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mass levels. In most cases, you won’t know which index, or set of indices and wavelengths, is best to use until you examine them as a part of your analyses. The best way
to identify useful vegetation indices is detailed in the next section.
6.5 Techniques for Early Stress Detection
While mapping severe or widespread forest decline can be relatively straightforward using simple vegetation indices, it can be much more challenging to identify
early or small-scale decline, particularly in mixed forests. For example, an insect
outbreak may cause severe decline symptoms in the host tree species, but this signal may be washed out in a heterogeneous forest where reflectance from the larger
canopy of other species dominates. Similarly, tree mortality is often accompanied
by the release and ingrowth of understory vegetation. This can make detection of
decline difficult as increased vegetation density from the understory masks the
reduction in vegetation density in the upper canopy. Further, different species
inherently have different chemical and structural characteristics, resulting in sometimes starkly different spectral signatures, even among healthy canopies. A healthy
oak may be spectrally similar to a declining sugar maple. This underscores the
importance of knowing the distribution of species across a landscape of interest
and the characteristics of a “healthy” vs. “declining” spectral signature for a target
forest type.
Because the identification of subtle stress characteristics relies on subtle changes
in spectral characteristics, RS of early decline is very sensitive to anything that
might alter spectral signatures. For example, an algorithm designed for early stress
detection with one instrument may not be appropriate to apply to imagery from a
different sensor. Even with a similar spectral, radiometric, and spatial configuration,
differences in calibration may introduce differences that have nothing to do with the
health of the canopy. Even when using the same instrument, atmospheric or illumination conditions may vary over time. For these reasons, it is important to calibrate
each image to the specific conditions (atmospheric, illumination, canopy condition)
at the time of acquisition.
There are several methodological approaches that can help to isolate and quantify decline symptoms, regardless of the sensor system (Pontius and Hallett 2014).
Here we summarize the key components to identifying and quantifying early vegetation stress:
1. Know the spectral characteristics of your baseline ecosystem. While all vegetation has a common spectral curve, there are distinct differences in the spectral
signature across different species and at different spatial resolutions. Because of
inherent differences in foliar chemistry and canopy structure, a sugar maple has
a spectral signature that is distinct from an eastern hemlock, even when both are
in optimal health. Because of the spectral contribution from surrounding surface
features, a healthy sugar maple in a heterogeneous forest will look different from
6 Remote Sensing for Early, Detailed, and Accurate Detection of Forest Disturbance…
mass levels. In most cases, you won’t know which index, or set of indices and wavelengths, is best to use until you examine them as a part of your analyses. The best way
to identify useful vegetation indices is detailed in the next section.
6.5 Techniques for Early Stress Detection
While mapping severe or widespread forest decline can be relatively straightforward using simple vegetation indices, it can be much more challenging to identify
early or small-scale decline, particularly in mixed forests. For example, an insect
outbreak may cause severe decline symptoms in the host tree species, but this signal may be washed out in a heterogeneous forest where reflectance from the larger
canopy of other species dominates. Similarly, tree mortality is often accompanied
by the release and ingrowth of understory vegetation. This can make detection of
decline difficult as increased vegetation density from the understory masks the
reduction in vegetation density in the upper canopy. Further, different species
inherently have different chemical and structural characteristics, resulting in sometimes starkly different spectral signatures, even among healthy canopies. A healthy
oak may be spectrally similar to a declining sugar maple. This underscores the
importance of knowing the distribution of species across a landscape of interest
and the characteristics of a “healthy” vs. “declining” spectral signature for a target
forest type.
Because the identification of subtle stress characteristics relies on subtle changes
in spectral characteristics, RS of early decline is very sensitive to anything that
might alter spectral signatures. For example, an algorithm designed for early stress
detection with one instrument may not be appropriate to apply to imagery from a
different sensor. Even with a similar spectral, radiometric, and spatial configuration,
differences in calibration may introduce differences that have nothing to do with the
health of the canopy. Even when using the same instrument, atmospheric or illumination conditions may vary over time. For these reasons, it is important to calibrate
each image to the specific conditions (atmospheric, illumination, canopy condition)
at the time of acquisition.
There are several methodological approaches that can help to isolate and quantify decline symptoms, regardless of the sensor system (Pontius and Hallett 2014).
Here we summarize the key components to identifying and quantifying early vegetation stress:
1. Know the spectral characteristics of your baseline ecosystem. While all vegetation has a common spectral curve, there are distinct differences in the spectral
signature across different species and at different spatial resolutions. Because of
inherent differences in foliar chemistry and canopy structure, a sugar maple has
a spectral signature that is distinct from an eastern hemlock, even when both are
in optimal health. Because of the spectral contribution from surrounding surface
features, a healthy sugar maple in a heterogeneous forest will look different from
6 Remote Sensing for Early, Detailed, and Accurate Detection of Forest Disturbance…