472
in geospatial data registration and alignment, and different processing frameworks
(see Quattrochi et al. 2017 for a thorough discussion).
Figure 17.3 presents an example workflow that depicts the steps required to
assess if there is a correlation between biomass and human population density in a
coastal area of Mexico. In this case, since biomass cannot be inferred directly from
RS sources (as described elsewhere in this book), several preprocessing and processing steps are required. Preprocessing steps include data preparation (data cleaning, atmospheric and geometric corrections), data transformation (e.g., from tabular
into rasterized data) and fusion, running of auxiliary methods such as classification
methods, and application of digital terrain models and height canopy models.
Processing steps incorporate the use of allometric equations using in-situ plot level
measurements of plant biomass in that area, which are also used as training and validation data to formulate the final aboveground biomass model fusing RS with in-situ
data. Here, we present examples of techniques dealing with some of the abovementioned challenges in aligning different sensors, fusing data from these different sources
across space and time, training fusion methods, and validating results.
17.5.1 Fusion
Data fusion is an invaluable tool to assess patterns and processes of biodiversity at
large spatial scales and integrate data across different aspects of remotely sensed
plant diversity, abiotic, and socioeconomic factors. Data fusion allows integration of
data from different sensors and of diverging spatial, spectral, and temporal extent to
produce outputs of increased fidelity and usefulness. Fusion is often performed to
account for limitation in one data source, e.g., where single data rather than time
series data are available in the sensor of interest (Carreiras et al. 2017) or to resample low-resolution data from one spaceborne RS channel using data from another,
high-resolution channel on the same sensor.
In the example mentioned above (Fig. 17.3), the authors chose to use optical data
from MODIS and radar data from ALOS PALSAR, and for their successful fusion,
it is important to consider the different spatial resolutions, temporal data availability, and sensor characteristics (Fig. 17.1). This kind of constellation is frequently
used to map a range of land cover and land use characteristics, including change,
conversion, and modification where detailed information on both broad land cover
classes from optical data and detailed surface roughness and moisture information
from radar images are required (Pereira et al. 2013; Dusseux et al. 2014; Stefanski
et al. 2014).
Different fusion techniques are applied to spaceborne or airborne sensors. For
example, Sankey et al. (2018) described an approach to fuse unmanned aerial vehicle (UAV) LiDAR with hyperspectral data using a decision tree classification technique and found the combined use of these sensors provided more accurate
assessments of 3D analyses of plant characteristics and plant species identification
at submeter spatial resolutions.
F. Schrodt et al.
in geospatial data registration and alignment, and different processing frameworks
(see Quattrochi et al. 2017 for a thorough discussion).
Figure 17.3 presents an example workflow that depicts the steps required to
assess if there is a correlation between biomass and human population density in a
coastal area of Mexico. In this case, since biomass cannot be inferred directly from
RS sources (as described elsewhere in this book), several preprocessing and processing steps are required. Preprocessing steps include data preparation (data cleaning, atmospheric and geometric corrections), data transformation (e.g., from tabular
into rasterized data) and fusion, running of auxiliary methods such as classification
methods, and application of digital terrain models and height canopy models.
Processing steps incorporate the use of allometric equations using in-situ plot level
measurements of plant biomass in that area, which are also used as training and validation data to formulate the final aboveground biomass model fusing RS with in-situ
data. Here, we present examples of techniques dealing with some of the abovementioned challenges in aligning different sensors, fusing data from these different sources
across space and time, training fusion methods, and validating results.
17.5.1 Fusion
Data fusion is an invaluable tool to assess patterns and processes of biodiversity at
large spatial scales and integrate data across different aspects of remotely sensed
plant diversity, abiotic, and socioeconomic factors. Data fusion allows integration of
data from different sensors and of diverging spatial, spectral, and temporal extent to
produce outputs of increased fidelity and usefulness. Fusion is often performed to
account for limitation in one data source, e.g., where single data rather than time
series data are available in the sensor of interest (Carreiras et al. 2017) or to resample low-resolution data from one spaceborne RS channel using data from another,
high-resolution channel on the same sensor.
In the example mentioned above (Fig. 17.3), the authors chose to use optical data
from MODIS and radar data from ALOS PALSAR, and for their successful fusion,
it is important to consider the different spatial resolutions, temporal data availability, and sensor characteristics (Fig. 17.1). This kind of constellation is frequently
used to map a range of land cover and land use characteristics, including change,
conversion, and modification where detailed information on both broad land cover
classes from optical data and detailed surface roughness and moisture information
from radar images are required (Pereira et al. 2013; Dusseux et al. 2014; Stefanski
et al. 2014).
Different fusion techniques are applied to spaceborne or airborne sensors. For
example, Sankey et al. (2018) described an approach to fuse unmanned aerial vehicle (UAV) LiDAR with hyperspectral data using a decision tree classification technique and found the combined use of these sensors provided more accurate
assessments of 3D analyses of plant characteristics and plant species identification
at submeter spatial resolutions.
F. Schrodt et al.