Simulating Historical Landscape 

Dynamics Using the Landscape 

Fire Succession Model 

LANDSUM version 4.0

Robert E. Keane, Lisa M. Holsinger, 

and Sarah D. Pratt

United States

Department

of Agriculture

Forest Service

Rocky Mountain

Research Station

General Technical Report

RMRS-GTR-171CD

April 2006
Keane, Robert E.; Holsinger, Lisa M.; Pratt, Sarah D. 2006. Simulating historical landscape 

dynamics using the landscape fire succession model LANDSUM version 4.0. Gen. Tech. 

Rep. RMRS-GTR-171CD. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, 

Rocky Mountain Research Station. 73 p.

Abstract

The range and variation of historical landscape dynamics could provide a useful reference for 

designing fuel treatments on today’s landscapes. Simulation modeling is a vehicle that can be 

used to estimate the range of conditions experienced on historical landscapes. A landscape fire 

succession model called LANDSUMv4 (LANDscape SUccession Model version 4.0) is presented 

here as a tool for estimating historical range and variation (HRV) of landscape characteristics. The 

model simulates fire and succession on fine scale landscapes for land management applications. 

It simulates vegetation development as a deterministic process by changing the species composition and stand structure assigned to a polygon. Disturbance initiation is modeled stochastically 

and disturbance effects are based on the current vegetation conditions of the polygon. Details of 

all model algorithms are discussed and the model is demonstrated for two applications. Results 

of an extensive sensitivity and model behavior analysis are also presented.

Key words: Historical range and variation (HRV), simulation modeling, LANDSUM, landscape 

modeling, landscape fire ecology, spatial modeling

The Authors

Robert E. Keane is a Research Ecologist with the USDA Forest Service, Rocky Mountain 

Research Station at the Missoula Fire Sciences Laboratory, Missoula, MT. Since 1985, Keane 

has developed various ecological computer models for the Fire Effects Project for research and 

management applications. His most recent research includes the synthesis of a First Order Fire 

Effects Model; construction of mechanistic ecosystem process models that integrate fire behavior 

and fire effects into succession simulation; restoration of whitebark pine in the Northern Rocky 

Mountains; spatial simulation of successional communities on the landscape using GIS and satellite imagery; and the mapping of fuels and fire regimes for fire behavior prediction and hazard 

analysis. He received his B.S. degree in forest engineering in 1978 from the University of Maine, 

Orono; his M.S. degree in forest ecology from the University of Montana, Missoula, in 1985; and 

his Ph.D. degree in forest ecology from the University of Idaho, Moscow, in 1994.

Lisa M. Holsinger is a GIS Specialist with the USDA Forest Service at the Fire Sciences 

Laboratory, Missoula, MT 59807. Lisa joined the Fire Sciences Lab in 2002, and has worked on 

developing and analyzing spatial data for simulation models applied to large landscapes. She has 

developed GIS data and input for the WX-FIRE and LANDSUM simulation models and HRVSTAT 

program, ran simulations, and produced associated spatial data. She has also conducted sensitivity analyses for both the WXFIRE and LANDSUM models. Prior to the Fire Sciences Laboratory, 

she worked as a fisheries biologist/GIS Specialist for the National Marine Fisheries Service on 

research and management for the conservation of west coast salmon populations. Lisa received 

her B.S. degree in Biological Sciences from U.C. Davis in 1984, and her M.S. at the University 

of Washington in Fisheries in 1988.

Sarah D. Pratt is a GIS Specialist with the USDA Forest Service, Rocky Mountain Research 

Station, Missoula Fire Sciences Laboratory, Missoula, MT. Sarah has been with the Fire Sciences 

Laboratory since 2003, where she prepares input data and analyzes and processes spatial 

outputs for simulation models including LANDSUMv4 and FIREHARM. Pratt received her B.A. 

from Kenyon College (Gambier, Ohio) in 1992 and her M.S. in Biological Sciences from Northern 

Michigan University in 2003 where she investigated the response of understory vegetation to a 

1998 crown fire in the Upper Peninsula of Michigan.
Research Summary

Current landscape management often requires an approximation of the range and variation of 

historical landscape characteristics and processes to use as a target or reference for designing 

effective ecosystem restoration or fuel reduction treatments. Temporally deep chronosequences 

of historical landscape conditions are rare because there are few useful spatial data layers and 

vegetation maps prior to the 1900s. Spatial simulation modeling provides a vehicle to understand 

historical landscape dynamics and to estimate the historical range of landscape conditions. This 

report presents a landscape fire succession model called LANDSUMv4 that is used to simulate fire 

and vegetation dynamics in a spatial domain and then details how this model was used to simulate 

the range and variation of historical landscape dynamics using examples from the LANDFIRE 

prototype project. LANDSUMv4 is the fourth version of the LANDSUM model and was developed 

specifically for the LANDFIRE prototype project, an effort to develop methods that integrate the 

sciences of remote sensing, biophysical modeling, and landscape simulation to produce nationally consistent and comprehensive maps of historical fire regimes, Fire Regime Condition Class 

(FRCC), and fuel characteristics. LANDSUMv4 contains a deterministic simulation of vegetation 

dynamics where successional communities are linked along multiple pathways of development that 

converge to a somewhat stable climax community in the absence of disturbance. Disturbances are 

stochastically modeled at the stand-level from probabilities specified by the user, except for fire. 

Fire is spread across the landscape based on simplistic slope and wind factors. Using historical 

disturbance probabilities and successional community pathways, LANDSUMv4 was applied to 

an example landscape to generate a time series of vegetation and disturbance conditions (area 

by vegetation or disturbance type) that is used to quantify the range and variation of historical 

landscape characteristics for the LANDFIRE project. An extensive sensitivity and model behavior 

analysis was performed on a number of important LANDSUMv4 parameters to determine their 

effect on simulation results and the results show that many polygon-level parameters, such as 

fire size, fire frequency, and succession transition times are important, but their importance varies by landscape and ecosystem.

Acknowledgments

This project was a joint effort between the USDA Forest Service Rocky Mountain Research Station 

Missoula Fire Sciences Laboratory, the USDA Forest Service Fire and Aviation Management 

(FAM), and NASA (National Aeronautics and Space Administration-ARS-0000-178). This project 

was started with funding from NASA Research Grant AFRR-0000-0175 and was completed using 

monies for LANDFIRE supplied by FAM. We recognize Brendan Ward, Ann Wolfe, Eva Kasav, 

Kathy Gray, Russ Parsons, Tracey Fresino, Matt Rollins, and Maureen Mislivets, USDA Forest 

Service Rocky Mountain Research Station Missoula Fire Sciences Laboratory for their hard work 

in testing and designing LANDSUM. We also thank CSIRO TERC, Darwin Australia, especially 

Dick Williams, for support during the writing of this document.

The use of trade or firm names in the publication is for reader information and 

does not imply endorsement by the U.S. Department of Agriculture of any 

product or service.

i
Contents

Research Summary ....................................................................................... i

Acknowledgments.......................................................................................... i

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

Historical Range and Variability (HRV).................................................. 2

Landscape Simulation Models............................................................... 4

The Model..................................................................................................... 5

General Description............................................................................... 5

Model Algorithms................................................................................... 9

Model Execution.................................................................................. 16

Model Output....................................................................................... 22

Methods ...................................................................................................... 23

The Test Landscapes .......................................................................... 23

Model Demonstration .......................................................................... 25

Sensitivity Analysis.............................................................................. 26

Results........................................................................................................ 27

Model Demonstration .......................................................................... 27

Sensitivity Analysis.............................................................................. 31

Discussion .................................................................................................. 34

Sensitivity Analysis.............................................................................. 35

Model Parameterization....................................................................... 35

Simulation Issues and Limitations........................................................ 40

Simulating HRV Issues........................................................................ 44

Conclusions ................................................................................................ 46

References.................................................................................................. 47

Appendix A.1 – LANDSUMv4 Model Execution .......................................... 53

Appendix A.2 – The Driver Input File .......................................................... 54

Appendix A.3 – The Simulation Input File ................................................... 56

Appendix A.4 – The Map Input File ............................................................. 58

Appendix A.5 – The Polygon Input File ...........................................................60

Appendix A.6 – The Spatial Disturbance Input File .........................................62

Appendix A.7 – The Vegetation Input File .......................................................64

Appendix A.8 – The Disturbance Input File .....................................................66

Appendix A.9 – The Vegetation Fix Input File..................................................67

Appendix A.10 – The Management Plan Input File .........................................68

Appendix A.11 – The Scenario Input File ........................................................69

Appendix A.12 – The Attribute Input File .........................................................70

Appendix A.13 – The Fire Year Input File........................................................71

Appendix B.1 – LANDSUMv4 Output File Formats.........................................72

ii
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-171CD. 2006

Simulating Historical Landscape Dynamics Using the 

Landscape Fire Succession Model 

LANDSUM version 4.0

Robert E. Keane, Lisa M. Holsinger, and Sarah D. Pratt

Introduction

The spatial arrangement, composition, and structure of vegetation communities 

on a landscape reflects the cumulative and interactive effects of past disturbance 

regimes, biophysical environments, and successional processes over long time 

periods (Baker 1989b, Bormann and Likens 1994, Crutzen and Goldammer 

1993, Pickett and White 1985, Wright 1974). Most landscapes in the western 

United States are shaped primarily by wildland fire and vegetation succession, 

and conversely, the current landscape condition will invariably influence future 

fire patterns, species migration, and plant community development (Hessburg 

and others 1999a, Keane and others 2002d, Turner and others 1994, Veblen 

and others 1994).

