the funding to survey, from the boats of local dive operators, Wolf Rock at Double Island Point, Gotham, Cherub's Cave, Henderson's Rock and China Wall at North Moreton and Flat Rock at Point Look Out during 2002 and 2003. These sites are situated along the south east Queensland coast and are known to be key Grey Nurse Shark aggregation sites. During the project UniDive members were trained in mapping and survey techniques that include identification of fish, invertebrates and substrate types. Training was conducted by experts from the University of Queensland (Centre of Marine Studies, Biophysical Remote Sensing) and the Queensland Parks and Wildlife Service who are also UniDive members. The monitoring methods (see methods) are based upon results of the UniDive Coastcare project from 2002, the international established Reef Check program and research conducted by Biophysical Remote Sensing and the Centre of Marine Studies. Habitats were mapped using a combination of towed GPS photo transects, aerial photography, bathymetry surveys and expert knowledge. This data provides georeferenced information regarding the major features of each of Sites mapped including Wolf Rock

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Antarctic glacier forefields are extreme environments and pioneer sites for ecological succession. Increasing temperatures due to global warming lead to enhanced deglaciation processes in cold-affected habitats, and new terrain is becoming exposed to soil formation and microbial colonization. However, only little is known about the impact of environmental changes on microbial communities and how they develop in connection to shifting habitat characteristics. In this study, using a combination of molecular and geochemical analysis, we determine the structure and development of bacterial communities depending on soil parameters in two different glacier forefields on Larsemann Hills, East Antarctica. Our results demonstrate that deglaciation-dependent habitat formation, resulting in a gradient in soil moisture, pH and conductivity, leads to an orderly bacterial succession for some groups, for example Cyanobacteria, Bacteroidetes and Deltaproteobacteria in a transect representing 'classical' glacier forefields. A variable bacterial distribution and different composed communities were revealed according to soil heterogeneity in a slightly 'matured' glacier forefield transect, where Gemmatimonadetes, Flavobacteria, Gamma- and Deltaproteobacteria occur depending on water availability and soil depth. Actinobacteria are dominant in both sites with dominance connected to certain trace elements in the glacier forefields.

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In the past few years, it has become increasingly apparent that perchlorate (ClO4-) is present on all continents, except the polar regions where it had not yet been assessed, and that it may have a significant natural source. Here, we report on the discovery of perchlorate in soil and ice from several Antarctic Dry Valleys (ADVs) where concentrations reach up to 1100/µg/kg. In the driest ADV, perchlorate correlates with atmospherically deposited nitrate. Far from anthropogenic activity, ADV perchlorate provides unambiguous evidence that natural perchlorate is ubiquitous on Earth. The discovery has significant implications for the origin of perchlorate, its global biogeochemical interactions, and possible interactions with the polar ice sheets. The results support the hypotheses that perchlorate is produced globally and continuously in the Earth's atmosphere, that it typically accumulates in hyperarid areas, and that it does not build up in oceans or other wet environments most likely because of microbial reduction on a global scale.

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To project the future development of the soil organic carbon (SOC) storage in permafrost environments, the spatial and vertical distribution of key soil properties and their landscape controls needs to be understood. This article reports findings from the Arctic Lena River Delta where we sampled 50 soil pedons. These were classified according to the U.S.D.A. Soil Taxonomy and fall mostly into the Gelisol soil order used for permafrost-affected soils. Soil profiles have been sampled for the active layer (mean depth 58±10 cm) and the upper permafrost to one meter depth. We analyze SOC stocks and key soil properties, i.e. C%, N%, C/N, bulk density, visible ice and water content. These are compared for different landscape groupings of pedons according to geomorphology, soil and land cover and for different vertical depth increments. High vertical resolution plots are used to understand soil development. These show that SOC storage can be highly variable with depth. We recommend the treatment of permafrost-affected soils according to subdivisions into: the surface organic layer, mineral subsoil in the active layer, organic enriched cryoturbated or buried horizons and the mineral subsoil in the permafrost. The major geomorphological units of a subregion of the Lena River Delta were mapped with a land form classification using a data-fusion approach of optical satellite imagery and digital elevation data to upscale SOC storage. Landscape mean SOC storage is estimated to 19.2±2.0 kg C/m**2. Our results show that the geomorphological setting explains more soil variability than soil taxonomy classes or vegetation cover. The soils from the oldest, Pleistocene aged, unit of the delta store the highest amount of SOC per m**2 followed by the Holocene river terrace. The Pleistocene terrace affected by thermal-degradation, the recent floodplain and bare alluvial sediments store considerably less SOC in descending order.

