mapping theory [modo de compatibilidad] - imedea...geodata can be projected, conventions,...
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Mapping Introduction to MATLAB mapping ToolboxMap projectionMATLAB mapping Toolbox and georeferencing
Laboratory exercises :
Romà Tauler (IDAEA, CSIC, Barcelona)
Mapping
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Maps
The simplest (although perhaps not the most general) definition of a map is arepresentation of geographic data.
Map data is any variable or set of variables representing a set of geographiclocations , properties of a region, or features on a planet’s surface, regardless of how large or complex the data is, or how it is formatted.
Geospatial data are spatial data (data where things are within a given coordinate system) that are absolutely or relatively positioned on a planet, or georeferenced (it has a terrestrial coordinate system that can be shared by other geospatial data)
Geodata is coded for computer storage and applications in two principal ways:vector and raster representations . It has been said that “raster is faster butvector is corrector
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Map examples
World
Example 1---------
% World map with coarse coastlinesworldmap('World')load coastplotm(lat, long)
180° W 135° W 90° W 45° W 0° 45° E 90° E 135° E 180° E
90° S
45° S
0°
45° N
90° N
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Map examples
Europe
15 ° W 0 ° 15° E
30° E
45° E
30 ° N
45 ° N
60 ° N
75 ° N
worldmap('Europe')
load coast
plotm(lat, long)
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Map examples
Spain
15.0 ° W 12.5° W 10.0° W 7.5° W 5.0° W 2.5° W 0.0° 2.5° E 5.0° E 7.5° E 10.0° E
35.0 ° N
37.5 ° N
40.0 ° N
42.5 ° N
45.0 ° N
worldmap('Spain')
load coast
plotm(lat, long)
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Example 2---------% Worldmap with land areas, major lakes and rivers, and cities and% populated placesax = worldmap('World');setm(ax, 'Origin', [0 180 0])land = shaperead('landareas', 'UseGeoCoords', true);geoshow(ax, land, 'FaceColor', [0.5 0.7 0.5])lakes = shaperead('worldlakes', 'UseGeoCoords', true);geoshow(lakes, 'FaceColor', 'blue')rivers = shaperead('worldrivers', 'UseGeoCoords', true);geoshow(rivers, 'Color', 'blue')cities = shaperead('worldcities', 'UseGeoCoords', true);geoshow(cities, 'Marker', '.', 'Color', 'red')
0° 45° E 90° E 135° E 180° E 135° W 90° W 45° W 0°
90° S
45° S
0°
45° N
90° N
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Example 3---------% Map of Antarcticaworldmap('antarctica')antarctica = shaperead('landareas', 'UseGeoCoords', true,...
'Selector',{@(name) strcmp(name,'Antarctica'), 'Name'});patchm(antarctica.Lat, antarctica.Lon, [0.5 1 0.5])
90 ° S
80° S
70° S
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Example 4---------% Map of Africa and India with major cities and populated placesworldmap({'Africa','India'})land = shaperead('landareas.shp', 'UseGeoCoords', true);geoshow(land, 'FaceColor', [0.15 0.5 0.15])cities = shaperead('worldcities', 'UseGeoCoords', true);geoshow(cities, 'Marker', '.', 'Color', 'blue')
0° 30° E 60° E 90° E
30° S
0°
30° N
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Example 5 ---------% Map of the geoid over South America and the central Pacific worldmap([-50 50],[160 -30])load geoidgeoshow(geoid, geoidrefvec, 'DisplayType', 'texturemap');load coastgeoshow(lat, long)
The geoid approximates mean sea level. The shape of the ellipsoid was calculated based on the hypothetical equipotential gravitational surface. A significant difference exists between this mathematical model and the real object. However, even the most mathematically sophisticated geoid can only approximate the real shape of the earth.
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geoid, model of the figure of the Earth—i.e., of the planet’s size and shape—that coincides with mean sea level over the oceans and continues in continental areas as an imaginary sea-level surface defined by spirit level. It serves as a reference surface from which topographic heights and ocean depths are measured. The scientific discipline concerned with the precise figure of the Earthand its determination and significance is known as geodesy.
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Example 6---------% Map of terrain elevations in Koreaload koreah = worldmap(map, refvec);set(h, 'Visible', 'off')geoshow(h, map, refvec, 'DisplayType', 'texturemap')colormap(demcmap(map))
DEMCMAP Colormaps appropriate to terrain elevation data
DEMCMAP(map) creates and assigns a colormap appropriate for elevation data.
The colormap has the number of land and sea colors in proportion to the maximum elevations and depths in the matrix map. With no output arguments, the colormap is applied to the current figure and the axes
CLim property is set so that the interface between the land and sea is correct.
not display the outside of the
projection
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Example 7---------% Map of the United States of Americaax = worldmap('USA');load coastgeoshow(ax, lat, long,...
'DisplayType', 'polygon', 'FaceColor', [.45 .60 .30])states = shaperead('usastatelo', 'UseGeoCoords', true);faceColors = makesymbolspec('Polygon',...
{'INDEX', [1 numel(states)], 'FaceColor', polcmap(numel(states))});geoshow(ax, states, 'DisplayType', 'polygon', 'SymbolSpec', faceColors)
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GEOSHOW Display map latitude and longitude data
GEOSHOW(LAT, LON) or GEOSHOW(LAT, LON, ..., 'DisplayType', DISPLAYTYPE, ...)GEOSHOW(LAT,LON,Z, ..., 'DisplayType', DISPLAYTYPE, ...)GEOSHOW(Z,R, ..., 'DisplayType', DISPLAYTYPE,...)GEOSHOW(LAT,LON,I) GEOSHOW(LAT,LON,BW) GEOSHOW(LAT,LON,X,CMAP) GEOSHOW(LAT,LON,RGB)GEOSHOW(..., 'DisplayType', DISPLAYTYPE, ...)GEOSHOW(I,R) GEOSHOW(BW,R) GEOSHOW(RGB,R) GEOSHOW(A,CMAP,R) GEOSHOW(..., 'DisplayType', DISPLAYTYPE, ...)GEOSHOW(S) or GEOSHOW(S, ..., 'SymbolSpec', SYMSPEC, ...)GEOSHOW(FILENAME)........
Look at HELP GEOSHOW, a very powerful MATLAB command
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Example 1 ---------% Display world land areas using a default Plate Carree
projection.figuregeoshow('landareas.shp', 'FaceColor', [0.5 1.0 0.5]);
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Example 2 ---------% Create a worldmap of North America, and% override default polygon properties.figureworldmap('na'); % Read the USA high resolution data.states = shaperead('usastatehi', 'UseGeoCoords', true); % Create a SymbolSpec to display Alaska and Hawaii as red polygons.symbols = makesymbolspec('Polygon', ...
{'Name', 'Alaska', 'FaceColor', 'red'}, ...{'Name', 'Hawaii', 'FaceColor', 'red'});
% Display all the other states in blue.geoshow(states, 'SymbolSpec', symbols, ...
'DefaultFaceColor', 'blue', ...'DefaultEdgeColor', 'black');
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Example 3 ---------% Create a worldmap of the Korean data grid% and display as a texture map.load koreafigureworldmap(map, refvec) % Display the Korean data grid as a texture map. geoshow(gca,map,refvec,'DisplayType','texturemap');colormap(demcmap(map)) % Display the land area boundary as black lines.S = shaperead('landareas','UseGeoCoords',true);geoshow([S.Lat], [S.Lon], 'Color' ,'black');
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Example 4 ---------% Display the EGM96 geoid heights as a texture map% using the Eckert projection.load geoid % Create a figure with an Eckert projection.figureaxesm eckert4; framem; gridm;axis off % Display the geoid as a texture map. geoshow(geoid, geoidrefvec, 'DisplayType', 'texturemap'); % Create a colorbar and title.hcb = colorbar('horiz');
set(get(hcb,'Xlabel'),'String','EGM96 Geoid Heights in Meters.') % Mask out all the land.geoshow('landareas.shp', 'FaceColor', 'black');
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Example 6---------% Display the moon albedo image using a default Plate Carree projection% and an orthographic projection. load moonalb % Plate Carree projection. figuregeoshow(moonalb,moonalbrefvec) % Orthographic projection.figureaxesm ortho geoshow(moonalb, moonalbrefvec, 'DisplayType', 'texturemap')colormap(gray(256))axis off
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% Example 7 Read and display the San Francisco South 24K DEM data.filenames = gunzip('sanfranciscos.dem.gz', tempdir); demFilename = filenames{1}; % Read every point of the 1:24,000 DEM file.
[lat, lon,Z] = usgs24kdem(demFilename,2); % Delete the temporary gunzipped file.
delete(demFilename); % Move all points at sea level to -1 to color them blue.
Z(Z==0) = -1;% Compute the latitude and longitude limits for the DEM.
latlim = [min(lat(:)) max(lat(:))];lonlim = [min(lon(:)) max(lon(:))];% Display the DEM values as a texture map. figureusamap(latlim, lonlim)geoshow(lat, lon, Z, 'DisplayType','texturemap')demcmap(Z)daspectm('m',1)% Overlay black contour lines onto the texturemap.geoshow(lat, lon, Z, 'DisplayType', 'contour', 'LineColor', 'black');% View the DEM values in 3-D.figureusamap(latlim, lonlim)geoshow(lat, lon, Z, 'DisplayType', 'surface')demcmap(Z)daspectm('m',1)view(3)
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Other Examples
Try: worldmap command (mapping Toolbox) and select region...
>> worldmap ---> Spain or worldmap('Spain')
>> load coast
>> plotm(lat,long)
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Geographic coordinates
Map coordinate systems - geographic and geomagnetic.
Latitude/Longitude is the usual coordinate system for maps.
In some cases UTM coords are also used, but these are really just a simple transformation based on the location of the equator and certain lines of longitude.
On the other hand, there are occasions when a coordinate system based on some other set of axes is useful. For example, in space physics data is often projected in coordinates based on the magnetic poles
Notice that longitudes are specified using a signed notation
East longitudes are positive, whereas West longitud es are negative !!!!Nord latitudes are positive, whereas Sud latitudes are negative!!!
Also note that a decimal degree notation is used, so that a longitude of 120 30'W is specified as -120.5. .
