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Displaying 96 - 100 of 661Estimating upper soil horizon carbon stocks in a permafrost watershed of Northeast Siberia by integrating field measurements with Landsat-5 TM and WorldView-2 satellite data
Arctic soils contain three times as much carbon (C) as all aboveground biomass distributed globally, much of which is stored in permafrost soils. Here, we (1) determine the predictability of estimating soil organic carbon (SOC) using different satellite data, classifications, and methods; (2) estimate the quantity and distribution of SOC for the top 10 cm for the Ambolikha River watershed (~121 km ²) in northeast Siberia, a sub-watershed of the Kolyma River; and (3) produce a hybrid SOC map through data fusion, combining the strengths of each data type.
Littoral macroinvertebrate communities of dune lakes in the far north of New Zealand
The littoral macroinvertebrate faunas of 17 dune lakes on the Aupouri Peninsula in northern New Zealand were examined. Land cover of individual catchments was principally sand dunes and scrub, plantation forest, pasture, or a mixture of plantation forest and pasture. Sampling was concentrated in the sedge beds, submerged macrophytes and surface sediment layers of the littoral zone. Sixty-eight invertebrate taxa were recorded, 11–30 per lake. Relative abundance of major faunal groups differed considerably among lakes but a core group of common species was found in three quarters of them.
effect of corn–soybean rotation on the NDVI-based drought indicators: a case study in Iowa, USA, using Vegetation Condition Index
Satellite remote sensing has become a popular tool to analyze agricultural drought through terrestrial vegetation health conditions using the normalized difference vegetation index (NDVI). Drought monitoring techniques using remote sensing-based drought indices assume that vegetation conditions vary year-to-year due to prevailing weather conditions (e.g., precipitation and temperature), and current conditions are evaluated based on the deviation from the long-term statistics such as mean, minimum, or maximum.
Possible impacts of climate change on water quality in streams of the Czech Republic
The impacts of changes in water temperature and flow on selected water quality parameters, as one of the consequences of climate change, were studied in river catchments in the Czech Republic with little anthropogenic influence. The impact of climate change was manifested by an increase in stream temperature by 1.15°C over 28 years. The selected water quality parameters were dependent on flow, with up to 10-fold increases in the concentrations of ammonia, phosphorus and chlorophyll- a at minimum flow levels.
Feed-forward vs recurrent neural network models for non-stationarity modelling using data assimilation and adaptivity
Artificial neural networks (ANN) are nonlinear models widely investigated in hydrology due to their properties of universal approximation and parsimony. Their performance during the training phase is very good, and their ability to generalize can be improved by using regularization methods such as early stopping and cross-validation. In our research, two kinds of generic models are implemented: the feed-forward model and the recurrent model.