134
Aerial Survey) ITC Satellite and Sensor Database: https://webapps.itc.utwente.nl/
sensor/default.aspx?view=allsensors
One particularly promising sensor for improved forest health detection includes
Sentinel 2 (A and B), recently launched by the European Space Agency. This is the
first civil Earth observation sensor to include three bands in the red edge, providing
additional information to quantify vegetation condition. Its 5-day repeat time and
10 m pixels also improve its ability to detect more subtle decline symptoms. This
temporal resolution has proven useful in identifying forest decline based on detecting changes in the spectra of declining trees relative to healthy ones over time
(Zarco-Tejada et al. 2018). Geostationary sensors like the GOES-R series also provide a unique opportunity to monitor forest condition at rapid time intervals across
large landscapes. With two visible and four infrared bands useful to inform vegetation condition, the Advanced Baseline Imager on GOES-16 can provide images
every 5 minutes with a spatial resolution of 0.5–2 km.
Improvements in computing technologies and modeling techniques have also
increased the utility of multispectral sensors in early vegetation decline detection
(Lausch et al. 2017). For example, Pontius (2014) demonstrated that using a multitemporal approach mimicking hyperspectral algorithms could successfully quantify
a detailed decline scale using Landsat TM data. Over time, ongoing improvements in
sensor resolution, computing capabilities, and modeling options will enable measurements of more subtle changes in reflectance associated with early decline detection.
Hyperspectral Sensors While multispectral sensors record electromagnetic radiation averaged over a broad “band” of wavelengths, a hyperspectral instrument
records many adjacent narrow bands to image most of the spectrum within a set
range. What makes these instruments so useful for vegetation assessment extends
beyond the simple availability of more bands to work with. Typically, these bands
record reflectance from much narrower regions of the electromagnetic spectrum.
This narrowband design provides two key modeling capabilities that are not possible with broadband sensors: (1) narrow bands are able to target specific absorption
features linked to specific physiological structures or processes that we can directly
relate to plant stress response and (2) narrow, contiguous bands allow us to consider
the overall shape of spectral signatures, including mathematical techniques (e.g.,
derivatives, area under the curve, slope of the line between key regions) that are not
possible with broadband data.
Building off of the science of spectroscopy (the study of constituents and materials using specific wavelengths), RS analysts have used hyperspectral imagery to
quantify specific vegetation constituents and processes. The best hyperspectral narrow bands to study vegetation are in the 400–2500 nm spectral range (Thenkabail
et al. 2013; Fig. 6.8), enabling direct links to species composition, foliar chemistry,
foliar function, and ecosystem characteristics (Smith et al. 2002; Williams and Hunt
2002; Kokaly et al. 2003; Asner and Heidebrecht 2003; Townsend et al. 2003; Carter
et al. 2005; Cheng et al. 2006; Singh et al. 2015).
While it is generally believed that spectral changes in stressed vegetation are
common across stress agents, the ability of hyperspectral sensors to target specific
J. Pontius et al.
Aerial Survey) ITC Satellite and Sensor Database: https://webapps.itc.utwente.nl/
sensor/default.aspx?view=allsensors
One particularly promising sensor for improved forest health detection includes
Sentinel 2 (A and B), recently launched by the European Space Agency. This is the
first civil Earth observation sensor to include three bands in the red edge, providing
additional information to quantify vegetation condition. Its 5-day repeat time and
10 m pixels also improve its ability to detect more subtle decline symptoms. This
temporal resolution has proven useful in identifying forest decline based on detecting changes in the spectra of declining trees relative to healthy ones over time
(Zarco-Tejada et al. 2018). Geostationary sensors like the GOES-R series also provide a unique opportunity to monitor forest condition at rapid time intervals across
large landscapes. With two visible and four infrared bands useful to inform vegetation condition, the Advanced Baseline Imager on GOES-16 can provide images
every 5 minutes with a spatial resolution of 0.5–2 km.
Improvements in computing technologies and modeling techniques have also
increased the utility of multispectral sensors in early vegetation decline detection
(Lausch et al. 2017). For example, Pontius (2014) demonstrated that using a multitemporal approach mimicking hyperspectral algorithms could successfully quantify
a detailed decline scale using Landsat TM data. Over time, ongoing improvements in
sensor resolution, computing capabilities, and modeling options will enable measurements of more subtle changes in reflectance associated with early decline detection.
Hyperspectral Sensors While multispectral sensors record electromagnetic radiation averaged over a broad “band” of wavelengths, a hyperspectral instrument
records many adjacent narrow bands to image most of the spectrum within a set
range. What makes these instruments so useful for vegetation assessment extends
beyond the simple availability of more bands to work with. Typically, these bands
record reflectance from much narrower regions of the electromagnetic spectrum.
This narrowband design provides two key modeling capabilities that are not possible with broadband sensors: (1) narrow bands are able to target specific absorption
features linked to specific physiological structures or processes that we can directly
relate to plant stress response and (2) narrow, contiguous bands allow us to consider
the overall shape of spectral signatures, including mathematical techniques (e.g.,
derivatives, area under the curve, slope of the line between key regions) that are not
possible with broadband data.
Building off of the science of spectroscopy (the study of constituents and materials using specific wavelengths), RS analysts have used hyperspectral imagery to
quantify specific vegetation constituents and processes. The best hyperspectral narrow bands to study vegetation are in the 400–2500 nm spectral range (Thenkabail
et al. 2013; Fig. 6.8), enabling direct links to species composition, foliar chemistry,
foliar function, and ecosystem characteristics (Smith et al. 2002; Williams and Hunt
2002; Kokaly et al. 2003; Asner and Heidebrecht 2003; Townsend et al. 2003; Carter
et al. 2005; Cheng et al. 2006; Singh et al. 2015).
While it is generally believed that spectral changes in stressed vegetation are
common across stress agents, the ability of hyperspectral sensors to target specific
J. Pontius et al.