Decision support for forest management:
The goal of Kangas, Kangas and Kurttila's Decision Support for Forest Management is to provide students and researchers with a toolbox of methods for approaching the different planning situations that may arise in practice. It draws together a wide range of methods used in planning forest manag...
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Dordrecht [u.a.]
Springer
2008
|
| Schriftenreihe: | Managing forest ecosystems
16 |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Zusammenfassung: | The goal of Kangas, Kangas and Kurttila's Decision Support for Forest Management is to provide students and researchers with a toolbox of methods for approaching the different planning situations that may arise in practice. It draws together a wide range of methods used in planning forest management regimes and presents a systematic overview of current methodological approaches. While earlier books concerning forest planning have tended to focus on linear programming, economic aspects, or specific multi-criteria decision aid tools, this book provides a much broader range of tools to meet a variety of planning situations. The methods themselves cover a range of decision situations - from cases involving single decision makers, through group decision making, to participatory planning. They include traditional decision support tools, from optimization to utility functions, as well as methods that are just gaining ground in forest planning - such as problem structuring methods and social choice theory. Including examples which illustrate the application of each technique to specific management planning problems, the book offers an invaluable resource for both researchers and advanced students specializing in management and planning issues relating to forestry. |
| Beschreibung: | XII, 222 S. graph. Darst. |
| ISBN: | 9781402067860 1402067860 9781402067877 |
Internformat
MARC
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| 100 | 1 | |a Kangas, Annika |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Decision support for forest management |c Annika Kangas ; Jyrki Kangas ; Mikko Kurttila |
| 264 | 1 | |a Dordrecht [u.a.] |b Springer |c 2008 | |
| 300 | |a XII, 222 S. |b graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 1 | |a Managing forest ecosystems |v 16 | |
| 520 | 3 | |a The goal of Kangas, Kangas and Kurttila's Decision Support for Forest Management is to provide students and researchers with a toolbox of methods for approaching the different planning situations that may arise in practice. It draws together a wide range of methods used in planning forest management regimes and presents a systematic overview of current methodological approaches. While earlier books concerning forest planning have tended to focus on linear programming, economic aspects, or specific multi-criteria decision aid tools, this book provides a much broader range of tools to meet a variety of planning situations. The methods themselves cover a range of decision situations - from cases involving single decision makers, through group decision making, to participatory planning. They include traditional decision support tools, from optimization to utility functions, as well as methods that are just gaining ground in forest planning - such as problem structuring methods and social choice theory. Including examples which illustrate the application of each technique to specific management planning problems, the book offers an invaluable resource for both researchers and advanced students specializing in management and planning issues relating to forestry. | |
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Datensatz im Suchindex
| _version_ | 1804137625180700672 |
|---|---|
| adam_text | CONTENTS PREFACE .. . . . .. . . . . . . . . . .. V ACKNOWLEDGEMENTS
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . .. VII 1 IUTRODUCTION . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. 1 1.1 PLANNING ANEL DECISION SUPPORT. . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . 1 1.2 FOREST MANAGEMENT PLANNING. . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.3 HISTORY OF
FOREST PLANNING . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . 6 REFERENCES . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 PART I
DISCRETE PROBLEMS 2 UNIDIRNENSIONAL PROBLEMS. . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . .. 13 2.1 DECISIONS UNELER
RISK ANEL UNCERTAINTY . . . . . . . . . . . . . . . . . . . . . . . ..
13 2.2 MEASURING UTILITY ANEL VALUE 15 2.2.1 ESTIMATING A UTILITY
FUNCTION .. . . . . . . . . . . . . . . . . . . . . . . .. 15 2.2.2
ESTIMATING A VALUE FUNCTION . . . . . . . . . . . . . . . . . . . . . .
. . . .. 17 REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . .. 21 3
MULTI-CRITERIA DECISION PROBLEMS . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . .. 23 3.1 THEORETICAJ ASPECTS . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 23 3.2
MULTI-ATTRIBUTE UTILITY FUNCTIONS. . . . . . . . . . . . . .. . . . . .