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Plotting vector geodata
>>clear>> load coast>> axesm mercator>> framem>> plotm(lat,long)whos
lat 9589x1 76712 double long 9589x1 76712 double
plotm(lat(1:20),long(1:20),'r')
MATLAB mapping toolbox
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Plotting raster (grid) data in MATLAB>> load topo>> whosName Size Bytes Class Attributes
topo 180x360 518400 double topolegend 1x3 24 double topomap1 64x3 1536 double topomap2 128x3 3072 double
>> axesm sinusoid>> geoshow(topo,topolegend,'DisplayType','texturemap')>> demcmap(topo)>> topolegend>> topolegendtopolegend =
1 90 0
geoshow shows geodata ingeographic unprojected ccordinatesdemcmap creates andassigns a colormapappropriate for elevation data.
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Combining vector and raster data in the same plot>> clma>> clear>> load coast>> load topo>> axesm robinson>> geoshow(topo,topolegend,'Displaytype','texturemap')>> demcmap(topo)>> geoshow(lat,long,'Color','r')
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From vectors to raster (grids)
>> indiana=shaperead ('usastatehi.shp','UseGeoCoords',true,'Selector',...{@(name)strcmpi('Indiana',name),'Name'});>> inLat=indiana.Lat;>> inLon=indiana.Lon;>> gridDensity=40;>> [inGrid,inRefVec]=vec2mtx (inLat,inLon,gridDensity);>> whosName Size Bytes Class Attributes
gridDensity 1x1 8 double inGrid 164x137 179744 double inLat 1x626 5008 double inLon 1x626 5008 double inRefVec 1x3 24 double indiana 1x1 10960 struct
Convert vectorinformation to
a raster grid image
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From vectors to rastrer (grids )
>> figure>> axesm eqdcyl>> meshm(inGrid,inRefVec)>> colormap jet(4)
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From vectors to rastrer (grids)>> [latlim,lonlim]=limitm(inGrid,inRefVec)latlim =
37.7107 41.8107lonlim =-88.1479 -84.7229
>> setm(gca,'Flatlimit',latlim,'Flonlimit',lonlim)>> tightmap
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Mapping Introduction to MATLAB mapping ToolboxMap projectionsMATLAB mapping Toolbox and georeferencing
Laboratory exercises :
Romà Tauler (IDAEA, CSIC, Barcelona)
Mapping
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Map Projections
All geospatial data must be flattened onto a display surface in order to visually portray what exists where. The mathematics and craft of map projection are central to this process. Although there is no limit to the ways geodata can be projected, conventions, constraints, standards, and applications generally prescribe its usage.
To represent a curved surface such as the Earth in two dimensions, you must geometrically transform (literally, and in the mathematical sense, “map”) that surface to a plane. Such a transformation is called a map projection.
map projection consists of constructing points on geometric objects such as cylinders, cones, and circles that correspond to homologous points on the surface of the planet being mapped according to certain rules and formulas.
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But what projection should I use?
This depends really on how large an area you are mapping.
Usually, maps of the whole world are Mercator, although often the Miller Cylindricalprojection looks better because it doesn't emphasize the polar areas as much.
Another choice is the Hammer-Aitoff or Mollweide (which has meridians curving together near the poles). Both are equal-area. It's probably not a good idea to use these projections for maps that don't have the equator somewhere near the middle.
The Robinson projection is not equal-area or conformal, but was the choice of National Geographic (for a while, anyway), and also appears in the IPCC reports.
If you are plotting something with a large north/south extent, but not very wide (say, North and South America, or the North and South Atlantic), then the Sinusoidal or Mollweide projections will look pretty good.
Another choice is the Transverse Mercator, although that is usually used only for very large-scale maps.
Map Projections
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Projection Name Map Projection ID --------------- -----------------Transverse Mercator tranmerc Mercator mercator Lambert Conformal Conic (2SP) lambert Lambert Conformal Conic (1SP) lambert Lambert Azimuthal Equal Area eqaazim Albers Equal-Area Conic eqaconic Azimuthal Equidistant eqdazim Equidistant Conic eqdconic Polar Stereographic ups Oblique Stereographic stereo Equirectangular eqdcylin Cassini-Soldner cassini Gnomonic gnomonic Miller Cylindrical miller Orthographic ortho Polyconic polycon Robinson robinson Sinusoidal sinusoid Van der Grinten vgrint1
Map ProjectionsMATLAB mapping toolbox
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But what projection should I use?
For smaller areas within one hemisphere or other (say, Australia, the United States, the Mediterranean, the North Atlantic) you might pick a conic projection. The differences between the two available conic projections are subtle, and if you don't know much about projections it probably won't make much difference which one you use.
If you get smaller than that, it doesn't matter a whole lot which projection you use. One projection useful in many cases is the Oblique Mercator, since you can align it along a long (but narrow) coastal area.
If map limits along lines of longitude/latitude are OK, use a Transverse Mercator or Conic Projection. The UTM projection is also useful.
Polar areas are traditionally mapped using a Stereographic projection, since for some reason it looks nice to have a "bullseye" pattern of latitude lines. If you want to get a quick idea of what any projection looks like, default parameters for all functions are set for a "typical" usage, i.e. to get a quick idea of what any projection looks like, you can do so without having to figure out a lot of numerical values:
Map Projections
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axesm Define map axes and set map properties
displaym Display geographic data from display structure
geoshow Display map latitude and longitude data
grid2image Display regular data grid as image
mapview Interactive map viewer
usamap Construct map axes for United States of America
worldmap Construct map axes for given region of world
How to specify map projections in MATLAB mapping Toolbox?
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MAPPING GUIs
AXESM GUIAXESM activates a GUI to define a map projection for the current axes.AXESM(PROPERTYNAME, PROPERTYVALUE,...)AXESM(MSTRUCT,...)AXESM(PROJFCN,...)
MAPVIEW Interactive map viewerUse the Map Viewer to work with vector, image, and raster data grids in a mapcoordinate system: load data, pan and zoom on the map, control the map scale ofyour screen display, control the order, visibility, and symbolization of map layers,annotate your map, and click to learn more about individual vector features.
MAPVIEW complements MAPSHOW and GEOSHOW, which are for constructingmaps in ordinary figure windows in a less interactive, script-oriented way.
MAPVIEW (with no arguments) starts a new Map Viewer in an empty state.
MAPSHOW Display map data without projection
MAKESYMBOLSPEC Construct vector symbolization specification
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HELP for Mapping MATLAB Toolbox• help map for computational functions
• mapdemos for a list of Mapping Toolbox demos
• maps lists all Mapping Toolbox map projections by class, name, and ID string.
• maplist returns a structure describing all Mapping Toolbox map
projections.
• projlist to list map projections supported by projfwd and projinv
• help functionname for help on a specific function, often including examples
• helpwin functioname to see the output of help displayed in the Help
browser window instead of the Command Window
• doc functionname to read a function’s reference page in the Help browser,
including examples and illustrations
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MAPPING DEMOS
• mapexkmlexport — Exporting Vector Point Data to KML
• mapexfindcity — Interactive Global City Finder
• mapexgeo — Creating Maps Using geoshow (for latitude, longitude data)
• mapexmap — Creating Maps Using mapshow (for x, y data)
• mapexrefmat — Creating and Using Referencing Matrices
• mapexreg — Georeferencing an Image to an Orthotile Base Layer
• mapex3ddome — Plotting a 3-D Dome as a Mesh Over a Globe
• mapexunprojectdem — Un-Projecting a Digital Elevation Model (DEM)
• mapexgshhs — Converting Coastline Data (GSHHS) to Shapefile Format
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MATLAB Functions that Access Internet Data Directly
The MATLAB functions in the table below read files from URLs. Then you can write the files to a local directory or load them into your MATLAB Workspace. With the exception of urlread and urlwrite, all the functions can read files on local and network file systems using path syntax as well.
geotiffread Read a georeferenced image from GeoTIFF file
gunzip Uncompress files in the GNU-Zip format
imread Read image from graphics file
untar Extract the contents of a Tar-file
unzip Extract the contents of a Zip-file
urlread Returns the contents of a URL as a string
urlwrite Save the contents of a URL to a file
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Mapping Introduction to MATLAB mapping ToolboxData ModelsMap projectionsMATLAB mapping Toolbox and georeferencing
Laboratory exercises :
Romà Tauler (IDAEA, CSIC, Barcelona)
Mapping
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Georeferencing
Raster geodata consists of georeferenced data grids and images that in MATLAB are stored as matrices. Raster geodata looks like any other matrix in MATLAB
Georeferencing can be done to the earth globe or to a specified map projection, so that each pixel of data occupies a known patch of territory of the planet. Whether a raster geodata set covers the entire planet or not, its placementand resolution must be specified.
Referencing vectors
In many instances (when the data grid or image is based on latitude and longitude and is aligned with the geographic graticule), a referencing matrix has more degrees of freedom than the data requires. In such cases, you can use a more compact representation, a three-element referencing vector.
A referencing vector defines the pixel size and northwest origin for a regular, rectangular data grid:
refvec= [cells-per-degree, north lat, west long]
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refvec= [cells-per-degree, north lat, west long]
In MAT-files, this variable is often called refvec or maplegend. The firstelement, cells-per-degree, describes the angular extent of each grid cell (e.g., if each cell covers five degrees of latitude and longitude, cells-per-degree would be specified as 0.2).
Note that if the latitude extent of cells differs from their longitude extent you cannot use a referencing vector, and instead must specify a referencing matrix.
The second element, north-lat, specifies the northern limit of the data grid (as a latitude), and the third element, west-lon, specifies the western extent of the data grid (as a longitude). In other words, north-lat, west-lon is the northwest corner of the data grid.
Note, however, that cell (1,1) is always in the southwest corner of the grid. This need not be the case for grids or images described by referencing matrices, as opposed to referencing vectors.
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Regular data grids
Dsta matrices that contain double values.
southermost edge is the first rownorthermost edge is the last rowwesternmost edge is the first columneastermnost edge is the last column
cell(1,1) is southwest corner of the grid
East longitudes are positive, whereas West longitud es are negative !!!!
Nord latitudes are positive, whereas Sud latitudes are negative!!!
