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Statistical algorithms for land/forest cover change detection using remote sensing data

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Anees, A (2016) Statistical algorithms for land/forest cover change detection using remote sensing data. PhD thesis, University of Tasmania.

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Abstract

Land cover changes significantly affect climate, hydrology, bio-diversity, socio-economic
stability and food security. Some of these changes being studied in remote sensing discipline
include, but are not limited to, anthropogenic changes e.g. clear-cutting of forests
for human settlements, and beetle/insect infestations in the forests. Beetle/insect infestations
cause considerable damage to the forests resulting in tree mortality on large scale
which provides fuel for fires and wastes valuable wood. Therefore, early detection of such
changes is often desired by the authorities in order to carry out timely actions to mitigate
them. However, manual monitoring using high resolution photography or field surveys
can become very difficult and time consuming or even infeasible because such changes
cover very large areas. This necessitates development of automated remote sensing algorithms
which can monitor large areas with minimal human intervention. Several land
cover change detection algorithms exist in literature which utilize remotely sensed imagery
captured by different satellites. However, there are a very few studies which detect
such changes in near-real time manner. Furthermore, there is still a room for improvement
in the detection accuracy, detection delays and computational complexity of such algorithms.
This thesis utilizes coarse (500 m) and moderate (30 m) spatial resolution satellite
imagery (MODIS and Landsat 7 ETM+, respectively) and proposes four statistical algorithms
for detection of land cover changes with significant improvements. The first
algorithm (published in IEEE Journal of Selected Topics in Applied Earth Observations
and Remote Sensing) is a supervised technique developed for near-real time detection of
beetle infestations in pine forests of North America (British Columbia and Colorado). It
models the hyper-temporal multi-spectral MODIS Vegetation Index (VI) time series with
a triply modulated cosine function using a sliding window non-linear least squares and
applies a change metric based on log-likelihood ratios to the trend parameter time-series
of the fitted model, instead of the raw vegetation index. Significant improvement, in the detection accuracy with reduced detection delays, was achieved with this first published
algorithm. The second algorithm (published in IEEE Geoscience and Remote Sensing
Letters) is unsupervised and makes use of properties of Martingale Central Limit Theorem
(MCLT) in the change metric derived from the parameter time series, in order to
avoid threshold tuning while detecting beetle infestation in MODIS vegetation index time
series. The third algorithm (published in IEEE Journal of Selected Topics in Applied
Earth Observations and Remote Sensing) avoids the Gaussian distribution based change
metrics, one of the limitations of the existing methods, and improves the change detection
accuracy and detection delays significantly by using assumption free, directly estimated
relative density-ratio based Repeated Sequential Probability Ratio Test (RSPRT) as test
statistic on the parameter time series. The fourth algorithm (submitted to ISPRS Journal of
Photogrammetry and Remote Sensing) is a supervised technique that utilizes bi-temporal
multi-spectral Landsat 7 ETM+ imagery and models class posterior probabilities of the
change and no change classes non-parametrically, using a linear combination of a large
number of Gaussian Kernels through Least Squares Probabilistic Classifier (LSPC) formulation.
This helps in avoiding the Gaussian assumption about the data, which is a
major drawback of the traditional Bayesian Classifiers e.g. Maximum Likelhood Classifier
(MLC) and Naive Bayes (NB). Another popular classifier which avoids this limitation
is Support Vector Machine (SVM) but its drawback is that it assigns only class labels to
the test samples and does not provide probabilistic class-memberships. Moreover, it is a
binary classifier in its original formulation and needs different strategies to be adopted in
order to use it for multi-class problems and also to make it probabilistic. The LSPC based
framework, on the other hand, is non-parametric as well as capable of assigning degree
of class-membership (probabilistic) to test samples and handling multi-class problems in
its original formulation. Its application to leaf beetle infestation problem in north-eastern
Tasmanian Eucalyptus plantations suggested improvement in detection accuracy. These methods have been compared with their respective counterparts in the literature in order
to demonstrate their effectiveness.
The contribution of this thesis is four folds: (i) advancing our understanding of land/forest
cover change detection using quantitative/statistical approaches, (ii) proposition of reliable
statistical approaches for near-real time land/forest cover change detection in hypertemporal
coarse spatial resolution MODIS data, with especial emphasis on changes due
to beetle infestations in pine forests, (iii) efficient threshold selection in complex scenario
of near-real time change detection, when an optimal trade off between more than 2 performance
indices is needed, and (iv) proposition of a non-parametric approach for detecting
land/forest cover changes (bark beetle problem in north-eastern Tasmanian Eucalyptus
plantations) in bi-temporal Landsat 7 EMT+ data.

Item Type: Thesis (PhD)
Keywords: Change detection, remote sensing, statistical algorithms, supervised classification
Copyright Information:

Copyright 2016 the author

Additional Information:

Chapter 2 appears to be the equivalent of a post-print of an article published as: Anees, A., Aryal, J., (2014), Near-real time detection of beetle infestation in pine forests using MODIS data, IEEE journal of selected topics in applied Earth observations and remote sensing, 7(9), 3713-3723. © © 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. 10.1109/JSTARS.2014.2330830

Chapter 3 appears to be the equivalent of a post-print of an article published as: Anees, A., Aryal, J., (2014), A Statistical framework for near-real time detection of beetle infestation in pine forests using MODIS data, IEEE geoscience and remote sensing letters, 11(10), 1717-1721. © © 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. 10.1109/LGRS.2014.2306712

Chapter 4 appears to be the equivalent of a post-print of an article published as: Anees, A., Aryal, J., O'Riley, M.M., Gale, T.J., (2016), A Relative density ratio based framework for detection of land cover changes in MODIS NDVI time-series, IEEE journal of selected topics in applied Earth observations and remote sensing, 9(8), 3359-3371. © © 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. 10.1109/JSTARS.2015.2428306

Date Deposited: 16 Sep 2016 03:21
Last Modified: 05 Sep 2017 23:14
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