. . . . . . . . .. 25 3.2.1 FUNCTION FORMS . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . .. 25 3.2.2 BASIS FOR
ESTIMATING THE WEIGHTS .... . . . . . . . . . . . . . . . . . .. 29
3.2.3 SN1I.UT..................... . . . . . . . . . . . . . . . . . . .
. . . . .. 30 3.3 EVEN SWAPS 34 3.4 ANALYTIC HIERARCHY PROCESS .. . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 37 3.4.1
DECISION PROBLEM. . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . .. 37 3.4.2 PHASES OF AHP . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . .. 39 IX X CONTENTS 3.4.3
UNCERTAINTY IN AHP , 44 3.4.4 ANP ********************* 47 3.5 A WOT
********************** 48 REFERENCES . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 50
4 UNCERTAINTY IN MULTI-CRITERIA DECISION MAKING . . . . . . . . . . . .
. . . . . . .. 55 4.1 NATURE OF UNCERTAINTY 55 4.2 FUZZY SET THEORY 56
4.2.1 MEMBERSHIP FUNCTIONS AND FUZZY NUMBERS , 56 4.2.2 FUZZY GOALS IN
DECISION MAKING . . . . . . . . . . . . . . . . . . . . . .. 60 4.2.3
FUZZY ADDITIVE WEIGHTING . . . . . . . . . . . . . . . . . . . . . . . .
. . . .. 65 4.3 POSSIBILITY THEORY IN DECISION MAKING. . . . . . . . . .
. . . . . . . . . . . . . .. 65 4.4 EVIDENCE THEORY . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 68 4.5
OUTRANKING METHODS . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . .. 70 4.5.1 OUTLINE . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 70 4.5.2
PROMETHEE METHOD 72 4.5.3 ELECTRE METHOD . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . .. 75 4.5.4 OTHER OUTRANKING METHODS
... . . . . . . . . . . . . . . . . . . . . . . . .. 81 4.6
PROBABILISTIC UNCERTAINTY IN DECISION ANALYSIS. . . . . . . . . . . . .
. . . .. 81 4.6.1 STOCHASTIC MULTICRITERIA ACCEPTABILITY ANALYSIS (SMAA)
... 81 4.6.2 SMAA-O . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . .. 91 4.6.3 PAIRWISE PROBABILITIES. . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . .. 91 REFERENCES .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . .. 94 PART II CONTINUOUS PROBLEMS 5
OPTIMIZATION. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . 101 5.1 LINEAR PRO GRAMMING . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . 101 5.1.1 PRIMAL PROBLEM 101 5.1.2 DUAL PROBLEM 105 5.1.3 FOREST
PLANNING PROBLEM WITH SEVERAL STANDS 109 5.1.4 JLP SOFTWARE 110 5.2 GOAL
PROGRAMMING 111 5.3 INTEGER PROGRAMMING 115 5.4 UNCERTAINTY IN
OPTIMIZATION 118 5.5 ROBUST PORTFOLIO MODELLING 120 5.5.1 PRINCIPLES OF
THE METHOD 120 5.5.2 USE OF RPM IN FOREST PLANNING 121 REFERENCES . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . 123 6 HEURISTIC OPTIMIZATION 127 6.1
PRINCIPLES 127 6.2 OBJECTIVE FUNCTION FORMS 129 6.3 HERO 130 CONTENTS XI
6.4 SIMULATED ANNEALING AND THRESHOLD ACCEPTING 132 6.5 TABU SEARCH 133
6.6 GENETIC ALGORITHMS 135 6.7 IMPROVING THE HEURISTIE SEARCH 136 6.7.1
PARAMETERS OF HEURISTIC OPTIMIZATION TEEHNIQUES 136 6.7.2 EXPANDING THE
NEIGHBOURHOOD 137 6.7.3 COMBINING OPTIMIZATION TEEHNIQUES 138 REFERENEES
