CheneBelgiqueV1, PascalFrancis, Corpus, bibRecord, 000102

Exploitation of very high resolution satellite data for tree species identification

Identifieur interne : 000102 ( PascalFrancis/Corpus ); précédent : 000101; suivant : 000103

Exploitation of very high resolution satellite data for tree species identification

Auteurs : A. Carleer ; E. Wolff

Source :

Photogrammetric engineering and remote sensing [ 0099-1112 ] ; 2004.

RBID : Pascal:04-0481282

Descripteurs français

Pascal (Inist)
- Belgique, Télédétection spatiale, Très haute résolution, Végétation, Identification, Arbre, Résolution spatiale, Photographie aérienne, Forêt, Modèle, Maximum vraisemblance, Classification, Pixel, Imagerie, Chenal, Précision, Quercus, Fagus, Pseudotsuga, Pinus, Larix.

English descriptors

KwdEn :
- Belgium, Fagus, Larix, Pinus, Pixel, Pseudotsuga, Quercus, Space remote sensing, accuracy, aerial photography, channels, classification, forests, identification, imagery, maximum likelihood, models, spatial resolution, trees, vegetation, very high resolution.

Abstract

With the emergence of very high spatial resolution satellite images, the spatial resolution gap which existed between satellite images and aerial photographs has decreased. A study of the potential of these images for tree species in "monoculture stands" identification was conducted. Two Ikonos images were acquired, one in June 2000 and the other in October 2000, for an 11- by 11-km area covering the Sonian Forest in the southeastern part of the Brussels-Capital region (Belgium). The two images were orthorectified using a digital elevation model and 1256 geodetic control points. The identification of the tree species was carried out utilizing a supervised maximum-likelihood classification on a pixel-by-pixel basis. Classifications were performed on the orthorectified data, NDVI transformed data, and principal components imagery. In order to decrease the intraclass variance, a mean filter was applied to all the spectral bands and neo-channels used in the classification process. Training and validation areas were selected and digitized using detailed geographical databases of the tree species. The selection of the relevant bands and neo-channels was carried out by successive addition of information in order to improve the classification results. Seven different tree species of one to two different age classes were identified with an overall accuracy of 86 percent. The seven identified tree species or species groups are Oaks (Quercus sp.), Beech (Fagus sylvatica L.), Purple Beech (Fagus sylvatica purpurea), Douglas Fir (Pseudotsuga menziesii (Mirb.) Franco), Scots Pine (Pinus sylvestris L.), Corsican Pine (Pinus nigra Arn. subsp. laricio (Poir.) Maire var. corsican), and Larch (Larix decidua Mill.).

Notice en format standard (ISO 2709)

Pour connaître la documentation sur le format Inist Standard.

