The Experts below are selected from a list of 279 Experts worldwide ranked by ideXlab platform

James F.w. Purdom - One of the best experts on this subject based on the ideXlab platform.

  • ramsdis contributions to NOAA Satellite data utilization
    Bulletin of the American Meteorological Society, 2000
    Co-Authors: Debra A Molenar, Kevin J. Schrab, James F.w. Purdom
    Abstract:

    Abstract The Regional and Mesoscale Meteorology (RAMM) Advanced Meteorological Satellite Demonstration and Interpretation System (RAMSDIS) was developed as part of an effort to get high quality digital Satellite data to field forecasters prior to the deployment of the Satellite component of the National Weather Service (NWS) Modernization Program. RAMSDIS was created by the National Oceanic and Atmospheric Administration (NOAA) National Environmental Satellite, Data and Information Service RAMM Team. RAMSDIS has made significant contributions to NOAA's Satellite training and technology transfer program. The project has had a major impact on the utilization of digital Satellite data, both nationally and internationally, providing the sole source for high–resolution digital Satellite data at some NWS Forecast Offices (FOs) since 1993. In addition to its use in the FO, RAMSDIS has also provided data distribution and research capabilities on a common platform to several NOAA laboratories, allowing for more ef...

  • RAMSDIS contributions to NOAA Satellite data utilization
    Bulletin of the American Meteorological Society, 2000
    Co-Authors: Debra A Molenar, Kevin J. Schrab, James F.w. Purdom
    Abstract:

    The Regional and Mesoscale Meteorology (RAMM) Advanced Meteorological Satellite Demonstration and Interpretation System (RAMSDIS) was developed as part of an effort to get high quality digital Satellite data to field forecasters prior to the deployment of the Satellite component of the National Weather Service (NWS) Modernization Program. RAMSDIS was created by the National Oceanic and Atmospheric Administration (NOAA) National Environmental Satellite, Data and Information Service RAMM Team. RAMSDIS has made significant contributions to NOAA's Satellite training and technology transfer program. The project has had a major impact on the utilization of digital Satellite data, both nationally and internationally, providing the sole source for high-resolution digital Satellite data at some NWS Forecast Offices (FOs) since 1993. In addition to its use in the FO, RAMSDIS has also provided data distribution and research capabilities on a common platform to several NOAA laboratories, allowing for more efficient collaboration on digital Satellite data applications and analysis tools, and has been used by the World Meteorological Organization in an effort to provide digital Satellite data to developing countries in Central America find the Caribbean. The RAMSDIS project was innovative for many reasons. This article describes the unique approaches that made the project a success and details RAMSDIS utilization within the NWS and NOAA. The next phase of RAMSDIS implementation in the international meteorological community is also described.

Seiichi Ohta - One of the best experts on this subject based on the ideXlab platform.

  • Leaf-Shedding Phenology in Lowland Tropical Seasonal Forests of Cambodia as Estimated From NOAA Satellite Images
    IEEE Transactions on Geoscience and Remote Sensing, 2008
    Co-Authors: Makoto Araki, Mamoru Kanzaki, Bora Tith, Craig Trotter, Seiichi Ohta
    Abstract:

    The seasonality of forest leaf area index and photosynthetic activity is an important element of ecosystem function. The ecohydrological models representing water and carbon fluxes in tropical forests need regional-scale spatial information on forest leaf phenology. We aimed to evaluate the utility of the National Oceanic and Atmospheric Administration (NOAA)/Advanced Very high resolution radiometer (AVHRR) imagery for monitoring evergreen and deciduous forest phenology in lowland Cambodia. The temporal sequences of the normalized difference vegetation index (NDVI) data were constructed from ten-day composite imagery acquired from May 2001 to April 2002. A local maximum fitting (LMF) technique that combined time-series filtering and local curve-fitting techniques was used to extract cloud-free data. With this approach, different phenology between evergreen and deciduous forests was clearly observed during the dry season: Deciduous forests exhibited marked and spatially uniform loss and (later) gain in new leaf area, whereas evergreen forests showed lesser and spatially/temporally heterogeneous changes in leaf phenology. A significant finding was that about 30% of evergreen forests shed detectable quantities of leaves at two times during a dry season (i.e., in the early, and late, dry season). In summary, the capability of composite NOAA/AVHRR NDVI data sets processed with the LMF technique was established as a viable approach for monitoring heterogeneous forest phenology at regional scales during the tropical dry season, although it is apparent that further improvements in cloud clearing are required if the approach is to be useful also in the wet season.

Makoto Araki - One of the best experts on this subject based on the ideXlab platform.