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . 139 PART 111 CASES WITH SEVERAL
DECISION MAKERS 7 GROUP DECISION MAKING AND PARTICIPATORY PLANNING 145
7. 1 DEEISION MAKERS AND STAKEHOLDERS 145 7.2 PUBLIE PARTICIPATION
PROEESS 147 7.2.1 TYPES OF PARTICIPATION PROEESS 147 7.2.2 SUCCESS OF
THE PARTIEIPATION PROEESS 148 7.2.3 DEFINING THE APPROPRIATE PROCESS 150
7.3 TOOLS FOR ELIEITING THE PUBLIC PREFERENCES 153 7.3.1 SURVEYS 153
7.3.2 PUBLIC HEARINGS 154 7.4 PROBLEM STRUCTURING METHODS 155 7.4.1
BACKGROUND 155 7.4.2 STRATEGIE OPTIONS DEVELOPMENT AND ANALYSIS 156
7.4.3 SOFT SYSTEMS METHODOLOGY 159 7.5 DEEISION SUPPORT FOR GROUP
DECISION MAKING 164 REFERENEES . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169 8
VOTING METHODS 173 8. 1 SOCIAL CHOICE THEORY 173 8.1.1 OUTLINE 173 8.1.2
EVALUATION CRITERIA FOR VOTING SYSTEMS 174 8.2 POSITIONAL VOTING SCHEMES
174 8.2.1 PLURALITY VOTING 174 8.2.2 APPROVAL VOTING 175 8.2.3 BORDA
COUNT 176 8.3 PAIRWISE VOTING 177 8.4 FUZZY VOTING ., 179 8.5
PROBABILITY VOTING 180 8.6 MULTICRITERIA APPROVAL 181 8.6.1 ORIGINAL
METHOCL 181 8.6.2 FUZZY MA 183 8.6.3 MULTICRITERIA APPROVAL VOTING 185
REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . 187 XII PART IV APPLICATION
VIEWPOINTS CONTENTS 9 BEHAVIOURAL ASPECTS 191 9.1 CRITICISM TOWARDS
DECISION THEORY . . . . . . . . . . . . . . . . . . . . . . . . 191
9.1.1 OUTLINE 191 9.1.2 SATISFICING OR MAXIMIZING? 192 9.1.3 RULES OR
RATIONAL BEHAVIOUR? 193 9.2 IMAGE THEORY 194 9.3 PROSPECT THEORY 197
REFERENCES 200 10 PRACTICAL EXAMPLES OF USING MCDS METHODS 201 10.1
LANDSCAPE ECOLOGICAL PLANNING 201 10.2 PARTICIPATORY PLANNING 203 10.3
SPATIAL OBJECTIVES AND HEURISTIC OPTIMIZATION IN PRACTICAL FOREST
PLANNING 209 REFERENCES 212 11 FINAL REMARKS 213 REFERENCES 216 INDEX ,
219
|
| adam_txt |
CONTENTS PREFACE '" . . . . . . . . . . . . . . . V ACKNOWLEDGEMENTS
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . VII 1 IUTRODUCTION . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. 1 1.1 PLANNING ANEL DECISION SUPPORT. . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . 1 1.2 FOREST MANAGEMENT PLANNING. . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.3 HISTORY OF
FOREST PLANNING . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . 6 REFERENCES . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 PART I
DISCRETE PROBLEMS 2 UNIDIRNENSIONAL PROBLEMS. . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . 13 2.1 DECISIONS UNELER
RISK ANEL UNCERTAINTY . . . . . . . . . . . . . . . . . . . . . . . .
13 2.2 MEASURING UTILITY ANEL VALUE 15 2.2.1 ESTIMATING A UTILITY
FUNCTION . . . . . . . . . . . . . . . . . . . . . . . . 15 2.2.2
ESTIMATING A VALUE FUNCTION . . . . . . . . . . . . . . . . . . . . . .
. . . . 17 REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . 21 3
MULTI-CRITERIA DECISION PROBLEMS . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 23 3.1 THEORETICAJ ASPECTS . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 3.2
MULTI-ATTRIBUTE UTILITY FUNCTIONS. . . . . . . . . . . . . . . . . . .
