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Displaying 221 - 225 of 661Growth characteristics, nutrient uptake, and metal accumulation of ryegrass (Lolium perenne L.) in sludge-amended mudflats
The objective of this study was to investigate the impact of sewage sludge amendment (SSA), with the rates of 0, 30, 75, 150, and 300 t ha⁻¹, on soil chemical properties and biomass, aboveground and root growth, and nitrogen and phosphorus uptake of ryegrass (Lolium perenne L.) seedling grown on a mudflat soil. Results showed that SSA modified the chemical properties of mudflat soil by decreasing soil salinity and pH, increasing soil organic matter, total and available N and P, and heavy metals.
Assessment of the MODIS global evapotranspiration algorithm using eddy covariance measurements and hydrological modelling in the Rio Grande basin
Remote sensing is considered the most effective tool for estimating evapotranspiration (ET) over large spatial scales. Global terrestrial ET estimates over vegetated land surfaces are now operationally produced at 1-km spatial resolution using data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the MOD16 algorithm. To evaluate the accuracy of this product, ground-based measurements of energy fluxes obtained from eddy covariance sites installed in tropical biomes and from a hydrological model (MGB-IPH) were used to validate MOD16 products at local and regional scales.
PolyFrag: a vector-based program for computing landscape metrics
Landscape fragmentation is important in investigating changing biodiversity patterns. Several current software programs calculate landscape fragmentation metrics. The most prevalent of these programs are only compatible with raster-format land cover maps. However, with advancing classification techniques, vector-format maps are becoming more popular. The new program PolyFrag computes fragmentation metrics for vector-based maps, is flexible and comprehensive, and outputs metrics similar to those of the widely used raster-based programs, like FRAGSTATS.
Analyst variation associated with land cover image classification of Landsat ETM + data for the assessment of coarse spatial resolution regional/global land cover products
This study examined analyst variation associated with land cover (LC) image classification using 30 × 30 m Landsat ETM+ data for the assessment of coarse spatial resolution regional/global LC products. The study was designed to test the effect of varying training site selections (location and number) among six analysts performing a supervised classification on a Landsat ETM + image. Design constraints maintained other aspects of the classification process constant (i.e., type of classifier, choice of band combinations, etc.).
Land-cover mapping in the Nujiang Grand Canyon: integrating spectral, textural, and topographic data in a random forest classifier
The integration of spectral, textural, and topographic information using a random forest classifier for land-cover mapping in the rugged Nujiang Grand Canyon was investigated in this study. Only a few land-cover categories were accurately discriminated using spectral information exclusively, with an overall accuracy of 0.56 and a kappa coefficient of 0.51.