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A02	`01`			`@0 PERSDV`
A03		`1`		`@0 Photogramm. eng. remote sensing`
A05				`@2 70`
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A08	`01`	`1`	`ENG`	`@1 Exploitation of very high resolution satellite data for tree species identification`
A11	`01`	`1`		`@1 CARLEER (A.)`
A11	`02`	`1`		`@1 WOLFF (E.)`
A14	`01`			`@1 Institut de Gestion de l'Environnement et d'Aménagement du Territoire, Université Libre de Bruxelles, CP 130/02, 50 av. F. Roosevelt @2 1050 Brussels @3 BEL @Z 1 aut. @Z 2 aut.`
A20				`@1 135-140 @7 2`
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C01	`01`		`ENG`	@0 With the emergence of very high spatial resolution satellite images, the spatial resolution gap which existed between satellite images and aerial photographs has decreased. A study of the potential of these images for tree species in "monoculture stands" identification was conducted. Two Ikonos images were acquired, one in June 2000 and the other in October 2000, for an 11- by 11-km area covering the Sonian Forest in the southeastern part of the Brussels-Capital region (Belgium). The two images were orthorectified using a digital elevation model and 1256 geodetic control points. The identification of the tree species was carried out utilizing a supervised maximum-likelihood classification on a pixel-by-pixel basis. Classifications were performed on the orthorectified data, NDVI transformed data, and principal components imagery. In order to decrease the intraclass variance, a mean filter was applied to all the spectral bands and neo-channels used in the classification process. Training and validation areas were selected and digitized using detailed geographical databases of the tree species. The selection of the relevant bands and neo-channels was carried out by successive addition of information in order to improve the classification results. Seven different tree species of one to two different age classes were identified with an overall accuracy of 86 percent. The seven identified tree species or species groups are Oaks (Quercus sp.), Beech (Fagus sylvatica L.), Purple Beech (Fagus sylvatica purpurea), Douglas Fir (Pseudotsuga menziesii (Mirb.) Franco), Scots Pine (Pinus sylvestris L.), Corsican Pine (Pinus nigra Arn. subsp. laricio (Poir.) Maire var. corsican), and Larch (Larix decidua Mill.).
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C03	`03`	`2`	`FRE`	`@0 Très haute résolution @5 03`
C03	`03`	`2`	`ENG`	`@0 very high resolution @5 03`
C03	`04`	`2`	`FRE`	`@0 Végétation @5 04`
C03	`04`	`2`	`ENG`	`@0 vegetation @5 04`
C03	`04`	`2`	`SPA`	`@0 Vegetación @5 04`
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C03	`06`	`2`	`ENG`	`@0 trees @5 09`
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C03	`07`	`2`	`ENG`	`@0 spatial resolution @5 11`
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C03	`08`	`2`	`ENG`	`@0 aerial photography @5 12`
C03	`08`	`2`	`SPA`	`@0 Fotografía aérea @5 12`
C03	`09`	`2`	`FRE`	`@0 Forêt @5 13`
C03	`09`	`2`	`ENG`	`@0 forests @5 13`
C03	`09`	`2`	`SPA`	`@0 Bosque @5 13`
C03	`10`	`2`	`FRE`	`@0 Modèle @5 14`
C03	`10`	`2`	`ENG`	`@0 models @5 14`
C03	`10`	`2`	`SPA`	`@0 Modelo @5 14`
C03	`11`	`2`	`FRE`	`@0 Maximum vraisemblance @5 15`
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C03	`12`	`2`	`FRE`	`@0 Classification @5 16`
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C03	`13`	`2`	`SPA`	`@0 Pixel @5 17`
C03	`14`	`2`	`FRE`	`@0 Imagerie @5 18`
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C03	`14`	`2`	`SPA`	`@0 Imaginería @5 18`
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C03	`16`	`2`	`ENG`	`@0 accuracy @5 20`
C03	`16`	`2`	`SPA`	`@0 Precisión @5 20`
C03	`17`	`2`	`FRE`	`@0 Quercus @2 NY @5 21`
C03	`17`	`2`	`ENG`	`@0 Quercus @2 NY @5 21`
C03	`17`	`2`	`SPA`	`@0 Quercus @2 NY @5 21`
C03	`18`	`2`	`FRE`	`@0 Fagus @2 NY @5 22`
C03	`18`	`2`	`ENG`	`@0 Fagus @2 NY @5 22`
C03	`18`	`2`	`SPA`	`@0 Fagus @2 NY @5 22`
C03	`19`	`2`	`FRE`	`@0 Pseudotsuga @2 NY @5 23`
C03	`19`	`2`	`ENG`	`@0 Pseudotsuga @2 NY @5 23`
C03	`20`	`2`	`FRE`	`@0 Pinus @2 NY @5 24`
C03	`20`	`2`	`ENG`	`@0 Pinus @2 NY @5 24`
C03	`20`	`2`	`SPA`	`@0 Pinus @2 NY @5 24`
C03	`21`	`2`	`FRE`	`@0 Larix @2 NY @5 25`
C03	`21`	`2`	`ENG`	`@0 Larix @2 NY @5 25`
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C07	`01`	`2`	`SPA`	`@0 Europa del Oeste @2 NG`
C07	`02`	`2`	`FRE`	`@0 Europe`
C07	`02`	`2`	`ENG`	`@0 Europe`
C07	`02`	`2`	`SPA`	`@0 Europa`
C07	`03`	`2`	`FRE`	`@0 Dicotyledoneae @2 NY`
C07	`03`	`2`	`ENG`	`@0 Dicotyledoneae @2 NY`
C07	`04`	`2`	`FRE`	`@0 Angiospermae @2 NY`
C07	`04`	`2`	`ENG`	`@0 angiosperms @2 NY`
C07	`04`	`2`	`SPA`	`@0 Angiospermae @2 NY`
C07	`05`	`2`	`FRE`	`@0 Spermatophyta @2 NY`
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C07	`08`	`2`	`SPA`	`@0 Gymnospermae @2 NY`
C07	`09`	`2`	`FRE`	`@0 Pinaceae @2 NY`
C07	`09`	`2`	`ENG`	`@0 Pinaceae @2 NY`
N21				`@1 271`
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N82				`@1 PSI`