  • Leaf-Shedding Phenology in Lowland Tropical Seasonal Forests of Cambodia as Estimated From NOAA Satellite Images
    IEEE Transactions on Geoscience and Remote Sensing, 2008
    Co-Authors: Makoto Araki, Mamoru Kanzaki, Bora Tith, Craig Trotter, Seiichi Ohta
    Abstract:

    The seasonality of forest leaf area index and photosynthetic activity is an important element of ecosystem function. The ecohydrological models representing water and carbon fluxes in tropical forests need regional-scale spatial information on forest leaf phenology. We aimed to evaluate the utility of the National Oceanic and Atmospheric Administration (NOAA)/Advanced Very high resolution radiometer (AVHRR) imagery for monitoring evergreen and deciduous forest phenology in lowland Cambodia. The temporal sequences of the normalized difference vegetation index (NDVI) data were constructed from ten-day composite imagery acquired from May 2001 to April 2002. A local maximum fitting (LMF) technique that combined time-series filtering and local curve-fitting techniques was used to extract cloud-free data. With this approach, different phenology between evergreen and deciduous forests was clearly observed during the dry season: Deciduous forests exhibited marked and spatially uniform loss and (later) gain in new leaf area, whereas evergreen forests showed lesser and spatially/temporally heterogeneous changes in leaf phenology. A significant finding was that about 30% of evergreen forests shed detectable quantities of leaves at two times during a dry season (i.e., in the early, and late, dry season). In summary, the capability of composite NOAA/AVHRR NDVI data sets processed with the LMF technique was established as a viable approach for monitoring heterogeneous forest phenology at regional scales during the tropical dry season, although it is apparent that further improvements in cloud clearing are required if the approach is to be useful also in the wet season.

  • Leaf-shedding phenology in tropical seasonal forests of Cambodia estimated from NOAA Satellite images
    2007 IEEE International Geoscience and Remote Sensing Symposium, 2007
    Co-Authors: Makoto Araki, Akihiro Tani, Mamoru Kanzaki, Khorn Saret, Det Seila, Pith Phearak, Lim Sopheap, Pol Sopheavuth
    Abstract:

    Forest seasonality is an important element to ecosystem functions. Eco-hydro models describing the Indochina bioregion under the seasonal tropical climate need regional phenological information about leaf dynamics. We examined the utility of remote sensing technology to leaf phenological research in Cambodian lowland forests. For this purpose, we aimed to detect any difference in leaf-shedding phenology between evergreen forests and deciduous forests. We analyzed the NOAA/AVHRR normalized differential vegetation index (NDVI) obtained from May 2001 to April 2002. The local maximum fitting (LMF) processing combines the time series filtering and the functional fitting was used for creating cloud/noise-free 10- day composites 1.1 km pixel data. Firstly, we estimated seasonal changes in the NDVI dominated by lowland evergreen forests and by lowland deciduous forests. Secondary, we identified local minimum points and the antecedent local maximum points of the fitting trigonometric function curve to each pixel as indicators of leaf-shedding events. The heterogeneous seasonal changes in the NDVI were well detected. Deciduous forests demonstrated drastic and uniform leaf phenology; while evergreen forests did spatially and temporally heterogeneous one during the dry season. It indicated the difficulty in getting information of regional forest seasonality; thus it displayed the utility of remote sensing for phenological investigation. Leaf-flushing was detected during the dry season both in evergreen forests and in deciduous forests. It suggested that leaf phenology was not completely governed by drought stress. Leaf-shedding and leaf flushing in twice within a single evergreen pixel was the most striking findings in this study. In summary, remote sensing technology was of great service to getting phenological information that was considerably different between evergreen forests and deciduous forests distributed in the seasonal tropical zones.

  • leaf shedding phenology in lowland tropical seasonal forests of cambodia as estimated from NOAA Satellite images
    International Geoscience and Remote Sensing Symposium, 2007
    Co-Authors: Makoto Araki, Akihiro Tani, Mamoru Kanzaki, Khorn Saret, Det Seila, Pith Phearak, Lim Sopheap, Pol Sopheavuth
    Abstract:

