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67 Cards in this Set
- Front
- Back
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Remote sensing |
collection and interpretation of information about objects on the earth’s surface by measuring from remote platforms with specialized sensors the amount of electromagnetic energy the objects reflect |
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multispectral satellite imagery |
characterized by their resolution in the spectral(number and range of wavelengths captured), spatial (size of pixel), and temporal (time of return tosame location). |
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Image classification |
- digital process of identifying objects by grouping pixels with similar spectral signatures - spatial data created by computer‐based classification of pixels based on spectral signatures - two methods: supervised and unsupervised classification. |
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Supervised image classification |
analyst identifies representative training sites for each class a priori and an algorithm identifies allpixels with spectral signatures similar to training sites to generate a classified map |
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Unsupervised image classification |
analyst chooses total number of classes desired a priori and an algorithm identifies statistical clustersin data (mean, standard deviation) to generate a classified map; classes identified a posteriori |
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Photointerpretation |
- visual process of identifying objects (trees, buildings, roads, etc.) in imagery - spatial data created by manually digitizing recognizable objects of interest based on visual cue |
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GPS |
system of radio-emitting and receiving satellites used to determine absolute positions on the earth - constellation of satellites in orbit: - each satellite transmit signals to receivers on eartho broadcasts its location in orbit and precise time based on atomic clocks - receiver on earth o picks up each satellite signal it “sees” and statistically computes the range (distance) to the satelliteo estimates its position using trilateration |
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GPS accuracy affected by: |
- type of receiver; recreational grade (garmin, magellan) vs survey grade (trimble) - alignment and geometry of satellites in sky - signals bouncing off objects (multipath) - signals blocked by objects (buildings, tree canopy, etc.) and terrain |
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Geoprocessing |
simplify data while maintaining its geographic characteristics and integrity - subsetting data through tabular or spatial queries: select data then export to a new dataset - reclassification: grouping (aggregating) features or cells together based on criteriao often used to assign new values, like ranks for a site suitability model - dissolve boundaries: eliminates boundaries between adjacent features with the same attribute |
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Vector overlay |
combine spatial features AND attributes from two or more map layers - geometry of features are changed and attributes merged together to form a new dataset |
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Types of vector overlay |
- point in polygon (attach polygon attributes to points) - line in polygon (attach polygon attributes to lines) - polygon on polygon (attach polygon attributes to polygons) - output layer usually takes geometry type of the lowest dimension -->point in polygon-->point will be the output geometry type -->line in polygon-->line will be the output geometry type |
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Intersect |
- two or more polygon datasets are combined retaining ONLY features and attributes from bothdatasets that OVERLAP - attributes and geometry are change |
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Union |
- two or more polygon datasets are combined retaining ALL features and attributes from both datasets. - attributes and geometry are changed. |
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Identity |
- two or more polygon datasets are combined retaining features from ONE dataset merged withportions of the other datasets that overlap it. - attributes and geometry are changed. |
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Clip |
- extracts the features from one dataset that overlap features in another dataset. - “cookie cutter” overlay but RETAINS the area of overlap - attributes are not changed, only geometry of the input dataset |
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Erase |
- removes or deletes features from one dataset that overlap features in another dataset. - “cookie cutter” overlay but REMOVES the area of overlap - attributes are not changed, only geometry of the input dataset |
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proximity analysis |
find near features or the distance to features; calculate the distances around features |
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buffers |
- for a proximity analysis - calculate regions a specified distance around one or more features - can be fixed, variable distance, or nested (multi‐ring) |
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euclidean distance |
- for a proximity analysis - calculate the straight‐line (as‐the‐crow flies) distance away to or from features - useful for finding closest feature or distance to multiple features |
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cost distance |
- for proximity analysis - calculate distances to or from features, but includes the cost of traveling over frictional surfaces - data assigned ranks of cost (e.g. 0‐9) based on unique attributes and combined to create cost surface - cans use to calculate least cost paths, the path across a surface incurring the least friction |
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raster overlay |
cell‐by‐cell combination of raster layers - uses map algebra, the analysis language for raster data - expression made of operations and functions are applied to each number (cell value) - expressions are through the Raster Calculator |