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Building a global data grid
Build a coarse world map where each cell represents 60o
1) Built a data minigrid:
minigrid=[1,2,3,4,5,6;7,8,9,10,11,12;13,14,15,16,17,18]
2) Make a referencing vector refvec= [cells-per-degree, north lat, west long]
minivec=[1/60,90,-180]
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minigrid =
1 2 3 4 5 67 8 9 10 11 12
13 14 15 16 17 1890,-180 NW
-90,-180 SW-90,180 SE
90,180 NE
Remember!
East longitudes are positive. West longitudes are negative . North latitudes are positive. South latitudes are negative.
minigrid=[1,2,3,4,5,6;7,8,9,10,11,12;13,14,15,16,17,18]
minivec=[1/60,90,-180]
Building and georeferencing a global data grid
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3) Set up an equidistant cylindrical map projection
axesm('MapProjection','eqdcylin')
setm(gca,'GlineStyle','-','Grid','on','Frame','on')
4) Draw a graticule with parallel and meridian labe ls at 60o intervals
setm(gca, 'MlabelLocation', 60,'PlabelLocation',[-30,30],...
'MlabelParallel','north','MeridianLabel','on',...
'ParallelLabel','on','MlineLocation',60,...
'PlineLocation',[-30,30])
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>> meshm(minigrid,minivec);
>> colorbar
5) Map de data using mesm and display with a color map legend
minigrid =1 2 3 4 5 67 8 9 10 11 12
13 14 15 16 17 18NW
SW
NE
SE
minigrid=[1,2,3,4,5,6;7,8,9,10,11,12;13,14,15,16,17,18]
minivec=[1/60,90,-180]
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5) Map de data using mesm and display with a color map legendmeshm(minigrid,minivec);colormap('autumn');colorbar
minigrid =1 2 3 4 5 67 8 9 10 11 12
13 14 15 16 17 1890,-180 NW
-90,-180 SW -90,180 SE
90,180 NE
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Computing Map limits from reference vectors
load korearefvec = 12.0000 45.0000 115.0000[latlimits,longlimits]=limitm(map,refvec)latlimits =
30 45longlimits =
115 135
This means that korea region extends from 30oN to 45oN and 115oE to 135oE
>> [rows,cols]=size(map)rows =
180cols =
240>> southlat=refvec(2)-rows/refvec(1)southlat =
30.0000>> eastlon=refvec(3)+cols/refvec(1)eastlon =
135.0000latitudes decrease southwards, longitudes increase eastwards
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Geographic interpretation of matrix elements
>> load russia>> [latlim,longlim]=limitm(map,refvec)latlim =
35 80longlim =
15 190>> row=23;col=79;[lat,long]=setltln(map,refvec,row,col)lat =
39.5000long =
30.7000This means that the element row,column = 23,79 has this lat and long
>> [r,c]=setpostn(map,refvec,lat,long)r =
23c =
79This means that this lat,long is in the 23,79 row coliumn
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BUILDING A GEOREFERENCE MATRIX, R
Raster geodata is georeferenced in the toolbox through a companion data structure called a referencing matrix. This 3-by-2 matrix of doubles describes the scaling, orientation, and placement of the data grid on the globe. For a given referencing matrix, R, one of the following relations holds between rows and columns and coordinates (depending on whether the grid is based on map coordinates or geographic coordinates, respectively):
[x y] = [row col 1] * R or [long lat] = [row col 1] * R
MAKEREFMAT Construct affine spatial-referencing matrixA spatial referencing matrix R ties the row and column subscripts of an image or regular data grid to 2-D map coordinates or to geographic coordinates (longitude and geodetic latitude). R is a 3-by-2 affine transformation matrix. R either transforms pixel subscripts (row, column) to/from map coordinates (x,y) according to
[x y] = [row col 1] * R
or transforms pixel subscripts to/from geographic coordinates according to[lon lat] = [row col 1] * R.
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BUILDING A GEOREFERENCE MATRIX, R
To construct a referencing matrix for use with geographic coordinates,use longitude in place of X and latitude in place of Y, as shown in thethird syntax below. This is one of the few places where longitudeprecedes latitude in a function call.
R = MAKEREFMAT(X11, Y11, DX, DY) with scalar DX and DY constructs areferencing matrix that aligns image/data grid rows to map X andcolumns to map Y. X11 and Y11 are scalars that specify the maplocation of the center of the first (1,1) pixel in the image or firstelement of the data grid, so that
[X11 Y11] = pix2map(R,1,1).
DX is the difference in X (or longitude) between pixels in successivecolumns and DY is the difference in Y (or latitude) between pixels insuccessive rows. More abstractly, R is defined such that
[X11 + (col-1) * DX, Y11 + (row-1) * DY] = pix2map(R, row, col).
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Mapping (MATLAB) georeferencing: makerefmatConstruct spatial referencing matrix of raster/grid files using commandmakerefmat.mExample 1
---------Create a referencing matrix for an image with square, four-meter pixelsand with its upper left corner (in a map coordinate system) atx = 207000 meters, y = 913000 meters. The image follows the typicalorientation: x increasing from column to column and y decreasing fromrow to row.
x11 = 207002; % Two meters east of the upper left cornery11 = 912998; % Two meters south of the upper left cornerdx = 4;dy = -4;R = makerefmat(x11, y11, dx, dy)x11 = 207002; % Two meters east of the upper left cornery11 = 912998; % Two meters south of the upper left cornerdx = 4;dy = -4;R = makerefmat(x11, y11, dx, dy)
R =0 -44 0
206998 913002
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Example 2---------Create a referencing matrix for a global geoid grid.
load geoid % Adds array 'geoid' to the workspace
% 'geoid' contains a model of the Earth's geoid sampled in% one-degree-by-one-degree cells. Each column of 'geoid' contains% geoid heights in meters for 180 cells starting at latitude% -90 degrees and extending to +90 degrees, for a given latitude.% Each row contains geoid heights for 360 cells starting at% longitude 0 and extending 360 degrees.
lat11 = -89.5; % Cell-center latitude corresponding to geoid(1,1)lon11 = 0.5; % Cell-center longitude corresponding to geoid(1,1)dLat = 1; % From row to row moving north by one degreedLon = 1; % From column to column moving east by one degree
geoidR = makerefmat(lon11, lat11, dLon, dLat)
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% It's well known that at its most extreme the geoid reaches a% minimum of slightly less than -100 meters, and that the minimum% occurs in the Indian Ocean at approximately 4.5 degrees latitude,% 78.5 degrees longitude. Check the geoid height at this location% by using LATLON2PIX with the new referencing matrix:
[row, col] = latlon2pix(geoidR, 4.5, 78.5)geoid(round(row),round(col))
geoidR =0 1.0000
1.0000 0-0.5000 -90.5000
row =95
col =79
ans =-106.9260
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Example 3---------Create a half-resolution version of a georeferenced TIFF image, usingImage Processing Toolbox functions IND2GRAY and IMRESIZE.
% Read the indexed-color TIFF image and convert it to grayscale.% The size of the image is 2000-by-2000.[X, cmap] = imread('concord_ortho_w.tif');I_orig = ind2gray(X, cmap);
% Read the corresponding worldfile. Each image pixel covers a% one-meter square on the map.R_orig = worldfileread('concord_ortho_w.tfw')
% Halve the resolution, creating a smaller (1000-by-1000) image.I_half = imresize(I_orig, size(I_orig)/2, 'bicubic');
% Find the map coordinates of the center of pixel (1,1) in the% resized image: halfway between the centers of pixels (1,1) and% (2,2) in the original image.[x11_orig, y11_orig] = pix2map(R_orig, 1, 1)[x22_orig, y22_orig] = pix2map(R_orig, 2, 2)
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% Average these to determine the center of pixel (1,1) in the new% image.x11_half = (x11_orig + x22_orig) / 2y11_half = (y11_orig + y22_orig) / 2
% Construct a referencing matrix for the new image, noting that its% pixels are each two meters square.R_half = makerefmat(x11_half, y11_half, 2, -2)
% Display each image in map coordinates.figure;subplot(2,1,1); h1 = mapshow(I_orig,R_orig); ax1 = get(h1,'Parent');subplot(2,1,2); h2 = mapshow(I_half,R_half); ax2 = get(h2,'Parent');set(ax1, 'XLim', [208000 208250], 'YLim', [911800 911950])set(ax2, 'XLim', [208000 208250], 'YLim', [911800 911950])
% Mark the same map location on top of each image.x = 208202.21;y = 911862.70;line(x, y, 'Parent', ax1, 'Marker', '+', 'MarkerEdgeColor', 'r');line(x, y, 'Parent', ax2, 'Marker', '+', 'MarkerEdgeColor', 'r');
% Graphically, they coincide, even though the same map location% corresponds to two different pixel coordinates.[row1, col1] = map2pix(R_orig, x, y)[row2, col2] = map2pix(R_half, x, y)
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Mapping Introduction to MATLAB mapping ToolboxMap projectionsMATLAB mapping Toolbox and georeferencing
Laboratory exercises :
Plotting images and mapsGoogle map loaderGetting satellite data from internetRepresenting results of a monitoring campaign
Romà Tauler (IDAEA, CSIC, Barcelona)
Mapping
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Reading images
IMREAD Read image from graphics file.A = IMREAD(FILENAME,FMT) reads a grayscale or color image from the filespecified by the string FILENAME. If the file is not in the currentdirectory, or in a directory on the MATLAB path, specify the full pathname.
The text string FMT specifies the format of the file by its standardfile extension. For example, specify 'gif' for Graphics Interchange Format files. To see a list of supported formats, with their file extensions, use the IMFORMATS function. If IMREAD cannot find a file named FILENAME, it looks for a file named FILENAME.FMT.
The return value A is an array containing the image data. If the file contains a grayscale image, A is an M-by-N array. If the file containsa truecolor image, A is an M-by-N-by-3 array. For TIFF files containingcolor images that use the CMYK color space, A is an M-by-N-by-4 array. See TIFF in the Format-Specific Information section for moreinformation.
Look at C:\Archivos de programa\MATLAB\R2007a\toolbox\images\imdemosfor images and demos..................................................
