Post on 07-Apr-2018
transcript
8/4/2019 TNM LiDAR Elevation Data Employed in Carolina Bay Survey
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!"#"$%&'()&*$+&,$-./01$%2/$23,*(456$.)$7&8(*.)&$9&4:$15:5&8;<$
/53():=8&')>$?)=5>8&'()$@.=<$#*(A&*$+&,,58B$
#((>*5$2&8=<$C.8=D&*$#*(A5$E$FD:.()$G&A*5:$$
G<5$%&'()&*$+&,$!:58$7()H585);5$
/5)I58B$7J$$$+&4$KLMKNB$LOKK$
+.;<&5*$2P$/&I.&:$ michael@cintos.org
Contents Copyright 2011, Michael E. DaviasGoogle Earth Imagery presented under the Fair Use Doctrine of the US Copyright Act (section 107 of title 17)
Mashathon
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!"#"$%&'()&*$+&,$-./01$%2/$23,*(456$.)$7&8(*.)&$9&4:$15:5&8;<$
!
"#$%&'(%!$
058.&*$,<(=(>8&,<:$(H$7&8(*.)&$A&4:$=&Q5)$.)$=<5$KRNOS:$:,&8Q56$85:5&8;<$.)=($=<5.8$>5(3(8,<(*(>4B$
AD=$85I5&*56$()*4$,&8=$(H$=<5.8$D).TD5$,*&)H(83:P$/.>.=&*$2*5I&'()$+&,:B$;85&=56$.)$#*(A&*$+&,,58$
D:.)>$-./01M658.I56$6&=&$H8(3$=<5$!"#"S:$%&'()&*$+&,$"58I58B$&;;5)=D&=5$=<5$I.:D&*$,85:5)=&'()$
(H$=<5:5$:<&**(@$A&:.):P$$G($:D,,(8=$&$>5(:,&'&*$:D8I54B$=<5$-./01$3&,:$@585$5U,(8=56$&:$V+-M
WX2#$'*5$:5=:$H(8$I.:D&*.Y&'()$.)$#((>*5$2&8=<P$G<5$85:D*')>$;&=&*(>D5$(H$7&8(*.)&$A&4:$35=&6&=&$.:$,DA*.;&**4$&;;5::.A*5$H8(3$&$#((>*5$FD:.()$G&A*5P$$
$$
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X<(=($7(D8=5:4$(H$
$#5(8>5$Z(@&86$
My thanks to those in attendance here, and to the USGS, for the opportunity to share
some of the USGS National Map facilities and resources we have applied in
researching Carolina bay landforms. We will also be demonstrating the integration of
LiDAR imagery with the Google Earth Virtual Globe.
So, What is a Carolina bay? Until 80 years ago, they were simply a scattered collection
of swamps and lakes that represented challenges for local farmers and road builders.
This photograph displays numerous bays under different land uses. Due to their water
retention characteristics, the bays outlines are often easy to see.
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+48=*5$95&;<B$"7$
[F&.8;<.*6$058.&*$"D8I54:$H(8$=<5$J;5&)$F(85:=$7(3,&)4\$$058.&*$I.5@$=&Q5)$.)$KRNO$]KLU^$Q3_$
Since the bays were first visualized in aerial photography of Myrtle Beach in the 1930s,
their presence on the landscape has generated controversy as to their geomorphology.
Differing from simple parabolic dunes, these landforms universally exhibit a closed rim.
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+48=*5$95&;<B$"7$
Here we take an original Fairchild Aerial Survey photograph and overlay it on theVirtual Globe, and as we fade out, the current satellite imagery becomes visible.
Our goal is to capture multiple planform metrics using remote sensing.