Format Inist (serveur)

NO :	PASCAL 04-0481282 INIST
ET :	Exploitation of very high resolution satellite data for tree species identification
AU :	CARLEER (A.); WOLFF (E.)
AF :	Institut de Gestion de l'Environnement et d'Aménagement du Territoire, Université Libre de Bruxelles, CP 130/02, 50 av. F. Roosevelt/1050 Brussels/Belgique (1 aut., 2 aut.)
DT :	Publication en série; Niveau analytique
SO :	Photogrammetric engineering and remote sensing; ISSN 0099-1112; Coden PERSDV; Etats-Unis; Da. 2004; Vol. 70; No. 1; Pp. 135-140; Bibl. 27 ref.
LA :	Anglais
EA :	With the emergence of very high spatial resolution satellite images, the spatial resolution gap which existed between satellite images and aerial photographs has decreased. A study of the potential of these images for tree species in "monoculture stands" identification was conducted. Two Ikonos images were acquired, one in June 2000 and the other in October 2000, for an 11- by 11-km area covering the Sonian Forest in the southeastern part of the Brussels-Capital region (Belgium). The two images were orthorectified using a digital elevation model and 1256 geodetic control points. The identification of the tree species was carried out utilizing a supervised maximum-likelihood classification on a pixel-by-pixel basis. Classifications were performed on the orthorectified data, NDVI transformed data, and principal components imagery. In order to decrease the intraclass variance, a mean filter was applied to all the spectral bands and neo-channels used in the classification process. Training and validation areas were selected and digitized using detailed geographical databases of the tree species. The selection of the relevant bands and neo-channels was carried out by successive addition of information in order to improve the classification results. Seven different tree species of one to two different age classes were identified with an overall accuracy of 86 percent. The seven identified tree species or species groups are Oaks (Quercus sp.), Beech (Fagus sylvatica L.), Purple Beech (Fagus sylvatica purpurea), Douglas Fir (Pseudotsuga menziesii (Mirb.) Franco), Scots Pine (Pinus sylvestris L.), Corsican Pine (Pinus nigra Arn. subsp. laricio (Poir.) Maire var. corsican), and Larch (Larix decidua Mill.).
CC :	225B04; 002A14A03; 001E01M04
FD :	Belgique; Télédétection spatiale; Très haute résolution; Végétation; Identification; Arbre; Résolution spatiale; Photographie aérienne; Forêt; Modèle; Maximum vraisemblance; Classification; Pixel; Imagerie; Chenal; Précision; Quercus; Fagus; Pseudotsuga; Pinus; Larix
FG :	Europe Ouest; Europe; Dicotyledoneae; Angiospermae; Spermatophyta; Plantae; Coniferales; Gymnospermae; Pinaceae
ED :	Belgium; Space remote sensing; very high resolution; vegetation; identification; trees; spatial resolution; aerial photography; forests; models; maximum likelihood; classification; Pixel; imagery; channels; accuracy; Quercus; Fagus; Pseudotsuga; Pinus; Larix
EG :	Western Europe; Europe; Dicotyledoneae; angiosperms; Spermatophyta; Plantae; Coniferales; gymnosperms; Pinaceae
SD :	Belgica; Teledetección espacial; Vegetación; Fotografía aérea; Bosque; Modelo; Clasificación; Pixel; Imaginería; Canal; Precisión; Quercus; Fagus; Pinus
LO :	INIST-3289.354000119032760060
ID :	04-0481282