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imformats
EXT ISA INFO READ WRITE ALPHA DESCRIPTION ------------------------------------------------------------------------------------------------bmp isbmp imbmpinfo readbmp writebmp 0 Windows Bitmap (BMP)
cur iscur imcurinfo readcur 1 Windows Cursor resources (CUR)
fts fits isfits imfitsinfo readfits 0 Flexible Image Transport System (FITS)
gif isgif imgifinfo readgif writegif 0 Graphics Interchange Format (GIF)
hdf ishdf imhdfinfo readhdf writehdf 0 Hierarchical Data Format (HDF)
ico isico imicoinfo readico 1 Windows Icon resources (ICO)
jpg jpeg isjpg imjpginfo readjpg writejpg 0 Joint Photographic Experts Group (JPEG)pbm ispbm impnminfo readpnm writepnm 0 Portable Bitmap (PBM)
pcx ispcx impcxinfo readpcx writepcx 0 Windows Paintbrush (PCX)pgm ispgm impnminfo readpnm writepnm 0 Portable Graymap (PGM)
png ispng impnginfo readpng writepng 1 Portable Network Graphics (PNG)
pnm ispnm impnminfo readpnm writepnm 0 Portable Any Map (PNM)
ppm isppm impnminfo readpnm writepnm 0 Portable Pixmap (PPM)
ras isras imrasinfo readras writeras 1 Sun Raster (RAS)
tif tiff istif imtifinfo readtif writetif 0 Tagged Image File Format (TIFF)xwd isxwd imxwdinfo readxwd writexwd 0 X Window Dump (XWD)
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A=imread('moon.tif')image(A)imagesc(A);
50 100 150 200 250 300 350
50
100
150
200
250
300
350
400
450
500
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Table: summary of supported image typesBMP 1-bit, 8-bit and 24-bit uncompressed images
GIF 8-bit images
HDF 8-bit raster image datasets, with or without associated
colormap; 24-bit raster image datasets; uncompressed or
with RLE or JPEG compression
JPEG 8-bit, 12-bit, and 16-bit Baseline JPEG images
PBM Any 1-bit PBM image, ASCII (plain) or raw (binary) encoding.
PCX 8-bit images
PGM Any standard PGM image. ASCII (plain) encoded with
arbitrary color depth. Raw (binary) encoded with up
to 16 bits per gray value.
PNG 1-bit, 2-bit, 4-bit, 8-bit, and 16-bit grayscale
images; 8-bit and 16-bit grayscale images with alpha
channels; 1-bit, 2-bit, 4-bit, and 8-bit indexed
images; 24-bit and 48-bit truecolor images; 24-bit
and 48-bit truecolor images with alpha channels
PNM Any of PPM/PGM/PBM (see above) chosen automatically
PPM Any standard PPM image. ASCII (plain) encoded with
arbitrary color depth. Raw (binary) encoded with up
to 16 bits per color component.
RAS Any RAS image, including 1-bit bitmap, 8-bit indexed,
24-bit truecolor and 32-bit truecolor with alpha
TIFF Baseline TIFF images, including 1-bit, 8-bit, 16-bit,
and 24-bit uncompressed images; 1-bit, 8-bit, 16-bit,
and 24-bit images with packbits compression; 1-bit
images with CCITT 1D, Group 3, and Group 4 compression;
CIELAB, ICCLAB, and CMYK images
XWD 8-bit ZPixmaps
imwrite(x,filename,fmt)
>> A=imread('moon.tif');
>> imagesc(A)
>> imwrite(A,'moon.jpeg','jpeg');
>> clear
>> A=imread('moon.jpeg');
>> imagesc(A)
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...\m_map\private
A=imread('exlamber.gif');
image(A)
imagesc(A)
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Mapping Introduction to MATLAB mapping ToolboxData ModelsMap projectionsMATLAB mapping Toolbox and georeferencingIntroduction to M_map Toolbox
Laboratory exercises :
Plotting imagesGoogle maps and Google EarthGetting satellite data from internetRepresenting results of a monitoring campaign
Romà Tauler (IDAEA, CSIC, Barcelona)
Mapping
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EXAMPLE
Google map of the river region was read as a ‘7.tif’ fileGPS limits of the map are in gpslimit (aprox. from Google)GPS positions of sampling pointys are in gpsxyValues to represent are in variable zAll these variables have been stored in mapex.mat
>> load('mapex.mat')>> whosName Size Bytes Class Attributes
gpslimit 2x2 32 double gpsxy 62x2 992 double sampnumbers 1x62 496 double z 122x1 976 double
LOADING A MAP AS AN IMAGE FROM GOOGLE MAPS
GEOREFERENCING IT AND PLOTTING VALUES
PLOTTING SYMBOLS ACCORDING THE SIZE OF A VARIABLE (E.G. FLUORESCENCE)
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function mapdatagpsRT(gpslimit,gpsxy,sampnumbers,z)
% function mapdatagps(gpslimit);
% INPUT
% gpslimit=[lat1,long1;lat2,long2]
% lat1,long1 is right down corner
% lat2,long2 is left up corner
% example gpslimit=[42.020003,2.426382;41.940318,2.217402];
% gpsxy are the GPS values where the representation has to be performed
% gpsxy(lat,long)
% sampnumbers are sample numbers used for representation
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2.25 2.3 2.35 2.4
41.88
41.9
41.92
41.94
% scatter(y(i),x(i),(z(i)-min(z)+0.1)*100,[a,b,c],'.')
% scatter(y(i),x(i),(z(i)-min(z)+0.1)*20,'o','MarkerEdgeColor','k','MarkerFaceColor','c')
Test with different symbols, sizes and color….
>> mapdatagpsRT(gpslimit,gpsxy,sampnumbers,z)
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function varargout = plot_google_map(varargin)
% function h = plot_google_map(varargin)
% Plots a google map on the current axes using the Google Static Maps API
%
% USAGE:
% h = plot_google_map(Property, Value,...)
% Plots the map on the given axes. Used also if no output is specified
%
% Or:
% [lonVec latVec imag] = plot_google_map(Property, Value,...)
% Returns the map without plotting it
%
Program plot_google_map
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% PROPERTIES:
% Height (640) - Height of the image in pixels (max 640)
% Width (640) - Width of the image in pixels (max 640)
% Scale (2) - (1/2) Resolution scale factor . using Scale=2 will
% double the resulotion of the downloaded image (up
% to 1280x1280) and will result in finer rendering,
% but processing time will be longer.
% MapType - ('roadmap') Type of map to return. Any of [roadmap,
% satellite, terrain, hybrid] See the Google Maps API for
% more information.
% Alpha (1) - (0-1) Transparency level of the map (0 is fully
% transparent). While the map is always
% moved to the bottom of the plot (i.e. will
% not hide previously drawn items), this can
% be useful in order to increase readability
% if many colors are ploted (using SCATTER
% for example).
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% Language - (string) A 2 letter ISO 639-1 language code for displaying labels in a local language instead of English (where available).
% For example, for Chinese use:
% plot_google_map('language','zh')
% For the list of codes, see:
% http://en.wikipedia.org/wiki/List_of_ISO_639-1_codes
% Marker - The marker argument is a text string with fields
% conforming to the Google Maps API. The
% following are valid examples:
% '43.0738740,-70.713993' (default midsize orange marker)
% '43.0738740,-70.713993,blue' (midsize blue marker)
% '43.0738740,-70.713993,yellowa' (midsize yellow
% marker with label "A")
% '43.0738740,-70.713993,tinyredb' (tiny red marker
% with label "B")
% Refresh (1) - (0/1) defines whether to automatically refresh the
% map upon zoom/pan action on the figure.
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% OUTPUT:
% h - Handle to the plotted map
% lonVect - Vector of Longidute coordinates (WGS84) of the image
% latVect - Vector of Latidute coordinates (WGS84) of the image
% imag - Image matrix (height,width,3) of the map
% EXAMPLE - plot a map showing some capitals in Europe:
% lat = [48.8708 51.5188 41.9260 40.4312 52.523 37.982];
% lon = [2.4131 -0.1300 12.4951 -3.6788 13.415 23.715];
% plot(lon,lat,'.r','MarkerSize',20)
% plot_google_map
% References:
% http://www.mathworks.com/matlabcentral/fileexchange/24113
% http://www.maptiler.org/google-maps-coordinates-tile-bounds-projection/
% http://developers.google.com/maps/documentation/staticmaps/
% Acknowledgement to Val Schmidt for his submission of get_google_map.m
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plot_google_map
% EXAMPLE - plot a map showing some capitals in Europe:
% lat = [48.8708 51.5188 41.9260 40.4312 52.523 37.982];
% lon = [2.4131 -0.1300 12.4951 -3.6788 13.415 23.715];
% plot(lon,lat,'.r','MarkerSize',20)
-5 0 5 10 15 20 2536
38
40
42
44
46
48
50
52
54
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plot_google_map
-5 0 5 10 15 20 2536
38
40
42
44
46
48
50
52
54
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>> axis([0,5,40,44]);
>> plot_google_map
0 1 2 3 4 540
40.5
41
41.5
42
42.5
43
43.5
44
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>> axis([2,5,38,41]);
>> plot_google_map('maptype','roadmap');
>> plot_google_map('maptype','satellite');
1.5 2 2.5 3 3.5 4 4.5 5 5.538
38.5
39
39.5
40
40.5
41
1.5 2 2.5 3 3.5 4 4.5 5 5.538
38.5
39
39.5
40
40.5
41
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2.24 2.26 2.28 2.3 2.32 2.34 2.36 2.38 2.4 2.42 2.44
41.94
41.96
41.98
42
42.02
42.04
>> load mapex
>> mapgoogle(gpsxy,sampnumbers,z)
Other possibilities???
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Simple Google Map Loader in MATLAB.
GoogleMap.m and test1.m programs in subdirectory Go ogle from lab_maps
Please, go to below link and then set your IP address in format of http://code.google.com/apis/maps/signup.htmlKey:ABQIAAAAvHZo1sBQ73orGDJ_B0nzbxSIXsr6Ik0NwFLW01lZrR0I VF8WgxTDcvvnARCYU7qv5P59bACbpikqdw
Nord 42 Est 2.0
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Simple Google Map Loader in MATLAB.
% Simple Google Map Loader in MATLAB.
% Key:
% Please, go to below link and then set your IP address in format of
% (http://xxx.xxx.xxx.xxx) for getting a KEY!
