138
a healthy sugar maple grown in someone’s front yard. Thus, it is important to
know what the spectral signature for a pixel of your target ecosystem would look
like in optimal condition.
There are many spectral libraries where “typical” spectra for a range of surface features can be downloaded and used for image calibration (e.g.,
ECOSTRESS Spectral Library https://speclib.jpl.nasa.gov/documents/jhu_desc
or US Geological Survey (USGS) Spectral Library https://crustal.usgs.gov/speclab/QueryAll07a.php?quick_filter=vegetation). However, because of inherent
differences between sensors, as well as atmospheric and illumination conditions
at the time of image acquisition, it is best to also collect field spectra or identify
homogeneous calibration pixels from across the imagery. Linking field data
directly to the pixels will provide a spectral signature that is specific to the imagery you are using and ecosystem you are working in. This will serve as an important baseline and provide essential calibration data to model the species and
stress condition of interest.
2. Identify, quantify, and gather calibration data for the specific stress symptoms
you expect to see. While there are many common stress responses across vegetation types and stress agents, many symptoms can be species- or stress-specific.
Of these, only some may be visible to the human eye. This is why it is important
to identify the common stress symptoms you expect to see, from the earliest
symptoms to the most obvious and severe decline, and design field data collection efforts that quantify each of those stress symptoms. Field calibration data
should include measurements from locations across the imagery and cover the
full range for each of these metrics that you would expect to manifest in the system you are studying and that you hope to quantify in your final product. These
field data will provide valuable information as you analyze your imagery and
model decline conditions across your study area.
For example, hemlock woolly adelgid feed on photosynthate stored within
hemlock twigs, limiting the ability of trees to put on new growth. This may not
be visible in a broad assessment of canopy vigor, but can be quantified in the
field by collecting multiple branches from across the canopy and assessing the
proportion of terminal branchlets that have put on new growth. This serves as a
relatively quick and low-tech way to quantify foliar productivity and the reductions in new growth that are often the first sign of infestation. Similarly, the most
obvious visible sign of emerald ash borer infestation in ash trees is often scarified
bark that results from increased woodpecker activity. Woodpeckers strip bark as
they feed on larvae, leaving obvious white markings. These telltale signs of early
infestation can serve as a proxy for subtle biophysical changes in the canopy that
are not yet visible to field crews.
Most often, decline manifests as many different concurrent stress symptoms
(e.g., chlorosis and defoliation and dieback in various parts of the canopy) or a
progression of decline symptoms that vary with the degree of impact (e.g., early
decline manifests as chlorosis, later stages dominated by reductions in the live
crown ratio, and ultimately mortality). In such cases, you may choose to develop
an aggregate “field health” index that mathematically normalizes a suite of stress
J. Pontius et al.
a healthy sugar maple grown in someone’s front yard. Thus, it is important to
know what the spectral signature for a pixel of your target ecosystem would look
like in optimal condition.
There are many spectral libraries where “typical” spectra for a range of surface features can be downloaded and used for image calibration (e.g.,
ECOSTRESS Spectral Library https://speclib.jpl.nasa.gov/documents/jhu_desc
or US Geological Survey (USGS) Spectral Library https://crustal.usgs.gov/speclab/QueryAll07a.php?quick_filter=vegetation). However, because of inherent
differences between sensors, as well as atmospheric and illumination conditions
at the time of image acquisition, it is best to also collect field spectra or identify
homogeneous calibration pixels from across the imagery. Linking field data
directly to the pixels will provide a spectral signature that is specific to the imagery you are using and ecosystem you are working in. This will serve as an important baseline and provide essential calibration data to model the species and
stress condition of interest.
2. Identify, quantify, and gather calibration data for the specific stress symptoms
you expect to see. While there are many common stress responses across vegetation types and stress agents, many symptoms can be species- or stress-specific.
Of these, only some may be visible to the human eye. This is why it is important
to identify the common stress symptoms you expect to see, from the earliest
symptoms to the most obvious and severe decline, and design field data collection efforts that quantify each of those stress symptoms. Field calibration data
should include measurements from locations across the imagery and cover the
full range for each of these metrics that you would expect to manifest in the system you are studying and that you hope to quantify in your final product. These
field data will provide valuable information as you analyze your imagery and
model decline conditions across your study area.
For example, hemlock woolly adelgid feed on photosynthate stored within
hemlock twigs, limiting the ability of trees to put on new growth. This may not
be visible in a broad assessment of canopy vigor, but can be quantified in the
field by collecting multiple branches from across the canopy and assessing the
proportion of terminal branchlets that have put on new growth. This serves as a
relatively quick and low-tech way to quantify foliar productivity and the reductions in new growth that are often the first sign of infestation. Similarly, the most
obvious visible sign of emerald ash borer infestation in ash trees is often scarified
bark that results from increased woodpecker activity. Woodpeckers strip bark as
they feed on larvae, leaving obvious white markings. These telltale signs of early
infestation can serve as a proxy for subtle biophysical changes in the canopy that
are not yet visible to field crews.
Most often, decline manifests as many different concurrent stress symptoms
(e.g., chlorosis and defoliation and dieback in various parts of the canopy) or a
progression of decline symptoms that vary with the degree of impact (e.g., early
decline manifests as chlorosis, later stages dominated by reductions in the live
crown ratio, and ultimately mortality). In such cases, you may choose to develop
an aggregate “field health” index that mathematically normalizes a suite of stress
J. Pontius et al.