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+48=*5$95&;<B$"7$
Current USGS NED elevation data is of no use here: the best offered for Myrtle
Beach is 1/3 arc-second, and it looks like this. The original survey seems to be the
best! $
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#2$?3&>584$]KRRR_B$"=P$X&D*:B$%7$
Here is another example of satellite imagery. While bays and their planforms arevisible, it is difficult and imprecise to trace the rimsP$`5$:D>>5:=$=<&=$I.:D&*$.3&>584$
85I5&*:$()*4$&$:3&**$,&8=$(H$=<5.8$D).TD5$,*&)H(83:P$$$
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#2$?3&>584$]KRRR_B$"=P$X&D*:B$%7$
/.>.=&*$5*5I&'()$3&,:$;85&=56$@.=<$=(6&4:$-&:58$?3&>.)>$&)6$1&)>5$/5=5;'()$]-./01_$:4:=53:$&;;5)=D&=5:$=<5.8$&*85&64M:=D)).)>$I.:D&*$,85:5)=&'()B$&**(@.)>$H(8$=<5$.65)'a;&'()$&)6$
;*&::.a;&'()$(H$5I5)$>85&=58$TD&)''5:$(H$A&4:P$G<.:$.3&>584$D:5:$<D5$:&=D8&'()$I&*D5$]Z"C_$:<&6.)>P$F*.,,.)>$A&;Q$&)6$H(D8=<B$.=$.:$(AI.(D:$=<&=$=<585$.:$3(85$<585$=<&)$355=:$=<5$;&358&:$545P$
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#2$?3&>584$`.*3.)>=()B$%7$
In a dense urban landscape, a bay in a park might be noticed
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#2$?3&>584$`.*3.)>=()B$%7$
– but that would be overlooking the big elephant in the room.LiDAR imagery to the rescue !
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15:5&8;<$15TD.8535)=:$
• 785&=5$7(3,85<5):.I5$7&=&*(>D5$(H$7&8(*.)&$A&4$*&)6H(83:$
• G8.&)>D*&'()$%5=@(8Q$85TD.85:$A8(&6$:,&'&*$6.:=8.AD'()$(H$A&4:$E$&*.>)35)=:$
• ?)=5>8&=5$@.=<$#((>*5$2&8=<$C.8=D&*$#*(A5$
$24=()$E$X&8Q<D8:=$$]KRbc_$ 7&8(*.)&$A&4$J8.5)=&'()$d$W(<):()$]KReL_$
Our proposal suggests that straight lines on flat maps may not be the best way
to correlate these! instead, we shall apply our data onto a virtual globe.
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G((*:$E$15:(D8;5:
• !"#"$%&'()&*$+&,$F&;.*.=4$
– KfR$08;M:5;()6$%&'()&*$2*5I&'()$/&=&$ – X8(I.65:$:5I58&*$H(83&=:B$@5$D:5$08;M#8.6$<585$
• #*(A&*$+&,,58$;(3358;.&*$#?"$,8(>8&3$
– "&I5$&:$V54<(*5$+&8QD,$*&)>D&>5$]V+-_$6&=&$a*5$ – #8.6656$&:$OPLcg$U$OPLcg$:5*HM65:;8.A56$h(;=&)=:i$ – 2U,(8=:$#((>*5$2&8=<$V+-$
• #((>*5$2&8=<$$
– 0D=(3&';&**4$&*.>):$JD8$G.*5:$()$I.8=D&*$>*(A5$ – 0**(@:$H(8$;&,=D85$(H$,*&)H(83$>5(:,&'&*$35=8.;:$ – X(.)=:B$JI58*&4:B$-.)5$"5>35)=:$E$X(*4>():$
• #((>*5$FD:.()$G&A*5:$ – 7*(D6$9&:56$#5(:,&'&*$15,(:.=(84$ – XDA*.;&**4$&;;5::.A*5$$
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KfR$&8;$:5;()6$-./01M658.I56$/&=&$
!"#"$%2/$
Here is the spatial distribution LiDAR-derived data in the areas of intereston the East Coast. We eagerly await similar data for other areas.
