United States

Department of

Agriculture

Forest

Service

North Central

Research Station

General Technical

Report NC-263

LANDIS: A Spatially Explicit Model 

of Forest Landscape Disturbance, 

Management, and Succession

LANDIS 4.0 Users Guide

Hong S. He, Wei Li, Brian R. Sturtevant, Jian Yang, Bo Z. Shang, 

Eric J. Gustafson, and David J. Mladenoff
He, Hong S.; Li, Wei; Sturtevant, Brian R.; Yang, Jian; Shang, Bo Z.; 

Gustafson, Eric J.; Mladenoff, David J. 2005. LANDIS 4.0 users guide. 

LANDIS: a spatially explicit model of forest landscape disturbance, 

management, and succession. Gen. Tech. Rep. NC-263. St. Paul, MN: 

U.S. Department of Agriculture, Forest Service, North Central Research 

Station. 93 p.

LANDIS 4.0 is new-generation software that simulates forest landscape

change over large spatial and temporal scales. It is used to explore how

disturbances, succession, and management interact to determine forest

composition and pattern. Also describes software architecture, model

assumptions and provides detailed instructions on the use of the model.

KEY WORDS: Simulation model, landscape ecology, software 

documentation, decision support, forest management, 

manual.

The U.S. Department of Agriculture (USDA) prohibits discrimination in all its programs 

and activities on the basis of race, color, national origin, sex, religion, age, disability, 

political beliefs, sexual orientation, and marital or family status. (Not all prohibited 

bases apply to all programs.) Persons with disabilities who require alternative 

means for communication of program information (Braille, large print, audiotape, etc.) 

should contact USDA’s TARGET Center at 202-720-2600 (voice and TDD). To file a 

complaint of discrimination, write USDA, Director, Office of Civil Rights, Room 326-W, 

Whitten Building, 1400 Independence Avenue SW, Washington, DC 20250-9410 or call 

202-720-5964 (voice and TDD). USDA is an equal opportunity provider and employer.

We believe the good life has its roots in clean air, sparkling water, rich soil, 

healthy economies and a diverse living landscape. Maintaining the good life for 

generations to come begins with everyday choices about natural resources. The 

North Central Research Station provides the knowledge and the tools to help 

people make informed choices. That’s how the science we do enhances the 

quality of people’s lives.

For further information, contact:

North Central Research Station

USDA Forest Service

1992 Folwell Avenue

St. Paul, MN 55108

Or visit our web site:

www.ncrs.fs.fed.us

MISSION STATEMENT
LANDIS 4.0 Users Guide Page 

i

ABOUT THE AUTHORS

Hong S. He

Assistant Professor

School of Natural Resources

University of Missouri

Columbia, MO

Wei Li

Computer Programmer

School of Natural Resources

University of Missouri

Columbia, MO

Brian R. Sturtevant

Research Ecologist

North Central Research Station

Rhinelander, WI

Jian Yang

PhD student

School of Natural Resources

University of Missouri

Columbia, MO

Bo Z. Shang

Post-doctoral Associate

School of Natural Resources

University of Missouri

Columbia, MO

Eric J. Gustafson

Research Ecologist

North Central Research Station

Rhinelander, WI

David J. Mladenoff

Professor

Department of Forest Ecology and Management

University of Wisconsin-Madison

Madison, WI
LANDIS 4.0 Users Guide Page ii

LANDIS 4.0 Dynamic Design

Hong S. He (1)

LANDIS 4.0 Overall Programming

Wei Li (1)

LANDIS 4.0 Succession/Dispersal Module

Design: David J. Mladenoff (2), Hong S. He (1)

Implementation: Hong S. He (1)

LANDIS 4.0 Fire Module

Design: Jian Yang (1), Hong S. He (1), Eric Gustafson (3) 

Implementation: Jian Yang (1)

LANDIS 4.0 Wind Module

Design: David J. Mladenoff (2), Hong S. He (1)

Implementation: Hong S. He (1)

LANDIS 4.0 Harvest Module

Design: Eric J. Gustafson (3), Kevin K. Nimerfro (3), Stephen R. Shifley (3), 

David J. Mladenoff (2), Hong S. He (2), Patrick A. Zollner (3)

Implementation: Kevin K. Nimerfro (3)

LANDIS 4.0 Fuel Module

Design: Hong S. He (1), Bo Z. Shang (1), Thomas R. Crow (3), 

Eric J. Gustafson (3), Stephen R. Shifley (3)

Implementation: Bo Z. Shang (1)

LANDIS 4.0 Biological Disturbance Module

Design: Brian R. Sturtevant (3), Eric J. Gustafson (3), Wei Li (1), 

Hong S. He (1)

Implementation: Wei Li (1)

LANDIS 4.0 Input and Output Interface

Design: Hong S. He (1)

Implementation: Shihua Sun (1)

Institutions of the above individuals when they made contributions to 

LANDIS 4.0

1. University of Missouri-Columbia

2. University of Wisconsin-Madison 

3. USDA Forest Service North Central Research Station

Funding Support for LANDIS 4.0 Development

USDA FS North Central Research Station (90%)

University of Missouri GIS Mission Enhancement Program (10%)

The following individuals also made contributions to LANDIS 4.0 development: 

Pat Zollner (USDA FS North Central Research Station), Robert Cummings 

(USDA FS North Central Research Station), Robert Scheller (University of 

Wisconsin-Madison), David Lytle (USDA FS North Central Research Station).

Contact

Dr. Hong S. He

School of Natural Resources, University of Missouri-Columbia, 203 ABNR 

Building, Columbia, MO 65211

heh@missouri.edu 
LANDIS 4.0 Users Guide Page iii

TABLE OF CONTENTS

1. WHAT IS THE LANDIS MODEL? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

2. LANDIS MODEL DESIGN CONSIDERATIONS . . . . . . . . . . . . . . . . . . 1

3. LANDIS IS SUITED TO ANSWER 

THE FOLLOWING QUESTIONS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

4. LANDIS 4.0 RELEASE NOTE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

5. THE NEW FEATURES IN LANDIS 4.0 . . . . . . . . . . . . . . . . . . . . . . . . . . 2

6. LANDIS 4.0 DISCLAIMER . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

7. LANDIS 4.0 COPYRIGHT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

8. HOW TO USE THIS GUIDE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

9. CONCEPTUAL BASIS FOR THE LANDIS MODEL . . . . . . . . . . . . . . . 4

9.1 Spatial and Temporal Scales of LANDIS . . . . . . . . . . . . . . . . . . . . . . . 4

9.2 Heterogeneity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

9.3 Stochasticity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

9.4 Component-based Model Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

9.5 Overview of LANDIS Model Components . . . . . . . . . . . . . . . . . . . . . . 6

9.5.1 Succession and Seed Dispersal . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

9.5.1.1 Birth, establishment, growth, and death . . . . . . . . . . . . . . . . 7

9.5.1.2 Vegetative reproduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

9.5.1.3 Competition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

9.5.1.4 Seed dispersal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

9.5.2 Natural Disturbance Overview and Definitions . . . . . . . . . . . . . . . 9

9.5.3 Fire Disturbance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

9.5.3.1 Fire occurrence simulation . . . . . . . . . . . . . . . . . . . . . . . . . 10

9.5.3.2 Fire spread simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

9.5.3.3 Fire effect simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

9.5.4 Wind Disturbance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

9.5.5 Harvest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

9.5.6 Biological Disturbance Agent (BDA) . . . . . . . . . . . . . . . . . . . . . 18

9.5.6.1 Site resource dominance . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

9.5.6.2 Site resource modifiers . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

9.5.6.3 Neighborhood resource dominance . . . . . . . . . . . . . . . . . . 19

9.5.6.4 Regional outbreak status . . . . . . . . . . . . . . . . . . . . . . . . . . 19

9.5.6.5 BDA effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

9.5.6.6 BDA dispersal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

9.5.6.6.1 Epicenters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

9.5.6.6.2 Spatial outbreak zones . . . . . . . . . . . . . . . . . . . . . . . 22

9.5.7 Fuel and fuel management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

9.5.7.1 Fine fuel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

9.5.7.2 Coarse fuels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

9.5.7.3 Fine fuel and coarse fuel modifier . . . . . . . . . . . . . . . . . . . 25

9.5.7.4 Potential fire intensity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

9.5.7.5 Live fuel-fire intensity modifier . . . . . . . . . . . . . . . . . . . . . 26

9.5.7.6 Potential fire risk . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
LANDIS 4.0 Users Guide Page iv

9.5.7.7 Fuel management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

9.5.7.7.1 Treatment size, interval, and intensity . . . . . . . . . . . 27

9.5.7.7.2 Prescribed burning . . . . . . . . . . . . . . . . . . . . . . . . . . 27

9.5.7.7.3 Physical fuel load reduction . . . . . . . . . . . . . . . . . . . 27

9.5.7.7.4 Other fuel treatments . . . . . . . . . . . . . . . . . . . . . . . . 27

10. OPERATING SYSTEMS AND CONFIGURATIONS . . . . . . . . . . . . . . 28

10.1 Operating Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

10.2 Memory and Storage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

11. BASIC LANDIS INPUT FILES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

11.1 Parameter File (parameter.dat) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

11.2 Species Attribute File (species.dat) . . . . . . . . . . . . . . . . . . . . . . . . . . 30

11.3 Land Type Attribute File (landtype.dat) . . . . . . . . . . . . . . . . . . . . . 31

11.4 Land Type Map File (landtype.gis) . . . . . . . . . . . . . . . . . . . . . . . . . . 32

11.5 Species Composition Map File (speciesMap.gis) . . . . . . . . . . . . . . . . . 33

11.6 Map Attribute File (mapAttribute.dat) . . . . . . . . . . . . . . . . . . . . . . . 33

11.7 Map Index File (mapIndex.dat) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

11.8 Reclassification File (RCS File) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

11.9 Reclassification Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

11.10 Age Map Index File (ageIndex.dat) . . . . . . . . . . . . . . . . . . . . . . . . . 37

11.11 Age File (.age) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

11.12 Frequency File (frequency.dat) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

12. LANDIS WIND MODULE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

13. LANDIS FIRE MODULE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

13.1 Fire Entry File (fireParameter.dat) . . . . . . . . . . . . . . . . . . . . . . . . . 39

13.2 Fire Regime Attribute File (fireRegimeAttr.dat) . . . . . . . . . . . . . . . 42

13.3 Fire regime and DEM maps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

14. LANDIS BDA MODULE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

14.1 Entry File (BDAInit.dat) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

14.2 BDA Parameter File . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

15. LANDIS FUEL MODULE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

15.1 Fuel Entry File (fuelInit.dat) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

15.2 Fuel module input file (fuelInput.dat) . . . . . . . . . . . . . . . . . . . . . . . 49

15.3 Fuel rule input file (fuelRule.dat) . . . . . . . . . . . . . . . . . . . . . . . . . . 53

15.3.1 Potential fire intensity class . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

15.3.2 Potential fire probability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

15.3.3 Potential fire risk . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

15.3.4 Fire regime attributes file (fireRegimeAttr.dat) . . . . . . . . . . . . . 56

15.3.5 Fuel management event file (fuelMan.dat) . . . . . . . . . . . . . . . . 56

15.3.5.1 Example 1 - Prescribed burning . . . . . . . . . . . . . . . . . . . . 57

15.3.5.2 Example 2 - Coarse woody debris reduction . . . . . . . . . . 57

15.3.5.3 Example 3 - Prescribed burning + 

 coarse woody debris reduction . . . . . . . . . . . . . . . . . . . . 58
LANDIS 4.0 Users Guide Page v

16. LANDIS HARVEST MODULE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58

16.1 Harvest Entry File (harvestInit.dat) . . . . . . . . . . . . . . . . . . . . . . . . 58

16.2 Harvest Module Input Files . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59

16.2.1 Management area identifier map (ma.gis) . . . . . . . . . . . . . . . . . 59

16.2.2 Stand identifier map (stand.gis) . . . . . . . . . . . . . . . . . . . . . . . . . 59

16.2.3 Harvest regime (harvest.dat) . . . . . . . . . . . . . . . . . . . . . . . . . . . 59

16.2.3.1 Harvest mask . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

16.2.3.2 Stand ranking algorithms . . . . . . . . . . . . . . . . . . . . . . . . . 60

16.2.4 Descriptions of harvest regimes (harvest.dat) . . . . . . . . . . . . . . 61

16.2.4.1 Harvest regime 1: one-entry, stand-filling . . . . . . . . . . . . 61

16.2.4.2 Harvest regime 2: periodic-entry, 

 stand-resampling, stand-filling . . . . . . . . . . . . . . . . . . . . 62

16.2.4.3 Harvest regime 3: two-entry, stand-filling . . . . . . . . . . . . 63

16.2.4.4 Harvest regime 4: one-entry, stand-spreading . . . . . . . . . 65

16.2.4.5 Harvest regime 5: two-entry, stand-spreading . . . . . . . . . 66

16.2.4.6 Harvest regime 6: group selection . . . . . . . . . . . . . . . . . . 68

16.2.4.7 Harvest regime 7: periodic-entry-fixed-stand, 

 two-entry, stand-filling . . . . . . . . . . . . . . . . . . . . . . . . . . 70

16.2.4.8 Harvest regime 8: periodic-entry-stand-resampling, 

 two-entry, stand-filling . . . . . . . . . . . . . . . . . . . . . . . . . . 71

17. OUTPUT FILES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73

17.1 GIS Map Files . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73

17.1.1 Species GIS file . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74

17.1.2 Species age GIS file . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74

17.1.3 Age group GIS file . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74

17.1.4 Disturbance GIS file . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74

17.1.5 Harvest GIS file . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74

17.2 Trailer File . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74

17.3 Log Files . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74

18. RUNNING THE PROGRAM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75

18.1 Using LANDIS Input Interface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75

18.1.1 LI.exe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75

18.1.2 Menu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75

18.1.3 Toolbar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78

18.2 Through Windows DOS Prompt . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78

19. LANDIS OUTPUT VIEWER (LV.EXE) . . . . . . . . . . . . . . . . . . . . . . . . . 79

19.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79

19.2 Main User Interface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79

19.3 How to use . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80

19.4 Key features of LANDIS Viewer . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81

19.4.1 Accept maps of any size . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81

19.4.2 Accept both 8-bit and 16-bit maps . . . . . . . . . . . . . . . . . . . . . . . 81

19.4.3 Support command line parameters . . . . . . . . . . . . . . . . . . . . . . 81

19.4.4 Species list and time sequence navigation . . . . . . . . . . . . . . . . . 82

19.4.5 Multiple instances of the viewer . . . . . . . . . . . . . . . . . . . . . . . . 82
LANDIS 4.0 Users Guide Page vi

20. LANDIS SUBSIDIARY PROGRAMS . . . . . . . . . . . . . . . . . . . . . . . . . . . 82

20.1 IAN-raster image analysis software program . . . . . . . . . . . . . . . . . 82

20.2 Map Converter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82

20.3 GIS2Grid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83

21. QUICK EVALUATION OF RESULTS . . . . . . . . . . . . . . . . . . . . . . . . . . 88

21.1 Mean Disturbance Sizes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88

21.2 Mean Disturbance Return Intervals . . . . . . . . . . . . . . . . . . . . . . . . . 88

22. TROUBLESHOOTING . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89

22.1 Missing Dlls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89

22.2 Program Terminated with an Error Message Displayed . . . . . . . . . 89

22.3 Program Hung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89

22.4 Abnormal Program Termination . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89

22.5 Out of Memory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90

22.6 Out of Disk Space . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90

22.7 Program is Very Slow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90

22.8 Results and Parameters do not Match . . . . . . . . . . . . . . . . . . . . . . . 90

22.9 LANDIS Users Forum . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90

22.10 Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90

23. REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
LANDIS 4.0 Users Guide Page 1

1. WHAT IS THE LANDIS MODEL?

LANDIS is a spatially explicit landscape model designed to simulate forest 

landscape change over large spatial and temporal scales (Mladenoff et al.

1996, Mladenoff and He 1999). LANDIS 4.0 simulates the dynamics of 

forest succession, seed dispersal, wind, fire, biological disturbance (insects 

and diseases), harvesting, fuel accumulation and decomposition, and fuel 

management. Differing from most landscape models, LANDIS simulates multiple 

landscape processes in combination with the simulation of succession dynamics 

at the tree species level.

2. LANDIS MODEL DESIGN CONSIDERATIONS

LANDIS is designed with these considerations:

• It simulates forest landscape change over large spatial (103-107 ha) 

and temporal (101-103 years) scales with flexible resolutions (10-500 m 

pixel size), balancing ecological complexity with current and foreseeable 

computational capability.

• It simulates the main natural and anthropogenic disturbances and their 

interactions with adequate mechanistic realism for these broad scales.

• It simulates species-level forest succession in combination with 

disturbances and management. 

• It assumes that detailed, individual tree information and within-stand 

processes can be simplified, allowing large-scale questions about spatial 

pattern, species distribution, and disturbances to be adequately addressed.

• It uses a component-based, object-oriented design that provides users with 

the flexibility of parameterizing and simulating only the processes of 

interest. 

• It uses classified satellite imagery as input, and output is compatible with 

most GIS software.

• It requires moderate parameter input since, for most landscapes in these 

scale ranges, available input data may be coarse and parameters may be 

poorly estimated. 

LANDIS does not predict specific disturbance or management events. Rather, it 

is a scenario model that compares long-term effects of various disturbance and 

management scenarios on the simulated landscape. 

3. LANDIS IS SUITED TO ANSWER THE FOLLOWING 

QUESTIONS

• How do disturbance and successional dynamics interact to change forest 

patterns on large, heterogeneous landscapes and what are the expected 

recovery paths of the tree species after disturbance in the simulated 

landscape?

• Will fire suppression lead to large catastrophic fires? If a large catastrophic 

fire were to occur, what are the long-term effects on forested ecosystems?

• Where are areas of high fire probability and high potential fire intensity 

on forest landscapes? How do these areas change over time under different 

fuel reduction plans? 

Landis 1 Model

Design 2 Considerations

Questions 3 Answered
LANDIS 4.0 Users Guide Page 2

• What are the effects of common fuel treatments such as prescribed 

burning and coarse woody debris reduction? What should be the treatment 

frequency, size, and intensity in order to maintain a forested landscape 

where fire is less likely to occur? 

• What are the effects of forest harvesting and do the harvest activities 

performed at the stand scale alter species composition and spatial pattern 

at the landscape scale? 

• How does seed dispersal influence landscape change and how significant 

is the initial seed source abundance and distribution for ecological 

restoration? 

• How does insect disturbance influence the spatio-temporal pattern of forest 

composition and age? Does insect damage significantly influence long-term 

likelihood of fire?

4. LANDIS 4.0 RELEASE NOTE

LANDIS 4.0 is a computer program that simulates forest landscape change 

over large spatial (103-107 ha) and temporal (101-103 years) scales. It is a 

new generation program based on earlier versions of LANDIS (Mladenoff et al.

1996, Mladenoff and He 1999). LANDIS 4.0 uses a component-based approach 

to software design, which breaks the monolithic program into multiple small, 

stand-alone, and functionally more specific components. In LANDIS 4.0, each 

component (module) simulates a particular process and collectively they simulate 

forest landscape change under natural and anthropogenic disturbances. 

The current version (LANDIS 4.0) has gone through considerable testing. 

However, it is possible that users will discover bugs that arise under conditions 

we did not test. Please report all bugs to Dr. Hong S. He, heh@missouri.edu. 

We anticipate correcting bugs periodically within the same revision with higher 

decimal version numbers (e.g., LANDIS 4.0.1). Substantial changes to the 

software, primarily associated with the addition of new features (e.g., new 

modules), will be released as a new version number (e.g., LANDIS 4.1). All 

changes associated with each release will be documented in the release notes.

5. THE NEW FEATURES IN LANDIS 4.0

• LANDIS 4.0 is a component-based program using multiple dynamically 

linked libraries (DLLs), each having a standard interface and simulating a 

distinct process. 

• A biological disturbance agent (BDA) module was added to LANDIS 4.0 to 

simulate the effects of one or more insect and/or disease disturbances. 

• A fuel module was added to track fine fuel, coarse fuel, and live fuel and 

to evaluate the effects of common fuel treatments on the simulated fire risk 

(see section 9.5.7.6 for definition).

• LANDIS 4.0 has a newly designed fire module that incorporates terrain, 

wind, and fuel information into the simulation of fire. 

• In LANDIS 4.0, landscape heterogeneity is no longer assumed to be 

stratified only by land type map (or ecoregion). Landscape heterogeneity 

can now be processed using individual disturbance regime maps as well 

as the land type map. 

4 Release Note

5 New Features
LANDIS 4.0 Users Guide Page 3

• LANDIS 4.0 is now a Windows program, with companion graphical 

interfaces for input (LI.exe) and viewing output (LV.exe), which are 

independent of GIS software. 

• LANDIS 4.0 automatically detects and accepts 16-bit input for all input 

maps. Consequently, the number of map classes that can be processed has 

increased from 256 to 65,536. 

• LANDIS 4.0 preserves all the functionality of previous versions of 

LANDIS (e.g., LANDIS 3.7).

6. LANDIS 4.0 DISCLAIMER

This software is in the public domain and is the intellectual property of the 

acknowledged individuals (see acknowledgments). The recipient may not assert 

any proprietary rights thereto nor represent it to anyone as other than a program 

of the University of Missouri-Columbia. LANDIS is provided without warranty 

of any kind. The user assumes all responsibility for the accuracy and suitability of 

this program for his/her application. In no event will the authors or the university 

be liable for any damages, including lost profits, lost savings, or other incidental 

or consequential damages arising from the use of or the inability to use this 

program. 

The computer program described in this publication is available on request with 

the understanding that the U.S. Department of Agriculture cannot assure its 

accuracy, completeness, reliability, or suitability for any other purpose than that 

reported.

7. LANDIS 4.0 COPYRIGHT

LANDIS 4.0 was developed in the GIS and Spatial Analysis Laboratory at 

the University of Missouri-Columbia in collaboration with the USDA Forest 

Service, North Central Research Station. It is the intellectual property of the 

acknowledged individuals. The copyright is held by the University of MissouriColumbia. LANDIS 4.0 is available free of charge by contacting Dr. Hong S. 

He, heh@missouri.edu, or by registering at the LANDIS 4.0 Web site (www.snr.

missouri.edu/LANDIS). 

8. HOW TO USE THIS GUIDE

This guide contains the necessary information and step-by-step instructions on 

how to prepare data for a LANDIS run. It also presents conventions for data 

organization and file naming. Most of the file formats are shown using examples. 

We suggest reading Chapter 9 completely before conducting a run. This guide 

provides a full description of all LANDIS parameters. However, since LANDIS 

is a fully modularized program, users need to parameterize only the succession 

module and the modules needed for their specific simulations. Users may find the 

troubleshooting tips helpful when doing their own runs. Please note that some 

recommendations from this chapter are from the developers’ experience with 

only limited tests. We encourage users to read the publications on LANDIS for 

more technical issues. Also, please feel free to e-mail suggestions for expansions, 

limitations, or solutions to technical problems to the address on the bottom of 

page ii.

6 Disclaimer

7 Copyright

How to Use 8 This Guide
LANDIS 4.0 Users Guide Page 4

9. CONCEPTUAL BASIS FOR THE LANDIS MODEL

9.1 Spatial and Temporal Scales of LANDIS

LANDIS is a spatially explicit landscape model designed to simulate forest 

landscape change over large spatial and temporal scales (Mladenoff et al.

1996, Mladenoff and He 1999). LANDIS 4.0 simulates the dynamics of 

forest succession, seed dispersal, wind, fire, biological disturbance (insects 

and diseases), harvesting, fuel accumulation and decomposition, and fuel 

management. Differing from most landscape models, LANDIS simulates multiple 

landscape processes in combination with the simulation of succession dynamics 

at the tree species level.

In LANDIS, a landscape is modeled as a grid of cells (or sites) with vegetation 

information stored as attributes for each cell (fig. 1). At each cell, the model 

tracks a matrix containing a list of species by rows and the 10-year age 

cohorts by columns. The temporal scales of LANDIS are largely dictated by 

the current 10-year age-cohort structure, and the 10-year time step makes the 

model suitable for simulating long-term dynamics (101-103 years). The spatial 

scales of LANDIS are between 103 and 107 ha, depending on the simulation 

cell size. Since LANDIS tracks only the presence/absence of species age cohorts, 

not individual trees, computational loads are greatly reduced. In addition, the 

essential information of presence/absence is relatively independent of cell size, 

and therefore LANDIS is capable of simulating forest succession at cell sizes 

ranging from 10 to 500 m (He et al. 1999a). 

Figure 1. LANDIS model structure. In LANDIS, a landscape is divided into equal-sized 

individual cells or sites. Each site (i, j) resides on a certain land type and a disturbance 

regime type, and includes a unique list of species present and their associated age 

cohorts. The species/age cohort information varies with establishment, succession, and 

seed dispersal, and interacts with disturbances.

Conceptual

9 Basis
LANDIS 4.0 Users Guide Page 5

9.2 Heterogeneity

In LANDIS, heterogeneity of vegetation, disturbance, and management activities 

is modeled at multiple hierarchical levels from the landscape to the pixel. 

For vegetation heterogeneity, LANDIS stratifies the heterogeneous landscape 

into land types (also called ecoregions for broad-scale studies), which are 

generated from GIS layers of climate, soil, or terrain attributes (slope, aspect, 

and landscape position). Land types capture the highest level (coarse grain) of 

spatial heterogeneity caused by various environmental controls. Within a land 

type, a somewhat uniform suite of ecological conditions that results in similar 

species establishment patterns is assumed, but the stochastic processes such as 

seed dispersal can result in intermediate level (fine grain, within land type) 

heterogeneity of a species distribution. Finally, succession, competition, and 

probabilistic establishment may result in heterogeneity of species presence 

and age cohorts even among pixels that were initially identical. Disturbance 

heterogeneity refers to various regimes a disturbance may have on the simulated 

landscape. For disturbance heterogeneity (except for wind and biological 

disturbances), LANDIS stratifies the heterogeneous disturbance regimes using 

disturbance regime maps (fig. 1). For example, fire regimes are characterized by 

ignition frequency and fire cycle (mean fire return interval) in the fire regime 

map (Yang et al. 2004). Within-regime heterogeneity is further simulated by 

the stochastic process of each disturbance regime, and pixel level heterogeneity 

is simulated through the interaction of disturbance and the vegetation in the 

particular pixel. Furthermore, land types or disturbance regimes can be defined by 

users to partition the landscape into strata that are most relevant for a particular 

application. 

9.3 Stochasticity

LANDIS is a stochastic model that uses random number generators to simulate 

the stochastic processes of seed dispersal, seedling establishment, disturbance, 

and management events. Therefore, LANDIS predicts the statistical properties 

of landscape composition, age structure, and spatial pattern under particular 

disturbance and management regimes, but it does not accurately predict 

individual disturbance events or optimize management actions. 

