Optical and SAR sensor synergies for forest and land cover mapping in a tropical site in West Africa
The classification of tropical fragmented landscapes and moist forested areas is a challenge due to the presence of a continuum of vegetation successional stages, persistent cloud cover and the presence of small patches of different land cover types. To classify one such study area in West Africa we...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Vaglio Laurin, Gaia [verfasserIn] Liesenberg, Veraldo [verfasserIn] Chen, Qi [verfasserIn] Guerriero, Leila [verfasserIn] Del Frate, Fabio [verfasserIn] Bartolini, Antonio [verfasserIn] Coomes, David [verfasserIn] Wilebore, Beccy [verfasserIn] Lindsell, Jeremy [verfasserIn] Valentini, Riccardo [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2012 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: International journal of applied earth observation and geoinformation - Amsterdam [u.a.] : Elsevier Science, 1999, 21, Seite 7-16 |
|---|---|
| Übergeordnetes Werk: |
volume:21 ; pages:7-16 |
| DOI / URN: |
10.1016/j.jag.2012.08.002 |
|---|
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ELV007774877 |
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| 520 | |a The classification of tropical fragmented landscapes and moist forested areas is a challenge due to the presence of a continuum of vegetation successional stages, persistent cloud cover and the presence of small patches of different land cover types. To classify one such study area in West Africa we integrated the optical sensors Landsat Thematic Mapper (TM) and the Advanced Visible and Near Infrared Radiometer type 2 (AVNIR-2) with the Phased Arrayed L-band SAR (PALSAR) sensor, the latter two on-board the Advanced Land Observation Satellite (ALOS), using traditional Maximum Likelihood (MLC) and Neural Networks (NN) classifiers. The impact of texture variables and the use of SAR to cope with optical data unavailability were also investigated. SAR and optical integrated data produced the best classification overall accuracies using both MLC and NN, respectively equal to 91.1% and 92.7% for TM and 95.6% and 97.5% for AVNIR-2. Texture information derived from optical images was critical, improving results between 10.1% and 13.2%. In our study area, PALSAR alone was able to provide valuable information over the entire area: when the three forest classes were aggregated, it achieved 75.7% (with MCL) and 78.1% (with NN) overall classification accuracies. The selected classification and processing methods resulted in fine and accurate vegetation mapping in a previously untested region, exploiting all available sensors synergies and highlighting the advantages of each dataset. | ||
| 650 | 4 | |a Classification | |
| 650 | 4 | |a West Africa | |
| 650 | 4 | |a Forests | |
| 650 | 4 | |a SAR | |
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| 650 | 4 | |a AVNIR-2 | |
| 650 | 4 | |a Texture | |
| 700 | 1 | |a Liesenberg, Veraldo |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Qi |e verfasserin |4 aut | |
| 700 | 1 | |a Guerriero, Leila |e verfasserin |4 aut | |
| 700 | 1 | |a Del Frate, Fabio |e verfasserin |4 aut | |
| 700 | 1 | |a Bartolini, Antonio |e verfasserin |4 aut | |
| 700 | 1 | |a Coomes, David |e verfasserin |4 aut | |
| 700 | 1 | |a Wilebore, Beccy |e verfasserin |4 aut | |
| 700 | 1 | |a Lindsell, Jeremy |e verfasserin |4 aut | |
| 700 | 1 | |a Valentini, Riccardo |e verfasserin |4 aut | |
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10.1016/j.jag.2012.08.002 doi (DE-627)ELV007774877 (ELSEVIER)S0303-2434(12)00165-1 DE-627 ger DE-627 rda eng 550 DE-600 KARTEN DE-1a fid 38.03 bkl 74.48 bkl 74.41 bkl Vaglio Laurin, Gaia verfasserin aut Optical and SAR sensor synergies for forest and land cover mapping in a tropical site in West Africa 2012 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The classification of tropical fragmented landscapes and moist forested areas is a challenge due to the presence of a continuum of vegetation successional stages, persistent cloud cover and the presence of small patches of different land cover types. To classify one such study area in West Africa we integrated the optical sensors Landsat Thematic Mapper (TM) and the Advanced Visible and Near Infrared Radiometer type 2 (AVNIR-2) with the Phased Arrayed L-band SAR (PALSAR) sensor, the latter two on-board the Advanced