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-./01$#5)58&'()$X8(;5::$d$/&=&$15=8.5I&*$
G<5$)5@$!"#"$%&'()&*$+&,$C.5@58$.:$&$@()658HD*$H&;.*.=4P$
2)=58$.)$*&'=D65B$*()>.=D65$](8$&$)&35$,*&;5_$E$:5&8;<B$<585$D:.)>$LeV$?)65U$$
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-./01$#5)58&'()$X8(;5::$d$/&=&$15=8.5I&*$
7*.;Q$()$&$65:.856$LeV$:5>35)=$&)6$h"55$&I&.*&A*5$6&=&iP$`5$D:5$e$(H$=<5:5$
=($;(3,8.:5$(D8$A&:.;$>8.66.)>$5*535)=B$NL$.)$=(=&*$H(8$&$KOOV$jD&68&)=P$
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-./01$#5)58&'()$X8(;5::$d$/&=&$15=8.5I&*$
! many options are presented. We are interested in Elevation data,
in ArcGrid format! others are GeoTIFF, GridFloat, BIL_16INT$
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-./01$#5)58&'()$X8(;5::$d$/&=&$15=8.5I&*$
! and select 1/9 arc second data, where available.$
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-./01$#5)58&'()$X8(;5::$d$/&=&$15=8.5I&*$
066$=($=<5$;&8=$&)6$,8(;5::B$&)6$&)$53&.*$.:$:5)=$@.=<$$6(@)*(&6$*.)Q:P$
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-./01$#5)58&'()$X8(;5::$d$/&=&$15=8.5I&*$
7*.;Q$()$=<5$*.)Q$=($.).'&=5$=<5$6(@)*(&6P$
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-./01$#5)58&'()$X8(;5::$d$/&=&$15=8.5I&*$
!"#"$&6I.:5:$6(@)*(&6$,8(>85::$E$:=&=D:$I.&$=<.:$64)&3.;$@5A$,&>5P$
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-./01$#5)58&'()$X8(;5::$d$#*(A&*$+&,,58$
Here, we have loaded eight 24K Quad segments (downloaded as zipfiles) into Global Mapper s interface. We use high-gain Hue-Saturation-
Value (hsv) shading, driven by the elevation value at each pixel.
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-./01$#5)58&'()$X8(;5::$d$#*(A&*$+&,,58$
One of Global Mapper s many tools is an elevation profile capability,The 2 kilometer-wide bay has only 5 meters of rim relief ! FL AT
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-./01$#5)58&'()$X8(;5::$
Once we have a scope of LiDAR data to define our “Octant”, we proceedwith the export of data in a form digestible by Google Earth: - KML
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-./01$?)=5>8&'()$@.=<$#((>*5$2&8=<$
The exported fie is opened in Google Earth, with the Image automaticallypositioned on the virtual globe. The tree of increasingly detailed image tiles is
shown on the left.
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-./01$JI58*&4$V+-$.)$#((>*5$2&8=<$
This LiDAR imagery covers 600 square km surrounding Rex, NC.
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-./01$JI58*&4$V+-$.)$#((>*5$2&8=<$
0:$@5$Y((3$.)B$@5$:55$=<5$.);85&:.)>$65=&.*$,8(I.656$A4$=<5$'*.)>$=855P$$
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-./01$JI58*&4$V+-$.)$#((>*5$2&8=<$
G<5$;8.:,$,*&)H(83$I.5@56$<585$.:$=<5$08;<5=4,5$(H$A&4:$.)$=<5$7&8(*.)&:P$G<54$&85$)(=$
,D85$(I&*:B$&:$=<54$<&I5$()5$k&l5)56$:.65$&)6$&$AD.*=MD,$8.3$()$=<5$"(D=<5&:=$5)6P$$
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-./01$JI58*&4$V+-$.)$#((>*5$2&8=<$
Here is the same landscape seen in Google Earth imagery. Flippingback and fourth, it is easy to see the value of LiDAR..
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9*&)Q5=$08'H&;=:$d$/&D><=58$9DAA*5:$
Now, lets look at some of the interesting planform features we have visualizedusing these LiDAR maps. This juxtaposition of adjacent bays is what might be
termed daughter bubbles: small bays at the southeastern end of a large bay.!
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X*&)H(83$d$!:.)>$#((>*5$2&8=<$?3&>5$JI58*&4$
An individual bays geospatial planform can be captured - here using an overlayrepresenting the central Carolina bay archetype . Default orientation is due north.
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X*&)H(83$JI58*&4$$
The overlay is rotated and sized to the outline of the bays actual rim.
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X*&)H(83$JI58*&4$$
The overlay is rotated and sized to the outline of the bays actual rim.
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X*&)H(83$JI58*&4$$
Orientation of bays is tightly constrained in any give area. C&8.&'():$(H$=<5$(I58*&4$&85$D:56$5*:5@<585B$3(65*56$&m58$=<5$,856(3.)&=5$,*&)H(83$.)$=<(:5$*(;&*5:P$
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X*&)H(83$JI58*&4$$
We note that there are tens of thousands of bays which are crisplyrepresented by this particular overlay. Cookie Cutter geomorphology.
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X*&)H(83$JI58*&4$$
Spatial sizes vary considerably. Length/width ratio tightly constrained.
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JI58*&4$2*535)=$7(,.56$H8(3$/J+$
With a bit of fine tuning, a very snug fit is obtained, and the overlay appears inthe object directory. That object can be copied, as it is comprised of a series of
meta data elements in the kml text format.