    Forest seasonality is an important element to ecosystem functions. Eco-hydro models describing the Indochina bioregion under the seasonal tropical climate need regional phenological information about leaf dynamics. We examined the utility of remote sensing technology to leaf phenological research in Cambodian lowland forests. For this purpose, we aimed to detect any difference in leaf-shedding phenology between evergreen forests and deciduous forests. We analyzed the NOAA/AVHRR normalized differential vegetation index (NDVI) obtained from May 2001 to April 2002. The local maximum fitting (LMF) processing combines the time series filtering and the functional fitting was used for creating cloud/noise-free 10- day composites 1.1 km pixel data. Firstly, we estimated seasonal changes in the NDVI dominated by lowland evergreen forests and by lowland deciduous forests. Secondary, we identified local minimum points and the antecedent local maximum points of the fitting trigonometric function curve to each pixel as indicators of leaf-shedding events. The heterogeneous seasonal changes in the NDVI were well detected. Deciduous forests demonstrated drastic and uniform leaf phenology; while evergreen forests did spatially and temporally heterogeneous one during the dry season. It indicated the difficulty in getting information of regional forest seasonality; thus it displayed the utility of remote sensing for phenological investigation. Leaf-flushing was detected during the dry season both in evergreen forests and in deciduous forests. It suggested that leaf phenology was not completely governed by drought stress. Leaf-shedding and leaf flushing in twice within a single evergreen pixel was the most striking findings in this study. In summary, remote sensing technology was of great service to getting phenological information that was considerably different between evergreen forests and deciduous forests distributed in the seasonal tropical zones.

Debra A Molenar - One of the best experts on this subject based on the ideXlab platform.

  • ramsdis contributions to NOAA Satellite data utilization
    Bulletin of the American Meteorological Society, 2000
    Co-Authors: Debra A Molenar, Kevin J. Schrab, James F.w. Purdom
    Abstract:

    Abstract The Regional and Mesoscale Meteorology (RAMM) Advanced Meteorological Satellite Demonstration and Interpretation System (RAMSDIS) was developed as part of an effort to get high quality digital Satellite data to field forecasters prior to the deployment of the Satellite component of the National Weather Service (NWS) Modernization Program. RAMSDIS was created by the National Oceanic and Atmospheric Administration (NOAA) National Environmental Satellite, Data and Information Service RAMM Team. RAMSDIS has made significant contributions to NOAA's Satellite training and technology transfer program. The project has had a major impact on the utilization of digital Satellite data, both nationally and internationally, providing the sole source for high–resolution digital Satellite data at some NWS Forecast Offices (FOs) since 1993. In addition to its use in the FO, RAMSDIS has also provided data distribution and research capabilities on a common platform to several NOAA laboratories, allowing for more ef...

  • RAMSDIS contributions to NOAA Satellite data utilization
    Bulletin of the American Meteorological Society, 2000
    Co-Authors: Debra A Molenar, Kevin J. Schrab, James F.w. Purdom
    Abstract:

    The Regional and Mesoscale Meteorology (RAMM) Advanced Meteorological Satellite Demonstration and Interpretation System (RAMSDIS) was developed as part of an effort to get high quality digital Satellite data to field forecasters prior to the deployment of the Satellite component of the National Weather Service (NWS) Modernization Program. RAMSDIS was created by the National Oceanic and Atmospheric Administration (NOAA) National Environmental Satellite, Data and Information Service RAMM Team. RAMSDIS has made significant contributions to NOAA's Satellite training and technology transfer program. The project has had a major impact on the utilization of digital Satellite data, both nationally and internationally, providing the sole source for high-resolution digital Satellite data at some NWS Forecast Offices (FOs) since 1993. In addition to its use in the FO, RAMSDIS has also provided data distribution and research capabilities on a common platform to several NOAA laboratories, allowing for more efficient collaboration on digital Satellite data applications and analysis tools, and has been used by the World Meteorological Organization in an effort to provide digital Satellite data to developing countries in Central America find the Caribbean. The RAMSDIS project was innovative for many reasons. This article describes the unique approaches that made the project a success and details RAMSDIS utilization within the NWS and NOAA. The next phase of RAMSDIS implementation in the international meteorological community is also described.

Mamoru Kanzaki - One of the best experts on this subject based on the ideXlab platform.

  • Leaf-Shedding Phenology in Lowland Tropical Seasonal Forests of Cambodia as Estimated From NOAA Satellite Images
    IEEE Transactions on Geoscience and Remote Sensing, 2008
    Co-Authors: Makoto Araki, Mamoru Kanzaki, Bora Tith, Craig Trotter, Seiichi Ohta
    Abstract:

