453
monitoring vegetation phenology (Cleland et al. 2007) and thus indirectly detecting
pollination (Neil and Wu 2006). In the next section, we describe how RS can be
used to measure phenology.
17.2.2 Phenology
Recent climate change has shifted phenology and associated species distributions
globally. This has in turn increased the risk of extinction for affected species through
the alteration of development rates of species or by altering the timing of environmental cues that affect a species’ presence in the community (Yang and Rudolf
2009). Ongoing climatic and phenological changes are expected to further increase
this risk. Moreover, the extent to which changes in vegetation phenology will feed
back into the climate system by modifying albedo and hence cloud formation is a
major source of uncertainty in climate change projections (Zhao et al. 2013). Recent
work on land surface phenology has focused on assessing changes in phenology
globally (rather than regionally) over long time periods as is now afforded by the
Earth Observation (EO) archive (Ganguly et al. 2010). Buitenwerf et al. (2015) used
phenomes (83 phenologically similar zones) in their global study of 32 years
(1980–2012) and showed via metrics extracted from the normalized difference vegetation index (NDVI) record that most of Earth’s land surface has undergone some
form of change in the seasonal pattern of vegetation activity. Other studies used
alternative indices, such as the MERIS Global Vegetation Index (MGVI) and
Terrestrial Chlorophyll Index (MTCI), from the now defunct Envisat platform and
MODIS Enhanced Vegetation Index (EVI). Indeed, comparison between these indices for a temperate deciduous forest showed that MTCI corresponded more closely
with vegetation phenology from ground observations and climatic proxies than any
of the other indices (Boyd and Foody 2011). This finding suggests that the Envisat
MTCI is best suited for monitoring vegetation phenology, advocated by its sensitivity to canopy chlorophyll content, a proxy for the canopy’s physical and chemical
alterations occurring during phenological cycles (Boyd et al. 2012). These studies
also point to the value of increasing both the temporal and spatial sampling of vegetation phenology. Limitations to spatial resolution mean that satellite-derived data
represent land surface phenology rather than direct vegetation phenology and are
therefore too coarse to detect critical individual, species, or community-level
responses. Further improving the temporal sampling means that those challenges to
using satellite data, including high sensitivity to effects of clouds and atmospheric
conditions, could be overcome. With the recent launch of the ESA Sentinel-2 and
Sentinel-3, this improvement in data characteristics is assured to go forward.
Key to applying data from these or any satellite for the derivation and study of
phenology, however, are accurate calibration and atmospheric correction to obtain
surface reflectance data. National Oceanic and Atmospheric Administration (NOAA)
satellite sensors have legacy calibrated data. For the Landsat and Terra/Aqua satellites, calibrated top-of-atmosphere radiance and surface reflectance products are
17 Integrating Biodiversity, Remote Sensing, and Auxiliary Information for the Study…
monitoring vegetation phenology (Cleland et al. 2007) and thus indirectly detecting
pollination (Neil and Wu 2006). In the next section, we describe how RS can be
used to measure phenology.
17.2.2 Phenology
Recent climate change has shifted phenology and associated species distributions
globally. This has in turn increased the risk of extinction for affected species through
the alteration of development rates of species or by altering the timing of environmental cues that affect a species’ presence in the community (Yang and Rudolf
2009). Ongoing climatic and phenological changes are expected to further increase
this risk. Moreover, the extent to which changes in vegetation phenology will feed
back into the climate system by modifying albedo and hence cloud formation is a
major source of uncertainty in climate change projections (Zhao et al. 2013). Recent
work on land surface phenology has focused on assessing changes in phenology
globally (rather than regionally) over long time periods as is now afforded by the
Earth Observation (EO) archive (Ganguly et al. 2010). Buitenwerf et al. (2015) used
phenomes (83 phenologically similar zones) in their global study of 32 years
(1980–2012) and showed via metrics extracted from the normalized difference vegetation index (NDVI) record that most of Earth’s land surface has undergone some
form of change in the seasonal pattern of vegetation activity. Other studies used
alternative indices, such as the MERIS Global Vegetation Index (MGVI) and
Terrestrial Chlorophyll Index (MTCI), from the now defunct Envisat platform and
MODIS Enhanced Vegetation Index (EVI). Indeed, comparison between these indices for a temperate deciduous forest showed that MTCI corresponded more closely
with vegetation phenology from ground observations and climatic proxies than any
of the other indices (Boyd and Foody 2011). This finding suggests that the Envisat
MTCI is best suited for monitoring vegetation phenology, advocated by its sensitivity to canopy chlorophyll content, a proxy for the canopy’s physical and chemical
alterations occurring during phenological cycles (Boyd et al. 2012). These studies
also point to the value of increasing both the temporal and spatial sampling of vegetation phenology. Limitations to spatial resolution mean that satellite-derived data
represent land surface phenology rather than direct vegetation phenology and are
therefore too coarse to detect critical individual, species, or community-level
responses. Further improving the temporal sampling means that those challenges to
using satellite data, including high sensitivity to effects of clouds and atmospheric
conditions, could be overcome. With the recent launch of the ESA Sentinel-2 and
Sentinel-3, this improvement in data characteristics is assured to go forward.
Key to applying data from these or any satellite for the derivation and study of
phenology, however, are accurate calibration and atmospheric correction to obtain
surface reflectance data. National Oceanic and Atmospheric Administration (NOAA)
satellite sensors have legacy calibrated data. For the Landsat and Terra/Aqua satellites, calibrated top-of-atmosphere radiance and surface reflectance products are
17 Integrating Biodiversity, Remote Sensing, and Auxiliary Information for the Study…