imagery. Depending on the satellite signals and processing, the precision of the
coordinate data can be enhanced by up to a centimeter level of error with the PPK
method (Iizuka et al. 2019).
Although this method initially appears to be more suitable for use in non-forested
areas or terrain, the increased accuracy and sophisticated data processing methods
are likely to be eventually applied to other land cover types. Therefore, aerial photo
collection using 3D-photogrammetry technology based on UAVs can supply a costeffective and technically robust alternative for accumulating topographic information on peatlands at the landscape scale.
5.6.3 Infrared Sensor
Satellite images that supply earth surface reflectance data from the visible to nearand mid-infrared spectrum (400–13,000 nm) with various spatial resolutions can be
trained to identify land-cover vegetation types. Analysis of these images might be
related to peat type, whereas fine-resolution object-based classification might be able
to map features such as drains (Minasny et al. 2019). As organic matter, peat has a
unique spectral signature with lower reflectance in the near-infrared (NIR) and
shortwave-infrared (SWIR) portion of the electromagnetic spectrum (Krankina
et al. 2008). Spectral indices calculated from the NIR and SWIR liquid water
absorption bands were used to indicate peat moisture status. Airborne hyperspectral
imaging (in the visible and NIR range) has been tested on small areas for mapping of
peatland vegetation types (Harris et al. 2015).
In addition to use in digital mapping of peatland area, infrared sensors on
satellites can also be used to detect and quantify peat fires, e.g., MODIS, VIIRS,
and Landsat. However, these methods lack the required spatial resolution to detect
hotspots associated with underground fires (typical resolution of 375 m to 1 km)
(Gumbricht et al. 2002). Although this method can cover a large area, the pixel size
is too wide, and thus it cannot clearly describe the field conditions. Moreover, for fire
detection, complete images of land are collected every 1–2 days, and thus the
systems cannot supply real-time detection.
A thermal infrared (TIR) sensor mounted on a UAV presents a potential solution
for detection and management of peat fires because it allows large areas to be
surveyed quickly from above and can detect the heat transferred to the surface
above a fire (Burke et al. 2019). A pilot trial conducted by WSL-MTI demonstrated
potential for use of UAVs to interpret the characteristics of thermal trends in peatland
environments with notably high spatial and temporal resolution. The trends in the
thermal information clearly show the differences among land cover types, and the
heating and cooling of the peat vary throughout the study area. The proposed method
can guide strategic approaches for monitoring of peatlands (Iizuka et al. 2018),
especially in prevention of peat fires. One of the areas that must be improved in use
of UAV technology is extension of the flight time to increase the effectiveness of
monitoring activity. The combination of satellite-based monitoring and UAV
5 Evaluation of Eco-Management of Tropical Peatlands
181
coordinate data can be enhanced by up to a centimeter level of error with the PPK
method (Iizuka et al. 2019).
Although this method initially appears to be more suitable for use in non-forested
areas or terrain, the increased accuracy and sophisticated data processing methods
are likely to be eventually applied to other land cover types. Therefore, aerial photo
collection using 3D-photogrammetry technology based on UAVs can supply a costeffective and technically robust alternative for accumulating topographic information on peatlands at the landscape scale.
5.6.3 Infrared Sensor
Satellite images that supply earth surface reflectance data from the visible to nearand mid-infrared spectrum (400–13,000 nm) with various spatial resolutions can be
trained to identify land-cover vegetation types. Analysis of these images might be
related to peat type, whereas fine-resolution object-based classification might be able
to map features such as drains (Minasny et al. 2019). As organic matter, peat has a
unique spectral signature with lower reflectance in the near-infrared (NIR) and
shortwave-infrared (SWIR) portion of the electromagnetic spectrum (Krankina
et al. 2008). Spectral indices calculated from the NIR and SWIR liquid water
absorption bands were used to indicate peat moisture status. Airborne hyperspectral
imaging (in the visible and NIR range) has been tested on small areas for mapping of
peatland vegetation types (Harris et al. 2015).
In addition to use in digital mapping of peatland area, infrared sensors on
satellites can also be used to detect and quantify peat fires, e.g., MODIS, VIIRS,
and Landsat. However, these methods lack the required spatial resolution to detect
hotspots associated with underground fires (typical resolution of 375 m to 1 km)
(Gumbricht et al. 2002). Although this method can cover a large area, the pixel size
is too wide, and thus it cannot clearly describe the field conditions. Moreover, for fire
detection, complete images of land are collected every 1–2 days, and thus the
systems cannot supply real-time detection.
A thermal infrared (TIR) sensor mounted on a UAV presents a potential solution
for detection and management of peat fires because it allows large areas to be
surveyed quickly from above and can detect the heat transferred to the surface
above a fire (Burke et al. 2019). A pilot trial conducted by WSL-MTI demonstrated
potential for use of UAVs to interpret the characteristics of thermal trends in peatland
environments with notably high spatial and temporal resolution. The trends in the
thermal information clearly show the differences among land cover types, and the
heating and cooling of the peat vary throughout the study area. The proposed method
can guide strategic approaches for monitoring of peatlands (Iizuka et al. 2018),
especially in prevention of peat fires. One of the areas that must be improved in use
of UAV technology is extension of the flight time to increase the effectiveness of
monitoring activity. The combination of satellite-based monitoring and UAV
5 Evaluation of Eco-Management of Tropical Peatlands
181