Links to Exploration step

Pascal:04-0481282

Le document en format XML

<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en" level="a">Exploitation of very high resolution satellite data for tree species identification</title>
<author><name sortKey="Carleer, A" sort="Carleer, A" uniqKey="Carleer A" first="A." last="Carleer">A. Carleer</name>
<affiliation><inist:fA14 i1="01"><s1>Institut de Gestion de l'Environnement et d'Aménagement du Territoire, Université Libre de Bruxelles, CP 130/02, 50 av. F. Roosevelt</s1>
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<affiliation><inist:fA14 i1="01"><s1>Institut de Gestion de l'Environnement et d'Aménagement du Territoire, Université Libre de Bruxelles, CP 130/02, 50 av. F. Roosevelt</s1>
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<term>forests</term>
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<term>spatial resolution</term>
<term>trees</term>
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<front><div type="abstract" xml:lang="en">With the emergence of very high spatial resolution satellite images, the spatial resolution gap which existed between satellite images and aerial photographs has decreased. A study of the potential of these images for tree species in "monoculture stands" identification was conducted. Two Ikonos images were acquired, one in June 2000 and the other in October 2000, for an 11- by 11-km area covering the Sonian Forest in the southeastern part of the Brussels-Capital region (Belgium). The two images were orthorectified using a digital elevation model and 1256 geodetic control points. The identification of the tree species was carried out utilizing a supervised maximum-likelihood classification on a pixel-by-pixel basis. Classifications were performed on the orthorectified data, NDVI transformed data, and principal components imagery. In order to decrease the intraclass variance, a mean filter was applied to all the spectral bands and neo-channels used in the classification process. Training and validation areas were selected and digitized using detailed geographical databases of the tree species. The selection of the relevant bands and neo-channels was carried out by successive addition of information in order to improve the classification results. Seven different tree species of one to two different age classes were identified with an overall accuracy of 86 percent. The seven identified tree species or species groups are Oaks (Quercus sp.), Beech (Fagus sylvatica L.), Purple Beech (Fagus sylvatica purpurea), Douglas Fir (Pseudotsuga menziesii (Mirb.) Franco), Scots Pine (Pinus sylvestris L.), Corsican Pine (Pinus nigra Arn. subsp. laricio (Poir.) Maire var. corsican), and Larch (Larix decidua Mill.).</div>
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<fC01 i1="01" l="ENG"><s0>With the emergence of very high spatial resolution satellite images, the spatial resolution gap which existed between satellite images and aerial photographs has decreased. A study of the potential of these images for tree species in "monoculture stands" identification was conducted. Two Ikonos images were acquired, one in June 2000 and the other in October 2000, for an 11- by 11-km area covering the Sonian Forest in the southeastern part of the Brussels-Capital region (Belgium). The two images were orthorectified using a digital elevation model and 1256 geodetic control points. The identification of the tree species was carried out utilizing a supervised maximum-likelihood