Land Observation Satellite (ALOS), using traditional Maximum Likelihood (MLC) and Neural Networks (NN) classifiers. The impact of texture variables and the use of SAR to cope with optical data unavailability were also investigated. SAR and optical integrated data produced the best classification overall accuracies using both MLC and NN, respectively equal to 91.1% and 92.7% for TM and 95.6% and 97.5% for AVNIR-2. Texture information derived from optical images was critical, improving results between 10.1% and 13.2%. In our study area, PALSAR alone was able to provide valuable information over the entire area: when the three forest classes were aggregated, it achieved 75.7% (with MCL) and 78.1% (with NN) overall classification accuracies. The selected classification and processing methods resulted in fine and accurate vegetation mapping in a previously untested region, exploiting all available sensors synergies and highlighting the advantages of each dataset. Classification West Africa Forests SAR Landsat AVNIR-2 Texture Liesenberg, Veraldo verfasserin aut Chen, Qi verfasserin aut Guerriero, Leila verfasserin aut Del Frate, Fabio verfasserin aut Bartolini, Antonio verfasserin aut Coomes, David verfasserin aut Wilebore, Beccy verfasserin aut Lindsell, Jeremy verfasserin aut Valentini, Riccardo verfasserin aut Enthalten in International journal of applied earth observation and geoinformation Amsterdam [u.a.] : Elsevier Science, 1999 21, Seite 7-16 Online-Ressource (DE-627)359784119 (DE-600)2097960-5 (DE-576)25927254X 1872-826X nnns volume:21 pages:7-16 GBV_USEFLAG_U SYSFLAG_U GBV_ELV FID-KARTEN SSG-OPC-GGO SSG-OPC-AST SSG-OPC-GEO GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2106 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.03 Methoden und Techniken der Geowissenschaften 74.48 Geoinformationssysteme 74.41 Luftaufnahmen Photogrammetrie AR 21 7-16 |
| spelling |
10.1016/j.jag.2012.08.002 doi (DE-627)ELV007774877 (ELSEVIER)S0303-2434(12)00165-1 DE-627 ger DE-627 rda eng 550 DE-600 KARTEN DE-1a fid 38.03 bkl 74.48 bkl 74.41 bkl Vaglio Laurin, Gaia verfasserin aut Optical and SAR sensor synergies for forest and land cover mapping in a tropical site in West Africa 2012 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The classification of tropical fragmented landscapes and moist forested areas is a challenge due to the presence of a continuum of vegetation successional stages, persistent cloud cover and the presence of small patches of different land cover types. To classify one such study area in West Africa we integrated the optical sensors Landsat Thematic Mapper (TM) and the Advanced Visible and Near Infrared Radiometer type 2 (AVNIR-2) with the Phased Arrayed L-band SAR (PALSAR) sensor, the latter two on-board the Advanced Land Observation Satellite (ALOS), using traditional Maximum Likelihood (MLC) and Neural Networks (NN) classifiers. The impact of texture variables and the use of SAR to cope with optical data unavailability were also investigated. SAR and optical integrated data produced the best classification overall accuracies using both MLC and NN, respectively equal to 91.1% and 92.7% for TM and 95.6% and 97.5% for AVNIR-2. Texture information derived from optical images was critical, improving results between 10.1% and 13.2%. In our study area, PALSAR alone was able to provide valuable information over the entire area: when the three forest classes were aggregated, it achieved 75.7% (with MCL) and 78.1% (with NN) overall classification accuracies. The selected classification and processing methods resulted in fine and accurate vegetation mapping in a previously untested region, exploiting all available sensors synergies and highlighting the advantages of each dataset. Classification West Africa Forests SAR Landsat AVNIR-2 Texture Liesenberg, Veraldo verfasserin aut Chen, Qi verfasserin aut Guerriero, Leila verfasserin aut Del Frate, Fabio verfasserin aut Bartolini, Antonio verfasserin aut Coomes, David verfasserin aut Wilebore, Beccy verfasserin aut Lindsell, Jeremy verfasserin aut Valentini, Riccardo verfasserin aut Enthalten in International journal of applied earth observation and geoinformation Amsterdam [u.a.] : Elsevier Science, 1999 21, Seite 7-16 Online-Ressource (DE-627)359784119 (DE-600)2097960-5 (DE-576)25927254X 1872-826X nnns volume:21 pages:7-16 GBV_USEFLAG_U SYSFLAG_U GBV_ELV FID-KARTEN SSG-OPC-GGO SSG-OPC-AST SSG-OPC-GEO GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2106 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.03 Methoden und Techniken der Geowissenschaften 74.48 Geoinformationssysteme 74.41 Luftaufnahmen Photogrammetrie AR 21 7-16 |
