[steven e. franklin] remote sensing for sustainabl(bookfi.org)

RemoteSensing forSustainableForestManagement

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LEWIS PUBLISHERSBoca Raton London New York Washington, D.C.

RemoteSensing forSustainableForestManagementSteven E. Franklin

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Library of Congress Card Number 2001029505Printed in the United States of America 1 2 3 4 5 6 7 8 9 0

Printed on acid-free paper

Library of Congress Cataloging-in-Publication Data

Franklin, Steven E.Remote sensing for sustainable forest management / Steven E. Franklin.

p. cm.Includes bibliographical references and index (p. ).ISBN 1-56670-394-8 (alk. paper)1. Sustainable forestryRemote sensing. 2. Forest management. I. Title.

SD387.R4 F73 2001634.9

2

028dc21 2001029505 CIP


Dedication

for Dawn Marie, Meghan, and Heather


Preface

Remote sensing has been defined as the

detection, recognition, or evaluation

ofobjects by means of distant sensing or recording devices. In recent decades, remotesensing technology has emerged to support data collection and analysis methods ofpotential interest and importance in forest management. Historically,

digital

remotesensing developed quickly from the technology of aerial photography and photoin-terpretation science. In forestry, information extracted visually from aerial photo-graphs is well-understood, well-used, and integrated with field surveys. Informationextracted from digital remote sensing data, on the other hand, is rarely used in forestmanagement. It is thought that many remote sensing data and methods are complex,and are not well understood by those who might best use them. The technologicalinfrastructure is not in place to make effective use of the data. The characteristicsof much remote sensing data are, perhaps, not well suited to the problems that havepreoccupied the forest management community.

But forest management is changing. Today, forest management problems aremultiscale and intricately linked to societys need to measure, preserve, and managefor multiple forest values. Population growth and climate change appear likely tocreate continual pressure on forests, making their preservation, even over relativelyshort time periods, seem largely in doubt. Human activities threaten the continuedphysical existence, biodiversity, and functioning of forests. It is probable that noforest on the planet can survive intact without conscious human decision making,and actual on-the-ground treatments and prescriptions that consider ecological pro-cesses and functioning. The forest ecosystem is complex and multifaceted; under-standing how forest ecosystems work requires new types of data, and data at a rangeof spatial and temporal scales not often contemplated. Remote sensing informationneeds to be integrated with other spatial and nonspatial data sets to form the infor-mation base upon which sound forest management decisions can be made. The goalis to predict the effects of human activities and natural processes on forests, and topromote forest practices that will ensure the worlds forests are sustainable.

A major issue facing those with forest management questions is not simply thecollection of data, but rather the interpretation of information extracted from thosedata. Converting remote sensing data to information is no simple task.

Remotesensing measurements have a physical or statistical relationship to the forest condi-tions of interest which may be uneconomical, impractical, or impossible to measuredirectly over large areas. The remote sensing technological approach is an appliedperspective applying remote sensing knowledge to satisfy information needsmotivated by a strong desire to understand the implications of management whilethere is still time to learn from prescriptions and to understand forest conditions andprocesses. A survey of the field of remote sensing in sustainable forest managementmay help those in direct operational contact with forests to better understand the


potential, and the implications, of adopting certain aspects of this new approach. Insome ways, the results and methods of remote sensing reviewed here represent theleast possible contribution that remote sensing can make, since the improvement ofremote sensing the sensors, data quality, methods of analysis, understanding ofgeospatial environments is the subject of an intensive and ongoing worldwideresearch agenda. This situation is virtually assured to help make remote sensingcontributions stronger in the future. It is this assurance that I have sought to identifyby highlighting the principal methods and accomplishments in the field, and byoutlining future implications and challenges.

I recognize that even successful conversion of remotely sensed data to forestryinformation products will not be enough; the process of acquiring vast amounts ofnew information about forests must be seen as part of the wider responsibility inservice of the generation of new knowledge about the current state of the forest andthe influences of management and natural processes to further the goal of forestsustainability. This book is written for university and college students with somebackground in forestry, physical geography, ecology, or environmental studies; butone key audience that I hope will see value in this material is the operational forestmanagers, practitioners, and scientists working with forest management problems.I perceive that remote sensing can be useful in solving problems that arise whenforest planning directs forest activities on the ground, but there is rarely time toconsider the larger context, the specific tool, the trade-offs in different approaches.Whether remote sensing can help those in positions of responsibility in forestoperations and management understand and improve the management of the forestresource is, perhaps, still uncertain. What does seem likely is that remote sensing,at the very least, can help detect and monitor forest conditions, forest changes, andforest growth over large spatial scales and at relevant time steps. Hopefully, withbetter information comes greater understanding and, in turn, practical improvements.It is hoped that increased confidence will be generated that sustainable forest man-agement is possible, and politically, economically, and socially desirable.

I have tried to provide an international flavor to the book, but as is evident inforest management and probably many other fields, remote sensing has been dispro-portionately developed and implemented in temperate and boreal forests, and partic-ularly in Europe and North America. It seems likely, though, that the methods thathave proven valuable in these forests can work well in many world forests, andreferences and examples have been sought to try and emphasize this key point. I owea great debt to the early pioneers of remote sensing the physicists, engineers, andnatural scientists who sought to discover, document, and summarize the principlesof the rapidly emerging remote sensing field; their papers and books are liberallyreferenced in this book, and should be consulted by those wishing to complete anunderstanding of the forestry remote sensing application. Recently, new remote sens-ing books that focus on the social, geographical, and environmental sciences havebeen added to the mix. Remote sensing has always benefitted as has forestmanagement from the inherently multidisciplinary nature of its practitioners,methodologists, experimentalists, and developers. I sincerely hope that the currentbook with its focus on remote sensing in forestry is viewed in this positive light.


Acknowledgments

This book is a development of my research and teaching in remote sensing appliedto forestry problems. From the time I was a forestry undergraduate student atLakehead University in the mid-1970s, such work has been marked in no small wayby an ever-widening collaborative experience among foresters, geographers, ecolo-gists, physicists, and others arriving with an interest in remote sensing from vastlydifferent and sometimes wildly circuitous routes. I consider myself very fortunateto have had the opportunity to work with many such excellent students, faculty, andcolleagues; by their efforts and enthusiasm I have been much inspired. I am partic-ularly indebted to Clayton Blodgett, Jeff Dechka, Elizabeth Dickson, GrahamGerylo, Philip Giles, Ron Hall, Medina Hansen, Ray Hunt, Mike Lavigne, EllsworthLeDrew, Julia Linke, Joan Luther, Alan Maudie, Tom McCaffrey, Greg McDermid,Monika Moskal, Derek Peddle, Richard Waring, Brad Wilson, Mike Wulder, andthe helpful staff and students at the organizations and institutions in which I havestudied, worked, or taught: Lakehead University, Ontario Ministry of NaturalResources, University of Waterloo, Ontario Centre for Remote Sensing, GeophysicalInstitute of the University of Bergen, Memorial University of Newfoundland, Uni-versity of Calgary, and Oregon State University, for some of the ideas and conceptsthat are mentioned in this book.

I would like to acknowledge an important influence on the direction and natureof my remote sensing research by the late John Hudak, Canadian Forest Service;his enormous enthusiasm and trust in the quality and significance of our forestryremote sensing work were both a challenge and a reward. Thank you, John.

