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Spatial Dependence Clusters in the Estimation of Forest Structural Parameters

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Spatial Dependence Clusters in the Estimation of Forest Structural Parameters Spatial Dependence Clusters in the Estimation of Forest Structural Parameters Michael Albert Wulder A thesis presented to the University of Waterloo in fulfillment of the thesis requirement for the degree of Doctor of Philosophy in Geography Waterloo, Ontario, Canada, 1998 O Michael Albert Wulder 1 998 National Library Bibliotheque nationale 191 of Canada du Canada Acquisitions and Acquisitions et Bibliographic Services services bibliographiques 395 Wellington Street 395, rue Wellington Ottawa ON K1A ON4 OttawaON KtAON4 Canada Canada Your file Votre reOrenw Our tile Notre rei6rencs The author has granted a non- L'auteur a accorde une licence non exclusive licence allowing the exclusive permettant a la National Library of Canada to Bibliotheque nationale du Canada de reproduce, loan, distribute or sell reproduire, peter, distribuer ou copies of this thesis in microform, vendre des copies de cette these sous paper or electronic formats. la fome de microfichelfilm, de reproduction sur papier ou sur format Bectronique. The author retains ownership of the L'auteur conserve la propriete du copyright in this thesis. Neither the droit d'auteur qui protege cette these. thesis nor substantial extracts fiom it Ni la these ni des extraits substantiels may be printed or otherwise de celle-ci ne doivent &re imprimes reproduced without the author's ou autrement reproduits sans son permission. autonsation. Borrower's Page The University of Waterloo requires the signatures of all persons using or photocopying this thesis. Please sign below, and give address and date. ABSTRACT In this thesis we provide a summary of the methods by which remote sensing may be applied in forestry, while also acknowledging the various limitations which are faced. The application of spatial statistics to high spatial resolution imagery is explored as a means of increasing the information which may be extracted from digital images. A number of high spatial resolution optical remote sensing satellites that are soon to be launched will increase the availability of imagery for the monitoring of forest structure. This technological advancement is timely as current forest management practices have been altered to reflect the need for sustainable ecosystem level management. The low accuracy level at which forest structural parameters have been estimated in the past is partly due to low image spatial resolution. A large pixel is often composed of a number of surface features, resulting in a spectral value which is due to the reflectance characteristics of all surface features within that pixel. In the case of small pixels, a portion of a surface feature may be represented by a single pixel. When a single pixel represents a portion of a surface object, the potential to isolate distinct surface features exists. Spatial statistics, such as the Getis statistic, provide for an image processing method to isolate distinct surface features. In this thesis, high spatial resolution imagery sensed over a forested landscape is processed with spatial statistics to combine distinct image objects into clusters, representing individual or groups of trees. Tree clusters are a means to deal with the inevitable foliage overlap which occurs within complex mixed and deciduous forest stands. The generation of image objects, that is, clusters, is necessary to deal with the presence of spectrally mixed pixels. The ability to estimate forest inventory and biophysical parameters from image clusters generated from spatially dependent image features is tested in this thesis. The inventory parameter of crown closure is successfully estimated from image clusters, yet the grouping of trees into clusters causes mixed results when estimating stem counts. The assignment of a cover class of each cluster is also undertaken. The knowledge of cluster cover class has also enabled the estimation of leaf area index. Further, spatial information alone may be used to estimate LA1 under described conditions. ACKNOWLEDGMENTS Many people have played a role in assisting my academic pursuits throughout my graduate experience. Some sources of encouragement are unmistakable, such as academic supervisor and committee, others are more subtle, such as family, friends, and colleagues. To all those who have, either put up with me, or assisted me though this graduate enterprise, I ex tend my gratitude. Special thanks are offered to my graduate supervisor, Dr. Ellsworth LeDrew. Dr. LeDrew provides students with a unique combination: both the space to pursue ideas and the direction to assist in this pursuit. As a result, under the tutelage of Dr. LeDrew, it is possible to use your imagination, follow scientific leads, and still have the technical and theoretical