Conference PaperPDF Available

Spectral Information System Development for Australia

  • December 2012
DOI:10.5167/uzh-86627
  • Conference: Geospatial Science Research SymposiumGeospatial Science Research Symposium
Authors:
Andreas Hueni at University of Zurich
Andreas Hueni
Laurie Chisholm at University of Wollongong
Laurie Chisholm
Lola Suárez at University of Melbourne
Lola Suárez
Cindy Ong at The Commonwealth Scientific and Industrial Research Organisation
Cindy Ong
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Abstract and Figures

Scientific efforts to observe the state of natural systems over time, allowing the prediction of future states, have led to a burgeoning interest for organised storage of spectral field data and associated metadata, seen as being key to the successful and efficient modeling of such systems. A centralised system for such data established for the Australian remote sensing community aims to standardise storage parameters and metadata thus fostering best practice protocols and collaborative research. Supported by funding from the Australian National Data Service (ANDS), whose aim is to promote connections between data, projects, researchers and institutions, a spectral database system based on the already operational SPECCHIO system is being augmented to specifically meet the needs of the Australian remote sensing community, and is aligned with the TERN Auscover facility. In this paper we outline the envisaged dataflow and usage of the database as a case study within the context of TERN Auscover. The development of a national spectral database will not only ensure the long-term storage of data but support scientists in data analysis activities, essentially leading to improved repeatability of results, superior reprocessing capabilities, and promotion of best practice.
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Content uploaded by Andreas Hueni
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All content in this area was uploaded by Andreas Hueni
Content may be subject to copyright.
Spectral)Database)Development)for)Australia))
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!
!
Figure'1:'DIKW'hierarchy'adapted'to'spectroradiometry'
!
Spectral)Information)System)Requirements)
!
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G1#(12+$'=&!'#$!M%+1%+=+(&!!(8(=&3!%&`*+%&3&#=(!=1!&#(*%&!!=0&!E+#'2!M%1$*G=!3&&=(!=0&!#&&$(!1E!!
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(+#G&!=0&#d!=0&(&!(*MM1%=&$!E&'=*%&(!'%&!2+(=&$!+#!O'B2&!,N!
Table'1:'List'of'features'supported'by'the'current'SPECCHIO'version'
]&'=*%&!
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9%:'#+(&$!(=1%':&!1E!$'='!
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"*=13'=&$!1*=M*=!E1%3'==+#:!>0&#!&YM1%=+#:!$'='!
5XJ//)H9!E+2&!1*=M*=!%1*=+#&(!
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5XJ//)H9!$&2='!21'$+#:!G'M'B+2+=8!
R&2&=&!$'='!E%13!$'='B'(&!
5XJ//)H9!$'='!%&31D&%!
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9#!(+#:2&!(M&G=%*3!2&D&2-!B*=!:%1*M!
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M%1G&((&(!
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P+'!5XJ//)H9!M12+G8!G1#G&M=!'#$!
5XJ//)H9!3&='$'='!(M'G&!$&#(+=8!
3&'(*%&!
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5XJ//)H9!E+2&!1*=M*=!%1*=+#&(!'#$!$+%&G=!
RI!'GG&((!*(+#:!i'D'!B%+$:+#:!
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/5P!'#$!JKPH!5.I!'%&!G*%%&#=28!(*MM1%=&$!
R1>#21'$!1E!3&='$'='!
]*22!3&='$'='!'%&!+#G2*$&$!+#!=0&!/5P!
H#=&%'G=+1#!>+=0!1=0&%!(8(=&3(!
P+'!i'D'!B%+$:+#:!U<'=2'B-!X8=01#-!A-!&=GV!
H3M1%=!1E!$'='!+#=1!(='=+(=+G(!M'GT':&(!
/5P!E+2&(!!
R'='!'#'28(+(!+#!1M&#!(1*%G&!(1E=>'%&!
/5P!E+2&(!1%!D+'!$+%&G=!$'='B'(&!'GG&((!
UX8=01#-!A!'#$!(+3+2'%!1M&#!(1*%G&!
2'#:*':&(!(*MM1%=+#:!i'D'!B%+$:+#:V!
9#2+#&!'#$!1EE2+#&!>1%T!M1((+B2&!
.1G'2!RI!+#(='22'=+1#N!O0+(!'221>(!=0&!*(&!
*#$&%!E+&2$!G1#$+=+1#(N!
5&2&G=+#:!$'='!E1%!$1>#21'$!
P+'!3&='$'='!(M'G&!%&(=%+G=+1#(!$&E+#&$!+#!
=0&!5XJ//)H9!`*&%8!B*+2$&%!1%!`*&%+&(!
>%+==&#!B8!*(&%!E1%!(M&G+E+G!G'(&(!
5*MM1%=!E1%!$+(=%+B*=&$!(8(=&3(!
R'='!&YG0'#:&!D+'!_<.!E1%3'=!
!