9.4 Component-based Model Design

LANDIS 4.0 is a new generation program based on earlier versions of LANDIS 

(Mladenoff and He 1999). LANDIS 4.0 uses a component-based approach to 

conduct simulation, which breaks the monolithic program into multiple small, 

stand-alone, and functionally more specific components (He et al. 2002). 

A component in a model is like a mini-model; it comes packaged as a binary 

code that is compiled, linked, and ready to perform certain tasks for the entire 

model. Components connect with each other at run time to form a complete 

model. With a component-based model, it is possible to replace some components 

while maintaining the integrity of the model. In LANDIS 4.0, each component 

(module) simulates a particular process and collectively they simulate forest 

landscape change under natural and anthropogenic disturbances (fig. 2).

Conceptual

9 Basis
LANDIS 4.0 Users Guide Page 6

Breaking up a single monolithic program into several logically independent 

modules has the following advantages (He et al. 2002): 1) modifying and testing 

a component is a much smaller task than tackling a whole model; 2) keeping 

the logic of an ecological process contained within a single module ensures that 

changes to a module will not have unintended effects in other parts of the model; 

3) components can be developed by different groups or in other programming 

languages; 4) upgrading the model is easier because existing components can 

easily be replaced by new components. This reduces the number of largely 

redundant versions of the model and allows the modeling community to focus 

on significant conceptual advances rather than managing cumbersome code. 

These advantages truly allow more groups to participate in the modeling work. 

For example, a disturbance may have several designs and implementations from 

different groups in the dynamically linked libraries format, which can be plugged 

into LANDIS 4.0 and run without recoding and recompiling the whole model. 

This provides an ideal platform for scientists to compare and analyze different 

disturbance module designs. Therefore, a component-based model can be more 

rigorously tested, evaluated, and modified than a monolithic model, and these 

processes can be conducted not only by the original developers, but by the larger 

modeling community (He et al. 2002). 

9.5 Overview of LANDIS Model Components

9.5.1 Succession and Seed Dispersal

Succession is a non-spatial, site level process. In LANDIS, it is assumed 

that detailed, individual tree information and within-stand processes can be 

simplified, allowing large-scale questions such as spatial pattern, species 

Figure 2. LANDIS 4.0 uses a component-based modeling approach. A component is like 

a mini-model with a published interface that can be updated or replaced by any group 

(Li and He 2004).

Conceptual

9 Basis
LANDIS 4.0 Users Guide Page 7

distribution, and disturbances to be adequately addressed. Succession at each 

site is a competitive process driven by species life history attributes. These are 

longevity, age of sexual maturity, shade tolerance class, fire tolerance class, 

maximum age of vegetative reproduction (sprouting), sprouting probability, and 

effective and maximum seeding distance based on 10-year time step. In contrast 

to most gap models, which track individual trees (Botkin et al. 1972; Botkin 

1993; Pastor and Post 1985; Shugart 1984, 1997; Urban et al. 1993), LANDIS 

tracks the presence and absence of species age cohorts. Therefore, succession 

dynamics is simplified and simulated as birth, growth, and death processes acting 

on species age cohorts. This approach is similar to a polygon-based landscape 

model, LANDSIM (Roberts 1996). During a single LANDIS iteration, birth, 

death, and growth routines are performed on species age cohorts and random 

background mortality is simulated. 

9.5.1.1 Birth, establishment, growth, and death

In succession, several parameters are treated as categorical inputs rather than 

modeled explicitly; these include species shade tolerance class and fire tolerance 

class. Other parameters are treated as numerical inputs, including a species’ 

effective and maximum dispersal distance. 

Birth adds the presence of the youngest age cohort (1-10) to a species. When 

seed dispersal is simulated for a given site, a uniform random number from 0 

to 1 is drawn to check against an establishment coefficient to decide if seed can 

establish. A species establishment coefficient is a number ranging from 0 to 1 

that expresses the species’ relative ability to grow on different site categories or 

land types. Coefficients are differentiated based on relative responses of species 

to soil moisture, climate, and nutrients and are not themselves modeled within 

LANDIS. They can be estimated empirically or derived from a gap model 

with ecosystem-process drivers (He et al. 1999b). A species can establish only 

when its establishment coefficient is greater than the random number drawn. 

Therefore, species with high establishment coefficients have a higher probability 

of establishment. 

Growth increases all species age cohorts by 10 years, and death deterministically 

removes the species age cohort when it reaches the longevity of the species. 

Random background mortality simulates tree mortality as it approaches its 

longevity, but not the mortality caused by disturbance and harvest. 

9.5.1.2 Vegetative reproduction

Vegetative reproduction may occur following the death of a species age cohort. 

The process of vegetative reproduction is simulated stochastically based on the 

species’ sprouting probability. Maximum sprouting age is used to determine the 

age at which species can re-sprout in this version of LANDIS. When checking 

for the sprouting of a species on a given site, a uniform random number from 0 

to 1 is drawn to check against the species’ sprouting probability to decide if the 

species can reproduce vegetatively. 

9.5.1.3 Competition

A species’ competitive ability is determined based upon simple logical rules 

applied to the combination of life history attributes and land type suitability 

(Mladenoff and He 1999). Shade-intolerant species (species with lower shadeConceptual

9 Basis
LANDIS 4.0 Users Guide Page 8

Figure 3. The negative exponential distribution of tree species seeding probability in 

relation to distance from available seed sources. ED—species effective seeding distance, 

MD—species maximum seeding distance. Other functions can also be used.

tolerance class) cannot establish either by seeding or by vegetative reproduction 

on a site where more shade tolerant and mature species are present. On the 

other hand, the most shade-tolerant species are delayed in being able to occupy 

an open site until the specified number of years of shade presence has passed. 

A shade-checking algorithm defines shade by the most shade-tolerant species 

cohort present that is also sexually mature. Species cohorts younger than the 

minimum seed-producing age are ignored in this shade-checking algorithm. This 

was implemented as a surrogate for crown closure. Without disturbance, shadetolerant species will tend to dominate the landscape if other attributes are not 

highly limiting and land types (reflected as species establishment coefficients) are 

generally suitable. 

9.5.1.4 Seed dispersal

Seed dispersal is modeled as a function of species effective and maximum 

seeding distances. The effective seed-dispersal distance is the distance at which 

seed has the highest probability (e.g., P>0.95) of reaching a site. The maximum 

seed-dispersal distance is that distance beyond which a seed has near zero 

probability (e.g., P<0.001) of reaching. These distances have been parameterized 

for common tree species in northern Wisconsin (Mladenoff and He 1999b) based 

on the literature for various tree species. Seed-dispersal probability (P) between 

the effective (ED) and maximum seeding distance (MD) follows a negative 

exponential distribution:

P = e 

-b.( x )

 (1)

where x is a given distance from the seed source (MD>x>ED), m is the maximum 

seeding distance, and b is an adjustable coefficient (b>0) (b=1 in the current 

version of LANDIS), which can change the shape of the exponential curve 

corresponding to various seed-dispersal patterns when information is available 

(fig. 3). If x ≤ ED, P=0.95, indicating that the probability of seed dispersing 

within its own effective seeding distance is very high, while if x ≥ MD, P=0.001, 

indicating that the probability of seed dispersing beyond its own maximum 

seeding distance is very low (He and Mladenoff 1999b). 

MD

Conceptual

9 Basis
LANDIS 4.0 Users Guide Page 9

Other types of seed dispersal that are implemented include no dispersal (no cell 

receives seed), uniform dispersal (all cells receive seed from all species), and 

neighboring dispersal (seed only disperse to the neighboring cell).

9.5.2 Natural Disturbance Overview and Definitions

The ability to simulate different types of disturbances and management and their 

effects on tree communities and landscape structure is central to the design and 

intent of LANDIS. LANDIS 4.0 can simulate three different types of natural 

disturbances (fire, wind, and biological), implemented as independent modules 

that can be applied in any combination. While each disturbance module 

follows its own set of “rules” that define its spatiotemporal dynamics and 

impacts on simulated vegetation, some organizing themes are common to all 

disturbance modules described in this section. Several terms used to define 

the LANDIS disturbance rules have ambiguous definitions in the disturbance 

ecology literature, and some terms with slightly different meanings have been 

used interchangeably in different publications describing various LANDIS 

components. We attempt to reconcile these inconsistencies here by clearly 

defining key LANDIS disturbance terms to be most consistent with the literature. 

At times these definitions will conflict with past LANDIS publications; these 

discrepancies are noted within each individual module description. 

Disturbance dynamics can be simplified into three steps: 1) selecting individual 

sites to be disturbed; 2) calculating disturbance intensity; and 3) removing 

susceptible and intolerant species-age cohorts (i.e., disturbance-caused mortality 

or effects). Disturbance site selection is a spatial process specific to each 

disturbance type. For example, a wind or fire event spreads across a subset of sites 

forming disturbance patches; a biological disturbance (e.g., insect) selects sites 

for disturbance using species and age composition; the harvest module selects 

group of sites (e.g., a stand) for a given treatment. In each case, the frequency and 

number of sites (i.e., area) disturbed by each disturbance module is controlled by 

user-defined but stochastic disturbance regimes. 

Once a site is selected for disturbance, a disturbance intensity class is calculated 

based on a set of rules. Sousa (1984, page 357) defines disturbance intensity as “a 

measure of strength of the disturbing force.” Examples include fire temperature, 

wind speed, or population size of defoliating insects. Intensity classes within 

LANDIS approximate the relative strength of the simulated disturbance event, 

and their specific calculation varies by disturbance module. Vulnerability to a 

given disturbance type can vary by both species and age. In LANDIS, tolerance 

class defines the relative vulnerability of a species to a given disturbance type 

and intensity, and susceptibility class defines the relative vulnerability of a 

species-age group to a given disturbance type and intensity (He and Mladenoff 

1999). For example, fire is simulated, in general, as a bottom-up disturbance, 

in which the youngest age cohorts are most susceptible to mortality. However, 

a low intensity fire may not kill species of high fire tolerance class even if the 

age cohorts are young. Disturbance severity results from the interaction of 

disturbance intensity and species tolerance and susceptibility at each site, and it 

is calculated for each species age cohort present on that site to determine which 

species age cohorts are removed by the disturbance. Fire and BDA modules 

require both species tolerance and susceptibility classes to be defined, whereas 

the wind module does not require species tolerance classes, assuming that all 

species have similar vulnerability to wind disturbance. 

Conceptual

9 Basis
LANDIS 4.0 Users Guide Page 10

Previous publications describing LANDIS fire (He and Mladenoff 1999) or 

biological disturbances (Sturtevant et al. 2004) used the term severity class 

to describe intensity class. However, severity is best defined as “the measure 

of damage caused by the disturbing force” (Sousa 1984, page 357), i.e., 

disturbance effects. He et al. (2004) and Shang et al. (2004) use the two terms 

interchangeably. Since actual disturbance effects in the form of species cohort 

mortality are implemented in a subsequent step, we use the term intensity 

class in the remainder of this users guide to represent the relative strength of a 

disturbance.

9.5.3 Fire Disturbance

Fire disturbance is an important landscape process. Fires appear to be stochastic 

for a single site, but have repeated patterns in terms of ignition, location, size, 

and shape at landscape scales. It has long been noted that some areas are more 

fire-prone than others. The differences are often represented by using mean 

fire-return interval, which is the mean number of years for fire to recur on a 

given area (Johnson 1992, Johnson et al. 1990, Pickett and Thompson 1978, 

Pickett and White 1985, Pickett et al. 1989). Depending on their extent, large 

landscapes can be stratified into ecoregions, relatively homogeneous sub-areas 

that are characterized by different climate, topography, and soils with similar fire 

characteristics. Such ecoregions can be used as the fire regime map in LANDIS 

in which each fire regime unit is characterized by its attributes in the fire regime 

attribute file.

As a disturbance module, a fire disturbance simulation module in LANDIS must 

address when and where such a disturbance occurs, how such a disturbance 

spreads over the landscape, and what effects it has on the forest landscape. 

Therefore, a fire module must include the following three major components:

• Fire occurrence simulates how many fires occur and when and where each 

fire occurs.

• Fire spread simulates how fires spread across the landscape from their 

ignition points.

• Fire effects simulates which species age cohorts are killed on each burned 

cell; the effects are quantified as fire intensity classes that are passed to the 

fuel module to determine how much fuel is consumed.

The fire module also reads its own input and writes its own output, just as the 

other modules do.

9.5.3.1 Fire occurrence simulation

The fire module in LANDIS 4.0 uses a hierarchical fire frequency model to 

simulate temporal patterns of fire regimes. This differs from past approaches 

(Baker et al. 1991, He and Mladenoff 1999a, Johnson 1992, Turner et al.

1994), which use a statistical distribution of fire frequency to simulate fire 

occurrence. The hierarchical fire frequency model divides a fire occurrence into 

two consecutive events—fire ignition and fire initiation (fig. 4). A fire occurrence 

begins with an ignition attempt from an external heat source that heats the forest 

fuel complex up to its ignition temperature. Fire ignition agents are either natural 

(lightning) or anthropogenic (e.g., arson or accidental). A fire initiation event 

starts with the ignition and is successful when an area equal to the cell size is 

burned (Li 2000, 2001). Whether a fire ignition can result in fire initiation is 

dependent on the fuel loading, fuel arrangement, and fuel moisture content. 

Conceptual

9 Basis
LANDIS 4.0 Users Guide Page 11

For a given time step (e.g., year 10), LANDIS first generates the number of 

ignitions ( X ) in the given fire regime unit from the Poisson distribution with the 

parameter ignition density λ (i.e., average fire ignitions per decade per hectare). 

For each ignition, LANDIS performs a Bernoulli trial, whose result is denoted by 

Yi (ignition result), with the parameter fire initiation probability Pi , whose value 

is determined by the time since last fire of the ignited cell if the fuel module is 

turned off (equation 1), or fine fuel class if the fuel module is turned on (specified 

in fire parameter file): 

P(t) = 1 – e–t/FC (2)

where FC is fire cycle, t is time since last fire, and P is fire initiation probability. 

If the ignition becomes an initiation, we assign 1 to Yi, otherwise we assign 0 to 

Yi. The summation of the result of all ignitions generated for a given fire regime 

unit per decade ( ∑Yi, i = 1,2,...,X ) is then the number of fire occurrences per 

decade for the given fire regime unit. For each initiation, LANDIS will randomly 

select a fire size, denoted by Z, from a log-normal distribution (fig. 4) with 

parameters μ (Mean Fire Size: MFS) and σ2 (Standard Deviation of Fire Size: 

STD) to simulate fire spread. The overall structure is depicted in figure 5. 

Since a landscape often consists of more than one fire regime unit, it is possible 

that a fire starts from one fire regime unit and spreads to another. When a fire 

reaches another fire regime unit, LANDIS will use a new fire ignition from 

the ignition pool of this new fire regime unit. If such a new ignition becomes a 

fire initiation, LANDIS will randomly draw another fire size from the fire size 

distribution defined on this new fire regime unit. Such a fire will be recorded 

as one single fire event in the fire log files, but it actually consists of two fire 

occurrences.

Figure 4. Fire size follows log-normal distribution with small fires occurring more 

frequently than large fires. Conceptual

9 Basis
LANDIS 4.0 Users Guide Page 12

9.5.3.2 Fire spread simulation

LANDIS 4.0 has two algorithms to simulate fire spread. The first one is a 

percolation algorithm similar to the algorithms of Gardner et al. (1999), Clarke 

et al. (1994), and Wimberley et al. (2000) to simulate fire spread. Fires simulated 

by the percolation algorithms spread from a burning cell to forested cells in 

the cardinal directions (up, down, left, and right). These cells are entered into a 

priority queue in a random order. The first cell in the queue has a higher priority 

of fire spread. The fire will continue to spread until it reaches its predetermined 

size. LANDIS does not allow a forested site to be burned more than once within 

one time step, and nonactive land types or ecoregions (e.g., roads, lakes) may 

serve as fuel breaks in the landscape. Therefore, it is possible for a fire to be 

extinguished prior to burning its predetermined size if the fire reaches fuel breaks 

or newly burned sites. In a real landscape, fires may spread across the boundaries 

of fire regime units where the fire size distribution changes. When a fire spreads 

into a different fire regime unit, the module will simulate a new ignition. If 

the new ignition results in an active fire, a new predetermined fire size will be 

selected based on the fire size distribution for the new fire regime unit.

The second algorithm used in LANDIS 4.0 is similar to Hargrove et al. (2000), 

a modified percolation method with a fire front, which can simulate fire spread 

behavior with respect to fuel configuration, topography, and prevailing wind. 

A fire front is defined as the part of a fire within which continuous flaming 

combustion is taking place. The fire front in LANDIS is assumed to be the 

edges of the fire perimeter. Fire will spread out from the fire front along eight 

directional (N, NE, E, SE, S, SW, W, NW) neighbor sites. The directional spread 

probability in each site is computed by its own fuel quality and quantity, biased 

with topographical aspect and slope, and wind direction and speed:

Figure 5. The overview of the fire occurrence simulation design.

Conceptual

9 Basis
LANDIS 4.0 Users Guide Page 13

P = 1 – e – (1 + r1

) y (1 + r2) z (1 + r3) w kx (3)

where r1 is wind coefficient, which serves as an adjustment so that the user 

can increase or decrease wind effect on the directional spread probability 

calculation. If r1 = 0, then there is no wind effect on the directional spread 

probability calculation no matter how much wind speed ( y ) is. Similarly, r2 is 

the topography coefficient, r3 is the predefined fire size distribution coefficient, 

and k is the fuel coefficient. In LANDIS parameter files, k is defined as the base 

probability for fuel class 3 (denoted by prbase) when there are no other factors 

on spread probability (i.e., r1 , r2 , r3 are all 0). Equation 4 shows the relation 

between k and base probability:

k = log( 1 – prbase ) / (– 3) (4)

Equation 4 denotes the relation between fuel coefficient and base probability for 

fuel class 3. There are four independent variables in the fire spread probability π

equation: y is wind speed class (0, 1, 2, 3, 4, 5), z is topographical slope (– π/2 to 

π/2), and x is fuel class (i.e., the potential fire intensity class (0, 1 ~ 5) in the fuel 

module). w is the predefined fire size effect, a normalized ratio of current fire size 

to predefined fire size (equation 5) used to determine how the current fire size 

affects fire spread probability. The range of w is 1 to negative infinity. However, 

in LANDIS practical simulations, w usually ranges from 1 (when current fire 

size equals 0) to –1 (when current size equals predefined size). It decreases as 

the current size increases, and reaches zero when fire spread reaches half of 

predefined size.

w = 1 – 2CS / FS (5)

where CS is current simulated fire size, and FS is predefined fire size.

9.5.3.3 Fire effect simulation

Fire intensity is determined by the quantity and quality of fuel. When the fuel 

module is turned off, a simple framework reflects the relationship between fuel 

quantity and years of accumulation on different fire regime units (i.e., fire curves 

defined for each fire regime unit level; refer to section 13.2 for details). Fire 

intensity is categorized into 6 classes (0 ~ 5, with a class 5 fire the most intense). 

When the fuel module is on, LANDIS will directly use potential fire-intensity 

class calculated in the fuel module instead. 

Fire is a bottom-up disturbance, such that fires of low intensity affect only 

younger age classes. Also, fire tolerance varies among species fire-tolerance 

classes. To implement these two characteristics, species fire-tolerance classes, 

containing five categories from 1 to 5, are designed to reflect the differences of 

fire tolerance among species. Species fire-susceptibility classes are designed 

to reflect differences related to age. A fire of a given intensity interacts with 

individual species and age cohorts through the species fire tolerance and age 

susceptibility. The interactions of fire with species fire tolerance and age 

susceptibility have been explicitly defined in He and Mladenoff (1999a) (fig. 6). 

Conceptual

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9.5.4 Wind Disturbance

Wind disturbance follows an approach similar to fire, except that wind is a 

top-down disturbance, where species susceptibility increases with age and size 

(Mladenoff and He 1999). A wind disturbance event can add fine fuel and 

coarse fuel and influence the potential fire-intensity class, depending on the 

decomposition dynamics of the particular ecoregion. Interactions between these 

two disturbances can be interesting and complex. Generally, wind disturbance 

becomes more important on ecoregions with long-lived species, and where fire 

frequency is low. 

Wind disturbance events are stochastic for a single site, but have repeated 

patterns in terms of breakout location, size, and shape at landscape scales. It has 

long been noted that some areas are more prone to wind disturbance than others. 

The differences are often represented by using mean wind-return intervals, 

i.e., the mean number of years for wind to recur on a given area. Similar to the 

approaches used in other studies, in LANDIS the mean wind-return interval is 

used to calculate wind probability using the following equation: 

Figure 6. Fire intensity vs. fire tolerance (species classes) and susceptibility (age 

classes). Each individual bar represents the removal range of species of a given age class 

under fire-intensity class three.

Conceptual

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P = B . lw . MI – (e+2) (6)

where P is the wind probability of a cell, MI is the mean wind return interval of 

a given ecoregion on which the cell resides, B is the wind probability coefficient 

designed for model calibration (B=MI by default), and lw is the number of 

years since last wind on that cell. With the above distribution, P varies among 

ecoregions with MIs, and it can be further altered by lw recorded for each single 

cell. For example, if wind occurs at a given cell in a given time step, lw of the 

cell is reset to 0, and P for that cell is calculated as 0 during that time step. This 

eliminates the possibility of cells being blown down twice in the same time step 

regardless of how short MI is. 

Another important feature of wind disturbance is wind size, defined from the 

following equation integrating random factors and the mean wind size: 

S = A . (10.0)r . MS (7)

where S is the wind size, MS is the mean wind size, A is the wind disturbance 

size coefficient designed for model calibration (A=0.34 by default), and r is a 

normalized random number. Under similar mean wind return interval, one can 

have very different wind regimes ranging from small, frequent wind to large, 

infrequent wind, which are defined by the distribution of S. 

9.5.5 Harvest

The timber harvest capability of LANDIS allows flexible simulation of the 

broad spectrum of silvicultural activities that are commonly implemented on 

managed forests (Gustafson et al. 2000). These capabilities are simulated across 

two distinct hierarchies of disturbance intensity and the spatial configuration. 

The intensity of management activity ranges from thinning through single-tree 

selective harvest to clearcutting. The specific details of how these activities 

affect species and cohort structure are controlled by the user, allowing an almost 

infinite range of management activity to be simulated. The spatial configuration 

of management activity is controlled by the designation of Management Areas 

(MA) in which distinct management activities and intensities can be simulated on 

the stands within that MA (fig. 7). 

LANDIS implements timber harvest within a specific hierarchical management 

structure. The overall landscape is divided into MAs, each to be treated with 

specific harvest regimes at specific intensities. The MAs need not be contiguous 

(i.e., multiple areas having the same MA designation may be delineated). 

Furthermore, some management units may be specified to have no harvest at all. 

Harvesting on land in other ownerships can be simulated by representing those 

ownerships as distinct MAs.

Within MAs that are to be harvested, LANDIS expects to find the land base 

delineated into stands. These are represented by a map layer in which stand 

polygons have been gridded so that each site (where site is the equivalent of a 

cell or a pixel in the raster) has the value of the stand ID number. Often, land in 

other ownerships will be interspersed among these stands. Any lands that will 

never have harvests allocated on them can be represented by zeros in the stand 

map. Based on the map layers of MAs and stands, each site becomes associated 

with an MA and a stand ID. 

Conceptual

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Harvests are implemented by removing specific cohorts of specific species on 

sites selected for harvest. The sites selected for harvest are determined by one of 

eight “harvest regimes” from which the user may choose. These harvest regimes 

vary in the number of entries required to complete the silvicultural treatment, and 

in whether they are applied to: 1) an entire stand (stand-filling), 2) a portion of 

a stand or to multiple stands (stand-spreading), and 3) multiple patches within a 

Figure 7. Flow chart of the LANDIS harvest module show harvest actions with one 

LANDIS iteration.

Conceptual

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single stand (i.e., group selection). The regimes currently available are: 

1) one-entry, stand-filling, 2) periodic-entry, stand-resampling, stand-filling, 

3) two-entry, stand-filling, 4) one-entry, stand-spreading, 5) two-entry, standspreading, 6) periodic-entry, group selection, 7) periodic-entry-fixed stand, 

two-entry, stand-filling, and 8) periodic-entry-stand-resampling, two-entry, standfilling. Stand resampling means that in each entry year, the stands within the 

management area will be ranked again using the initial ranking algorithm. Thus, 

the specific stands treated in each entry may vary, but the treatment applied 

will not. 

These regimes allow simulation of multiple-entry silvicultural treatments such as 

shelterwood, seed-tree, and group selection. The periodic entry options allow for 

automatic harvesting of stands at some specified interval (e.g., rotation length). 

Stand-filling regimes are applied to every site in a single stand, while standspreading regimes begin at a focal site in a stand; harvest terminates when a 

certain harvest size is reached. This size may be reached before the stand is 

completely harvested, or the harvest may spill over into an adjacent stand. All 

eligible sites within a stand must be harvested before harvest can spill over into 

another stand, but the process may continue until the size has been reached. This 

feature allows timber management to be used to change the patch size distribution 

of the landscape, and to allow patterns to emerge that are less constrained by the 

underlying stand map than are the stand-filling harvest regimes.

Stands for the application of these regimes are selected using a ranking algorithm 

chosen by the user. Stands within each MA are prioritized for harvest (ranked) 

according to rules that reflect criteria that might be used by forest planners 

or researchers. The ranking algorithms currently include Stand Age (oldest 

first), Economic Importance (most valuable stands first), Regulate Age Class 

Distribution (attempt to produce an even distribution), and Random (choose 

stands randomly). Because stands are ranked independently for each MA, 

different ranking algorithms can be applied to different MAs. When an adjacent 

stand needs to be harvested by a stand-spreading regime, the highest ranked of 

all the adjacent stands is selected.

Variation in the intensity of harvest activity is controlled by rules governing the 

removal of age cohorts of species found on the site being harvested. The rules 

for each harvest regime are specified by the user in the form of removal masks 

that for each species specify which age cohorts (if present on the site) are to 

be removed. For example, the prescribed burning mask might specify that the 

youngest cohort of all species be removed. A shelterwood mask might specify 

that all cohorts be removed for all species except one or two older cohorts 

of one or two species during the first entry, and all older cohorts during the 

second entry. Because the removal masks are generated by the user, the user has 

unlimited flexibility to tailor the masks to the treatments to be simulated and the 

characteristics of the species found on the landscape.

The extent of harvesting is controlled by the user. For each MA the user must 

specify the regimes to be applied, the total area to be harvested under each 

regime, and the ranking algorithm to be used for each regime. If a standspreading regime is chosen, the user must specify the size distribution for the 

harvests. Simulations are controlled by a parameter input file. Once this file is 

produced, it can be modified to introduce changes to the scenario simulated, 

allowing alternatives and replicates to be readily generated.