Extensive reviews of the manuscript were received from Dr. Ron Hall (NorthernForestry Centre, Canadian Forest Service), Mr. Stephen Joyce (Department of ForestResources and Geomatics, Swedish University of Agricultural Sciences), Dr. PeterMurtha (Faculty of Forestry, University of British Columbia), and Dr. Warren Cohen(Forestry Sciences Laboratory, Pacific Northwest Research Station, USDA ForestService). Portions of the book were reviewed by Dr. Mike Wulder (Pacific ForestryCentre, Canadian Forest Service), and Dr. Ferdinand Bonn (CARTEL, Departmentof Geography, Universit de Sherbrooke). I am very grateful to these individuals fortheir dedicated efforts to read through the text and provide many suggestions forimprovement. I believe their comments and insights have helped create a morecomprehensive and worthwhile contribution, but of course I retain sole responsibilityfor any errors or oversights that remain.

I thank Graham Gerylo and Medina Hansen for their exemplary work on thefigures and plates, respectively. To those who agreed to help by providing imagesand graphics, thank you: Joseph Cihlar, Doug Davison, Ron Hall, Doug King,Monika Moskal, Derek Peddle, Miriam Presutti, Benot St-Onge, and Mike Wulder.These numerous contributions were instrumental in ensuring an effective set of plates


and figures for the book. I am also grateful to Pat Roberson, Randi Gonzalez, andSheryl Koral of CRC Press for their help in turning a manuscript into this book.The following organizations granted permission to use figures, tables, or shortquotations from their publications: American Society for Photogrammetry andRemote Sensing, Canadian Aeronautics and Space Institute, IEEE Intellectual Prop-erty Rights Office, Soil Science Society of America, Academic Press, NaturalResources Canada, Elsevier Science, Taylor & Francis, Heron Publishing, KluwerAcademic Publishers, CRC Press, Canadian Institute of Forestry, American Chem-ical Society, and Island Press.

Part of this book was written while I was supported by a University of CalgarySabbatical Leave Fellowship at the National Center for Geographic Information andAnalysis, University of California Santa Barbara. This leave was made possiblewith administrative support by Dr. Stephen Randall (Dean, Faculty of Social Sci-ences, University of Calgary), Dr. Ronald Bond (Vice-President Academic, Univer-sity of Calgary), and Dr. Michael Goodchild (Director, NCGIA, University of Cal-ifornia Santa Barbara). I acknowledge gratefully the financial support of myresearch activities in forestry remote sensing by the Natural Sciences and Engineer-ing Research Council of Canada and the Canadian Forest Service.

Steven E. FranklinUniversity of Calgary


About the Author

Steven E. Franklin, Ph.D.,

is a professor engaged in teaching and research in thefield of remote sensing at the University of Calgary, Alberta, Canada. He has studiedforestry, geography, and environmental studies, and has received his Ph.D. in geo-graphy from the Faculty of Environmental Studies, University of Waterloo in 1985.

Dr. Franklin taught classes in remote sensing at Memorial University of New-foundland (19851988) and has been teaching at the University of Calgary since1988. He has had visiting appointments at Oregon State University College ofForestry (1994) and the University of California Santa Barbara National Center forGeographical Information and Analysis (2000). At the University of Calgary, Dr.Franklin has held the positions of Associate Dean (Research) from 1998 to 1999and Head of the Geography Department from 1995 to 1998. He has also beenChairman of the Canadian Remote Sensing Society (19951997) and is an AssociateFellow of the Canadian Aeronautics and Space Institute.

Dr. Franklin has published more than 70 journal articles on remote sensing andforest management issues in Canada, the United States, and South America. Hispapers focused on remote sensing applications such as forest defoliation, forestharvesting monitoring, and forest inventory classification.

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Table of Contents

Chapter 1

Introduction ..........................................................................................1

Forest Management Questions.........................................................................1A Technological Approach ..................................................................5

Remote Sensing Data and Methods ................................................................6Definition and Origins of Remote Sensing .........................................8The Experimental Method .................................................................10The Normative Method......................................................................14

Categories of Applications of Remote Sensing.............................................16Growth of Remote Sensing................................................................18User Adoption of Remote Sensing ....................................................22Current State of the Technological Infrastructure

and Applications ..............................................................................25Three Views of Remote Sensing in Forest Management..................30

Organization of the Book ..............................................................................35Overview ............................................................................................35Chapter Summaries ............................................................................36

Chapter 1: Introduction ......................................................36Chapter 2: Sustainable Forest Management.......................36Chapter 3: Acquisition of Imagery.....................................37Chapter 4: Image Calibration and Processing ...................37Chapter 5: Forest Modeling and GIS.................................37Chapter 6: Forest Classification .........................................38Chapter 7: Forest Structure Estimation..............................38Chapter 8: Forest Change Detection ..................................38Chapter 9: Conclusion ........................................................38

Chapter 2

Sustainable Forest Management ........................................................39

Definition of Sustainable Forest Management ..............................................39Forestry in Crisis................................................................................42

Ecosystem Management ................................................................................45Forest Stands and Ecosystems...........................................................47Achieving Ecologically Sustainable Forest Management.................50

Criteria and Indicators of Sustainable Forest Management..........................53Conservation of Biological Diversity ................................................58Maintenance and Enhancement of Forest Ecosystem Condition

and Productivity...............................................................................64Conservation of Soil and Water Resources .......................................66Forest Ecosystem Contributions to Global Ecological Cycles .........68


Multiple Benefits of Forestry to Society ...........................................70Accepting Societys Responsibility for Sustainable

Development ....................................................................................71Role of Research and Adaptive Management ...................................72

Information Needs of Forest Managers.........................................................73Some Views on the Way Forward .....................................................77

Role of Remote Sensing ................................................................................79Two Hard Examples...........................................................................81

Chapter 3

Acquisition of Imagery ......................................................................85

Field, Aerial, and Satellite Imagery...............................................................85Data Characteristics .......................................................................................90

Optical Image Formation Process......................................................91At-Sensor Radiance and Reflectance.................................................93SAR Image Formation Process..........................................................95SAR Backscatter ................................................................................96

Resolution and Scale......................................................................................97Spectral Resolution ............................................................................98Spatial Resolution ..............................................................................98Temporal Resolution ..........................................................................99Radiometric Resolution....................................................................100Relating Resolution and Scale.........................................................100

Aerial Platforms and Sensors ......................................................................103Aerial Photography ..........................................................................104Airborne Digital Sensors .................................................................108Multispectral Imaging ......................................................................108Hyperspectral Imaging.....................................................................110Synthetic Aperture Radar.................................................................111Lidar .................................................................................................112

Satellite Platforms and Sensors ...................................................................113General Limits in Acquisition of Airborne and Satellite Remote

Sensing Data ..............................................................................................117

Chapter 4

Image Calibration and Processing ...................................................121

Georadiometric Effects and Spectral Response ..........................................121Radiometric Processing of Imagery ................................................123Geometric Processing of Imagery ...................................................130

Image Processing Systems and Functionality .............................................132Image Analysis Support Functions ..............................................................137

Image Sampling ...............................................................................138Image Transformations ....................................................................139Data Fusion and Visualization .........................................................143

Image Information Extraction......................................................................145Continuous Variable Estimation ......................................................146


Image Classification.........................................................................150Modified Classification Approaches ................................................156Increasing Classification Accuracy ..................................................161Image Context and Texture Analysis...............................................166Change Detection Image Analysis...................................................169

Image Understanding ...................................................................................171

Chapter 5

Forest Modeling and GIS ................................................................177

Geographical Information Science ..............................................................177Remote Sensing and GIScience.......................................................179GIS and Models ...............................................................................182

Ecosystem Process Models..........................................................................184Hydrologic Budget and Climate Data .............................................187Forest Covertype and LAI ...............................................................191Model Implementation and Validation ............................................193

Spatial Pattern Modeling .............................................................................195Remote Sensing and Landscape Metrics.........................................198

Chapter 6

Forest Classification .........................................................................205

Information on Forest Classes .....................................................................205Mapping, Classification, and Remote Sensing................................206Purpose and Process of Classification.............................................211