% http://code.google.com/apis/maps/signup.html
clc
clear all
key='ABQIAAAAteHND9YP6ctZEpCJsGZpWxRQWo9-sWXbVvdU2wQSDpuKctuXhBQYpf8Mm2875572Jwd2ge0XshBKBg';
pos='http://maps.google.com/staticmap?center=42.0,2.0&zoom=8&size=512x512';
address=strcat(pos,'&key=',key);
[I map]=imread(address,'gif');
RGB=ind2rgb(I,map);
imshow(RGB);
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Distribution of dissolved organic matter in freshwa ters using excitation emission fluorescence and Multivariate C urve Resolution
Xin Zhang, Rafael Marcé, Joan Armengol, Romà Tauler
Chemosphere 111 (2014) 120–128
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Figure 1
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Emission
D1
λEm
λE
x-C
D2
D3
Daug SEx-C STEm
1
2
3
4D4
Exc
itatio
n D1
D2D
D4
D3
1 2 3 4
240 260 280 300 320 340 360 380 400 420 4400
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
Wavelength (nm)
Load
ings
Dn
Dn
……
n
n……
……
Emission
Excitation
Concentration
SEx1-C1=C1 ⊗⊗⊗⊗ SEx1
MCR
ALS
SVD
240
260
280
300
320
34
0360
380
40
0420
44
00
0.0
5
0.1
0.1
5
0.2
0.2
5
0.3
Wavele
ngth
(nm
)
Loading
……1 2 3 4 n
0 20 40 60 80 100 120 1400
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
λE
x-C
λE
x-C
λE
x-C
λE
x-C
1st component
SEx1-C1 new
Figure 2
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240 260 280 300 320 340 360 380 400 420 4400
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
Wavelength(nm)
Load
ings
MCR-ALS Ex
MCR-ALS Em
PARAFAC Ex
PARAFAC Em
240 260 280 300 320 340 360 380 400 420 4400
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
Wavelength(nm)
Load
ings
MCR-ALS Ex
MCR-ALS EmPARAFAC Ex
PARAFAC Em
240 260 280 300 320 340 360 380 400 420 4400
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
Wavelength(nm)
Load
ings
MCR-ALS Ex
MCR-ALS EmPARAFAC Ex
PARAFAC Em
240 260 280 300 320 340 360 380 400 420 4400
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
Wavelength(nm)
Load
ings
MCR-ALS Ex
MCR-ALS EmPARAFAC Ex
PARAFAC Em
240 260 280 300 320 340 360 380 400 420 4400
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
Wavelength(nm)
Load
ings
MCR-ALS Ex
MCR-ALS Em
PARAFAC Ex
PARAFAC Em
240 260 280 300 320 340 360 380 400 420 4400
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
Wavelength(nm)
Load
ings
MCR-ALS Ex
MCR-ALS EmPARAFAC Ex
PARAFAC Em
240 260 280 300 320 340 360 380 400 420 4400
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
Wavelength(nm)
Load
ings
MCR-ALS Ex
MCR-ALS EmPARAFAC Ex
PARAFAC Em
240 260 280 300 320 340 360 380 400 420 4400
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
Wavelength(nm)
Load
ings
MCR-ALS Ex
MCR-ALS Em
PARAFAC Ex
PARAFAC Em
240 260 280 300 320 340 360 380 400 420 4400
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
Wavelength(nm)
Load
ings
MCR-ALS Ex
MCR-ALS Em
PARAFAC Ex
PARAFAC Em
240 260 280 300 320 340 360 380 400 420 4400
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
Wavelength(nm)
Load
ings
MCR-ALS Ex
MCR-ALS EmPARAFAC Ex
PARAFAC Em
240 260 280 300 320 340 360 380 400 420 4400
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
Wavelength(nm)
Load
ings
MCR-ALS Ex
MCR-ALS Em
PARAFAC Ex
PARAFAC Em
240 260 280 300 320 340 360 380 400 420 4400
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
Wavelength(nm)
Load
ings
MCR-ALS Ex
MCR-ALS Em
PARAFAC Ex
PARAFAC Em
240 260 280 300 320 340 360 380 400 420 4400
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
Wavelength(nm)
Load
ings
MCR-ALS Ex
MCR-ALS Em
PARAFAC Ex
PARAFAC Em
240 260 280 300 320 340 360 380 400 420 4400
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
Wavelength(nm)
Load
ings
MCR-ALS Ex
MCR-ALS EmPARAFAC Ex
PARAFAC Em
240 260 280 300 320 340 360 380 400 420 4400
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
Wavelength(nm)
Load
ings
MCR-ALS Ex
MCR-ALS EmPARAFAC Ex
PARAFAC Em
240 260 280 300 320 340 360 380 400 420 4400
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
Wavelength(nm)
Load
ings
MCR-ALS Ex
MCR-ALS Em
PARAFAC Ex
PARAFAC Em
240 260 280 300 320 340 360 380 400 420 4400
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
Wavelength(nm)
Load
ings
MCR-ALS Ex
MCR-ALS Em
PARAFAC Ex
PARAFAC Em
240 260 280 300 320 340 360 380 400 420 4400
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
Wavelength(nm)
Load
ings
MCR-ALS Ex
MCR-ALS EmPARAFAC Ex
PARAFAC Em
3 components 4 components 5 components 6 components
Figure 3
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10 20 30 40 50 601
2
3
4
5
6
7
8
9
Stations
%F
max
10 20 30 40 50 601
2
3
4
5
6
7
8
9
Stations
%F
max
2.22 2.24 2.26 2.28 2.3 2.32 2.34 2.36 2.38 2.4 2.42
41.87
41.88
41.89
41.9
41.91
41.92
41.93
41.94
2.22 2.24 2.26 2.28 2.3 2.32 2.34 2.36 2.38 2.4 2.42
41.87
41.88
41.89
41.9
41.91
41.92
41.93
41.94
2.22 2.24 2.26 2.28 2.3 2.32 2.34 2.36 2.38 2.4 2.42
41.87
41.88
41.89
41.9
41.91
41.92
41.93
41.94
a
b
c
10 20 30 40 50 601
2
3
4
5
6
7
8
9
Stations
%F
max
Figure 4
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Lat 40.01 N, Lon 2.09 E
Lat 39.09 N, Lon 3.56 E
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Make a map of Cap Salines in Mallorca using Google Earth and store in an image jpg file capsalines.jpg
>> A=imread('capsalines2.jpg','jpg');>> size(A)ans =
758 1280 3
>> latmin=39.1538>> latmax=39.1612>> lonmin=3.0221>> lonmax=3.0327>> dlon=(lonmax-lonmin)/1280>> dlat=(latmax-latmin)/758
refvec=[1/dlon,39.1612,3.0327]refvec =1.0e+005 *
1.2075 0.0004 0.0000
>> R = MAKEREFMAT (39.15, 3.02, dlon, dlat)R =
0 0.00000.0000 0
39.1500 3.0200
AI=A(end:-1:1,:,:);
mapview
Check for the correct georeferences
Save image as a GeoTiffIt creates two files: one .tiff and another .tfw
Now it can already be read directly as a GeoTiff referecned image for further work
Producing map images (ex. from google earth) and ge orefencing them
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Google Earth
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geoshow(AI,R)
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>> refvec=[1/dlon,39.1612,3.0327]refvec = 1.0e+005 * 1.2075 0.0004 0.0000>> geoshow(AI,refvec)
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Map Viewer
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capsalines.tif, capsalines.tfw
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Mapping Introduction to MATLAB mapping ToolboxData ModelsMap projectionsMATLAB mapping Toolbox and georeferencingIntroduction to M_map Toolbox
Laboratory exercises :
Plotting imagesGoogle map loaderGetting satellite data from internetRepresenting results from a monitoring campaign
Romà Tauler (IDAEA, CSIC, Barcelona)
Mapping
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Examples of satellite data manipulation1. Global SST (or any variable on a global Lat/Long grid)% NOAA/NASA Pathfinder AVHRR SST product% http://podaac.jpl.nasa.gov/sst/
For example:in subdirectory nasa from lab_mapsbsst2007.hdfUse import bizard of MATLABChoose bsstIt is a matrix of dimensions 4096 8192It can be represented directly using image
image(bsst)
But the units are not correct!
Geeting satellite data from internet
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Georeferencing the map
To calculate the geographic location of a specific pixel use the following equations for 9 km equal-angle imagery (other resolutions will be similar):
longitude = (-180. + (x * dx)) + (dx/2)latitude = (90. - (y * dy)) - (dy/2)
where for 9km resolution: x = grid point in x-direction (0-4095); dx = grid x-direction spacing (360 deg / 4096); y = grid point in y-direction (0-2047); and dy = grid y-direction spacing (180 deg / 2048.)
for 5.0 4 km resolution: x = grid point in x-direction (0-8191); dx = grid x-direction spacing (360 deg / 8192.); y = grid point in y-direction (0-4095); anddy = grid y-direction spacing (180 deg / 4096.)
>> x=0:8192;>> y=0:4095;>> dx=360/8192;>> dy=180/4096;>> latitude = (90. - (y * dy)) - (dy/2);>> longitude = (-180. + (x * dx)) + (dx/2);
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Data Characteristics:Parameter/Variable:
Sea Surface Temperature Variable Description/Definition:SST - temperature of the sea surface.
Units of Measurement: for version 4.1 and below pixel value with slope of 0.15 and y-intercept of -3.0 to convert to °C. for version 5.0 pixel value with slope of 0.075 and y-intercept of -3.0 to convert to °C.
