The Experts below are selected from a list of 327 Experts worldwide ranked by ideXlab platform
Simon J Hook - One of the best experts on this subject based on the ideXlab platform.
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evaluating spectral indices for assessing fire severity in chaparral ecosystems southern california using modis aster master airborne simulator data
Remote Sensing, 2011Co-Authors: Sarah Harris, Sander Veraverbeke, Simon J HookAbstract:Wildland fires are a yearly recurring phenomenon in many terrestrial ecosystems. Accurate fire severity estimates are of paramount importance for modeling fire-induced trace gas emissions and rehabilitating post-fire landscapes. We used high spatial and high spectral resolution MODIS/ASTER (MASTER) airborne simulator data acquired over four 2007 southern California burns to evaluate the effectiveness of 19 different spectral indices, including the widely used Normalized Burn Ratio (NBR), for assessing fire severity in southern California chaparral. Ordinal logistic regression was used to assess the goodness-of-fit between the spectral index values and ordinal field data of severity. The NBR and three indices in which the NBR is enhanced with surface temperature or emissivity data revealed the best performance. Our findings support the operational use of the NBR in chaparral ecosystems by Burned Area Emergency Rehabilitation (BAER) projects, and demonstrate the potential of combining optical and thermal data for assessing fire severity. Additional testing in more burns, other ecoregions and different vegetation types is required to fully understand how (thermally enhanced) spectral indices relate to fire severity.
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evaluating spectral indices for burned area discrimination using modis aster master airborne simulator data
Remote Sensing of Environment, 2011Co-Authors: Sander Veraverbeke, Sarah Harris, Simon J HookAbstract:Abstract Wildland fires are an annually recurring phenomenon in many terrestrial ecosystems. Accurate burned area estimates are important for modeling fire-induced trace gas emissions and rehabilitating post-fire landscapes. High spatial and spectral resolution MODIS/ASTER (MASTER) airborne simulator data acquired over three 2007 southern California burns were used to evaluate the sensitivity of different spectral indices at discriminating burned land shortly after a fire. The performance of the indices, which included both traditional and new band combinations, was assessed by means of a separability index that provides an assessment of the effectiveness of a given index at discriminating between burned and unburned land. In the context of burned land applications results demonstrated (i) that the highest sensitivity of the longer short wave infrared (SWIR) spectral region (1.9 to 2.5 μm) was found at the band interval from 2.31 to 2.36 μm, (ii) the high discriminatory power of the mid infrared spectral domain (3 to 5.5 μm) and (iii) the high potential of emissivity data. As a consequence, a newly proposed index which combined near infrared (NIR), longer SWIR and emissivity outperformed all other indices when results were averaged over the three fires. Results were slightly different between land cover types (shrubland vs. forest-woodland). Prior to use in the indices the thermal infrared data were separated into temperature and emissivity to assess the benefits of using both temperature and emissivity. Currently, the only spaceborne sensor that provides moderate spatial resolution (
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the modis aster airborne simulator master a new instrument for earth science studies
Remote Sensing of Environment, 2001Co-Authors: Simon J Hook, Jeffrey J Myers, Kurtis J Thome, Michael Fitzgerald, Anne B KahleAbstract:Abstract The MODIS/ASTER Airborne Simulator was developed for the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Moderate Resolution Imaging Spectroradiometer (MODIS) projects. ASTER and MODIS are both spaceborne imaging instruments on the Terra platform launched in the fall of 1999. Currently MASTER is flown on the Department of Energy (DOE) King Air Beachcraft B200 aircraft and the NASA DC-8. In order to validate the in-flight performance of the instrument, the Jet Propulsion Laboratory and the University of Arizona conducted a joint experiment in December 1998. The experiment involved overflights of the MASTER instrument at two sites at three elevations (2000, 4000, and 6000 m). The two sites: Ivanpah Playa, California, and Lake Mead, Nevada, were selected to validate the visible–shortwave infrared and thermal infrared (TIR) channels, respectively. At Ivanpah Playa, a spectrometer was used to determine the surface reflectance and a sun photometer used to obtain the optical depth. At Lake Mead contact and radiometric surface lake temperatures were measured by buoy-mounted thermistors and self-calibrating radiometers, respectively. Atmospheric profiles of temperature, pressure, and relative humidity were obtained by launching an atmospheric sounding balloon. The measured surface radiances were then propagated to the at-sensor radiance using radiative transfer models driven by the local atmospheric data. There was excellent agreement between the predicted radiance at sensor and the measured radiance at sensor at all three altitudes. The percent difference between the channels not strongly affected by the atmosphere in the visible–shortwave infrared was typically 1–5% and the percent difference between the TIR channels not strongly affected by the atmosphere was typically less than 0.5%. These results indicate the MASTER instrument should provide a well-calibrated instrument for Earth Science Studies. It should prove particularly valuable for those studies that leverage information across the electromagnetic spectrum from the visible to the TIR.