Conceptual

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The Harvest module completes two major loops to simulate one time period 

of timber harvest: 1) It sequentially visits each MA in which timber harvest is 

prescribed, and 2) within each MA, it sequentially simulates each harvest regime 

specified for that MA, beginning with scheduled re-entries. Different harvesting 

rules can be specified for each MA. For each harvest regime, the module 

allocates harvests to stands until the target number of sites has been allocated. 

The order in which stands are chosen is determined by the ranking procedure. 

At the completion of the model run, LANDIS produces a log file allowing 

comparison of the harvest targets with the allocations actually made. LANDIS 

also creates output files that report how many sites containing each cohort (by 

species) were cut (useful to estimate volume and value generated by harvest 

activity), and how many sites containing cohorts (by species) remain on each 

stand (useful to estimate stand vertical structure). 

9.5.6 Biological Disturbance Agent (BDA)

Biological disturbances, such as insect and disease outbreaks, are critically 

important agents of forest change that cause tree mortality at scales ranging from 

individual trees of a single species to entire regions. The BDA module is designed 

to simulate tree mortality following major outbreaks of insects and/or disease, 

where major outbreaks are defined as those significant enough to influence forest 

succession, fire disturbance, or harvest disturbance at landscape scales. 

Biological disturbances in LANDIS are probabilistic at the site (i.e., cell) scale, 

where each site is assigned a probability value called biological disturbance 

probability (BDP) and compared with a uniform random number to determine 

whether the site is disturbed or not. Disturbance causes species- and cohortspecific mortality in the cell. In the simplest case, BDP equals Site Resource 

Dominance, a number that ranges from 0 (no host) to 1 (most preferred 

host) based on the tree species and age cohorts present on the site. Four 

additional optional factors may also modify BDP: 1) environmental and/or other 

disturbance-related stress (Site Resource Modifiers); 2) the abundance of host in 

the neighborhood surrounding the site (Neighborhood Resource Dominance); 3) 

user-defined temporal functions (e.g., cyclic, random, or chronic) that affect the 

temporal pattern of disturbances across the entire spatial domain of the simulation 

(Regional Outbreak Status); and 4) spatial epidemic zones defined via simulated 

dispersal of a BDA through a heterogeneous landscape (Dispersal). The above 

combinations of optional factors allow the BDA module to accommodate several 

types of destructive insect and disease species, and more than one BDA may be 

simulated concurrently to examine their interactions. 

More detail on the BDA module and its behavior can be found in Sturtevant et 

al. (2004). However, several key terms were modified from this publication to be 

consistent with the terminology of other natural disturbance modules in LANDIS 

4.0. In this users guide, we use the term BDP for site vulnerability, and all 

references to “vulnerability” or “susceptibility” in Sturtevant et al. (2004) have 

been changed here to either tolerance class (for species) or susceptibility class 

(for species age cohort). The rank order of these two classes is also consistent 

with the design of the other natural disturbance modules. Finally, all references 

in Sturtevant et al. (2004) to the “severity” class of a disturbance have been 

changed here to “intensity” class.

Conceptual

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9.5.6.1 Site resource dominance

Site resource dominance (SRD) indicates the relative quantity/quality of food 

resources on a given site and is a combined function of tree species composition 

and the age cohorts present on that site. The relative resource value of a given 

species cohort is defined by its host preference class, where preferred host = 1.0, 

secondary host = 0.66, minor host = 0.33, and nonhost = 0. The BDA module 

compares a look-up table (see section 14.2) with the species cohort list generated 

by LANDIS to calculate SRD using one of two methods: 1) SRD = the maximum 

host preference class present, or 2) SRD = the average resource value of all tree 

species present, where the resource value of each species is represented by the 

cohort with the oldest host preference. 

9.5.6.2 Site resource modifiers

Site resource modifiers are optional parameters used to adjust SRD to reflect 

variation in the quality of food resources introduced by site environment 

(i.e., land type) and recent disturbance. Both land type modifiers (LTMs) and 

disturbance modifiers (DMs) can range between –1 and +1, and will be added 

to the SRD value of all active sites where host species are present. LTMs are 

assumed to be constant for the entire simulation, while DMs decline linearly with 

the time since last disturbance. Disturbances that may affect a given BDA include 

fire and wind. Disturbance effects from another BDA and user-specified harvest 

prescriptions are currently not implemented. SRD is then modified by LTM and 

the sum of all DMs:

SRDm = SRD + LTM + (DMwind + DMfire + ...) (8)

The user should calibrate the above modifiers to reflect the relative influence 

of species composition/age structure, the abiotic environment, and recent 

disturbance. For example, an LTM value of 0.33 is equal to a full step increase 

in disturbance intensity above that calculated using species composition alone 

(see section 14). 

9.5.6.3 Neighborhood resource dominance

Several recent studies suggest that the landscape context of a site also influences 

the probability and intensity of disturbance (Cappuccino et al. 1998, Radeloff 

et al. 2000). A neighborhood effect is modeled in LANDIS as the mean SRDm

of each cell within a user-defined radius R, using one of three radial distance 

weighting functions listed in increasing order of local dominance: uniform, 

linear, and Gaussian (Orr 1996; see Sturtevant et al. 2004). Neighborhood 

resource dominance (NRD) is calculated for all sites containing host species 

(i.e., SRD > 0). An optional subsampling procedure calculates the NRD for every 

other site, and the NRD of the remaining sites are estimated by the mean NRD 

of adjacent sites in the four cardinal directions. For large neighborhoods, this 

subsampling routine can increase the processing speed of the BDA by over 40 

percent (Sturtevant et al. 2004). 

9.5.6.4 Regional outbreak status

Several simple temporal patterns may be simulated in the BDA module to 

represent general outbreak trends for the entire study landscape. Temporal 

patterns in a given BDA are assumed constant for the length of the simulation, 

Conceptual

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and are defined by a suite of temporal disturbance functions that define the 

landscape scale intensity of the BDA at a given time step, termed Regional 

Outbreak Status (ROS). ROS units are integer classes ranging from 0 (no 

outbreak) to 3 (intense outbreak). The time to the next outbreak is calculated 

following each outbreak event using either a uniform or a normal random 

function. Though the actual time periods between outbreaks will be constrained 

by the time step of LANDIS (currently set at 10 years), the random outbreak 

functions may be used to vary the outbreak interval so that the average interval 

between outbreaks observed during the length of the simulation approximates 

that expected by the user. 

The magnitude of simulated regional outbreaks is controlled by the MinROS and 

MaxROS parameters. MinROS defines the “background” outbreak activity that 

will occur in each time step. Outbreak type (“TempType” in the BDA parameter 

file) determines whether outbreaks are binary (either MinROS or MaxROS; 

TempType = “pulse”) or if the ROS can range between those values (TempType 

= “variable pulse”). For the variable pulse outbreak type, the ROS value is 

randomly selected for each outbreak event from the range between MinROS+1 

and MaxROS. 

9.5.6.5 BDA effects

Both the probability that a site is disturbed by a given BDA and the intensity of 

that disturbance are controlled by biological disturbance probability (BDP). BDP 

is defined by the following equation: 

BDP = a ·{[SRDm + (NRD*NW)]/(1+NW)} · (ROS/3) (9)

where a is a user-defined calibration parameter (by default, a should = 1); 

SRDm = the species and age composition of the site (SRD), optionally modified 

by land type and/or past disturbance (equation 8); NRD = the mean SRDm

of sites within the neighborhood surrounding a site; NW = Neighborhood 

Weight, a parameter designed to define the relative importance between site and 

neighborhood resources; and ROS = Regional Outbreak Status.

Sites are selected for disturbance by comparing BDP with a uniform random 

number ranging from 0-1. Note that while equation 8 (see section 9.5.5.2) 

allows SRDm to exceed 1.0, by definition BDP cannot exceed 1.0 (i.e., 100 

percent probability of disturbance). SRDm values exceeding 1.0 can therefore 

only further enhance the probability of disturbance if additional variables such 

as neighborhoods or temporal disturbance functions are applied. Once a site is 

disturbed, the disturbance intensity class is calculated for the site to determine 

which species cohorts die, based on their tolerance class. Disturbance intensity 

is a direct function of BDP, where BDP < 0.33 = intensity class 1; 0.33 < BDP < 

0.67 = intensity class 2; BDP > 0.67 = intensity class 3 disturbance. Unlike fire 

or wind disturbance, there is no predefined function that estimates susceptibility 

class as a function of species tolerance class. Instead, susceptibility class is 

defined directly by a look-up table similar to that used for host preference class 

(see section 9.5.6.1). Cohort mortality follows these rules: intensity class 1 

disturbance kills all susceptibility class 1 cohorts, intensity class 2 disturbance 

kills susceptibility classes 1 and 2, and intensity class 3 disturbance kills 

susceptibility classes 1-3. Susceptibility class 4 species age cohorts, including all 

nonhost species, cannot be disturbed by the BDA. If no other BDA options are 

Conceptual

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simulated, the BDA module finishes by updating species cohort lists, updating 

the time since last biological disturbance, outputting a map of BDA disturbance 

events, and updating the BDA log (fig. 8).

9.5.6.6 BDA dispersal 

Some epidemics occur at spatial scales smaller than the typical simulation area 

of LANDIS. Accounting for BDA dispersal and spread will be necessary for 

these cases. The BDA dispersal procedure defines smaller spatial zones within 

the modeled landscape where BDA disturbance may occur within a given time 

step. Within these restricted spatial zones, the BDA operates exactly the same as 

if the outbreak were synchronous. Note that the dispersal procedures for the BDA 

module are still under development, but there are some preliminary dispersal 

procedures available in LANDIS 4.0. 

9.5.6.6.1 Epicenters

Epicenters are defined as central sites from which a BDA may disperse. There 

are three types of epicenters: 1) initial epicenters—sites randomly selected at 

time = 0 to initiate new outbreak zones in the first time step; 2) seed epicenters—

sites randomly selected at each time step an outbreak occurs to initiate new 

outbreak zones outside the outbreak zone defined at time t–1 during the 

simulation; and 3) outbreak zone epicenters—sites randomly selected from 

within the last outbreak zone (i.e., time = t–1) to continue the spread of an 

outbreak in consecutive time steps. The BDA module will randomly select 

epicenters from a subset of sites that are above user-specified threshold site 

Figure 8. BDA flow diagram.

Conceptual

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vulnerability. Initial epicenters can be selected anywhere in the landscape where 

sites meet this criterion; seed and outbreak zone epicenters are selected from 

outside and inside (respectively) the outbreak zone defined at time t–1.

The number of initial epicenters is a simple user-defined parameter. The 

following negative exponential equation determines how many new epicenters 

will be generated both inside and outside existing outbreak zones:

Yi = Ai*exp (– ciXi) (10)

Here, Ai = the number of qualified potential epicenter sites (i.e., the number of 

sites either inside or outside the last outbreak zone where BDP > the epidemic 

threshold), Xi = the current number of selected epicenters of a given type, and 

Yi = the number of remaining sites that can be checked. Coefficient ci is a userdefined parameter that controls statistically how many new epicenters may be 

generated for either seed epicenter or outbreak zone epicenter type. The number 

of epicenters will decrease with increasing c.

9.5.6.6.2 Spatial outbreak zones

Outbreak zones are defined using dispersal routines that spread from an epicenter 

to a circular boundary with a radius defined by the annual dispersal distance 

of a BDA, multiplied by the number of years in a time step (i.e., 10). An 

outbreak zone either automatically expands to this maximum limit (termed 

“regular dispersal”) or occurs as a percolation process through a binary landscape, 

where it may only spread through sites containing host tree species. Ability to 

spread over nonhost cells is defined by a user-defined neighborhood rule (sensu

Gardner 1999) termed a “structuring element” in the BDA Module. Available 

structuring elements include 4, 8, 12, and 24 nearest neighbors (fig. 9). 

The dispersal routines will attempt to spread each epicenter to its maximum 

dispersal distance using the neighborhood rule defined by the user. An outbreak 

zone from a given epicenter may overlap one created from a nearby epicenter. 

The cumulative area of all zones created during the time step defines the spatial 

extent over which the BDA may disturb sites during that time step. 

9.5.7 Fuel and fuel management

The LANDIS fuel module is a new addition to LANDIS. It tracks fine fuel, 

coarse fuel, and live fuels, simulates various fuel management practices, and 

estimates the impact of fuel reduction activities (He et al. 2004). In the fuel 

module, fine fuels (FF) represent primarily foliage litter fall and small dead twigs 

Figure 9. Available structuring elements.

Conceptual

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LANDIS 4.0 Users Guide Page 23

less than 1⁄4 inch in diameter. They are the primary prerequisite for fire ignitions. 

Coarse fuels (CF) include any dead tree materials that have a diameter ≥ 3 inches. 

These include snags, stems, boles, harvest stumps, and standing dead trees, which 

affect the intensity class of fire. Live fuels, also called canopy fuels, are live trees 

that may be ignited during high intensity fires (e.g., crown fires). 

9.5.7.1 Fine fuel

Calculating the amount of fine fuel involves deriving a fine fuel amount based on 

species age and modified by the fuel quality coefficient (FQC). Fine fuel from 

different species may have different flammability due to differences in physical 

and chemical attributes (Brown et al. 1982). We use a fuel quality coefficient 

(0<FQC<=1) to summarize such differences on a relative scale. Species with low 

FQC contribute less to the flammability of fine fuels than species with high FQC. 

FQCs are ranked and parameterized by users, and the same value (e.g., FQC=1) 

can be used for all species if there are no discernible differences among species. 

The result is not the absolute quantity of fine fuels, but rather an “effective index” 

of the amount of fine fuel that accounts for its flammability. If there are n species 

in a cell, the total amount of fine fuel (FF) in this cell is calculated as 

FF = ( ∑ Agei

 / Longi

. FQCi

 ) / n (11)

where Agei is the age of the oldest cohort of the i th species, Longi is the maximum 

longevity of the i th species (also used elsewhere in LANDIS), and FQC is as 

previously defined. Dividing by n averages the amount of fuel across all species 

present in the cell. Because LANDIS tracks only the presence and absence of 

species/age cohorts, the design for simulating fine fuels assumes that all species 

present in a cell have the same density. Such an assumption may not be realistic 

for individual cells, but at the landscape scale with millions of cells, relative 

species abundance can be realistically approximated (He et al. 1998). Fine fuel 

production changes through the lifespan of a given species. In this example (fig. 

10), the amount of fine fuel created by each species (the thin line) is positively 

correlated with age until approximately 70 percent of the species lifespan 

is reached, and becomes negatively correlated with age as the species age 

approaches maximum species longevity. The actual value calculated is translated 

into five categorical fine fuel classes using the user-defined relationship (fig. 10, 

see section 15.3). Such relationships can be defined for each species and specific 

ecosystems based upon empirical data. 

9.5.7.2 Coarse fuels

Unlike fine fuels, coarse fuels are not derived using species-specific age cohorts. 

Instead, stand age (the oldest age cohorts) in combination with disturbance 

history (time since last disturbance) is used to determine the coarse fuel 

accumulation for a cell (Brown and See 1981, Harmon et al. 1986, Spetich 

et al. 1999, Spies et al. 1988, Sturtevant et al. 1997). Coarse fuel amount is the 

interplay between input and decomposition (Spies et al. 1988, Sturtevant 

et al. 1997). Such interplay may vary by land type (Harmon et al. 1986), which 

encapsulate environmental variables (e.g., climate, soil, slope, and aspect) 

(fig. 10). 

In the absence of disturbance, the accumulation process dominates until the 

amount of coarse fuel reaches a level where decomposition and accumulation 

n

i=1

Conceptual

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Figure 10. Fine fuel and coarse fuel accumulation and decomposition. The relationship 

can be further modified by land type, disturbance, harvest, and fuel treatment.

Conceptual

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are in balance (Bergeron and Flannigan 2000, Sturtevant et al. 1997), as depicted 

in figure 10. For example, on a mesic land type with high decomposition rates, 

the amount of coarse fuel can be low, whereas on a xeric land type with low 

decomposition rates, the amount of coarse fuel can be high. The decomposition 

process is modeled using the decomposition curve as described in previous 

studies (Foster and Lang 1982, Hale and Pastor 1998, Lambert et al. 1980, 

MacMillan 1988) (fig. 10). Such a decomposition curve is also user-defined for 

each land type. The example suggests two decomposition trajectories of coarse 

fuels on two different land types (fig. 10). 

The accumulation and decomposition curves together form the general 

“U-shaped” temporal pattern observed in many forest ecosystems (Spetich 

et al. 1999, Sturtevant et al. 1997). In many boreal and northern hardwood forest 

ecosystems, a land type can seldom accumulate enough coarse fuel to reach 

class five unless there are other disturbance events occurring such as wind, BDA, 

and/or harvest. Users can define these disturbance-related accumulations using 

coarse fuel accumulation and decomposition curves (fig. 10).

Due to the long temporal scales involved in estimating the amount of coarse fuels, 

uncertainty is high. Collapsing the estimates of the quantity of coarse fuel into 

five categorical classes (very low to very high) reduces the potential for false 

precision and the parameterization burden for the module.

The elapsed time of fuel accumulation (TFC) is used to determine the current 

amount of coarse fuel as shown in figure 10. The various modifiers, once 

activated, will determine how much the coarse fuel class is increased or reduced. 

The relationships defined for each modifier (section 15.2) and the decomposition 

status defined in figure 10 is used to determine the final coarse fuel amount. The 

highest class (up to class 5) will be retained as the final coarse fuel class. For 

example, based upon the time of coarse fuel accumulation for a site, the coarse 

fuel class is determined to be class 2. However, an intense wind disturbance and 

an insect defoliation would each raise the coarse fuel class by 3 (e.g., section 

15.2). This leads to a final coarse fuel class for this site that is larger than 5 

(2+3+3). In such a case, the model will set the coarse fuel class to 5. 

9.5.7.3 Fine fuel and coarse fuel modifier

Land type, fire, wind, harvest, and biological disturbance can modify the fine fuel 

and coarse fuel classes derived from the relationships discussed above. Fine fuel 

decomposition rates may vary by land types (Agee and Huff 1987). Thus, a land 

type modifier may decrease or increase the fine fuel class derived from species 

age cohorts. For example, a fine fuel class 3 on a mesic land type might decrease 

to class 2 because the decomposition rate is relatively high, while the same fuel 

class (3) on a xeric land type might increase to class 4, since the decomposition 

rate is relatively low. The user defines the modifier in the fuel module input file, 

and the default is no modification. Disturbances may increase or decrease the fuel 

class in a similar way. For example, as a parameter for a Biological Disturbance 

Agent (BDA) (e.g., insect pest), the user defines how a BDA disturbance 

event will increase the fine fuel and coarse fuel class, depending on the type 

and intensity of the event. Similarly, fire events can reduce the fine and coarse 

fuel class (Armour et al. 1984) based on user-defined rules. The simplest and 

most common case is that fires remove all fine fuels. Alternatively, a rule could 

remove fine and coarse fuels in proportion to the intensity class of the fire. Wind 

disturbances primarily increase coarse fuel. However, they can also increase fine 

Conceptual

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fuels by producing dead leaves and needles. Increases in fine fuel classes caused 

by wind can be determined by the intensity class of the wind. Harvest activities 

can also modify the fine and coarse fuel class. The user can also define how 

each harvest prescription defined in the Harvest module will modify the fine and 

coarse fuel class. 

9.5.7.4 Potential fire intensity

Potential fire intensity is determined by the combination of fine fuel and coarse 

fuel in each cell. A set of rules can be defined (see section 15.3.1) based upon the 

assumption that coarse fuel is the primary contributor to the fire intensity class, 

since in many forest ecosystems coarse fuel accounts for about 90 percent of 

forest floor mass (Grier and Logan 1977, Lambert et al. 1980, Lang and Forman 

1978). Users can define other rules according to the ecosystems they study. In 

the example in section 15.3.1, high-intensity fires are not common compared to 

low-intensity fires. Seven fine and coarse fuel combinations result in fire intensity 

= 1 (very low), seven in fire intensity = 2, six in fire intensity = 3, three in fire 

intensity = 4, and two in fire intensity = 5 (very high) (see section 15.3.1). 

9.5.7.5 Live fuel-fire intensity modifier

Live fuels are live trees that may be ignited in high-intensity fire situations (such 

as crown fires). Thus, live fuels can be a fire intensity modifier. A mid-level 

intensity fire (≥3) may change into a crown fire (intensity class = 5) if there are 

suitable conifer species present (e.g., FQC=1). However, changing from lowintensity fire to crown fire is not a deterministic event and a probability function 

is used to predict its occurrence. For example, the probability of low-intensity 

fire changing to a crown fire is 0.01-0.05 based on the empirical knowledge for 

Missouri central hardwoods (B. Cutter, Department of Forestry, University of 

Missouri-Columbia, personal communication). Such a probability (P) can be 

user defined. In the fuel module, when fire intensity reaches level 3 and there 

are species with FQC = 1 present, the fire intensity can reach 5 if the uniform 

random number >P. 

9.5.7.6 Potential fire risk

Fire risk has a variety of definitions under different modeling frameworks. In 

the Fire Regime Condition Class, fire risk is defined as the risk of loss of key 

ecosystem components (Hardy et al. 2001). In the National Fire Plan, fire risk 

refers to the risk that communities and environment will experience a damaging 

fire (www.nwfireplan.gov). When the Forest Service forecasts fire risk, it defines 

that risk as the probability or chance of fire starting determined by weather index. 

In LANDIS 4.0, the definition of fire risk reflects the two key aspects: 1) the 

probability of fire occurrence, and 2) the intensity and spread once a fire ignition 

occurs (He et al. 2004). Fire probability is derived from the fire cycle and the 

time since last fire for each cell and is intended to account for climate and other 

factors that can influence ignition probability, whereas fire intensity and spread 

are related to biotic factors affecting the level of various fuels (Yang et al. 2004). 

In the LANDIS 4.0 fuel module, fire probability is converted into five classes, 

from very low to very high, based upon the “equal area” (the fire probability 

density function is divided into 5 areas of equal size) or equal interval approach. 

Potential fire intensity is determined by the combination of fine fuel and coarse 

fuel in each cell (see section 9.5.7.4). 

Conceptual

9 Basis
LANDIS 4.0 Users Guide Page 27

Fire risk is classified into five classes based upon fire probability and fire 

intensity, from very low (class 1) to very high (class 5). We assume that potential 

fire probability and fire intensity equally contribute to the fire risk. Thus, in 

the default fire risk table (see section 15.3.3), five unique combinations of fire 

probability and potential intensity classes are identified for each fire risk class. 

Again, users can define this table based upon the characteristics of their study 

area. The module allows users to track the high fire risk areas on the landscape 

over time and to explore the effects of various fuel load reduction methods on the 

spatial and temporal dynamics of fire risk.

9.5.7.7 Fuel management

9.5.7.7.1 Treatment size, interval, and intensity

The LANDIS fuel module simulates fuel management practices that fall into 

two categories: prescribed burning and physical fuel load reduction (removal 

and mechanical thinning). LANDIS fuel management has spatial, temporal, and 

treatment components. The spatial component uses parameters on the desired 

treatment size (e.g., the percent area of a management unit to be treated) and 

determines where such a treatment can be spatially allocated (e.g., how stands 

are selected for treatments). The allocation criteria can be based upon rankings 

of potential fire risk, where stands with highest potential fire risk are treated 

first, or by using random selection. The temporal component of fuel management 

determines what year (decade) a given treatment is performed and how often 

it is repeated. Single, multiple, or periodical entry years can be specified. The 

treatment component specifies the treatment types (e.g., prescribed burning) and 

treatment intensity. Since the three components are independent, combinations 

of the three are capable of simulating most fuel treatment practices (He et al.

2004, Shang et al. 2004). 

9.5.7.7.2 Prescribed burning

In LANDIS, prescribed burning mainly affects fine fuel, but it can also reduce 

coarse fuel based upon the user specification. The choice of low- vs. highintensity prescribed burning treatments depends on the field conditions, resources, 

and potential fire risk (Brose and Wade 2002). For example, a low-intensity 

prescribed burning might reduce the fine fuel load by a maximum of 2 classes. A 

high-intensity prescribed burn might remove most fine fuel loads (reduced to 1).

9.5.7.7.3 Physical fuel load reduction

Mechanical thinning primarily targets coarse fuels, including reducing the fuel 

size and removing/reducing coarse fuel load. The low-intensity treatment reduces 

coarse fuel load by 1-2 classes when the coarse fuel class is >3. The highintensity treatment removes most coarse fuels (reduced to class 1). 

9.5.7.7.4 Other fuel treatments

The following example illustrates how to specify chipping and thinning of coarse 

fuel. The chipping and thinning treatment is prescribed only for high (=4) and 

very high (=5) coarse fuel classes, and classes 4 and 5 are reduced to class 2. 

Note that coarse fuel treatments can result in an increase in fine fuels. In this 

example, all fine fuel classes are increased by 1. 

Coarse fuel load class before treatment: 0 1 2 3 4 5

Coarse fuel load class after treatment: 0 1 2 3 2 2

Fine fuel load class before treatment: 0 1 2 3 4 5

Fine fuel load class after treatment: 1 2 3 4 5 5

Conceptual

9 Basis
LANDIS 4.0 Users Guide Page 28

10. OPERATING SYSTEMS AND CONFIGURATIONS

10.1 Operating Systems

LANDIS 4.0 is a 32-bit Windows application implemented with Visual C++. 

It runs under Windows 2000/XP with at least 215 MB of memory. There is no 

UNIX version of LANDIS 4.0. 

10.2 Memory and Storage

The LANDIS 4.0 runs with 256 MB of internal memory. However, most runs 

will require substantially more RAM. If the necessary RAM is not available, 

virtual memory will be automatically allocated on the hard drive. Successful runs 

on large models have been performed on computers with 512 MB to 1 GB RAM. 

However, 1 GB or more RAM is recommended. 

11. BASIC LANDIS INPUT FILES

This section will describe basic LANDIS input files that are required to conduct 

a model run. Basic LANDIS input files are always needed regardless of which 

disturbance and management modules are turned on or off. LANDIS input 

files for individual disturbance and management processes will be discussed in 

subsequent chapters. 