>> temp=bsst.*0.075-3;>> imagesc(temp)
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temp2=temp(1:4:4096,1:4:8192);dx=360/8192dy=180/4096axesm Robinsontemp2=temp(1:4:4096,1:4:8192);refmat2=makerefmat(-180,90,dx*4,-dy*4)geoshow(temp2,refmat2,'DisplayType','texturemap')
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gridm
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Mapping Introduction to MATLAB mapping ToolboxData ModelsMap projectionsMATLAB mapping Toolbox and georeferencingIntroduction to M_map Toolbox
Laboratory exercises :
Plotting imagesGoogle map loaderGetting satellite data from internetRepresenting results from a monitoring campaign
Romà Tauler (IDAEA, CSIC, Barcelona)
Mapping
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1
2
3
4
5 6 7 8 910
1112131415161718192021222324
252627282930313233343536373839404142
43444546474849
30 ° W 20 ° W 10°
W 0°
10° E
20° E
70 ° N
80 ° N
90 ° N
Decluttered
10 20 30 40 50 60 70 80-20
-15
-10
-5
0
5
10
15
20
Sample
long
1 1b
2b
3b
4b
5b
6b 7 8 9a 9c 10 11 12a 12c
13 14 15a 15c
16 18a 18c
20a 21 22 23a 23c 25 26b 27b
28 29 30a
31 32
33a 33c 34 36a 37 39a 39c 40 42a 42c
43b 44 45
46a 46c 48 49b
Decluttered
10 20 30 40 50 60 70 8068
70
72
74
76
78
80
82
Sample
latd
1 1b
2b
3b
4b
5b 6b 7
8 9b 10 11 12b 13 15a 15c
17 18b 20a 21 22 23a 23c
25 26b 27b 29 31 33a 33c 35 36b 38 39b 40 42a 42c 43b 44
46a 46c 48 49b
longitud
latitud
PROYECTO ATOS (Julio 2007)Deposición atmosférica de carbono y contaminates a los ecosistemas polares
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6 7
8 9
10
11
12
13
1415
161718
19
2021
22
23
24
2526
27
28
29
30
31
32
333435
363738
39404142
4344
45
46
474849
Ice
Open sea
Approximateice limit
North Pole
PROYECTO ATOS (Julio 2007)Deposición atmosférica de carbono y contaminates a los ecosistemas polares
group tasks:
POP analysis in samples:-air and aerosols-sea-water-Ice-Fito- and zooplancton
Experiments:-fotodegradation-biodegradation (zoo)-accumulation-toxicity
Method Implementation -SBSE+TD+GC-MS
Data evaluation andIntegration
Higher Latdand long
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CTD= Conductivity, Temperature, Depth and other sensors: salt and dissoved O2
conc., beam light transmission, fluorescence, turbidimetry
Example: CTD data evaluation and Integration
Buque Hespérides CTD
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El CTD proporciona medidas de 10 variables a diferentes profundidades en cada una de las estaciones de parada del buque
depths(100,..1000 m)
10 variables
Estación1
10 variables
Estación49
depths(100,…1000 m)
10 variables
Estación20
depths(100,…1000 m)
1 'press‘ or depths2 'temp'3 'cond'4 'salt'5 'oxyg'6 'btra'7 'fluo'8 'turb'9 'long'10 'latd'
......... .........
La adquisición de datos se realiza muy rápidamente de forma continua por lo que se requiere realizar un promediado y filtradoprevio de los datos. Tabla o Matriz final de datos a procesar : X(53367x10)provenientes de 81 experimentos CTD (49 estaciones con algunas réplicas y profundidades diferentes, total 80 estac.)
Se seleccionan algunos casos de interés:1) Matrices X1(80,10) XDCM(80,10), X100(80,10) respectivamente en superfície, al máximo fluor y a 100m prof.)2) Matrices de fluorescencia Xfluor(80,200)
Example: CTD data evaluation and Integration
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Decluttered
10 20 30 40 50 60 70 800
5
10
15
20
25
30
35
40
45
Sample
pres
s 1
1b
2a 2b
3a 3b 4a
4b
5a
5b
6a
6b
6c
7
8 9a
9b
9c 10
11
12a 12b
12c
13
14
15a
15b
15c
16
17
18a
18b
18c
20a 20b 21
22
23a
23b
23c
24
26a
26b
27a
27b
28
29
30a
31
32
33a
33b
33c
34 35
36a
36b
37
38
39a
39b
40
41 42a
42b 42c
43a
43b 43c
44
45
46a 46c
47
48
49b
Depth (press)
Decluttered
10 20 30 40 50 60 70 80-2
-1
0
1
2
3
4
5
6
7
Sample
tem
p
1 1b
2a 2b
3b
4a
4b
5a 6a 6b 6c
7 8 9a 9b
9c
11
12a 12c
13 14
15b
15c
16
17
18a
18b
18c
20a 20b
21
22
23a 23c
24
25
26a
26b
27b
28
29
30a
31
32
33b
33c 35
36a
36b
37
38
39a
39b 39c
40
41 42b 43a 43c
44
45
46b 47 48 49a 49b
Temperatures
10 20 30 40 50 60 70 8026
27
28
29
30
31
32
33
34
35
36
Sample
cond
1 1b
2b
3b
4a
4b
5a 6a 6b 6c
7 8 9a 9b
9c
10 11
12a 12c
13 14
15b
15c
16
17
18a
18b
18c
20a 20b
21
22
23a 23c
24
25
26a
26b
27b
28
29 30a
31 32
33b
33c 35
36a
36b
37
38
39a
39b
39c
40
42a 42c 43a 43c
44
45
46a 46c 47 48
49b
Decluttered
Conductivity
Decluttered
10 20 30 40 50 60 70 8032.5
33
33.5
34
34.5
35
35.5
Sample
salt
1 1b
2a 2b
3a 3b
4b 5b
6a 6b 6c 7
8 9a 9c
10 11
12a 12b 13
15a 15b
15c
16
17 18a
18b
18c
20b
21
22 23a 23c
24 25
26a
26b
27b
28
30a
31 32
33b
33c 35
36a
36b
37
38 39a
39b
39c
40
41 42b 42c
43a 43c
44
45
46a 46b 46c
47
48
49a
49b
Salt conc
AT THEMAXIMUM
FLUORESCENCE.
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Decluttered
10 20 30 40 50 60 70 80300
320
340
360
380
400
420
Sample
oxyg
1
1b
2a
2b 3b
4a 4b
5a
5b
6b
6c
7
8 9a
9b
9c
10
11
12a 12b
12c
13
14
15a 15b
15c
16
17
18a
18b 18c
20b
21
22
23b 23c
24
25
26a
26b
27a 27b
28
29
30a
31
32 33b
33c 34
35
36a
36b
37
38
39a
39b
39c
40
42a 42b 42c
43b
43c
44
45
46a 46b 46c
47
48
49a
49b
Decluttered
10 20 30 40 50 60 70 8060
65
70
75
80
85
90
95
Sample
btra
1
1b
2a 2b 3b
4a 4b
5a
5b
6b
6c
7 8 9a 9b
9c
10
11
12a 12b 12c
14
15a 15b
15c
16
17
18a
18c
20b
21
22
23a
23b
23c
24
25
26a 26b
27a 27b
28
29
30a
31
32
33a 33b
33c
34 35 36a
36b
38 39a
39b
39c
40
41
42a
42c
43a
43b
43c
44
45
46a
46b
46c
47
48
49a
49bConc. O2 diss. Beam transm.
Decluttered
10 20 30 40 50 60 70 800
5
10
15
20
25
30
35
40
45
50
Sample
fluo
1
1b
2a 2b
3b 4a
4b
5b
6a
6c
7
8
9b
9c 11 12a 12b 13
14
15a
15b
15c
16
17
18a
18c
20a
20b 21
22
23a 23c
24 25
26a
26b
27a
27b
28
29
30a
31
32
33a 33b 33c
34
35 36a
36b
37
38
39a
39b
39c
40
42a
42b
43a 43c
44 45
46a
46b
46c
47
48
49a 49b
Decluttered
10 20 30 40 50 60 70 800
10
20
30
40
50
60
70
80
90
100
Sample
turb 1
1b
2a 2b
3b
4a
4b
5a
5b
6a
6b
7
8 9b
9c
10
11
12a 12b 12c 13
14 15a 15b
15c
16
17
18a
18b 18c
20a 20b 21
22
23a
23b
23c
24 25
26a
26b
27a 27b
28
30a
31 32
33b
33c
35 36a
36b
37 39a
39b
39c
40
41
42a
42b 42c
43a
43b 43c
44
45 46a
46c
47
48
49a 49b
Turb.
Int. Fluor.
AT THEMAXIMUM
FLUORESCENCE.
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1
2
3
4
5
6
7
8
9
10
Scale Gives Value of R for Each Variable Pair
Correlation Map, Variables in Original Order
1 2 3 4 5 6 7 8 9 10
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
correlation in dcm (máximo de clorofilamáximo de fluorescencia)
1 'press'2 'temp'3 'cond'4 'salt'5 'oxyg'6 'btra'7 'fluo'8 'turb'9 'long'10 'latd'
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1 2 3 4 5 6 7 8 9 10-0.5
-0.4
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
0.4
0.5
Variable
Load
ings
on
PC
2 (
31.0
0%)
press
temp cond
salt
oxyg
btra
fluo turb
long latd
Variables/Loadings Plot for yred11
1 2 3 4 5 6 7 8 9 10-0.4
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
0.4
0.5
Variable
Load
ings
on
PC
1 (
39.5
2%)
press
temp cond
salt
oxyg
btra
fluo turb
long latd
Variables/Loadings Plot for yred11
-0.4 -0.2 0 0.2 0.4 0.6-0.5
-0.4
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
0.4
0.5
Loadings on PC 1 (39.52%)
Load
ings
on
PC
2 (
31.0
0%)
press
temp cond
salt
oxyg
btra
fluo turb
long latd
Variables/Loadings Plot for yred11
1 2 3 4 5 6 7 8 9 100
0.5
1
1.5
2
2.5
3
3.5
Principal Component Number
RM
SE
CV
, R
MS
EC
Eigenvalues and Cross-validation Results for yred11
Principal Eigenvalue % Variance Component of Captured Number Cov(X) This PC --------- ---------- ----------
1 3.95e+000 39.522 3.10e+000 31.00 (70.53)3 1.54e+000 15.42 (85.95)4 8.46e-001 8.46 (94.41)
PCA
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-5 -4 -3 -2 -1 0 1 2 3-3
-2
-1
0
1
2
3
4
5
Scores on PC 1 (39.52%)
Sco
res
on P
C 2
(31
.00%
)
1
1b 2a 2b 3a 3b 4a
4b
5a
5b
6a 6b
6c
7 8 9a
9b
9c
10
11
12a 12b 12c
13
14
15a 15b
15c
16
17
18a
18b
18c
20a 20b
21
22
23a
23b
23c
24
25
26a 26b
27a
27b
28
29
30a
31
32 33a 33b 33c
34 35
36a
36b
37 38
39a
39b
39c 40
41
42a
42b 42c
43a
43b 43c
44
45
46a
46b 46c
47
48
49a
49b
Samples/Scores Plot of yred11
Higher fluorescenceand turbidimetry
HigherSalt
ConductTemp
Higher dissolved O2
Higher beam transmission
Initialsamples
Last samples
Intermediatesamples
closer to the ice
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Fluorescenceat 425 nm excitationand 685 nm emission
(chlorophila fluorescence.)
