458
nutritional value (Sheppard et al. 2007), food resources (Coops et al. 2009), seasonal variation (Bischof et al. 2012), and combined effects of climate and land use
changes (Santos et al. 2017). Upcoming sensors are expected to provide even better
and more diverse measurements of ecosystem processes and other information that
might be relevant to map species distributions at finer spatial, temporal, and spectral
resolutions (e.g., Sentinel satellites; Berger and Aschbacher 2012). Further, through
SDMs we can expand our capacity to monitor taxonomic groups beyond plants,
providing a broader understanding of the dynamics and dimensions of biodiversity,
its feedbacks and interactions, and its change.
Yet another way in which RS can be useful in biodiversity monitoring is to detect
animals using unmanned aerial vehicles with visible and thermal sensors and
LiDAR data. Nagendra et al. (2013) concluded that despite the potential for RS in
monitoring habitat, the integration has not happened yet because of technical challenges of conducting and accurately interpreting image analyses, insufficient integration between in-situ data and expert knowledge RS data, and lack of funding and
platforms that provide such services and capacity in an accessible way globally.
However, progress in this domain includes the provision of environmental data layers from RS sources in Movebank, a major animal movement data repository.
Environmental data are directly extracted at the recorded locations and interpolated
to the date and time of each GPS fix (Dodge et al. 2013). This allows animal movement ecologists to easily extract environmental variables co-registered in space and
time with their animal location data.
Current RS capabilities allow for improving species mapping and monitoring
such as the local species pool (i.e., alpha diversity, Feret and Asner 2014) as well as
to move beyond species (Jetz et al. 2016) to measure and monitor other components
of diversity such as compositional turnover (i.e., beta diversity, Leitão et al. 2015;
Schwieder et al. 2016; Rocchini et al. 2017). There were a few attempts to map species richness using Landsat TM multispectral data to calculate NDVI as a proxy for
species richness with limited success (Gould 2000). The spectral resolution of
imaging spectrometer data today is sufficiently fine to implement and test the spectral diversity hypothesis (Feret and Asner 2014) because plant species exhibit a set
of traits that respond to light at different wavelengths (plant optical types, Ustin and
Gamon 2010). Novel findings, however, show some limitations to the application of
the spectral diversity principles at larger spatial resolutions (Schmidtlein and
Fassnacht 2017), and more studies are needed to identify challenges and opportunities of this approach to mapping and monitoring biodiversity.
17.3.2 Vegetation Condition
There have been many efforts to move beyond species assessments toward functional
aspects, which include vegetation condition. The current wealth of time series data
from sensors like NOAA AVHRR, MODIS, or Landsat allows measurements of ecosystem phenology, seasonality, and changes in onset of seasons and assessment of
F. Schrodt et al.
nutritional value (Sheppard et al. 2007), food resources (Coops et al. 2009), seasonal variation (Bischof et al. 2012), and combined effects of climate and land use
changes (Santos et al. 2017). Upcoming sensors are expected to provide even better
and more diverse measurements of ecosystem processes and other information that
might be relevant to map species distributions at finer spatial, temporal, and spectral
resolutions (e.g., Sentinel satellites; Berger and Aschbacher 2012). Further, through
SDMs we can expand our capacity to monitor taxonomic groups beyond plants,
providing a broader understanding of the dynamics and dimensions of biodiversity,
its feedbacks and interactions, and its change.
Yet another way in which RS can be useful in biodiversity monitoring is to detect
animals using unmanned aerial vehicles with visible and thermal sensors and
LiDAR data. Nagendra et al. (2013) concluded that despite the potential for RS in
monitoring habitat, the integration has not happened yet because of technical challenges of conducting and accurately interpreting image analyses, insufficient integration between in-situ data and expert knowledge RS data, and lack of funding and
platforms that provide such services and capacity in an accessible way globally.
However, progress in this domain includes the provision of environmental data layers from RS sources in Movebank, a major animal movement data repository.
Environmental data are directly extracted at the recorded locations and interpolated
to the date and time of each GPS fix (Dodge et al. 2013). This allows animal movement ecologists to easily extract environmental variables co-registered in space and
time with their animal location data.
Current RS capabilities allow for improving species mapping and monitoring
such as the local species pool (i.e., alpha diversity, Feret and Asner 2014) as well as
to move beyond species (Jetz et al. 2016) to measure and monitor other components
of diversity such as compositional turnover (i.e., beta diversity, Leitão et al. 2015;
Schwieder et al. 2016; Rocchini et al. 2017). There were a few attempts to map species richness using Landsat TM multispectral data to calculate NDVI as a proxy for
species richness with limited success (Gould 2000). The spectral resolution of
imaging spectrometer data today is sufficiently fine to implement and test the spectral diversity hypothesis (Feret and Asner 2014) because plant species exhibit a set
of traits that respond to light at different wavelengths (plant optical types, Ustin and
Gamon 2010). Novel findings, however, show some limitations to the application of
the spectral diversity principles at larger spatial resolutions (Schmidtlein and
Fassnacht 2017), and more studies are needed to identify challenges and opportunities of this approach to mapping and monitoring biodiversity.
17.3.2 Vegetation Condition
There have been many efforts to move beyond species assessments toward functional
aspects, which include vegetation condition. The current wealth of time series data
from sensors like NOAA AVHRR, MODIS, or Landsat allows measurements of ecosystem phenology, seasonality, and changes in onset of seasons and assessment of
F. Schrodt et al.