Historical fluctuations in landscape characteristics, such as species composition and structure, can provide an important reference for evaluating the current 

condition of the same landscape. Moreover, a quantification of the fluctuations 

can be used to guide the design and implementation of management treatments. 

The historical range and variability (HRV) of patch sizes or percent species composition on a landscape can be used to plan the fuel treatments and prescribed 

fire activities (Cissel and others 1994, Mladenoff and others 1994, Swetnam 

and others 1999). For example, a preponderance of large patches in the historical record may indicate a fire regime dominated by large, severe fires and any 

prescribed fire program should attempt to mimic these types of historical burns 

(Baker 1989a, Baker and others 1991, Keane and others 1999, Keane and others 

2002c). Current landscape conditions can also be compared with historical landscape conditions to detect ecologically significant change, such as that incurred 

by fire exclusion and timber harvesting (Baker 1992, Baker 1994, Hessburg and 

others 1999b, Landres and others 1999). This information can be used to plan 

and prioritize treatment areas where stands that have significantly departed from 

historical conditions may warrant treatment first.

This report presents the use of simulation modeling as a method of estimating 

historical landscape dynamics for reference to determine the departure of current 

landscapes from their historical fluctuations. The concepts of historical range 

and variation (HRV) are presented first, along with methods for their computation 

including a summary of landscape modeling. Then, a model called LANDSUMv4 

is presented in detail. LANDSUMv4 is a modification of previous LANDSUM 

models (Keane and others 1996b, Keane and others 1997, Keane and others 

2002c) but modified specifically as the primary vehicle to estimate landscape 

HRV for the LANDFIRE project (www.landfire.gov; Rollins and others, in press). 

The LANDFIRE project is a national mapping effort that integrates the sciences 

of remote sensing, biophysical modeling, and landscape simulation to produce 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-171CD. 2006

nationally consistent and comprehensive maps of historical fire regimes, Fire 

Regime Condition Class (FRCC), and fuel characteristics for prioritizing, planning, 

and implementing fuel treatments (www.landfire.gov). The LANDSUMv4 section 

includes a full discussion of all algorithms, assumptions, and parameters and then 

instructions on how to run the model are presented. Next, LANDSUMv4 results 

for several nested landscapes in Utah and Montana are shown to demonstrate 

the simulation output needed to approximate landscape composition HRV. Then, 

the behavior of model was explored to document the strengths and limitations 

of using this simulation model to describe HRV dynamics using a hierarchical 

sensitivity analysis. Recommendations and limitations on running the model are 

finally presented, including demonstrations of using HRV calculations to compute 

departure indices. Results from this effort can be used to plan and implement 

landscape-scale ecosystem management activities.

Historical Range and Variability (HRV)

Landscape structure and composition describe patch distributions and biotic 

characteristics for a spatially defined area that will be referred to as a “landscape” 

in this report. The spatial and size distribution of patches describes landscape 

structure; while landscape composition is described by the relative abundance 

of ecosystem features across the spatial domain (percent area by cover types, 

for example). The term “patches” is synonymous with stands or polygons in this 

paper, where polygons refer to mapped stands. Ecosystem features, such as the 

dominant plant species (cover type) or vertical stand structure (structural stage), 

or disturbance processes, such as fuel models and fire regime, can be related 

to each polygon. Landscape composition can be summarized from spatial data 

layers using standard GIS techniques to estimate the area by composition category (area by cover type, for example).

Many types of landscape metrics are used to quantitatively describe patch 

dynamics for landscape structure such as mean patch size and largest patch 

index. They are calculated by importing spatial thematic data layers, usually from 

a Geographic Information System (GIS), into any of the many landscape metrics 

programs available (Baker and Cai 1992, McGarigal and Marks 1995, Turner and 

Gardner 1991). However, this report is concerned only with landscape composition to determine HRV because it was the primary characteristic used by the 

LANDFIRE effort (www.landfire.gov) to describe historical reference conditions 

(Rollins and others, in press). However, the methods and models discussed here 

can also be used to determine the HRV of landscape structure (Keane and others 

1999, Keane and others 2002b, Stewart and Arno 1997, for examples).

As mentioned, historical range and variability (HRV) of landscape compositional characteristics provides a useful concept for prioritizing, planning and 

designing landscape treatments at multiple scales (Keane and others 2002b, 

Landres and others 1999, Swetnam and others 1999). In this report, we define 

HRV as the quantification of temporal fluctuations in ecological processes and 

characteristics prior to European settlement (before 1900). Naturally, HRV is 

highly scale-dependent and inherently unstable due to climate change, human 

land use, and geologic processes. The variability of ponderosa pine cover across 

a landscape, for example, depends on the range of years used to compute the 

HRV statistics; fluctuations over a 1,000 year period taken 10,000 years ago at 

the end of the last ice age would be dramatically different from a recent 1,000-

year time slice.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-171CD. 2006 

To use HRV in an operational context, it must be assumed that the record of 

historical conditions more or less reflects the range of possible conditions for 

future landscapes; an assumption that we now know is overly simplistic because 

of documented climate change, exotic introductions, and human land use. But, 

despite its drawbacks, the HRV concept has the potential to be indispensable 

to ecosystem management because it can be used to define limits of acceptable change and compare historical and current stand or landscape condition to 

prioritize for restoration treatments (Hessburg and others 1999a, Swetnam and 

others 1999). Since HRV estimates do not integrate past and future trends resulting from climate change and human activities, we feel that HRV is NOT the final 

answer to land management planning, but it does provide a good reference point 

or target for planning future management projects (Keane and others 2002c).

The range and variation of historical landscape dynamics can be quantified from 

three main sources. The best sources are spatial chronosequences defined as 

sequences of maps from one landscape over many time periods. These maps can 

be digitized with GIS software so that landscape analysis programs can be used 

to compute HRV in landscape composition and pattern. Unfortunately, temporally 

deep spatially explicit chronosequences of historical landscape conditions are 

absent for many western landscapes because aerial photography and satellite 

imagery were rare or non-existent before 1930 and comprehensive maps of forest 

vegetation are scarce and inconsistent prior to 1900. Other sources are vegetation maps from many similar, unmanaged landscapes, taken from one or more 

time periods, that are gathered across a geographic region and used to quantify 

the HRV of landscape characteristics (Hessburg and others 1999a, Hessburg 

and others 1999b). This spatial series essentially substitutes space for time 

and assumes all landscapes in the series contain highly similar environmental, 

disturbance, topographical, and biological conditions (Pickett and others 1987). 

The primary limitation of this assumption is that subtle differences in landform, 

relief, soils, and climate make each landscape unique (Keane and others 2002c). 

Landscapes may be similar in terms of the processes that govern vegetation, 

such as climate, disturbance, and species succession (Hessburg and others 

2000), but this does not ensure that fire growth, pattern, and occurrence dynamics will be similar across the similar landscapes because of subtle differences in 

topography, orientation, and wind direction (Keane and others 2002b).

The third method of quantifying HRV involves simulating historical dynamics to 

produce a chronosequence of simulated maps to compute landscape statistics 

and metrics. This approach assumes that succession and disturbance processes 

are simulated accurately in space and time, and that the spatial properties of 

the disturbance and succession simulation are reflected in the patch dynamics 

(Keane and others 1999). Many spatially explicit ecosystem simulation models 

are available for quantifying HRV patch dynamics (Gardner and others 1999, 

Keane and Finney 2003, Keane and others 2004b, Mladenoff and Baker 1999), 

but most are computationally intensive, difficult to parameterize and initialize, and 

complex in design, thereby making them difficult to use across large regions over 

long time periods. On the other hand, those landscape models designed specifically for management planning tend to oversimplify successional development 

and disturbance initiation, spread and effects (Keane and others 2004b). Even 

the most complex landscape models rarely simulate direct spatial interactions 

between biophysical environments, fire dynamics, and vegetation development 

because of the lack of research in those areas and the great computer resources 

required for such an effort. However, simulation models can include explicit 
 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-171CD. 2006

simulations of climate and human activities to generate more relevant estimates 

of the range and variation of landscape dynamics.

The problem then is how to obtain an historical time series (a record of landscape composition and structure over time) that could be used as reference to 

calculate HRV landscape statistics consistently and comprehensively for any 

landscape in the United States. Spatial chronosequences derived from historical 

maps are impossible because of the limited historical depth and inadequate and 

inconsistent coverage across the nation. Space-for-time substitutions are difficult because landscape similarity is difficult to assess and it is highly dependent 

on scale, recent disturbance history, and topography. It appears that simulated 

chronosequences may be the only alternative for large scale mapping efforts, 

but they also have limitations. This report is concerned only with the simulation 

approach for generating HRV landscape statistics because we feel that it is the 

most viable alternative at this time.