80 stations
100x2 deepths (CTD down and up)
0 20 40 60 80 100 120 140 160 180 2000
10
20
30
40
50
60
0 10 20 30 40 50 60 70 800
10
20
30
40
50
60
Initialstations
Laststations
Xfluor(80,200)
Study of the fluorescence at different depths for multiple samples
50 100 150 200
10
20
30
40
50
60
70
80
5
10
15
20
25
30
35
40
45
50
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Decluttered
20 40 60 80 100 120 140 160 180 2000
0.05
0.1
0.15
0.2
0.25
0.3
0.35
Variable
Mul
tiple
Ite
ms
(see
Leg
end)
1 4 6 7
9
10
11
14
16 18
20
23
24 26
29
31 33 37
38 41 43 45
49 53 54 56
60
64 68 69 71 73 77
78 82 83
87 91 95 99 96 92 88 84 80 76 72 68 64
60 56 52 49
45
41 37
36 32
31
29
28 27
26 25 23
21
20
19
18
17 14
13
12 11
7
6
5
4 1
CTD way down depths CTD way updepths
MCR-ALS resolution of 4 fluorescence patterns
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Decluttered
10 20 30 40 50 60 70 800
20
40
60
80
100
120
140
160
180
200
Sample
Col
umn
3
1 1b 2a 3a 4a 4b
5a
5b
6a 6b 6c
7
8
9b
9c
10
11
12a
12b
12c
13
14
15a
15b
15c
16
17 18a
18b
18c
20a 20b 21
22
23a
23b
23c
24 25 26a 26b
27a
28
29 30a
31
32
33a
33b 33c 34 35 36a
37 39a 39b 39c 40 41
42a 42b
42c
43a
43c
44
45 46a
46b
46c 47
49a 49b
Decluttered
20 40 60 80 100 120 140 160 180 2000
0.05
0.1
0.15
0.2
0.25
Variable
Row
3
1 3 5 6 7
8 9
10
11
14 15 16 17
18
19
20
21
22
23
24
27 28
31 32 33 34 35 36 37 38 39 40 41 42 44 46
50 51 53
55 59 61 63 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 87 89 90 91 92 93 94 95 96 97 98 99 100 100 99 98 97 96 95 94 93 92 91 90 89 88 87 86 85 84 83 82 81 80 78 77 76 75 74 73 72 71 70 69 67 63 59 55 51
47 43 39 38
35 31 27 26
24
20
19 16
15 14
13
12
11
10 7
6
5
4 1
max. fluor.at low depth.
MCR-ALSC1
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Decluttered
10 20 30 40 50 60 70 800
20
40
60
80
100
120
140
160
180
200
Sample
Col
umn
1
1 1b 2a 2b 3a 3b 4a
4b
5a
5b
6a 6b 6c
7
8
9a
9b
9c
10 11
12a
12b
13
14
15b
15c
16
17
18a
18b 18c
20a
20b
21
22
23a
23b
23c
24
25 26a
26b
27b
28
29
30a
31
32
33a 33b
33c
34 35
36a
36b
37
38 39a
39b
39c
40
41
42a
42b
42c
43a
43b
43c
44
45
46a
46b 46c
47
48
49a
49b
Decluttered
20 40 60 80 100 120 140 160 180 2000
0.05
0.1
0.15
0.2
0.25
0.3
0.35
Variable
Row
1
1 2 3 4 5 6 7 8 9 10 11 12 13
14
15
16
17
20
21
23 24 25
26 27 28 30
33 37 41 45 49 53 57 61 62 66 68 70 74 78 79 80 83 87 91 95 99 100 99 98 97 96 93 92 88 87 83 79 78 77 75 71 67 63 62 58 54 50 46 42 38 37 35 33 32 31 30 29 28 27
25
23
22
21
20
19
18
17 14
13
12
11
10
9
8
7 6 5 4 3 2 1
max. fluor.At interm.depthts
MCR-ALSC1
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Decluttered
10 20 30 40 50 60 70 800
20
40
60
80
100
120
140
160
180
Sample
Col
umn
2
1
1b 2a 2b
3a 3b
4a 4b
5a
5b
6a
6b 6c 7
8
9a
9b
9c
10
11
12a
12b
12c 13
14
15a
15b
15c
16
17
18a
18b
18c
20a
20b
21
22
23a
23b
23c
24
25
26a
26b
27a
27b 28 29
30a
31
32
33a
33b
34
35
36a
36b
37
38 39a
39b
39c
40
41
42a
42b
43a 43b
43c
44 45 46a
46b
47 48 49a
49b
Decluttered
20 40 60 80 100 120 140 160 180 2000
0.05
0.1
0.15
0.2
0.25
Variable
Row
2
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19
20
21
22
23
24 28 29
31
32
34
35
37 38
39 40
44 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 67 68 72 73 76 77 82 83 89 94 99 95 90 84 79 73 68 62 61 60 59 58 57 56 55 54 53 51 50 49 48 47 46 44 43 42
39 38 36
35
33
32
31
29
28
26
25 23
22
21
20 19
18
17
16
15 14 13 12 11 10 9 7 4 1
max. fluor.at interm.-high depths
MCR-ALSC3
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Decluttered
10 20 30 40 50 60 70 800
50
100
150
200
250
Sample
Col
umn
4
1 1b 2a 2b 3a 3b
4a
4b 5a 5b 6a
6b 6c 7
8 9a 9b
9c
10 11
12a 12c 13
14
15a
15b
15c
16
17 18a
18c
20a 21 22
23a 23b 23c 24
25 26a
26b
27a
27b
28 29
30a
31
32
33a 33b 33c
34
35
36a 36b
37
38
39a 39b
40
41 42a 42c 43a 43c
44
45 46a
46b
46c 47 48 49a
49b
Decluttered
20 40 60 80 100 120 140 160 180 2000
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
0.2
Variable
Row
4
3 6 8
10 13
15 16 17 18 19 20 21 22 23 25
26
27
28
29 30
31
32
33 37
38
41 42 43
45
48 52 53
54 56
59 60
62 64
68
69 71 73
75 77
81 84 86 88 92 96 100
98 94 90 88 84 80 76 72
71 68 64
63 60 58 56 54 52
49
47 45
43 39
36
34 32
29 28
27
25
23 22
21 18
17
16 15
14 11
10 9
8 7 6 5 3 2 1
max. fluor.at high depths
MCR-ALSC4
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Linear regression model usingPartial Least Squares calculated with SIMPLS Cross validation: random samples w/ 8 splits Percent Variance Captured by Regression Model
-----X-Block----- -----Y-Block-----Comp This Total This Total ---- ------- ------- ------- -------1 29.00 29.00 76.95 76.952 38.72 67.72 2.51 79.46
1 2 3 4 5 6 7 8 96
6.2
6.4
6.6
6.8
7
7.2
7.4
7.6
7.8
8
Latent Variable Number
RM
SE
CV
, R
MS
EC
SIMPLS Variance Captured and Statistics for yred11
1 2 3 4 5 6 7 8 9-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
Variable
Load
ings
on
LV 1
(29
.00%
)
press
temp cond
salt
oxyg
btra
turb
long latd
Variables/Loadings Plot for yred11
1 2 3 4 5 6 7 8 9-0.4
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
0.4
Variable
Reg
Vec
tor
for
Y 1
press
temp cond salt
oxyg
btra
turb
long latd
Variables/Loadings Plot for yred11
Fluorescence PLS prediction from others
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Linear regression model usingPartial Least Squares calculated with the SIMPLS Cross validation: random samples w/ 8 splits Percent Variance Captured by Regression Model
-----X-Block----- -----Y-Block-----Comp This Total This Total ---- ------- ------- ------- -------1 34.94 34.94 33.23 33.232 39.55 74.49 10.53 43.763 11.72 86.21 12.44 56.20
Fluorescence PLS prediction from othersExcluding beam transmission and turbidity
1 2 3 4 5 6 79.5
10
10.5
11
11.5
12
12.5
13
13.5
14
Latent Variable Number
RM
SE
CV
, R
MS
EC
SIMPLS Variance Captured and Statistics for yred11
1 2 3 4 5 6 7-0.4
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
0.4
0.5
0.6
Variable
Load
ings
on
LV 3
(11
.72%
)
press
temp cond
salt
oxyg
long
latd
Variables/Loadings Plot for yred11
1 2 3 4 5 6 70
0.5
1
1.5
2
2.5
Variable
VIP
Sco
res
for
Y 1
press
temp cond
salt
oxyg
long
latd
Variables/Loadings Plot for yred11
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function mapatos(latlim,lonlim,latdr,longr,ident,y)% function mapatos(latlim,lonlim,latdr,longr,ident,y)% mapatos([77,81],[2,19],latdr2(9:80,:),longr2(9:80,:),identc3(9:80,:),yred(9:80,6));
% worldmap of the desired lat and long, with the coast lines worldmap(latlim,lonlim)load coastgeoshow('landareas.shp', 'FaceColor', [0.15 0.5 0.15])plotm(latdr,longr,'r--')textm(latdr,longr+0.4,ident)scatterm(latdr,longr,y,y,'filled');gridm on
linem(latdr,longr,y);
Representation of the fluorescence maxima
10 CTD measured variables:1 Depth, press2 Temperature, temp3 Conductivity, cond4 Salt concentration, salt5 Oxygent dissolved, oxyg6 beam light transmission, btrm7 fluorescence, fluor8 turbidimetry, turb9 latitude, latd10 longitude, long
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Fluoresc in dcm
19° E 18° E 17° E 16° E 15° E
18b17 18c18a
14° E 13° E
16
36b36a35 34 37
39a
38 39c39b42a
33a33b
41 42b40
33c
12° E
42c
15c15b15a
11° E
28
14 21
30a20a20b32
10° E
29
27a13 27b22
9° E
26a26b
24
43a43b43c
31 25 44
12a12c12b
8° E
23c23b23a
45
11
7° E 6° E
10
46a46c46b
5° E
47
81 ° N
9c
48
9b
8
49b49a
4° E
80 ° N
9a
7 6c 6b
79 ° N
6a
3 ° E
78 ° N
2 ° E 77 ° N
65
70
75
80
85
90
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19° E 18° E 17° E 16° E 15° E
18b17 18c18a
14° E 13° E
16
36b36a35 34 37
39a
38 39c39b42a
33a33b
41 42b40
33c
12° E
42c
15c15b15a
11° E
28
14 21
30a20a20b32
10° E
29
27a13 27b22
9° E
26a26b
24
43a43b43c
31 25 44
12a12c12b
8° E
23c23b23a
45
11
7° E 6° E
10
46a46c46b
5° E
47
81 ° N
9c
48
9b
8
49b49a
4° E
80 ° N
9a
7 6c 6b
79 ° N
6a
3 ° E
78 ° N
2 ° E 77 ° N
33
33.2
33.4