O0&!E&'=*%&(!2+(=&$!+#!O'B2&!4!!%&M%&(&#=!'!G13M+2'=+1#!1E!3'#$'=1%8!'(!>&22!'(!$&(+%'B2&!
E*#G=+1#'2+=8!=0'=!>+22!B&!M%+1%+=+(&$!!
Table'2:'Additional'desired''feature'list'
]&'=*%&!
R&(G%+M=+1#!
A&(&'%G0!:%1*M!G1#G&M=!
R'='!G'#!B&!1>#&$!B8!'!:%1*M!+#(=&'$!1E!1#28!'!(+#:2&!*(&%!
C(&%!$'='!*M$'=&!D+'!>&B!+#=&%E'G&!
!
/1#G&M=!1E!XH^(!
"!$'='!+(!'2(1!1>#&$!B8!'!XH!
.1'$+#:!1E!'#G+22'%8!E+2&(!
"*:3&#=+#:!(M&G=%'2!G122&G=+1#(!>+=0!3&='$'='!0&2$!B8!
E+2&(!
R'='!+3M1%=!E%13!'#1=0&%!(8(=&3!
"$$!#&>!E+2&!%&'$+#:!%1*=+#&(!'(!+#=&%E'G&(!=1!(*G0!(8(=&3(!
<&='$'='!*M$'=&(!1D&%>%+=+#:!
&Y+(=+#:!&#=%+&(!(01*2$!B&!21::&$!!
<&='$'='!&$+=+#:!'G=+1#!21:(!>+=0!%&E&%&#G&!=1!+#D12D&$!
*(&%!
"((&((+#:!$'='!`*'2+=8!
C(&!G1#G&M=(!$&D&21M&$!'=!A<HO-!<&2B1*%#&!
5*MM1%=!1E!*(':&!M%1E+2&(!
C(&%!:%1*M!(M&G+E+G!$'='!&$+=+#:!3'(T(!
"%G0+D&!$'='!
]%&&6&!$'='!'=!'!:+D&#!(='=&!'#$!$+('B2&!&$+=+#:!
H#=&#$&$!G1#=&Y=!+#$+G'=1%!
J$*G'=+1#'2-!=%'+#+#:-!=&(=-!%&(&'%G0-!&=GN!!
R&E+#&$!1#!G'3M'+:#!2&D&2N!
H#D+=&!7%$!M'%=+&(!=1!'GG&((!$'='!
"$$+#:!#&>!M&%(1#(!=1!'#!&Y+(=+#:!:%1*M!E1%!'!2+3+=&$!=+3&-!
&N:N!E1%!%&D+&>+#:!
/133*#+=8!B*+2$+#:!
]'G&B11T!(+3+2'%!E&'=*%&(!
?&B!'GG&((!1E!RI!
R'='!G'#!B&!'GG&((&$!>+=01*=!21G'2!+#(='22'=+1#!1E!
5XJ//)H9!$&(T=1M!'MM2+G'=+1#!
X*B2+GQM%+D'=&!D+(+B+2+=8!1E!
G'3M'+:#(!
513&!$'='!G'#!B&!&3B'%:1&$!B8!=0&!XH!E1%!'!G&%='+#!=+3&!
>0+2&!>1%T!1%!M*B2+(0+#:!+(!1#:1+#:!
R1>#21'$!1E!1%+:+#'2!$'='!E+2&(!
5=1%&!'!G1M8!1E!=0&!E+2&(!1#!'!E+2&(&%D&%!$*%+#:!$'='!
+#:&(=+1#!'#$!3'T&!=0&3!'D'+2'B2&!E1%!$1>#21'$!
JYM1%=!1E!$'='!=1!&N:N!LH5!E1%3'=(!
"$$!&YM1%=+#:!%1*=+#&(!E1%!+$&#=+E+&$!(1E=>'%&!%&`*+%&$!B8!
&#$!*(&%(!
!
Methods)
"#!+#+=+'2!:'M!'#'28(+(!+$&#=+E+&$!=0&!M1=&#=+'2!1E!=0&!5XJ//)H9!(8(=&3!=1!(&%D&!'(!'!B'(+(!E1%!=0&!
$&D&21M3&#=!1E!'!#'=+1#'2!(M&G=%'2!$'='B'(&!E1%!=0&!"*(=%'2+'#!%&31=&!(&#(+#:!G133*#+=8N!!O0&!
E1221>+#:!3&=01$(!=0*(!%&M%&(&#=!'!3+Y!1E!=0&!G*%%&#=!G'M'B+2+=+&(!1E!=0&!5XJ//)H9!(8(=&3!'#$!
1E!E&'=*%&(!=0'=!>+22!B&!$&D&21M&$!1%!&#0'#G&$!+#!=0&!(G1M&!1E!=0&!(M&G=%'2!$'='B'(&!$&D&21M3&#=N!