Classification Systems for Use with Remote Sensing Data .......................213Level I Classes .............................................................................................217

Climatic and Physiographic Classifications ....................................217Large Area Landscape Classifications .............................................219

Level II Classes............................................................................................223Structural Vegetation Types .............................................................224Using Forest Successional Classes ..................................................232

Level III Classes ..........................................................................................237Species Composition........................................................................237Ecological Communities ..................................................................244Understory Conditions .....................................................................247

Chapter 7

Forest Structure Estimation .............................................................251

Information on Forest Structure ..................................................................251Forest Inventory Variables ...........................................................................257

Forest Cover, Crown Closure, and Tree Density ............................257Canopy Characteristics on High Spatial Detail Imagery ................259Forest Age ........................................................................................261Tree Height.......................................................................................264Structural Indices .............................................................................265

Biomass ........................................................................................................269


Volume and Growth Assessment .................................................................276Volume and Growth .........................................................................276Leaf Area Index (LAI) .....................................................................280

Chapter 8

Forest Change Detection..................................................................287

Information on Forest Change.....................................................................287Harvesting and Silviculture Activity ...........................................................293

Clearcut Areas ..................................................................................293Partial Harvesting and Silviculture..................................................298Regeneration.....................................................................................301

Natural Disturbances....................................................................................302Forest Damage and Defoliation.......................................................302Mapping Stand Susceptibility and Vulnerability.............................309Fire Damage.....................................................................................312

Change in Spatial Structure .........................................................................313Fragmentation Analysis....................................................................314Habitat Pattern and Biodiversity......................................................317

Chapter 9

Conclusion........................................................................................321

The Technological Approach Revisited..................................................321Understanding Pixels Multiscale and Multiresolution...............325Aerial Photography and Complementary Information....................326Actual Measurement vs. Prediction The Role of Models .........328Remote Sensing Research................................................................329

References

.............................................................................................................333

Index

......................................................................................................................393


1

Introduction

Satellite imagery and related technology: one of the top ten advances in forestry inthe past 100 years (Society of American Foresters, Web site accessed 17 July 2000,http://www.safnet.org/about/topten.htm)

FOREST MANAGEMENT QUESTIONS

Human activities in forests are increasingly organized within plans that have at theircore sustainability and the preservation of biodiversity. These plans lie at the heartof

sustainable forest management, whose practices are designed to maintain andenhance the long-term health of forest ecosystems, while providing ecological,economic, social, and cultural opportunities for the benefit of present and futuregenerations (Canadian Council of Forest Ministers, 1995). Sustainable forest man-agement supplements a concern with economic values with concerns that speciesdiversity, structure, and the present and future functioning and biological productivityof the ecosystem be maintained or improved (Landsberg and Gower, 1996). Thegrowing acceptance of sustainable forest management has been characterized as aparadigm shift of massive proportions within the forest science and forest manage-ment communities that is only now reaching maturation (Franklin, 1997).

The roots of sustainable forest management can be traced back much earlier;for example, the Royal Ordonnance on Forests was enacted in Brunoy on 29 May,1346 by Philippe of Valois (Birot, 1999: p. 1):

The Masters of Forests [] shall survey and visit all forests and all woods which theyinclude, and they shall effect the sales as needed, with a view to continuously main-taining the said forests and woods in good condition.

People have long been interested in extracting immediate value today from forests,while preserving their characteristics for future generations. These interests havebeen at various times, as in the present, heatedly debated. During the seventeenthcentury in England, for example, the fundamental interests of the realm prosperity,security, and liberty were invoked to support the positions of

both

conservationistsand developers (Schama, 1995: p. 153): The greenwood was a useful fantasy; theEnglish forest was serious business.

In

the century just past, the discussion has

1

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2

Remote Sensing for Sustainable Forest Management

been no less intense (Leopold, 1949) as greater understanding of the fundamentalconcepts of conservation, ecology, community, economics, and ethics has emerged.

In a global context in current times, as populations and technological ability toextract resources from what were once considered vast and inexhaustible forestscontinue to expand, world forests increasingly appear finite, vulnerable, dangerouslydiminished, perhaps already subject to irreparable damage. Recently, efforts haveincreased to provide a social accommodation to the issues of sustainable forestmanagement, and it is thought that this social emphasis will have far-reachingimplications for the way in which society will view and use the forest resources ofthe planet. Perhaps the ideas have not changed as much as the actual practice offorestry and understanding of forestry goals. Current human ability to change theglobal forest environment is unprecedented. Sustainable forest management may notrepresent a fundamental shift in the way humans view forests inasmuch as it repre-sents a recognition of this global influence.

The change has already meant a difference in human interaction with naturalforest systems. There is increased emphasis on scientific management (Oliver et al.,1999) and the recognition of the need to understand better ecosystem functioning(Waring and Running, 1998; Landsberg and Coops, 1999) and patterns (Forman,1995) over large areas and long periods of time (Kohm and Franklin, 1997). Suchunderstanding is increasingly required regardless of any philosophical stanceassumed on the role of forests, management, and continued human use of forests.Sustainable forest management has encouraged continued wide-ranging philosoph-ical discussion within the forest science, applied forestry, and ecological communi-ties (Maser, 1994).

There may be much more to come. Heeding the clarion calls for new directionsin forestry has resulted in many instances of direct action (Hansen et al., 1998;Bordelon et al., 2000). In the past few years, the actual practice of forest managementin some parts of the world has moved swiftly to accommodate sustainable forestrywith uneven-age (single-tree and group selection) and even-age stand management(clearcutting, shelterwood, seed tree), increased rotation times, reduced harvestingamounts, and new patterns of resource utilization and cooperative management. Thechanges from an older, traditional forest management approach with an emphasison timber values to sustainable forest management are profound. The process ofchange is subject to continuing discussion, understanding, clarification, and modi-fication. This is an exciting and intellectually challenging time in which to considerfuture directions in forests and forestry.

Some believe that the cumulative effect of human needs has resulted in a worldforestry in a crisis that can only deepen as populations continue to rise and theresource base declines. To them, the historical and current mismanagement anderadication of whole forests has all but reached the critical point, after which thedamage is overwhelming and final (Berlyn and Ashton, 1996; Meyers, 1997). Thereis abundant evidence that destructive land use practices, widespread pollution,exploitation of species, and perhaps global climate change (Stoms and Estes, 1993),have caused damage to the Earths ecosystems and consigned many species tooblivion. The current rate of species extinction is estimated at 50 times the base


Introduction

3

level of the last 400 years, and 100 times the base level of the last half of the twentiethcentury (Raven and McNeely, 1998). In some areas, the species extinction rate maybe 10,000 times the background level (Wilson, 1988). Exactly how much of thismay be traced to unsustainable forest practices such as clearcutting in areas notable to regenerate is not known. But the message is unmistakable; the humanspecies must desist in knowingly engaging in destructive forest activities that resultin continuing, massive, even irreversible damage to the biosphere.

Others view the current problems as symptomatic of a fundamental shift in thebalance of human management of resources and economics. During this time ofchange, the actual direction is not yet clear. Initial indications are that support ismoving away from applying best practices over fine spatial scales, as managementis reoriented to address concerns over larger areas and longer time periods (Swansonet al., 1997). To help accomplish this necessary transition, an overarching theme isthe continued search for fast, consistent, versatile, accurate, and cost-effective infor-mation inputs to management problems, but now with the full knowledge of thewide range of scales local to global over which forest communities are affected.There is time to adjust, to experiment, to adapt, to understand better the impactsand implications of forest management there is time, but not much (Boyce andHaney, 1997). Still others believe that human populations will soon stabilize, thenbegin an orderly decline to sustainable levels. Resources will not become limiting.To them, the forest resource simply must be managed more scientifically; forexample, by more careful application of the principles of management science(Oliver et al., 1999). The wide range of opinion and the large number of unknownssuggest the difficulties in charting the future of forest management and the fate ofthe worlds remaining forests.