A simulation approach was taken in the LANDFIRE prototype project for estimating HRV because it was determined to be the only approach that could guarantee 

a consistent estimation of HRV statistics for all ecosystems, landscapes, and 

geographic regions of the United States (Pratt and others, in press, Rollins and 

others, in press). The LANDFIRE prototype project was dedicated to developing 

methods to map vegetation, fuels, and fire regime condition classes across the 

entire contiguous United States (National LANDFIRE Project; www.landfire.gov). 

A fire regime condition class (FRCC) is a three category index that reflects the 

degree of departure of current landscape conditions from historical landscape 

conditions. The LANDFIRE effort uses output from the spatial simulation model 

presented in this paper to describe historical landscape conditions that, in turn, 

are used as a reference for computing FRCC. This simulation approach also allows the generation of multiple time series reflecting alternate landscape histories 

so that a full complement of range and variation statistics can be computed to 

more extensively manage landscapes. For example, a climate change scenario 

could be simulated to generate a temporal stream of landscape composition data 

reflecting landscape behavior under future climate, or the invasion of exotics can 

be integrated into the succession parameters to include their influence on historical 

departures. The simulation approach is easily replicated so new HRV landscape 

statistics can be generated as new and improved spatial models are developed, 

and as more accurate input parameters are measured on the landscape.

Landscape Simulation Models

A class of simulation models, called landscape fire succession models (LFSMs), 

can be used to generate time series of landscape characteristics to quantify 

HRV (Keane and others 2004a). These models simulate the linked processes 

of fire and succession in a spatial domain. Although the complexity of spatial 

relationships of vegetation, fire ignition, and fire spread may vary from model to 

model, all LFSMs, by definition, produce time-dependent, georeferenced results 

in the form of digital maps or GIS layers. Several references provide excellent 

reviews of existing LFSMs. Keane and others (2004b) reviewed 44 LFSMs and 

then classified them into similar groups based on scale of application, simulation 

detail, and fire modeling approaches. Baker (1989b) examined several models 

of landscape change and grouped them into whole, distributional, and spatial 

landscape models depending on the level of data aggregation. Details of some 

landscape models are also presented in Mladenoff and Baker (1999). A review of 

spatial fire spread and effects models is provided by Gardner and others (1999), 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-171CD. 2006 

while and Keane and Finney (2003) present a summary of fire and succession 

modeling efforts.

Several existing landscape models provide examples of the diverse approaches 

used to simulate landscape, climate, and fire dynamics. At the complex end, FireBGC integrates the FOREST-BGC biogeochemical model (Running and Coughlan 

1988, Running and Gower 1991) with the FIRESUM gap model (Keane and others 

1989) to simulate climate-fire-vegetation dynamics (Keane and others 1996c). 

The LANDIS model was used to evaluate fire, windthrow, and harvest disturbance 

regimes on landscape pattern and structure (He and Mladenoff 1999, Mladenoff 

and others 1996, Mladenoff and He 1999). Fire is indirectly simulated at the standlevel by quantifying fire effects based on age class structure, and succession 

is simulated as a competitive process driven by species life history parameters. 

Roberts and Betz (1999) used life history parameters or vital attributes (Noble 

and Slatyer 1977) to drive succession in their polygon-based model LANDSIM 

that also simulates fire effects at the polygon level without a fire spread model. 

The DISPATCH model of Baker (1992, 1993, 1999) stochastically simulates fire 

occurrence and spread based on dynamically simulated weather, fuel loadings 

and topographic setting, and then simulates subsequent forest succession as 

a change in cover type and stand age. Miller and Urban (1999) implemented a 

spatial application of fire in the Zelig gap model to assess the interaction of fire, 

climate, and pattern in Sierra Nevada forests. More simplistic approaches include 

the SIMPPLLE model (Chew 1997, Chew and others 2003) that uses a multiple 

pathway approach to simulate succession on landscape polygons and a stochastic 

approach to simulate fire. This same theme can be found in the FETM, VDDT, 

and LANDSUM models by Schaaf and Carlton (1998), Kurz and others (2000), 

Keane and others (2002c), respectively. Any one of the models mentioned here 

could be used to estimate landscape composition HRV for evaluation of potential departure to use in planning and implementation. This paper concerns one 

particular model – the LANDSUM model (Keane and others 1997, Keane and 

others 2002c) – selected for application in the LANDFIRE project.

The Model

General Description

The LANDscape SUccession Model version 4.0 (LANDSUMv4) is a spatially 

explicit vegetation dynamics simulation C program wherein succession is mostly 

simulated as a deterministic process and disturbances (for example, fire, insects, 

and disease) are modeled as stochastic processes. LANDSUMv4 is the fourth 

major revision of the original LANDSUM developed to simulate alternative management scenarios on small landscapes delineated by polygons (Keane and 

others 1997). The current model is a spatial state-and-transition stand-level 

succession model with a spatially explicit fire model that simulates fire growth 

using a cell percolation method. LANDSUM is a descendent of the CRBSUM 

model used to simulate coarse scale vegetation dynamics in the interior Columbia 

River Basin at a 1 km pixel resolution (Keane and others 1996b). CRBSUM was 

altered to create LANDSUM by changing spatial resolution from the 1 km pixel 

to a polygon level to simulate fine scale disturbance and succession processes 

(Keane and others 1997). A spatially explicit fire spread algorithm was included 

later to realistically simulate landscape patch dynamics over time and the new 

model revision (LANDSUM version 2.0) was used to explore the limitations and 

implications of using a simulation approach to describe landscape dynamics 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-171CD. 2006

(Keane and others 2002b, 2002c). A simplistic climate driver was then included 

to create LANDSUM version 3.0 to simulate fire regimes (Keane and others 

2004b) and contrast results to other LFSMs (Cary and others 2005). This current version (version 4.0) contains extensive refinements to the fire spread and 

successional development algorithms, and it allows options for easily importing 

and exporting LANDFIRE data layers.

LANDSUMv4 simulates succession within a patch (adjacent similar pixels) or 

polygon using the multiple pathway succession modeling approach presented 

by Kessell and Fischer (1981) that was based on the seminal work of Noble and 

Slatyer (1977), Cattelino and others (1979), and Davis and others (1980). This 

approach assumes all pathways of successional development will eventually 

converge to a stable or climax plant community called a Potential Vegetation 

Type (PVT) (fig. 1). A PVT identifies a distinct biophysical setting that supports 

a unique and stable climax plant community under a constant climate regime 

(Daubenmire 1966, Pfister and Arno 1980). In LANDSUMv4, a PVT can have 

multiple climax plant species indicators to identify broad trends in successional 

dynamics. There is a single set of successional pathways for each PVT on the 

Figure 1—An example of a subalpine fir succession PVT (Potential Vegetation Type) pathway model 

used in LANDSUMv4. Succession pathways are defined by sequences of succession classes 

named for cover type and structural stage. Cover type names are as follows: SH-mountain shrub, 

WP-whitebark pine, SF-subalpine fir. Structural stage names are defined as: LCLH-low cover 

low height early succession stage, HCLH-high cover low height mid-seral stage, HCHH-high 

cover high height late succession stage, LCHH-low cover high height disturbance maintained 

late succession stage. Labels T1-11 identifies unique succession classes. Fire, beetle, and blister 

rust disturbance pathways are also shown.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-171CD. 2006

simulation landscape (Arno and others 1985, Steele 1984) (fig. 1). Each pathway 

is composed of a sequence of plant communities called succession classes that 

are linked along gradients of vegetation development. Each succession class is 

represented by a cover type (dominant species) and a structural stage (vertical 

and horizontal stand structure). Successional development within a polygon is 

simulated at an annual time step where the polygon’s succession class would 

change if the length of time spent in the current succession class (transition time) 

exceeds a user-defined input parameter (called maximum residence time in a 

succession class) that is held constant throughout the simulation. The PVT of a 

polygon never changes throughout the simulation, but the succession class is dynamic depending on disturbance regime and successional development rate.

Static PVTs are a major limitation of LANDSUMv4. Since PVTs are determined 

over time by biophysical processes, such as climate, that are constantly changing (global climate change, for example), ideally the PVTs should also change 

throughout simulation time. Unfortunately, the simplistic structure and limited input 

parameters in LANDSUMv4 do not allow a dynamic simulation of the biophysical 

processes that affect PVTs. An effort is underway to develop empirical models 

that migrate PVTs across the landscape as the climate changes and human land 

use affects long-term successional trajectories.

Disturbances can disrupt succession by delaying or advancing the time spent 

in a succession class, or they can cause an abrupt change to another succession class (see disturbance pathways in fig. 1). Occurrences of human-caused 

and natural disturbances are stochastically modeled from probabilities based on 

historical frequencies. All disturbances are simulated at a polygon-level, except 

for wildland fire, which is simulated as a spatial cell spread process across the 

landscape, as discussed in the next section. Only one disturbance can be simulated in a polygon during a simulation year.

