40
1: Richard Lucas, Aled Rowlands, Olaf Niemann, Ray Merton
the canopy level which could be used subsequently to assess fire fuel loading
and hence assist in the prediction of wildfires. Asner et al. (1999) also indicated
that observed variations in AVIRIS hyperspectral signatures were indicative
of the 3-dimensional variation in LAI and dry carbon and that simultaneous
observations of both LAI and dry carbon area index (NPVAI) allowed the production of maps of both structural and functional vegetation types as well as
fire fuel load.
1.7
Summary
This chapter has provided a brief history of hyperspectral sensors and an
overview of the main airborne and spaceborne instruments. The particular
spatial, spectral and temporal benefits of both airborne and spaceborne hyperspectral sensors, which make them well suited to observation of Earth
surfaces, have also been highlighted. Applications of hyperspectral remote
sensing have been reviewed in brief and readers are encouraged to consult the
cited literature for more detailed information.
The review comes at a time when the remote sensing community is at
the start of a new era in which spaceborne observations of hyperspectral
data, often acquired at different view angles, will be acquired routinely. The
information content within these new datasets is enormous and will present
many challenges to scientists and land managers charged with analysing and
transforming these to derive practical output products. New research will not
only need to focus on the nature of information that can be extracted across
a wide and disparate range of disciplines, but also attention will be drawn
towards the way in which the vast data-streams are handled, particularly
with the advent of multi-angle and ultraspectral sensors. The development
of algorithms and models to optimise processing and analysis of these data
will also be a significant growth area of research, and methods for efficient
and appropriate extraction of data, especially from time-series datasets, will
need to be developed as historical archives of hyperspectral data increase.
The following chapters in this book discuss a selection of data processing
and extraction tools which can be applied to both existing and forthcoming
hyperspectral data.
References
Aber JD, Bolster KL, Newman SD, Soulia M, Martin ME (1994) Analysis of forest foliage II:
Measurement of carbon fraction and nitrogen content by end-member analysis. Journal
of Near Infrared Spectroscopy 2: 15-23
Adams JB, Sabol DE, Kapos V, Filho RA, Roberts DA, Smith MO (1995) Classification
of multispectral images based on fractions of endmembers: application to land-cover
change in the Brazilian Amazon. Remote Sensing of Environment 52: 137-154
Adams JB, Smith MO (1986) Spectral mixture modeling: a new analysis of rock and soil
types at the Viking Lander 1 site. Journal of Geophysical Research 91B8: 8098-8112
1: Richard Lucas, Aled Rowlands, Olaf Niemann, Ray Merton
the canopy level which could be used subsequently to assess fire fuel loading
and hence assist in the prediction of wildfires. Asner et al. (1999) also indicated
that observed variations in AVIRIS hyperspectral signatures were indicative
of the 3-dimensional variation in LAI and dry carbon and that simultaneous
observations of both LAI and dry carbon area index (NPVAI) allowed the production of maps of both structural and functional vegetation types as well as
fire fuel load.
1.7
Summary
This chapter has provided a brief history of hyperspectral sensors and an
overview of the main airborne and spaceborne instruments. The particular
spatial, spectral and temporal benefits of both airborne and spaceborne hyperspectral sensors, which make them well suited to observation of Earth
surfaces, have also been highlighted. Applications of hyperspectral remote
sensing have been reviewed in brief and readers are encouraged to consult the
cited literature for more detailed information.
The review comes at a time when the remote sensing community is at
the start of a new era in which spaceborne observations of hyperspectral
data, often acquired at different view angles, will be acquired routinely. The
information content within these new datasets is enormous and will present
many challenges to scientists and land managers charged with analysing and
transforming these to derive practical output products. New research will not
only need to focus on the nature of information that can be extracted across
a wide and disparate range of disciplines, but also attention will be drawn
towards the way in which the vast data-streams are handled, particularly
with the advent of multi-angle and ultraspectral sensors. The development
of algorithms and models to optimise processing and analysis of these data
will also be a significant growth area of research, and methods for efficient
and appropriate extraction of data, especially from time-series datasets, will
need to be developed as historical archives of hyperspectral data increase.
The following chapters in this book discuss a selection of data processing
and extraction tools which can be applied to both existing and forthcoming
hyperspectral data.
References
Aber JD, Bolster KL, Newman SD, Soulia M, Martin ME (1994) Analysis of forest foliage II:
Measurement of carbon fraction and nitrogen content by end-member analysis. Journal
of Near Infrared Spectroscopy 2: 15-23
Adams JB, Sabol DE, Kapos V, Filho RA, Roberts DA, Smith MO (1995) Classification
of multispectral images based on fractions of endmembers: application to land-cover
change in the Brazilian Amazon. Remote Sensing of Environment 52: 137-154
Adams JB, Smith MO (1986) Spectral mixture modeling: a new analysis of rock and soil
types at the Viking Lander 1 site. Journal of Geophysical Research 91B8: 8098-8112