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Oil polluted and not oil polluted soils (crude oil hydrocarbons contents: 20-92500 mg/kg dry soil mass) under natural grass and forest vegetation and in a bog in the Russian tundra were compared in their principal soil ecological parameters, the oil content and the microbial indicators. CFE biomass-C, dehydrogenase and arylsulfatase activity were enhanced with the occurrence of crude oil. Using these parameters for purposes of controlling remediation and recultivation success it is not possible to distinguish bctween promotion of microbial activity by oil carbon or soil organic carbon (SOC). For this reason we think that these parameters are not appropriate to indicate a soil damage by an oil impact. In contrast the metabolie quotient (qC02), calculated as the ratio between soil basal respiration and the SIR biomass-C was adequate to indicate a high crude oil contamination in soil. Also, the ß-glucosidase activity (parameter ß-GL/SOC) was correlated negatively with oil in soil. The indication of a soil damage by using the stress parameter qCO, or the specific enzyme activities (activity/SOC) minimizes the promotion effect of the recent SOC content on microbial parameters. Both biomass methods (SIR, CFE) have technical problems in application for crude oil-contaminated and subarctic soils. CFE does not reflect the low C_mic level of the cold tundra soils. We recommend to test every method for its suitability before any data collection in series as well as application for cold soils and the application of ecophysiological ratios as R_mic/C_mic, C_mic/SOC or enzymatic activity/SOC instead of absolute data.

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This collection contains measurements on physical soil properties of the plots of the different sub-experiments at the field site of a large grassland biodiversity experiment (the Jena Experiment; see further details below). In the main experiment, 82 grassland plots of 20 x 20 m were established from a pool of 60 species belonging to four functional groups (grasses, legumes, tall and small herbs). In May 2002, varying numbers of plant species from this species pool were sown into the plots to create a gradient of plant species richness (1, 2, 4, 8, 16 and 60 species) and functional richness (1, 2, 3, 4 functional groups). Plots were maintained by bi-annual weeding and mowing

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Soil porosity is the fraction of total volume occupied by pores or voids measured at matric potential 0. To measure soil porosity, soil samples were taken from each plot using sample rings with an internal diameter of 57 mm and height of 40.5 mm (inner volume of Vs=100 cm3). The samples were placed on a sand bed box with water level set to allow saturation of the samples with water. After 48 h the samples were weighed (ms), oven dried at 105 °C and weighed again to determine the dry weight (md). We calculated soil porosity (n [%]) using the density of water (?w=1 g cm?3), n=100 ? (mw-md) / (?w?Vs). To account for the spatial variation of soil properties, three replicates were taken per plot, approximately 2, 3 and 4 weeks after the flood that occurred at the field site during June 2013. Data are the average soil porosity values per plot. All data where measured in the main experiment plots of a large grassland biodiversity experiment (the Jena Experiment; see further details below). In the main experiment, 82 grassland plots of 20 x 20 m were established from a pool of 60 species belonging to four functional groups (grasses, legumes, tall and small herbs). In May 2002, varying numbers of plant species from this species pool were sown in the plots to create a gradient of plant species richness (1, 2, 4, 8, 16 and 60 species) and functional richness (1, 2, 3, or 4 functional groups). Plots were maintained by bi-annual weeding and mowing.