Single Tree Detection from Airborne Laser Scanning Data: A Stochastic Approach

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dc.contributor.advisorSohn, Gunho
dc.creatorZhang, Junjie
dc.date.accessioned2015-08-28T15:46:31Z
dc.date.available2015-08-28T15:46:31Z
dc.date.copyright2015-05-04
dc.date.issued2015-08-28
dc.date.updated2015-08-28T15:46:31Z
dc.degree.disciplineEarth & Space Science
dc.degree.levelDoctoral
dc.degree.namePhD - Doctor of Philosophy
dc.description.abstractCharacterizing and monitoring forests are of great scientific and managerial interests, such as understanding the global carbon circle, biodiversity conservation and management of natural resources. As an alternative or compliment to traditional remote sensing techniques, airborne laser scanning (ALS) has been placed in a very advantageous position in forest studies, for its unique ability to directly measure the distribution of vegetation materials in the vertical direction, as well as the terrain beneath the forest canopy. Serving as basis for tree-wise forest biophysical parameter and species information retrieval, single tree detection is a very motivating research topic in forest inventory. The objective of the study is to develop a method from the perspective of computer vision to detect single trees automatically from ALS data. For this purpose, this study explored different aspects of the problem. It starts from an improved pipeline for canopy height model (CHM) generation, which alleviates the distortion of tree crown shapes presented on CHMs resulted from conventional procedures due to the shadow effects of ALS data and produces pit-free CHM. The single tree detection method consists of a hybrid framework which integrates low-level image processing techniques, i.e. local maxima filtering (LM) and marker-controlled watershed segmentation (MCWS), into a high-level probabilistic model. In the proposed approach, tree crowns in the forest plot are modelled as a configuration of circular objects. The configuration containing the best possible set of detected tree objects is estimated by a global optimization solver in a probabilistic framework. The model features an accelerated optimization process compared with classical stochastic models, e.g. marked point processes. The parameter estimation is another issue: the study investigated both a reference-based supervised and an Expectation-Maximization (EM) based unsupervised method to estimate the parameters in the model. The model was tested in a temperate mature coniferous forest in Ontario, Canada, as well as simulated coniferous forest plots with various degrees of crown overlap. The experimental results showed the effectiveness of our proposed method, which was capable of reducing the commission errors produced by local maxima filtering based methods, thus increasing the overall detection accuracy by approximately 10% on all of the datasets.
dc.identifier.urihttp://hdl.handle.net/10315/30123
dc.language.isoen
dc.rightsAuthor owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.
dc.subjectRemote sensing
dc.subjectForestry
dc.subjectComputer engineering
dc.subject.keywordsLiDAR
dc.subject.keywordsAirborne laser scanning
dc.subject.keywordsForestry
dc.subject.keywordsSingle tree detection
dc.subject.keywordsIndividual tree detection
dc.subject.keywordsCanopy height model
dc.subject.keywordsCHM
dc.subject.keywordsHole-filling
dc.subject.keywordsForest plot simulation
dc.subject.keywordsConiferous forest
dc.subject.keywordsPoint processes
dc.subject.keywordsMarked point processes
dc.subject.keywordsProbabilistic model
dc.subject.keywordsStochastic model
dc.subject.keywordsLocal maxima filtering
dc.subject.keywordsMarker-controlled watershed segmentation
dc.subject.keywordsParameter estimation
dc.subject.keywordsMaximum likelihood
dc.subject.keywordsLogistic regression model
dc.subject.keywordsExpectation-Maximization
dc.subject.keywordsEM
dc.subject.keywordsEnergy minimization
dc.subject.keywordsMarkov Chain Monte Carlo
dc.subject.keywordsMCMC
dc.subject.keywordsReversible Jump Markov Chain Monte Carlo
dc.subject.keywordsRJMCMC
dc.subject.keywordsPrior-guided Markov Chain Monte Carlo
dc.subject.keywordsPGMCMC
dc.subject.keywordsSimulated annealing
dc.subject.keywordsOptimization
dc.titleSingle Tree Detection from Airborne Laser Scanning Data: A Stochastic Approach
dc.typeElectronic Thesis or Dissertationen_US

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