Tropical forests are extremely important ecosystems which play a substantial role
in the global carbon budget and are increasingly dominated by anthropogenic
disturbance through deforestation and forest degradation, contributing to emissions
of greenhouse gases to the atmosphere.
There is an urgent need for forest monitoring over extensive and inaccessible
tropical forest which can be best accomplished using spaceborne satellite data.
Currently, two key processes are extremely challenging to monitor: forest
degradation and post-disturbance re-growth.
The thesis work focuses on these key processes by considering change indicators
derived from radar remote sensing signal that arise from changes in forest structure.
The problem is tackled by exploiting spaceborne Synthetic Aperture Radar (SAR) and
Interferometric SAR (InSAR) observations, which can provide forest structural
information while simultaneously being able to collect data independently of cloud
cover, haze and daylight conditions which is a great advantage over the tropics.
The main principle of the work is that a connection can be established between
the forest structure distribution in space and signal variation (spatial statistics) within
backscatter and Digital Surface Models (DSMs) provided by SAR. In turn, forest
structure spatial characteristics and changes are used to map forest condition (intact
or degraded) or disturbance.
The innovative approach focuses on looking for textural patterns (and their
changes) in radar observations, then connecting these patterns to the forest state
through supporting evidence from expert knowledge and auxiliary remote sensing
observations (e.g. high resolution optical, aerial photography or LiDAR). These
patterns are descriptors of the forest structural characteristics in a statistical sense, but
are not estimates of physical properties, such as above-ground biomass or canopy
height. The thesis tests and develops methods using novel remote sensing technology
(e.g. single-pass spaceborne InSAR) and modern image statistical analysis methods
(wavelet-based space-scale analysis). The work is developed on an experimental basis and articulated in three test
cases, each addressing a particular observational setting, analytical method and
thematic context.
The first paper deals with textural backscatter patterns (C-band ENVISAT ASAR
and L-band ALOS PALSAR) in semi-deciduous closed forest in Cameroon. Analysis
concludes that intact forest and degraded forest (arising from selective logging) are
significantly different based on canopy structural properties when measured by
wavelet based space-scale analysis. In this case, C-band data are more effective than
longer wavelength L-band data. Such a result could be explained by the lower wave
penetration into the forest volume at shorter wavelength, with the mechanism
driving the differences between the two forest states arising from upper canopy
heterogeneity.
In the second paper, wavelet based space-scale analysis is also used to provide
information on upper canopy structure. A DSM derived from TanDEM-X acquired in
2014 was used to discriminate primary lowland Dipterocarp forest, secondary forest,
mixed-scrub and grassland in the Sungai Wain Protection Forest (East Kalimantan,
Indonesian Borneo) which was affected by the 1997/1998 El NiΓ±o Southern Oscillation
(ENSO). The Jeffries- Matusita separability of wavelet spectral measures of InSAR
DSMs between primary and secondary forest was in some cases comparable to results
achieved by high resolution LiDAR data.
The third test case introduces a temporal component, with change detection
aimed at detecting forest structure changes provided by differencing TanDEM-X
DSMs acquired at two dates separated by one year (2012-2013) in the Republic of
Congo. The method enables cancelling out the component due to terrain elevation
which is constant between the two dates, and therefore the signal related to the forest
structure change is provided. Object-based change detection successfully mapped a
gradient of forest volume loss (deforestation/forest degradation) and forest volume
gain (post-disturbance re-growth).
Results indicate that the combination of InSAR observations and wavelet based
space-scale analysis is the most promising way to measure differences in forest structure arising from forest fires. Equally, the process of forest degradation due to
shifting cultivation and post-disturbance re-growth can be best detected using
multiple InSAR observations.
From the experiments conducted, single-pass InSAR appears to be the most
promising remote sensing technology to detect forest structure changes, as it provides
three-dimensional information and with no temporal decorrelation. This type of
information is not available in optical remote sensing and only partially available
(through a 2D mapping) in SAR backscatter. It is advised that future research or
operational endeavours aimed at mapping and monitoring forest degradation/regrowth
should take advantage of the only currently available high resolution
spaceborne single-pass InSAR mission (TanDEM-X).
Moreover, the results contribute to increase knowledge related to the role of SAR
and InSAR for monitoring degraded forest and tracking the process of forest
degradation which is a priority but still highly challenging to detect. In the future the
techniques developed in the thesis work could be used to some extent to support
REDD+ initiatives