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mathematical operations |
- for map algebra - adding or multiplying cell values together, applying trigonometric functions, etc. - return numeric values - grid1 + grid2 |
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relational and boolean |
- map algebra function - create simple and complex logical tests; similar to tabular queries - relational: =, >, <, etc.; boolean: AND (&), OR (|), XOR (!) and NOT (^) - return logical values of 1 (true) and 0 (false) - grid1 > 5 AND grid 2 = 16 |
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conditional processing |
- map algebra function - assign values to cells based on if‐then conditions; conditions are logical tests - if condition is TRUE, the cell value is set to one value; if condition is FALSE, set to a different value - con(grid > 5, 100, 50) |
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NODATA values |
if any input cell is NODATA in an operation or function, the output cell is generally NODATA - exceptions are using setnull or isnull |
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isnull |
if a cell contains NODATA value, the output cell value is set to 1, otherwise to a 0 |
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setnull |
- if condition is TRUE, the cell value is set to NODATA; if FALSE, set to a different value - setnull(grid>8, 10) |
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local functions |
- derive an output value cell‐by‐cell - often used to calculate statistics (mean, min, max, etc.) across multiple datasets - max(grid1, grid2) |
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focal (neighborhood) functions |
- derive output value from a neighborhood of cells centered on an output cell - often used to calculate statistics (mean, min, max, etc.) within the neighborhood - slope, aspect, and curvature are special case focal functions |
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zonal functions |
derive output value for each ZONE in a raster or vector dataset. - zones are all cells having the same value (raster) or a single polygon feature (vector) - often used to calculate statistics (mean, min, max, etc.) within a zone - zonalmean(grid1, grid2) |
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slope |
maximum rate of change from each cell and its eight neighbors - calculated as degree slope or percent slope |
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curvature |
defines the shape of the slope, i.e. morphometrics of the surface - shows if part of a surface is convex (e.g. a ridge) or concave (e.g. a valley, channel) |
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aspect |
- slope direction or the compass direction a hill faces - calculated in degrees measured clockwise from 0 (due north) to 360 |
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Shaded relief (hillshade) |
hypothetical illumination of a surface from the sun - calculates the relative radiance value; values ranges from 0 – 1 multiplied by 255 |
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Visibility (viewshed) |
-portion of a surface visible (cells that can be “seen”) from one or more observation points or lines - visible cells receive a value of 1, “hidden” cells a value of 0 |
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Density |
concentration of features across a landscape per unit area |
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Simple density |
magnitude per unit area from features that fall within a specified distance around each cell |
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Kernel density |
magnitude per unit area from features (with a kernel function fit to it) that fall within a specifieddistance around each cell |
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Spatial interpolation |
estimate (predict) a value at unsampled points based on known values of surrounding points - distance from known values are used to weight estimated unknown values - common application of Tobler’s 1st law of geography: things that are closer are more similar than things farther away - hat is, spatial autocorrelation |
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deterministic methods of spatial interpolation |
use “simple” mathematical equations - inverse distance weighted, spline, trend, |
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stochastic methods of spatial interpolation |
use probabilistic methods based on statistical models that incorporate spatial autocorrelation - - kriging |
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spatial modeling |
means to describe a distribution based on a set of static conditions and multiple criteria - involves weighting variables positively or negatively based on their importance - combine datasets through overlays to develop overall scores of “suitability” - often referred to as site suitability analysis; most common modeling approach in GIS |
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binary |
- input data are classified as 0 or 1 (true or false) based on unique attributes - datasets multiplied together - output is 1 (true) for areas that meet ALL criteria and 0 (false) for areas that do not - output = elev *slope *landuse*distance |
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weighted index |
uses within layer rankings like the index model, but applies weights to each layer when combined - weights represent a layers importance relative to other layers in the model output = ( (elev * 0.25) + (slope * 0.1) + (landuse * 0.5) + (distance * 0.15) ) / 4 |
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how ranks are decided |
suitability scales (ranks) are synthetic and subjective - ranking may be based on measured data but assigning a “suitability” is generally subjective |
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Spatial statistics |
means for measuring and analyzing spatial patterns (arrangement) and distributions - statistics that take the spatial dependence of entities into consideration - used to test for clustering or dispersion of spatial features - used to test for spatial autocorrelation or to treat for spatial autocorrelation as a “nuisance” |