classification on a pixel-by-pixel basis. Classifications were performed on the orthorectified data, NDVI transformed data, and principal components imagery. In order to decrease the intraclass variance, a mean filter was applied to all the spectral bands and neo-channels used in the classification process. Training and validation areas were selected and digitized using detailed geographical databases of the tree species. The selection of the relevant bands and neo-channels was carried out by successive addition of information in order to improve the classification results. Seven different tree species of one to two different age classes were identified with an overall accuracy of 86 percent. The seven identified tree species or species groups are Oaks (Quercus sp.), Beech (Fagus sylvatica L.), Purple Beech (Fagus sylvatica purpurea), Douglas Fir (Pseudotsuga menziesii (Mirb.) Franco), Scots Pine (Pinus sylvestris L.), Corsican Pine (Pinus nigra Arn. subsp. laricio (Poir.) Maire var. corsican), and Larch (Larix decidua Mill.).</s0>
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<s2>NG</s2>
<s5>01</s5>
</fC03>
<fC03 i1="01" i2="2" l="ENG"><s0>Belgium</s0>
<s2>NG</s2>
<s5>01</s5>
</fC03>
<fC03 i1="01" i2="2" l="SPA"><s0>Belgica</s0>
<s2>NG</s2>
<s5>01</s5>
</fC03>
<fC03 i1="02" i2="2" l="FRE"><s0>Télédétection spatiale</s0>
<s5>02</s5>
</fC03>
<fC03 i1="02" i2="2" l="ENG"><s0>Space remote sensing</s0>
<s5>02</s5>
</fC03>
<fC03 i1="02" i2="2" l="SPA"><s0>Teledetección espacial</s0>
<s5>02</s5>
</fC03>
<fC03 i1="03" i2="2" l="FRE"><s0>Très haute résolution</s0>
<s5>03</s5>
</fC03>
<fC03 i1="03" i2="2" l="ENG"><s0>very high resolution</s0>
<s5>03</s5>
</fC03>
<fC03 i1="04" i2="2" l="FRE"><s0>Végétation</s0>
<s5>04</s5>
</fC03>
<fC03 i1="04" i2="2" l="ENG"><s0>vegetation</s0>
<s5>04</s5>
</fC03>
<fC03 i1="04" i2="2" l="SPA"><s0>Vegetación</s0>
<s5>04</s5>
</fC03>
<fC03 i1="05" i2="2" l="FRE"><s0>Identification</s0>
<s5>08</s5>
</fC03>
<fC03 i1="05" i2="2" l="ENG"><s0>identification</s0>
<s5>08</s5>
</fC03>
<fC03 i1="06" i2="2" l="FRE"><s0>Arbre</s0>
<s5>09</s5>
</fC03>
<fC03 i1="06" i2="2" l="ENG"><s0>trees</s0>
<s5>09</s5>
</fC03>
<fC03 i1="07" i2="2" l="FRE"><s0>Résolution spatiale</s0>
<s5>11</s5>
</fC03>
<fC03 i1="07" i2="2" l="ENG"><s0>spatial resolution</s0>
<s5>11</s5>
</fC03>
<fC03 i1="08" i2="2" l="FRE"><s0>Photographie aérienne</s0>
<s5>12</s5>
</fC03>
<fC03 i1="08" i2="2" l="ENG"><s0>aerial photography</s0>
<s5>12</s5>
</fC03>
<fC03 i1="08" i2="2" l="SPA"><s0>Fotografía aérea</s0>
<s5>12</s5>
</fC03>
<fC03 i1="09" i2="2" l="FRE"><s0>Forêt</s0>
<s5>13</s5>
</fC03>
<fC03 i1="09" i2="2" l="ENG"><s0>forests</s0>
<s5>13</s5>
</fC03>
<fC03 i1="09" i2="2" l="SPA"><s0>Bosque</s0>
<s5>13</s5>
</fC03>
<fC03 i1="10" i2="2" l="FRE"><s0>Modèle</s0>
<s5>14</s5>
</fC03>
<fC03 i1="10" i2="2" l="ENG"><s0>models</s0>
<s5>14</s5>
</fC03>
<fC03 i1="10" i2="2" l="SPA"><s0>Modelo</s0>
<s5>14</s5>
</fC03>
<fC03 i1="11" i2="2" l="FRE"><s0>Maximum vraisemblance</s0>
<s5>15</s5>
</fC03>
<fC03 i1="11" i2="2" l="ENG"><s0>maximum likelihood</s0>
<s5>15</s5>
</fC03>
<fC03 i1="12" i2="2" l="FRE"><s0>Classification</s0>
<s5>16</s5>
</fC03>
<fC03 i1="12" i2="2" l="ENG"><s0>classification</s0>
<s5>16</s5>
</fC03>
<fC03 i1="12" i2="2" l="SPA"><s0>Clasificación</s0>
<s5>16</s5>
</fC03>
<fC03 i1="13" i2="2" l="FRE"><s0>Pixel</s0>
<s5>17</s5>
</fC03>
<fC03 i1="13" i2="2" l="ENG"><s0>Pixel</s0>
<s5>17</s5>
</fC03>
<fC03 i1="13" i2="2" l="SPA"><s0>Pixel</s0>
<s5>17</s5>