11.1 Parameter File (parameter.dat)

The parameter file is an entry file in ASCII format. It contains the settings of 

simulation scenarios such as number of years of simulation, dispersal mode, and 

particular processes simulated. Comments can be included in the parameter file 

as well as in any other attribute files. They can be located anywhere in a file as 

long as they are bracketed by the “#” character. The sequence of the parameters 

in the parameter file is fixed. The following is an example of a parameter input 

file, in which the first column stands for various parameters and the second 

column contains the brief explanations of each parameter. 

species.dat #1. Species life history attribute file#

landType.dat #2. Attributes describing each land type class#

landType.gis #3. 8- or 16-bit ERDAS GIS map of land type or ecoregion#

speciesMap.gis #4. 8- or 16-bit ERDAS GIS map of species and age cohort classes#

mapAttribute.dat #5. Attributes describing each map class#

mapIndex.dat #6. Map names for tree species or forest cover output#

ageIndex.dat #7. Age map name for output#

c:\project\output #8. Path for LANDIS output#

default.plt #9. Required in the parameter file directory#

frequency.dat #10. Required in the parameter file directory#

50 #11. Number of iterations (10 year time steps)#

1 #12. 0 - generate randomly; other - fixed seed#

30 #13. Cell size in meters#

DISPERSAL #14. The name of the seed dispersal routine#

windInit.dat #15. Flag-on of wind. N/A for flag-off#

fireInit.dat #16. Flag-on of fire. N/A for flag-off#

BDAInit.dat #17. Flag-on of BDA. N/A for flag-off#

fuelInit.dat #18. Flag-on of fuel module. N/A for flag-off#

harvestInit.dat. #19. Flag-on of wind disturbance. N/A for flag-off#

Operating 10 Systems

11 Input Files
LANDIS 4.0 Users Guide Page 29

• The first 7 lines of the file are names of auxiliary LANDIS input files. 

• Line 8 is the output directory name. All LANDIS output will be placed 

in this directory. If the last level of the directory does not exist, it will 

be created at run time. If more than one level of the path does not exist, 

LANDIS will generate an error message. Full descriptions of subsidiary 

files are in the following sections of documentation. 

• Line 9 is a predefined LANDIS input file that defines the color palettes of 

various LANDIS output maps. These color palettes will be used only if the 

user explicitly informs the LANDIS Viewer or a GIS to do so. 

• Line 10 is the LANDIS output frequency file that can be modified to affect 

the model output intervals for various processes.

• Line 11 is the number of model iterations. Multiplying the number of 

iterations by 10 will give the number of years in a model run. 

• Line 12 is the random number seed. When this value is set to 0, LANDIS 

chooses a seed randomly. A valid number is any integer. Choosing a 

random number seed gives users control over the random number series and 

may help with model verification. The default value is 1. 

• Line 13 is the length of the side of a map cell in meters. All LANDIS 

cells must be square (not rectangular). Recommended cell sizes are integers 

from 10 to 500 meters. 

• Line 14 is the seed dispersal method identifier. Any identifier must be in 

upper case. The following identifiers are valid:

NO_DISPERSAL—No seed movement at all.

UNIFORM—All cells receive all seeds every iteration.

NEIGHBORS—Each cell seeds its neighboring cell.

DISPERSAL—Each cell seeds out to the species effective distance 

attribute. Within this distance species have a 95 percent chance of 

seeding out; beyond this distance species have a 5 percent chance of 

seeding out.

RAND_ASYM—Each cell seeds in a random asymptotic manner for 

an infinite distance. This regime runs very slowly. Most runs will not 

complete in a reasonable amount of time.

MAX_DIST—Each cell seeds out to the species maximum distance 

attribute. 

SIM_RAND_ASYM—Each cell seeds out to the species maximum 

distance attribute. Outside of this range cells have a small random 

chance of receiving seed of any species. This simulates the RAND_

ASYM seeding regime but runs much faster. 

• Lines 15-19 are entry file names (flags) for wind, fire, biological 

disturbance, fuel, and harvesting, respectively. If set to N/A, the particular 

disturbance or management will not be simulated. The use of these flags is 

described in the following chapters. 

11 Input Files
LANDIS 4.0 Users Guide Page 30

11.2 Species Attribute File (species.dat)

This file contains all of the life history attributes for every species in use. The 

maximum number of species allowed is 30. The species names are not fixed 

except for poputrem (trembling aspen) and popugran (big-tooth aspen). Species 

can be input in any order. However, because several other input files must use the 

same order, the order should not be changed once it is established. 

The life history attributes are longevity, mature age, shade tolerance, fire 

tolerance, effective seeding distance, maximum seeding distance, vegetation 

propagation probability, maximum sprouting age, and reclassification coefficient. 

Species not present in the base year map can be omitted in the species attribute 

file. The following sample species attribute file was used in a LANDIS run for 

northern Wisconsin.

The set of attributes for each species is listed on a single line. Each of the nine 

data values in species.dat is described below:

#NAME LONG MTR SHD FIRE EFFD MAXD VGP SPAG RCLC#

prunpenn 30 10 1 1 30 3000 0.0 10 0.25

poputrem 120 15 1 2 –1 –1 1.0 10 0.25

popugran 90 20 1 2 –1 –1 1.0 10 0.25

pinubank 90 15 1 2 20 40 0.0 0 0.25

pinuresi 250 35 2 4 12 275 0.0 0 0.75

querelli 300 35 2 5 30 3000 0.5 20 0.25

betupapy 120 30 2 2 200 5000 0.5 200 0.25

pinustro 450 15 3 3 100 250 0.0 0 1.00

acerrubr 150 10 3 1 100 200 0.5 20 0.50

querrubr 250 25 3 3 30 3000 0.5 20 0.75

piceglau 300 25 3 2 30 200 0.0 0 0.50

thujocci 400 30 4 1 45 60 0.5 20 0.50

fraxamer 150 30 4 1 70 140 0.1 20 0.75

betualle 350 40 4 2 100 400 0.1 20 0.75

tiliamer 250 15 4 2 30 120 0.5 20 0.75

abiebals 200 25 5 1 30 160 0.0 0 0.25

acersac2 400 40 5 1 100 200 0.1 20 0.75

tsugcana 640 30 5 3 30 100 0.0 0 1.00

LONG—Longevity of the species in years.

MTR—Maturity age of the species in years. The species will begin to seed 

when this age is reached.

SHD—Shade tolerance value (1-5). 1= least tolerant; 5= most tolerant.

FIRE—Fire tolerance value (1-5). The most fire-tolerant species are fire 

tolerance 5 while the least fire-tolerant species are fire tolerance 1.

EFFD—Species effective distance seeding range in meters. Within this 

distance species have a 95 percent chance of seeding out; beyond this 

distance species have a 5 percent chance of seeding out. –1 represents 

unlimited effective seeding range.

MAXD—Species maximum distance seeding range in meters. –1 represents 

unlimited maximum seeding range.

VGP—Probability of vegetative propagation following disturbance. It is 

specific to resprouting, not other types of reproduction such as root 

suckering. 

11 Input Files
LANDIS 4.0 Users Guide Page 31

SPAG—Maximum age to be able to resprout (vegetative propagation). The 

maximum age of resprouting is not currently implemented. 

RCLC—Reclassification coefficient (0-1). This number is used in the output 

reclassification algorithm (see section 11.9). Functionally, these values 

are used to weight the classification of cells in output maps created by 

LANDIS. It can be considered as the theoretical importance coefficient 

of a species in comparison with other species. 0 is least important and 1 

is most important.

11.3 Land Type Attribute File (landtype.dat)

A successful LANDIS run requires land type data reflecting differential species 

establishment among land types. To achieve this, a synthetic land type or 

ecoregion coverage is created from abiotic data layers such as climate, soil, 

geology, and topography. The land type attribute file contains the descriptions 

of each land type class in a LANDIS compatible format. LANDIS assumes 

that species may behave differently on different land types by having their 

own establishment coefficients (or reproduction probabilities). This assumption is 

supported by numerous experimental and empirical studies (He and Mladenoff 

1999a). These coefficients must be developed for each land type and scaled from 

0.00 to 1.00 (He et al. 1999b). 

Land types are generally divided into two categories, active and nonactive. In 

LANDIS 4.0, nonactive land types are not processed by the model although they 

will be represented in output maps as their respective types. The current version 

includes empty, water, wetland, bog, lowland, and nonforest as the nonactive land 

types. The empty land type denotes any pixel outside the extent of the map. 

Active land types are any other land types. If the user needs to simulate fires 

on a lowland type, a land type name other than “lowland” can be used in the 

land type attribute file. The total number of land types should not exceed 65,536, 

as limited by the 16-bit ERDAS GIS map. The following is a sample land type 

attribute file.

moraine 40 700

#prunpenn# 1

#poputrem# 0.8

#popugran# 1

#pinubank# 0.5

#pinuresi# 1

#querelli# 0.5

#betupapy# 1

#pinustro# 1

#acerrubr# 1

#querrubr# 1

#piceglau# 0.5

#thujocci# 0

#fraxamer# 0.3

#betualle# 0.5

#tiliamer# 0.8

#abiebals# 0.3

#acersac2# 0.5

#tsugcana# 0.1

11 Input Files
LANDIS 4.0 Users Guide Page 32

outwash 40 700

#prunpenn# 1

#poputrem# 0.3

#popugran# 0.5

#pinubank# 1

#pinuresi# 0.8

#querelli# 1

#betupapy# 0.3

#pinustro# 0.8

#acerrubr# 0.5

#querrubr# 0.5

#piceglau# 0.3

#thujocci# 0

#fraxamer# 0

#betualle# 0

#tiliamer# 0

#abiebals# 0

#acersac2# 0

#tsugcana# 0

In this example, there are two land types in total. Each land type is grouped 

together with one empty line in between. The first line contains three variables:

• The first identifier is the land type name. This name is case sensitive. 

• The second variable represents the minimum age of cohort growth required 

before enough shade is created so that a shade tolerance 5 species can seed 

into the site on this land type. For instance, in order for a shade tolerance 

class 5 species such as balsam fir (abiebals), sugar maple (acersac2), or 

eastern hemlock (tsugcana) to seed into an outwash site, at least one other 

species must be present in an age class older than 40.

• The third variable is the initial time since last wind disturbance associated 

with all cells in the land type. This can be retrieved from disturbance 

history maps (if available). If adequate data are not available, we 

recommend using half the wind return interval for this variable. This can be 

set without requiring GIS processing.

• The next X lines where X equals the number of species modeled, are the 

species name in comments and their establishment coefficients (SEC) on 

the land type. The species order should be exactly the same as it is in the 

species attribute file. 

Environmental changes encapsulated by land type can be incorporated in 

model simulations by specifying the “-e” argument followed by the year of 

interpolation. The land type attribute files must be derived per iteration. For 

example, if the land type attribute file name at year 0 is LT, the land type attribute 

file at year 10 should be LT10, at year 20 LT20, and so on, and each new land 

type attribute file will have different species establishment coefficients reflecting 

the environmental changes.

11.4 Land Type Map File (landtype.gis) 

The land type input map is processed from several environmental data layers 

such as climate, soil, and topography. There are many ways to create the map 

using GIS. The final map file should be an ERDAS 7.4 8-bit or 16-bit GIS 

file. The model does not currently accept 4-bit GIS files. Within the GIS file, 

11 Input Files
LANDIS 4.0 Users Guide Page 33

a continuous range of integers beginning from 0 should be used to represent 

the final overlaid map classes. For example, if the land type map contains 50 

classes, 0 will represent the first class and 49 will represent the 50th class. A 

land type map class can represent any land type a user may wish to define. For 

example, 1 may represent a class of water land type with no species present; 10 

may represent a class of moraine land type. Thus each integer corresponds to a 

class description in the land type attribute file (described above). The final class 

number can be as large as 65,536.

11.5 Species Composition Map File (speciesMap.gis)

The species composition map consists of species and species age classes. In many 

cases a species age map is not available for the study region. Interpolation of age 

information from other sources, such as the Forest Inventory and Analysis (FIA) 

database, can be used (He et al. 1998). 

As with the land type map file, the forest composition map should be an ERDAS 

7.4 8- or 16-bit GIS file. Within the GIS file, a continuous range of integers 

beginning from 0 should be used to represent the final overlaid map classes. If 

the map has 200 classes, 0 will represent the first class and 199 will represent 

the 200th class. For example, pixel value 10 may represent a class with hemlock, 

yellow birch, and sugar maple present and hemlock in the ages of 150, 200, 250, 

yellow birch in the ages of 80, 90, 100, and sugar maple in the ages of 30, 50, 

150, 200. Each integer corresponds to a class description in the map attribute 

file (described below). Note that 0 should represent areas without data if such 

area exists, which will not be simulated. The final class number can be as large 

as 65,536.

11.6 Map Attribute File (mapAttribute.dat)

The map attribute file contains information about each of the classes present in 

the species composition map described above. The class descriptions are listed 

in sequential order where the first entry describes class 0 in the map file, the 

second entry describes class 1, and so forth. Any class in the map file should be 

represented in the map attribute file. The following is a sample map attribute file.

#class 0#

#prunpenn# 0 000

#poputrem# 0 000000000000

#popugran# 0 000000000

#pinubank# 0 000000000

#pinuresi# 0 0000000000000000000000000

#querelli# 0 000000000000000000000000000000

#betupapy# 0 000000000000

#pinustro# 0 000000000000000000000000000000000000000000000

#acerrubr# 0 000000000000000

#querrubr# 0 0000000000000000000000000

#piceglau# 0 000000000000000000000000000000

#thujocci# 0 00000000000000000000000000000000000

#fraxamer# 0 000000000000000

#betualle# 0 00000000000010000000000000000000000

#tiliamer# 0 0000000000000000000000000

#abiebals# 0 00000000000000000000

#acersac2# 0 0000000000000000000000000000000000000000

#tsugcana# 0 00000000000000000000000000000000000000000000000000

11 Input Files
LANDIS 4.0 Users Guide Page 34

#class 1# 

#prunpenn# 0 000

#poputrem# 0 000000000000

#popugran# 0 000000000

#pinubank# 0 000000000

#pinuresi# 0 0000000000000000000000000

#querelli# 0 000000000000000000000000000000

#betupapy# 0 000001000000

#pinustro# 0 000000000000000010000000000000000000000000000

#acerrubr# 0 000000000000000

#querrubr# 0 0000000000000000000000000

#piceglau# 0 000000000000000000000000000000

#thujocci# 0 0000000010000000100000000000000000000000

#fraxamer# 1 000000000000000

#betualle# 0 00000000000010000000000000000000000

#tiliamer# 0 0000000000000000000000000

#abiebals# 1 11100101110000000000

#acersac2# 0 1001101101100000000000000000000000000000

#tsugcana# 0 00000000000100100010000001000001010010000110000010

In the above example, class 0 is empty and class 1 is an old growth hemlock. The 

species should be listed in the same order as they appear in the species attributes 

file. Each species has two data elements associated with it: the first number is a 

vegetative propagation flag, and the numbers that follow represent an age list. 

If a species has a 1 in the vegetative propagation flag, all cells of the cover 

type will be eligible for vegetative reproduction subject to the maximum age of 

resprouting and resprouting probability defined in the species attribute file. 

Each column in the age list represents a 10-year cohort up to species longevity. 

A 0 denotes that the cohort is not present at that age class, while a 1 denotes 

the presence of that age class. For instance, abiebals has cohorts present at the 

10, 20, 30, 60, 80, 90, and 100-year age classes. All class 2 cells within the 

forest composition map will be filled with the ages given above for abiebals at 

the beginning of a model run. In similar fashion, the other species with 1’s listed 

in their age list will be present.

For class 1 in the above example, betupapy, pinustro, thujocci, betualle, abiebals, 

acersac2, and tsugcana are present on the site. Vegetative reproduction has been 

enabled for fraxamer and abiebals (note that fraxamer is not present). 

11.7 Map Index File (mapIndex.dat)

Note: The map index file used to be called the reclassification output description 

file in earlier versions of LANDIS (LANDIS 2.0 -3.7). 

LANDIS’ internal data structures often contain more than 40MB of data 

depending on the map size. Since runs may be as long as 2,000 years, it may not 

be necessary to save out the complete internal state at each iteration. Instead, 

LANDIS attempts to create various maps (GIS files) using user-defined cover 

types by converting LANDIS’ internal data format to user-defined thematic maps. 

This process is called output reclassification and is implemented through the map 

index file and the reclassification files (.RCS).

The map index file contains a list of names. Each name is used during the 

reclassification phase. Each name in the list consists of three or fewer characters, 

11 Input Files
LANDIS 4.0 Users Guide Page 35

since for some operating systems the maximum file name allowed is still eight 

characters. An example map index file may look like this:

#mapindex.dat#

map oak std

After running LANDIS with this map index file you should find the following 

ERDAS 8-bit GIS files and their respective trailer files (TRL) in the output 

directory.

map0.gis, map10.gis, ..., ..., mapn.gis (n is the final simulation year)

map0.trl, map10.trl, ..., ..., mapn.gis

oak0.gis, oak10.gis, ..., ..., oakn.gis

oak0.trl, oak10.trl, ..., ..., oakn.trl

std0.gis, std10.gis, ..., ..., stdn.gis

std0.trl, std10.trl, ..., ..., stdn.trl

11.8 Reclassification File (RCS File)

For each name in the map index file there should be an associated RCS file. The 

RCS file specifies how to assign cover types for a given output map. 

Using the map index file from the example in the last section, the user must 

provide three RCS files in the default directory: map.rcs, oak.rcs, and std.rcs. 

LANDIS searches for these files at run time. If any of these files are not present, 

output reclassification will not occur for the corresponding type.

Reclassification will reclassify maps based on the presence or absence of one 

or more species into a single, named GIS map. The method is effective and 

flexible. Users can examine single species or collections of species classified at 

different levels according to ecological principles. Continuing with our example 

may clarify the matter. “map.rsc,” “oak.rcs,” and “std.rcs” might look like this:

#map.rcs#

Maple: acersac2

#oak.rcs#

Oak: querelli querrubr

#std.rcs#

Aspen: prunpenn poputrem popugran !tsugcana !acersac2 !acerrubr

P_Birch: betupapy !tsugcana !acersac2 !acerrubr

Maple: acersac2 acerrubr !abiebals !tsugcana

Maple/Conifer: acersac2 acerrubr abiebals !tsugcana

Hemlock: tsugcana

Pine: pinubank pinuresi pinustro !tsugcana

Y_Birch: betualle

Oak: querelli querrubr

In the first case the output map series mapn.gis will contain acersac2. In the 

second case the oakn.gis series will contain both querelli and querrubr. The third 

case represents a more complicated situation. There are eight cover types defined: 

Aspen, P_Birch, Maple, Maple/Conifer, Hemlock, Pine, Y_Birch, and Oak. 

11 Input Files
LANDIS 4.0 Users Guide Page 36

The name denoting each cover type is defined by the user and is saved in each 

TRL file. Each name must be followed by a colon and then a list of one or more 

species names on the same line. There is no limit to the number of species in 

the list. 

Each species following the cover type name is characteristic of the cover type 

unless the species name is preceded by an exclamation point. These species 

are not characteristic of the cover type. Presence of a noncharacteristic species 

will have a negative impact upon the likelihood of a cell being represented as 

the corresponding cover type. A fuzzy-logic-based algorithm determines which 

defined cover type most closely represents the current state. 

11.9 Reclassification Algorithm

Some of the output reclassification algorithms are straightforward and not 

described here. These include the land type maps, the fire and wind maps, and 

the age maps.

The generic reclassification algorithm implementing the .RCS files is not nearly 

as straightforward. It can be summarized as follows. 

For each site:

For each species s:

 Determine dominance fuzzy value d(s).

End For.

Determine fuzzy reclassification vector v.

Output correct reclassification class dependent on decision boundary within v.

End For.

The dominance fuzzy value is found by the following formula:

d(s)=actual age of s / maximum age of s * reclassification coefficient of s.

The fuzzy reclassification vector v may be found by the following procedure:

For i=1 to n (where n is size of reclassification vector v).

For each species s:

If s is a positive influence on v(i)

 v(i)=v(i)+d(s)

Else if s is a negative influence on v(i)

 v(i)=v(i)–d(s)

End For

End For

The greatest value in vector v(1), v(2), ..., v(n) will be the correct reclassification 

class. Note that in the above equations all mathematics are confined to fuzzy 

set rules. Thus all numbers are bounded in the range [0..1] and the result of any 

equation may never exceed 1 or be less than 0. If a number exceeds 1, it will be 

set to 1. If a number is less than 0, it will be set to 0.

11 Input Files
LANDIS 4.0 Users Guide Page 37

11.10 Age Map Index File (ageIndex.dat)

Sometimes it is desirable to trace the dynamics of one or more species throughout 

the simulation run. The age map index file allows users to do just that. The 

age index file contains a list of names. Each name is used during the map 

output phase for each iteration. Each name in the list consists of three or fewer 

characters. A sample age map index file might look like the following: 

#ageindex.dat#

ySM yRO yQA

After the model simulation using this age map index file, you should find the 

following ERDAS 8-bit GIS and TRL files in the output directory. The definition 

of each age map (e.g., ySM, yRO, and yQA) is discussed in the next section. 

ySM0.gis, ySM10.gis, ..., ..., ySMn.gis (n is the final year)

ySM0.trl, ySM10.trl, ..., ..., ySMn.trl

yRO0.gis, yRO10.gis, ..., ..., yROn.gis

yRO0.trl, yRO10.trl, ..., ..., yROn.trl

yQA0.gis, yQA10.gis, ..., ..., yQAn.gis

yQA0.trl, yQA10.trl, ..., ..., yQAn.trl

11.11 Age File (.age)

For each name in the age map index file there should be an associated AGE 

file. The age file specifies which species to create age maps for. Using the age 

map index file from the example in the last section the user must provide three 

AGE files in the default directory: ySM.age , yRO.age , and yQA.age. LANDIS 

searches for these files at runtime. If any of these files are not present, output 

maps will not be created for the corresponding species. Example age files might 

look like the following:

#ySM.age#

 acersac2

#yRO.age#

 querrubr

#yQA.age#

 Poputrem

As shown above, each AGE file contains the name of an individual species and 

the name of the species is defined in species attribute file (species.dat). 

11.12 Frequency File (frequency.dat)

The output generated by LANDIS defaults to every iteration (a 10-year time 

step). If this is not desired, the LANDIS output frequency file can be modified 

to affect the output intervals. Users can set their preferred output interval to 

something other than every iteration. This feature is useful when disk space is a 

concern. The format of the frequency file is as follows:

11 Input Files
LANDIS 4.0 Users Guide Page 38

#LANDIS output frequency file::freq.dat#

#map indexed and age indexed species output interval# 1

#fire map output interval# 1

#wind map output interval# 1

#harvesting map output interval# 1

#30 year group age map output interval# 1

When the value specified falls in the range of 1 to 9, it is interpreted as a cyclic 

increment. Specifying 4, for instance, results in maps being output every fourth 

iteration (40 years). Specifying 0 or negative value results in no output for the 

category. When the value specified is greater than 9, it is interpreted as a single 

specific iteration. Specifying 50, for instance, results in maps being output on 

the 50th iteration (500th year). The above example outputs each iteration. Since 

LANDIS fire, wind, BDA, fuel, and harvesting modules incorporate stochastic 

algorithms, the time step of a disturbance event cannot be predicted until it occurs 

at a certain year. Thus, alternative map output does not apply, since it may omit 

the stochastic events. Currently, LANDIS 4.0 outputs disturbance maps every 

iteration regardless of the number specified in the output frequency file. 

12. LANDIS WIND MODULE 

The wind disturbance entry file (windInit.dat) is the only file needed by the 

LANDIS 4.0 wind module. Wind regimes are not differentiated by land types, 

so only a single prevailing wind regime can be defined and simulated in the 

LANDIS 4.0 WIND module. If windInit.dat or other wind entry file name is set in 

the LANDIS parameter file, wind disturbance will be simulated. The following is 

a sample of a wind disturbance entry file.

1 #1. standard wind disturbance mode#

900 #2. Minimum wind size.#

200000 #3. Maximum wind size.#

10000 #4. Mean wind size.#

800 #5. Mean return interval. changed from 1500 to 1000#

0.1 #6. Wind breakout coefficient#

2000 #7. Wind probability coefficient was 10#

1000 #8. Wind size coefficient was 50#

5 #9. Lower percentage of pseudomean#

10 #10. Upper percentage of pseudomean#

10 #11. pseudostrong#

5 #12. pseudolight#

wind #13. Yearly output file.#

windfin #14. Final output file.#

wind.log #15. Wind log file.#

• The first variable is standard wind disturbance mode. Other disturbance 

modes are not available at this time. 

• The second variable is the minimum wind size in square meters. 

• The third variable is the maximum wind size in square meters. 

• The fourth variable is the mean wind size in square meters. Over the course 

of a long model run (several thousand years) the mean wind size should be 

accurately simulated.

• The fifth variable is the mean wind return interval or wind disturbance 

cycle. This is the average years for wind to disturb all sites during the 

model run. 

11 Input Files

12 Wind Module
LANDIS 4.0 Users Guide Page 39

• The sixth variable is a wind breakout coefficient, the percent number of 

cells of the study area having wind events per decade. It ranges from 0.0 

to 1.0, with 0.0 indicating that no cell is checked for breakout and 1.0 

indicating that all cells are checked for possible wind occurrence. 

• The seventh variable is wind probability coefficient. Probability 

coefficients can be adjusted interactively so that results match the 

predetermined mean return intervals. We recommend varying the 

probability coefficient by 10 each run. For example, if simulated wind 

disturbances happened more frequently than the predetermined mean return 

intervals, increase the probability coefficient by 10 each time. If fewer 

disturbances than expected happened, decrease the number by 10 each 

time. The scale of adjustment also depends on how different the simulated 

values are from the theoretical values. 

• The eighth variable is wind size coefficient. When changing the value of the 

size coefficients for wind, we also recommend modifying it in increments 

of 10. For example, if simulated mean fire or wind disturbance size is 

less than the predetermined value in the disturbance file, increase the size 

coefficient by 10 each run. If the mean fire or wind disturbance size is 

greater than the predetermined value in the disturbance file, decrease the 

size coefficient by 10 each run. The size of the total adjustment will depend 

on how much the simulated values and the theoretical values differ. 

• The following variables (9-12) are for the ratio mode disturbance modes 

(see LANDIS 3.7 Users Guide). They are no longer used, but need to be 

set as dummy numbers. 

• Lines 13 to 15 contain file-naming information. The first name is the base 

for the name of the GIS map file for each 10-year time step or the step 

specified in freq_out.put. The year will be appended to the base. The GIS 

file will contain a visual representation of the wind damage for the current 

time step. The second name is the name of the final GIS file. This GIS file 

will contain a visual representation of the wind damage accumulated during 

the entire model run. The third name is the name of an ASCII text file that 

holds a record of all wind disturbances occurring in the model run. 

13. LANDIS FIRE MODULE

13.1 Fire Entry File (fireParameter.dat)

If fireParameter.dat or other fire entry file name is set in the LANDIS parameter 

file, the fire module will be activated. The following is a sample of fire entry file.