    The seasonality of forest leaf area index and photosynthetic activity is an important element of ecosystem function. The ecohydrological models representing water and carbon fluxes in tropical forests need regional-scale spatial information on forest leaf phenology. We aimed to evaluate the utility of the National Oceanic and Atmospheric Administration (NOAA)/Advanced Very high resolution radiometer (AVHRR) imagery for monitoring evergreen and deciduous forest phenology in lowland Cambodia. The temporal sequences of the normalized difference vegetation index (NDVI) data were constructed from ten-day composite imagery acquired from May 2001 to April 2002. A local maximum fitting (LMF) technique that combined time-series filtering and local curve-fitting techniques was used to extract cloud-free data. With this approach, different phenology between evergreen and deciduous forests was clearly observed during the dry season: Deciduous forests exhibited marked and spatially uniform loss and (later) gain in new leaf area, whereas evergreen forests showed lesser and spatially/temporally heterogeneous changes in leaf phenology. A significant finding was that about 30% of evergreen forests shed detectable quantities of leaves at two times during a dry season (i.e., in the early, and late, dry season). In summary, the capability of composite NOAA/AVHRR NDVI data sets processed with the LMF technique was established as a viable approach for monitoring heterogeneous forest phenology at regional scales during the tropical dry season, although it is apparent that further improvements in cloud clearing are required if the approach is to be useful also in the wet season.

  • Leaf-shedding phenology in tropical seasonal forests of Cambodia estimated from NOAA Satellite images
    2007 IEEE International Geoscience and Remote Sensing Symposium, 2007
    Co-Authors: Makoto Araki, Akihiro Tani, Mamoru Kanzaki, Khorn Saret, Det Seila, Pith Phearak, Lim Sopheap, Pol Sopheavuth
    Abstract:

    Forest seasonality is an important element to ecosystem functions. Eco-hydro models describing the Indochina bioregion under the seasonal tropical climate need regional phenological information about leaf dynamics. We examined the utility of remote sensing technology to leaf phenological research in Cambodian lowland forests. For this purpose, we aimed to detect any difference in leaf-shedding phenology between evergreen forests and deciduous forests. We analyzed the NOAA/AVHRR normalized differential vegetation index (NDVI) obtained from May 2001 to April 2002. The local maximum fitting (LMF) processing combines the time series filtering and the functional fitting was used for creating cloud/noise-free 10- day composites 1.1 km pixel data. Firstly, we estimated seasonal changes in the NDVI dominated by lowland evergreen forests and by lowland deciduous forests. Secondary, we identified local minimum points and the antecedent local maximum points of the fitting trigonometric function curve to each pixel as indicators of leaf-shedding events. The heterogeneous seasonal changes in the NDVI were well detected. Deciduous forests demonstrated drastic and uniform leaf phenology; while evergreen forests did spatially and temporally heterogeneous one during the dry season. It indicated the difficulty in getting information of regional forest seasonality; thus it displayed the utility of remote sensing for phenological investigation. Leaf-flushing was detected during the dry season both in evergreen forests and in deciduous forests. It suggested that leaf phenology was not completely governed by drought stress. Leaf-shedding and leaf flushing in twice within a single evergreen pixel was the most striking findings in this study. In summary, remote sensing technology was of great service to getting phenological information that was considerably different between evergreen forests and deciduous forests distributed in the seasonal tropical zones.

  • leaf shedding phenology in lowland tropical seasonal forests of cambodia as estimated from NOAA Satellite images
    International Geoscience and Remote Sensing Symposium, 2007
    Co-Authors: Makoto Araki, Akihiro Tani, Mamoru Kanzaki, Khorn Saret, Det Seila, Pith Phearak, Lim Sopheap, Pol Sopheavuth
    Abstract:

    Forest seasonality is an important element to ecosystem functions. Eco-hydro models describing the Indochina bioregion under the seasonal tropical climate need regional phenological information about leaf dynamics. We examined the utility of remote sensing technology to leaf phenological research in Cambodian lowland forests. For this purpose, we aimed to detect any difference in leaf-shedding phenology between evergreen forests and deciduous forests. We analyzed the NOAA/AVHRR normalized differential vegetation index (NDVI) obtained from May 2001 to April 2002. The local maximum fitting (LMF) processing combines the time series filtering and the functional fitting was used for creating cloud/noise-free 10- day composites 1.1 km pixel data. Firstly, we estimated seasonal changes in the NDVI dominated by lowland evergreen forests and by lowland deciduous forests. Secondary, we identified local minimum points and the antecedent local maximum points of the fitting trigonometric function curve to each pixel as indicators of leaf-shedding events. The heterogeneous seasonal changes in the NDVI were well detected. Deciduous forests demonstrated drastic and uniform leaf phenology; while evergreen forests did spatially and temporally heterogeneous one during the dry season. It indicated the difficulty in getting information of regional forest seasonality; thus it displayed the utility of remote sensing for phenological investigation. Leaf-flushing was detected during the dry season both in evergreen forests and in deciduous forests. It suggested that leaf phenology was not completely governed by drought stress. Leaf-shedding and leaf flushing in twice within a single evergreen pixel was the most striking findings in this study. In summary, remote sensing technology was of great service to getting phenological information that was considerably different between evergreen forests and deciduous forests distributed in the seasonal tropical zones.