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V+-$+5=&$/&=&$.)$JI58*&4
• n#8(D)6JI58*&4o$
• $ n)&35oA&4p9ONccnf)&35o$
• $ n?;()o$• $ $ n<85Ho<l,\ff;.)=(:P(8>f>5f(I58*&4:fA&4pX8(=(=4,5P,)>nf<85Ho$
• $ $ nI.5@9(D)6";&*5oOPbcnfI.5@9(D)6";&*5o$
• $ nf?;()o$
• $ n-&=-()9(Uo$
• $ $ n)(8=<oNePqNLcLKe^RNqKObnf)(8=<o$• $ $ n:(D=<oNePqKcOqROqLNLNqenf:(D=<o$
• $ $ n5&:=oMbRPcbLRNLcbqNbeqbnf5&:=o$
• $ $ n@5:=oMbRPc^c^KqbRRRb^qbnf@5:=o$
• $ $ n8(=&'()oMKNcPLNqRNRqONRNOenf8(=&'()o$
• $ nf-&=-()9(Uo$
• nf#8(D)6JI58*&4o$
Pasting this in a text editor, we see the overlay information: The LatLonBox’s metrics
yields ( with a bit of trig ) the length of the major and minor axis and an estimate the bay’s
surface area. The rotation angle from due north documents the bay’s orientation.
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"D8I54B$A&4MA4MA&4
The survey currently includes metrics from about 22,000 individual bays.
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78.=58.&$H(8$?65)'a;&'()$&:$&$7&8(*.)&$9&4
• F(D)6$03()>:=$7(**5;'()$JH$".3.*&8$-&)6H(83:$
• 2U<.A.=$0$7*(:56$7.8;D3H58&*$1.3$
• "&=5**.=5$?3&>584$"D,,(8=56$94$-./01f/2+$0:$0$9&:.)$
• X*&)H(83$F.=:$08;<5=4,5$F(8$-(;&*5$
There are some of our criteria for selecting a Carolina bay. They should not be
singularities in the area, their rims should be closed, or at least a significant hint that a
closed rim exists, if we are primarily using satellite imagery for selection, there should be
support by DEM data to insure it is a depression. And finally, it should conform to one of
our planform overlays, such as the ones shown here. Left is the Archetype; right is the
shape seen a bit further south in South Carolina and Georgia.
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78.=58.&$H(8$?65)'a;&'()$&:$&$7&8(*.)&$9&4
Douglas Johnson documented his version of these two bay types in his book The Origins of the CarolinaBays, and we note them in the same general areas. Obviously there is a gently transformation from one
style to the other, as the bays are found to exists on a near-continuum across the region.
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78.=58.&$H(8$?65)'a;&'()$&:$&$7&8(*.)&$9&4
)*('+,! %7B$"7$ "7B$#0$
-.'/0%1! KNBOOO$ NBcOO$
2((,/%&3(3%1! OPbK$ OPqR$
)*('+,! %7B$"7$ "7B$#0$ #0B$0-$
-.'/0%1! KNBOOO$ NBcOO$ KOOO$
2((,/%&3(3%1! OPbK$ OPqR$ OPcR$
)*('+,! +/P$/2B$%W$ %7B$"7$ "7B$#0$ #0B$0-$
-.'/0%1! LBOOO$ KNBOOO$ NBcOO$ KOOO$
2((,/%&3(3%1! OPNR$ OPbK$ OPqR$ OPcR$
)*('+,! +/B$/2B$%W$ %7B$"7$ "7B$#0$ #0B$0-$ %5A8&:Q&$
-.'/0%1! LBOOO$ KNBOOO$ NBcOO$ KOOO$ cOO$
2((,/%&3(3%1! OPNR$ OPbK$ OPqR$ OPcR$ OPq^$
9(=<$=<5:5$=4,5:$:<(@$&$35&)$5;;5)=8.;.=4$(H$&A(D=$OPbP$`5$.)=58,85=$=<5$,*&)H(83:$(H$
A&4:$.)$(=<58$&85&:$=($&*:($I&84$:*.><=*4P$jD&)''5:$)(=56$A5*(@$&85$H8(3$=<5$"D8I54P$
?)$=<5$)(8=<58)$&85&:$(H$=<5$;(&:=$@5$&*:($:55$&$:TD&l58$:<&,5PP$?)$#5(8>.&$&)6$HD8=<58$@5:=B$@5$:55$&)$5I5)$H&l58$:<&,5P$$F.)&**4B$(D=$@5:=$.)$%5A8&:Q&B$@<.;<$<&:$&$:<&,5$
@.=<$()5$k&l5)56$:.65B$:.3.*&8$=($=<5$5&:=58)$&8;<5=4,5P$
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!"#"$KOOV$jD&6:$H(8$"D8I54$]2&:=$7(&:=_
Our survey encompasses these 100K Quads on the East Coast. Where 1/9 arc sec data available,we have produced eight high resolution LiDAR overlay sets in each Octant. Elsewhere we have
produced 1/3 arc sec hsv shaded DEM/s,
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"&.)=$#5(8>5$jD&68&)=$E$J;=&)=$#8.6
Here’s the Saint George USGS Quadrant in Google Earth, with our LiDAR tiled elevation mapoverlays. Each survey segment encompasses a quarter degree grid- which we call an Octant, as
there are eight of them per USGS 100k Quadrant. We subdivide each octant into a 1000 x 1000 grid,
allowing us to uniquely name up to 10,000 bays in each Octant.
