135
chemical, physiological, and morphological traits allows RS analysts to target and
assess specific, early symptoms of decline and target detection efforts based on
known physiological responses to a particular pest or pathogen. Lausch et al. (2013)
targeted changes in chlorophyll absorption as an indicator of bark beetle-induced
decline; Pontius et al. (2008) targeted chlorophyll fluoresce to map the invasive
emerald ash borer (Pontius et al. 2008) and canopy density for detailed monitoring
impacts of hemlock woolly adelgid (Pontius et al. 2005b).
Hyperspectral imagery has historically been limited in availability. NASA’s
Airborne Visible/Infrared Spectrometer (AVIRIS; Porter and Enmark 1987) hyperspectral sensor was the pioneer of airborne applications. But the launch of the
NASA Hyperion Instrument (Pearlman et al. 2003) on the EO-1 satellite in 2000,
and the addition of commercial vendors with aerial hyperspectral platforms (e.g.,
ITRES http://www.itres.com/; SPECIM http://www.specim.fi/hyperspectral-RS/),
has increased the availability of hyperspectral imagery. The promise of new hyperspectral satellites such as the Environmental Mapping and Analysis Program
(EnMAP http://www.enmap.org/mission.html) suggests there is potential for
expanding applications in forest health monitoring and assessment. Recent examples include assessments of hemlock woolly adelgid-induced decline in the Catskills
region of New York (Hanavan et al. 2015) and detection of drought-induced decline
in the chaparral ecosystems of California (Coates et al. 2015). Fused hyperspectral
and LiDAR imagery have also enabled the assessment of early decline at the canopy
level in urban environments (e.g., Degerickx et al. 2018; Pontius et al. 2017).
6.4 Spectroscopy of Early Decline Detection
While different species have unique spectral signatures, there are similar changes in
general spectral characteristics in response to stress (Buschmann and Nagel 1993).
Many of these spectral features can be directly linked to the stress symptoms and
physiological characteristics described above (Fig. 6.8). For example, changes in
leaf chemistry and physiology are captured in the 480–520 nm (blue) and
600–680 nm (red) regions, where chlorophyll absorption is strong. But changes in
this region are relatively small compared with the dramatic changes that can be seen
with stress between 750 and 1300 nm. The sharp rise in reflectance between the red
and NIR regions (red edge inflection point) can be used to quantify changes in both
the slope of the spectral signature and the location of the inflection point of the slope
in response to changes in leaf chemistry and canopy density. Spectral information at
longer wavelengths (1650–2200 shortwave infrared) has also been useful in quantifying changes in leaf water content, often a key signal of early vegetation stress.
Often the most useful information about general canopy condition, density, and
function is derived from combining bands from various regions in mathematical
expressions referred to as vegetation indices (Elvidge and Chen 1995; Pinty et al.
1993). Sometimes these indices incorporate information from multiple wavelengths
with known absorption features. But other times a nonresponsive “control” band
6 Remote Sensing for Early, Detailed, and Accurate Detection of Forest Disturbance…
chemical, physiological, and morphological traits allows RS analysts to target and
assess specific, early symptoms of decline and target detection efforts based on
known physiological responses to a particular pest or pathogen. Lausch et al. (2013)
targeted changes in chlorophyll absorption as an indicator of bark beetle-induced
decline; Pontius et al. (2008) targeted chlorophyll fluoresce to map the invasive
emerald ash borer (Pontius et al. 2008) and canopy density for detailed monitoring
impacts of hemlock woolly adelgid (Pontius et al. 2005b).
Hyperspectral imagery has historically been limited in availability. NASA’s
Airborne Visible/Infrared Spectrometer (AVIRIS; Porter and Enmark 1987) hyperspectral sensor was the pioneer of airborne applications. But the launch of the
NASA Hyperion Instrument (Pearlman et al. 2003) on the EO-1 satellite in 2000,
and the addition of commercial vendors with aerial hyperspectral platforms (e.g.,
ITRES http://www.itres.com/; SPECIM http://www.specim.fi/hyperspectral-RS/),
has increased the availability of hyperspectral imagery. The promise of new hyperspectral satellites such as the Environmental Mapping and Analysis Program
(EnMAP http://www.enmap.org/mission.html) suggests there is potential for
expanding applications in forest health monitoring and assessment. Recent examples include assessments of hemlock woolly adelgid-induced decline in the Catskills
region of New York (Hanavan et al. 2015) and detection of drought-induced decline
in the chaparral ecosystems of California (Coates et al. 2015). Fused hyperspectral
and LiDAR imagery have also enabled the assessment of early decline at the canopy
level in urban environments (e.g., Degerickx et al. 2018; Pontius et al. 2017).
6.4 Spectroscopy of Early Decline Detection
While different species have unique spectral signatures, there are similar changes in
general spectral characteristics in response to stress (Buschmann and Nagel 1993).
Many of these spectral features can be directly linked to the stress symptoms and
physiological characteristics described above (Fig. 6.8). For example, changes in
leaf chemistry and physiology are captured in the 480–520 nm (blue) and
600–680 nm (red) regions, where chlorophyll absorption is strong. But changes in
this region are relatively small compared with the dramatic changes that can be seen
with stress between 750 and 1300 nm. The sharp rise in reflectance between the red
and NIR regions (red edge inflection point) can be used to quantify changes in both
the slope of the spectral signature and the location of the inflection point of the slope
in response to changes in leaf chemistry and canopy density. Spectral information at
longer wavelengths (1650–2200 shortwave infrared) has also been useful in quantifying changes in leaf water content, often a key signal of early vegetation stress.
Often the most useful information about general canopy condition, density, and
function is derived from combining bands from various regions in mathematical
expressions referred to as vegetation indices (Elvidge and Chen 1995; Pinty et al.
1993). Sometimes these indices incorporate information from multiple wavelengths
with known absorption features. But other times a nonresponsive “control” band
6 Remote Sensing for Early, Detailed, and Accurate Detection of Forest Disturbance…