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10.1016/j.jag.2012.08.002 doi (DE-627)ELV007774877 (ELSEVIER)S0303-2434(12)00165-1 DE-627 ger DE-627 rda eng 550 DE-600 KARTEN DE-1a fid 38.03 bkl 74.48 bkl 74.41 bkl Vaglio Laurin, Gaia verfasserin aut Optical and SAR sensor synergies for forest and land cover mapping in a tropical site in West Africa 2012 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The classification of tropical fragmented landscapes and moist forested areas is a challenge due to the presence of a continuum of vegetation successional stages, persistent cloud cover and the presence of small patches of different land cover types. To classify one such study area in West Africa we integrated the optical sensors Landsat Thematic Mapper (TM) and the Advanced Visible and Near Infrared Radiometer type 2 (AVNIR-2) with the Phased Arrayed L-band SAR (PALSAR) sensor, the latter two on-board the Advanced Land Observation Satellite (ALOS), using traditional Maximum Likelihood (MLC) and Neural Networks (NN) classifiers. The impact of texture variables and the use of SAR to cope with optical data unavailability were also investigated. SAR and optical integrated data produced the best classification overall accuracies using both MLC and NN, respectively equal to 91.1% and 92.7% for TM and 95.6% and 97.5% for AVNIR-2. Texture information derived from optical images was critical, improving results between 10.1% and 13.2%. In our study area, PALSAR alone was able to provide valuable information over the entire area: when the three forest classes were aggregated, it achieved 75.7% (with MCL) and 78.1% (with NN) overall classification accuracies. The selected classification and processing methods resulted in fine and accurate vegetation mapping in a previously untested region, exploiting all available sensors synergies and highlighting the advantages of each dataset. Classification West Africa Forests SAR Landsat AVNIR-2 Texture Liesenberg, Veraldo verfasserin aut Chen, Qi verfasserin aut Guerriero, Leila verfasserin aut Del Frate, Fabio verfasserin aut Bartolini, Antonio verfasserin aut Coomes, David verfasserin aut Wilebore, Beccy verfasserin aut Lindsell, Jeremy verfasserin aut Valentini, Riccardo verfasserin aut Enthalten in International journal of applied earth observation and geoinformation Amsterdam [u.a.] : Elsevier Science, 1999 21, Seite 7-16 Online-Ressource (DE-627)359784119 (DE-600)2097960-5 (DE-576)25927254X 1872-826X nnns volume:21 pages:7-16 GBV_USEFLAG_U SYSFLAG_U GBV_ELV FID-KARTEN SSG-OPC-GGO SSG-OPC-AST SSG-OPC-GEO GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2106 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.03 Methoden und Techniken der Geowissenschaften 74.48 Geoinformationssysteme 74.41 Luftaufnahmen Photogrammetrie AR 21 7-16 |
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10.1016/j.jag.2012.08.002 doi (DE-627)ELV007774877 (ELSEVIER)S0303-2434(12)00165-1 DE-627 ger DE-627 rda eng 550 DE-600 KARTEN DE-1a fid 38.03 bkl 74.48 bkl 74.41 bkl Vaglio Laurin, Gaia verfasserin aut Optical and SAR sensor synergies for forest and land cover mapping in a tropical site in West Africa 2012 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The classification of tropical fragmented landscapes and moist forested areas is a challenge due to the presence of a continuum of vegetation successional stages, persistent cloud cover and the presence of small patches of different land cover types. To classify one such study area in West Africa we integrated the optical sensors Landsat Thematic Mapper (TM) and the Advanced Visible and Near Infrared Radiometer type 2 (AVNIR-2) with the Phased Arrayed L-band SAR (PALSAR) sensor, the latter two on-board the Advanced Land Observation Satellite (ALOS), using traditional Maximum Likelihood (MLC) and Neural Networks (NN) classifiers. The impact of texture variables and the use of SAR to cope with optical data unavailability were also investigated. SAR and optical integrated data produced the best classification overall accuracies using both MLC and NN, respectively equal to 91.1% and 92.7% for TM and 95.6% and 