O0&!:&#&%'2!$'='E21>!U]+:*%&!7V!(='%=(!>+=0!'!$'='!+#:&(=+1#!M%1G&((-!21'$+#:!(M&G=%1%'$+13&=&%!
:&#&%'=&$!$'='!E+2&(!=1!=0&!$'='B'(&N!O0+(!(=&M!+#D12D&(!&Y=%'G=+#:!'(!3'#8!3&='M'%'3&=&%(!'(!
M1((+B2&!E%13!=0&!E+2&(!=1!%&$*G&!=0&!3'#*'2!3&='$'='!+#M*=N!H#M*=!E+2&(!(*MM1%=&$!'=!=0&!=+3&!1E!
>%+=+#:!'%&\!!
Table'3:'File'formats'support'by'SPECCHIO'version'2.2.2'delta'
]+2&!]1%3'=!
/133&#=(!
"5R!B+#'%8!!
"#'28=+G'2!5M&G=%'2!R&D+G&(-!12$!'#$!#&>!E+2&!E1%3'=(!
LJA!
LJA!+#(=%*3&#=(!=&Y=!E+2&(!
<]A!
<]A!5*#!X01=13&=&%!=&Y=!E+2&(!
5P/!
5P/!)AZ,a4;!E+2&(!
"M1:&&!
"M1:&&!=&Y=!E+2&(-!M%&2+3+#'%8!(*MM1%=!
JKPH!5.I!
JKPH!(M&G=%'2!2+B%'%8!E+2&(!
9G&'#!9M=+G(!!
9G&'#!9M=+G(!5M&G=%'5*+=&!=&Y=!E+2&(!
C#+5M&G!!
C#+5M&G!5+#:2&!I&'3!'#$!R*'2!I&'3!=&Y=!E+2&(!
]LH!)R]@!
)R]@!E+2&(!>+=0!$'='!(=%*G=*%&!M%1M%+&='%8!=1!=0&!]+##+(0!L&1$&=+G!H#(=+=*=&!
O&Y=!
/12*3#'%-!(M'G&!(&M'%'=&$!(M&G=%'2!$'='!
5XJ/XA!
5M&G=%'2!E+2&!E1%3'=!B8!C5L5-!'2(1!:&#&%'=&$!B8!=0&!C5L5!XAH5<!(1E=>'%&!
!
<&='$'='!1E!$'='!G1#='+#&$!+#!=0&!$'='B'(&!G'#!B&!'*:3&#=&$!B8!*(+#:!'!3&='$'='!&$+=+#:!LCHN!
O0+(!M%1G&((!+(!(=%&'32+#&$!B8!=0&!G1#G&M=!1E!:%1*M!*M$'=&(-!>0&%&!'!(M&G=%'2!G122&G=+1#!G'#!B&!
*M$'=&$!+#!1#&!1M&%'=+1#N!R'='!0&2$!B8!=0&!(8(=&3!G'#!B&!M%1G&((&$!'#$!D+(*'2+(&$!>+=0+#!=0&!
5XJ//)H9!i'D'!'MM2+G'=+1#-!B*=!=0&(&!G'M'B+2+=+&(!'%&!%'=0&%!:&#&%+G!'#$!%*$+3&#='%8!'(!+=!+(!
D+%=*'228!+3M1((+B2&!=1!G'=&%!E1%!'22!M1((+B2&!*(&%!#&&$(!>+=0+#!=0&!i'D'!'MM2+G'=+1#N!R'='!3'8!B&!
&YM1%=&$!=1!%&:*2'%!E+2&(!>+=0!/5P!'#$!JKPH!5.I!B&+#:!=0&!G*%%&#=!1*=M*=!1M=+1#(N!
<1%&!G13M2&Y!1%!*(&%!(M&G+E+G!M%1G&((+#:-!'#'28(+(!'#$!D+(*'2+6'=+1#!G'#!B&!+3M2&3&#=&$!+#!
M%1:%'33+#:!2'#:*':&(!=0'=!(*MM1%=!(G+&#=+E+G-!&YM21%'=1%8!>1%T!'=!'!3*G0!:%&'=&%!&Y=&#=!=0'#!
i'D'N!5*G0!0+:0&%Z2&D&2!'2:1%+=03(!G'#!M%'G=+G'228!B&!&'(+28!+3M2&3&#=&$!+#!'#8!2'#:*':&!=0'=!
(*MM1%=(!i'D'!B%+$:+#:!+#!(13&!E'(0+1#N!O0&!:&#&%'2!G1#G&M=!+(!=1!*(&!5XJ//)H9!i'D'!9BW&G=(!=1!
G1##&G=!=1!=0&!$'='B'(&!'#$!3'T&!(M&G=%'2!$'='!'#$!3&='$'='!'D'+2'B2&!=1!=0&!2'#:*':&!1E!G01+G&N!
<'=2'B!'#$!X8=01#!0'D&!B&&#!M%1D&#!=1!(&'32&((28!+#=&%'G=!>+=0!5XJ//)H9!$'='B'(&(!'#$!=0&!
