annual map of forest loss for persistently cloudy Indonesia at Landsat spatial
resolution. Their approach used annual MODIS forest loss detections to temporally
disaggregate epochal Landsat-mapped forest loss.
Schmidt et al. (2012) investigated the utility of the STARFM algorithm with
MODIS and Landsat time series to map and monitor subtle changes in vegetation
cover in a heterogeneous savanna and wetland landscape. They found that the
synthesized high spatial and temporal resolution time series allowed the detailed
phenological description of various vegetation communities that would have not
been possible across large areas otherwise.
The majority satellite sensor measurements are made using broad spectral
bands that are limited in their ability to capture fine resolution biochemical spectral
variability associated with multi-species canopies, leaf age spectral variations, and
variable plant stress responses. Broadband vegetation and water indices often lack
the fidelity to capture subtle absorption variations associated with vegetation stress
and changes in biochemistry. Hyperspectral remote sensing measurements add
spectral fidelity that enable the retrieval of important biochemical canopy features.
Their fusion with high temporal frequency satellite measurements can provide
powerful monitoring tools for the characterization of landscape phenology, ecosystem processes, and ecosystem health.
Hyperspectral indices can be formulated with narrow bandwidths that offer
greater sensitivity in the retrieval of foliage biochemical properties (Carter and
Knapp 2001), and many narrow-band indices have been developed that aim to
quantify canopy absorption processes associated with pigments, water, and lignocellulose compounds from litter and woody material (see Ustin et al. 2004). The
photochemical reflectance index (PRI) is a hyperspectral index that provides a
scaled LUE measure as (Middleton et al. 2011; Gamon et al. 1992),
PRI ¼ q 531nm Àq 570nm
ð
Þ = q 531nm þ q 570nm
ð
Þ
ð 1:16Þ
Spectral variations at 531 nm are closely associated with the dissipation of
excess light energy by xanthophyll pigments in order to protect the photosynthetic
leaf apparatus (Ripullone et al. 2011). The upcoming potential launches of new
hyperspectral missions, such as Hyperspectral Infrared Imager (HyspIRI), will
provide future data fusion opportunities for the scaling and extension of leaf
physiologic processes and phenology from species and ecosystem to regional and
global scales.
Opportunities to fuse dynamic VI optical measurements and hyperspectral data
with Lidar (light detection and ranging) sensors also have promising potentials to
improve the assessments of standing wood biomass, forest disturbance and biomass loss, carbon accumulation through forest regrowth, and mapping the spread
of invasive species (Lefsky et al. 2002; Baccini et al. 2008; Asner et al. 2011).
1 Indices of Vegetation Activity
31
resolution. Their approach used annual MODIS forest loss detections to temporally
disaggregate epochal Landsat-mapped forest loss.
Schmidt et al. (2012) investigated the utility of the STARFM algorithm with
MODIS and Landsat time series to map and monitor subtle changes in vegetation
cover in a heterogeneous savanna and wetland landscape. They found that the
synthesized high spatial and temporal resolution time series allowed the detailed
phenological description of various vegetation communities that would have not
been possible across large areas otherwise.
The majority satellite sensor measurements are made using broad spectral
bands that are limited in their ability to capture fine resolution biochemical spectral
variability associated with multi-species canopies, leaf age spectral variations, and
variable plant stress responses. Broadband vegetation and water indices often lack
the fidelity to capture subtle absorption variations associated with vegetation stress
and changes in biochemistry. Hyperspectral remote sensing measurements add
spectral fidelity that enable the retrieval of important biochemical canopy features.
Their fusion with high temporal frequency satellite measurements can provide
powerful monitoring tools for the characterization of landscape phenology, ecosystem processes, and ecosystem health.
Hyperspectral indices can be formulated with narrow bandwidths that offer
greater sensitivity in the retrieval of foliage biochemical properties (Carter and
Knapp 2001), and many narrow-band indices have been developed that aim to
quantify canopy absorption processes associated with pigments, water, and lignocellulose compounds from litter and woody material (see Ustin et al. 2004). The
photochemical reflectance index (PRI) is a hyperspectral index that provides a
scaled LUE measure as (Middleton et al. 2011; Gamon et al. 1992),
PRI ¼ q 531nm Àq 570nm
ð
Þ = q 531nm þ q 570nm
ð
Þ
ð 1:16Þ
Spectral variations at 531 nm are closely associated with the dissipation of
excess light energy by xanthophyll pigments in order to protect the photosynthetic
leaf apparatus (Ripullone et al. 2011). The upcoming potential launches of new
hyperspectral missions, such as Hyperspectral Infrared Imager (HyspIRI), will
provide future data fusion opportunities for the scaling and extension of leaf
physiologic processes and phenology from species and ecosystem to regional and
global scales.
Opportunities to fuse dynamic VI optical measurements and hyperspectral data
with Lidar (light detection and ranging) sensors also have promising potentials to
improve the assessments of standing wood biomass, forest disturbance and biomass loss, carbon accumulation through forest regrowth, and mapping the spread
of invasive species (Lefsky et al. 2002; Baccini et al. 2008; Asner et al. 2011).
1 Indices of Vegetation Activity
31