</fC03>
<fC03 i1="14" i2="2" l="FRE"><s0>Imagerie</s0>
<s5>18</s5>
</fC03>
<fC03 i1="14" i2="2" l="ENG"><s0>imagery</s0>
<s5>18</s5>
</fC03>
<fC03 i1="14" i2="2" l="SPA"><s0>Imaginería</s0>
<s5>18</s5>
</fC03>
<fC03 i1="15" i2="2" l="FRE"><s0>Chenal</s0>
<s5>19</s5>
</fC03>
<fC03 i1="15" i2="2" l="ENG"><s0>channels</s0>
<s5>19</s5>
</fC03>
<fC03 i1="15" i2="2" l="SPA"><s0>Canal</s0>
<s5>19</s5>
</fC03>
<fC03 i1="16" i2="2" l="FRE"><s0>Précision</s0>
<s5>20</s5>
</fC03>
<fC03 i1="16" i2="2" l="ENG"><s0>accuracy</s0>
<s5>20</s5>
</fC03>
<fC03 i1="16" i2="2" l="SPA"><s0>Precisión</s0>
<s5>20</s5>
</fC03>
<fC03 i1="17" i2="2" l="FRE"><s0>Quercus</s0>
<s2>NY</s2>
<s5>21</s5>
</fC03>
<fC03 i1="17" i2="2" l="ENG"><s0>Quercus</s0>
<s2>NY</s2>
<s5>21</s5>
</fC03>
<fC03 i1="17" i2="2" l="SPA"><s0>Quercus</s0>
<s2>NY</s2>
<s5>21</s5>
</fC03>
<fC03 i1="18" i2="2" l="FRE"><s0>Fagus</s0>
<s2>NY</s2>
<s5>22</s5>
</fC03>
<fC03 i1="18" i2="2" l="ENG"><s0>Fagus</s0>
<s2>NY</s2>
<s5>22</s5>
</fC03>
<fC03 i1="18" i2="2" l="SPA"><s0>Fagus</s0>
<s2>NY</s2>
<s5>22</s5>
</fC03>
<fC03 i1="19" i2="2" l="FRE"><s0>Pseudotsuga</s0>
<s2>NY</s2>
<s5>23</s5>
</fC03>
<fC03 i1="19" i2="2" l="ENG"><s0>Pseudotsuga</s0>
<s2>NY</s2>
<s5>23</s5>
</fC03>
<fC03 i1="20" i2="2" l="FRE"><s0>Pinus</s0>
<s2>NY</s2>
<s5>24</s5>
</fC03>
<fC03 i1="20" i2="2" l="ENG"><s0>Pinus</s0>
<s2>NY</s2>
<s5>24</s5>
</fC03>
<fC03 i1="20" i2="2" l="SPA"><s0>Pinus</s0>
<s2>NY</s2>
<s5>24</s5>
</fC03>
<fC03 i1="21" i2="2" l="FRE"><s0>Larix</s0>
<s2>NY</s2>
<s5>25</s5>
</fC03>
<fC03 i1="21" i2="2" l="ENG"><s0>Larix</s0>
<s2>NY</s2>
<s5>25</s5>
</fC03>
<fC06><s0>ILS</s0>
<s0>TAS</s0>
</fC06>
<fC07 i1="01" i2="2" l="FRE"><s0>Europe Ouest</s0>
<s2>NG</s2>
</fC07>
<fC07 i1="01" i2="2" l="ENG"><s0>Western Europe</s0>
<s2>NG</s2>
</fC07>
<fC07 i1="01" i2="2" l="SPA"><s0>Europa del Oeste</s0>
<s2>NG</s2>
</fC07>
<fC07 i1="02" i2="2" l="FRE"><s0>Europe</s0>
</fC07>
<fC07 i1="02" i2="2" l="ENG"><s0>Europe</s0>
</fC07>
<fC07 i1="02" i2="2" l="SPA"><s0>Europa</s0>
</fC07>
<fC07 i1="03" i2="2" l="FRE"><s0>Dicotyledoneae</s0>
<s2>NY</s2>
</fC07>
<fC07 i1="03" i2="2" l="ENG"><s0>Dicotyledoneae</s0>
<s2>NY</s2>
</fC07>
<fC07 i1="04" i2="2" l="FRE"><s0>Angiospermae</s0>
<s2>NY</s2>
</fC07>
<fC07 i1="04" i2="2" l="ENG"><s0>angiosperms</s0>
<s2>NY</s2>
</fC07>
<fC07 i1="04" i2="2" l="SPA"><s0>Angiospermae</s0>
<s2>NY</s2>
</fC07>
<fC07 i1="05" i2="2" l="FRE"><s0>Spermatophyta</s0>
<s2>NY</s2>
</fC07>
<fC07 i1="05" i2="2" l="ENG"><s0>Spermatophyta</s0>
<s2>NY</s2>
</fC07>
<fC07 i1="05" i2="2" l="SPA"><s0>Spermatophyta</s0>
<s2>NY</s2>
</fC07>
<fC07 i1="06" i2="2" l="FRE"><s0>Plantae</s0>
</fC07>
<fC07 i1="06" i2="2" l="ENG"><s0>Plantae</s0>
</fC07>
<fC07 i1="07" i2="2" l="FRE"><s0>Coniferales</s0>
<s2>NY</s2>
</fC07>
<fC07 i1="07" i2="2" l="ENG"><s0>Coniferales</s0>
<s2>NY</s2>
</fC07>
<fC07 i1="07" i2="2" l="SPA"><s0>Coniferales</s0>
<s2>NY</s2>
</fC07>
<fC07 i1="08" i2="2" l="FRE"><s0>Gymnospermae</s0>
<s2>NY</s2>
</fC07>
<fC07 i1="08" i2="2" l="ENG"><s0>gymnosperms</s0>
<s2>NY</s2>
</fC07>
<fC07 i1="08" i2="2" l="SPA"><s0>Gymnospermae</s0>
<s2>NY</s2>
</fC07>
<fC07 i1="09" i2="2" l="FRE"><s0>Pinaceae</s0>
<s2>NY</s2>
</fC07>
<fC07 i1="09" i2="2" l="ENG"><s0>Pinaceae</s0>
<s2>NY</s2>
</fC07>
<fN21><s1>271</s1>
</fN21>
<fN44 i1="01"><s1>PSI</s1>
</fN44>
<fN82><s1>PSI</s1>
</fN82>
</pA>
</standard>
<server><NO>PASCAL 04-0481282 INIST</NO>
<ET>Exploitation of very high resolution satellite data for tree species identification</ET>