The future of forest management remains unclear. What will be the end resultof changing values in society and the societal view of forests? Will increasedeconomic and social needs be met with forest products? Will foresters and otherresource management professionals find better ways to manage forests and to meetthese needs? Will there be more or less direct (e.g., prescribed treatments, suppres-sion of natural processes) and indirect (e.g., climate change) human intervention inforest growth patterns? Will our understanding of the characteristics of landscapedynamics improve fast enough to allow the incorporation of natural disturbances inplanning sustainable forest management? Can human needs and forests coexistsustainably? Can a sustainable forest management approach can any managementapproach succeed in ending the terminal threat of destruction faced by many, ifnot most, of Earths remaining intact old-growth forests?

What

is

clear is that increasing amounts of scientific information must beacquired to support the emerging, practical, ongoing goals and objectives in man-aging forests (Bricker and Ruggiero, 1998; Noss, 1999; Simberloff, 1999). One goalis to adapt forest management continually to accept new objectives. One goal is tolearn how to manage forests sustainably so benefits continue and future generationsare not compromised. Another goal is to acquire knowledge about the current stateof the forest and about how management and natural processes affect future out-comes. These goals require that new information be obtained by:


4


1. Increasing understanding of forests through field trials, observations onlong-term sampling plots, analysis of historical outcomes, growth, suc-cession, and competition observations and models,

2. Transforming and interpreting data from new and existing forest inventories,3. Developing and accessing data from various purpose-designed national

and regional resources, including forest health networks, decision supportsystem networks, and ecological or biogeoclimatic classification systems,

4. Obtaining new data and insights through development and deployment ofa suite of new information technologies, including geographical informa-tion systems (GIS), computer modeling and spatial databases, and remotesensing of all types and descriptions.

Driving these demands for new information are basic science and managementquestions, coupled with evolving models of forest economics and forest certificationinitiatives. However, if generating and accumulating data were the only impedimentsto sustainable forest management, humanitys problems would be over. A commonview in the natural sciences is that there is difficulty handling the data currentlyavailable without losing critical key components; in forestry,

We now have moredata than we can interpret (Lachowski et al., 2000: p. 15). There are probablyenough data in all the critical areas, and

spatial

data types and volumes are notimmediately limiting (Graetz, 1990; Vande Castle, 1998). With increased inten-sive/extensive monitoring at instrumented research sites, this situation will continueto exist, perhaps developing beyond capabilities to manage the data flow. But dataare not information. What may be lacking is a way of understanding these data, offinding the right interpretation of the data, of ensuring the conversion of data toinformation, and ultimately, the conversion of information to usable knowledge aboutthe current state of the forest and the influences of management and natural pro-cesses. It appears that converting data to information is the highest priority for remotesensing to contribute to sustainable forest management.

It has been suggested that compared to previous forest management approaches,all new forest management strategies will require even more record keeping andeven wider access to information (Bormann et al., 1994). It is not yet known if thenew spatial information technologies such as GIS, remote sensing, computermodeling, decision support systems, and digital databases are going to be ableto handle all of the new data requirements (Bormann et al., 1994; MacLean andPorter, 1994). Can remote sensing data provide the required information with thegreatest accuracy for a given cost? Even if this challenge can be met, informationis not understanding; it is not yet known whether increased human understandingof the central issues will result such that management will be improved. There is acritical lack of understanding in several key areas related to human activities on thelandscape. For example, it is not clear in what ways, if at all, human-altered spatialstructures can mimic natural disturbance regimes, or what consequences human-induced climate change will have on net ecosystem productivity.

The spatial information on patterns of disturbance and productivity is relativelyeasy to come by; as will be shown in this book, mapping forest insect defoliation,patterns of forest harvesting, and changes in photosynthetic capacity across broad


Introduction

5

forest ecosystems are operational with current satellite and airborne remote sensingtechnology and methods. What is not so simple is the understanding of what thesepatterns mean, and how to implement the more certain power to make decisions thatsuch understanding confers on those responsible for sustainable forest management.

A T

ECHNOLOGICAL

A

PPROACH

Remote sensing has been a valuable source of information over the course of thepast few decades in mapping and monitoring forest activities. As the need forincreased amounts and quality of information about such activities becomes moreapparent, and remote sensing technology continues to improve, it is felt that remotesensing as an information source will be increasingly critical in the future. A powerfulline of thought in remote sensing is to consider problems in a

technological approach(Curran, 1987). By this, it is meant that remote sensing can sometimes proceeddifferently than traditional scientific deductive and inductive approaches. These areconsidered the pure science approaches, and can be contrasted to the scientifictechnological approach in which the emphasis is shifted to a

methodological

or

applied

perspective. A successful remote sensing application proceeds from thedesign of methodology. In a remote sensing technological approach, the goal is theapplication of knowledge the use of what has been learned to solve problems.

Forest managers are concerned with the spatial distribution of forest resourceswithin their management area and in the surrounding ecosystems; with the timelyacquisition of information on conditions and changes to these resources; with thesmall and large impacts associated with changing patterns and processes at differentscales in time and space; with interpretation of the effect of those changes onunmapped components such as wildlife; and with economic, social, and environ-mental implications of human activities and impacts on forests. There is a need tohave as much relevant information as possible on the conditions of the forest toprescribe treatments, to help formulate policy, and to provide insight and predictionson future forest condition, and health. Typically, there are few choices in how toacquire all the different types of information. The goal of remote sensing, then, isto help satisfy as many of these multidimensional needs for information as possible.This is the application of knowledge: that is, the application of remote sensingknowledge in response to forest management questions.

While remote sensing technology must help in providing information to satisfythe needs that forest managers have, remote sensing must be a cost-effective andeasily understandable technology. These are probably two of the most importantreasons that aerial photographs are still the most common form of remote sensingin forestry; relative to information content, they are inexpensive and easy to use (Pittet al., 1997; Caylor, 2000). The field of remote sensing began with fully manualmethods of analysis applied to aerial photographs, but has since come to rely onnew data and methods. As these data and methods evolve and improve, it appearslikely that remote sensing will be increasingly useful in satisfying needs for forestmanagement information.

A

methodological

design

is necessary to show how remote sensing data can beused to determine the spatial distribution of forest resources, can detect changes in


6


those resources, and predict changes in other aspects of the forest not captureddirectly. Remote sensing methodology can be a powerful aspect of a technology todetect changes accurately and to help explain more fully the implications of forestchanges and activities. To accomplish this, there must be a series of direct mappingand modeling applications of remote sensing consistent with the needs of forestmanagers. This is the role of methodological design to convert data to informa-tion in a scientific, understandable, and repeatable way. As in all scientificapproaches, the hallmark of good science is the use of the knowledge gained touncover general laws and to predict future conditions; then, the methodologicaldesign becomes part of the established scientific method, the analytical approachfor the field (Lunetta, 1999).

A useful way to consider the diversity of the remote sensing data input tosustainable forest management is to examine the kinds of issues and questions aroundwhich sustainable forest management revolves. In Table 1.1 some example questionsare listed; each sustainable forest management question is paired with an inferentialhypothesis which can be suggestive of the ways in which remote sensing cancontribute to providing an answer or generating new insight into the question. Allquestions of forest value are first rooted in an accurate description of the resource,and it is the responsibility of the forest inventory to provide that description (Erdleand Sullivan, 1998). This book presents the technological approach of remote sensingto sustainable forest management questions, but does not attempt to illustrate exhaus-tively how specific answers are best derived. The infinite variety of such questionsand answers prevents that level of detail; this is not a remote sensing cookbook.Rather, the idea is to present to remote sensing data users a review of the achieve-ments of remote sensing and forestry scientists and professionals in addressing keymapping, monitoring, and modeling applications in forests.