1 #1. Flag of new fire algorithms incorporation#

1 #2. Flag of wind incorporation#

1 #3. Flag of using DEM data#

1 #4. Flag of using independent fire regime data#

1 #5. Flag of output TSLF#

0.1 0.2 0.3 0.4 0.5 #6. fire initiation probability for each fine fuel class#

0.300000 #7. Base probability for fuel class 3#

0.200000 #8. Wind coefficient#

0.000000 #9. Topography coefficient#

1.000000 #10. Predefined fire size distribution coefficient#

30 #11. Percentage (%) of nonwind events in wind regime#

10 15 20 25 30 #12. Cumulative probabilities for SW direction wind with 

intensity class 1 to 5#

12 Wind Module

13 Fire Module
LANDIS 4.0 Users Guide Page 40

30 30 35 40 40 #13. Cumulative probabilities for W direction wind with 

intensity class 1 to 5#

40 40 40 40 40 #14. Cumulative probabilities for NW direction wind with 

intensity class 1 to 5#

45 50 55 60 60 #15. Cumulative probabilities for N direction wind with 

intensity class 1 to 5#

60 60 60 60 60 #16. Cumulative probabilities for NE direction wind with 

intensity class 1 to 5#

60 60 60 60 60 #17. Cumulative probabilities for E direction wind with 

intensity class 1 to 5#

65 70 75 75 75 #18. Cumulative probabilities for SE direction wind with 

intensity class 1 to 5#

80 85 90 95 100 #19. Cumulative probabilities for S direction wind with 

intensity class 1 to 5#

fireRegimeAttr.dat #20. Fire regime attribute input file#

fireRegimeData.gis #21. Fire regime GIS input data file#

DEM500.dat #22. Landscape elevation ASCII input data file#

Fire #23. File name used to output fire map each iteration#

Firefin #24. Final output file#

Fire.log #25. Fire log file#

30 #26. Cell size. Unit: meters#

• If the Using-New-Fire-Algorithm-Flag (line 1) is set to 1, the fire module 

will use fine fuel to simulate fire initiation if the fuel module is also turned 

on, or use TSLF (Time Since Last Fire algorithm) to calculate fire initiation 

probability when fuel module is off. The fire module will also use the 

new fire spread algorithm, which incorporates wind, topography, fuel, and 

fire size distribution. The fire module will use fuel class provided by fuel 

module to determine fire intensity when the fuel module is also on, or use 

TSLF when the fuel module is off. If the flag is set to 0, the fire module 

will use TSLF to simulate fire initiation and fire intensity, and use the old 

fire spread algorithm from LANDIS 3.7 that does not contain capabilities to 

simulate the effects of terrain, wind regime, and fuel.

• If the Wind-Incorporation-Flag (line 2) is set to 0, the fire module will not 

simulate effects of wind on fire spread, even if wind coefficient (line 8) 

is non-zero. To simulate wind effects on fire spread, both the Using-NewFire-Algorithm-Flag and Wind-Incorporation-Flag must be set to 1. 

• If the Using-DEM-Flag (line 3) is set to 1, the fire module will try to read 

DEM data (line 22) and use it when the Using-New-Fire-Algorithm-Flag 

is also on. 

• If the Using-Independent-Fire-Regime-Flag (line 4) is set to 1, the fire 

module will read fire regime GIS file (line 21). Otherwise, it will use the 

land type map. 

• If the Output-TSLF-Flag (line 5) is set to 1, the fire module will output 

time since last fire map. The file name is fixed: TSLF0.gis is the initial 

time-since-last-fire GIS map; TSLF1.gis is the time-since-last-fire GIS map 

after the first iteration simulation, etc.

• Fire initiation probability for each fine fuel class 1 to 5 is set in line 6. Each 

value ranges from 0 to 1. Fine fuel class of a site is dependent on species/

age composition of the site, and this composition mainly determines 

whether or not an ignition can initiate a fire. Since it is simulated by a 

Bernoulli function with the probability specified here, the probability 

13 Fire Module
LANDIS 4.0 Users Guide Page 41

indicates the proportion of fire ignitions that become fires given each fine 

fuel class. For example, given an initiation probability in fuel class 1 of 

0.1, if there are 100 ignitions simulated in an area with fine fuel class 1, 

there will be about 100 * 0.1= 10 fire occurrences.

• Base probability (line 7) is used in the new fire spread algorithm when the 

fuel module is on. This probability will be the fire spread probability of 

fuel class (potential fire intensity class in fuel module) 3. A preliminary 

sensitivity analysis has shown that fire spread probability has a nonlinear 

effect on the burned patch size and the critical probability is around 0.29. 

That is to say, for a homogeneous landscape where all sites have fuel class 

3, fire will spread across the landscape if the base probability is above 0.29 

provided there are no other effects (i.e., wind, topography, predefined fire 

size distribution) taken into account. 

• Wind coefficient (line 8) is used to weight the effects of wind on fire spread 

probability. The range of this coefficient is 0 to 2. Please refer to fire spread 

probability equation in fire module design for details (section 7.5.2.2). 

Increasing the wind coefficient will increase the fire spread probability in 

the wind direction. If the wind coefficient is 0, there will be no effects on 

fire spread probability. 

• Topography coefficient (line 9) is used to weight the terrain effects on fire 

spread probability. The range of this coefficient is 0 to 2. Please refer to 

fire spread probability equation in fire module design for details (section 

7.5.2.2). If the topography coefficient is 0, there will be no topographic 

effects on spread probability. Otherwise, an uphill slope will increase 

the spread probability, and the magnitude of such increase is decided by 

the slope angle and topography coefficient. If the landscape is flat, the 

topography coefficient should be a little higher (above 0.5) to simulate 

topographical effects. However, in a mountainous landscape, the coefficient 

should be smaller (under 0.5) to avoid unrealistic simulation of the effects 

of topography on fire spread behavior.

• Predefined fire size distribution coefficient (line 10) is used to weight the 

effects of fire size distribution on fire spread. The range of this coefficient is 

0 to 2. Please refer to fire spread probability equation in fire module design 

for details (section 7.5.2.2). If this coefficient is set to 0, the predefined 

fire size distribution will have no effect on the fire spread simulation, and 

only fuel, wind, and topography will decide fire size and spread behavior. 

Therefore, if the users intend to have the fire regime observe the fire 

size distribution described by mean fire size and standard deviation, the 

coefficient should be towards the high end (above 1).

• Line 11 defines the percentage of nonwind events that are simulated in 

one iteration, when both the Using-New-Fire-Algorithm-Flag (line 1) and 

Wind-Incorporation-Flag (line 2) are turned on. The range is 0 to 100. In 

the example data, the value is 30, which means 30 percent of simulated fire 

events in one iteration will not have wind events simulated.

• Line 12 to line 19 set the cumulative probabilities of all 40 wind event 

classes. There are eight directions and each direction has five wind 

intensity classes. Each line represents one direction. They are SW, W, 

NW, N, NE, E, SE, and S sequentially. The values on each line are the 

cumulative probabilities for each of the five wind intensity classes in that 

wind direction, and the probabilities are cumulative over all 40 classes. The 

13 Fire Module
LANDIS 4.0 Users Guide Page 42

range of each value is 0 to 100. Notice that the prior value must be 

less than or equal to the following value and the last of the 40 numbers 

must be 100 to observe cumulative probability rules. From the example 

data, line 12 indicates that the percentages of SW wind with intensity 

class 1, 2, 3, 4, and 5 are 10 percent, 5 percent (10-15), 5 percent (15-20), 

5 percent (20-25), and 5 percent (25-30), respectively. Line 14 indicates 

that there are no NW winds in any intensity class since the cumulative 

probabilities do not change.

• Line 20 and line 21 specify the name of fire regime attribute data file and 

the associated fire regime map file.

• Line 22 specifies the name of the DEM data file. The DEM file must be an 

ASCII file containing height information for each cell in meters.

• Line 23, line 24, and line 25 specify file names used for fire output. The 

names are used for the simulated fire intensity map at each time step, the 

final cumulative fire intensity map, and the log of each simulated fire event, 

respectively.

• Line 26 specifies the cell size of the maps (meters), which must be 

consistent with that specified in the LANDIS parameter file.

Figure 11 shows how different flags and the fuel module control the flow of the 

simulation by the fire module.

13.2 Fire Regime Attribute File (fireRegimeAttr.dat)

The fire regime attribute file specifies the attributes of each fire regime unit for 

the fire module. The first fire regime unit that appears in the fire regime attribute 

file is coded 0 in the fire regime map (e.g., fireRegimeData.gis) when UsingIndependent-Fire-Regime-Flag is 1 or in the land type map (e.g., landType.gis) 

when Using-Independent-Fire-Regime-Flag is 0. Similarly, the second fire regime 

unit that appears in the fire regime attribute file is coded 1 in the fire regime map 

or in the land type map, and so on. For each fire regime unit, a set of properties 

will be described in a fixed order, as the following example shows:

swslope #1. Fire regime name#

415 #2. Mean fire return interval#

0.1 #3. Fire ignition density#

4.5 #4. MFS, unit: hectare#

1.8 #5. STD#

50 #6. Last fire disturbance#

10 60 90 120 150 #7. fire curve, time since last fire#

1 2 3 4 5 #8. fire intensity classes#

10 20 60 80 100 #9. wind curve, time since last wind#

1 2 3 3 3 #10. modified fire classes#

• Line 1 is the name of the fire regime. It does not have to be the same as the 

name of the land type. The name is case sensitive.

• Line 2 specifies the mean fire return interval (i.e., fire cycle) of the fire 

regime. The unit is years.

• Line 3 is ignition density (i.e., ignition rate) of the fire regime. The unit is 

#per decade per hectare. 

• Line 4 and line 5 specify two statistics of the fire size distribution of the 

fire regime (mean and standard deviation of fire size). The unit is hectares. 

13 Fire Module
LANDIS 4.0 Users Guide Page 43

• Line 6 is the time since last fire that is used as initial conditions for land 

under this fire regime. It is needed even when a land type map is used as 

fire regime input map.

• Lines 7, 8, 9, and 10 specify the fire curve, fire intensity class, wind curve, 

and modified fire intensity class, respectively. They are inherited from 

LANDIS 3.7 and used only when the fuel module is turned off. The fire 

curve represents the fuel accumulation processes, and fire intensity classes 

are respective fire intensity classes when time since last fire reaches a 

certain period as specified in the fire curve. For example, if the last fire 

occurred 50 years ago, the fire intensity class should be 1 since the curve 

in the example specifies that when time since last fire is between 10 and 

50, the fire intensity class is 1 for this fire regime unit. Time since last 

wind and modified fire intensity classes (wind curve) are also used in the 

fire intensity calculation if wind module is turned on, and the final fire 

intensity class is the larger value between the two curves. In addition to this 

Figure 11. Fire and fuel module flags.

13 Fire Module
LANDIS 4.0 Users Guide Page 44

example, if last wind also happened 50 years ago, since the modified fire 

intensity class specified by the wind curve is 2, the final fire intensity class 

will be 2, which is the larger of 1 and 2. When wind module is turned off, 

these parameters are still required but will not be used. Note that LANDIS 

4.0 fire module uses its own wind parameters (wind direction and intensity) 

to simulate fire spread. 

13.3 Fire regime and DEM maps

Fire regime map is an (optional) input for LANDIS 4.0 fire module and the 

file (e.g., fireRegimeData.gis) is an ERDAS 7.4 8-bit or 16-bit GIS file. Within 

the GIS file, a continuous range of integers beginning with 0 should be used to 

represent the fire regime classes specified in the fire regime attribute file. For 

example, if the fire regime map contains 50 classes, 0 will represent the first fire 

regime and 49 will represent the 50th fire regime. Thus each integer corresponds 

to a class description in the fire regime attribute file. The final class number can 

be as large as 65,536. 

A DEM map is also needed for the new LANDIS fire module if the Using-DEMFlag is set to 1. The file (e.g., DEM500.dat) must be an ASCII file containing 

height information for each cell in meters. There are no header files or header 

part needed for the DEM map. LANDIS assumes that the DEM map has the same 

rows, columns, and cell size attributes as the other input ERDAS 7.4 GIS files.

14. LANDIS BDA MODULE

14.1 Entry File (BDAInit.dat)

If BDAInit.dat or other BDA entry file name is set in the LANDIS parameter file, 

the BDA module will be activated. The following is an example of a BDA entry 

file.

2 #BDA number# 

0 #Neighbor speed up# 

#BDA name 1# budworm.dat

#BDA name 2# beetle.dat

• The BDA number defines the number of different disturbance agents the 

BDA will simulate. 

• The Neighborhood speed up flag determines whether the BDA module 

will use every cell in a neighborhood to calculate Neighborhood Resource 

Dominance (0), or use the subsampling procedure to calculate NRD (1).

• Each BDA simulated must have a corresponding BDA parameter file. The 

file names for each are defined here. Note that the number of file names 

must match the BDA number.

13 Fire Module

14 BDA Module
LANDIS 4.0 Users Guide Page 45

14.2 BDA Parameter File

budworm #1. BDA Name# 

Budworm.gis #2. Initial Conditions map#

1 #3. BDP Calibrator#

mean #4. SRDMod#

10 #5. time step (years)#

5 #6. number of LTM#

Empty -999 #7. Landtype Name##Modifier Value#

Water -999 #8. Landtype Name##Modifier Value#

Xeric 0.16 #9. Landtype Name##Modifier Value#

Mesic 0.0 #10. Landtype Name##Modifier Value#

Hydric -0.16 #11. Landtype Name##Modifier Value#

1 #12. Number of Disturbance Modifiers#

Wind #13. Disturbance Type#

20 #14. Modifier Duration (years)#

0.33 #15. Disturbance Modifier Value#

20 #16. TimeSinceLastEpidemic(years)#

cyclic #17. TempPattern not used in new version#

pulse #18. TempType: pulse, variablepulse, continuous#

RFnormal #19. RandomFuncton: RFnormal or RFuniform#

25 #20. Random Parameter 1#

10 #21. Random Parameter 2#

10 #22. TemporalFreq#

0 #23. MinROS#

3 #24. MaxROS#

5 #26. number of species#

#Species Name# Minor Secondary Primary Susceptibility Susceptibility Susceptibility

Host Age Host Age Host Age Class 3 Age Class 2 Age Class 1 Age

Abiebals 0 20 30 0 20 40

Betupapy 999 999 999 999 999 999

Piceglau 0 20 70 30 60 999

Picemari 20 60 999 20 60 999

Poputrem 999 999 999 999 999 999

1 #34. NeighborhoodFlag#

1000 #35. NeighborRadius (meters)#

uniform #36. NeighborShape (uniform, linear, gaussian)#

10 #37. NeighborWeight#

1 #39. Dispersal Flag, not used#

1 #40. Spatial##[1 = synchronous, 0 = asynchronous]#

100 #41. DispersalRate (meters/year)#

0.3 #42. EpidemicThresh#

5 #43. InitialEpicenterNum#

0 #44. SeedEpicenter#

0.01 #45. outbreak epicenter coeff# 

20 #46. seed epicenter coeff#

none #47. InitialCondition: “map”, “none”]#

0 #48. Dispersal template#

14 BDA Module
LANDIS 4.0 Users Guide Page 46

• BDA name will define the name of the disturbance output, where the 

year of simulation and .gis extension are added to the corresponding 

Erdas output map. The BDA will automatically place all output in a BDA 

subfolder within the output folder defined in the parameter.dat file.

• Initial Conditions map is an optional map that may be used to characterize 

current outbreak activity for a BDA. This line for optional map is for future 

enhancement and is not currently used. Its purpose will be to provide 

site locations for current insect activity that may act as dispersal sites for 

future outbreaks. The format of the map will be an ERDAS 8-bit map with 

attributes identical to the BDA disturbance maps currently produced by the 

BDA module.

• BDP Calibrator represents the “a” parameter of equation 9 (section 

9.5.5.5).

• Site Resources Dominance Mode may be set to either “max” or “mean.”

• The time step is currently restricted by LANDIS 4.0 to be 10, but may be 

modified here for future variable time step versions of LANDIS.

• The number of Land Type Modifiers (i.e., line 6; LTM) must match the 

number of land types in the landtype.dat file. Lines 7-10 here represent 

the input for the LTMs. Each must have a name that corresponds with the 

landtype.dat file, and a real value from –1.0 to 1.0 that represents the land 

type influence on site resource dominance (see section 9.5.5.2). Note that 

a value of -999 indicates the land type will be ignored when calculating a 

neighborhood resource dominance value.

• Lines 12-16 represent the disturbance modifier parameters. For each 

disturbance that may modify the BDA (e.g., Wind, Fire, Harvest, or another 

BDA), three parameters are required: Disturbance Type, the duration of the 

modifying effect (in years), and the modifier value (between –1.0 to 1.0) 

for the first time step following the disturbance. Note that the disturbance 

modifier value represents the influence of a specific disturbance type on site 

resource dominance (see section 9.5.5.2) and is assumed to decline linearly 

with time since that disturbance for the duration of the modifying effect.

▪ Lines 16-24 represent the temporal parameters that control the landscape 

scale intensity of the BDA at a given time step, termed Regional Outbreak 

Status (ROS). ROS units are integer classes ranging from 0 (no outbreak) 

to 3 (intense outbreak).

▪ TimeSinceLastEpidemic represents the time in years since the last 

outbreak. 

▪ TempPattern is an old parameter no longer used. A value is still 

required for this LANDIS version.

▪ TempType valid inputs = pulse, variablepulse, and continuous. 

TempType determines whether outbreaks are binary (either MinROS 

or MaxROS—see definitions below; TempType = “pulse”) or if the 

ROS can range between those values (TempType = “variable pulse”). 

Note that continuous temporal types are still under development and 

currently not operational.

▪ RandomFunction represents the type of function used to control 

the pattern of outbreaks. Options are “RFnormal” for a normally 

distributed random function defined by a mean μ and standard 

deviation σ, and “RFuniform” for a uniformly distributed random 

function defined by a minimum interval (MinI) and a maximum value 

(MaxI).

14 BDA Module
LANDIS 4.0 Users Guide Page 47

▪ The definition of RandomParameter1 depends on the RandomFunction 

selected: μ if using RFnormal, or MinI if using RFuniform.

▪ The definition of RandomParameter2 also depends on the 

RandomFunction selected: σ if using RFnormal, or MaxI if using 

RFuniform.

▪ TemporalFreq is another old parameter no longer used. A value is still 

required for this LANDIS version.

▪ MaxROS = Maximum Outbreak Status; defines the maximum intensity 

of a regional outbreak. Parameter value must be an integer value 

between 1 (light outbreak) and 3 (intense outbreak).

▪ MinROS = Minimum Outbreak Status; defines the “background” 

outbreak activity that will occur in each time step. Parameter value 

must be an integer value between 0 (no outbreak) and 3 (intense 

outbreak). It can equal MaxROS, but cannot exceed it.

• Lines 26-32 represent tree species parameters. 

▪ The number of species must match the number of species in the 

species.dat file.

▪ Each tree species must have a name that corresponds with a name in 

the species.dat file. 

▪ Minor, Secondary, and Primary Host Ages indicate the minimum age 

at which a species enters the respective Host Preference Class. These 

classes are used to calculate Site Resource Dominance (SRD). A value 

greater than the tree species longevity (e.g., 999) indicates that the 

species never reaches the indicated class.

▪ Susceptibility Class Ages indicate the minimum age at which a species 

enters a respective tree species cohort susceptibility class. These 

classes determine the age class thresholds of biological disturbance 

probability (BDP) required to kill a species cohort if a site is disturbed. 

A value greater than the tree species longevity (e.g., 999) indicates that 

the species never reaches the indicated class. Cohorts younger than the 

minimum age for susceptibility class 3 are assigned a susceptibility 

class of 4 and are immune to the BDA.

• Lines 34-37 are Neighborhood Resource Dominance parameters.

▪ NeighborhoodFlag determines whether NRD is used in calculating 

BDP.

▪ NeighborhoodRadius defines the radius of the neighborhood influence 

in meters.

▪ NeighborShape defines the radial function used to calculate NRD. 

Valid entries are: uniform, linear, or Gaussian.

▪ NeighborWeight (NW) defines the importance of NRD relative to SRD 

when calculating BDP.

• Lines 39-48 are Dispersal parameters.

▪ The DispersalFlag is not currently used, but an entry is still required 

for the current LANDIS version.

▪ Spatial is a flag that determines whether dispersal is used. Here 1 = 

“spatially synchronous” (i.e., dispersal routines are NOT used), and 0 = 

“spatially asynchronous” (i.e., dispersal routines are used).

14 BDA Module
LANDIS 4.0 Users Guide Page 48

▪ DispersalRate defines the annual rate of dispersal in meters per year. 

▪ EpidemicThresh defines the minimum BDP required for an Epicenter 

to be selected.

▪ InitialEpicenterNum defines the number of epicenters that will be 

selected at time = 0. This is typically used to initiate an outbreak(s) that 

will spread over the course of the simulation.

▪ SeedEpicenter is a flag that determines when new epicenters will 

“seed” new outbreaks outside of current outbreak zones.

▪ OutbreakEpicenterCoeff is the “c” parameter corresponding with 

Equation 10 (section 9.5.5.6.1) for epicenters that will start from within 

the outbreak zone that occurred at time = t–1.

▪ SeedEpicenterCoeff is the “c” parameter corresponding with Equation 

10 for new epicenters that will start outside of the outbreak zone 

defined at time = t–1.

▪ InitialCondition specifies whether an input map is used to define 

known outbreaks at time = 0.

▪ DispersalTemplate defines the structuring element (i.e., the 

neighborhood rule) controlling the percolation of the BDA from an 

epicenter to its dispersal radius defined by DispersalRate×TimeStep. 

Numeric options are: 0 = regular dispersal (i.e., disperse to maximum 

radius); 1 = 4-neighbor structuring element; 2 = 8-neighbor structuring 

element; 3 = 12-neighbor structuring element; 4 = 24-neighbor 

structuring element.

15. LANDIS FUEL MODULE

15.1 Fuel Entry File (fuelInit.dat)

If fuelInit.dat or other fuel entry file name is set in the LANDIS parameter file, 

the fuel module will be activated and fuel status will be used by the fire module 

(see discussions under fire module for situations when fuel module is turned off). 

The following is an example of the fuel entry file.

fuelinput.dat #1. fuel module input file#

FuelRule.dat #2. fuel module rules file#

fireRegimeAttr.dat #3. Fire regime attributes#

fireRegimeData.gis #4. Fire regime map#

fuelman.dat #5. N/A or Fuel management event file#

fuel.log #6. Fuel tracking output file#

fuelman.log #7. Fuel management output file#

stand_fuel.log #8. Fuel management output for each stand#

stand500.gis #9. Stand identifier input map file#

ma_500.gis #10. Management area identifier input map#

• Fuel module input file (line 1) contains parameters of fuel accumulation 

and decomposition by land type, as well as fuel modifiers including fire, 

wind, BDA, and harvest. See section 15.3.

• Fuel module rule file (line 2) contains user-defined rules for defining 

potential fire intensity based upon coarse fuel and fine fuel. See 

section 15.3.

14 BDA Module

15 Fuel Module
LANDIS 4.0 Users Guide Page 49

• Fire regime attributes file (line 3) has fire regime parameters of LANDIS 

3.7. Fire regime map (line 4) is the 8-bit ERDAS GIS map that defines 

various fire regimes. If fire module is turned on, these two files (fire regime 

attributes file and fire regime map) should be consistent with the files used 

in fire module. If the fire module is turned off, the user must provide these 

two files as they are both used in fuel module. 

• Fuel management event file (line 5) contains a fuel management treatment 

prescription. If set to N/A, fuel management will not be simulated. 

• Fuel log file (line 6) outputs some general information for each simulation 

year, e.g., fine fuel loads, coarse fuel loads, and potential fire risk. Detailed 

information can be found in section 15.3.

• Fuel management log file (line 7) logs all fuel treatments simulated based 

upon the treatment prescriptions by management area (or unit). 

• Fuel stand log file (line 8) logs all fuel treatments simulated based upon the 

treatment prescriptions by stands.

• Line 9 and line 10 are 16-bit ERDAS input maps of stand and management 

area, respectively. 

15.2 Fuel Module Input File (fuelInput.dat)

1 [SPECIES]

#Live fuel quality coefficient (FQC) by species#

2 #Species 1# 0.2

3 #Species 2# 0.8

4 ... ...

5 #Species N# 0.6

 #Fine fuel accumulation curves by species#

 ##

6 #Fine fuel classes for species 1# 1 2 3 4 5

7 #Age of species 1# 0 20 50 110 180

8 #Fine fuel classes for species 2# 1 2 3 3 3

9 #Age of species 2# 20 60 140 200 240

10 #Fine fuel classes for species 3# 2 1 2 3 4

11 #Age of species 3# 10 30 60 110 180

12 ... ...

13 #Fine fuel classes for species N# 1 2 3 4 5

14 #Age of species N# 0 20 50 110 180

15 [LANDTYPE]

#Probability of low/medium intensity fire 

 to become a crown fire#

16 #Landtype 1# 0.02

17 ... ...

18 #Landtype M# 0.15

#Landtype modifiers of fine fuel and coarse fuel#

19 #Landtype name# Landtype1

 #Fine fuel classes# #1 2 3 4 5#

20 #Fine fuel loads modified by landtype1# 1 1 2 3 4

 #Coarse fuel accumulation curve for landtype1#

 #Coarse fuel classes# #1 2 3 4 5#

21 #Years of coarse fuel accumulation 

 to reach the corresponding classes# 20 60 110 170 220

 #Coarse fuel decomposition curve for landtype1#

 #Coarse fuel classes# #4 3 2 1 0#

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22 #Years of decomposition 

 since last disturbance# 20 60 100 140 20

23 ... ...

24 #Landtype name# LandtypeN

 #Fine fuel classes# #1 2 3 4 5#

25 #Fine fuel loads modified by landtypeN# 2 3 4 5 5

 #Coarse fuel accumulation curve for landtypeN#

 #Coarse fuel classes# #1 2 3 4 5#

26 #Years of coarse fuel accumulation 

 to reach the corresponding classes# 30 100 250 600 700

 #Coarse fuel decomposition curve for landtypeN#

 #Coarse fuel classes# #4 3 2 1 0#

27 #Years of decomposition 

 since last disturbance# 10 50 80 120 160

28 [FIRE]

 #Fire intensity classes# #1 2 3 4 5#

29 #Fine fuel reduction# –1 –2 –3 –3 –3

30 #Coarse fuel reduction# 0 –1 –2 –3 –3

31 [WIND]

 #Wind intensity classes# #1 2 3 4 5#

32 #number of fine fuel classes increased# 1 1 2 2 3

33 #number of coarse fuel classes increased# 1 2 3 4 4

34 [BDA]

35 #Total BDA types# 2

 #BDA type 1#

 #BDA intensity classes# #1 2 3#

36 #number of fine fuel classes increased# 1 1 2

37 #number of coarse fuel classes increased# 1 2 3

 #BDA type 2#

 #BDA intensity classes# #1 2 3#

38 #number of fine fuel classes increased# 0 1 2

39 #number of coarse fuel classes increased# 1 2 2

40 [HARVEST]

41 #Total harvest events# 2

 #Harvest event 1#

 #Fine fuel load before harvest# #0 1 2 3 4 5#

42 #Fine fuel load after harvest# 2 2 3 4 5 5

 #Coarse fuel load before harvest# #0 1 2 3 4 5#

43 #Coarse fuel load after harvest# 2 3 3 4 5 5

 #Harvest event 2#

 #Fine fuel load before harvest# #0 1 2 3 4 5#

44 #Fine fuel load after harvest# 1 2 3 4 5 5#

 #Coarse fuel load before harvest# #0 1 2 3 4 5#

45 #Coarse fuel load after harvest# 1 2 3 4 5 5

Species section

• The species section begins with a string named “[SPECIES]” (line 1). The 

name is specified and upper case is required. 