. . . . . . . . . 25 3.2.1 FUNCTION FORMS . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . 25 3.2.2 BASIS FOR
ESTIMATING THE WEIGHTS . . . . . . . . . . . . . . . . . . . 29
3.2.3 SN1I.UT. . . . . . . . . . . . . . . . . . . .
. . . . . 30 3.3 EVEN SWAPS " 34 3.4 ANALYTIC HIERARCHY PROCESS . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 3.4.1
DECISION PROBLEM. . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . 37 3.4.2 PHASES OF AHP . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . 39 IX X CONTENTS 3.4.3
UNCERTAINTY IN AHP , 44 3.4.4 ANP ********************* 47 3.5 A'WOT
********************** 48 REFERENCES . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
4 UNCERTAINTY IN MULTI-CRITERIA DECISION MAKING . . . . . . . . . . . .
. . . . . . . 55 4.1 NATURE OF UNCERTAINTY 55 4.2 FUZZY SET THEORY 56
4.2.1 MEMBERSHIP FUNCTIONS AND FUZZY NUMBERS , 56 4.2.2 FUZZY GOALS IN
DECISION MAKING . . . . . . . . . . . . . . . . . . . . . . 60 4.2.3
FUZZY ADDITIVE WEIGHTING . . . . . . . . . . . . . . . . . . . . . . . .
. . . . 65 4.3 POSSIBILITY THEORY IN DECISION MAKING. . . . . . . . . .
. . . . . . . . . . . . . . 65 4.4 EVIDENCE THEORY . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 4.5
OUTRANKING METHODS . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . 70 4.5.1 OUTLINE . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 4.5.2
PROMETHEE METHOD 72 4.5.3 ELECTRE METHOD . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . 75 4.5.4 OTHER OUTRANKING METHODS
. . . . . . . . . . . . . . . . . . . . . . . . . 81 4.6
PROBABILISTIC UNCERTAINTY IN DECISION ANALYSIS. . . . . . . . . . . . .
. . . . 81 4.6.1 STOCHASTIC MULTICRITERIA ACCEPTABILITY ANALYSIS (SMAA)
. 81 4.6.2 SMAA-O . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . 91 4.6.3 PAIRWISE PROBABILITIES. . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . 91 REFERENCES .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . 94 PART II CONTINUOUS PROBLEMS 5
OPTIMIZATION. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . 101 5.1 LINEAR PRO GRAMMING . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . 101 5.1.1 PRIMAL PROBLEM 101 5.1.2 DUAL PROBLEM 105 5.1.3 FOREST
PLANNING PROBLEM WITH SEVERAL STANDS 109 5.1.4 JLP SOFTWARE 110 5.2 GOAL
PROGRAMMING 111 5.3 INTEGER PROGRAMMING 115 5.4 UNCERTAINTY IN
OPTIMIZATION 118 5.5 ROBUST PORTFOLIO MODELLING 120 5.5.1 PRINCIPLES OF
THE METHOD 120 5.5.2 USE OF RPM IN FOREST PLANNING 121 REFERENCES . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . 123 6 HEURISTIC OPTIMIZATION 127 6.1
PRINCIPLES 127 6.2 OBJECTIVE FUNCTION FORMS 129 6.3 HERO 130 CONTENTS XI
6.4 SIMULATED ANNEALING AND THRESHOLD ACCEPTING 132 6.5 TABU SEARCH 133
6.6 GENETIC ALGORITHMS 135 6.7 IMPROVING THE HEURISTIE SEARCH 136 6.7.1
PARAMETERS OF HEURISTIC OPTIMIZATION TEEHNIQUES 136 6.7.2 EXPANDING THE
NEIGHBOURHOOD 137 6.7.3 COMBINING OPTIMIZATION TEEHNIQUES 138 REFERENEES
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . 139 PART 111 CASES WITH SEVERAL
DECISION MAKERS 7 GROUP DECISION MAKING AND PARTICIPATORY PLANNING 145
7. 1 DEEISION MAKERS AND STAKEHOLDERS 145 7.2 PUBLIE PARTICIPATION
PROEESS 147 7.2.1 TYPES OF PARTICIPATION PROEESS 147 7.2.2 SUCCESS OF
THE PARTIEIPATION PROEESS 148 7.2.3 DEFINING THE APPROPRIATE PROCESS 150
7.3 TOOLS FOR ELIEITING THE PUBLIC PREFERENCES 153 7.3.1 SURVEYS 153
7.3.2 PUBLIC HEARINGS 154 7.4 PROBLEM STRUCTURING METHODS 155 7.4.1