('3&!+(!&YM&G=&$!=1!012$!=%*&!E1%!E*%=0&%!2'#:*':&(!(*G0!'(!A!'#$!HR.N!"XH(!U"MM2+G'=+1#!
X%1:%'33&%!H#=&%E'G&(V!M%1D+$&$!B8!=0&!5XJ//)H9!(8(=&3!(1E=>'%&!(*MM1%=!$'='B'(&!`*&%+&(-!
(&2&G=+1#(!'#$!+#(&%=(-!+N&N!'22!+#=&%'G=+1#(!>+=0!=0&!%&2'=+1#'2!$'='B'(&!3'#':&3&#=!(8(=&3!
UARI<5V!U]+:*%&!4VN!
!
Figure'2:'SPECCHIO'system'layers'
!
O0+(!G'M'B+2+=8!+(!&Y=%&3&28!M1>&%E*2-!'(!+=!:+D&(!(G+&#=+(=(!=0&!E%&&$13!=1!$&D&21M!=0&+%!1>#!
'2:1%+=03(!'#$!$'='!'#'28(+(QM%1G&((+#:!E21>(!>0+2&!B'(+#:!1#!'!(='B2&!'#$!'B(=%'G=&$!
E1*#$'=+1#!=0'=!0'#$2&(!'22!=0&!`*&%8+#:-!$'='!21'$+#:-!+#(&%=+#:!'(!>&22!'(!01*(&T&&M+#:!
%&:'%$+#:!3&='$'='!'#$!G1#(+(=&#G8!1E!(M&G=%'2!(M'G&(N!
!
!
Figure'3:'Main'dataflows'of'the'SPECCHIO'system'
O0&!5XJ//)H9!(8(=&3!G'#!B&!&'(+28!$&M218&$!1#!$'='B'(&!(&%D&%(!>+=0!1%!>+=01*=!H#=&%#&=!
'GG&((!1%!1#!21G'2!>1%T(='=+1#(!1%!&D&#!1#!E+&2$!2'M=1M(N!"!M1((+B2&!1#=121:8!+(!(01>#!+#!]+:*%&!;N!
O0+(!'221>(!'!E2&Y+B2&!'#$!*(&%Z#&&$!='+21%&$!*(&!1E!=0&!(8(=&3-!&N:N!=1!2+3+=!=0&!$'='!'GG&((!=1!+#Z
01*(&!%&(&'%G0&%(-!1%!=1!E'G+2+='=&!$'='!(=1%':&!'#$!M%1G&((+#:!$*%+#:!E+&2$!G'3M'+:#(!>0&%&!
H#=&%#&=!'GG&((!3'8!B&!+3M1((+B2&N!5XJ//)H9!1EE&%(!'!E+2&ZB'(&$!1M=+1#!=1!&YG0'#:&!$'='!
B&=>&&#!$+(=%+B*=&$!$'='B'(&(!'#$!=0*(!'221>(!G1#(12+$'=+#:!=0&!$'='!+#!'!G&#=%'2!(&%D&%!1#G&!
$&&3&$!'MM%1M%+'=&!=1!$1!(1!B8!=0&!$'='!M%1$*G&%!U)*&#+!&=!'2N-!4a,,VN!
!
!
Figure'4:'Possible'ontology'of'database'instances'
!
TERN)Auscover)Case)Study!
O0&!O&%%&(=%+'2!JG1(8(=&3!A&(&'%G0!K&=>1%T!UOJAKV!"*(/1D&%!E'G+2+=8!>'(!G%&'=&$!=1!E'G+2+='=&!
=0&!M%1$*G=+1#!1E!D'2+$'=&$!('=&22+=&Z$&%+D&$!B+1M08(+G'2!3'M!M%1$*G=(!E1%!=0&!&G1(8(=&3!
%&(&'%G0!G133*#+=8!'#$!#'=*%'2!%&(1*%G&!3'#':&%(!UOJAK!4a,aVN!!O0&!"*(G1D&%!#&=>1%T!
(=%+D&(!=1!G%&'=&!=0&(&!M%1$*G=(!>+=0+#!=0&!E%'3&>1%T!1E!&(='B2+(0&$!"*(=%'2+'#!D'2+$'=+1#!
M%1:%'3(!E1221>+#:!&(='B2+(0&$!+#=&%#'=+1#'2!&'%=0!1B(&%D'=+1#!M%1=1G12(N!!!
C(+#:!'!OJAK!"*(G1D&%!E+&2$!G'3M'+:#!!'(!'!G'(&!(=*$8!+22*(=%'=&(!=0&!*(&!1E!=0&!G*%%&#=!
5XJ//)H9!(8(=&3!'(!$'='!%&M1(+=1%8!'#$!'(!'!M2'=E1%3!E1%!M1(=ZM%1G&((+#:!'#$!(=1%':&!1E!
'GG1%$+#:!%&(*2=(!+#!=0&!$'='B'(&N!!