direction to produce robust results. The insight and encouragement of Dr. Phillip Howarth while at &heUniversity of Waterloo is also very much appreciated. Dr. Barry Boots, of Wilfrid Laurier University, has continued to be a source of academic insight and inspiration. I very much appreciate the willingness of Dr. Boots to assist me in my quarrels with data analysis and statistics. Dr. Steven Franklin, of the University of Calgary, has provided to me, throughout my academic career, valuable encouragement and advice. Also very much appreciated is the variety of opportunities and responsibilities Dr. Franklin presented me with and encouraged me to pursue. Sincere thanks are extended to Dr. Mike Lavigne, of the Canadian Forest Service, for sharing with me his deep understanding of the composition, structure, and functioning of forests. DEDICATION To my wife Karen Laberee, for her love, encouragement, and understanding. TABLE OF CONTENTS .. Authors Declaration ....................................e...o.......... ..................................................t .........LC ... Borrower's Page ................... .......o........,....................m........m..........................................w..........rtr ABSTRACT............................................................................................................................... iv .. TABLE OF CONTENTS ..........................................................................o.........e.................. VLL LIST OF TABLES ............................................m............................ ................................... xi LIST OF FORMULAS ................................................................................................ xvi 1. INTRODUCTION .................... .... ...................................m.m................................................. I lml. RATIONALE aa.~.~~o~.~e~~..~.m~~mm~~~~~~~~~m~~mm~~~mmmmwrnme1 1.2. THESIS RESEARCH OBJECTIVES.... ............a...~..........e....e........m........m.m...........m........7 2. FOREST STRUCTURE FOR ECOSYSTEM MONITORING AND MANAGEMENT .. 9 23. THE CANADIAN FORESTS ...................~,.oaa.o....o......................m.. ................... .e......m......... 13 2.3.1. Canadian Forest Coverage ................................................................................. ,,. ......... 14 2.4. FOREST SUSTAINABILITY IN A CANADIAN CONTEXT...~~......~~m~~o.~~mm~m~~~~I~~.~em15 2.5. FOREST MANAGEMENT: FOREST INVENTORY PARAMETERS ................... .,..... 18 2.6. FOREST BIOPHYSICAL PARAMETERS.~.m~.~~.m~~~.~~~~~~~.~~~~m~~~~~om.....~~~~~oaaoo~~e~~~o~~oo~~~~~~me19 2.6.1 . Within Stand Partitioning of LA1 ................. ........... ........... .,.... ... ...... ..... ......... ........... ...... ................ -22 3. MEASUREMENT OF FOREST STRUCTURE .............................................................. 25 3.2. FIELD MEASUREMENT OF FOREST INVENTORY PARAMETERS ....................... 26 3.2.1. Forest Mensuration - Direct Measures, Sampling, and Prediction..................................................... 27 33. FIELD MEASUREMENT OF FOREST BIOPHYSICAL PARAMETERS..,.,.... ........... 28 3.3.1. In situ Assessment of LA1................................................. ,,..,,.,..,...................................................... 28 3.3.1.1 . Direct In situ LA1 Estimation................................................. ...................................... ....... ....+..30 3.3.1.2. Indirect In siru LA1 Estimation ............................................................................................... 3 I 3.4. FACTORS WHICH AFFECT THE REMOTE SENSING OF FORESTS ................... .,. 33 3.4.1. Spectral Response of Forest Canopies.................................. ,..... ..,.,,.,, ...... , ...... ....... ,......... .............. 33 3.4.2. Scale in Remote Sensing................................. ..,..,,, .... ,........................ ....................................... ...... 34 3.4.3. The Nature of Models in Remote Sensing .......................................... ,*.....................,.... ,, ................. 36 3.4.4. Scale and the Representation of Geographic Data ............................................................................. 36 vii 3.6. SATELLITE REMOTE SENSING OF FOREST STRUCTUREo~o.omo.o....mooo.omoomoooooo*omom 40 3.6.1. Satellite Estimation of LA1 .................................................,,,, ....................... ,,,.............................. ...41 3.6.2. Satellite Estimation of Inventory Parameters........................... ,.,..,........................................... ...... ...43 3.7. NEW GENERATION OF COMMERCIAL HIGH RESOLUTION SATELLITESma.ooo. 45 3.8. HIGH SPATIAL RESOLUTION AND AIRBORNE MULTISPECTRAL REMOTE SENSING ~....~mom.ooooo...~ooom.omoomo..m.m.~mo~~~m~m~om~o~ao~mooomo..oo...o.~~...moo..m.omo.o.mo.oom.~~m.o.mm~om.m~o.o.o~..o.omommomm~o.mo..m..
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