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The biological monitoring working party (BMWP) is a procedure for measuring water quality using families of macroinvertebrates as biological indicators. [1]The method is based on the principle that different aquatic invertebrates have different tolerances to pollutants.
Consequently, shoreline development index values cannot be compared for lakes with shorelines measured from maps with different scales. [ 9 ] [ 11 ] [ 12 ] Additionally, the shoreline development index cannot be compared for lakes with different surface areas because large lakes automatically have higher values than smaller lakes, even if they ...
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McFeeters index: If looking for water bodies or change in water level (e.g. flooding), then it is advisable to use the green and NIR spectral bands [18] or green and SWIR spectral bands. Modification of normalised difference water index (MNDWI) has been suggested for improved detection of open water by replacing NIR spectral band with SWIR. [19]
TIGER includes both land features such as roads, rivers, and lakes, as well as areas such as counties, census tracts, and census blocks. Some of the geographic areas represented in TIGER are political areas, including state and federally recognized tribal lands, cities, counties, congressional districts, and school districts.
Not all National Topographic System maps strictly follow the National Tiling System's linear grid. Some maps also, as an "overedge", cover land in an area which would otherwise be covered by an adjacent map sheet, simply because the latter area does not contain enough land in Canada to warrant a separate printing. [4] [clarification needed]
Water quality modeling helps people understand the eminence of water quality issues and models provide evidence for policy makers to make decisions in order to properly mitigate water. [1] Water quality modeling also helps determine correlations to constituent sources and water quality along with identifying information gaps. [2] Due to the ...
A supervised classification is a system of classification in which the user builds a series of randomly generated training datasets or spectral signatures representing different land-use and land-cover (LULC) classes and applies these datasets in machine learning models to predict and spatially classify LULC patterns and evaluate classification accuracies.