Large-scale vegetation maps derived from the combined L-band GRFM and C-band CAMP wide area radar mosaics of Central Africa

P. Mayaux, G. de Grandi, Yrjö Rauste, M. Simard, S. Saatchi

    Research output: Contribution to journalArticleScientificpeer-review

    32 Citations (Scopus)

    Abstract

    A new dataset has been compiled by combining the wide area Synthetic Aperture Radar (SAR) mosaics over Central Africa generated in the context of the NASDA Global Rain Forest Mapping (GRFM) and the ESA/EC Central Africa Mosaic Projects (CAMP). The CAMP mosaic consists of more than 700 SAR scenes acquired over the Central Africa region (6° S-8° N and 5° E-26° E) by the ESA ERS satellites; the acquisitions were performed in 1994 (July, August) and in 1996 (January, February) in two different seasonal conditions. The GRFM Africa mosaic consists of some 3900 JERS-1 images acquired over the region (10° S-10° N, 14° W and 42° E) at two dates (January-March 1996 and October-November 1996). In this paper the methods used for combining the two wide area radar mosaics are at first presented. The GRFM Africa mosaic was processed using a block adjustment algorithm with the inclusion of external observations derived from high precision maps along the coastline, which assures an absolute geolocation residual mean squared error of 240 m with respect to ground control points. On the other hand, the CAMP mosaic was compiled taking into account only the internal relative geometric accuracy. Therefore the GRFM dataset was taken as the reference system and the C-band ERS layer composed by rectifying each ERS frame, after down-sampling at 100 m pixel spacing, to the reference mosaic. The rectification procedure uses a set of tie-points measured automatically between each ERS frame and the homologous subset in the JERS mosaic. Due to the different characteristics of the two sensors (microwave centre frequency, viewing geometry, polarization) and the different acquisition dates, each mosaic presents a different window over the same ecosystem. This fact suggests that a new dimension in terms of thematic information content can be added by the fusion of the two datasets. In support of this statement, the complementary characteristics of the two sensors are first discussed with respect to observations related to the vegetation cover in the Congo River floodplain. The potential of the combined dataset for vegetation mapping at the regional scale is further demonstrated by a classification pursuit of the main vegetation types in the central part of the Congo Basin. The main land-cover classes are: lowland rain forest, permanently flooded forest, periodically flooded forest, swamp grassland, and savannah. The classification map is validated using a compilation of national vegetation maps derived from other high resolution remote sensing data or by ground surveys. This first thematic result already confirms that the combined contributions from the L-band and the C-band sensors improve the information extraction capability. Indeed, the radar-derived vegetation map contains better spatial detail than any existing map, especially with respect to the extent of flooded formations.
    Original languageEnglish
    Pages (from-to)1261-1282
    JournalInternational Journal of Remote Sensing
    Volume23
    Issue number7
    DOIs
    Publication statusPublished - 2002
    MoE publication typeA1 Journal article-refereed

    Fingerprint

    radar
    vegetation
    sensor
    rain forest
    mosaic
    Central Africa
    project
    synthetic aperture radar
    vegetation mapping
    swamp forest
    ground control
    vegetation type
    vegetation cover
    floodplain
    pixel
    land cover
    spacing
    polarization
    grassland
    remote sensing

    Keywords

    • remote sensing
    • GIS
    • SAR
    • SAR mosaic
    • forest biomass
    • forests