It is clear that the information needs of the past and the future differ, and newdemands will be placed on the forest inventory and other information resourcesavailable in forestry. Here, in a few pages, several decades of progress in forestryremote sensing are rushed through, hopefully with due process, in an attempt toenable the reader to understand the full range of possibilities in remote sensingparticipation in the forest management questions of the day.

REMOTE SENSING DATA AND METHODS

The general role that remote sensing data might play in forest management, withinthe relatively narrow range of information sources that are available, is probably notwell understood (Cohen et al., 1996b). Generally, remote sensing is understoodrelative to the more familiar aerial photography (Graham and Read, 1986; Howard,1991; Avery and Berlin, 1992) and of course, field observations (Avery and Burkhard,1994). In many situations, such as site or forest stand disease assessment (Innes,1993) and studies in old-growth or other forests where rare or endangered speciesor ecosystems may occur, field observations are the only possible way to acquirethe needed information (Ferguson, 1996). In other situations, aerial photographs aresuggested (Pitt et al., 1997); no other source of information would be appropriate.Currently, it is thought that there are few or no substitutes for

in situ

observation;


Introduction

7

and, there are few or no acceptable alternatives to aerial photography. Only undercertain rare circumstances is it appropriate or productive to consider remote sensinga

substitute

information source. But is remote sensing a legitimate method of acquir-ing forest information that cannot be obtained in other ways? There appears to beinsufficient awareness of the complementarity of field observations, aerial photog-raphy, and specific remote sensing data sources and methods, and the ways thesevarious information sources can work together (Czaplewski, 1999; Oderwald andWynne, 2000; Bergen et al., 2000).

There are many situations in forest management in which managers and forestscientists are concerned with larger areas and differing time periods; field observa-tions are necessary, but not sufficient; aerial photographs are necessary, but notsufficient. How do field observations, aerial photography, and digital remote sensingfit together? What kind of remote sensing can and should be done in support ofsustainable forest management? Information is a management resource. Understand-

TABLE 1.1Sustainable Forest Management Questions and Corresponding Remote Sensing Hypotheses

Sustainable Forest Management Question Driven by Human Need

Remote Sensing Inferential Hypothesis Driven by Technology

What is the spatial distribution of forest covertypes/classes? Species composition?

Remote sensing observations can be used to differentiate forest covertypes on the basis of forest structure and species composition.

Is there a cost-effective way to map annual changes resulting from harvesting operations and natural disturbances?

Multitemporal remote sensing observations can be used to separate forest management treatments (such as cutovers, thinnings, plantings), new roads, insect damage, windthrow, burned or flooded areas, from surrounding covertypes over time.

How can remote sensing data be compared to existing forest inventory data stored in the GIS?

For some attributes (e.g., stand density) over large areas or within forest stands the information content of remote sensing data is consistent with the accuracy and level of confidence that we now possess in the GIS database. For other attributes (e.g., leaf area index) the remote sensing data are superior.

Can we map more detail within each forest stand, but also see the big picture the ecosystems in which stands are embedded and areas surrounding my management unit?

Remote sensing observations acquired at multiple scales and resolutions can be used to continuously estimate forest conditions from plots to stands to ecosystems.

Can habitat fragmentation and connectedness be measured and quantified?

Landscape pattern and structure can be detected and quantified using remote sensing observations.

What is the best way to monitor forest production? Remote sensing observations can be used to obtain precise estimates of driving variables (e.g., LAI, biomass) for use in initiating and verifying functioning ecosystem process models.


8


ing what can and cannot be remotely sensed with accuracy and efficiency is a keypiece of knowledge which those faced with management problems should possess.

D

EFINITION

AND

O

RIGINS

OF

R

EMOTE

S

ENSING

The field of remote sensing has throughout its development been relatively poorlydefined (Fisher and Lindenberg, 1989). Any number of reasons for this situationcould be cited: the truly multidisciplinary nature of the field; the phenomenal growthof automation in the various founding disciplines, such as cartography (Hegyi andQuesnet, 1983); the increasing dominance of GIS in the marketplace (Longley etal., 1999). Has a loose definition of remote sensing led to poor remote sensingscience and applications? Some might feel that there is at least one advantage ofworking in a poorly defined field the feeling among remote sensing practitionersthat virtually anything goes. Without a restrictive definition of what is and is notremote sensing, there is great freedom in selecting approaches, methods, even prob-lems to address; accordingly, there are few boundaries to remote sensing establishedby convention. In remote sensing, one strong focus has always been on methodology;how sensors, computers, and humans can be used together to solve real-worldproblems (Landgrebe, 1978a,b). This situation has persisted since the early days inremote sensing, perhaps leading to, or at least not preventing, great creativity andbreadth in the emerging field.

An original problem in defining remote sensing can be traced to a fundamentalphilosophical problem, long since resolved, of whether remote sensing was solelythe reception of stimuli (data collection) or whether it also included the collective(i.e., analytical) response to such stimuli (Gregory, 1972). But a glance at the titlesof early papers in remote sensing journals, or at any of the many remote sensingconferences and symposia throughout the 1960s and 1970s, provides ample evidencethat to the pioneers in the field, remote sensing was always much more than simplycollection of data that remote sensing was the science of

deriving

information

about an object from measurements at a distance from the object, i.e., withoutactually coming into contact with it (Landgrebe, 1978a: p. 1, italics added). Or thisone from Avery (1968: p. 135, italics added): Remote sensing may be defined asthe

detection, recognition, or evaluation

of objects by means of distant sensing orrecording devices.

By specifying the key words deriving information and detection, recognition,or evaluation these definitions suggested that the most important contribution prom-ised by remote sensing was in the conversion of the collected data to informationproducts; the true value and challenge of remote sensing would be realized in thedata interpretation and subsequent applications. The developers of applications ofremote sensing data understood from the beginning that remote sensing was bothtechnology and methodology. The term remote sensing has come to be stronglyassociated with Earth-observing satellite technology, but more properly has beenunderstood to include all sensing with distant instruments, and that is the meaningthat is assumed in this book.

Today, remote sensing is usually defined as comprised of two distinct activities:


Introduction

9

1. Data collection by sensors designed to detect electromagnetic energy frompositions on ground-based, aerial, and satellite platforms, and

2. The methods of interpreting those data.

Working in those early days of satellites and digital sensors, Avery (1968)intended to cover the emerging field of Earth-orbiting satellite remote sensingseparate from aerial photography. Remote sensing is sometimes now considered toencompass aerial photography (Lillesand and Kieffer, 1994) or at least occupy acompanion, parallel or perhaps not yet fully integrated position (Avery and Berlin,1992). Between 1960 and about 1980, at least, there were always two types ofremote sensing:

1. Imagery (or visually)-based remote sensing, and2. Numerically-based remote sensing.

The differences were found in the way the data were acquired, but more significantly,in the way the data were interpreted; in other words, the way information wasextracted from the data. To many, this distinction is no longer relevant; the focushas shifted decisively to remote sensing interpretations which best serve the purposeat hand with the available technology (Buiten, 1993). In any given application, amix of visual and numerical methods is needed.