BACKGROUND 155 7.4.2 STRATEGIE OPTIONS DEVELOPMENT AND ANALYSIS 156
7.4.3 SOFT SYSTEMS METHODOLOGY 159 7.5 DEEISION SUPPORT FOR GROUP
DECISION MAKING 164 REFERENEES . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169 8
VOTING METHODS 173 8. 1 SOCIAL CHOICE THEORY 173 8.1.1 OUTLINE 173 8.1.2
EVALUATION CRITERIA FOR VOTING SYSTEMS 174 8.2 POSITIONAL VOTING SCHEMES
174 8.2.1 PLURALITY VOTING 174 8.2.2 APPROVAL VOTING 175 8.2.3 BORDA
COUNT 176 8.3 PAIRWISE VOTING 177 8.4 FUZZY VOTING ., 179 8.5
PROBABILITY VOTING 180 8.6 MULTICRITERIA APPROVAL 181 8.6.1 ORIGINAL
METHOCL 181 8.6.2 FUZZY MA 183 8.6.3 MULTICRITERIA APPROVAL VOTING 185
REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . 187 XII PART IV APPLICATION
VIEWPOINTS CONTENTS 9 BEHAVIOURAL ASPECTS 191 9.1 CRITICISM TOWARDS
DECISION THEORY . . . . . . . . . . . . . . . . . . . . . . . . 191
9.1.1 OUTLINE 191 9.1.2 SATISFICING OR MAXIMIZING? 192 9.1.3 RULES OR
RATIONAL BEHAVIOUR? 193 9.2 IMAGE THEORY 194 9.3 PROSPECT THEORY 197
REFERENCES 200 10 PRACTICAL EXAMPLES OF USING MCDS METHODS 201 10.1
LANDSCAPE ECOLOGICAL PLANNING 201 10.2 PARTICIPATORY PLANNING 203 10.3
SPATIAL OBJECTIVES AND HEURISTIC OPTIMIZATION IN PRACTICAL FOREST
PLANNING 209 REFERENCES 212 11 FINAL REMARKS 213 REFERENCES 216 INDEX ,
219 |
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| author | Kangas, Annika Kangas, Jyrki Kurttila, Mikko |
| author_facet | Kangas, Annika Kangas, Jyrki Kurttila, Mikko |
| author_role | aut aut aut |
| author_sort | Kangas, Annika |
| author_variant | a k ak j k jk m k mk |
| building | Verbundindex |
| bvnumber | BV023298275 |
| callnumber-first | S - Agriculture |
| callnumber-label | SD431 |
| callnumber-raw | SD431 |
| callnumber-search | SD431 |
| callnumber-sort | SD 3431 |
| callnumber-subject | SD - Forestry |
| classification_tum | FOR 640f WIR 543f |
| ctrlnum | (OCoLC)180474063 (DE-599)HBZHT015294267 |
| dewey-full | 634.92 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 634 - Orchards, fruits, forestry |
| dewey-raw | 634.92 |
| dewey-search | 634.92 |
| dewey-sort | 3634.92 |
| dewey-tens | 630 - Agriculture and related technologies |
| discipline | Forstwissenschaft Agrar-/Forst-/Ernährungs-/Haushaltswissenschaft / Gartenbau Wirtschaftswissenschaften |
| discipline_str_mv | Forstwissenschaft Agrar-/Forst-/Ernährungs-/Haushaltswissenschaft / Gartenbau Wirtschaftswissenschaften |
| format | Book |
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| id | DE-604.BV023298275 |
| illustrated | Illustrated |
| index_date | 2024-07-02T20:45:32Z |
| indexdate | 2024-07-09T21:15:16Z |
| institution | BVB |
| isbn | 9781402067860 1402067860 9781402067877 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-016482771 |
| oclc_num | 180474063 |
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| owner_facet | DE-M49 DE-BY-TUM |
| physical | XII, 222 S. graph. Darst. |
| publishDate | 2008 |
| publishDateSearch | 2008 |
| publishDateSort | 2008 |
| publisher | Springer |
| record_format | marc |
| series | Managing forest ecosystems |
| series2 | Managing forest ecosystems |