    Cite this

    @article{bda89fbbcccb4f50b1bf4c2b54562c53,
    title = "Large-scale vegetation maps derived from the combined L-band GRFM and C-band CAMP wide area radar mosaics of Central Africa",
    abstract = "A new dataset has been compiled by combining the wide area Synthetic Aperture Radar (SAR) mosaics over Central Africa generated in the context of the NASDA Global Rain Forest Mapping (GRFM) and the ESA/EC Central Africa Mosaic Projects (CAMP). The CAMP mosaic consists of more than 700 SAR scenes acquired over the Central Africa region (6° S-8° N and 5° E-26° E) by the ESA ERS satellites; the acquisitions were performed in 1994 (July, August) and in 1996 (January, February) in two different seasonal conditions. The GRFM Africa mosaic consists of some 3900 JERS-1 images acquired over the region (10° S-10° N, 14° W and 42° E) at two dates (January-March 1996 and October-November 1996). In this paper the methods used for combining the two wide area radar mosaics are at first presented. The GRFM Africa mosaic was processed using a block adjustment algorithm with the inclusion of external observations derived from high precision maps along the coastline, which assures an absolute geolocation residual mean squared error of 240 m with respect to ground control points. On the other hand, the CAMP mosaic was compiled taking into account only the internal relative geometric accuracy. Therefore the GRFM dataset was taken as the reference system and the C-band ERS layer composed by rectifying each ERS frame, after down-sampling at 100 m pixel spacing, to the reference mosaic. The rectification procedure uses a set of tie-points measured automatically between each ERS frame and the homologous subset in the JERS mosaic. Due to the different characteristics of the two sensors (microwave centre frequency, viewing geometry, polarization) and the different acquisition dates, each mosaic presents a different window over the same ecosystem. This fact suggests that a new dimension in terms of thematic information content can be added by the fusion of the two datasets. In support of this statement, the complementary characteristics of the two sensors are first discussed with respect to observations related to the vegetation cover in the Congo River floodplain. The potential of the combined dataset for vegetation mapping at the regional scale is further demonstrated by a classification pursuit of the main vegetation types in the central part of the Congo Basin. The main land-cover classes are: lowland rain forest, permanently flooded forest, periodically flooded forest, swamp grassland, and savannah. The classification map is validated using a compilation of national vegetation maps derived from other high resolution remote sensing data or by ground surveys. This first thematic result already confirms that the combined contributions from the L-band and the C-band sensors improve the information extraction capability. Indeed, the radar-derived vegetation map contains better spatial detail than any existing map, especially with respect to the extent of flooded formations.",
    keywords = "remote sensing, GIS, SAR, SAR mosaic, forest biomass, forests",
    author = "P. Mayaux and Grandi, {G. de} and Yrj{\"o} Rauste and M. Simard and S. Saatchi",
    year = "2002",
    doi = "10.1080/01431160110092894",
    language = "English",
    volume = "23",
    pages = "1261--1282",
    journal = "International Journal of Remote Sensing",
    issn = "0143-1161",
    publisher = "Taylor & Francis",
    number = "7",

    }

    Large-scale vegetation maps derived from the combined L-band GRFM and C-band CAMP wide area radar mosaics of Central Africa. / Mayaux, P.; Grandi, G. de; Rauste, Yrjö; Simard, M.; Saatchi, S.

    In: International Journal of Remote Sensing, Vol. 23, No. 7, 2002, p. 1261-1282.

    Research output: Contribution to journalArticleScientificpeer-review

    TY - JOUR

    T1 - Large-scale vegetation maps derived from the combined L-band GRFM and C-band CAMP wide area radar mosaics of Central Africa

    AU - Mayaux, P.

    AU - Grandi, G. de

    AU - Rauste, Yrjö

    AU - Simard, M.

    AU - Saatchi, S.