While the methods and technology of remote sensing have shown tremendousadvances, remote sensing is obviously not a completely new idea having itsmodern antecedants in the use of cameras and balloons in the nineteenth century(Olson and Weber, 2000). The first use of this technology is not well documented,but there are suggestions that camera and balloon remote sensing technology wasused during the Franco-Austrian War of 1859, the American Civil War, and the Siegeof Paris in 1870 (Graham and Read, 1986; Landgrebe, 1978a). The first knownforestry remote sensing application was recorded in the

Berliner Tageblatt

of Sep-tember 10, 1887 (Spurr, 1960). The notice concerned the experiments of an unnamedGerman forester who constructed a forest map from photos acquired from a hot-airballoon. Interestingly, the power of the perspective from above was regarded asthe principal advantage, and many of the same problems that have since preoccupiedaerial mappers and digital image analysts were identified: geometric distortion,spatial coverage, uncertain species identification, within-stand variability, visibleindicators of growth and development, and so on. Aerial photography was establishedas a reconnaissance tool in the first World War (Graham and Read, 1986). The growthof digital remote sensing as a field from these humble but practical beginnings isdescribed in a later section.

By far, the most common remote sensing in forestry, historically and today, isconducted using optical/infrared sensors; and by far the most common of thesesensors is the aerial camera (film). However, other sensors in the passive microwave,thermal, and ultraviolet portions of the spectrum are under rapid development andinstruments may soon reach operational status.

Li

ght

d

etection

a

nd

r

anging (lidar)instruments, in particular, appear poised to transform forest measurements and


10


remote sensing as an information source (Lefsky et al., 1999a). It is not possible toconsider all of these remote sensing devices, but some are introduced in this bookand briefly discussed. All of these sensors generate data that are complex andsometimes unique. The forestry user community could no doubt benefit if an entirebook were devoted separately to the science, technology, and forestry applicationsof remote sensing by means of lidar, hyperspectral sensors, thermal sensors, andother instruments. These have not, though, achieved the level of market penetrationand user acceptance that aerial photographs, and to a lesser extent, optical/infraredand

ra

dio

d

etection

a

nd

r

anging (radar) sensors have enjoyed. This book is not aboutaerial photography, instead focusing on the digital remote sensing data and methodsgenerated by multispectral and radar instruments. It is hoped that there may be somecommon understanding that can emerge from considering the field of remote sensing,as it has been applied in forestry, and focusing on these relatively common sensorsand the digital analysis tools that have emerged to extract information from theimage data.

Here, those sensors that have been, and will likely continue to be, the mainsource of digital remote sensing information in support of forest management andpractices are discussed. The assumption is that by reviewing forestry remote sensingapplications by multispectral and radar sensors, readers will gain an appreciation ofsome key methodological issues. For example:

1. An appropriate and properly prepared remote sensing database for thetask at hand;

2. A fully functional image processing system, perhaps coupled with theability to write needed computer codes in-house (Sanchez and Canton,1999); and

3. Access to other sources of digital information, most notably throughavailable geographical information systems.

In this book, the intention is to review remote sensing accomplishments andpotential in a way that may make sustainable forest management questions moreclear, and their resolution more likely through application of remote sensing tech-nology. What forest practices will ensure our forests are being managed sustainably?Remote sensing provides some of the information that will support managementdecisions. Several examples drawn from the literature will include case study sum-maries of work that address some of the forest management questions and remotesensing hypotheses (see Table 1.1). Before examining these issues in detail, aninterpretation of two different remote sensing methods used as ways of tacklingforestry questions is provided.

T

HE

E

XPERIMENTAL

M

ETHOD

The experimental method in science is used when the control of variables is possibleand desirable (Haring et al., 1992). In many remote sensing applications, the exper-imental method has been used to better understand the relationship between theforest condition of interest, and the information available about that condition from


Introduction

11

remotely sensed data. In a remote sensing experiment, the remote sensing observa-tion is the dependent variable, and the independent variables influencing the depen-dent variable are the forest conditions of interest. If all of these variables can becontrolled, then a precise and accurate predictive model can be created by whichthe independent variable (the forest condition) can be used to predict the dependentvariable (the remote sensing observation). Then, the model can be inverted so thatthe independent variable can be predicted by the remote sensing observations. Inforestry applications, a desirable set of independent variables would include forestvegetative, structural, and biophysical conditions such as forest canopy closure,species, density, height, volume, age, roughness, leaf area, and biochemical ornutrient status.

An example of this approach was provided by Ranson and Saatchi (1992) intheir study of the microwave backscattering characteristics of balsam fir (

Abiesbalsamea

). On a movable platform, small balsam fir seedlings were arranged andthen observed by a truck-mounted microwave scatterometer at controlled polariza-tions and incidence angles. By altering the angle of the platform and the spacing ofthe seedlings, different forest canopy densities were created. With careful biophysicalmeasurements of the seedlings (as the independent variables) and the remote sensingobservations (as the dependent variables), strong predictive relationships were devel-oped and used to calibrate a mathematical model of the energy interactions of themicrowave beams with the canopies. For example, it was found that the measuredleaf area index (LAI the independent variable) and measured backscatteringcoefficient (the dependent variable) increased together. Typically, LAI is considereda dynamic forest structure variable, and is estimated by representing all of the uppersurfaces of leaves projected downward to a unit of ground area beneath the canopy(Waring and Schlesinger, 1985). The finding that LAI and microwave backscatteringwere related positively was in accordance with the predictions of the theoreticalmodel relating needle-shaped leaves and microwave energy (Figure 1.1). More leavescreated more scattering of the microwave wavelengths, and the deployed microwavesensor was sensitive to this increase in reflected energy.

The control of many confounding variables in an actual forest microwave imageacquisition from aerial or space-borne sensors is not possible. In this situation, oftenit is not known

a priori

which variables will influence remote sensing measurements;for example, the soil background and topography will variably influence the mea-surements of microwave energy. These influences can overwhelm and confound theinfluence of the leaves; such influences are not uniquely determined. Typically, thescientists would have little or no ability to control the experiment for topographicor soil differences over large areas. Even the range of leaf area conditions and thetype of imagery are typically very difficult to control; often the satellite or airbornesensor that may be available for the mission is not the ideal sensor that one wouldchoose to develop the relationship between leaves and microwave energy. The actualrelationship between aerial and satellite remotely sensed microwave backscattercoefficients and leaf area index is typically much less predictable than that obtainedusing experimental methods (e.g., Ranson and Sun, 1994a,b; Franklin et al., 1994).

A second example of the experimental approach involves the identification ofnutrients in foliage using spectral measurements. The remote sensing of foliar


12


nutrients and stress has long been of interest, and new technology has been developedto satisfy the needs of managers for whole leaf, plant, and canopy nutrient estimation(Curran, 1992; Dungan et al., 1996; Johnson and Billow, 1996). Applications mightinclude stress detection (Murtha and Ballard, 1983) and identification of agents ofstress before they cause damage or after damage has occurred (Murtha, 1978).

FIGURE 1.1

Relationship between balsam fir leaf area index at two incidence angles and C-band SAR backscatter coefficients measured in a controlled experiment. Higher backscatteris related to higher leaf area index because of increased scattering by the conifer needles.The effect is often more pronounced at lower incidence angle and in cross-polarization data.(From Ranson, K. J., and S. S. Saatchi. 1992.

IEEE Trans. Geosci. Rem. Sensing,

30: 924932.With permission.)

-5

-10

-15

-20

-25

-300.50.0 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5

a. HH

Leaf Area Index

o(d

B)

2050

oo

-5

-10

-15

-20

-25

-300.50.0 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5

c. HV

Leaf Area Index

o(d

B)

-5

-10

-15

-20

-25

-300.50.0 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5

b. VV

Leaf Area Index

o(d

B)

-5

-25

-300.50.0 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5

a. HH

Leaf Area Index

o(d

B)

2050

oo

-5

-25

-300.50.0 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5

c. HV

Leaf Area Index

o(d

B)

-5

-25

-300.50.0 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5

b. VV

Leaf Area Index

o(d

B)


Introduction

13

Typically, the first step in these applications is to apply the experimental method inremote sensing to examine the relationship between leaf reflectance properties andpigment concentration (Blackburn, 2000).