• Fuel quality coefficient (FQC) (lines 2-5). Fuel from different species 

may have different flammability due to differences in physical and 

chemical attributes (Brown et al. 1982). We use a fuel quality coefficient 

(0<FQC<=1) to summarize such differences on a relative scale. Species 

with low FQC are less flammable than species with high FQC. FQCs are 

ranked and parameterized by users, and the same value (e.g., FQC=1) 

can be used for all species if there are no discernible differences among 

species. 

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• Fine fuel accumulation curve (lines 6-14). Fine fuel accumulation curve 

is defined for each species and depicts the relationship between fine fuel 

production and species age. For some species (e. g., species 1, lines 6-7), 

the fine fuel loads may accumulate up to class 5 when this species reaches 

its longevity. Other species may not accumulate to such a level. For 

example, in species 2 (lines 8-9), the fine fuel load can only accumulate to 

class 3. Fine fuel accumulation curves can have multiple peaks (e.g., lines 

10-11) and do not have to follow an ascending order. However, the data 

line of tree age must follow the ascending order, and only five values can 

be set for each line.

Land type section

• The land type section begins with a string named “[LANDTYPE]” (line 

15). The name is specified and upper case is required.

• The probability that low/medium fire class will become crown fire by land 

type (lines 16-18). Because of the existence of live fuels, a fire may jump 

from surface fire to a crown fire, and this probability may vary differently 

among land types. Lines 16-18 define this probability (P) for each land 

type.

• Land type name (line 19). This gives the name of each land type. The name 

and the order of each land type should be consistent with that in land type 

attribute file (landtype.dat).

• Fine fuel modifier (line 20). Fine fuel decomposition rates may vary by 

land types. Thus, a land type modifier may decrease or increase the fine fuel 

class derived from species age cohorts. For example, a fine fuel class 3 

on a mesic land type might decrease to class 2 because the decomposition 

rate is relatively high, while the same fuel class (3) on a xeric land type 

might increase to class 4, since the decomposition rate is relatively low. The 

user defines the modifier in the land type parameter file, and the default 

is no modification. 

• Curve of coarse fuel accumulation (line 21). Stand age (the oldest 

age cohorts) in combination with disturbance history (time since last 

disturbance) are used to determine the coarse fuel accumulation. In 

the absence of disturbance, the accumulation process dominates until 

the amount of coarse fuel reaches a level where decomposition and 

accumulation are in balance. The curve of coarse fuel accumulation 

describes the relation of coarse fuel loading as a function of accumulation 

year, in the absence of disturbance. In this example, the coarse fuel load 

will accumulate to class 1 after 20 years of accumulation, class 2 after 60 

years, class 3 after 110 years, class 4 after 170 years, and class 5 after 220 

years.

• Curve of coarse fuel decomposition (line 22). Disturbances, e.g., wind, 

insect and disease, and harvest, can cause tree mortality and increase coarse 

fuel. The increased coarse fuel will decompose and the decomposition 

rate varies by land types. In this example, when additional coarse fuels 

are added by disturbance, the coarse fuels will gradually decompose from 

class 5 to class 4 in 20 years, to class 3 in 60 years, to class 2 in 100 years, 

to class 1 in 140 years, and to class 0 in 200 years. However, coarse fuels 

will rarely reach the very low classes (e.g. 0) because coarse fuels also 

accumulate through time. 

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• Lines 23-27 give the input of all other land types, which repeat the 

information in lines 19-22.

Fire section

• Fire modifier Section (line 28). This section begins with a string named 

“[FIRE]”. The name is specified and upper case is required. This section 

and all the inputs under this section are required, whether the fire module is 

turned on or off. If the fire module is turned on, the fire modifier can have 

effects on fuel loading. Otherwise, this information will be ignored.

• Fire modifier on fine fuel (line 29). This line defines the fine fuel reduction 

by each fire intensity class.

• Fire modifier on coarse fuel (line 30). This line defines the coarse fuel 

reduction or increase by each fire intensity class.

Wind section

• Wind Section (line 31). This section begins with a string named “[WIND]”. 

The name is specified and upper case is required. This section and all the 

inputs under this section are required, whether the wind module is turned 

on or off. If the wind module is turned on, the wind modifier can have 

effects on fuel loading. Otherwise, this information will be ignored.

• Wind modifier on fine fuel (line 32). This line defines the fine fuel increase 

by each wind intensity class.

• Wind modifier on coarse fuel (line 33). This line defines the coarse fuel 

increase by each wind intensity class.

BDA section

• BDA modifier Section (line 34). This section begins with a string named 

“[BDA]”. The name is specified and upper case is required. This section 

and all the inputs under this section are required, whether the BDA module 

is turned on or off. If BDA module is turned on, the BDA modifier may 

have effects on fuel loading. Otherwise, this information will be ignored.

• Total BDA types (line 35). This gives the total number of BDA types. This 

number should be consistent with the number defined in BDA attribute 

files.

• BDA modifier on fine fuel (line 36). This line defines the fine fuel increase/

decrease by each BDA intensity class for BDA type 1.

• BDA modifier on coarse fuel (line 37). This line defines the coarse fuel 

reduction/increase by each BDA intensity class for BDA type 1.

• BDA modifier on fine fuel (line 38). This line defines the fine fuel increase/

decrease based on the BDA intensity class for BDA type 2. If there are 

more than two BDA types, the modifiers for each BDA should be input 

sequentially by BDA.

• BDA modifier on coarse fuel (line 39). This line defines the coarse fuel 

reduction/increase based on the BDA intensity class for BDA type 2. If 

there are more than two BDA types, the modifiers for each BDA should be 

input sequentially by BDA.

Harvest section

• Harvest section (line 40). This section begins with a string named 

“[HARVEST]”. The name is specified and upper case is required. This 

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section and all the inputs under this section are required, whether the 

harvest module is turned on or off. If the harvest module is turned on, 

the harvest modifier may have effects on fuel loading. Otherwise, this 

information will be ignored.

• Total harvest events (line 41). This gives the total number of harvest 

events. This number should be consistent with the number defined in 

harvest management files.

• Harvest modifier on fine fuel (line 42). This line gives the fine fuel load 

after harvest event 1. In this example, if the before-harvest fine fuel load 

was class 0, the after-harvest fine fuel load will increase to class 2.

• Harvest modifier on coarse fuel (line 43). This line gives the coarse fuel 

load after harvest event 1. In this example, if the before harvest coarse 

fuel load was class 0, then the after-harvest coarse fuel load will increase 

to class 3.

• Harvest modifier on fine fuel (line 44). This line gives the fine fuel load 

after harvest event 2. In this example, if the before-harvest fine fuel load 

was class 0, then the after-harvest fine fuel load will increase to class 1. 

• Harvest modifier on coarse fuel (line 45). This line gives the coarse fuel 

load after harvest event 2. In this example, if before-harvest coarse fuel load 

was class 0, then after-harvest coarse fuel load will increase to class 1. If 

there are more than two harvest events, the modifiers for each harvest event 

should be input sequentially by harvest event.

15.3 Fuel Rule Input File (fuelRule.dat)

In the fuel rule input file, two tables and some criteria are defined for determining 

potential fire intensity, potential fire probability, and potential fire risk classes.

15.3.1 Potential fire intensity class

This table lists all the possible combination of fine fuel and coarse fuel classes 

and defines for each combination the resulting potential fire intensity class. Users 

can modify this matrix based on their empirical studies.

#1 1 1 1 1# #Fine fuel classes#

#1 2 3 4 5# #Coarse fuel classes#

1 1 2 2 3 #Potential fire intensity classes#

#2 2 2 2 2# #Fine fuel classes#

#1 2 3 4 5# #Coarse fuel classes#

1 1 2 3 3 #Potential fire intensity classes#

#3 3 3 3 3# #Fine fuel classes#

#1 2 3 4 5# #Coarse fuel classes#

1 1 2 3 4 #Potential fire intensity classes#

#4 4 4 4 4# #Fine fuel classes#

#1 2 3 4 5# #Coarse fuel classes#

1 2 3 4 5 #Potential fire intensity classes#

#5 5 5 5 5# #Fine fuel classes#

#1 2 3 4 5# #Coarse fuel classes#

2 2 3 4 5 #Potential fire intensity classes#

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15.3.2 Potential fire probability

This table gives the criteria for classifying potential fire probability (5 classes) 

from decimal probability data (0.0 ~ 1.0), which is estimated from mean fire 

return interval and fire history (time since last fire). There are at least two options 

to generate these probabilities: 1) equal interval, which means the probabilities 

for the classes are set at equal intervals; and 2) equal area, which means 

approximately an equal number of active sites (cells) for each class.

The following gives an example of option 1, equal interval. In this example, we 

calculated the decimal fire probability for all the active cells in the landscape 

(from mean fire return interval and time since last fire). Then we drew the curve to 

show in figure 12. Based on the figure, we set the rule as follows:

#Based on equal interval#

0.1 #If decimal fire probability ranges from 0 to 0.1, then it belongs to class 1#

0.2 #If decimal fire probability ranges from 0.1 to 0.2, then it belongs to class 2#

0.3 #If decimal fire probability ranges from 0.2 to 0.3, then it belongs to class 3#

0.4 #If decimal fire probability ranges from 0.3 to 0.4, then it belongs to class 4#

1.0 #If decimal fire probability ranges from 0.4 to 1, then it belongs to class 5#

The following provides an example for option 2, equal area. In this example, 

fire probability for all the cells in the landscape (from mean fire return interval 

and time since last fire) is calculated using a utility program discussed in section 

20.1.4. Based on the calculation, about 1/5 of the total active area has fire 

probabilities ranging from 0 to 0.08; 1/5 ranging from 0.08 to 0.18; 1/5 from 0.18 

to 0.31; 1/5 from 0.31 to 0.51; and 1/5 from 0.51 to 1.0 (fig. 13). Therefore, we 

set the criteria as:

#Based on equal area#

0.08 #If decimal fire probability ranges from 0 to 0.08, then it belongs to class 1#

0.18 #If decimal fire probability ranges from 0.08 to 0.18, then it belongs to class 2#

0.31 #If decimal fire probability ranges from 0.18 to 0.31, then it belongs to class 3#

0.51 #If decimal fire probability ranges from 0.31 to 0.51, then it belongs to class 4#

1.0 #If decimal fire probability ranges from 0.51 to 1, then it belongs to class 5# 15 Fuel Module
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Figure 12. Fire probability classes based on equal interval.

Figure 12. Fire probability classes based on equal area.

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15.3.3 Potential fire risk

This table lists all the possible combinations of potential fire intensity classes and 

potential fire probability classes and defines the potential fire risk class for each 

combination. Users can modifier this table based on their empirical studies. The 

following is an example in Missouri, which gives high weight to the fine fuel 

loads.

#1 1 1 1 1# #Potential fire probability class#

#1 2 3 4 5# #Potential fire intensity class#

1 1 2 2 3 #Potential fire risk class#

 #2 2 2 2 2# #Potential fire probability class#

#1 2 3 4 5# #Potential fire intensity class#

1 1 2 3 3 #Potential fire risk class#

#3 3 3 3 3# #Potential fire probability class#

#1 2 3 4 5# #Potential fire intensity class#

1 1 2 3 4 #Potential fire risk class#

#4 4 4 4 4# #Potential fire probability class#

#1 2 3 4 5# #Potential fire intensity class#

1 2 3 4 5 #Potential fire risk class#

#5 5 5 5 5# #Potential fire probability class#

#1 2 3 4 5# #Potential fire intensity class#

2 2 3 4 5 #Potential fire risk class#

15.3.4 Fire regime attributes file (fireRegimeAttr.dat)

Same as section 13.2.

15.3.5 Fuel management event file (fuelMan.dat)

Fuel management event file describes the treatment event. Treatment options 

include either single or multiple fuel treatment prescriptions. Each prescription 

is specified by the proportion of cells to be treated, the rotation and frequency of 

treatment, and the intensity of fine fuel and coarse fuel removal. The treatment 

events are specified for each management area, and these management boundary 

maps are defined with a 16-bit Erdas GIS map format. Treatment can be done 

for fine fuel reduction, coarse fuel reduction, or both fine fuel and coarse fuel 

reduction. Here we give three examples of different treatment prescriptions. In 

these examples we have three management areas, and we specify a different fuel 

treatment prescription for each management area.

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15.3.5.1 Example 1—Prescribed burning

Prescribed burning may remove most fine fuels as in the following example:

PRESCRIBEDBURNING #EVENT LABEL#

1 #management area identifier#

1 #rank algorithm (1=potential fire risk)#

1 #initial decade#

20 #final decade#

2 #reentry interval#

0.10 #proportion of management area for treatment#

2 #minimum potential fire risk for management#

#Fuel treatment mask#

#0 1 2 3 4 5# #fine fuel load before management#

 0 1 1 1 1 1 #fine fuel load after management#

#0 1 2 3 4 5# #Coarse fuel load before management#

0 1 2 3 4 5 #Coarse fuel load after management#

• Line 1 is the name of the treatment.

• Line 2 is the identifier of the management area. This number should be 

consistent with the management boundary map.

• Line 3 is the rank algorithm. At this time there is only one rank algorithm, 

which is based on potential fire risk.

• Line 4 is the initial decade in which the fuel management will start.

• Line 5 is the final decade in which the fuel management will end.

15.3.5.2 Example 2—Coarse woody debris reduction

Coarse woody debris reduction is used to lower coarse fuels, and the intensity of 

the removal is defined by the user. For example:

CWDREDUCTION #EVENT LABEL#

2 #management area identifier#

1 #rank algorithm (1=potential fire risk)#

1 #entry decade#

20 #final decade#

2 #reentry interval#

0.20 #proportion of management area to cut#

2 #minimum potential fire risk for management#

#Fuel treatment mask#

#0 1 2 3 4 5# #fine fuel load before management#

1 2 3 4 5 5 #fine fuel load after management#

#0 1 2 3 4 5# #Coarse fuel load before management#

0 0 0 0 1 2 #Coarse fuel load after management#

The differences of this management prescription with the fine fuels example are:

• Line 2: this management is set for area 2.

• Line 11: this example shows that the high intensity CWD reduction 

increases fine fuel loads. 

• Line 13: here the high intensity CWD reduction results in low coarse fuel 

loads. 

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15.3.5.3 Example 3—Prescribed burning + coarse woody debris 

reduction

Users can define a treatment event that includes both fine fuel and coarse fuel 

reduction. For example:

PRESCRIBEDFIRE+CWDREDUCTION #EVENT LABEL#

3 #management area identifier#

1 #rank algorithm (1=potential fire risk)#

1 #entry decade#

20 #final decade#

1 #reentry interval#

0.15 #proportion of management area to cut#

2 #minimum potential fire risk for 

 management#

#Fuel treatment mask#

#0 1 2 3 4 5# #fine fuel load before management#

0 0 0 0 0 1 #fine fuel loads after management#

#0 1 2 3 4 5# #Coarse fuel load before management#

0 0 0 0 1 2 #Coarse fuel load after management#

The differences between this management prescription and the others are:

• Line 2: this management is set for area 3.

• Lines 11 and 13: this example shows a high intensity of CWD reduction 

followed by a high intensity of prescribed burning, resulting in low fine 

and coarse fuel loads. 

16. LANDIS HARVEST MODULE 

16.1 Harvest Entry File (harvestInit.dat)

If the harvest entry file (harvestInit.dat) or other file name is set in the LANDIS 

parameter file, the harvest module will be activated. The following is an example 

of the harvest entry file. 

0 #1. flag of harvesting, 0-no; 1-on DISABLED#

1 #2. flag of harvesting event, 0-no; 1-on#

0 #3. Stand adjacency flag (0=off, 1=on). Don’t harvest stand 

 if a neighbor was “recently harvested”.#

0 #4. n=decade span: Consider an adjacent stand “recently harvested” 

 if it was harvested less than this number of decades ago 

0.5 #5. p=proportion: of cells cut in the last n decades is at least p.#

harvest.dat #6. Harvest event file#

stand500.gis #7. Stand identifier input map file#

ma_500.gis #8. Management area identifier input map file usually rsrvmng8.gis#

stands.log #9. Harvest stand output file#

ma.log #10. Harvest management area output file#

• Line 1 is old LANDIS (version 2-3.6) timber module switch and this 

parameter is no longer used.

• Line 2 is the harvest module switch of LANDIS 3.7 and is no longer used. 

• Line 3 is stand adjacency switch. If it is turned on, stand adjacency 

constraints on harvest activity will be enforced. An adjacency constraint 

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prohibits a harvest from occurring on a stand adjacent to another stand that 

has been recently harvested, in order to prevent very large openings.

• Line 4 defines “recently harvested.” If an adjacent stand was harvested less 

than the number of decades given here, then adjacency constraints must be 

applied when the stand adjacency switch is turned on. 

• Line 5 defines how much of an adjacent stand must have been harvested 

to trigger the adjacency constraint. For example, if the threshold is 0.5, the 

adjacency constraint will apply if the proportion of recently harvested cells 

(defined in line#4) within the stand is larger than this threshold. 

• Line 6 is the file name of harvest event file that contains all harvest 

prescriptions. 

• Lines 7 and 8 are file names of stand and management area maps. 

• Lines 9 and 10 are file names. These files are used to tabulate harvest 

history by stand and management area, respectively.

16.2 Harvest Module Input Files 

The LANDIS harvest module requires three input files in addition to those 

already needed by other LANDIS modules: 1) the management area identifier 

map, 2) the stand identifier map, and 3) the harvest regime input file. Based on 

the MA identifier map and the stand identifier map each site becomes associated 

with a single MA and a single stand. The harvest regime input file specifies how 

harvests will be applied to each MA. These are described in more detail below.

16.2.1 Management area identifier map (ma.gis)

The management area (MA) identifier map indicates which sites are in each MA. 

MAs are indicated by integers ranging from 1 to the total number of MAs on the 

map, labeled sequentially. The map value for each site is its corresponding MA. 

A value of 0 indicates a site that does not belong to any MA and will never be 

harvested. The format of the MA identifier map is a 16-bit ERDAS GIS file. 

16.2.2 Stand identifier map (stand.gis)

The stand identifier map indicates which sites are in each stand. Stands are 

indicated by integers ranging from 1 to the total number of stands on the map. 

Note that stand identifiers must be unique over the entire landscape and not just 

within their designated MA. The map value for each site is its corresponding 

stand. A value of 0 indicates a site that does not belong to any stand and will 

never be harvested. The format of the stand identifier map is a 16-bit ERDAS 

GIS file. 

16.2.3 Harvest regime (harvest.dat)

There are six different harvest regimes available for use: 1) one-entry, standfilling, 2) periodic-entry, stand-filling, 3) two-entry, stand-filling, 4) oneentry, stand-spreading, 5) two-entry, stand-spreading, 6) group selection, 7) 

periodic-entry-fixed stand, two-entry, stand-filling, and 8) periodic-entry-standresampling, two-entry, stand-filling. These regimes can be implemented one 

or more times in any management area. Singly or in combination these harvest 

regimes can be used to simulate a wide variety of harvesting practices. Standfilling harvest regimes are applied to every site in the stand and do not cross stand 

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boundaries. Stand-spreading harvest regimes begin at a focal site in the stand and 

the harvest terminates when the specified harvest size is reached. If necessary to 

meet the specified harvest size, stand-spreading harvest regimes will spread into 

adjacent stands. Stands are prioritized for harvest according to one of four userspecified ranking algorithms that are described in a later section. 

16.2.3.1 Harvest mask

The harvest mask specifies by species and age class which cohorts will be 

removed when a harvest regime is applied, and it specifies which species, if any, 

will be planted. Planting simply establishes the youngest age cohort of a given 

species following the harvest of each cell. The format of the harvest mask is 

similar to the map attribute file in many respects. There is one line for each 

species, and each line contains a 0 or 1 for each age class. A value of 0 for an 

age class indicates that the age class should not be harvested for that species. A 

value of 1 indicates that the age class should be harvested when the regime calls 

for harvesting on a given site. The first digit in the harvest mask is the planting 

code. If the planting code is 0 the species is not planted; if the planting code is 1 

the species is planted. Harvest regimes and planting codes are further illustrated 

in later in this section.

16.2.3.2 Stand ranking algorithms

Stands are prioritized for harvest according to one of four user-specified ranking 

algorithms:

1) Random—stands in a management area are randomly selected for harvest.

2) Stand age—oldest stands in a management area are harvested first. Stand 

age is computed as the mean of the oldest cohort on each site within the 

stand.

3) Economic importance—stands are ranked on an index of economic value. 

Each species is assigned a relative economic value. The value of each age 

cohort within a species is linearly weighted so that older cohorts are more 

valuable. The economic value of a site is the sum of the weighted value 

for each age cohort present, but only includes those age cohorts marked for 

removal by the harvest mask. The economic value of a stand is the mean 

of the economic value for each site in the stand. The most valuable stands 

are harvested first. This ranking algorithm requires additional parameters 

that indicate the relative economic values of each species and the age of 

economic maturity for each species. 

4) Regulate distribution—stands are ranked by age class so that harvesting 

over time will result in an even distribution of sites by age classes. This 

algorithm gives the highest harvest priority to sites and those in age classes 

found on the greatest number of sites across the management area. The 

probability of harvest for any given site is calculated by

ph = rfs × eac (12)

where ph is the probability of harvest for any given site; rfs is relative 

frequency of sites in the age class; and ac is age class.

5) Flexible regulate distribution—as with regulate distribution, stands can 

be ranked based on age to produce an even distribution of age classes. 

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However, this algorithm incorporates a scaling factor that allows the user 

to control the degree to which stand age influences the probability that a 

stand will be selected. Low values of the scaling factor result in stands 

being selected in proportion to their availability on the landscape (thus a 

scaling factor of 1 behaves similarly to a random rank algorithm), while 

intermediate values of the scaling factor emulate a regulate distribution, 

and high values of the scaling factor cause the oldest stands to consistently 

be selected. The principal advantage of this algorithm is that the user 

can control the extent to which harvest regulates the distribution of age 

classes on the landscape and thus the strength of the regulating influence of 

harvest. Use of this rank algorithm requires the user to provide this scaling 

factor (as a positive integer) at the end of the list of input variables for a 

management prescription. When using this algorithm, the probability of 

harvest for any given site is calculated by

ph = rfs × e oac × sc (13)

where ph is the probability of harvest for any given site, rfs is relative 

frequency of sites in the age class and ac is age class, oac is the age of 

oldest age class in management area, and sc is scaling factor.

16.2.4 Descriptions of harvest regimes (harvest.dat)

16.2.4.1 Harvest regime 1: one-entry, stand-filling

This regime performs one harvest operation on each site in the entire stand 

during a given decade. This harvest regime can be used to simulate clearcutting, 

intermediate thinning, single species removal, and removal of trees in specified 

age classes. Harvested area is specified as a number of sites. A harvest mask 

(described more fully below) is used to control the removal of age cohorts by 

species during each one-entry, stand-filling operation.

This regime requires eight arguments:

1) harvest-regime-id (integer). The integer identifying the type of harvest 

regime. 

2) harvest-regime-label (string, 255 character max). A one-word label for the 

harvest regime. This label is only used to identify entries in the harvest 

output log files. The label cannot include spaces.

3) management-area-id (integer). The integer identifying the management 

area where this harvest regime will be implemented. It must be an integer 

from 1 to the number of management areas. 

4) min-stand-age (integer). The minimum age (in years) that a stand must 

attain to be eligible for harvest.

5) rank-algorithm (integer). The integer identifying the rank algorithm 

that will be used to determine the order in which stands are harvested. 

1 = Random (choose stands randomly), 2 = Stand age (oldest first), 3 = 

Economic importance (most valuable stands first), 4 = Regulate age class 

distribution (attempt to produce an even distribution).

6) entry-decade (integer). The decade in which the harvest regime will be 

implemented. The first iteration in LANDIS is decade 1.

ac

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7) target-cut (integer). The total number of sites to harvest over the entire 

management area. The number of sites harvested may exceed this value 

because the harvest algorithm will continue to harvest entire stands until 

the cut target is met or exceeded. This number of sites multiplied by the 

area of each site (i.e., the area of a cell or pixel) equals the total area to be 

harvested. 

8) removal-mask.

Example:

Harvest 100 (target-cut) sites from management area 3 (management-area-id) in 

decade 2 (entry-decade). Harvest stands in their entirety. Harvest only stands that 

are at least 70 years (min-stand-age) old. Prioritize stands in the management area 

for harvest using random order (rank-algorithm). Remove all age cohorts of every 

species (i.e., clearcut). No planting.

Coding for harvest regime input file. Note that variables must be entered in 

the order shown. Comments delimited by # signs are optional. Several harvest 

regimes can be specified in sequence for a single management area. This example 

assumes there are four species calibrated in the LANDIS model. The number and 

order of species in the removal mask must always match that of the LANDIS 

species attribute file. For clarity in the harvest mask, the planting code is shown in 

bold-face type followed by the age class harvest codes in normal type.

#harvest.dat#

1 #harvest-regime-id#

SAMPLE-CLEARCUT #harvest-regime-label#

3 #management-area-id#

70 #min-stand-age#

1 #rank-algorithm# 

2 #entry-decade#

100 #target-cut#

0 111...repeat for a total of 64 age classes...11 #removal-mask, species 1#

0 111...repeat for a total of 64 age classes...11 #removal-mask, species 2#

0 111...repeat for a total of 64 age classes...11 #removal-mask, species 3#

0 111...repeat for a total of 64 age classes...11 #removal-mask, species 4#

16.2.4.2 Harvest regime 2: periodic-entry, stand-resampling, 

stand-filling

This regime operates like the one-entry, stand-filling regime except that harvest 

operations are repeated for a specified number of decades. Harvested area is 

specified as a proportion of the MA. A harvest mask (described more fully below) 

is used to control the removal of age cohorts by species during each periodicentry, stand-filling operation. The algorithm does not necessarily cut the same 

cells upon each periodic entry.

This regime requires 10 arguments:

1) harvest-regime-id (integer). 

2) harvest-regime-label (see section 16.2.4.1).

3) management-area-id (integer) (see section 16.2.4.1).

4) min-stand-age (integer) (see section 16.2.4.1).

5) rank-algorithm (integer) (see section 16.2.4.1).

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6) initial-decade (integer). The decade in which the harvest regime will start.

7) final-decade (integer). The decade in which the harvest regime will end.

8) reentry-interval (integer). The number of decades between repeated 

implementation of the harvest action. The harvest action is always 

implemented in initial-decade and it is repeated every reentry-interval

decades. 

9) target-proportion (real). The proportion of sites in the management area to 

be treated with each harvest action.