H#!"M%+2!4a,4!08M&%(M&G=%'2!'#$!.+$'%!+3':&%8!>&%&!'G`*+%&$!1D&%!A*(0>1%=0!5='=&!]1%&(=!+#!
P+G=1%+'N!O0&!(=*$8!(+=&!G1#(+(=(!1E!'!%&E&%&#G&!'%&'!21G'=&$!>+=0+#!=0&!3'+#!B1YZ+%1#B'%T!E1%&(=!
,e!T3!K?!E%13!K':'3B+&N!N!5+3*2='#&1*(28-!E+&2$!$'='!G122&G=+1#!>'(!G1#$*G=&$!G1#(+(=+#:!1E!
D&:&='=+1#!(=%*G=*%&!3&'(*%&3&#=(!'#$!2&'E!('3M2+#:N!O0&!'+3!1E!=0+(!E+&2$!G'3M'+:#!!>'(!=1!
G0'%'G=&%+(&!2&'E!G0&3+(=%8!'#$!(M&G=%1(G1M8!E1%!'!#*3B&%!1E!=%&&(!+#!=0&!(=*$8!(+=&N!"!=1='2!1E!eh!
(='#$(!%&M%&(&#='=+D&!1E!=0&!E+D&!31(=!'B*#$'#=!(M&G+&(!>&%&!(&2&G=&$N!9#28!=%&&(!>0+G0!G%1>#!
(M&G=%'!G1*2$!B&!&Y=%'G=&$!E%13!=0&!+3':&%8!>&%&!G1#(+$&%&$!E1%!(&2&G=+1#N!"(!'!%&(*2=-!#1#&!1E!
=0&!(&2&G=&$!=%&&(!>&%&!(*MM%&((&$!1%!(0'$1>&$!B8!=0&!(*%%1*#$+#:!G%1>#(N!]%13!&'G0!(&2&G=&$!
=%&&-!1#&!B%'#G0!E%13!=0&!*MM&%Z31(=!=0+%$!1E!=0&!G%1>#!>'(!(01=!$1>#!'#$!'!(&=!1E!3'=*%&!
2&'D&(!>'(!='T&#!E1%!E*%=0&%!'#'28(+(N!"$$+=+1#'2!3&='$'='!G122&G=&$!E1%!&D&%8!=%&&!G1#(+(=&$!1E!
(='#$!G11%$+#'=&(-!=%&&!(M&G+&(-!0&+:0=-!=%*#T!$+'3&=&%!'=!B%&'(=!0&+:0=!URI)V-!G%1>#!$+'3&=&%-!
G%1>#!M&%G&#=':&!G1D&%!'#$!M1(+=+1#!1E!=0&!G%1>#!+#!%&2'=+1#!=1!=0&!(*%%1*#$+#:!=%&&(!U+N&N!
$13+#'#=-!G1Z$13+#'#=!1%!+(12'=&$VN!H#!=0&!E1221>+#:!01*%(-!'#!+#=&:%'=+#:!(M0&%&!U"#'28=+G'2!
5M&G=%'2!R&D+G&(-!I1*2$&%-!/9V!>'(!*(&$!=1!3&'(*%&!%&E2&G='#G&!'#$!=%'#(3+=='#G&!(M&G=%'!1E!7!1E!
=0&!2&'D&(!G122&G=&$!M&%!=%&&N!"#1=0&%!:%1*M!1E!2&'D&(!>&%&!(G'##&$!'#$!>&+:0&$!=1!&(=+3'=&!
(M&G+E+G!2&'E!'%&'-!'#$!'!=0+%$!(&=!>'(!(&#=!=1!'!2'B1%'=1%8!E1%!G0&3+G'2!'#'28(+(N!!
O0&!(M&G=%'2!3&'(*%&3&#=(!>&%&!'G`*+%&$!*(+#:!'!]+&2$5M&G!X%1!(M&G=%13&=&%!U"#'28=+G'2!
5M&G=%'2!R&D+G&(-!I1*2$&%-!/9V!'=='G0&$!=1!=0&!+#=&:%'=+#:!(M0&%&!=0%1*:0!'!B'%&!E+B&%N!!
<&'(*%&3&#=(!=1!G1%%&G=!E1%!=0&!3+('2+:#3&#=!1E!=0&!2+:0=!(1*%G&!>&%&!='T&#!E1%!&D&%8!
%&E2&G='#G&!'#$!=%'#(3+=='#G&!3&'(*%&3&#=N!!
R*%+#:!$'='!+#:&(=+1#!=0&!5XJ//)H9!"5R!E+2&!E1%3'=!%&'$&%!21'$(!$'='!E%13!=0&!E+2&!(8(=&3!+#=1!