We selected LANDSUMv4 as the landscape model to use in LANDFIRE 

because its minimal number of inputs and generalized structure allows it to be 

portable, flexible, and robust with respect to geographic area, ecosystem, disturbance regime, and available expertise (Keane and others 1996b). More complex 

models, such as LANDIS (Mladenoff and others 1996), might have generated 

more realistic landscape simulations, but the extensive parameterization required 

to run the model for every ecosystem and landscape in the United States probably would have been difficult. The more complex models probably would also 

have had prohibitively long execution times to generate sufficient time series to 

adequately capture historical fluctuations, especially on landscapes with infrequent fires. Less complex models, such as TELSA (Kurz and others 1999) or 

SIMPPLLE (Chew and others 2003), would have been easy to parameterize but 

these models did not adequately simulate the spatial dynamics of fire spread 

and effects to allow variation in landscape composition and structure to be fully 

assessed. LANDSUMv4 seemed a good balance between feasibility and realism, 

especially for the LANDFIRE project. The LANDSUM design allowed the addition 

of any stand-level disturbance, succession pathway, and succession development 

sequence as long as the event occurred within the one-year internal time step. 

Moreover, the aspatial succession model VDDT (Beukema and Kurz 1998) was 

modified for the LANDFIRE prototype project so that pathways in LANDSUM 

could be easily created and tested outside of the spatial environment to facilitate 

efficient pathway development and implementation (see later sections).

Some terminology must be defined to fully understand the following sections. 

First, polygons are stands or patches that have been mapped in a digital environ-
 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-171CD. 2006

ment such as a GIS (Geographical Information System). Polygons in LANDSUMv4 

are delineated by pixels of identical PVT, cover type, and structural stage combinations. All spatial data in LANDSUMv4 are stored in raster format, which is a 

grid of square pixels often measuring 30 meters on a side. A succession class 

is defined by a cover type and structural stage and the developmental sequence 

of succession classes is called a pathway (fig. 1).

The succession simulation in LANDSUMv4 is similar to that presented in 

Keane and others (1996b) for CRBSUM, while the spatial fire spread and effects 

Figure 2—The flowchart for LANDSUMv4 execution. Square and retangular boxes indicate input 

and output data while the circles represent algorithms.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-171CD. 2006 

simulation is detailed in Keane and others (2002c). The following sections were 

taken mostly from these two references and modified to reflect the extensive 

refinements in LANDSUMv4. The following sections also contain references to 

input and output files utilized by LANDSUMv4 and these files are documented 

in Appendices A.1 thru A.13 (input files) and Appendix B.1 (output files). The 

uses of these files are detailed in the Model Execution section. A flow diagram 

showing the simulation logic within the model is shown in figure 2 for reference 

while reading the following sections.

Model Algorithms

Simulating succession—LANDSUMv4 simulates succession within a polygon using the multiple pathway modeling approach at an annual time step. This 

approach assumes all pathways of successional development will eventually 

converge to a stable or climax plant community called the potential vegetation 

type (PVT) (fig. 1). There is a single set of successional pathways for each 

PVT present on a given landscape (Arno and others 1985). The length of time 

a polygon remains in a succession class is called the residence time (years). 

It is defined by a range of succession ages (beginning and ending succession 

age, for example, 50-100 years) and this range is held constant throughout the 

simulation. The beginning and ending succession ages and transition succession classes are specified for every PVT-succession class combination in the 

Vegetation Input File (see later sections, Appendix A.7). Transition times for the 

last succession class in a pathway should be longer than the simulation time 

span. The number of successional pathways in a PVT is dictated by the input 

map legend, diversity of succession communities in the PVT, and the number 

of disturbances possible on the landscape.

Succession is simulated in LANDSUMv4 by increasing the succession age 

of a polygon by a single year after one year of simulation if no disturbances are 

simulated (see next section). When the succession age of a polygon exceeds 

the user-defined ending succession age for its succession class, a succession 

event is modeled where the polygon transitions to a new succession class. 

The polygon is assigned a new succession class as specified in the Vegetation 

Input File (Appendix A.7) and a new set of parameters are used for disturbance 

simulation (see next section).

Simulation time (number of years in simulation) and succession age are not 

always related in a LANDSUMv4 simulation because disturbances can retard 

or advance the succession clock. Succession age is simulated in years but it is 

more a measure of successional development rather than the actual age of the 

polygon. For example, a polygon in a grass-herb succession class, which normally 

has a succession class lifespan (transition time) of 30 years, might spend over 

200 years in this succession class because disturbances such as grazing and 

fire keep decrementing the succession age and thereby preventing the transition to the next succession class. So, the succession age of the polygon might 

be 25 years but it might have been 200 years since it transitioned to that class 

because of repeated disturbance. Since the model outputs succession age in 

maps and tables, it is important that the user understand that this output age is 

not the same as the time since the last disturbance event.

Simulating disturbances—Except for fire, all disturbances in LANDSUMv4 

are simulated at the polygon or stand level as a simple stochastic process using occurrence probabilities. The program generates a random number, and if 
10 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-171CD. 2006

the random number is less than the user-specified probability, a disturbance is 

simulated. The probabilities are assigned by the user to PVT and succession 

class combinations in the Scenario Input File (Appendix A.11). It is assumed that 

the occurrence and effects of all non-fire disturbances simulated for a polygon 

do not depend on the spatial relationships of adjacent polygons or the position 

the polygon occupies on the landscape. The spread of simulated non-fire disturbances is confined to the boundaries of the polygon. This assumption may 

be valid for many biotic and abiotic disturbances, such as grazing and browsing, but it is certainly not realistic for multi-host, wide-spread disturbances, 

such as mountain pine beetle. However, the lack of research and modeling in 

landscape level disturbance dynamics coupled with the complexity of interactions of disturbance processes with vegetation dynamics preclude a landscape 

level simulation approach for all disturbances at this time. LANDSUMv4 has the 

ability to spread non-fire disturbances across the landscape once the process 

has been fully studied and estimation of spatial parameters is possible (Keane 

and others 2002c).

Disturbances and succession processes that act at temporal scales finer than 

the one-year LANDSUMv4 time step, such as drought and frost kill, should not 

be included in the Disturbance Input File (Appendix A.8) unless their effects are 

manifest at the end of a single year. Mechanistic disturbance processes, such 

as insect population dynamics, spread, and migration, are not explicitly modeled 

in LANDSUMv4. Rather, the incidence or initiation of a disturbance is simulated 

from probabilities in the Scenario Input File (Appendix A.11) and the effect of that 

disturbance is simulated as a change in succession age and/or succession class 

as specified in the Disturbance Input File (Appendix A.8). Those disturbances 

that don’t significantly affect the successional development of a polygon should 

not be included in a simulation. For example, dwarf mistletoe infections in some 

lodgepole pine landscapes may not significantly alter the amount of time spent 

in a succession class so it probably should not be included in the Disturbance 

or Scenario Input Files (Appendix A8, A.11).

In LANDSUMv4, there are two phases of disturbance simulation: the initiation 

phase and the effect phase. As mentioned, all fire and non-fire disturbances in 

LANDSUMv4 are initiated for a polygon using a stochastic approach based on 

probabilities specified for PVT and succession class combinations in the Scenario 

Input File (see Appendix A.11). Each year, the model cycles through the list of 

polygons in the exact order specified in the Polygon Input File (fig. 2, Appendix 

A.5). For each polygon, the model then evaluates each disturbance listed for the 

polygon’s PVT-succession class combination in the order entered in the Scenario 

Input File (Appendix A.11) for the scenario specified for that management zone 

and simulation time span in the Management Plan Input File (Appendix A.10). 

The model generates a random number from a uniform distribution for the first 

disturbance evaluated, and if the random number is less than the probability 

of the disturbance, that disturbance is simulated and no other disturbance is 

evaluated. If the random number is greater than the disturbance probability, 

then the probability of the next disturbance in the list is added to the first to obtain a cumulative probability. If the random number is less than this cumulative 

probability, the second disturbance is then simulated and no other disturbances 

are evaluated. This stochastic process is repeated for every disturbance in the 

list. Succession (incrementing succession age by one year) is only simulated 

(see above section) if no disturbances occur. If the new succession age is greater 

than the ending age for the polygon’s current succession class, the polygon 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-171CD. 2006 11

would be assigned a new succession class according to the sequence specified 

in the Vegetation Input File (Appendix A.7). It is assumed that all disturbance 

occurrences are independent of each other and that the PVT-succession class 

combination and succession age are sufficient to represent changes in stand 

composition and structure caused by disturbance. The same probabilities are 

used for each year of simulation unless otherwise specified in the Management 

Plan Input File (Appendix A.10).

The modification of the disturbances and their associated probabilities in the 

Scenario Input File (Appendix A.11) represents the only way to develop simulation 

scenarios in LANDSUMv4. A simulation scenario is actually a set of disturbances 

for each PVT-succession class combination coupled with their relevant occurrence probabilities. For example, an historical scenario would consist of a unique 

set of native disturbances with occurrence probabilities that represent their frequency on the historical landscape for each PVT-succession class combination 

(for example, stand-replacement fire with a probability of 0.01 or 100 year fire 

return interval). However, a scenario describing current fire management would 

probably have the same native fire disturbances (stand-replacement fire) but at 

significantly lower probabilities (0.005 or 200-year fire return interval) to reflect 

fire suppression activities, and, the disturbance list would also include a set of 

human-induced disturbances such as weed invasion, prescribed burning, thinning, or timber harvest with associated probabilities.