    PY - 2002

    Y1 - 2002

    N2 - A new dataset has been compiled by combining the wide area Synthetic Aperture Radar (SAR) mosaics over Central Africa generated in the context of the NASDA Global Rain Forest Mapping (GRFM) and the ESA/EC Central Africa Mosaic Projects (CAMP). The CAMP mosaic consists of more than 700 SAR scenes acquired over the Central Africa region (6° S-8° N and 5° E-26° E) by the ESA ERS satellites; the acquisitions were performed in 1994 (July, August) and in 1996 (January, February) in two different seasonal conditions. The GRFM Africa mosaic consists of some 3900 JERS-1 images acquired over the region (10° S-10° N, 14° W and 42° E) at two dates (January-March 1996 and October-November 1996). In this paper the methods used for combining the two wide area radar mosaics are at first presented. The GRFM Africa mosaic was processed using a block adjustment algorithm with the inclusion of external observations derived from high precision maps along the coastline, which assures an absolute geolocation residual mean squared error of 240 m with respect to ground control points. On the other hand, the CAMP mosaic was compiled taking into account only the internal relative geometric accuracy. Therefore the GRFM dataset was taken as the reference system and the C-band ERS layer composed by rectifying each ERS frame, after down-sampling at 100 m pixel spacing, to the reference mosaic. The rectification procedure uses a set of tie-points measured automatically between each ERS frame and the homologous subset in the JERS mosaic. Due to the different characteristics of the two sensors (microwave centre frequency, viewing geometry, polarization) and the different acquisition dates, each mosaic presents a different window over the same ecosystem. This fact suggests that a new dimension in terms of thematic information content can be added by the fusion of the two datasets. In support of this statement, the complementary characteristics of the two sensors are first discussed with respect to observations related to the vegetation cover in the Congo River floodplain. The potential of the combined dataset for vegetation mapping at the regional scale is further demonstrated by a classification pursuit of the main vegetation types in the central part of the Congo Basin. The main land-cover classes are: lowland rain forest, permanently flooded forest, periodically flooded forest, swamp grassland, and savannah. The classification map is validated using a compilation of national vegetation maps derived from other high resolution remote sensing data or by ground surveys. This first thematic result already confirms that the combined contributions from the L-band and the C-band sensors improve the information extraction capability. Indeed, the radar-derived vegetation map contains better spatial detail than any existing map, especially with respect to the extent of flooded formations.

    AB - A new dataset has been compiled by combining the wide area Synthetic Aperture Radar (SAR) mosaics over Central Africa generated in the context of the NASDA Global Rain Forest Mapping (GRFM) and the ESA/EC Central Africa Mosaic Projects (CAMP). The CAMP mosaic consists of more than 700 SAR scenes acquired over the Central Africa region (6° S-8° N and 5° E-26° E) by the ESA ERS satellites; the acquisitions were performed in 1994 (July, August) and in 1996 (January, February) in two different seasonal conditions. The GRFM Africa mosaic consists of some 3900 JERS-1 images acquired over the region (10° S-10° N, 14° W and 42° E) at two dates (January-March 1996 and October-November 1996). In this paper the methods used for combining the two wide area radar mosaics are at first presented. The GRFM Africa mosaic was processed using a block adjustment algorithm with the inclusion of external observations derived from high precision maps along the coastline, which assures an absolute geolocation residual mean squared error of 240 m with respect to ground control points. On the other hand, the CAMP mosaic was compiled taking into account only the internal relative geometric accuracy. Therefore the GRFM dataset was taken as the reference system and the C-band ERS layer composed by rectifying each ERS frame, after down-sampling at 100 m pixel spacing, to the reference mosaic. The rectification procedure uses a set of tie-points measured automatically between each ERS frame and the homologous subset in the JERS mosaic. Due to the different characteristics of the two sensors (microwave centre frequency, viewing geometry, polarization) and the different acquisition dates, each mosaic presents a different window over the same ecosystem. This fact suggests that a new dimension in terms of thematic information content can be added by the fusion of the two datasets. In support of this statement, the complementary characteristics of the two sensors are first discussed with respect to observations related to the vegetation cover in the Congo River floodplain. The potential of the combined dataset for vegetation mapping at the regional scale is further demonstrated by a classification pursuit of the main vegetation types in the central part of the Congo Basin. The main land-cover classes are: lowland rain forest, permanently flooded forest, periodically flooded forest, swamp grassland, and savannah. The classification map is validated using a compilation of national vegetation maps derived from other high resolution remote sensing data or by ground surveys. This first thematic result already confirms that the combined contributions from the L-band and the C-band sensors improve the information extraction capability. Indeed, the radar-derived vegetation map contains better spatial detail than any existing map, especially with respect to the extent of flooded formations.

    KW - remote sensing

    KW - GIS

    KW - SAR

    KW - SAR mosaic

    KW - forest biomass

    KW - forests

    U2 - 10.1080/01431160110092894

    DO - 10.1080/01431160110092894

    M3 - Article

    VL - 23

    SP - 1261

    EP - 1282

    JO - International Journal of Remote Sensing

    JF - International Journal of Remote Sensing

    SN - 0143-1161

    IS - 7

    ER -