97.5% for AVNIR-2. Texture information derived from optical images was critical, improving results between 10.1% and 13.2%. In our study area, PALSAR alone was able to provide valuable information over the entire area: when the three forest classes were aggregated, it achieved 75.7% (with MCL) and 78.1% (with NN) overall classification accuracies. The selected classification and processing methods resulted in fine and accurate vegetation mapping in a previously untested region, exploiting all available sensors synergies and highlighting the advantages of each dataset. Classification West Africa Forests SAR Landsat AVNIR-2 Texture Liesenberg, Veraldo verfasserin aut Chen, Qi verfasserin aut Guerriero, Leila verfasserin aut Del Frate, Fabio verfasserin aut Bartolini, Antonio verfasserin aut Coomes, David verfasserin aut Wilebore, Beccy verfasserin aut Lindsell, Jeremy verfasserin aut Valentini, Riccardo verfasserin aut Enthalten in International journal of applied earth observation and geoinformation Amsterdam [u.a.] : Elsevier Science, 1999 21, Seite 7-16 Online-Ressource (DE-627)359784119 (DE-600)2097960-5 (DE-576)25927254X 1872-826X nnns volume:21 pages:7-16 GBV_USEFLAG_U SYSFLAG_U GBV_ELV FID-KARTEN SSG-OPC-GGO SSG-OPC-AST SSG-OPC-GEO GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2106 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.03 Methoden und Techniken der Geowissenschaften 74.48 Geoinformationssysteme 74.41 Luftaufnahmen Photogrammetrie AR 21 7-16 |
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10.1016/j.jag.2012.08.002 doi (DE-627)ELV007774877 (ELSEVIER)S0303-2434(12)00165-1 DE-627 ger DE-627 rda eng 550 DE-600 KARTEN DE-1a fid 38.03 bkl 74.48 bkl 74.41 bkl Vaglio Laurin, Gaia verfasserin aut Optical and SAR sensor synergies for forest and land cover mapping in a tropical site in West Africa 2012 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The classification of tropical fragmented landscapes and moist forested areas is a challenge due to the presence of a continuum of vegetation successional stages, persistent cloud cover and the presence of small patches of different land cover types. To classify one such study area in West Africa we integrated the optical sensors Landsat Thematic Mapper (TM) and the Advanced Visible and Near Infrared Radiometer type 2 (AVNIR-2) with the Phased Arrayed L-band SAR (PALSAR) sensor, the latter two on-board the Advanced Land Observation Satellite (ALOS), using traditional Maximum Likelihood (MLC) and Neural Networks (NN) classifiers. The impact of texture variables and the use of SAR to cope with optical data unavailability were also investigated. SAR and optical integrated data produced the best classification overall accuracies using both MLC and NN, respectively equal to 91.1% and 92.7% for TM and 95.6% and 97.5% for AVNIR-2. Texture information derived from optical images was critical, improving results between 10.1% and 13.2%. In our study area, PALSAR alone was able to provide valuable information over the entire area: when the three forest classes were aggregated, it achieved 75.7% (with MCL) and 78.1% (with NN) overall classification accuracies. The selected classification and processing methods resulted in fine and accurate vegetation mapping in a previously untested region, exploiting all available sensors synergies and highlighting the advantages of each dataset. Classification West Africa Forests SAR Landsat AVNIR-2 Texture Liesenberg, Veraldo verfasserin aut Chen, Qi verfasserin aut Guerriero, Leila verfasserin aut Del Frate, Fabio verfasserin aut Bartolini, Antonio verfasserin aut Coomes, David verfasserin aut Wilebore, Beccy verfasserin aut Lindsell, Jeremy verfasserin aut Valentini, Riccardo verfasserin aut Enthalten in International journal of applied earth observation and geoinformation Amsterdam [u.a.] : Elsevier Science, 1999 21, Seite 7-16 Online-Ressource (DE-627)359784119 (DE-600)2097960-5 (DE-576)25927254X 1872-826X nnns volume:21 pages:7-16 GBV_USEFLAG_U SYSFLAG_U GBV_ELV FID-KARTEN SSG-OPC-GGO SSG-OPC-AST SSG-OPC-GEO GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2106 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.03 Methoden und Techniken der Geowissenschaften 74.48 Geoinformationssysteme 74.41 Luftaufnahmen Photogrammetrie AR 21 7-16 |
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Classification West Africa Forests SAR Landsat AVNIR-2 Texture |