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Explanatory/predictive spatial models |
means for estimating (predicting) values at unobserved locations based on statistical models - involves developing statistical relationships (mathematical equations) that predict the spatial distributionof an object based on some set of environmental variables - often use traditional regression models (linear, logistic, etc.) - predictive surfaces (rasters) can be generated by rebuilding regression equations using map algeb |
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problems with regressive modeling and spatial data |
spatial autocorrelation modifiable areal unit problem (MAUP) scale nonstationarity and edge issues |
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Dynamic simulation models |
means to describe and visualize spatial distributions over space and time (spatio‐temporal) - involve sets of rules and mathematical equations for quantifying physical processes (mechanistic) - can integrate dynamic (temporal) and stochastic (random) processes - typically more detailed, complex, and less flexible than other models |
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Types of maps |
- general purpose/reference maps - thematic maps: focus on a particular phenomena or relationship |
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Thematic maps |
- chorochromatic - chloropleth - graduated, proportional symbol - dot density - isoline - flow |
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Chorochromatic map |
map of a qualitative surface distribution of any feature - features are classified by their “uniqueness” (unique values) - no suggestion of hierarchy; shows distinctly different things - symbols filled with distinctly different colors; reinforces differences - legend rectangle boxes are separated, there is a gap between them |
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Choropleth map |
map of a quantitative surface distribution of any feature - portray the magnitude per unit area of a feature and classified by ranges (graduated) - suggests hierarchy; shows degrees of one thing - symbols filled with tints of one hue (color); reinforces magnitude - legend rectangle boxes are adjacent or joined, there are no gaps between them |
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map symbols |
- primary symbol; represent the main theme of the map and should stand out - secondary symbols; should support the primary symbol; should not attract too much attention - uses figure‐to‐ground and visual contrast to promote primary symbols |
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Georeferencing |
associating locations (coordinates) stored in unreferenced (e.g. “image”, tablet, screen) space with theircorresponding coordinates in geographic or real‐world space (e.g. UTM). - uses control points - coordinate transformations - Root mean square error: goodness of fit of a transformation |
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RMSE |
a measure of the “goodness of fit” of a transformation - the average deviation (in distance) of estimated control point coordinates from “true” locations - is a good assessment of the transformation's accuracy (low value generally suggests a better fit), butdon't confuse a low RMS error with an accurate registration; not always the case. |
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coordinate transformations |
mathematical equations that estimate new coordinates based on control points and correspondingreal‐world coordinates |
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control points |
locations that can be accurately identified on an unreferenced map and in real‐world coordinates |
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Geocoding |
the process of assigning coordinates to an address based on a reference dataset, usually a streets dataset - requires complete street data with information on odd‐even, left‐right address - assigns an x,y by linearly interpolating between start and end address range of a street segment - interpolation method generates rough, not an exact (absolute) location |
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Problems in geocoding |
- errors in the street address (street name, house number, zip code) - address does not correspond to a street address (P.O. box, trailer park, etc.) - outdated or incomplete street base map (newly developed area, rural areas, etc.) |
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Metadata |
- data about data; describes what a user needs to know about a data set - informs on any assumptions, limitations, approximations, simplifications |
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Important elements to get from metadata |
- scale, minimum mapping unit - method of capture - fields & associated values - projection |
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vector |
points, lines, polygons; location explicitly defined by pairs of coordinates - point is the basic building blocko resolution defined by map scale or minimum mapping unit - ArcINFO coverage, shapefile, geodatabase feature class, TIN |
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raster |
cells; location implicit by the size/area of the cell and the cell layout - cell is the basic building blocko resolution defined by cell size (area) - ESRI (ArcINFO) GRID, imagery |
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defining a projection |
recording (registering) the coordinate system information of a dataset, including any associatedprojection parameter, datum, and ellipsoid with the software |
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(re)projecting |
permanently changing the native coordinate system of a dataset, including its datum or ellipsoid, toanother coordinate system |
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on‐the‐fly projection |
temporarily displaying a dataset with its native coordinates stored in one coordinate system as if itwere in another coordinate system |
