470
optical, LiDAR, and radar RS have been applied to study interactions between biotic
(i.e., vegetation) and abiotic (i.e., soil, geomorphology) elements at landscape scales
and to quantify the carbon cycle and biomass.
Land cover is another commonly analyzed measure (in their recent review, Ma
et al. (2017) analyzed 254 experimental cases and 173 scientific papers on the
subject) that has been shown to be highly sensitive to the classification method
applied, with the optimal approach depending to a large extent on spatial resolution,
differences between land cover types, and training set size. Land cover maps are
often used to derive landscape structural features such as patch size, isolation, and
perimeter- to-area ratio.
Such landscape metrics can be assessed using patch matrix models (PMM),
which are most suitable for high-hemeroby (low naturalness and high anthropogenic pressure, e.g., urban) landscapes due to reduced spatiotemporal heterogeneity,
while gradient models (GM) are recommended for low-hemeroby landscapes (e.g.,
undisturbed forest) (Lausch et al. 2015). While PMMs are relatively well established and easy to use, disadvantages include that heterogeneity information might
be lost, patches tend to have sharp boundaries, and results are highly sensitive to
misclassifications of land cover and use metrics (Lausch et al. 2015). GMs, on the
other hand, are more complex to use and require more computing capacity and RS
expertise while being less susceptible to loss of heterogeneity information and artificially sharp boundaries (Lausch et al. 2015). Both models use a variety of data as
inputs, including hyperspectral and LiDAR RS as well as in-situ data, thereby taking full advantage of opportunities offered by each methodology.
17.5 Methods to Integrate Remotely Sensed Measures
of Plant Biodiversity with In-Situ Plant Diversity,
Abiotic, and Socioeconomic Data
Studies of plant diversity at the landscape level frequently require a mix of data
sources from various sensors as well as in-situ data (Table 17.1, Fig. 17.3). There
are thus three main reasons for integrating measures of biodiversity-related variables across different data sources: (i) combining data from different sensors to
make use of different vegetation aspects measured (e.g., MODIS vs. Advanced
Land Observation Satellite Phased Array type L-band Synthetic Aperture Radar
(ALOS PALSAR)); (ii) combining different sensors to simulate higher spatiotemporal and spectral resolutions to save financial resources or account for gaps in
available RS data (e.g., Zeng et al. 2017); and (iii) combining in-situ with RS data
for upscaling and validation.
The process of integrating, combining, and correlating data from different sensors and data types of different temporal and spatial scales is not straightforward.
Challenges are numerous and include sensor calibration, the propagation of uncertainties from individual data sets with inherent and variable uncertainty and impreciseness, outliers and spurious data, bias due to spatial autocorrelation, differences
F. Schrodt et al.
optical, LiDAR, and radar RS have been applied to study interactions between biotic
(i.e., vegetation) and abiotic (i.e., soil, geomorphology) elements at landscape scales
and to quantify the carbon cycle and biomass.
Land cover is another commonly analyzed measure (in their recent review, Ma
et al. (2017) analyzed 254 experimental cases and 173 scientific papers on the
subject) that has been shown to be highly sensitive to the classification method
applied, with the optimal approach depending to a large extent on spatial resolution,
differences between land cover types, and training set size. Land cover maps are
often used to derive landscape structural features such as patch size, isolation, and
perimeter- to-area ratio.
Such landscape metrics can be assessed using patch matrix models (PMM),
which are most suitable for high-hemeroby (low naturalness and high anthropogenic pressure, e.g., urban) landscapes due to reduced spatiotemporal heterogeneity,
while gradient models (GM) are recommended for low-hemeroby landscapes (e.g.,
undisturbed forest) (Lausch et al. 2015). While PMMs are relatively well established and easy to use, disadvantages include that heterogeneity information might
be lost, patches tend to have sharp boundaries, and results are highly sensitive to
misclassifications of land cover and use metrics (Lausch et al. 2015). GMs, on the
other hand, are more complex to use and require more computing capacity and RS
expertise while being less susceptible to loss of heterogeneity information and artificially sharp boundaries (Lausch et al. 2015). Both models use a variety of data as
inputs, including hyperspectral and LiDAR RS as well as in-situ data, thereby taking full advantage of opportunities offered by each methodology.
17.5 Methods to Integrate Remotely Sensed Measures
of Plant Biodiversity with In-Situ Plant Diversity,
Abiotic, and Socioeconomic Data
Studies of plant diversity at the landscape level frequently require a mix of data
sources from various sensors as well as in-situ data (Table 17.1, Fig. 17.3). There
are thus three main reasons for integrating measures of biodiversity-related variables across different data sources: (i) combining data from different sensors to
make use of different vegetation aspects measured (e.g., MODIS vs. Advanced
Land Observation Satellite Phased Array type L-band Synthetic Aperture Radar
(ALOS PALSAR)); (ii) combining different sensors to simulate higher spatiotemporal and spectral resolutions to save financial resources or account for gaps in
available RS data (e.g., Zeng et al. 2017); and (iii) combining in-situ with RS data
for upscaling and validation.
The process of integrating, combining, and correlating data from different sensors and data types of different temporal and spatial scales is not straightforward.
Challenges are numerous and include sensor calibration, the propagation of uncertainties from individual data sets with inherent and variable uncertainty and impreciseness, outliers and spurious data, bias due to spatial autocorrelation, differences
F. Schrodt et al.