| spelling | Kangas, Annika Verfasser aut Decision support for forest management Annika Kangas ; Jyrki Kangas ; Mikko Kurttila Dordrecht [u.a.] Springer 2008 XII, 222 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Managing forest ecosystems 16 The goal of Kangas, Kangas and Kurttila's Decision Support for Forest Management is to provide students and researchers with a toolbox of methods for approaching the different planning situations that may arise in practice. It draws together a wide range of methods used in planning forest management regimes and presents a systematic overview of current methodological approaches. While earlier books concerning forest planning have tended to focus on linear programming, economic aspects, or specific multi-criteria decision aid tools, this book provides a much broader range of tools to meet a variety of planning situations. The methods themselves cover a range of decision situations - from cases involving single decision makers, through group decision making, to participatory planning. They include traditional decision support tools, from optimization to utility functions, as well as methods that are just gaining ground in forest planning - such as problem structuring methods and social choice theory. Including examples which illustrate the application of each technique to specific management planning problems, the book offers an invaluable resource for both researchers and advanced students specializing in management and planning issues relating to forestry. Forest management Forstverwaltung (DE-588)4017964-3 gnd rswk-swf Entscheidungsunterstützung (DE-588)4202171-6 gnd rswk-swf Forstwirtschaft (DE-588)4017966-7 gnd rswk-swf Entscheidungsfindung (DE-588)4113446-1 gnd rswk-swf Forstverwaltung (DE-588)4017964-3 s Entscheidungsunterstützung (DE-588)4202171-6 s DE-604 Forstwirtschaft (DE-588)4017966-7 s Entscheidungsfindung (DE-588)4113446-1 s b DE-604 Kangas, Jyrki Verfasser aut Kurttila, Mikko Verfasser aut Managing forest ecosystems 16 (DE-604)BV013701624 16 OEBV Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016482771&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Kangas, Annika Kangas, Jyrki Kurttila, Mikko Decision support for forest management Managing forest ecosystems Forest management Forstverwaltung (DE-588)4017964-3 gnd Entscheidungsunterstützung (DE-588)4202171-6 gnd Forstwirtschaft (DE-588)4017966-7 gnd Entscheidungsfindung (DE-588)4113446-1 gnd |
| subject_GND | (DE-588)4017964-3 (DE-588)4202171-6 (DE-588)4017966-7 (DE-588)4113446-1 |
| title | Decision support for forest management |
| title_auth | Decision support for forest management |
| title_exact_search | Decision support for forest management |
| title_exact_search_txtP | Decision support for forest management |
| title_full | Decision support for forest management Annika Kangas ; Jyrki Kangas ; Mikko Kurttila |
| title_fullStr | Decision support for forest management Annika Kangas ; Jyrki Kangas ; Mikko Kurttila |
| title_full_unstemmed | Decision support for forest management Annika Kangas ; Jyrki Kangas ; Mikko Kurttila |
| title_short | Decision support for forest management |
| title_sort | decision support for forest management |
| topic | Forest management Forstverwaltung (DE-588)4017964-3 gnd Entscheidungsunterstützung (DE-588)4202171-6 gnd Forstwirtschaft (DE-588)4017966-7 gnd Entscheidungsfindung (DE-588)4113446-1 gnd |
| topic_facet | Forest management Forstverwaltung Entscheidungsunterstützung Forstwirtschaft Entscheidungsfindung |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016482771&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV013701624 |
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