33.6
33.8
34
34.2
34.4
34.6
34.8
35
Salt conc in dcm
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19° E 18° E 17° E 16° E 15° E
18b17 18c18a
14° E 13° E
16
36b36a35 34 37
39a
38 39c39b42a
33a33b
41 42b40
33c
12° E
42c
15c15b15a
11° E
28
14 21
30a20a20b32
10° E
29
27a13 27b22
9° E
26a26b
24
43a43b43c
31 25 44
12a12c12b
8° E
23c23b23a
45
11
7° E 6° E
10
46a46c46b
5° E
47
81 ° N
9c
48
9b
8
49b49a
4° E
80 ° N
9a
7 6c 6b
79 ° N
6a
3 ° E
78 ° N
2 ° E 77 ° N
27
28
29
30
31
32
33
34
35
conduct in dcm
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19° E 18° E 17° E 16° E 15° E
18b17 18c18a
14° E 13° E
16
36b36a35 34 37
39a
38 39c39b42a
33a33b
41 42b40
33c
12° E
42c
15c15b15a
11° E
28
14 21
30a20a20b32
10° E
29
27a13 27b22
9° E
26a26b
24
43a43b43c
31 25 44
12a12c12b
8° E
23c23b23a
45
11
7° E 6° E
10
46a46c46b
5° E
47
81 ° N
9c
48
9b
8
49b49a
4° E
80 ° N
9a
7 6c 6b
79 ° N
6a
3 ° E
78 ° N
2 ° E 77 ° N
31
32
33
34
35
36
37
38
39
40
41
diss oxyg in dcm
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19° E 18° E 17° E 16° E 15° E
18b17 18c18a
14° E 13° E
16
36b36a35 34 37
39a
38 39c39b42a
33a33b
41 42b40
33c
12° E
42c
15c15b15a
11° E
28
14 21
30a20a20b32
10° E
29
27a13 27b22
9° E
26a26b
24
43a43b43c
31 25 44
12a12c12b
8° E
23c23b23a
45
11
7° E 6° E
10
46a46c46b
5° E
47
81 ° N
9c
48
9b
8
49b49a
4° E
80 ° N
9a
7 6c 6b
79 ° N
6a
3 ° E
78 ° N
2 ° E 77 ° N
65
70
75
80
85
90
beam transm in dcm
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19° E 18° E 17° E 16° E 15° E
18b17 18c18a
14° E 13° E
16
36b36a35 34 37
39a
38 39c39b42a
33a33b
41 42b40
33c
12° E
42c
15c15b15a
11° E
28
14 21
30a20a20b32
10° E
29
27a13 27b22
9° E
26a26b
24
43a43b43c
31 25 44
12a12c12b
8° E
23c23b23a
45
11
7° E 6° E
10
46a46c46b
5° E
47
81 ° N
9c
48
9b
8
49b49a
4° E
80 ° N
9a
7 6c 6b
79 ° N
6a
3 ° E
78 ° N
2 ° E 77 ° N
10
20
30
40
50
60
70
80
90
turbidimertry in dcm
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From vectors to raster (grids)
[Z, refvec] = geoloc2grid(latdr2(9:80,:),longr2(9:80,:),yred(9:80,7),cellsize);
load atosmaps[Z, refvec] = geoloc2grid(latdr2(9:80,:),longr2(9:80,:),yred(9:80,7),0.1);worldmap([77,81],[2,16])
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From vectors to raster (grids)
function [Z,refvec]=mapgridatos(latlim,longlim,latw,longw,y,cellsize)% function [Z,refvec]=mapgridatos(latlim,longlim,lat,long,y,cellsize)close[Z, refvec] = geoloc2grid(latw,longw,y,cellsize);worldmap(latlim,longlim)meshm(Z,refvec)load coastgeoshow('landareas.shp', 'FaceColor', [0.15 0.5 0.15])plotm(latw,longw,'k+','LineWidth',1)linem([79.5,80.5],[2,18],'w--','LineWidth',3)colorbar
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From vectors to raster (grids)
meshm(Z,refvec)
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From vectors to raster (grids)>> load coast>> geoshow('landareas.shp', 'FaceColor', [0.15 0.5 0.15])
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From vectors to raster (grids)plotm(latdr2(9:80,:),longr2(9:80,:),'k-','LineWidth',5)colorbar
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[Z,refvec]=mapgridatos([77,81],[2,16],latdr(9:80,:),longr(9:80,:),ydeep(9:80,2),0.1);
From vectors to raster (grids)
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Investigation of Arctic and Antarctica spatial and depth patterns of sea water in CTD profiles using chemometric data analys is.
Ewelina Kotwa, Silvia Lacorte, Carlos Duarte, Roma Tauler
Polar Science, in press
In this paper a comparative study of CTD (Conductivity-Temperature-Depth) sensor using 2- and 3-way chemometric methods was performed for exploratory analysis and spatial patterns discovery from two distinct polar geographical areas: Arctic and Antarctic Seas. Results from the two oceanographic expeditions taking place in July 2007 (Arctic) and February 2009 (Antarctica), spanning the areas of 68°N-81°N, 20°W-20°E and 60°S-70°S, 50°W-77°50´W respectively, will be presented
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CTD data is obtained by standard sensor devices frequently used in oceanographic research type of studies. These data sets can be arranged in 3-way data structures (according to sea water depth, measured variables and geographical sample location modes). Measured variables were collected within the total number of 174 locations. Special importance was given to correlation and possible prediction of fluorescence (mostly related to biological activity), from other physical variables.
Data pre-processing together with outliers and missing values detection and elimination strategies were applied before the actual exploratory data analysis was performed. Classical and robust versions of common chemometric tools such as PCA, PARAFAC, PLS and nPLS were applied for data exploration and calibration, and MATLAB's mapping toolbox was used for results geo-referencing and visualization.
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Measured variables.
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Control plots for predicted Fluorescence values;
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No of PC
” full data” ”5,10,25,50,100m data”
ROBPCA Classical PCA ROBPCA Classical PCA
This PC
Total This PC
Total This PC
Total This PC
Total
1 61,26 61,26 62,92 62,92 59.78 59.78 55.67 55.67
2 17,02 78,28 14,26 77,18 19.67 79.46 20.78 76.45
3 8,33 86,61 6,56 83,74 7.04 86.49 8.37 84.81
Variance explained (%) by different number of PCs for station-wise unfolded “full” and “selected depth” data;
No. of PCs
”Full data” ”5,10,25,50,100m” ”5,10,DCM,50,100m”
ROBPCA Classical PCA ROBPCA Classical PCA ROBPCA Classical PCA
This PC
Total This PC
Total This PC
Total This PC
Total This PC
Total This PC
Total
1 61,64
61,64 52,2 52,2 50.98 50.98 50,04 50,04 50.07 50.07 50,10 50,10
2 23,44
85,09 30,96 83,16 34.33 85.31 32,30 82,34 34.43 84.51 33,03 83,13
3 7,11
92,19 8,29 91,46 6.29 91.60 8,79 91,13 5.95 90.46 8,80 91,93
Variance explained (%) by different number of PCs for 3 data sets: “full”, “selected data” and “DCM”;
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Robust and Classical scores for full and DMC data sets for 3 component PCA model..
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PCA loadings 1 vs 2 for a) classical and b) robust “depth” data;
-4 -2 0 2 4 6 8 10 12-10
-8
-6
-4
-2
0
2
4Classic PCA loading polt of PCs 1 and 2
AntarcticArctic
-5 0 5 10-8
-6
-4
-2
0
2
4
6Robust PCA loading polt of PCs 1 and 2
AntarcticArctic
0 20 40 60 80 100 120 140 160-8
-6
-4
-2
0
2
4
6
8
10
PCs 1, 2 and 3
Pc1PC2PC3
PCA loadings 1, 2 and 3 for a) classical and b) robust “depth” data;
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Geo-map of 1st PC for a) Arctic Sea, b) Antarctica
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No. of com
”full” dta ”depth”-data
Var. expl.
Core cons.
Iter. Time Var. expl.
Core cons.
Iter. Time
1 42,05 100 11 1,52 39.49 100 11 0,3
2 68,01 100 6 0,42 64.22 100 9 0,2
3 76.33 41,17 35 1,42 73.9 -194 9 2,8
Chosing amount of components in PARAFAC model
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1 2 3 4 5 6 7
-0.4
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
0.4
0.5
Temp Cond. Salin. Oxy. Beamtr Fluor. Seaturb
PARAFAC Mode 2
Comp 1Comp 2
Resolved PARAFAC modes: a) depth, b) variables, c) locations
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Geo-map of 2nd component for a) Arctic Sea, b) Antartctica
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LV.
All variables Only phisical variables
X-Block Y-Block X-Block Y-Block
This Total This Total This Total This Total
1 48,55 48,55 62,60 62,60 52,48 52,48 20,83 20,83
2 32,50 81,05 9,37 71,97 39.91 92,40 4,27 25,10
3 10,47 91,52 9,62 81,60 7,60 100 0,07 25,17
Variance coverage by 3 components PLS
RMSEC and RMSECV for PLS model.