273 research outputs found
Spatial and temporal statistics of SAR and InSAR observations for providing indicators of tropical forest structural changes due to forest disturbance
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 Niño 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
Wavelet Based Analysis of TanDEM-X and LiDAR DEMs across a Tropical Vegetation Heterogeneity Gradient Driven by Fire Disturbance in Indonesia
Three-dimensional information provided by TanDEM-X interferometric phase and airborne Light Detection and Ranging (LiDAR) Digital ElevationModels (DEMs) were used to detect differences in vegetation heterogeneity through a disturbance gradient in Indonesia. The range of vegetation types developed as a consequence of fires during the 1997-1998 El Niño. Two-point statistic (wavelet variance and co-variance) was used to assess the dominant spatial frequencies associated with either topographic features or canopy structure. DEMs wavelet spectra were found to be sensitive to canopy structure at short scales (up to 8 m) but increasingly influenced by topographic structures at longer scales. Analysis also indicates that, at short scale, canopy texture is driven by the distribution of heights. Thematic class separation using the Jeffries-Matusita distance (JM) was greater when using the full wavelet signature (LiDAR: 1.29 ≤ JM ≤ 1.39; TanDEM-X: 1.18 ≤ JM ≤ 1.39) compared to using each decomposition scale individually (LiDAR: 0.1 ≤ JM ≤ 1.26; TanDEM-X: 0.1 ≤ JM ≤ 1.1). In some cases, separability with TanDEM-X was similar to the higher resolution LiDAR. The study highlights the potential of 3D information from TanDEM-X and LiDAR DEMs to explore vegetation disturbance history when analyzed using two-point statistics.</p
Change detection and landscape similarity comparison using computer vision methods
Human-induced disturbances of terrestrial and aquatic ecosystems continue at alarming rates. With the advent of both raw sensor and analysis-ready datasets, the need to monitor ecosystem disturbances is now more imperative than ever; yet the task is becoming increasingly complex with increasing sources and varieties of earth observation data. In this research, computer vision methods and tools are interrogated to understand their capability for comparing spatial patterns. A critical survey of literature provides evidence that computer vision methods are relatively robust to scale and highlights issues involved in parameterization of computer vision models for characterizing significant pattern information in a geographic context. Utilizing two widely used pattern indices to compare spatial patterns in simulated and real-world datasets revealed their potential to detect subtle changes in spatial patterns which would not otherwise be feasible using traditional pixel-level techniques. A texture-based CNN model was developed to extract spatially relevant information for landscape similarity comparison; the CNN feature maps proved to be effective in distinguishing agriculture landscapes from other landscape types (e.g., forest and mountainous landscapes). For real-world human disturbance monitoring, a U-Net CNN was developed and compared with a random forest model. Both modeling frameworks exhibit promising potential to map placer mining disturbance; however, random forests proved simple to train and deploy for placer mapping, while the U-Net may be used to augment RF as it is capable of reducing misclassification errors and will benefit from increasing availability of detailed training data
Study on Co-occurrence-based Image Feature Analysis and Texture Recognition Employing Diagonal-Crisscross Local Binary Pattern
In this thesis, we focus on several important fields on real-world image texture analysis and recognition. We survey various important features that are suitable for texture analysis. Apart from the issue of variety of features, different types of texture datasets are also discussed in-depth. There is no thorough work covering the important databases and analyzing them in various viewpoints. We persuasively categorize texture databases ? based on many references. In this survey, we put a categorization to split these texture datasets into few basic groups and later put related datasets. Next, we exhaustively analyze eleven second-order statistical features or cues based on co-occurrence matrices to understand image texture surface. These features are exploited to analyze properties of image texture. The features are also categorized based on their angular orientations and their applicability. Finally, we propose a method called diagonal-crisscross local binary pattern (DCLBP) for texture recognition. We also propose two other extensions of the local binary pattern. Compare to the local binary pattern and few other extensions, we achieve that our proposed method performs satisfactorily well in two very challenging benchmark datasets, called the KTH-TIPS (Textures under varying Illumination, Pose and Scale) database, and the USC-SIPI (University of Southern California ? Signal and Image Processing Institute) Rotations Texture dataset.九州工業大学博士学位論文 学位記番号:工博甲第354号 学位授与年月日:平成25年9月27日CHAPTER 1 INTRODUCTION|CHAPTER 2 FEATURES FOR TEXTURE ANALYSIS|CHAPTER 3 IN-DEPTH ANALYSIS OF TEXTURE DATABASES|CHAPTER 4 ANALYSIS OF FEATURES BASED ON CO-OCCURRENCE IMAGE MATRIX|CHAPTER 5 CATEGORIZATION OF FEATURES BASED ON CO-OCCURRENCE IMAGE MATRIX|CHAPTER 6 TEXTURE RECOGNITION BASED ON DIAGONAL-CRISSCROSS LOCAL BINARY PATTERN|CHAPTER 7 CONCLUSIONS AND FUTURE WORK九州工業大学平成25年
Characterisation and monitoring of forest disturbances in Ireland using active microwave satellite platforms
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
Remote Sensing
This dual conception of remote sensing brought us to the idea of preparing two different books; in addition to the first book which displays recent advances in remote sensing applications, this book is devoted to new techniques for data processing, sensors and platforms. We do not intend this book to cover all aspects of remote sensing techniques and platforms, since it would be an impossible task for a single volume. Instead, we have collected a number of high-quality, original and representative contributions in those areas
Improving Wishart Classification of Polarimetric SAR Data Using the Hopfield Neural Network Optimization Approach.
This paper proposes the optimization relaxation approach based on the analogue Hopfield Neural Network (HNN) for cluster refinement of pre-classified Polarimetric Synthetic Aperture Radar (PolSAR) image data. We consider the initial classification provided by the maximum-likelihood classifier based on the complex Wishart distribution, which is then supplied to the HNN optimization approach. The goal is to improve the classification results obtained by the Wishart approach. The classification improvement is verified by computing a cluster separability coefficient and a measure of homogeneity within the clusters. During the HNN optimization process, for each iteration and for each
pixel, two consistency coefficients are computed, taking into account two types of relations between the pixel under consideration and its corresponding neighbors. Based on these coefficients and on the information coming from the pixel itself, the pixel under study is re-classified. Different experiments are carried out to verify that the proposed approach outperforms other strategies, achieving the best results in terms of separability and a
trade-off with the homogeneity preserving relevant structures in the image. The performance is also measured in terms of computational central processing unit (CPU) times
ALOS-2/PALSAR-2 Calibration, Validation, Science and Applications
Twelve edited original papers on the latest and state-of-art results of topics ranging from calibration, validation, and science to a wide range of applications using ALOS-2/PALSAR-2. We hope you will find them useful for your future research
Computational and Numerical Simulations
Computational and Numerical Simulations is an edited book including 20 chapters. Book handles the recent research devoted to numerical simulations of physical and engineering systems. It presents both new theories and their applications, showing bridge between theoretical investigations and possibility to apply them by engineers of different branches of science. Numerical simulations play a key role in both theoretical and application oriented research
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