=0&!$'='B'(&N!O0+(!+(!'!(='#$'%$!M%1G&((!'#$!$'='!'%&!+33&$+'=&28!'D'+2'B2&!E1%!(M0&%&!M1(=Z
M%1G&((+#:N!O1!:+D&!*(&%(!'!B&==&%!G1#=%12!1E!=0&!M%1G&((+#:!'MM2+&$-!'#!+#=&%'G=+D&!:%'M0+G'2!*(&%!
+#=&%E'G&!>%+==&#!+#!<'=2'B!*(+#:!5XJ//)H9!i'D'!G13M1#&#=(!+(!*(&$!U]+:*%&!hVN!O0&!2&E=!0'#$!
(+$&!1E!=0&!LCH!012$(!=0&!5M&G=%'2!R'='!I%1>(&%!G13M1#&#=-!$+(M2'8+#:!=0&!G1#=&#=!1E!=0&!
$'='B'(&!+#!0+&%'%G0+G'2!E1%3!'#$!'221>+#:!=0&!(&2&G=+1#!1E!(M&G=%'2!$'='N!5*G0!'!(&2&G=+1#!+(!=0&!
(='%=+#:!M1+#=!E1%!=0&!$'='E21>!&#(*+#:!'(!+22*(=%'=&$!+#!]+:*%&!@N!9#G&!$'='!'%&!(&2&G=&$-!=0&+%!
$'='B'(&!+$&#=+E+&%(!>+22!B&!21'$&$N!O0&(&!'%&!=0&#!+=&%'=&$!1D&%!$*%+#:!=0&!(M&G=%'!21'$+#:-!
(&2&G=+#:!(M&G=%*3!%&G1%$(!E%13!=0&!$'='B'(&!'#$!3'T+#:!=0&!(M&G=%'2!$'='!'D'+2'B2&!'(!<'=2'B!
3'=%+G&(N!O>1!31$&(!'%&!(*MM1%=&$!B8!=0&!LCH\!U'V!:%1*M!B8!:%1*M!+#=&%'G=+D&!M%1G&((+#:!'#$!
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Information(Science,!77,!,h7j,faN!
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?"59K-!ON!RN!k!?H.Jm-!RN!4aaaN!5=%*G=*%&$!<&='$'='!5M'G&(N!Journal(of(Internet(Cataloging,!7,!
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... Airborne imaging spectrometer data are typically stored and processed within processing and archiving facilities (PAF), comprising integrated hardware and software components for data storage and processing [60]. Ground-based point spectroradiometer data are ideally handled by spectral databases in combination with processing algorithms, for example, the SPEC-CHIO spectral information system [61], [62]. ...
... Such metadata should be sufficient to enable the contextual awareness, i.e., a full understanding of the circumstances of data acquisition. Modern spectral information systems are essentially capable of storing unlimited numbers of metaparameters [61], [62]. However, entering these data in a consistent [54] and timely manner while field records are still available and personal recollections yet fresh is definitely problematic. ...
Field and Airborne Spectroscopy Cross Validation—Some Considerations
Article
  • Oct 2016
Field spectroscopy is increasingly used in various fields of science: either as a research tool in its own right or in support of airborne- or space-based optical instruments for calibration or validation purposes. Yet, while the use of the instruments appears deceptively simple, the processes of light and surface interactions are complex to be measured in full and are further complicated by the multidimensionality of the measurement process. This study exemplifies the cross validation of in situ point spectroscopy and airborne imaging spectroscopy data across all processing stages within the spectroscopy information hierarchy using data from an experiment focused on vegetation. In support of this endeavor, this study compiles the fundamentals of spectroscopy, the challenges inherent to field and airborne spectroscopy, and the best practices proposed by the field spectroscopy community. This combination of theory and case study shall enable the reader to develop an understanding of 1) some of the commonly involved sources of errors and uncertainties, 2) the techniques to collect high-quality spectra under natural illumination conditions, and 3) the importance of appropriate metadata collection to increase the long-term usability and value of spectral data.
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... Spectral information systems take spectral databases a step further by making data held in the databases retrievable and usable by other users or systems and by adding processing functionalities that further transform the data or information held in the system, in turn generating more information. This could, for example, involve the generation of higher-level products or spectral data corrected for sampling equipment or sensor artefacts (Hueni et al 2012). Adopted by the Australian remote sensing community, and enhanced with funds provided by the Australian National Data Service Data Capture Program, (ANDS Project DC-10) AUS-SPECCHIO, is a system designed to support scientists in not only storing spectral data, but analysing the data using the full potential of combined metadata spaces (Wason and Wiley, 2000) and spectral spaces (Hueni et al 2012). ...
... This could, for example, involve the generation of higher-level products or spectral data corrected for sampling equipment or sensor artefacts (Hueni et al 2012). Adopted by the Australian remote sensing community, and enhanced with funds provided by the Australian National Data Service Data Capture Program, (ANDS Project DC-10) AUS-SPECCHIO, is a system designed to support scientists in not only storing spectral data, but analysing the data using the full potential of combined metadata spaces (Wason and Wiley, 2000) and spectral spaces (Hueni et al 2012). The system incorporates a defacto metadata standard to improve interoperability and data sharing, has spatial search capabilities, and contains mechanisms to house validation data associated with spectra and several enhancements which facilitate ease-of-use for individuals and research groups. ...