An example of this approach is the rapid, nondestructive ground-based foliar N,P, and total chlorophyll estimation technique using measurements obtained from atripod-mounted spectroradiometer developed by Bracher and Murtha (1994). Thisapproach is based on measurements most useful in nursery and field situations inwhich light and growth conditions can be controlled. First, treatments of Douglas-fir (

Pseudotsuga menziesii

) seedlings in greenhouses were varied with differentsolutions of micronutrients and macronutrients. Second, under controlled light con-ditions leaves were sampled for reflectance, then analyzed for nutrients with variouswet chemistry techniques. As pigment concentration of the leaves increased, reflec-tance measurements in the visible portion of the spectrum decreased in a predictableway. More leaf pigments, such as chlorophyll, meant less red reflectance, thus morered light absorption (more photosynthesis). The relationships to leaf nutrients, suchas N and P, were more complex and were indirect; for example, foliar N was notinvolved directly in the absorption and scattering processes that produced leaf reflec-tance. Instead, foliar N was highly correlated with leaf chlorophyll concentration.As leaf N increased, leaf reflectance measured in the green portion of the spectrumdecreased and the position of the red-edge (the difference between the red and nearinfrared reflectances) was shifted to longer wavelengths (Figure 1.2). Through therelationships between reflectance, N, and pigment concentrations, usable models(5% error) of foliar N were developed.

Extending the knowledge gained through experimental methods of remote sens-ing to create an actual remote sensing application to predict chlorophyll or nutrientstatus of forests has proven difficult. Even when all factors are controlled, variablerelationships have been reported (increased green peak reflectance with increasedpigmentation of leaves, for example). Reflectance is influenced by the chemicalproperties of consitutents and also their geometric arrangement. Field, aerial, andsatellite remote sensing measurements of canopy reflectance are subject to a largenumber of uncontrolled factors that reduce the strength and applicability of the directrelationship between leaf reflectance and chlorophyll. The sensitivity of the remotesensing instruments, as measured by the signal-to-noise ratio, has only recentlyapproached the level required for biochemical estimation (Wessman, 1990; Gholzet al., 1997). Satellite remote sensing instruments are not yet able to acquire datawith these characteristics. Airborne (and satellite) remote sensing data are a com-posite signal from leaves, branches and boles, tree canopies, ground cover and soils,and are influenced by shadowing due to height differences and defoliation, sun-target-sensor geometry, topography, leaf orientation, canopy architecture, and atmo-spheric turbidity (Bracher and Murtha, 1994; Blackburn, 2000). These and otherfactors mean that the relationship between leaf chlorophyll and leaf reflectanceobtained under controlled conditions cannot be expected to be found in the sameform and strength when those conditions of measurement are allowed to vary in anuncontrolled experiment.

What is important to note is that the experimental approach provides superiorunderstanding of the precise form of the relationship, but typically fails in most real


14


applications because there is much less control of confounding variables. What isthe point in developing a precise relationship between leaf chlorophyll and reflec-tance under lab-controlled conditions, if the moment one leaves the lab the relation-ship cannot be used? The point is that

the experimental method reveals the natureand exact form of the relationship which, it is understood, will not be found exceptunder ideal conditions.

T

HE

N

ORMATIVE

M

ETHOD

Because of the difficulty, or even the impossibility, of controlling more than a fewof the many independent variables during most remote sensing applications, thenormative method of scientific procedure (Haring et al., 1992) is often employed.That is, what is sought in remote sensing is an understanding of the normal rela-tionship (for example, between forest characteristics such as leaf area index, canopy

FIGURE 1.2

Relationship between foliar N and percent spectral reflectance for Douglas-firseedlings in different fertilization treatments measured in a controlled experiment. In (a) adecrease in reflectance at 554 nm (known as the green reflectance peak) is shown withincreased foliar N. In (b) the position of the red-edge is shown to occur at longer wavelengthswith increased foliar N. Measurements suggested that as foliar N increased, pigment concen-tration increased, and more absorption of visible light and less reflectance in the green andred portion of the spectrum occurred. The third graph (c) shows a much poorer relationshipbetween foliar P and red light absorption because of the weaker relationship between P andleaf pigment content. (From Bracher, G., and P. Murtha. 1994.

Can. J. Rem. Sensing,

20:102115. With permission.)

4.0

3.5

3.0

2.5

2.0

1.5

1.0

0.5

5 10 15 20 25 30

Spectral Reflectance (%) at 554 nm

Nitrogen(%)

+

+

+

+

+

+

+

+

+

+

+

+

+

+

+

+

+

+

+

+

+

+

+

+

N = 22.1300 (GRP)

-0.9388

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

Spectral Reflectance at 630 nm

Phosphorus(%)

4.0

3.5

3.0

2.5

2.0

1.5

1.0

0.5

Wavelength of the Red Edge (nm)

Nitrogen(%)

685 690 700 705 710 715 720 725 730695

+

+

+

+

+

+

+

+

+

+

+ +

+

+

+

+

+

+

+

+

++

N = -31.4723 + 0.0468 (RE)

r = 0.79**, p

Introduction

15

chlorophyll or N content, and reflectance or backscatter measured remotely) so thatapplications of that relationship can be developed to satisfy the practical need fornew information. Since not all the independent variables are controlled, the relation-ships that are found are subject to caveats and constraints that must be carefullydocumented and described.

An airborne spectrographic image might be acquired to map forest canopychlorophyll based on the experimental relationship between pigmentation andreflectance discussed in the previous section. If the optical properties of the atmo-sphere were not observed, and no accounting of the atmosphere was made in theuse of the remote sensing measurements, then a description of the effect of theatmosphere on the relationship between remote sensing observation and canopychlorophyll must accompany the presentation of the relationship. Even if the opticalproperties of the atmosphere were observed during image acquisition, it is impos-sible to know all the effects of the atmosphere for every pixel location in the image;assumptions of atmospheric homogeneity are needed to reduce the complexity ofthe radiative transfer equations through the atmosphere to a manageable level.Alternatively, a number of image processing steps can be taken to simulate controlof variables; instead of actual atmospheric measurements and the use of thosemeasurements to correct for the atmospheric contribution to the reflectance, a modelor an approximation of the atmosphere conditions can be employed, and an estimateof the atmospheric contribution, perhaps based on the image data themselves, isgenerated and applied

post hoc

. In this way, the observed relationship can beevaluated against the normal form of the relationship or the form of the relationshipthat would be expected given complete control of all the variables (in this case, theatmospheric contribution).

An example of this approach was presented by Gong et al. (1995) in estimationof forest LAI at six sites in Oregon using airborne imagery acquired with a varietyof bandsets at different altitudes. First, an atmospheric and sensor noise correctionwas applied with models available in an image processing system. Then, spectraldata were compared to field-measured LAI. Imagery with higher spatial resolutionhad stronger relationships to LAI than did imagery at coarser spatial resolution. Thereason suggested was that as the airplane flew at higher altitudes over the sites, thepixels in the imagery covered a larger area. This coarser spatial resolution meant thatthe sensor measurements contained more non-leaf features, such as background soil,shadows, and different understory characteristics which contributed their own reflec-tance characteristics, and degraded, subsumed, or hid the direct relationship betweenleaf area index and reflectance. The differences in spectral, spatial, and radiometricresolution that resulted when the image data were acquired at different altitudes gaverise to very different relationships between spectral reflectance and leaf area index.Typically, a negative relationship between red reflectance and leaf area index wasfound when the data were averaged over a larger area; but with very small pixel sizesthere may be a positive relationship between red reflectance and leaf area, based onpigment concentration differences between individual plant species.