10) removal-mask.

Example:

Harvest 1/10 (target-proportion) of sites from management area 15 (managementarea-id) every 2 decades (reentry-interval) beginning in decade 1 (initial-decade) 

and ending in decade 25 (final-decade). Harvest stands in their entirety. Harvest 

only stands that are at least 50 years (min-stand-age) old. Prioritize stands in 

the management area for harvest using stand age (rank-algorithm). Remove age 

cohorts of every species, but only harvest those cohorts older than 30 years. No 

planting.

Coding for harvest regime input file. Note that variables must be entered in 

the order shown. Comments delimited by # signs are optional. Several harvest 

regimes can be specified in sequence for a single management area. This example 

assumes there are four species calibrated in the LANDIS model. The number and 

order of species in the removal mask must always match that of the LANDIS 

species attribute file. For clarity in the harvest mask, the planting code is shown in 

bold-face type followed by the age class harvest codes in normal type.

2 #harvest-regime-id#

SAMPLE-PERIODIC-HARVEST #harvest-regime-label#

15 #management-area-id#

50 #min-stand-age#

2 #rank-algorithm#

1 #initial-decade#

25 #final-decade#

2 #reentry-interval#

0.1 #target-proportion#

0 000111...repeat for a total of 64 age classes...11 #removal-mask, species 1#

0 000111...repeat for a total of 64 age classes...11 #removal-mask, species 2#

0 000111...repeat for a total of 64 age classes...11 #removal-mask, species 3#

0 000111...repeat for a total of 64 age classes...11 #removal-mask, species 4#

16.2.4.3 Harvest regime 3: two-entry, stand-filling

This regime performs one harvest operation on each site in the entire stand during 

a given decade. It differs from the one-entry, stand-filling regime in that it keeps 

track of which sites were harvested. After a specified number of decades the 

previously harvested sites are revisited and a second harvest operation is applied. 

This harvest regime can be used to simulate shelterwood and seedtree harvests. 

Harvested area is specified as a number of sites. A harvest mask (described more 

fully below) is used to control the removal of age cohorts by species during each 

of the two harvest activities.

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This regime requires 10 arguments:

1) harvest-regime-id (integer) (see section 16.2.4.1).

2) harvest-regime-label (see section 16.2.4.1).

3) management-area-id (integer) (see section 16.2.4.1).

4) min-stand-age (integer) (see section 16.2.4.1).

5) rank-algorithm (integer) (see section 16.2.4.1).

6) first-entry-decade (see section 16.2.4.1).

7) reentry-decade (see section 16.2.4.1).

8) target-cut (integer) (see section 16.2.4.1).

9) first-entry-removal-mask (integers: planting code followed by a 0 or 1 for 

each age class cohort by species). This removal mask applies only to the 

first harvest entry. The removal mask has one line for each species in the 

map attribute file. The first digit in each line is a planting code that is set 

to 1 if this species is to be planted after harvest, or set to 0 otherwise. 

(Planting places the species in the 10-year age class.) The remaining digits 

in each line have a value of 1 (indicating cut) or a value of 0 (indicating 

leave) for each age cohort for each species in the map attribute file. 

10) reentry-removal-mask (integers: planting code followed by a 0 or 1 for 

each age class cohort by species). This removal mask applies only to the 

second (final) harvest entry. The removal mask has one line for each species 

in the map attribute file. The first digit in each line is a planting code that 

is set to 1 if this species is to be planted after harvest, or set to 0 otherwise. 

(Planting places the species in the 10-year age class.) The remaining digits 

in each line have a value of 1 (indicating cut) or a value of 0 (indicating 

leave) for each age cohort for each species in the map attribute file. See 

definition above and example below.

Example:

Harvest 2,500 (target-cut) sites from management area 43 (management-area-id). 

Perform a shelterwood harvest with the initial harvest in decade 5 (first-entrydecade) and the reentry in decade 7 (reentry-decade). In the initial harvest retain 

only cohorts of species 2 and species 4 that are at least 70 years old. In the 

reentry, harvest the residual overstory (i.e., cohorts of species 2 and 4 that are 

at least 70 years old). Harvest only stands that are at least 80 years (min-standage) old. Prioritize stands in the management area for harvest using stand age 

(rank-algorithm). No planting.

Coding for harvest regime input file. Note that variables must be entered in 

the order shown. Comments delimited by # signs are optional. Several harvest 

regimes can be specified in sequence for a single management area. This example 

assumes there are four species calibrated in the LANDIS model. The number and 

order of species in the removal mask must always match that of the LANDIS 

species attribute file. For clarity in the harvest mask, the planting code is shown 

in bold-face type followed by the age class harvest codes in normal type.

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3 #harvest-regime-id#

SAMPLE-SHELTERWOOD #harvest-regime-label#

43 #management-area-id#

80 #min-stand-age#

2 #rank-algorithm#

5 #first-entry-decade#

7 #reentry-decade#

2500 #target-cut#

0 11111111...repeat for 64 age classes...11 #first-entry-removal-mask, species 1#

0 11111100...repeat for 64 age classes...00 #first-entry-removal-mask, species 2#

0 11111111...repeat for 64 age classes...11 #first-entry-removal-mask, species 3#

0 11111100...repeat for 64 age classes...00 #first-entry-removal-mask, species 4#

0 00000000...repeat for 64 age classes...00 #reentry-removal-mask, species 1#

0 00000011...repeat for 64 age classes...11 #reentry-removal-mask, species 2#

0 00000000...repeat for 64 age classes...00 #reentry-removal-mask, species 3#

0 00000011...repeat for 64 age classes...11 #reentry-removal-mask, species 4#

16.2.4.4 Harvest regime 4: one-entry, stand-spreading

This regime performs like the one-entry, stand-filling regime except that harvests 

can spread beyond stand boundaries. The total harvested area for a given decade 

is specified as a number of sites. The size of individual harvest events is drawn 

from a normal distribution with mean and standard deviation specified by the 

user. Each harvest event starts at a random site on the stand. It expands until the 

specified target size is reached. If necessary to meet the specified harvest size, the 

harvest will spread into adjacent stands, but each stand is completely harvested 

before adjacent stands are harvested. This harvest regime can be used to simulate 

clearcutting, intermediate thinning, single-species removal, and removal of trees 

in specified age classes. A harvest mask (described more fully below) is used to 

control the removal of age cohorts by species during each harvest operation.

This regime requires 10 arguments:

1) harvest-regime-id (integer). 

2) harvest-regime-label (see section 16.2.4.1).

3) management-area-id (integer) (see section 16.2.4.1).

4) min-stand-age (integer) (see section 16.2.4.1).

5) rank-algorithm (integer) (see section 16.2.4.1).

6) entry-decade (integer) (see section 16.2.4.1).

7) target-cut (see section 16.2.4.1).

8) mean-harvest-size. Mean number of sites included in each harvest. Mean 

must be ≥ 1. The size of each harvest event is drawn from a normal 

distribution with this mean and a specified standard deviation (see below).

9) standard-deviation. Standard deviation (expressed as number of sites) of 

the distribution used to specify harvest size. The range of harvest sizes will 

generally not exceed the mean size ± 3 standard deviations.

10) removal-mask (integers: planting code followed by a 0 or 1 for each age 

class cohort by species). The removal mask has one line for each species 

in the map attribute file. The first digit in each line is a planting code that 

is set to 1 if this species is to be planted after harvest, or set to 0 otherwise. 

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(Planting places the species in the 10-year age class.) The remaining digits 

in each line have a value of 1 (indicating cut) or a value of 0 (indicating 

leave) for each age cohort for each species in the map attribute file. See 

definition above and example below.

Example:

Harvest 5,000 (target-cut) sites from management area 6 (management-area-id) 

in decade 3 (entry-decade). Harvests should have a mean size of 100 sites and a 

standard deviation of 20. Initiate harvests only in stands that are at least 60 years 

(min-stand-age) old. Prioritize stands in the management area for harvest using 

random order (rank-algorithm). Remove only age cohorts that are at least 20 

years old. Plant species 2 and species 3 following harvest. 

Coding for harvest regime input file. Note that variables must be entered in 

the order shown. Comments delimited by # signs are optional. Several harvest 

regimes can be specified in sequence for a single management area. This example 

assumes there are four species calibrated in the LANDIS model. The number and 

order of species in the removal mask must always match that of the LANDIS 

species attribute file. For clarity in the harvest mask, the planting code is shown in 

bold-face type followed by the age class harvest codes in normal type.

4 #harvest-regime-id#

LARGE-OPENINGS #harvest-regime-label#

6 #management-area-id#

60 #min-stand-age#

1 #rank-algorithm#

3 #entry-decade#

5000 #target-cut#

100 #mean-harvest-size#

20 #standard-deviation#

0 011...repeat for a total of 64 age classes...11 #removal-mask, species 1#

1 011...repeat for a total of 64 age classes...11 #removal-mask, species 2#

1 011...repeat for a total of 64 age classes...11 #removal-mask, species 3#

0 011...repeat for a total of 64 age classes...11 #removal-mask, species 4#

16.2.4.5 Harvest regime 5: two-entry, stand-spreading

This regime performs like the two-entry, stand-filling regime except that harvests 

can spread beyond stand boundaries as they do in the one-entry, stand-spreading 

regime. The total harvested area for a given decade is specified as a number of 

sites. The size of individual harvest events is drawn from a normal distribution 

with mean and standard deviation specified by the user. Each harvest event starts 

at a random site on the stand. It expands until the specified target size is reached. 

If necessary to meet the specified harvest size, the harvest will spread into 

adjacent stands, but each stand is completely harvested before adjacent stands are 

harvested. After a specified number of decades the previously harvested sites are 

revisited and a second harvest operation is applied. This harvest regime can be 

used to simulate shelterwood and seedtree harvests. A harvest mask (described 

more fully below) is used to control the removal of age cohorts by species during 

each of the two activities.

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This regime requires 12 arguments:

1) harvest-regime-id.

2) harvest-regime-label (see section 16.2.4.1).

3) management-area-id (integer) (see section 16.2.4.1).

4) min-stand-age (integer) (see section 16.2.4.1).

5) rank-algorithm (integer) (see section 16.2.4.1).

6) first-entry-decade (see section 16.2.4.1).

7) reentry-decade (see section 16.2.4.1).

8) target-cut (see section 16.2.4.1).

9) mean-harvest-size. Mean number of sites included in each harvest. Mean 

must be ≥ 1. The size of each harvest event is drawn from a normal 

distribution with this mean and a specified standard deviation (see below).

10) standard-deviation. Standard deviation (expressed as number of sites) of 

the distribution used to specify harvest size. The range of harvest sizes will 

generally not exceed the mean size ± 3 standard deviations.

11) first-entry-removal-mask (integers: planting code followed by a 0 or 1 for 

each age class cohort by species). This removal mask applies only to the 

first harvest entry. The removal mask has one line for each species in the 

map attribute file. The first digit in each line is a planting code that is set 

to 1 if this species is to be planted after harvest, or set to 0 otherwise. 

(Planting places the species in the 10-year age class.) The remaining digits 

in each line have a value of 1 (indicating cut) or a value of 0 (indicating 

leave) for each age cohort for each species in the map attribute file. See 

definition above and example below. 

12) reentry-removal-mask (integers: planting code followed by a 0 or 1 for 

each age class cohort by species). This removal mask applies only to the 

second (final) harvest entry. The removal mask has one line for each species 

in the map attribute file. The first digit in each line is a planting code that 

is set to 1 if this species is to be planted after harvest, or set to 0 otherwise. 

(Planting places the species in the 10-year age class,) The remaining digits 

in each line have a value of 1 (indicating cut) or a value of 0 (indicating 

leave) for each age cohort for each species in the map attribute file. See 

definition above and example below.

Example:

Harvest 7,000 (target-cut) sites from management area 19 (management-areaid). Perform a shelterwood harvest with the initial harvest in decade 1 (firstentry-decade) and the reentry in decade 2 (reentry-decade). Harvests should 

have a mean size of 30 sites and a standard deviation of 5. Initiate harvests only 

in stands that are at least 40 years (min-stand-age) old. In the initial harvest 

retain only cohorts of species 2 and species 4 that are at least 60 years old. In the 

reentry, harvest the residual overstory (i.e., cohorts of species 2 and 4 that are at 

least 60 years old). Prioritize stands in the management area for harvest using 

stand age (rank-algorithm). No planting.

Coding for harvest regime input file. Note that variables must be entered in 

the order shown. Comments delimited by # signs are optional. Several harvest 

regimes can be specified in sequence for a single management area. This example 

assumes there are four species calibrated in the LANDIS model. The number and 

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order of species in the removal mask must always match that of the LANDIS 

species attribute file. For clarity in the harvest mask, the planting code is shown 

in bold-face type followed by the age class harvest codes in normal type.

5 #harvest-regime-id#

SAMPLE-SHELTERWOOD #harvest-regime-label#

19 #management-area-id#

40 #min-stand-age#

2 #rank-algorithm#

1 #first-entry-decade#

2 #reentry-decade#

7000 #target-cut#

30 #mean-harvest-size#

5 #standard-deviation#

0 11111111...repeat for 64 age classes...11 #first-entry-removal-mask, species 1#

0 11111000...repeat for 64 age classes...00 #first-entry-removal-mask, species 2#

0 11111111...repeat for 64 age classes...11 #first-entry-removal-mask, species 3#

0 11111000...repeat for 64 age classes...00 #first-entry-removal-mask, species 4#

0 00000000...repeat for 64 age classes...00 #reentry-removal-mask, species 1#

0 00000111...repeat for 64 age classes...11 #reentry-removal-mask, species 2#

0 00000000...repeat for 64 age classes...00 #reentry-removal-mask, species 3#

0 00000111...repeat for 64 age classes...11 #reentry-removal-mask, species 4#

16.2.4.6 Harvest regime 6: group selection

As the name implies, this management regime is used to simulate group selection 

harvest. The area to be treated with a group selection harvest is specified as a 

proportion of the stands in the management area. Once a stand is selected for 

group selection no other harvest regime will treat that stand. Group selection 

harvests begin at a specified decade and are reapplied to the same stands at a 

specified interval. The user specifies the proportion of sites in each stand to be 

removed at each entry. Group size is drawn from a normal distribution with mean 

and standard deviation specified by the user. Group placement is random but 

constrained so that groups do not touch one another. Once harvested, sites cannot 

be re-harvested until the number of elapsed years is equal to the reentry interval 

divided by the proportion of sites to be harvested in each entry.

This regime requires 12 arguments:

1) harvest-regime-id.

2) harvest-regime-label (see section 16.2.4.1).

3) management-area-id (see section 16.2.4.1).

4) min-stand-age (see section 16.2.4.1).

5) rank-algorithm (integer) (see section 16.2.4.1).

6) initial-decade (integer) (see section 16.2.4.1).

7) reentry-interval (integer) (see section 16.2.4.1).

8) target-proportion (real) (see section 16.2.4.1).

9) stand-proportion-denominator (integer). The denominator of the fractional 

portion of the stand that is to be harvested (i.e., placed into group openings) 

during each entry. For example, to harvest 1/5 of the stand in each entry, set 

the value of stand-proportion-denominator to 5.

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10) mean-group-size (real). The mean size of group openings expressed as a 

number of sites. Mean must be ≥ 1. 

11) standard-deviation (real). The standard deviation of group opening size 

expressed as a number of sites. 

12) removal-mask (integers: planting code followed by a 0 or 1 for each age 

class cohort by species). The removal mask has one line for each species 

in the map attribute file. The first digit in each line is a planting code that 

is set to 1 if this species is to be planted after harvest, or set to 0 otherwise. 

(Planting places the species in the 10-year age class). The remaining digits 

in each line have a value of 1 (indicating cut) or a value of 0 (indicating 

leave) for each age cohort for each species in the map attribute file. See 

definition above and example below.

Example:

Perform a repeated series of group selection harvests on 75 percent (targetproportion) of the stands in management area 19 (management-area-id). Begin 

group selection harvest in decade 1 (first-entry-decade) and repeat each decade 

(reentry-interval) for the duration of the simulation. The stands selected for group 

selection harvesting will not be harvested by any other regime for the duration 

of the simulation. Prioritize stands in the management area for harvest using 

stand age (rank-algorithm). At each entry the area in group openings in each 

stand should equal 1/12 (1/stand-proportion-denominator) of the total stand area. 

Group selection openings should have a mean size of 3 sites and a standard 

deviation of 1. Initiate harvests only in stands that are at least 40 years (minstand-age) old. Harvest all species in all age classes. Plant species 1 after harvest.

Coding for harvest regime input file. Note that variables must be entered in 

the order shown. Comments delimited by # signs are optional. Several harvest 

regimes can be specified in sequence for a single management area. This example 

assumes there are four species calibrated in the LANDIS model. The number and 

order of species in the removal mask must always match that of the LANDIS 

species attribute file. For clarity in the harvest mask, the planting code is shown in 

boldface type followed by the age class harvest codes in normal type.

6 #harvest-regime-id#

SAMPLE-GROUP-SELECTION #harvest-regime-label#

19 #management-area-id#

40 #min-stand-age#

2 #rank-algorithm#

1 #initial-decade#

1 #reentry-interval#

0.75 #target-proportion#

12 #stand-proportion-denominator

3 #mean-group-size#

1 #standard-deviation#

1 111...repeat for a total of 64 age classes...11 #removal-mask, species 1#

0 111...repeat for a total of 64 age classes...11 #removal-mask, species 2#

0 111...repeat for a total of 64 age classes...11 #removal-mask, species 3#

0 111...repeat for a total of 64 age classes...11 #removal-mask, species 4#

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16.2.4.7 Harvest regime 7: periodic-entry-fixed-stand, two-entry, 

stand-filling

This regime closely resembles harvest regime 3 (two-entry, stand-filling). It 

performs one harvest operation on each site in stands during a given decade. It 

keeps track of which sites were harvested and after a specified number of decades 

the previously harvested sites are revisited and a second harvest operation (with 

its own harvest mask) is applied. It differs from regime 3 in that this pair of 

harvest entries is repeated periodically throughout the course of the simulation. 

Note this pair of entries is always applied to the same stands that were selected 

based upon the ranking algorithm during the first-entry-decade. This periodic 

design allows users to repeated a pair of entries many times over the coarse of 

a long simulation. Thus, the need for users to rewrite similar harvest regimes 

multiple times in the harvest.dat file is reduced and two entry regimes like 

shelterwood and seed tree harvests can be simulated conveniently in long runs. 

The harvested area is specified as a number of sites. A pair of harvest masks 

(described more fully below) is used to control the removal of age cohorts by 

species during each of the two harvest activities. Note this pair of entries is 

always applied to the same stands that were selected based upon the ranking 

algorithm during the first-entry-decade (fixed stand).

This regime requires 11 arguments:

1) harvest-regime-id (integer) (set this value to 7 to select this regime).

2) harvest-regime-label (see section 16.2.4.1).

3) management-area-id (integer) (see section 16.2.4.1).

4) min-stand-age (integer in years not decades) (see section 16.2.4.1).

5) rank-algorithm (integer) (see section 16.2.4.1).

6) first-entry-decade (see section 16.2.4.1).

7) decades until second entry. This value is the number of decades that will 

elapse between the first entry and second entry for each application of this 

regime to a stand.

8) return interval. This is the number of decades after the first-entry when 

the two entries will be applied to the exact same stands. Note this pair 

of entries will continue to repeat at this interval throughout the course of 

the simulation.

9) target-cut (integer) (see section 16.2.4.1).

10) first-entry-removal-mask. This removal mask applies to the first harvest 

entry for each pair of entries that are applied to the selected stands 

throughout the course of the simulation. The removal mask has one line for 

each species in the map attribute file. The digits in each line have a value of 

1 (indicating cut) or a value of 0 (indicating leave) for each age cohort for 

each species in the map attribute file.

11) reentry-removal-mask. This removal mask applies to the second harvest 

entry for each pair of entries that are applied to the selected stands 

throughout the course of the simulation. The removal mask has one line for 

each species in the map attribute file. The digits in each line have a value 

of 1 (indicating cut) or a value of 0 (indicating leave) for each age cohort 

for each species in the map attribute file. See definition above and example 

below.

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Example:

Harvest 5,753 (target-cut) sites from management area 15 (management-areaid). During decade 1 (first-entry-decade) stands will be selected based upon 

the stand age rank algorithm and set aside for the duration of the simulation. 

Only stands that are at least 40 years (min-stand-age) old will be selected for 

harvest. These stands will be cut according to the initial harvest mask of a 

shelterwood prescription. These same stands will be revisited in decade 3 (firstentry + decades until second entry) and cut according to the reentry mask of a 

shelterwood prescription. Initial harvests for this shelterwood prescription will 

retain only cohorts of species 2 and species 4 that are at least 70 years old. In the 

reentry harvest for this shelterwood prescription, residual overstory (i.e., cohorts 

of species 2 and 4 that are at least 70 years old) will be harvested. This same 

pair of harvests will be repeated in these same stands during decade 90 (firstentry + return interval) and at other 8-decade increments for the duration of the 

simulation (e.g., decade 170, 250, etc.).

Coding for harvest regime input file. Note that variables must be entered in 

the order shown. Comments delimited by # signs are optional. Several harvest 

regimes can be specified in sequence for a single management area. This example 

assumes that there are four species calibrated in the LANDIS model. The number 

and order of species in the removal mask must always match that of the LANDIS 

species attribute file.

7 #harvest-regime-id#

Periodic-fixed-Shelterwood #harvest-regime-label#

15 #management area identifier#

40 #minimum stand age (in years)#

2 #rank algorithm#

1 #entry decade#

2 #decades until second entry#

8 #return interval#

5753 #target number of sites to cut#

0 11111111...repeat for 64 age classes...11 #first-entry-removal-mask, species 1#

0 11111100...repeat for 64 age classes...00 #first-entry-removal-mask, species 2#

0 11111111...repeat for 64 age classes...11 #first-entry-removal-mask, species 3#

0 11111100...repeat for 64 age classes...00 #first-entry-removal-mask, species 4#

0 00000000...repeat for 64 age classes...00 #reentry-removal-mask, species 1#

0 00000011...repeat for 64 age classes...11 #reentry-removal-mask, species 2#

0 00000000...repeat for 64 age classes...00 #reentry-removal-mask, species 3#

0 00000011...repeat for 64 age classes...11 #reentry-removal-mask, species 4#

16.2.4.8 Harvest regime 8: periodic-entry-stand-resampling, 

two-entry, stand-filling

This regime closely resembles harvest regime 7 (periodic-entry-fixed-stand, twoentry, stand-filling) and is valuable for many of the same reasons. It supplies a 

convenient way to repeat two-entry, stand-filling harvests throughout the course 

of a simulation without having to write multiple harvest regimes. The principal 

difference is that during each period when this regime revisits a management area 

to implement an initial harvest it selects a new stand based upon the designated 

stand ranking algorithm and the current conditions on the landscape. In contrast, 

regime 7 selected stands during the initial entry decade and revisited these 

same stands during all second entries and periods of reentry for the duration of 

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LANDIS 4.0 Users Guide Page 72

the simulation. Thus, stands that are selected by regime 8 are only set aside as 

unavailable to other harvest regimes during the period between an initial entry 

and a second entry. This makes regime 8 a more convenient regime to use if you 

want to initiate new two-entry harvests during each decade of the simulation 

and are not worried about locking particular stands into management by a single 

regime for the duration of the simulation. As with regime 7, the harvested area is 

specified as a number of sites, and 2 harvest masks (described more fully below) 

are used to control the removal of age cohorts by species during each of the two 

entries into a particular stand.

This regime requires 11 arguments:

1) harvest-regime-id (integer) (set this value to 8 to select this regime).

2) harvest-regime-label (see section 16.2.4.1).

3) management-area-id (integer) (see section 16.2.4.1).

4) min-stand-age (integer in years not decades) (see section 16.2.4.1).

5) rank-algorithm (integer) (see section 16.2.4.1).

6) first-entry-decade (see section 16.2.4.1).

7) decades-until-second-entry. This value is the number of decades that will 

elapse between the first entry and second entry for each application of this 

regime to a stand.

8) return interval. This number of decades after the first entry when the 

two entries will be applied to the exact same stands. Note this pair of 

entries will continue to repeat at this interval throughout the course of the 

simulation.

9) target-cut (integer) (see section 16.2.4.1).

10) first-entry-removal-mask. This removal mask applies to the first harvest 

entry for each pair of entries that are applied to the selected stands 

throughout the course of the simulation. The removal mask has one line for 

each species in the map attribute file. The digits in each line have a value of 

1 (indicating cut) or a value of 0 (indicating leave) for each age cohort for 

each species in the map attribute file.

11) reentry-removal-mask. This removal mask applies to the second harvest 

entry for each pair of entries that are applied to the selected stands 

throughout the course of the simulation. The removal mask has one line 

for each species in the map attribute file. The digits in each line have a 

value of 1 (indicating cut) or a value of 0 (indicating leave) for each age 

cohort for each species in the map attribute file. See definition above and 

example below.

Example:

Select 1,513 (target-cut) sites from management area 1 (management-area-id) 

during each decade of the simulation (reentry interval) for use in a shelterwood 

harvest. Only stands that are at least 30 years (min-stand-age) old will be selected 

for harvest. During decade 1 (first-entry decade), begin this regime by selecting 

stands according to the stand age rank algorithm and cutting them according 

to the initial harvest mask of a shelterwood prescription. These same stands 

will be revisited in decade 4 (first-entry + decades-until-second-entry) and cut 

according to the reentry mask of a shelterwood prescription. Apply this regime 

to management area 1 during each decade (return interval) for the duration of 

Harvest 16 Module
LANDIS 4.0 Users Guide Page 73

the simulation. Note new stands will be selected based upon the stand age rank 

algorithm during each reapplication of this prescription and selected stands are 

only unavailable to other regimes during the interval between the initial harvest 

and the reentry harvest. Initial harvests for this shelterwood prescription will 

retain only cohorts of species 2 and species 4 that are at least 70 years old. In the 

reentry harvest for this shelterwood prescription, residual overstory (i.e., cohorts 

of species 2 and 4 that are at least 70 years old) will be harvested.

Coding for harvest regime input file. Note that variables must be entered in 

the order shown. Comments delimited by # signs are optional. Several harvest 

regimes can be specified in sequence for a single management area. This example 

assumes there are four species calibrated in the LANDIS model. The number and 

order of species in the removal mask must always match that of the LANDIS 

species attribute file.