Quality Assurance Steps for AusCover Hyper-Spectral Data. In A. Held, S. Phinn, M. Soto-Berelov, & S. Jones (Eds.), AusCover Good Practice Guidelines: A technical handbook supporting calibration and validation activities of remotely sensed data product. Version 1.1. TERN
Chapter
Full-text available
  • Aug 2015
Between January 2011 and June 2013, AusCover collected field, airborne hyper-spectral and airborne LiDAR data coincidently from nine locations across Australia. This chapter outlines a process to use for Quality Assurance (QA) of the airborne hyper-spectral data. All the data sets are available for the general public to download for use via the AusCover Visualisation Portal (http://data.auscover.org.au/Portal2/) to support ecosystem science in Australia. This chapter explains how to geo-reference and atmospherically correct the hyper-spectral data and how to assess the quality of the geo-referencing, the spatial coverage of the data set and the spectral at-surface reflectance image pixel values when compared against in-situ spectrophotometer measurements of ground calibration targets. These QA methods may be used to any hyper-spectral image data set.
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... In combination with the measurement protocol and processing descriptors, these metadata should account for the base spectral variability in the signal acquired with the given illumination-target-sensor setup (Figure 1). Modern spectral information systems are capable of storing all potentially relevant meta parameters [132] and target descriptors (including photographs of the target), or they can be easily extended to do so [133]. The selection of the required metadata is thus essentially dictated by science rather than the capabilities of information systems. ...
Sun-Induced Chlorophyll Fluorescence II: Review of Passive Measurement Setups, Protocols, and Their Application at the Leaf to Canopy Level
Article
Full-text available
  • Apr 2019
Imaging and non-imaging spectroscopy employed in the field and from aircraft is frequently used to assess biochemical, structural, and functional plant traits, as well as their dynamics in an environmental matrix. With the increasing availability of high-resolution spectroradiometers, it has become feasible to measure fine spectral features, such as those needed to estimate sun-induced chlorophyll fluorescence (F), which is a signal related to the photosynthetic process of plants. The measurement of F requires highly accurate and precise radiance measurements in combination with very sophisticated measurement protocols. Additionally, because F has a highly dynamic nature (compared with other vegetation information derived from spectral data) and low signal intensity, several environmental, physiological, and experimental aspects have to be considered during signal acquisition and are key for its reliable interpretation. The European Cooperation in Science and Technology (COST) Action ES1309 OPTIMISE has produced three articles addressing the main challenges in the field of F measurements. In this paper, which is the second of three, we review approaches that are available to measure F from the leaf to the canopy scale using ground-based and airborne platforms. We put specific emphasis on instrumental aspects, measurement setups, protocols, quality checks, and data processing strategies. Furthermore, we review existing techniques that account for atmospheric influences on F retrieval, address spatial scaling effects, and assess quality checks and the metadata and ancillary data required to reliably interpret retrieved F signals.
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... Spectral data and metadata were loaded into the spectral information system SPECCHIO with the temperature and humidity values linearly interpolated to fit the ASD spectra acquisition time stamps. All data analysis was carried out within MATLAB using a direct connection to SPECCHIO [17]. ...
Cause, Effect, and Correction of Field Spectroradiometer Interchannel Radiometric Steps
Article
Full-text available
  • Jan 2017
Field spectroradiometers are often comprised of several spectral detectors to sample the full range of reflected solar irradiance. An example of such an instrument is the Analytical Spectral Devices (ASD) full-range spectroradiometer, featuring three spectral detectors to capture spectra between 350 and 2500 nm. The resulting spectra often exhibit radiometric steps at the joints of these detectors. This study investigates the influence of external temperature and humidity on the magnitude of these steps by experiments based on a climate chamber. Relative radiometric errors at the detector borders were found to reach up to 16% for the visible and near infrared and 21% for the shortwave infrared 2 (SWIR2), whereas relative reflectance errors are target dependent, typically ranging between 2% and 6%. The derived sensor model provides a physically based explanation of the changes in radiometry due to temperature and demonstrates that all spectral bands are affected to a higher or lesser degree. The model can be used to correct for the effect of temperature on the recorded radiances. Applying the model to ASD instruments that were not tested in the climate chamber still leads to reasonable correction results with RMSE values of 0.6%.
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... Currently, there are only a few available and persisting spectral information systems, the most prominent one being the opensource SPECCHIO ( Hueni et al., 2009). SPECCHIO has seen many upgrades over time with a large contribution by the Australian National Data Service ( Hueni et al., 2012) and support from EUROSPEC. The challenges for the future are numerous, but most pressing appears the issue of automated data quality and metadata standards. ...