Each set of disturbance probabilities for the PVT-succession class combinations on the simulation landscape represent a “scenario” in LANDSUMv4. The 

user can assign scenarios across time and space during the simulation using 

the Management Plan Input File (Appendix A.10). Scenarios can be assigned 

in space by assigning each polygon a management zone as requested in the 

Polygon Input File (Appendix A.5) and a unique scenario can be specified for 

each management zone in the Management Plan Input File (Appendix A.10). 

The user can also specify the application of a scenario across a specific span of 

years during the simulation for each management zone. For example, all polygons 

within a wilderness area might be assigned a unique management zone and a 

set of three scenarios would be built to reflect disturbance occurrence for the 

wilderness area during three time phases: a pre-settlement era, a post-settlement era, and during the enactment of the Wilderness Act. These three phases 

could be given starting and ending years that are again specified, along with 

scenarios for all other zones and time periods, in the Management Plan Input 

File (Appendix A.10).

As mentioned, the effects of an initiated disturbance are simulated as a change 

in succession age, succession class, or both. The simulation of some disturbances can leave the polygon in the same succession class but the succession 

age can be decremented (set back; after grazing, for example) or incremented 

(accelerated; after a beetle attack kills seral trees, for example). Effects of severe 

disturbances are simulated by changing the succession class and assigning a 

new succession age. For example, a stand-replacement fire in an old growth 

Douglas-fir stand may change the succession class of the polygon to a ponderosa 

pine seedling succession class with a resultant succession age of one year. Or, a 

non-lethal surface fire may change an old growth multiple strata ponderosa pine 

polygon to an old growth single strata ponderosa pine polygon with a resultant 

succession age of 250 years. All disturbance effects parameters are entered 

in the Disturbance Input File by PVT-succession class combination (Appendix 

A.8). Usually, the effect of a disturbance within a PVT-succession class does 
12 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-171CD. 2006

not change across scenarios. Disturbance histories for all polygons are stored 

by LANDSUMv4 and summarized in program output.

In review, all non-fire disturbances are independently simulated at the polygon level. This means that the position of the polygon on the landscape and the 

conditions and disturbance histories of other adjacent polygons do not affect the 

simulation of disturbance on a target polygon. This assumption was made because 

the spatial simulation of spread for all disturbances is impracticable due to the 

complexity of interacting factors, the lack of fundamental research in landscape 

disturbance dynamics, and insufficient computing resources. However, wildland 

fire is one disturbance where there have been significant advances in simulating 

spatial interactions and spread (Finney 2001, Gardner and others 1999, Keane 

and others 2004a). Since fire is one of the most dominant disturbances in the 

many ecosystems of the United States, it has a major effect on the composition and structure of many landscapes (Agee 1993, DeBano and others 1998, 

Heinselman 1981). Therefore, it was decided that fire deserved an independent 

and more comprehensive treatment in LANDSUMv4. A separate fire module was 

created that simulated the spread of fire across a landscape as a pixel-to-pixel 

growth process. This means that fires can cleave or divide polygons to create 

new burned polygons that will be assigned different succession classes or succession ages. These new polygons are added to the bottom of the polygon list 

in LANDSUMv4. The fire module implemented in LANDSUMv4 is discussed 

next and it is thoroughly detailed in Keane and others (2002c). Although this 

module was primarily designed for fire, it can also be used to spread any other 

disturbance that occurs within one year of simulation.

Simulating fire ignition and spread—The spatial simulation of fire behavior 

and effects presented a special challenge because of LANDSUMv4’s simplistic 

simulation structure. Some spatial models assume a random or patch-to-patch 

fire spread (Chew 1997, Kurz and others 2000), which maintains map integrity 

but misrepresents the dynamics of fire growth (Gardner and others 1999, Keane 

2000, Keane and Finney 2003). Wildland fires tend to split patches along topographic, fuel, moisture, or wind gradients and rarely follow patch boundaries 

(Finney 2001). Inclusion of a detailed mechanistic fire growth model, such as 

FARSITE (Finney 1998a), into LANDSUMv4 was problematic because it would 

have required the addition of fine scale fuels and weather input data that would 

have created an overly complex model that would have required extensive 

parameterization and computer resources for millennia simulations across the 

entire nation. The second version of LANDSUM (version 2.0) simulates spatial 

fire dynamics and its effect on landscape pattern and composition using an 

approach that balanced simplicity and applicability with realism (Keane and others 2002c). A discussion of the fire spread algorithm follows with all spatial fire 

input parameters mentioned in this section specified by the user in the Spatial 

Disturbance Input File (Appendix A.6).

The simulation of fire in LANDSUMv4 is represented by three phases: initiation, spread, and effects (fig. 2). Ignition is stochastically simulated from the fire 

probabilities assigned to each initial polygon based on its PVT and succession 

class. The following three-parameter Weibull hazard function is employed to 

account for fuel buildup (using years since burn –YSB, years) and a no-burn 

period directly after a fire (REBURN, years).

pf  ( fmult)(pscale)( B

FRI

)[

YSB 
 REBURN

FRI

]

(B 
1) (1)
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-171CD. 2006 13

where ß is the shape parameter (parameterized at 2.0 for this study), FRI is the 

fire return interval or the inverse of fire probability as entered in the Scenario 

Input File (years), and pƒ is the probability of fire. We assigned the REBURN 

variable a value of 3.0 years in the LANDFIRE effort. The probability pƒ represented the probability that any point on the landscape would burn in a year 

and that point is defined by size of the pixel. The pƒ was adjusted for scale to 

account for the size of the polygon and the size of the fire using the following 

scaling factor (pscale):

pscale  [

AREApixel

AREAfire

][

AREApolygon

AREApixel

] (2)

where AREApixel and AREApolygon are the areas of the pixel and polygon (m2), 

respectively, and AREAfire is the average fire size as entered in the Spatial 

Disturbance Input File (Appendix A.6). The first term in the equation scales the 

probability using the relationship of total area that is burned to the pixel area to 

account for the point-level fire probabilities. The second term scales the probability to a polygon level because ignition is evaluated on a polygon-by-polygon 

sequence.

The effect of weather on fire initiation and spread is simulated using a fire 

multiplier (fmult, index from 0-1 in equation 1). The user must decide how many 

years out of ten there will be a dry, moderate, and wet fire season (specified in 

the Spatial Disturbance Input File; Appendix A.6). The program calculates the 

probability of these weather years (pd, pm, and pw for probability of dry, moderate 

and wet years, respectively) and selects the fire year by generating a random 

number and comparing it with the cumulative probability distribution. Once a 

fire year is selected (wet, moderate, dry), the ignition probability is increased by 

a factor (fmult) that is computed from the wet-moderate-dry probabilities under 

the assumption that the fmult for a moderate year is always 1.0 in the simulation. 

The equation for a wet and dry year multiplier is:

fmult

w  (1
 pm )

K (p d pw ) (3)

fmult

d  K( fmultw ) (4)

where the coefficient K is the increase in size and frequency of fire from a wet 

to dry year. For the LANDFIRE effort, the factor K was assigned a value of 100 

determined from an analysis of fire occurrence data in Hardy and others (2001) 

and Schmidt and others (2002). This approach maintains the integrity of the fire 

occurrence probabilities specified in the Scenario Input File (Appendix A.11) by 

keeping the long-term average fire frequency probability close to that specified 

by the user. The user can bypass this entire step by specifying a weather year 

multiplier for every simulation year in the Fire Year Input File (Appendix A.13).

Other probability functions are available in LANDSUMv4 besides the threeparameter Weibull function detailed above. The model also contains options to 

use a normal or exponential distribution if these are more appropriate for the 

ecosystem or landscape. Most studies use the three-parameter Weibull because 

it was proven effective (Johnson and Gutsell 1994, Johnson and Van Wagner 

1985, Keane and others 1996c). We actually used the survivor function of the 
14 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-171CD. 2006

Weibull probability equation to compute instantaneous estimates of annual 

probabilities.

The program generates a random number for each polygon, and if that number 

is less than pf, a fire is started in that polygon. It is assumed that the fire has an 

equal chance of starting anywhere within the polygon. The pixel where the fire 

first starts is randomly determined from the number of pixels within the polygon. 

The program calculates a cumulative probability distribution for all pixels, selects 

a random number, and compares the number with the distribution to choose the 

pixel.