Gilberto Guiterrez - One of the best experts on this subject based on the ideXlab platform.
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DCC - A Compact Representation of Raster Time Series
2019 Data Compression Conference (DCC), 2019Co-Authors: Nataly Cruces, Diego Seco, Gilberto GuiterrezAbstract:The Raster model is widely used in Geographic Information Systems to represent data that vary continuously in space, such as temperatures, precipitations, elevation, among other spatial attributes. In applications like weather forecast systems, not just a single Raster, but a sequence of Rasters covering the same region at different timestamps, known as a Raster time series, needs to be stored and queried. Compact data structures have proven successful to provide space-efficient representations of Rasters with query capabilities. Hence, a naive approach to save space is to use such a representation for each Raster in a time series. However, in this paper we show that it is possible to take advantage of the temporal locality that exists in a Raster time series to reduce the space necessary to store it while keeping competitive query times for several types of queries.
Sarah Harris - One of the best experts on this subject based on the ideXlab platform.
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evaluating spectral indices for assessing fire severity in chaparral ecosystems southern california using modis aster master airborne simulator data
Remote Sensing, 2011Co-Authors: Sarah Harris, Sander Veraverbeke, Simon J HookAbstract:Wildland fires are a yearly recurring phenomenon in many terrestrial ecosystems. Accurate fire severity estimates are of paramount importance for modeling fire-induced trace gas emissions and rehabilitating post-fire landscapes. We used high spatial and high spectral resolution MODIS/ASTER (MASTER) airborne simulator data acquired over four 2007 southern California burns to evaluate the effectiveness of 19 different spectral indices, including the widely used Normalized Burn Ratio (NBR), for assessing fire severity in southern California chaparral. Ordinal logistic regression was used to assess the goodness-of-fit between the spectral index values and ordinal field data of severity. The NBR and three indices in which the NBR is enhanced with surface temperature or emissivity data revealed the best performance. Our findings support the operational use of the NBR in chaparral ecosystems by Burned Area Emergency Rehabilitation (BAER) projects, and demonstrate the potential of combining optical and thermal data for assessing fire severity. Additional testing in more burns, other ecoregions and different vegetation types is required to fully understand how (thermally enhanced) spectral indices relate to fire severity.
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evaluating spectral indices for burned area discrimination using modis aster master airborne simulator data
Remote Sensing of Environment, 2011Co-Authors: Sander Veraverbeke, Sarah Harris, Simon J HookAbstract:Abstract Wildland fires are an annually recurring phenomenon in many terrestrial ecosystems. Accurate burned area estimates are important for modeling fire-induced trace gas emissions and rehabilitating post-fire landscapes. High spatial and spectral resolution MODIS/ASTER (MASTER) airborne simulator data acquired over three 2007 southern California burns were used to evaluate the sensitivity of different spectral indices at discriminating burned land shortly after a fire. The performance of the indices, which included both traditional and new band combinations, was assessed by means of a separability index that provides an assessment of the effectiveness of a given index at discriminating between burned and unburned land. In the context of burned land applications results demonstrated (i) that the highest sensitivity of the longer short wave infrared (SWIR) spectral region (1.9 to 2.5 μm) was found at the band interval from 2.31 to 2.36 μm, (ii) the high discriminatory power of the mid infrared spectral domain (3 to 5.5 μm) and (iii) the high potential of emissivity data. As a consequence, a newly proposed index which combined near infrared (NIR), longer SWIR and emissivity outperformed all other indices when results were averaged over the three fires. Results were slightly different between land cover types (shrubland vs. forest-woodland). Prior to use in the indices the thermal infrared data were separated into temperature and emissivity to assess the benefits of using both temperature and emissivity. Currently, the only spaceborne sensor that provides moderate spatial resolution (
Nataly Cruces - One of the best experts on this subject based on the ideXlab platform.
