The accurate estimation of methane, a greenhouse gas, from natural sources such as peatlands
is ... more The accurate estimation of methane, a greenhouse gas, from natural sources such as peatlands
is vital for overall assessment of global climate change. Investigations that rely on process-based
models that derive CH4 emissions from natural wetlands as a function of variables such as soil temperature,
water table depth and aerenchymateous vegetation measured at plot scales may produce
biased results when extrapolated to landscape, regional and global levels. This is because assumptions
such as average water table depth, vegetation patchiness, and microclimate that must be ‘scaled up’
from the plot to larger scales. Heterogeneity is an inherent characteristic of wetlands, and the relationships
between CH4 and variables deriving methane emissions are non-linear. These limitations can
be overcome by independent remote sensing data with multi-spatio-temporal resolutions acquired
from different sensors and platforms. This paper discusses novel techniques from information theory
applied to preserve the information content of edaphic and vegetative characteristics of a peatland
ecosystem in Scotland. Our results demonstrate shifting patterns in the information retrieved as the
pixel resolution decreases. We report the changes in processes and patterns across different scales
which would help improve the overall performance of the process based models at global scales.
Keywords: Methane, Peatland, Up Scaling, Heterogeneity, Information theory, Shannon entropy,
Kulback-Leibler distance, Quadratic approximation, Remote Sensing, Spatio-Temporal Resolution,
Global Change
Within the frame of a larger project funded by the High Tech Offensive Bayern (HTO) hyperspectral... more Within the frame of a larger project funded by the High Tech Offensive Bayern (HTO) hyperspectral HyMap data have been investigated on their spectral content in order to determine bio-geo-chemo-physical parameters of land cover units in the catchment of Lake Waging-Taching in Upper Bavaria, Germany. The focus of the current investigations was on parameter extraction from grasslands and the aim was to identify differences in management intensities of meadows and pastures in the area. The results would contribute to a more adequate modeling of the transport of fertilizers, pesticides, etc., that potentially influence the water quality of the lake ecosystem and serve as an input to object based radiative transfer model inversion studies.
The accurate estimation of methane, a greenhouse gas, from natural sources such as peatlands
is ... more The accurate estimation of methane, a greenhouse gas, from natural sources such as peatlands
is vital for overall assessment of global climate change. Investigations that rely on process-based
models that derive CH4 emissions from natural wetlands as a function of variables such as soil temperature,
water table depth and aerenchymateous vegetation measured at plot scales may produce
biased results when extrapolated to landscape, regional and global levels. This is because assumptions
such as average water table depth, vegetation patchiness, and microclimate that must be ‘scaled up’
from the plot to larger scales. Heterogeneity is an inherent characteristic of wetlands, and the relationships
between CH4 and variables deriving methane emissions are non-linear. These limitations can
be overcome by independent remote sensing data with multi-spatio-temporal resolutions acquired
from different sensors and platforms. This paper discusses novel techniques from information theory
applied to preserve the information content of edaphic and vegetative characteristics of a peatland
ecosystem in Scotland. Our results demonstrate shifting patterns in the information retrieved as the
pixel resolution decreases. We report the changes in processes and patterns across different scales
which would help improve the overall performance of the process based models at global scales.
Keywords: Methane, Peatland, Up Scaling, Heterogeneity, Information theory, Shannon entropy,
Kulback-Leibler distance, Quadratic approximation, Remote Sensing, Spatio-Temporal Resolution,
Global Change
Within the frame of a larger project funded by the High Tech Offensive Bayern (HTO) hyperspectral... more Within the frame of a larger project funded by the High Tech Offensive Bayern (HTO) hyperspectral HyMap data have been investigated on their spectral content in order to determine bio-geo-chemo-physical parameters of land cover units in the catchment of Lake Waging-Taching in Upper Bavaria, Germany. The focus of the current investigations was on parameter extraction from grasslands and the aim was to identify differences in management intensities of meadows and pastures in the area. The results would contribute to a more adequate modeling of the transport of fertilizers, pesticides, etc., that potentially influence the water quality of the lake ecosystem and serve as an input to object based radiative transfer model inversion studies.
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Papers by ABU ABDULLAH
is vital for overall assessment of global climate change. Investigations that rely on process-based
models that derive CH4 emissions from natural wetlands as a function of variables such as soil temperature,
water table depth and aerenchymateous vegetation measured at plot scales may produce
biased results when extrapolated to landscape, regional and global levels. This is because assumptions
such as average water table depth, vegetation patchiness, and microclimate that must be ‘scaled up’
from the plot to larger scales. Heterogeneity is an inherent characteristic of wetlands, and the relationships
between CH4 and variables deriving methane emissions are non-linear. These limitations can
be overcome by independent remote sensing data with multi-spatio-temporal resolutions acquired
from different sensors and platforms. This paper discusses novel techniques from information theory
applied to preserve the information content of edaphic and vegetative characteristics of a peatland
ecosystem in Scotland. Our results demonstrate shifting patterns in the information retrieved as the
pixel resolution decreases. We report the changes in processes and patterns across different scales
which would help improve the overall performance of the process based models at global scales.
Keywords: Methane, Peatland, Up Scaling, Heterogeneity, Information theory, Shannon entropy,
Kulback-Leibler distance, Quadratic approximation, Remote Sensing, Spatio-Temporal Resolution,
Global Change
is vital for overall assessment of global climate change. Investigations that rely on process-based
models that derive CH4 emissions from natural wetlands as a function of variables such as soil temperature,
water table depth and aerenchymateous vegetation measured at plot scales may produce
biased results when extrapolated to landscape, regional and global levels. This is because assumptions
such as average water table depth, vegetation patchiness, and microclimate that must be ‘scaled up’
from the plot to larger scales. Heterogeneity is an inherent characteristic of wetlands, and the relationships
between CH4 and variables deriving methane emissions are non-linear. These limitations can
be overcome by independent remote sensing data with multi-spatio-temporal resolutions acquired
from different sensors and platforms. This paper discusses novel techniques from information theory
applied to preserve the information content of edaphic and vegetative characteristics of a peatland
ecosystem in Scotland. Our results demonstrate shifting patterns in the information retrieved as the
pixel resolution decreases. We report the changes in processes and patterns across different scales
which would help improve the overall performance of the process based models at global scales.
Keywords: Methane, Peatland, Up Scaling, Heterogeneity, Information theory, Shannon entropy,
Kulback-Leibler distance, Quadratic approximation, Remote Sensing, Spatio-Temporal Resolution,
Global Change