Fire is spread across the landscape at a pixel-level using directional vectors 

of wind and slope. Wind direction (degrees azimuth) is an input to the model 

and specified in the Spatial Disturbance Input File (Appendix A.6), but then it is 

randomly modified within 45 degrees of the input direction for each simulated 

fire. Wind speed (m sec-1) is also an input parameter that is randomly adjusted 

within 0.5 times of a user-specified input value for each fire. Slope (percent) is 

calculated from a digital elevation model (DEM), which is a required input map 

in LANDSUMv4 (see the Map Input File in Appendix A.4). The number of pixels 

to spread the fire in eight possible directions (N, NE, E, SE, S, SW, W, NW) is 

calculated from the following relationship, which was modified from Rothermel 

(1991).

spix  (wind f )(slopef ) (5)

where spix is the number of pixels to spread in a direction, windf and slopef are 

wind and slope factors that are computed from the following equations.

wind f  (1 0.125V)(cos(abs(Qs 
Qw ))

V 0.6

(6)

slopef  5

(1 3.5e
10 $ ) (7)

where V is wind speed (mph), abs is absolute value operator, Qs is the spread 

direction, Qw is the wind direction, and Δ is percent slope (rise over run) (Rothermel 

1991). The slope factor applies to only positive slope values (upslope spread). 

Downslope spread is computed as:

slopef  e3($ )

2

(8)

These equations were solved for each pixel ignited by the fire, originating from 

the randomly selected fire start pixel mentioned above. Only those pixels within 

polygons having assigned fire probabilities in the Scenario Input File (Appendix 

A.11) less than 0.002 (500 year MFRI) were allowed to burn, except for those 

patches where pf was zero, such as in a recently burned patch or rock polygon. 

Rounding of the computed spix to the nearest pixel (pixel size was 30 meters 

in this study) was stochastically determined from a uniform random number 

generator.

Initially, fires were allowed to burn until they hit the landscape boundary or 

an unburnable patch, but this resulted in abnormally high burned areas on the 

simulated landscapes. Fire spread was then limited by stochastically calculating 

a maximum fire size (FIRESIZE, ha) for each fire from the following equation:

FIRESIZE  A ln(RN)

1 /S (9)
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-171CD. 2006 15

where A is the magnitude parameter that approximates the average fire size 

(ha) estimated by the user from sources such as the NIFMID data base (Hardy 

and others 2001), RN is a random number from a uniform probability distribution 

(number between zero and one), and σ is a shape parameter estimated as 1.33 

for this effort. Both α and σ parameters are specified in the Spatial Disturbance 

Input File (Appendix A.6). Some users can confuse α , the fire scale parameter 

in the Weibull function, with average fire size, but the main difference is that α

is a fire size equation parameter that is the expected average from the Weibull 

distribution and is “approximated” by average fire size. A maximum size of the 

simulated fire is determined by first generating a random number and then solving 

the above equation. Then, the fire’s spread is simulated using the cell percolation approach described above until it reaches the maximum fire size or until it 

reaches an unburnable boundary such as the edge of the simulation landscape. 

The program stores the map of burned pixels and the fire size characteristics 

(see Appendix B.1). The above fire size equation is derived from a heavy-tailed 

Weibull distribution but there are several other options in LANDSUMv4 including 

Pareto, Exponential, Uniform, and Logistic distributions (Appendix A.6).

The fire spread cell percolation method detailed above is not the only method 

available in LANDSUMv4 to simulate fire growth across the landscape. The model 

also allows two very simplistic, and somewhat adequate, methods of creating fire 

perimeters to simulate patch dynamics. The simplest method involves using a 

“cookie cutter” approach where ellipses encompassing the area determined by 

FIRESIZE in the above equation are placed on the landscape originating at the 

fire-start pixel (discussed above). The width and length of the ellipses are specified by an elongation parameter in the Spatial Disturbance Input File (Appendix 

A.6). There is also a cell automata option where fire is stochastically spread to 

adjacent pixels based probabilities computed from slope, wind, and fuel loadings 

(Hargrove and others 2000, Li 2000, 2001). We used only the cell percolation 

approach in the LANDFIRE effort and found this far superior to all others.

The effect of the fire on the burned polygon is stochastically determined 

from probabilities of each fire severity type in the Scenario Input File (Appendix 

A.11). The program cycles through all polygons in the burned area and slices all 

burned polygons into the sections that burned and sections that did not burn. The 

unburned and burned polygons retain all characteristics of the mother polygon 

prior to the fire. The burned polygons get new succession classes, stand ages, 

and year since fire attributes using the following procedure.

The model retrieves the fire occurrence probabilities for each of these burned 

polygons for the pre-burn PVT-succession class combination. The fire occurrence probabilities in LANDSUMv4 are usually specified by three fire severity 

types: stand-replacement, mixed severity, and non-lethal surface fires. This set 

of probabilities is assigned to each mapped polygon based on PVT and succession class. The inverse of the sum of these probabilities is used as the mean fire 

return interval in Equation 1. In calculating fire effects, these three probabilities 

for the pre-burn polygon’s PVT-succession class combination are relativized 

(scaled from 0.0 to 1.0) and a random number is compared to the cumulative 

relativized probability distribution to stochastically select the severity of the fire 

to simulate. A slight chance (0.01 probability) that the polygon would not burn 

was also included in the cumulative probability distribution in earlier versions 

of LANDSUM. The selected fire severity would then determine the appropriate 

successional pathway (fig. 1). The disturbance effects for the PVT-succession 

class are determined from the information specified in the Disturbance Input File 
16 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-171CD. 2006

(Appendix A.8) for the old polygon’s PVT-succession class. These new burned 

polygons are added to the end of the polygon list. Succession is not modeled 

for any polygon that was burned during that year of simulation.

The above algorithms for spatially simulating fire on the simulation landscape 

can also be used for any other disturbance process in LANDSUMv4. Any number 

of disturbances can be added to the Spatial Disturbance Input File (Appendix 

A.8) as long as data exists to quantify all input parameters and the disturbance 

has effects that are manifest in the same year of simulation. An example of a 

disturbance that might be considered for spatial simulation would be mountain 

pine beetle epidemic where the spread and mortality must be simulated within a 

year of simulation. This may be true for some lower elevation landscapes but it 

takes much longer for beetles to spread in high elevation pine stands.

Model Execution

LANDSUMv4 is a command line program that is initiated at the DOS command 

prompt by entering the program name (landsumv4) and then the name of an input file (Appendix A.1), called a Driver Input File (Appendix A.2), which contains 

the file pathnames of several other files that contain input parameters and initial 

values. All LANDSUMv4 input files are ASCII text files that are organized by 

simulation task and are structured by unique formats (see Appendix A.2-A.13). 

The Simulation Input File (Appendix A.11) contains user specifications for general 

simulation parameters including simulation time span, output options, random 

number options, age initialization options, and fire code specifications (Appendix 

A.3). A flowchart illustrating the steps needed for LANDSUMv4 execution is 

shown in figure 3 for reference throughout the following sections.

Figure 3—A flowchart showing the steps needed to parameterize and initialize LANDSUMv4 for 

execution on a landscape.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-171CD. 2006 17

Creating simulation maps—Only two digital maps are needed for input to 

LANDSUMv4 (fig. 3). These maps are input into LANDSUMv4 in the ARC/INFO 

ASCII GRID format. The extent of the simulation landscape in LANDSUMv4 is 

defined by the DEM (Digital Elevation Model) Input Layer and landscape composition is described by the Polygon Input Layer. An error will occur if these layers 

are not congruent; the model uses the DEM to spatially define the simulation 

landscape. During the initialization phase, the model will create a raster map of 

memory locations for each pixel on the landscape and each memory location will 

have all descriptive parameters for that pixel including succession age, time since 

last fire, cover type and structural stage. All output maps and spatial simulations 

are created from this memory location map. It is important that all pixels within 

the simulation landscape have values for PVT and succession class because 

these values are needed in the simulation of fire spread. The DEM layer can be 

created from any digital elevation model (DEM) and a good source is the EROS 

Data Center’s National Elevation Database (NED, www.usgs.eros.gov).

The Polygon Input Layer can be created from many sources. A digitized stand 

map with PVT, cover type, and structural stage attributes is perhaps the best 

source, but it can also be created from satellite imagery and gradient modeling 

(fig. 4 illustrative diagram) (Keane and others 2002a). The Polygon Input File 

Figure 4—An example of a simulation landscape with mapped polygons and the associated list of 

those polygons.
18 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-171CD. 2006

(Appendix A.5) is created from the Polygon Input Layer by creating an ASCII file 

where each line is a polygon on the simulation landscape (in the Polygon Input 

Layer) and each polygon is given a set of attributes. The first set of attributes 

is used to stratify simulation results so that predictions can be summarized by 

specific land areas or management units. The first stratification is the region 

code that represents the region of the country for which the succession pathways and disturbances parameters were developed (fig. 4). The next attribute is 

the management zone that is used to further delineate areas on the simulation 

landscape based on management objectives (for example, wilderness, roadless, and wildland urban interface). The third stratification parameter is generic 

and can be used for any purpose including finer geographical divisions or land 

use allotments. In the LANDFIRE prototype project, we used this stratification 

as the spatial landscape reporting unit for mapping FRCC to describe historical 

fluctuations in succession class (Pratt and others, in press).

The next set of attributes concern those variables used to model succession 

and disturbance: PVT, succession class, and succession age. These values are 

usually modeled from other GIS layers using topography, remotely sensed imagery, and other biophysical layers (Keane and others 2002a) and then assigned 

to mapped polygons using various GIS techniques (Keane and others 2000). 

