320
3-D vegetation structure, for example, by deriving canopy height models to characterize the structural complexity (Saarinen et al. 2017) or by describing vegetation
structures directly based on the vertical profiles of the 3-D point clouds (Wallace
et al. 2016).
Currently, the efficient use of UASs is limited to areas of less than a couple of
square kilometers. Therefore, their main application in the context of biodiversity
assessments is for sample-based observations (e.g., plots, transects) where relationships between spectral and structural traits and components of vegetation diversity
can be established. Given the option to create dense time series at user-defined frequencies with low effort, UASs offer scientists new opportunities for scaleappropriate measurement of ecological phenomena (Anderson and Gaston 2013)
such as phenological and other seasonal effects on canopy reflectance. Thus, they
can be used to bridge the gap between scale of observation and the scale of the ecological phenomena that long existed in the temporal and spatial domain when using
air- or spaceborne platforms. Therefore, UAS technology needs to be considered as
an important intermediate-scale technology for biodiversity monitoring systems
and upscaling from field-based measurements and models to larger area estimation.
13.2.2.2 Optical RS
The relationship between optical spectral variability over space or time and species
diversity can be used to optimize the inventory of species diversity, so priority may
be given to sites that are spectrally more different and hence more diverse in species
composition (Rocchini et al. 2005). Such analyses can be conducted at different
spatial extents and resolutions, from a few meters [e.g., using high-resolution
(~1–3 m multispectral) satellite data such as Worldview or GeoEye] to 10–30 m
(e.g., Sentinel, Landsat) up to large spatial grain and extent [e.g., Moderate
Resolution Imaging Spectroradiometer (MODIS) data from 250 m to 1000 m].
Alpha Diversity
Alpha diversity is the number of species living within a given local area and is a
measure of within-ecosystem species richness. Most research dealing with RS-based
estimates of alpha diversity has focused on mapping localized biodiversity hot
spots, based on the spectral variation hypothesis (SVH, (Palmer et al. 2002)). The
SVH states that the spatial variability in the remotely sensed signal, i.e., the spectral
heterogeneity, is expected to be positively related to environmental heterogeneity
and could therefore be used as a powerful proxy of species diversity. In other terms,
the greater the habitat heterogeneity, the greater the local species diversity within it,
regardless of the taxonomic group under consideration. Besides random variation in
species distribution, higher heterogeneity habitats will host a higher number of species each occupying a particular niche (niche difference model, Nekola and
White 1999).
A. Lausch et al.
3-D vegetation structure, for example, by deriving canopy height models to characterize the structural complexity (Saarinen et al. 2017) or by describing vegetation
structures directly based on the vertical profiles of the 3-D point clouds (Wallace
et al. 2016).
Currently, the efficient use of UASs is limited to areas of less than a couple of
square kilometers. Therefore, their main application in the context of biodiversity
assessments is for sample-based observations (e.g., plots, transects) where relationships between spectral and structural traits and components of vegetation diversity
can be established. Given the option to create dense time series at user-defined frequencies with low effort, UASs offer scientists new opportunities for scaleappropriate measurement of ecological phenomena (Anderson and Gaston 2013)
such as phenological and other seasonal effects on canopy reflectance. Thus, they
can be used to bridge the gap between scale of observation and the scale of the ecological phenomena that long existed in the temporal and spatial domain when using
air- or spaceborne platforms. Therefore, UAS technology needs to be considered as
an important intermediate-scale technology for biodiversity monitoring systems
and upscaling from field-based measurements and models to larger area estimation.
13.2.2.2 Optical RS
The relationship between optical spectral variability over space or time and species
diversity can be used to optimize the inventory of species diversity, so priority may
be given to sites that are spectrally more different and hence more diverse in species
composition (Rocchini et al. 2005). Such analyses can be conducted at different
spatial extents and resolutions, from a few meters [e.g., using high-resolution
(~1–3 m multispectral) satellite data such as Worldview or GeoEye] to 10–30 m
(e.g., Sentinel, Landsat) up to large spatial grain and extent [e.g., Moderate
Resolution Imaging Spectroradiometer (MODIS) data from 250 m to 1000 m].
Alpha Diversity
Alpha diversity is the number of species living within a given local area and is a
measure of within-ecosystem species richness. Most research dealing with RS-based
estimates of alpha diversity has focused on mapping localized biodiversity hot
spots, based on the spectral variation hypothesis (SVH, (Palmer et al. 2002)). The
SVH states that the spatial variability in the remotely sensed signal, i.e., the spectral
heterogeneity, is expected to be positively related to environmental heterogeneity
and could therefore be used as a powerful proxy of species diversity. In other terms,
the greater the habitat heterogeneity, the greater the local species diversity within it,
regardless of the taxonomic group under consideration. Besides random variation in
species distribution, higher heterogeneity habitats will host a higher number of species each occupying a particular niche (niche difference model, Nekola and
White 1999).
A. Lausch et al.