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symptoms over many years. This slow and highly variable decline (some good
years, some bad years) limits the ability to identify causal relationships, understand
potential impacts to ecosystem function, and develop management strategies. As a
result, we need to be able to quantify decline symptoms with greater detail and sensitivity to subtle changes, from the gradual loss of photosynthetic apparatus in
response to initial stress, to reductions in canopy density, dieback, and ultimate
mortality across the landscape.
Remote sensing (RS) has long been used to assess relative vegetation density,
decline, and mortality. But landscape-scale assessment of small-scale or subtle
decline symptoms has been more difficult. The spatial patial resolution of many
sensors has limited our ability to detect small-scale decline in highly mixed pixels,
while spectral resolution has limited our ability to detect early biogeochemical precursors to more severe decline symptoms. But as new sensors and modeling algorithms have come on board, there is a growing list of successful early decline
detection efforts.
Here we present the science behind RS for the assessment of vegetation condition, with a focus on using these tools for more detailed and accurate monitoring of
forest decline and disturbance. We also highlight the importance of this approach to
inform the sustainable management of forested ecosystems and preservation of forest biodiversity.
6.2 The Basics of Forest Decline
In order to better understand how RS instruments can detect vegetation stress, and
be used to quantify forest decline, it is important to understand the structural and
physiological response of vegetation to stress. Any RS effort to detect or monitor
decline is based on the sensor’s ability to detect these biophysical changes that
manifest following stress.
Trees adjust their physiology and form in response to environmental stimuli
(e.g., light, temperature, moisture). Stress occurs when environmental conditions
fall outside of the normal or optimal levels to which plants are adapted. As sessile
organisms that cannot flee from the many stresses that they are routinely exposed to
over their long life spans, trees have evolved enumerable mechanisms to avoid,
mitigate, or rebound from stress. Some of these adaptations (e.g., protective pigments such as the yellow/orange carotenoids and red anthocyanins in leaves) can
directly influence RS spectral measurements. Other stress adaptations (e.g., changes
in carbohydrate storage and lipid and protein metabolism; Strimbeck et al. 2015)
influence spectral characteristics indirectly through changes in leaf retention and
life span. Here we walk through some of these physiological and structural changes
relevant to RS efforts in more detail.
Leaf Size Small, emerging leaves can be difficult to detect via RS (e.g., White et al.
2014). Therefore, factors that delay or expedite bud break and leaf expansion, or
lead to leaf wilting, curling, and folding can influence spectral signatures
6 Remote Sensing for Early, Detailed, and Accurate Detection of Forest Disturbance…
symptoms over many years. This slow and highly variable decline (some good
years, some bad years) limits the ability to identify causal relationships, understand
potential impacts to ecosystem function, and develop management strategies. As a
result, we need to be able to quantify decline symptoms with greater detail and sensitivity to subtle changes, from the gradual loss of photosynthetic apparatus in
response to initial stress, to reductions in canopy density, dieback, and ultimate
mortality across the landscape.
Remote sensing (RS) has long been used to assess relative vegetation density,
decline, and mortality. But landscape-scale assessment of small-scale or subtle
decline symptoms has been more difficult. The spatial patial resolution of many
sensors has limited our ability to detect small-scale decline in highly mixed pixels,
while spectral resolution has limited our ability to detect early biogeochemical precursors to more severe decline symptoms. But as new sensors and modeling algorithms have come on board, there is a growing list of successful early decline
detection efforts.
Here we present the science behind RS for the assessment of vegetation condition, with a focus on using these tools for more detailed and accurate monitoring of
forest decline and disturbance. We also highlight the importance of this approach to
inform the sustainable management of forested ecosystems and preservation of forest biodiversity.
6.2 The Basics of Forest Decline
In order to better understand how RS instruments can detect vegetation stress, and
be used to quantify forest decline, it is important to understand the structural and
physiological response of vegetation to stress. Any RS effort to detect or monitor
decline is based on the sensor’s ability to detect these biophysical changes that
manifest following stress.
Trees adjust their physiology and form in response to environmental stimuli
(e.g., light, temperature, moisture). Stress occurs when environmental conditions
fall outside of the normal or optimal levels to which plants are adapted. As sessile
organisms that cannot flee from the many stresses that they are routinely exposed to
over their long life spans, trees have evolved enumerable mechanisms to avoid,
mitigate, or rebound from stress. Some of these adaptations (e.g., protective pigments such as the yellow/orange carotenoids and red anthocyanins in leaves) can
directly influence RS spectral measurements. Other stress adaptations (e.g., changes
in carbohydrate storage and lipid and protein metabolism; Strimbeck et al. 2015)
influence spectral characteristics indirectly through changes in leaf retention and
life span. Here we walk through some of these physiological and structural changes
relevant to RS efforts in more detail.
Leaf Size Small, emerging leaves can be difficult to detect via RS (e.g., White et al.
2014). Therefore, factors that delay or expedite bud break and leaf expansion, or
lead to leaf wilting, curling, and folding can influence spectral signatures
6 Remote Sensing for Early, Detailed, and Accurate Detection of Forest Disturbance…