8 #harvest-regime-id#

Periodic-Resampling-Shelterwood #harvest-regime-label#

1 #management area identifier#

30 #minimum stand age (in years)#

2 #rank algorithm#

1 #entry decade#

3 #decades until second entry#

1 #return interval#

1513 #target number of sites to cut#

0 11111111...repeat for 64 age classes...11 #first-entry-removal-mask, species 1#

0 11111100...repeat for 64 age classes...00 #first-entry-removal-mask, species 2#

0 11111111...repeat for 64 age classes...11 #first-entry-removal-mask, species 3#

0 11111100...repeat for 64 age classes...00 #first-entry-removal-mask, species 4#

0 00000000...repeat for 64 age classes...00 #reentry-removal-mask, species 1#

0 00000011...repeat for 64 age classes...11 #reentry-removal-mask, species 2#

0 00000000...repeat for 64 age classes...00 #reentry-removal-mask, species 3#

0 00000011...repeat for 64 age classes...11 #reentry-removal-mask, species4#

17. OUTPUT FILES

17.1 GIS Map Files

The majority of LANDIS output is as ERDAS GIS files. These files are in 

8-bit format and have accompanying trailer (.TRL) files. The trailer files are not 

complete; they do not contain output statistics. 

For each iteration a GIS file (and its accompanying trailer file) will be created 

for each output reclassification method. A GIS file showing the ages of the oldest 

cohort on each cell is also created. Additional files will be created for fire, wind, 

BDA, fuel, and harvest modules if they are turned on before the model run. 

The names of the files will be AGE, FIRE, WIND, HARVEST (in each module 

parameter file discussed in the previous chapters), or the appropriate output 

reclassification method name followed by the year number. 

Three other GIS files are also created and placed in the output directory. The 

first is a map of the land types named LU.GIS (same as land type map input file 

landtype.gis). The second is a final map of fires. It shows all fires that occurred 

throughout time. The third is the final map of wind disturbances. In the case of an 

Harvest 16 Module

17 Output Files
LANDIS 4.0 Users Guide Page 74

overlap the most recent fire or wind is displayed. The names of the files are given 

in the disturbance file.

17.1.1 Species GIS file

The pixel values within species GIS files are described by the corresponding 

RCS file. Pixel value 0 always denotes area outside the simulation boundaries; 1 

denotes water; and 2 denotes lowland and other nonactive simulated areas. Pixel 

value 3 will correspond to the first type defined in the RCS file, 4, the second, 

and so on. Pixel value 255, the highest value, always denotes “other.” 

17.1.2 Species age GIS file

The first three pixel values (0-2) within species age GIS files are defined exactly 

as they are for the species GIS file. Pixel value 3 denotes species age from 0-10, 4 

denotes 10-20, 5 denotes 20-30, and so forth. Pixel value 255 denotes “other.” 

17.1.3 Age group GIS file

The first three pixel values (0-2) within species age GIS files are defined exactly 

as they are for the species GIS file. Pixel value 3 denotes the oldest species 

age 0-30, 4 denotes 30-60, 5 denotes 60-90, and so forth. Pixel value of 255 

denotes “other.”

17.1.4 Disturbance GIS file

The first three pixel values (0-2) within disturbance GIS files are defined exactly 

as they are for the species GIS file. Pixel values 3-7 represent fire or wind 

intensity classes 1-5, respectively. For BDA disturbance GIS files, pixel values 

3-5 represent BDA intensity classes 1-3, respectively. 

17.1.5 Harvest GIS file

There are two types of harvest output GIS files, harvest types and harvest history. 

Harvest type maps record IDs of the last harvest regime applied on each pixel. 

Harvest history maps record the year since last harvest for each pixel. 

17.2 Trailer File

Each output GIS file has an associated ERDAS 7.4 trailer file that contains the 

map legend.

17.3 Log Files

The files WIND.LOG and FIRE.LOG contain a spreadsheet-compatible log of 

the fire and wind events that occurred during the simulation. Each wind log file 

entry records the year when the disturbance occurred, the map coordinates where 

the wind started, disturbance size, damage size, and number of cohorts killed. 

Disturbance size is the number of cells that were checked for a given disturbance, 

whereas damage size is the number of cells in which at least one age cohort was 

removed due to the disturbance. 

The fire log file records additional disturbance information for each land type.

The files MA.LOG and STAND.LOG contain spreadsheet-compatible logs of 

harvest summarized by management area and stand, respectively. 

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The file FUEL.LOG contains a spreadsheet-compatible log of the fine fuel load, 

coarse fuel load, potential fire probability, potential fire intensity, and potential 

fire risk for each simulation year. The output follows this order: simulation 

year, number of cells in each fine fuel load class (0 to 5), average fine fuel load, 

number of cells in each coarse fuel load class (0 to 5), average coarse fuel load, 

number of cells in each potential fire probability class (0 to 5), average potential 

fire probability, number of cells in each potential fire intensity class (0 to 5), 

average potential fire intensity, number of cells in each potential fire risk class 

(0 to 5), and average potential fire risk.

18. RUNNING THE PROGRAM

18.1 Using LANDIS Input Interface

18.1.1 LI.exe

LANDIS Interface (LI) is an integrated tool that can be used to edit LANDIS 

input parameter files, to run the LANDIS system, and to view the LANDIS 

output maps in an integrated environment. LANDIS Interface is an MFC MDI 

application developed with Visual C++ 6.0.

The parameter files used by the LANDIS system are composed of a main 

configuration file, several first-level configuration files linked to the main 

configuration file, and several second-level configuration files linked to the firstlevel configuration files. The LANDIS system accepts a main configuration file 

as a command line parameter that passes the information and setting into the 

program, and each module of the LANDIS system uses the first-level and the 

second-level configuration files to retrieve more information. 

LANDIS Interface program can automatically open the main configuration file 

and the corresponding first-level configuration files for editing. In addition, 

the program can open any configuration file specified by users manually. The 

program can also call LANDIS Viewer to display the *.gis map files that are used 

as part of the input of LANDIS system.

Once a main configuration file is opened, the program can cause the LANDIS 

system to run by using this main configuration file as the input parameter. After 

that, the program can call LANDIS Viewer to display the output results (fig. 14).

18.1.2 Menu

The menu of the LANDIS Interface is divided into the following sub-menus: 

1. File Menu

a. New

Create a new document (application project). The content of this 

document is automatically set by the template. 

b. Open

Open an existing main configuration file.

c. Close

Close the active document.

d. Save

Save the active document.

17 Output Files

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e. Save as

Save the active document with a new name.

f. Open related configuration file

Open any configuration file except the main configuration file. (If a file 

name is selected in the document, the file will be opened; otherwise, a 

dialog is shown to let user select the file name.)

g. Open *.gis map file

Open an existing *.gis map file and call LANDIS Viewer to display 

this file. (If a file name is selected in the document, the file will be 

opened; otherwise, a dialog is shown to let user select the file name.)

h. Print

Print the active document.

i. Print Preview

Preview the pages before printing.

j. Print Setup

Change the printer and printing options.

k. Most Recent Files List

Show the most recently opened file list, and allow opening of these 

files directly.

l. Exit

Exit the LANDIS Interface.

Figure 14. The main user interface of LANDIS. The interface is a typical MFC MDI 

program composed of menu bar, tool bar, edit windows, and status bar.

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2. Edit Menu

a. Undo

Undo the last action.

b. Cut

Cut the selection and put it on clipboard.

c. Copy

Copy the selection and put it on clipboard.

d. Paste

Insert the clipboard contents.

3. View Menu

a. Toolbar

Show or hide the Toolbar.

b. Status Bar

Show or hide the Status Bar.

4. Run Menu

a. Open All Config Files

Open all the first-level configuration files related to the main 

configuration file.

b. Save All Config Files

Save all the first-level configuration files related to the main 

configuration file.

c. Close All Config Files

Close all the first-level configuration files related to the main 

configuration file.

d. Launch LANDIS System

Call the LANDIS system and use the main configuration file as the 

input parameter.

e. Launch LANDIS Viewer

Call LANDIS Viewer to display the running results of the LANDIS 

system.

f. Options

Change the options, including the file location of the LANDIS system 

and LANDIS Viewer, and the default color palette for LANDIS 

Viewer.

5. Window Menu

a. Main Configuration File

Jump to the Main Configuration File.

b. Windows list

Show the windows list and jump to the selected window.

6. Help Menu

a. About

Display the program information, version, and copyright. 

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18.1.3 Toolbar

The toolbar is shown in the upper part of the window. Each button works exactly 

the same as its corresponding menu item. The corresponding menu item for each 

button (from left to right) is:

1. File > New

2. File > Open

3. File > Save

4. Edit > Cut

5. Edit > Copy

6. Edit > Paste

7. Run > Open All Config Files

8. Run > Close All Config Files

9. Run > Save All Config Files

10. File > Open related configuration file

11. File > Open *.gis map file

12. Window > Windows List

13. Window > Main Configuration File

14. Windows List for the first-level configuration files. It can be used to rapidly 

jump to the main configuration file or a first-level configuration file.

15. Run > Launch LANDIS System

16. Run > Launch LANDIS Viewer

17. File > Print

18. Help > About

18.2 Through Windows DOS Prompt

At a Windows Command prompt, go to the directory that contains your LANDIS 

input file, then type LANDIS followed by the parameter file name. If you do 

not specify a parameter file then LANDIS will show the correct syntax and 

terminate:

Usage: LANDIS [-argument [...]] <input file> 

arguments:

-e: the years environmental change interpreted (page 5.9)

-h: this help menu

-p: input file help

[ ] indicates that the argument is optional. < > indicates the argument is 

required. For more information about LANDIS input files, refer to chapters 

8-13.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

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19. LANDIS OUTPUT VIEWER (LV.EXE)

19.1 Overview

LANDIS Viewer is a tool to view 8-bit/16-bit GIS maps or LANDIS output 

maps. The viewer is a dialog-based windows application developed using Visual 

C++ 6.0. It loads one GIS map file or a whole LANDIS output directory and 

displays those maps using a user-specified color palette. When it loads a whole 

LANDIS output directory, LANDIS Viewer arranges those LANDIS outputs by 

species/disturbances and time sequences and provides an efficient way to choose 

species/disturbances and to navigate between time sequences.

19.2 Main User Interface

The main user interface of LANDIS Viewer is shown in figure 15. 

The main user interface of LANDIS Viewer is composed of six components: title 

bar, time sequence navigation controls, color palette selection control, species 

list, map display, and legend list. 

The title bar is used to show status information for the currently displayed map 

including file path, species name, and the number of years in time sequence. It 

also shows error messages if errors occur.

The title bar is used to show status information for the currently displayed map 

including its file path, species name, and year index. It also shows error messages 

if errors occur.

Time sequence navigation controls are used to navigate in the time sequences. 

Figure 15. The main user interface of LANDIS Viewer.

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Users may choose to display the first, the last, the previous, and the next map 

in a time sequence by clicking the corresponding buttons. Moreover, users may 

also display any map they want by inputting the number in the time sequence 

or selecting the number from the list. In addition, the input control has an 

input assistant, so that when a number is entered that does not exist in the time 

sequence, the input control will automatically select the nearest number and 

display the corresponding map.

The color palette selection control is used to select the color table to draw 

the map. There are four predefined color tables. They are “6 color distinct,” 

“6 color continuous,” “48 color continuous,” and “256 color continuous.” Users 

may also choose to use the color table stored in the trailer file if the trailer file 

corresponding to the gis map file exists.

The species list stores all species and disturbance names in the LANDIS output 

directory and enables users to choose which species and disturbances will be 

shown.

The map display shows the currently selected map rendered using the color table 

specified by the user. The legend list shows the legends for the current map. Only 

the colors used in the map are shown in the legend list.

These components are not visible at all times. They may be disabled or hidden in 

some situations. Furthermore, the layout of the user interface may be changed in 

some situations. For example, time sequence navigation controls may be disabled 

when a species or disturbance that is currently shown only has a single file. And 

the navigation controls will be enabled again when a species or disturbance that 

has a time sequence is chosen. 

19.3 How To Use

LANDIS Viewer can be run with or without command line parameters. 

When LANDIS Viewer runs without command line parameters, a dialog is shown 

for selecting a map or a directory to be displayed. The initial color palette is 

also chosen at this time. Then the main user interface is presented to show the 

map files. 

When it runs with command line parameters, LANDIS Viewer gets the file or 

directory name and the initial color palette from command line parameters instead 

of extracting the name and color palette from the dialog. LANDIS Viewer skips 

the first step, which is showing a dialog to select file/directory name and color 

palette, and displays the map file/files specified in command line.

LANDIS Viewer can work in two modes: directory mode when a LANDIS output 

directory is loaded, and file mode when a gis map is loaded.

In directory mode, all of the six user interface components are shown. LANDIS 

Viewer analyzes the file names under the specified directory, extracts the species/

disturbance names, and then fills these names into the species list component. 

The viewer also extracts the number of years in time sequence and fills the 

numbers into the time sequence navigation controls component. Therefore, no 

matter what time step was used in the LANDIS system, LANDIS Viewer can 

find the right time step and navigate the time sequence correctly. After these 

procedures are done, the species/disturbance name and the number in the time 

sequence can be selected to show the map. The legend information will be loaded 

from the trailer file and shown in the legend list component when the map is 

loaded.

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In file mode, the procedure is quite simple. Species list and time sequence 

navigation controls are hidden, and legend list will be hidden if there is no trailer 

file with the map file. The map file specified in the dialog or command line 

parameter is loaded and shown on the map component using the color palette 

selected by the user.

19.4 Key Features of LANDIS Viewer

19.4.1 Accept maps of any size

LANDIS Viewer accepts maps of any size, but when the map size is larger than 

the size of physical memory, it will slow system response time. The user interface 

of LANDIS Viewer automatically resizes to fit the size of map. If the map size is 

too large to be shown in one screen, the user interface will be shown as the size 

of screen and the horizontal/vertical scrolling bar will be shown to scroll the map. 

The user can also choose to proportionally scale the viewing size by specifying 

1/16, 1/8, 1/4, 1/2, 2, 4, 8, and 16 times the original map size. 

Important note: In directory mode, all files under the directory are assumed by 

LANDIS Viewer to have the same map size. LANDIS Viewer only resizes itself 

when starting up.

19.4.2 Accept both 8-bit and 16-bit maps

LANDIS Viewer accepts maps both 8-bit and 16-bit maps. Since human eyes 

cannot distinguish more than 256 colors, the viewer uses an index-based color 

system that only supports 256 colors to draw the map. Therefore, the 16-bit maps 

must be projected to the 256 color space. The method to project the color space 

from 16-bit to 8-bit is to classify the values with the same lower 8 bits into one 

group, so all values in the 16-bit are divided into 256 groups, and each group is 

assigned one color.

19.4.3 Support command line parameters 

LANDIS Viewer accepts command line parameters; thus, the viewer can be 

called from other programs to show maps specified in command line parameters. 

The command line parameter format is a file/directory name string followed by 

a number with value from 0 to 4 that indicates the initial color palette. The 

file/directory name can be enclosed with quote symbol if it contains a blank 

space character. LANDIS Viewer decides whether the name is a file name or 

a directory name and runs in the correct mode. For the number of initial color 

palette, values 0 to 3 indicate the use of one of the three predefined color palettes, 

“6 color distinct,” “6 color continuous,” “48 color continuous,” and “256 color 

continuous” correspondingly. Value 4 causes LANDIS Viewer to use the color 

palette stored in the trailer file. The examples of command line parameters are 

as follows:

LV.exe c:\output 0

LANDIS Viewer shows all maps in c:\output using “6 color distinct” 

color palette.

LV.exe “c:\output\age0.gis” 4

LANDIS Viewer shows the map c:\output\age0.gis using the color 

palette stored in trailer file c:\output\age0.trl.

19 Output Viewer
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19.4.4 Species list and time sequence navigation

LANDIS output maps are a pair of files composed of a map file with extension 

name “*.gis” and a trailer file with extension name “*.trl.” These maps can be 

classified to two types, single maps and time sequence maps. The file name for 

single maps is formed by “species/disturbances name” + “extension name.” And 

the file name for time sequence maps is formed by “species/disturbances name” 

+ “number of time sequence” + “extension name.” For instance, file firefin.gis is 

the map file for cumulative fire damage assessment, which is a single map file; 

and file age0.gis is the map file of age sequence at year 0, which is a map file in a 

time sequence. 

In directory mode, LANDIS Viewer assumes all of the time sequence results have 

the same time step. LANDIS Viewer arranges files by species/disturbances and 

time sequence and shows Species List and Time Sequence Navigation Controls 

to let users select a file. LANDIS Viewer puts all species/disturbances names into 

the Species List. When users choose a single map species, the time sequence 

navigation controls are disabled. And when users choose a time sequence map 

species, these navigation controls are enabled. Users can iterate the time sequence 

results by using navigation controls.

19.4.5 Multiple instances of the viewer

LANDIS Viewer supports multi-instances. Users can open as many LANDIS 

Viewer windows as they need under the limitation of the operation system. These 

windows won’t interrupt each other in either file mode or directory mode.

20. LANDIS SUBSIDIARY PROGRAMS

20.1 IAN-Raster Image Analysis Software Program

IAN generates reports from the statistical analysis of raster images. It is 

programmed in the open source scripting language Ruby, enabling technically 

savvy users and the developers to easily extend the program. The existing 

framework is designed to easily support additional metrics, reports, image file 

formats, options, and units. The flexible architecture of IAN streamlines the 

process of new releases. IAN runs on all 32-bit Windows platforms. It comes 

with two user interfaces-a user friendly windowing user interface (IAN) and a 

command line user interface (IANC) for console and programmatic access. If you 

have any questions about IAN, send them to ian-mail@mailplus.wisc.edu.

20.2 Map Converter

Mapconvert.exe is a stand-alone program to convert maps among text format, 

ERDAS GIS 7.4 8-bit, and ERDAS GIS 7.4 16-bit. To run the program type

mapconvert

Enter input file: enter input file here

Enter format 1) ASCII, 2) 8-bit, 3) 16-bit: 1 or 2 or 3

Enter output file: enter output file here

Enter format 1) ASCII, 2) 8-bit, 3) 16-bit: 1 or 2 or 3

Or you can enter all the information at one time, e.g.,

mapconvert [input file name] [format 1|2|3] [output file name] 

[format 1|2|3]

19 Output Viewer

Subsidiary 20 Programs
LANDIS 4.0 Users Guide Page 83

20.3 GIS2Grid

GIS2Grid is a utility that generates three script files used for converting LANDIS 

output (or any folder where there are .gis files) into ascii files, converting such 

ascii files into grid files, and assisting Fragstats to read such grid files for further 

analysis, respectively. 

GIS2Grid package includes two exe files: gis2grid.exe and Mapconvert.exe. 

Mapconvert.exe is a stand-alone program used for converting gis files into ASCII 

files or vice versa. Gis2grid.exe can generate the three script files.

Here are the instructions for using it:

1) When you click gis2grid.exe, a dialog window pops up. You need to tell 

the program where mapconvert.exe is located in your local computer. Click 

“open the mapconvertor” button and select the file from the dialog (fig. 16).

2) Now, tell the program which folder contains the LANDIS output (or any 

folder that contains gis files). Click “Open a file from the LANDIS output 

folder” button. You can select any file under the folder (fig. 17).

3) Specify the folder where you want to save your converted ASCII and grid 

files (fig. 18).

4) When you finish the above steps, click “OK” to run it. The program will 

generate three script files and save them in the selected folder (fig. 19).

5) Convert GIS files into ASCII files. Just click the first script files called 

“ascii.bat.” It will run mapconvert.exe in the batch mode (fig. 5). The 

converted ASCII files will be saved in the folder “converted folder\ascii\” 

(fig. 20).

6) Now you can invoke ArcToolbox (Tucker 2000) to convert ASCII files to 

raster (grid) files. You need to run “ASCII to Grid” in batch mode (fig. 21).

7) When the “ASCII to Grid” tool reads the “grid.aml” file that was generated 

from this utility (fig. 7), it will convert all ASCII files into grids under the 

folder: “converted folder\grid\” (fig. 22).

8) If you have the Windows version of FRAGSTATS (which can be 

downloaded from http://www.umass.edu/landeco/research/fragstats/

fragstats.html), you can let FRAGSTATS read the batch file “frag.fbt” to 

read all converted grids into FRAGSTATS for further analysis (fig. 23).

Subsidiary 20 Programs
LANDIS 4.0 Users Guide Page 84

Figure 16. Window interface of the mapconvert program.

Figure 17. Choose LANDIS output folder using the mapconvert program.

Subsidiary 20 Programs
LANDIS 4.0 Users Guide Page 85

Figure 18. Choose the converted folder.

Figure 19. The pop-up window tells you where you can find the three script files.

Subsidiary 20 Programs
LANDIS 4.0 Users Guide Page 86

Figure 20. Mapconvert.exe is now converting GIS files into ASCII files.

Figure 21. Converting ASCII to Grid using ArcToolbox.

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LANDIS 4.0 Users Guide Page 87

Figure 22. Reading in the “grid.aml” file.

Figure 23. FRAGSTATS can read frag.fbt. Subsidiary 20 Programs
LANDIS 4.0 Users Guide Page 88

21. QUICK EVALUATION OF RESULTS

There are no strict criteria for evaluating LANDIS results since LANDIS is a 

stochastic model driven by millions of random numbers. We recommend that 

users read the Major References regarding a specific discussion. Still, there are 

three aspects we check when examining LANDIS results. 

21.1 Mean Disturbance Sizes

If a natural disturbance is turned on, you can calculate the means for Damage 

Size from the LANDIS disturbance log file. It is usually called WIND.LOG or 

FIRE.LOG in the default output directory. 

For wind disturbance, compare the mean disturbance size summarized from 

WIND.LOG with respective mean disturbance size set for wind in the LANDIS 

disturbance file. If the simulated value differs significantly, it implies that the 

wind disturbance size curve may not represent the correct wind disturbance size 

distribution for the simulated area. Adjust the corresponding parameters in the 

parameter file and conduct another run.

For fire disturbance, compare mean disturbance sizes summarized for each land 

type in FIRE.LOG with the specific mean disturbance sizes set for each land type 

in the land type attribute input file. Sometimes it can be hard to determine if the 

run is valid, since simulated mean fire disturbance sizes match well on certain 

land types but not on others. A t-test is suggested to check the significance of 

differences for such cases. If there is a significant difference at the 85 percent 

level, we suggest that the fire disturbance size curve may not represent the correct 

fire disturbance size distribution of the simulated area. Adjust the corresponding 

parameters in the parameter file and conduct another run.

21.2 Mean Disturbance Return Intervals

Mean disturbance return interval can be considered as the expected (or average) 

time required to disturb an area equal in size to the study area. This can also be 

extracted from disturbance log files.

For wind disturbance, compare the simulated total disturbance size with the 

expected total disturbance size. The latter can be calculated as follows:

Expected total disturbance size = Area * Years / meanReturn

where Area is the total study area, Years is the final simulated year, and 

meanReturn is the mean wind disturbance return interval. If the simulated total 

disturbance size does not match the expected total disturbance size, the wind 

disturbance probability curve may not be correct for the simulation. Adjust the 

corresponding parameters in the parameter file and conduct another run.

For fire disturbance, compare the simulated total disturbance sizes summarized 

for each land type with the expected total disturbance sizes calculated for each 

land type using the above formula. As in the evaluation for mean disturbance 

size discussed earlier, a t-test may be used to check for significant differences. 

If there is a significant difference at the 85 percent level, we suggest that the fire 

disturbance probability curve may not represent the correct fire probability of the 

simulated area. Adjust the corresponding parameters in the parameter file and 

conduct another run.

Evaluation 21 of Results
LANDIS 4.0 Users Guide Page 89

22. TROUBLESHOOTING

Here are solutions to some common problems when running LANDIS. For other 

technical problems contact Hong S. He at the address provided on page ii.

22.1 Missing Dlls

This error indicates that Msvcrt.dll or Mfc42.dll is not found in any subdirectory 

specified in the PATH environmental variable. Add the subdirectory where these 

files reside to your PATH.

22.2 Program Terminated with an Error Message Displayed

Usually the error message explains the reason why the program was terminated. 

Follow the instructions in Preparing Files for a LANDIS Run. If the error 

message is unclear, check your parameter input file and all the files required. The 

following are the most common errors when conducting a LANDIS run:

1) Each ‘#’ has a matching ‘#’ in all comments.

2) Nonactive land types must be one these types: empty, water, lowland, 

wetland, bog, nonforest. Land type names are case sensitive.

3) The upper level of specified output directory does not exist (look for typos).

4) The species listed in the land type file do not match the species listed in 

the species file.

5) The species listed in the map attribute file do not match the species listed 

in the species file.

6) Some index names specified in the map index file do not have the 

corresponding RCS files defined. Reclassification will not occur for that 

specific type. 

7) The RCS file contains a species name that does not match the name in 

the species file.

8) The format of one or more RCS files is not correct.

9) Some age index names specified in the age index files do not have the 

corresponding AGE files defined.

10) Some species names in an AGE file do not match the species names in 

the species file.

22.3 Program Hung

After LANDIS is started, it should not take long before signs of LANDIS 

processing appears on the screen. If nothing appears on the screen except for the 

command you typed, the program might be allocating memory. If you hear the 

hard disk working, the program is probably allocating virtual memory. If you are 

waiting more than a few minutes (depending on your computer), the program 

is probably hung. Make sure that the parameter file and all other required files 

were created correctly.

22.4 Abnormal Program Termination

This message usually means there is not enough virtual memory available on 

your machine. It usually happens at the very beginning of a LANDIS run. To 

alleviate the problem you can:

22 Troubleshooting
LANDIS 4.0 Users Guide Page 90

1) Increase virtual memory.

2) Run your simulation on a smaller area or at a coarser resolution.

3) Decrease the total number of species or the total number of land types in 

your simulation.

22.5 Out of Memory

This can happen during processing. Occasionally LANDIS needs to allocate 

memory during a run. To alleviate the problem see Abnormal Program 

Termination.

22.6 Out of Disk Space

There is not enough space left on your hard drive to complete your simulation. 

Do one or more of the following:

1) Reorganize your reclassification file so that each map contains more 

information.

2) Decrease the number of reclassification methods in the map index file to 

output fewer maps.

3) Change the frequency.out file to decrease output intervals.

22.7 Program is Very Slow

1) You are running a simulation on a very large map.

2) The species file contains many species that are not present in your work 

area; delete unnecessary species definitions.

3) The seeding routine chosen is very slow. Choose a different one.

4) Inactive land types are treated as active land types. Check land type names.

22.8 Results and Parameters Do Not Match

LANDIS is a random number driven stochastic model. There will never be 

a perfect match for disturbance size and mean return interval between the 

LANDIS input files and the LANDIS output files. The equations generating 

disturbance frequencies and disturbance sizes rely on random number generation 

and empirical coefficients. These equations can lead to stable matches when total 

simulation years reach several thousands.

22.9 LANDIS Users Forum

LANDIS 4.0 update and bug fixing will be posted on www.snr.missouri/

LANDIS. LANDIS Users Forum provides free access for LANDIS users to share 

news and various issues in using LANDIS. The Web address is www.snr.missouri/

LANDIS. 

22.10 Acknowledgments

We would like to thank John Waldron, Pat Zollner, and many others who helped 

improve this users guide. 

22 Troubleshooting
LANDIS 4.0 Users Guide Page 91

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23 References