The succession age presents a special challenge because it is difficult to obtain 

stand age from remotely sensed imagery or biophysical layers. Therefore, the 

user has the option of simulating a realistic succession age inside LANDSUMv4 

by specifying a zero for this polygon attribute. The simulated succession age 

can be computed as 1) a midpoint of the beginning and ending age assigned 

to the PVT-succession class combination (see the Simulating Succession section); 2) a randomly selected age between the beginning and ending succession 

class time span; or 3) the beginning age for that succession class (this option is 

specified in the Simulation Input File, Appendix A.3). The last polygon attribute 

is the initial area (m2) of the polygon, which is used to check for discontinuities 

in the Initial Polygon Map.

The most important polygon attributes are potential vegetation type (PVT) 

and succession class. As mentioned, a PVT identifies a distinct biophysical setting that supports a unique and stable climax plant community under a constant 

climate regime in the absence of disturbance (Daubenmire 1966, Fischer and 

Bradley 1987, Pfister and Arno 1980). A PVT is usually identified in the field by 

overstory and understory indicator plants. These plants indicate a biophysical 

setting where, in the absence of disturbance, a unique plant community will 

persist. The PVT concept is used as a framework for simulating succession in 

LANDSUMv4. It is hypothesized that, if a unique climax community is supported 

by this biophysical setting when there are no disturbances, then there should 

be a unique set of successional pathways that converge on this unique plant 

community (Arno and others 1985, Steele 1984). And, each of these pathways 

originates from a specific disturbance(s).

There are three ways to stratify LANDSUMv4 parameters and results within 

a simulation. First, each polygon and each successional parameter is assigned 

a region code. The region code was designed for the LANDFIRE project where 

the region refers to the mapping zone that was used to stratify the entire United 

States. Another use for the region code in LANDFIRE is to assign variants of 

succession pathways for a PVT across large regions. Some simulation landscapes are so large that the successional dynamics for a Douglas-fir PVT, for 

example, are different across regions on that landscape. Therefore, the user 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-171CD. 2006 19

has the option of stratifying succession parameters for one PVT over different 

regions of the landscape. The next stratification field is the management zone 

(see Disturbance Simulation section). The management zone is used to assign 

a disturbance scenario to different parts of the landscape. An area on the landscape that is in wilderness, for example, might get a different set of disturbance 

probabilities than another zone that is actively managed for timber production. All 

polygons are assigned a management zone in the Polygon Input File (Appendix 

A.5) and the simulation scenarios are assigned to each management zone in the 

Management Plan Input File (see Disturbance Simulation section and Appendix 

A.10). The last stratification field is called the landscape stratum and is assigned 

to a polygon in any strategy desired by the user. The landscape strata have no 

intrinsic use in most LANDSUMv4 applications, but, as mentioned, it is used 

in LANDFIRE to spatially define areas to summarize landscape fluctuations in 

succession classes.

Creating input files—There are two types of input files in LANDSUMv4: 

scenario files and description files. Scenario files describe various aspects that 

are unique to the simulation while the description files portray site or polygon 

existing conditions. Scenario files reflect a particular simulation application, while 

the description files must accurately describe mapped or measured ecological 

conditions and characteristics.

All input and output files in LANDSUMv4 are in ASCII format and are space 

or comma delimited. This is often referred to as text format. Each input or output 

ASCII file is configured as a vertical or horizontal file. Vertical files contain only 

one input value per line with a descriptor of that input value following the number. 

Horizontal files contain more than one value per line and resemble a table. In 

general, LANDSUMv4 input files are both vertical and horizontal, while all output 

files are only horizontal. The horizontal file structure is used for efficiency while 

the vertical file structure is used for ease of development, greater understanding, and error management. Files in LANDSUMv4 are also cycling or discrete. 

Cycling files contain the same format that is cycled downward or repeated for 

each similar instance on the next line or set of lines. For example, disturbance 

parameters are cycled vertically for each PVT on the landscape. Discrete files 

do not cycle and always contain a discrete number of vertical records.

The first line of every LANDSUMv4 input file is called a title line and is used 

to describe the file and its origins. There are 256 characters available to craft a 

descriptive title that can uniquely identify the file while it is being viewed within 

a text editor. The title line should describe who, what, when, where, and why the 

file was developed. For example, one header might have the project for which 

the file was developed, who developed the file, when the file was developed 

(version), where the data were taken (data sources), and why this file was used 

for this particular simulation scenario.

The Polygon Input File (fig. 3, Appendix A.5) is a horizontal, cycling ASCII 

file that contains the list of all polygons on the landscape with values for each 

of the stratification, simulation, and area variables. The simulation landscape is 

composed of polygons defined by groups of pixels of the same PVT and succession class, and the number of polygons on the landscape dictates the speed of 

model execution. As mentioned, LANDSUMv4 simulations are performed at the 

polygon-level, which means succession and disturbance processes (except for 

fire) are simulated stand-by-stand in the order of the polygons in the input list. 

For example, all non-fire disturbances are evaluated for the first stand, then for 

the second stand, and so on. So, the more stands that are in the polygon list, 
20 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-171CD. 2006

the longer LANDSUMv4 will take to run. The initial list of polygons is input to the 

model using the Polygon Input File and it is important that this list reflects the 

diversity of vegetation on the landscape with the minimal number of polygons. 

In LANDFIRE, we defined polygons by unique clusters of PVT-succession class 

combinations. But, long polygon lists will mean long LANDSUMv4 execution times. 

The simulation of fire across the landscape will dissect these initial polygons to 

create new ones and therefore the number of polygons will increase throughout 

simulation time. So, long simulation periods coupled with frequent fire regimes 

will always result in long execution times.

The Map Input File (fig. 3, Appendix A.4) contains all the geo-referenced 

coordinates and map specifications (upper X and Y coordinates, missing value 

number, cell size, number rows, and number columns) for the input and output 

maps. The program compares these parameters to those specified in the header 

of the input maps in ASCII format (ARCGRID) to ensure consistency. The program also has an option where a small box within the simulation landscape can 

be specified, and all output maps generated from the model will be confined to 

the specified box.

The Spatial Disturbance Input File (Appendix A.6) contains all parameters 

to simulate the spread of any disturbance across a landscape (fig. 3, 4). Any 

number of disturbances can be specified in this file, but only fire was spatially 

simulated in LANDFIRE. A set of parameters are used to simulate the initiation of 

the disturbance (for example, fire ignition) and another set are used to calculate 

the size of the disturbance. The user must also select the spread algorithm: cell 

automata, cell spread (suggested), and “cookie cutter” techniques. Wind speed 

and direction are specified in this file to account for wind effects on spread, and 

a distribution of wet, normal, and dry weather years is specified to account for 

climate effects on fire ignition. The user can also specify the weather year (wet, 

dry, normal) by simulation year in the Fire Year Input File (Appendix A.13) for 

the LANDSUMv4 model.

Succession parameters are entered in the Vegetation Input File (Appendix 

A.7) and disturbance effects parameters are entered in the Disturbance Input 

File (Appendix A.8). The beginning and ending year of a succession class, along 

with the next transitional succession class, are specified in the Vegetation Input 

File by PVT to deterministically simulate succession. The effects of a disturbance 

are specified in the Disturbance Input File including the succession class that 

the polygon would transition to if a disturbance were initiated and the succession 

age or increment this polygon would receive. All these effects parameters are 

stratified by PVT and succession class.

Disturbance occurrence probabilities are specified in the Scenario Input File 

(Appendix A.11) by PVT and succession class. These probabilities reflect the 

chance that a polygon will experience a disturbance in any given year. There 

can be multiple scenarios contained in this file to reflect the change in climate 

and land use over time. Each scenario is assigned to a management zone and 

a simulation time period in the Management Plan Input File (Appendix A.10). 

A management zone is a region in the simulation landscape where historical 

or current disturbance probabilities would be significantly different because of 

changes over time or changes in land use.

Two input files in LANDSUMv4 allow the assignment of additional attributes 

to polygons. The Vegetation Fix Input File (Appendix A.9) allows the user to assign more consistent succession classes to polygons that have been assigned 

the wrong succession class for its PVT. The program scans all polygons for 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-171CD. 2006 21

errors in the Polygon Input File, and if it finds a succession class that does not 

belong in a PVT based on information in the Vegetation Input File, it then scans 

the Vegetation Fix Input File for the appropriate fix for that situation. The fix is 

only for the current simulation and the program does not fix the Polygon Input 

File. The Attribute Input File (Appendix A.12) allows the user to assign up to five 

attributes to combinations of PVT and succession class. Examples of attributes 

include fuel models, canopy bulk density, hiding cover, and canopy cover. The 

cover type and structural stage attributes (two characteristics that define a 

succession class; see discussions above) are also assigned in the Vegetation 

Attribute File. These attributes can be mapped over the simulation, or the area 

of these attributes can be summarized in generated output.

Other simulation characteristics—There are a number of other algorithms 

used in LANDSUMv4 simulations that deserve mentioning as they are important 

to the interpretation of the results. The random number generation is perhaps the 

most critical computation in this stochastic model and several random number 

generators are available in LANDSUMv4 and are specified in the Simulation Input 

File (Appendix A.3). However, these random number generators all generate 

the same random number sequence so run-to-run variations are not possible. 

