Characterisation and monitoring of forest disturbances in Ireland using active microwave satellite platforms
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Date
2020
Authors
Malur Balaji, Preethi
Journal Title
Journal ISSN
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Publisher
University College Cork
Published Version
Abstract
Forests are one of the major carbon sinks that significantly contribute towards achieving
targets of the Kyoto Protocol, and its successors, in reducing greenhouse (GHG)
emissions. In order to contribute to regular National Inventory Reporting, and as part of
the on-going development of the Irish national GHG reporting system (CARBWARE),
improvements in characterisation of changes in forest carbon stocks have been
recommended to provide a comprehensive information flow into CARBWARE. The Irish
National Forest Inventory (NFI) is updated once every six years, thus there is a need for
an enhanced forest monitoring system to obtain annual forest updates to support
government agencies and forest management companies in their strategic decision making
and to comply with international GHG reporting standards. Sustainable forest
management is imperative to promote net carbon absorption from forests. Based on the
NFI data, Irish forests have removed or sequestered an average of 3.8 Mt of atmospheric
CO2 per year between 2007 and 2016. However, unmanaged and degraded forests become
a net emitter of carbon. Disturbances from human induced activities such as clear felling,
thinning and deforestation results in carbon emissions back into the atmosphere. Funded
by the Department of Agriculture, Food and the Marine (DAFM, Ireland), this PhD study
focuses on exploring the potential of data from L-band Synthetic Aperture Radar (SAR)
satellite based sensors for monitoring changes in the small stand forests of Ireland.
Historic data from ALOS PALSAR in the late 2000s and more recent data from ALOS-2
PALSAR-2 sensors have been used to map forest areas and characterise the different
disturbances observed within three different regions of Ireland. Forest mapping and
disturbance characterisation was achieved by combining the machine learning supervised
Random Forests (RF) and unsupervised Iterative Self-Organizing Data Analysis
(ISODATA) classification techniques. The lack of availability of ground truth data
supported use of this unsupervised approach which forms natural clusters based on their
multi-temporal signatures, with divergence statistics used to select the optimal number of
clusters to represent different forest classes. This approach to forest monitoring using SAR imagery has not been reported in the peer-review literature and is particularly beneficial
where there is a dearth of ground-based information. When applied to the forests, mapped
with an accuracy of up to 97% by RF, the ISODATA technique successfully identified
the unique multi-temporal pattern associated with clear-fells which exhibited a decrease
of 4 to 5 decibels (dB) between the images acquired before and after the event. The
clustering algorithm effectively highlighted the occurrence of other disturbance events
within forests with a decrease of 2±0.5dB between two consecutive years, as well as areas
of tree growth and afforestation.
A highlight of the work is the successful transferability of the algorithm, developed using
ALOS PALSAR, to ALOS-2 PALSAR-2 data thereby demonstrating the potential
continuity of annual forest monitoring. The higher spatial and radiometric resolutions of
ALOS-2 PALSAR-2 data have shown improvements in forest mapping compared to
ALOS PALSAR data. From mapping a minimum forest size of 1.8 ha with ALOS
PALSAR, a minimum area of 1.1 ha was achieved with the ALOS-2 PALSAR-2 images.
Moreover, even with some different backscatter characteristics of images acquired in
different seasons, similar signature patterns between the sensors were retrieved that helped
to define the cluster groups, thus demonstrating the robustness of the algorithm and its
successful transferability.
Having proven the potential to monitor forest disturbances, the results from both the
sensors were used to detect deforestation over the time period 2007-2016. Permanent
land-use changes pertaining to conversion of forests to agricultural lands and windfarms
were identified which are important with respect to forest monitoring and carbon reporting
in Ireland.
Overall, this work has presented a viable approach to support forest monitoring operations
in Ireland. By providing disturbance information from SAR, it can supplement projects
working with optical images which are generally limited by cloud cover, particularly in
parts of northern, western and upland Ireland. This approach adds value to ground based
forest monitoring by mapping distinct forests over large areas on an annual basis. This
study has demonstrated the ability to apply the algorithm to three different study areas,
with a vision to operationalise the algorithm on a national scale. The main limitations
experienced in this study were the lack of L-band SAR data availability and reference
datasets. With typically only one image acquired per year, and discrepancies and
omissions existing within reference datasets, understanding the behaviour of certain
cluster groups representing disturbances was challenging. However, this approach has
addressed some issues within the reference datasets, for example locating areas for which
a felling licence was granted but where trees were never cut, by providing detailed
systematic mapping of forests. Future satellites such as Tandem-L, SAOCOM-2A and 2B,
P-band BIOMASS mission and ALOS-4 PALSAR-3 may overcome the issue of limited
SAR image acquisitions provided more images per year are available, especially during
the summer months.
Description
Keywords
Forest , Forest disturbances , Image classification , Radar remote sensing , SAR
Citation
Malur Balaji, P. 2020. Characterisation and monitoring of forest disturbances in Ireland using active microwave satellite platforms. PhD Thesis, University College Cork.