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Vaglio Laurin, Gaia @@aut@@ Liesenberg, Veraldo @@aut@@ Chen, Qi @@aut@@ Guerriero, Leila @@aut@@ Del Frate, Fabio @@aut@@ Bartolini, Antonio @@aut@@ Coomes, David @@aut@@ Wilebore, Beccy @@aut@@ Lindsell, Jeremy @@aut@@ Valentini, Riccardo @@aut@@ |
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2012-01-01T00:00:00Z |
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Vaglio Laurin, Gaia |
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Vaglio Laurin, Gaia ddc 550 fid KARTEN bkl 38.03 bkl 74.48 bkl 74.41 misc Classification misc West Africa misc Forests misc SAR misc Landsat misc AVNIR-2 misc Texture Optical and SAR sensor synergies for forest and land cover mapping in a tropical site in West Africa |
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550 DE-600 KARTEN DE-1a fid 38.03 bkl 74.48 bkl 74.41 bkl Optical and SAR sensor synergies for forest and land cover mapping in a tropical site in West Africa Classification West Africa Forests SAR Landsat AVNIR-2 Texture |
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Optical and SAR sensor synergies for forest and land cover mapping in a tropical site in West Africa |
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Optical and SAR sensor synergies for forest and land cover mapping in a tropical site in West Africa |
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International journal of applied earth observation and geoinformation |
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Vaglio Laurin, Gaia Liesenberg, Veraldo Chen, Qi Guerriero, Leila Del Frate, Fabio Bartolini, Antonio Coomes, David Wilebore, Beccy Lindsell, Jeremy Valentini, Riccardo |
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optical and sar sensor synergies for forest and land cover mapping in a tropical site in west africa |
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Optical and SAR sensor synergies for forest and land cover mapping in a tropical site in West Africa |
| abstract |
The classification of tropical fragmented landscapes and moist forested areas is a challenge due to the presence of a continuum of vegetation successional stages, persistent cloud cover and the presence of small patches of different land cover types. To classify one such study area in West Africa we integrated the optical sensors Landsat Thematic Mapper (TM) and the Advanced Visible and Near Infrared Radiometer type 2 (AVNIR-2) with the Phased Arrayed L-band SAR (PALSAR) sensor, the latter two on-board the Advanced Land Observation Satellite (ALOS), using traditional Maximum Likelihood (MLC) and Neural Networks (NN) classifiers. The impact of texture variables and the use of SAR to cope with optical data unavailability were also investigated. SAR and optical integrated data produced the best classification overall accuracies using both MLC and NN, respectively equal to 91.1% and 92.7% for TM and 95.6% and 97.5% for AVNIR-2. Texture information derived from optical images was critical, improving results between 10.1% and 13.2%. In our study area, PALSAR alone was able to provide valuable information over the entire area: when the three forest classes were aggregated, it achieved 75.7% (with MCL) and 78.1% (with NN) overall classification accuracies. The selected classification and processing methods resulted in fine and accurate vegetation mapping in a previously untested region, exploiting all available sensors synergies and highlighting the advantages of each dataset. |
| abstractGer |
The classification of tropical fragmented landscapes and moist forested areas is a challenge due to the presence of a continuum of vegetation successional stages, persistent cloud cover and the presence of small patches of different land cover types. To classify one such study area in West Africa we integrated the optical sensors Landsat Thematic Mapper (TM) and the Advanced Visible and Near Infrared Radiometer type 2 (AVNIR-2) with the Phased Arrayed L-band SAR (PALSAR) sensor, the latter two on-board the Advanced Land Observation Satellite (ALOS), using traditional Maximum Likelihood (MLC) and Neural Networks (NN) classifiers. The impact of texture variables and the use of SAR to cope with optical data unavailability were also investigated. SAR and optical integrated data produced the best classification overall accuracies using both MLC and NN, respectively equal to 91.1% and 92.7% for TM and 95.6% and 97.5% for AVNIR-2. Texture information derived from optical images was critical, improving results between 10.1% and 13.2%. In our study area, PALSAR alone was able to provide valuable information over the entire area: when the three forest classes were aggregated, it achieved 75.7% (with MCL) and 78.1% (with NN) overall classification accuracies. The selected classification and processing methods resulted in fine and accurate vegetation mapping in a previously untested region, exploiting all available sensors synergies and highlighting the advantages of each dataset. |