EUROSPEC: at the interface between remote-sensing and ecosystem CO2 flux measurements in Europe
Article
Full-text available
  • Oct 2015
  • BG
Resolving the spatial and temporal dynamics of gross primary productivity (GPP) of terrestrial ecosystems across different scales remains a challenge. Remote sensing is regarded as the solution to upscale point observations conducted at the ecosystem level, using the eddy covariance (EC) technique, to the landscape and global levels. In addition to traditional vegetation indices, the photochemical reflectance index (PRI) and the emission of solarinduced chlorophyll fluorescence (SIF), now measurable from space, provide a new range of opportunities to monitor the global carbon cycle using remote sensing. However, the scale mismatch between EC observations and the much coarser satellite-derived data complicate the integration of the two sources of data. The solution is to establish a network of in situ spectral measurements that can act as a bridge between EC measurements and remote-sensing data. In situ spectral measurements have already been conducted for many years at EC sites, but using variable instrumentation, setups, and measurement standards. In Europe in particular, in situ spectral measurements remain highly heterogeneous. The goal of EUROSPEC Cost Action ES0930 was to promote the development of common measuring protocols and new instruments towards establishing best practices and standardization of these measurements. In this review we describe the background and main tradeoffs of in situ spectral measurements, review the main results of EUROSPEC Cost Action, and discuss the future challenges and opportunities of in situ spectral measurements for improved estimation of local and global estimates of GPP over terrestrial ecosystems.
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... Currently, there 5 are only a few available and persisting spectral information systems, the most prominent one being the open source SPECCHIO (Hueni et al., 2009). SPECCHIO has seen many upgrades over time with a large contribution by the Australian National Data Service ( Hueni et al., 2012) and support from EUROSPEC. The challenges for the future are numerous, but most pressing appears the issue 10 of automated data quality and metadata standards. ...
EUROSPEC: at the interface between remote sensing and ecosystem CO<sub>2</sub> flux measurements in Europe
Article
Full-text available
Resolving the spatial and temporal dynamics of gross primary productivity (GPP) of terrestrial ecosystems across different scales remains a challenge. Remote sensing is regarded as the solution to upscale point observations conducted at the ecosystem level, using the eddy covariance (EC) technique, to the landscape and global levels. In addition to traditional vegetation indices, the photochemical reflectance index (PRI) and the emission of solar-induced chlorophyll fluorescence (SIF), now measurable from space, provide a new range of opportunities to monitor the global carbon cycle using remote sensing. However, the scale mismatch between EC observations and the much coarser satellite-derived data complicates the integration of the two sources of data. The solution is to establish a network of in situ spectral measurements that can act as bridge between EC measurements and remote sensing data. In situ spectral measurements have been already conducted for many years at EC sites, but using variable instrumentation, setups, and measurement standards. In Europe in particular, in situ spectral measurements remain highly heterogeneous. The goal of EUROSPEC Cost Action ES0930 was to promote the development of common measuring protocols and new instruments towards establishing best practices and standardization of in situ spectral measurements. In this review we describe the background and main tradeoffs of in situ spectral measurements, review the main results of EUROSPEC Cost Action, and discuss the future challenges and opportunities of in situ spectral measurements for improved estimation of local and global carbon cycle.
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Structured Metadata Spaces
Article
Full-text available
  • Nov 2000
This paper presents the concepts of a metadata space as it relates to cataloging and discovery. A space has multiple dimensions; in the case of resource metadata, these are descriptive dimensions. We explain the needs for orthogonal descriptive dimensions, and present a method for achieving maximally efficient, independent dimensions using semantic structures realized in structured metadata. A specific example of this system as developed in the IEEELearning Technology Standards Committee (LTSC P1484) Learning Object Metadata (LOM) is presented. The LOMis the collaborative work of many organizations including ADL, AJCC, ARIADNE, GESTALT, and IMS(see acronym list at the end of the article, following references). The scope of the concepts presented in this paper encompasses general concepts of metadata systems.
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The spectral database SPECCHIO for improved long-term usability and data sharing
Article
The organised storage of spectral data described by metadata is important for long-term use and data sharing with other scientists. Metadata describing the sampling environment, geometry and measurement process serves to evaluate the suitability of existing data sets for new applications. There is a need for spectral databases that serve as repositories for spectral field campaign and reference signatures, including appropriate metadata parameters. Such systems must be (a) highly automated in order to encourage users entering their spectral data collections and (b) provide flexible data retrieval mechanisms based on subspace projections in metadata spaces. The recently redesigned SPECCHIO system stores spectral and metadata in a relational database based on a non-redundant data model and offers efficient data import, automated metadata generation, editing and retrieval via a Java application. RSL is disseminating the database and software to the remote sensing community in order to foster the use and further development of spectral databases.
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The wisdom hierarchy: representations of the DIKW hierarchy
  • Jan 2007
  • J INF SCI
  • 163-180
ROWLEY, J. 2007. The wisdom hierarchy: representations of the DIKW hierarchy. Journal of Information Science, 33, 163-180.