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J;=&)=$KNNNLN$2*5I&'()$JI58*&4$G.*5:
LiDAR & NED Overlay for Octant 1333323. Unique gridding name is comprised of 3-digit count of "ºoctants west of Prim Meridian (333) and 3-digit count of octants north of Equator (323). We subdivide
each octant into a 1000 x 1000 grid, allowing us to uniquely name up to 10,000 bays in each Octant.
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J;=&)=$KNNNLN$2*5I&'()$JI58*&4$G.*5:
Full Tile set is 5-levels deep, 1024x1024 pixels resolutions each. All jpg images hosted on our web siteusing network download feature of Google Earth. A partial tile set with only 4 layers is also
generated , such that the HSV-shaded DEM drops out when below ~3km eye view altitude, and
satellite image can easily be contrasted with the DEM.
" <. H 9 . " . = # KOOV J = =
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"5&8;<.)>$H(8$9&4:$.)$"&.)=$#5(8>5$KOOV$J;=&)=
Let’s look at this LiDAR from the Saint George USGS quadrant. We see the bays diminish as theterrain become more complex. But here’s a bay, in a smooth hill top setting.
/ .* H 9 r Re 0+"-
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/5=&.*$(H$9&4$r$Re3$0+"-$
LiDAR gives a good visualization of a bay landform.
* H
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/5=&.*$(H$9&4$r$Re3$0+"-$
Using our overlay tools, we measure the bay and annotate it .
/5=&.* (H 9& r Re3 0+"-
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/5=&.*$(H$9&4$r$Re3$0+"-$
Visualized In LiDAR
/5=&.* (H 9&4 r Re3 0+"-
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/5=&.*$(H$9&4$r$Re3$0+"-$
Visualized in Google Earth Historical Satellite Imagery (1994)
# * F . G A*
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#((>*5$FD:.()$G&A*5
Our collection of fitted bay overlays are programmatically processed into table entries which are placed in an on-line Google Fusion Table. Our goal is to catalogue 50,000 unique bays.
F . G A* " < 9 0A ^O
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FD:.()$G&A*5$"5&8;<$d$9&4:$0A(I5$^O$3
The data in the Fusion Table can be queried with this GUI, or through an API using SQL statements.Here we retrieve all bays existing at over 80 m and under 400 m in elevation
F . G A* " < 9 0A ^O
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FD:.()$G&A*5$"5&8;<$d$9&4:$0A(I5$^O$3
We select Visualize>Map
! . F . G A* + C. *. '
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!:.)>$FD:.()$G&A*5:$+&,$C.:D&*.Y&'()
Fusion Table; elevation > 80m http://google.com/fusiontables/DataSource?snapid=166010
"45)! 6(*/! -%1!^O$=($KOO$ qK^$
KOO$=($KLO$ eNb$
KLO$=($KeO$ LK^$
KeO$=($KqO$ $$RN$
0A(I5$KqO$ KLN$
And we get a nice map of the selected bay data. We have edited the icon styles to correspond to 20 meter rangesof elevation, as shown in the legend.
!:.)> FD:.() +&, C.:D&*.Y&'()
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!:.)>$FD:.()$+&,$C.:D&*.Y&'()
Instead of the map view, we can select Satellite in the upper right! The Fusion facility will only show500 total placemarks at a time to keep from cluttering the view, but as you zoom in..