Use of the normative method does encourage the development and use ofrelationships developed under less than ideal conditions. The approach can alsosuffer from a lack of generality that could lead to highly spurious local insights.


16


What is the point in developing a normative relationship between leaf chlorophylland reflectance on one day in one type of forest, if the moment one leaves thoseconditions the relationship cannot be used? The point is that, provided the designis sound, estimates of the relationship can be obtained that can be used to solveproblems even if the relationship does not generate insight into the true relation-ship. The relationship can be used cautiously, and the methods used to find therelationship can be applied elsewhere.

Many more examples can be found to illustrate these information extractionmethods and the scientific procedures in remote sensing. The lessons that have beenlearned in using the experimental and normative approaches in remote sensingforestry applications are reviewed in later chapters in this book. It seems apparentthat a balanced approach, using both experimental and normative methods, hasresulted in progress in the application of remote sensing to real-world problems inresource management. It is expected that experimental results will continue to beincrementally tested and refined using the normative approach, and through this finebalance progress in applying new information can be achieved.

CATEGORIES OF APPLICATIONSOF REMOTE SENSING

Applications of remote sensing to sustainable forest management are presented infour categories that include classification of forest covertype, estimation of foreststructure, forest change detection, and forest modeling. Each of these categories isfurther subdivided for discussion and review into individual and separate issues,variables, or model parameters according to the logic shown in Figure 1.3. Theconsideration of each category proceeds from an understanding of the role thatspecific forest covertype, forest inventory, change detection, and forest modelingapplications may play in meeting information needs for sustainable forest manage-ment. It can be anticipated that these information needs will be identified in stand-level and landscape-level planning (Hunter, 1997); generally, to support forest plan-ning there is a need for information related to strategic, tactical, and operationalactivities in forests, and to monitor the results of these activities. The criteria andindicators of sustainable forest management

can be considered a focus for themonitoring issues, and the discussion is tailored to include remote sensing methods,and references to examples of the methods that actually work with known error.

Any categorization of the many remote sensing applications into a small numberof categories is constrained by the need to put a limit on the type of applicationincluded in the category. The use of remote sensing to estimate some of the morecommon forest inventory variables height, age, crown closure is reviewedbefore considering other forest information that has potential to be provided byremote sensing technology. Still other variables available by remote sensing, not inhigh demand presently for forest management, are considered because of theirpotential role in future management scenarios. An example is the forest leaf areaindex (LAI); this forest measure is considered by many to be a potentially powerfulnew forest inventory variable (Buckley et al., 1999) even though LAI is not part of


Introduction

17

many forest inventories, is better suited for ecological applications and modeling(Running et al., 1989), and most current applications of LAI could be consideredas part of forest change detection and modeling efforts. LAI may never be useddirectly by managers; rather, the LAI estimate obtained by remote sensing can beused to drive models of forest productivity. These models predict a number offorestry-relevant outputs that would be used in management planning. Here, thedistinction between inventory variables and indirect input/output variables is notconsidered relevant; in future it is expected that forest management demand forecological applications, models, and comprehensive forest structure information, ofwhich LAI is only one example, will continue to grow until the potential role ofsuch information alongside the more traditional data in a forest inventory isbetter defined and understood.

An early distinction between landcover mapping and biophysical remote sensing(Jensen, 1983) can be seen to persist in the general categories used in this book and

FIGURE 1.3

Categories of remote sensing applications reviewed in this book organizedaccording to broad categories in forest covertype mapping, inventory variables, changedetection, and modeling. These applications are made possible, or at least are more readilyaccomplished, by the existence and continuing development of a technological and organi-zational infrastructure.

ORGANIZATIONAL INFRASTRUCTURE

Covertype Mapping

Change Detection

Forest Modeling

TECHNOLOGICAL INFRASTRUCTURE

Inventory Mapping

Science and Technology Advancement

Physiognomic Classification- Level I & II Classes

Harvesting & Silviculture- Clearcuts- Partial Cuts

Ecosystem Process Models

Acquisition of Imagery

Cover, Age, DBH, Height

Improved Understanding

Floristic Classification- Level III Classes

Natural Disturbances- Defoliation, Damage

Landscape Structure

Ecological Models Geographical Information ScienceImage Processing & Calibration

Biomass

Infrastructure Development

Integrated Classification- Ecological Classes

Volume & Growth


18


in the field in general. A few applications of remote sensing have transcended thisdistinction, having combined the goals of mapping landcover and estimating bio-physical attributes within landcover or newly created landscape units. In fact, increas-ingly, landscape units of interest are actually based on biophysical variables (Graetz,1990). While the details of the remote sensing categories used are not critical, whatis important is that an opportunity is provided to gain an understanding and appre-ciation of the essential methods of remote sensing in providing key input andmonitoring variables in support of sustainable forest management activities.

G

ROWTH

OF

R

EMOTE

S

ENSING

The technology of remote sensing originated in the science and technology of aerialphotointerpretation (Silva, 1978). Interpretation of aerial photographs had beenoperational for much of the twentieth century, at least since the first World War, andforestry applications were well established after the second World War (Graham andRead, 1986). In 1968, aerial photointerpretation was thought to have finally com-manded the respect and interest of scientists in many disciplines (Colwell, 1968:p. 1). Then, this young science was sharply challenged. New imagery and sensingtechniques quickly emerged based on various computer technologies, engineeringdesigns, launch and deployment opportunities, and developments in sensor science(Gregory, 1971). The use of the term remote sensing began as a way of describingsome of the new imagery and analysis techniques acquired alongside aerial photog-raphy during the height of the Cold War. The methods of analysis soon diverged although, in the view of many, including Buiten (1993) and Beaubien (1994), visualand digital analysis methods have recently reconverged, to the benefit of the overallremote sensing approach. Remote sensing has since developed into a scientific fieldthat can stand on its own, as evidenced by the many journals and scientific societiesnow devoted to the subject.

In 1985, Curran suggested that rapid technological advancements (e.g., comput-ers) and improved sensor systems (both introduced and envisioned) had propelledremote sensing into a stage of exponential growth. An exponential growth stage canbe predicted as the second in the normal progression of a scientific discipline throughfour distinct growth stages represented by an S-curve model (Figure 1.4). First, aslow steady rise occurs in information and activity as the field begins. This stage isfollowed by a steeper rise as knowledge accumulates exponentially, followed by alessening of the curve slope. Finally, a relatively flat or even declining conditionexists in which the science is (apparently) exhausted. The field has reached maturity.

At what point on this curve is remote sensing in the year 2001? Vital researchactivity in remote sensing seems destined to continue for some time; hardly any ofthe fundamental questions in remote sensing have been completely and exhaustivelyaddressed. Evidently, the exponential growth stage identified in 1985 has continued(Jensen, 2000). Developments are such that remote sensing seems some way fromentering the fourth stage, in which reliable information can, as a matter of routine,be generated for the management of our fragile planet (Curran, 1985: p. 7). Inforestry, digital remote sensing applications have been adopted slowly, if at all(Bergen et al., 2000); few routine applications have been established.


Introduction

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Empirical evidence for the broader interpretation of the growth of remote sensingis found by considering the occurrence of milestones in the development of sensingand space technology (Jensen, 2000), such as the development of radar in the 1940s,thermal imagery in the 1950s, the routine availability of meteorological satellite datain the 1960s, the launch of the ERTS and Landsat satellites in the 1970s, thecontinuation of medium- and coarse-scale monitoring of the globe in the 1980s, theadvent of satellite high spatial detail imagery in the late 1990s (Landgrebe, 1997),the rapid refinement and anticipated widespread application of lidar in the 2000s(Lefsky et al., 1999b). The development of new aerial films and sensors or the launchof a new satellite has often provided an immediate stimulus to those interested indeveloping the field (Colwell, 1968). As in the early days of aeri

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