<AU>CARLEER (A.); WOLFF (E.)</AU>
<AF>Institut de Gestion de l'Environnement et d'Aménagement du Territoire, Université Libre de Bruxelles, CP 130/02, 50 av. F. Roosevelt/1050 Brussels/Belgique (1 aut., 2 aut.)</AF>
<DT>Publication en série; Niveau analytique</DT>
<SO>Photogrammetric engineering and remote sensing; ISSN 0099-1112; Coden PERSDV; Etats-Unis; Da. 2004; Vol. 70; No. 1; Pp. 135-140; Bibl. 27 ref.</SO>
<LA>Anglais</LA>
<EA>With the emergence of very high spatial resolution satellite images, the spatial resolution gap which existed between satellite images and aerial photographs has decreased. A study of the potential of these images for tree species in "monoculture stands" identification was conducted. Two Ikonos images were acquired, one in June 2000 and the other in October 2000, for an 11- by 11-km area covering the Sonian Forest in the southeastern part of the Brussels-Capital region (Belgium). The two images were orthorectified using a digital elevation model and 1256 geodetic control points. The identification of the tree species was carried out utilizing a supervised maximum-likelihood classification on a pixel-by-pixel basis. Classifications were performed on the orthorectified data, NDVI transformed data, and principal components imagery. In order to decrease the intraclass variance, a mean filter was applied to all the spectral bands and neo-channels used in the classification process. Training and validation areas were selected and digitized using detailed geographical databases of the tree species. The selection of the relevant bands and neo-channels was carried out by successive addition of information in order to improve the classification results. Seven different tree species of one to two different age classes were identified with an overall accuracy of 86 percent. The seven identified tree species or species groups are Oaks (Quercus sp.), Beech (Fagus sylvatica L.), Purple Beech (Fagus sylvatica purpurea), Douglas Fir (Pseudotsuga menziesii (Mirb.) Franco), Scots Pine (Pinus sylvestris L.), Corsican Pine (Pinus nigra Arn. subsp. laricio (Poir.) Maire var. corsican), and Larch (Larix decidua Mill.).</EA>
<CC>225B04; 002A14A03; 001E01M04</CC>
<FD>Belgique; Télédétection spatiale; Très haute résolution; Végétation; Identification; Arbre; Résolution spatiale; Photographie aérienne; Forêt; Modèle; Maximum vraisemblance; Classification; Pixel; Imagerie; Chenal; Précision; Quercus; Fagus; Pseudotsuga; Pinus; Larix</FD>
<FG>Europe Ouest; Europe; Dicotyledoneae; Angiospermae; Spermatophyta; Plantae; Coniferales; Gymnospermae; Pinaceae</FG>
<ED>Belgium; Space remote sensing; very high resolution; vegetation; identification; trees; spatial resolution; aerial photography; forests; models; maximum likelihood; classification; Pixel; imagery; channels; accuracy; Quercus; Fagus; Pseudotsuga; Pinus; Larix</ED>
<EG>Western Europe; Europe; Dicotyledoneae; angiosperms; Spermatophyta; Plantae; Coniferales; gymnosperms; Pinaceae</EG>
<SD>Belgica; Teledetección espacial; Vegetación; Fotografía aérea; Bosque; Modelo; Clasificación; Pixel; Imaginería; Canal; Precisión; Quercus; Fagus; Pinus</SD>
<LO>INIST-3289.354000119032760060</LO>
<ID>04-0481282</ID>
</server>
</inist>
</record>

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Exploitation of very high resolution satellite data for tree species identification

Exploitation of very high resolution satellite data for tree species identification

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