| abstract_unstemmed |
The classification of tropical fragmented landscapes and moist forested areas is a challenge due to the presence of a continuum of vegetation successional stages, persistent cloud cover and the presence of small patches of different land cover types. To classify one such study area in West Africa we integrated the optical sensors Landsat Thematic Mapper (TM) and the Advanced Visible and Near Infrared Radiometer type 2 (AVNIR-2) with the Phased Arrayed L-band SAR (PALSAR) sensor, the latter two on-board the Advanced Land Observation Satellite (ALOS), using traditional Maximum Likelihood (MLC) and Neural Networks (NN) classifiers. The impact of texture variables and the use of SAR to cope with optical data unavailability were also investigated. SAR and optical integrated data produced the best classification overall accuracies using both MLC and NN, respectively equal to 91.1% and 92.7% for TM and 95.6% and 97.5% for AVNIR-2. Texture information derived from optical images was critical, improving results between 10.1% and 13.2%. In our study area, PALSAR alone was able to provide valuable information over the entire area: when the three forest classes were aggregated, it achieved 75.7% (with MCL) and 78.1% (with NN) overall classification accuracies. The selected classification and processing methods resulted in fine and accurate vegetation mapping in a previously untested region, exploiting all available sensors synergies and highlighting the advantages of each dataset. |
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Optical and SAR sensor synergies for forest and land cover mapping in a tropical site in West Africa |
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Liesenberg, Veraldo Chen, Qi Guerriero, Leila Del Frate, Fabio Bartolini, Antonio Coomes, David Wilebore, Beccy Lindsell, Jeremy Valentini, Riccardo |
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To classify one such study area in West Africa we integrated the optical sensors Landsat Thematic Mapper (TM) and the Advanced Visible and Near Infrared Radiometer type 2 (AVNIR-2) with the Phased Arrayed L-band SAR (PALSAR) sensor, the latter two on-board the Advanced Land Observation Satellite (ALOS), using traditional Maximum Likelihood (MLC) and Neural Networks (NN) classifiers. The impact of texture variables and the use of SAR to cope with optical data unavailability were also investigated. SAR and optical integrated data produced the best classification overall accuracies using both MLC and NN, respectively equal to 91.1% and 92.7% for TM and 95.6% and 97.5% for AVNIR-2. Texture information derived from optical images was critical, improving results between 10.1% and 13.2%. In our study area, PALSAR alone was able to provide valuable information over the entire area: when the three forest classes were aggregated, it achieved 75.7% (with MCL) and 78.1% (with NN) overall classification accuracies. The selected classification and processing methods resulted in fine and accurate vegetation mapping in a previously untested region, exploiting all available sensors synergies and highlighting the advantages of each dataset.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Classification</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">West Africa</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Forests</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">SAR</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Landsat</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">AVNIR-2</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Texture</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Liesenberg, Veraldo</subfield><subfield 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