469
without explicitly taking into account human modification of the landscape through
management, engineered novel communities, land use history, and heterogeneous
landscapes (Abelleira Martínez et al. 2016). Anthropogenic aspects that can be
assessed easily and incorporated into landscape scale analyses include data on population density, socioeconomics, and pollution (see Table 17.1).
Land use is another important anthropogenic aspect but less easily assessed at
the landscape scale using RS techniques alone. This is mainly due to the same land
cover (which refers to the physical characteristics of a landscape) having the potential of belonging to different land use categories (referring to the human use of this
landscape). For example, the land cover class forest could be within the land use
category natural primary forest or heavily managed degraded forest. Consequently,
large-scale land use mapping depends on auxiliary data such as that coming from
open access crowdsourced land cover and land use data to improve ground-truthing
and validation (Fritz et al. 2017) and a combination of RS data sources, such as
fusion of spaceborne optical data with radar data (Joshi et al. 2016).
At the other end of the spectrum of land uses are the human modified, urbanized,
and infrastructure types such as cities and roads. RS has demonstrated a great potential to map impervious surface and more limited success in detecting roads. There
has been a growing interest in urban ecology because more than half of the global
population now lives in cities, and there is a growing interest to increase healthy
urban living that combines well-being and biodiversity (Botzat et al. 2016). The first
step to reach this goal is to create urban green belts (Hostetler et al. 2011), which are
expected to bring about increasing numbers of native species and increased connectivity (Aronson et al. 2017). However, urban areas are also linked to high richness (Gavier-Pizarro et al. 2010) and spread of invasive species (Hui et al. 2017),
and small urban centers are sources of invasive plants into natural areas (McLean
et al. 2017).
RS of urban (invasive) plant species is covered in another chapter in this book
(see Bolch et al., Chap. 12). Roads are more difficult to retrieve with RS alone,
although the fishbone patterns in the Brazilian Amazon are evident in Landsat data
(Alves and Skole 1996). Initiatives like OpenStreetMap can provide auxiliary data
to improve the accuracy of RS-only estimates. These data are fundamental to assess
global roadless areas and fundamental for maintaining biodiversity processes and
avoiding deleterious effects of fragmentation.
17.4.4 Land Cover
Among the most traditional applications of RS are those related to the estimation of
biophysical variables (e.g., tree density, vegetation health). AVHRR, Landsat,
Sentinel-2, and MODIS are the most widely used sensors for this purpose, but integration of optical RS with LiDAR technology significantly improves the estimation and
assessment of vegetation structure due to added horizontal and vertical information
of vegetation properties (e.g., canopy height) (Lim et al. 2003). Studies combining
17 Integrating Biodiversity, Remote Sensing, and Auxiliary Information for the Study…
without explicitly taking into account human modification of the landscape through
management, engineered novel communities, land use history, and heterogeneous
landscapes (Abelleira Martínez et al. 2016). Anthropogenic aspects that can be
assessed easily and incorporated into landscape scale analyses include data on population density, socioeconomics, and pollution (see Table 17.1).
Land use is another important anthropogenic aspect but less easily assessed at
the landscape scale using RS techniques alone. This is mainly due to the same land
cover (which refers to the physical characteristics of a landscape) having the potential of belonging to different land use categories (referring to the human use of this
landscape). For example, the land cover class forest could be within the land use
category natural primary forest or heavily managed degraded forest. Consequently,
large-scale land use mapping depends on auxiliary data such as that coming from
open access crowdsourced land cover and land use data to improve ground-truthing
and validation (Fritz et al. 2017) and a combination of RS data sources, such as
fusion of spaceborne optical data with radar data (Joshi et al. 2016).
At the other end of the spectrum of land uses are the human modified, urbanized,
and infrastructure types such as cities and roads. RS has demonstrated a great potential to map impervious surface and more limited success in detecting roads. There
has been a growing interest in urban ecology because more than half of the global
population now lives in cities, and there is a growing interest to increase healthy
urban living that combines well-being and biodiversity (Botzat et al. 2016). The first
step to reach this goal is to create urban green belts (Hostetler et al. 2011), which are
expected to bring about increasing numbers of native species and increased connectivity (Aronson et al. 2017). However, urban areas are also linked to high richness (Gavier-Pizarro et al. 2010) and spread of invasive species (Hui et al. 2017),
and small urban centers are sources of invasive plants into natural areas (McLean
et al. 2017).
RS of urban (invasive) plant species is covered in another chapter in this book
(see Bolch et al., Chap. 12). Roads are more difficult to retrieve with RS alone,
although the fishbone patterns in the Brazilian Amazon are evident in Landsat data
(Alves and Skole 1996). Initiatives like OpenStreetMap can provide auxiliary data
to improve the accuracy of RS-only estimates. These data are fundamental to assess
global roadless areas and fundamental for maintaining biodiversity processes and
avoiding deleterious effects of fragmentation.
17.4.4 Land Cover
Among the most traditional applications of RS are those related to the estimation of
biophysical variables (e.g., tree density, vegetation health). AVHRR, Landsat,
Sentinel-2, and MODIS are the most widely used sensors for this purpose, but integration of optical RS with LiDAR technology significantly improves the estimation and
assessment of vegetation structure due to added horizontal and vertical information
of vegetation properties (e.g., canopy height) (Lim et al. 2003). Studies combining
17 Integrating Biodiversity, Remote Sensing, and Auxiliary Information for the Study…