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A Compact Representation of Raster Time Series
arXiv: Data Structures and Algorithms, 2019Co-Authors: Nataly Cruces, Diego Seco, Gilberto GutiérrezAbstract:The Raster model is widely used in Geographic Information Systems to represent data that vary continuously in space, such as temperatures, precipitations, elevation, among other spatial attributes. In applications like weather forecast systems, not just a single Raster, but a sequence of Rasters covering the same region at different timestamps, known as a Raster time series, needs to be stored and queried. Compact data structures have proven successful to provide space-efficient representations of Rasters with query capabilities. Hence, a naive approach to save space is to use such a representation for each Raster in a time series. However, in this paper we show that it is possible to take advantage of the temporal locality that exists in a Raster time series to reduce the space necessary to store it while keeping competitive query times for several types of queries.
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DCC - A Compact Representation of Raster Time Series
2019 Data Compression Conference (DCC), 2019Co-Authors: Nataly Cruces, Diego Seco, Gilberto GuiterrezAbstract:The Raster model is widely used in Geographic Information Systems to represent data that vary continuously in space, such as temperatures, precipitations, elevation, among other spatial attributes. In applications like weather forecast systems, not just a single Raster, but a sequence of Rasters covering the same region at different timestamps, known as a Raster time series, needs to be stored and queried. Compact data structures have proven successful to provide space-efficient representations of Rasters with query capabilities. Hence, a naive approach to save space is to use such a representation for each Raster in a time series. However, in this paper we show that it is possible to take advantage of the temporal locality that exists in a Raster time series to reduce the space necessary to store it while keeping competitive query times for several types of queries.
Sander Veraverbeke - One of the best experts on this subject based on the ideXlab platform.
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evaluating spectral indices for assessing fire severity in chaparral ecosystems southern california using modis aster master airborne simulator data
Remote Sensing, 2011Co-Authors: Sarah Harris, Sander Veraverbeke, Simon J HookAbstract:Wildland fires are a yearly recurring phenomenon in many terrestrial ecosystems. Accurate fire severity estimates are of paramount importance for modeling fire-induced trace gas emissions and rehabilitating post-fire landscapes. We used high spatial and high spectral resolution MODIS/ASTER (MASTER) airborne simulator data acquired over four 2007 southern California burns to evaluate the effectiveness of 19 different spectral indices, including the widely used Normalized Burn Ratio (NBR), for assessing fire severity in southern California chaparral. Ordinal logistic regression was used to assess the goodness-of-fit between the spectral index values and ordinal field data of severity. The NBR and three indices in which the NBR is enhanced with surface temperature or emissivity data revealed the best performance. Our findings support the operational use of the NBR in chaparral ecosystems by Burned Area Emergency Rehabilitation (BAER) projects, and demonstrate the potential of combining optical and thermal data for assessing fire severity. Additional testing in more burns, other ecoregions and different vegetation types is required to fully understand how (thermally enhanced) spectral indices relate to fire severity.
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evaluating spectral indices for burned area discrimination using modis aster master airborne simulator data
Remote Sensing of Environment, 2011Co-Authors: Sander Veraverbeke, Sarah Harris, Simon J HookAbstract:Abstract Wildland fires are an annually recurring phenomenon in many terrestrial ecosystems. Accurate burned area estimates are important for modeling fire-induced trace gas emissions and rehabilitating post-fire landscapes. High spatial and spectral resolution MODIS/ASTER (MASTER) airborne simulator data acquired over three 2007 southern California burns were used to evaluate the sensitivity of different spectral indices at discriminating burned land shortly after a fire. The performance of the indices, which included both traditional and new band combinations, was assessed by means of a separability index that provides an assessment of the effectiveness of a given index at discriminating between burned and unburned land. In the context of burned land applications results demonstrated (i) that the highest sensitivity of the longer short wave infrared (SWIR) spectral region (1.9 to 2.5 μm) was found at the band interval from 2.31 to 2.36 μm, (ii) the high discriminatory power of the mid infrared spectral domain (3 to 5.5 μm) and (iii) the high potential of emissivity data. As a consequence, a newly proposed index which combined near infrared (NIR), longer SWIR and emissivity outperformed all other indices when results were averaged over the three fires. Results were slightly different between land cover types (shrubland vs. forest-woodland). Prior to use in the indices the thermal infrared data were separated into temperature and emissivity to assess the benefits of using both temperature and emissivity. Currently, the only spaceborne sensor that provides moderate spatial resolution (