!:.)> FD:.() +&, C.:D&*.Y&'()
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!:.)>$FD:.()$+&,$C.:D&*.Y&'()
As you zoom in, The individual placemarks become resolved.
!:.)> FD:.() +&, C.:D&*.Y&'()
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!:.)>$FD:.()$+&,$C.:D&*.Y&'()
As you zoom in, The individual placemarks become resolved.
!:.)> FD:.() +&, C.:D&*.Y&'()
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!:.)>$FD:.()$+&,$C.:D&*.Y&'()
Zoomed in on a single bay’s placemark
!:.)> FD:.() +&, C.:D&*.Y&'()
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!:.)>$FD:.()$+&,$C.:D&*.Y&'()
Selecting the placemark, we get a popup with the bay metrics displayed; bay is at 119.76 meters, avalue provided by the USGS through a web query during our processing of this bay’s overlay. We can
retrieve the overlay for viewing in Google Earth by clocking the link in the balloon. Note that we are still
in the web browser window here, simply working with the Fusion Table map visualization feature.
-(&656 9&4 JI58*&4 ?)=( #((>*5 2&8=<
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-(&656$9&4$JI58*&4$?)=($#((>*5$2&8=<
By selecting the link and opening the downloaded kml file, , we switch to the Google Earth
application and are brought to the bay’s location on the virtual globe, with the overlay in
place as we had positioned it during the survey capture. Historic Imagery view 2/7/1994
-(&656 9&4 JI58*&4 ?)=( #((>*5 2&8=<
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-(&656$9&4$JI58*&4$?)=($#((>*5$2&8=<
LiDAR View
9&4 ".Y5: 2U<.A.= -(>M%(83&* /.:=8.AD'()
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9&4$".Y5:$2U<.A.=$-(> %(83&*$/.:=8.AD'()
On your left is a histogram of sizes of the ~20,000 bays in the survey , exhibiting a classic log normaldistribution. Contrasted on the right is the log-normal distribution of bays found above 80 m in five
ranges.
9&4 J8.5)=&'() "4:=53&'; A4 -&'=D65 E -()>.=D65
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9&4$J8.5)=&'()$"4:=53&';$A4$-&'=D65$E$-()>.=D65$
Graph showing the relationship between the average measured Eastern Bay bearing by Octantnumber, which are based on Latitude (most significant 3 digits) & Longitude (least significant 3 digits.
"D33&84
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"D33&84
• 15=8.5I56$-./01$5*5I&'()$6&=&$H8(3$!"#"$%&'()&*$+&,$C.5@58$
• #5)58&=56$s$bO$KOOV$jD&68&)=$KfN$&8;M:5;()6$Z"CM:<&656$2*5I&'()$+&,:$@.=<$#*(A&*$+&,,58$
• #5)58&=56$seOO$OPLcg$U$OPLcg$-./01M85:(*D'()$Z"CM:<&656$2*5I&'()$+&,:$@.=<$#*(A&*$+&,,58
• ?)=5>8&=56$-./01$/2+$.3&>5:$.)=($#((>*5$2&8=<$
• ?65)'a56$&)6$/(;D35)=56$s$LLBOOO$?)6.I.6D&*$9&4:$
• 7&,=D856$?)6.I.6D&*$A&4$+5=8.;:$
– -(;&'()$
– +&t(8$E$+.)(8$0U.:\$4.5*6:$085&$E$2;;5)=8.;.=4$
– J8.5)=&'()$
– 2*5I&'()$
– #5)58&*$X*&)H(83$
• 785&=56$()M*.)5$#5(:,&'&*$/&=&A&:5$.)$FD:.()$G&A*5:$
– XDA*.;&**4$0;;5::.A*5$]85&6$()*4_$
– C.:D&*.Y56$.)$+&,MA&:56$H&;.*.=4$
• JA:58I&'():$
– 9&4:$5U<.A.=$'><=*4$;():=8&.)56$,*&)H(83$:<&,5:$ – 9&4:$5U<.A.=$*(>M)(83&*$:.Y5$6.:=8.AD'()$
– 9&4:$&A(I5$^O3$0+"-$:<(@$:.3.*&8$35=8.;:$=($=<(:5$()$,*&.)$
• "D8I54$.:$153(=5$"5):.)>$9&:56$J)*4$
– #8(D)6M,8((a)>$5u(8=:$&85$)()M5U.:=5)=$
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