The system default random number generator is used to generate entirely different random number streams across simulation runs but some system random 

number generators are not always statistically random. The random number 

scheme (same or different random numbers across simulations) and generator 

(see Appendix A.3 and the Simulation Input File) are selected according to the 

simulation objective. If the evaluation of alternative management scenarios is 

desired, then the same random number scheme should be used. However, if the 

variation between simulations needs to be quantified, then a different random 

number sequence should be used for each run using the system generator.

One of the most difficult tasks in preparing a LANDSUMv4 simulation is the 

development of the Polygon Input File (Appendix A.5) and Initial Polygon Map 

(Appendix A.4). Often, the GIS synthesis of cover type, structural stage, and PVT 

maps into a polygon layer results in some polygons having successional class 

assignments that are not present in the successional pathways for that polygon’s 

PVT. This is a common problem and, in previous LANDSUM versions, it took a 

long time to rectify these illogical combinations because the misfits had to be 

dealt with one at a time (Keane and others 1996a, Keane and others 1996b). This 

required that new versions of the PVT and vegetation input maps be developed 

where pixels were modified so that the PVT-succession classes were congruent. 

This version of LANDSUMv4 has the ability of assigning a new succession class 

to an illogical succession class inside the model so that new maps need not be 

developed and time is saved in the initialization phase. This is done with parameters found in the Vegetation Fix Input File (Appendix A.9) where each illogical 

PVT-succession class combination found during the error scanning process (see 

next section) is entered into the file along with the new succession class assignment. This routine also allows for the stochastic assignment of multiple valid 

succession classes for each illogical combination. For example, a Douglas-fir 

PVT might have a succession class that has a white pine cover type that is not 

found in the Douglas-fir pathway. The user may reassign all white pine succession 

classes to ponderosa pine, or the user may decide to reassign 50 percent of the 

white pine polygons to ponderosa pine and 50 percent to lodgepole pine. See 

Appendix A.9 for details. To help facilitate the process, all illogical combinations 
22 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-171CD. 2006

are written to the end of the Error Output File (see next section) and this can be 

used to start the development of the Vegetation Fix Input File.

Model Output

Results generated from LANDSUMv4 can be summarized in tabular ASCII 

files and mapped into grid layers in the ARC/INFO Grid format (fig. 3). Most 

tabular results summarize total area (m2) by simulation year, project, region, 

management zone, landscape strata, PVT, and succession class. Simulation 

data are summarized in five different tabular formats and written to separate files 

as specified in the Simulation Input File (Appendix A.3): landscape, disturbance, 

fire, and LANDFIRE summaries. Model output is written to these files at userspecified intervals, such as every 20 or 100 years, and the user enters a year 

to start printing simulation results (see Simulation Input File). For example, the 

user could start printing results at year 250 and print those results every 20 years 

until year 5,000. This is especially handy if the user does not want to include the 

first couple of simulation centuries to allow a period for the model to come into 

equilibrium (see Recommendations section).

Two output files are essential for evaluating the validity of LANDSUMv4 executions and these files should be examined after every simulation. The Echo 

Output File contains a summary of all input parameters specified for all input 

files (fig. 3). This file should be examined to detect if any parameters are in error 

or if the parameters were not input to the model correctly. The Error Output File 

contains all warnings and errors the program encountered as it was performing a scan of the input parameters. LANDSUMv4 conducts a thorough error 

scan by comparing all input parameters across files to ensure no errors will be 

encountered during the simulation. Warnings are issued if the input parameters 

are inconsistent across or within files but these inconsistencies do not prevent a 

successful execution. An error issued in the Error Output File must be fixed for 

the model to initiate a simulation.

All other LANDSUMv4 output files are formatted to be used as input to statistical 

programs or database software for further analysis (see Appendix B.1). Output to 

these files is generated at a simulation year interval specified in the Simulation 

Input File (Appendix A.3). The user can also select the files to be generated in 

the Simulation Input File. The Polygon Output File generates a list of polygons 

present on the simulation landscape and writes the attributes of each polygon 

including region, zone, PVT, and succession class, and the simulated area (m2) 

by simulation year. This output file can be used as a Polygon Input File for any 

output year. The Landscape Output File summarizes simulated landscape area 

(m2) at each output year by region, management zone, PVT, and succession 

class. The Disturbance Output File summarizes the estimated area (m2) affected 

by each disturbance stratified by region, management zone, PVT, and succession 

class. The Fire Output File is a file that contains the starting coordinate, PVT 

of the starting pixel, weather year, and fire size of every fire simulated on the 

landscape. The LANDFIRE Output File generates summaries of simulated area 

(m2) for all succession classes by the region, zone, strata, and PVT stratifications 

(as specified in the Vegetation Attribute Input File; Appendix A.7). Formats for 

these files are detailed in Appendix B.1.

Spatial data layers or digital maps are another form of output generated by 

LANDSUMv4. The model can generate up to 13 digital data layers in the ARC/INFO 

ASCII GRID format for the same simulation years as printed in the tabular output 

of the above files (specified in the Simulation Input File, see Appendix A.3). The 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-171CD. 2006 23

desired output layers are specified in the Map Input File as a pathname; the word 

“none” or “NONE” is entered if that particular layer is not desired. The Polygon 

Output Map is a layer where each pixel is assigned a unique polygon number that 

can be cross-referenced with the Polygon Output File. The Fire Output Map is a 

fire atlas of the number of times each pixel has been burned during the simulation. The Succession Class Output Map has each pixel assigned a succession 

class and the Cover Type and Structural Stage Output Maps have each pixel assigned the cover type or structural stage code specified for that PVT-successional 

class combination in the Attribute Input File (Appendix A.12). The Disturbance 

Output Map has the unique disturbance code assigned to each pixel for each 

disturbance simulated for that year of execution. There is also an output map 

where each unique combination of PVT and succession class is assigned to a 

pixel (PVT-Class Output Map). The Age Output Map has the succession age of 

each pixel on the simulation landscape. Last, the user can generate five output 

maps from the five attributes specified for each PVT-succession class combination in the Attribute Input File (Appendix A.12). These maps can be especially 

useful in understanding the behavior of the model and for estimating the range 

and variation of patch dynamics (for example, size, shape).

Methods

The Test Landscapes

The two landscapes that were used to demonstrate, test, and explore the 

model are from the two prototype areas in the LANDFIRE prototype project (Pratt 

and others, in press) (fig. 5). Square landscapes approximately 100,000 ha were 

taken from the Central Utah prototype area and the Northern Rockies prototype 

area (fig. 5). A PVT layer was created from a statistical model using biophysical 

process layers (Holsinger and others, in press), and cover type and structural 

stage layers were computed from a merging of satellite imagery (Landsat 7 

Thematic Mapper), biophysical modeling, and pattern analysis (Zhu and others, 

in press). The PVT, cover type, and structural stage layers were combined using standard GIS techniques to create polygons of contiguous pixels of similar 

PVT-cover type-structural stage combinations. The cover type-structural stage 

combinations were then assigned succession classes based on those defined 

in the successional pathways (discussed next). Both test landscapes were not 

stratified by management zones or regions for simplicity.

Successional pathways were constructed by LANDFIRE personnel for every 

PVT in both prototype areas (Long and others, in press). These pathways include all successional development trajectories with linked disturbance paths. 

All succession and disturbance parameters were quantified from existing data 

or study results found in the literature, and if no data were available, the parameters were estimated from the most similar succession class or disturbance type 

using opinions from local experts. Only one simulation scenario was employed 

for both areas and it was an historical scenario that attempts to simulate landscape dynamics prior to the turn of the 20th century (circa 1600 to 1900 AD). 

Fire occurrence probabilities were estimated from a host of fire history studies 

for both areas. Documentation of the succession and disturbance parameters 

are presented in Long and others (in press).

The fire size parameters were estimated from a summary of the NIFMID database for both areas (Hardy and others 2001, Schmidt and others 2002). The 

fire climate (wet, moderate, and dry years per decade) was estimated from the 
24 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-171CD. 2006

distribution of the Palmer Drought Severity Index (PDSI) over a century long 

weather record as estimated from tree chronologies where years with an average PDSI less than –2.99 were considered dry fire years, 1.99 to –2.99 were 

considered normal years, and those with PDSI above 1.99 were considered wet 

years.

Figure 5—The two prototype areas used in the LANDFIRE project and the test simulation landscapes 

within these prototype areas that were selected for model demonstration.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-171CD. 2006 25

Model Demonstration

The demonstration of the LANDSUMv4 model was completed for the two areas 

to illustrate the wide range of spatial and tabular outputs available to the user. 

First, a typical application of LANDSUMv4 was simulated for the test landscapes 

where the initial polygon map was created by assigning the most dominant succession class for each PVT to all pixels associated with that PVT in the PVT 

map. The most dominant succession class was determined by a GIS summary of 

the succession class layer described in the previous section. The most common 

succession class was used because previous investigations into LANDSUMv4 

behavior showed that the model comes into equilibrium with the input parameters much quicker if the input landscape is simplified and the current conditions 

(cover type and structural stage layers developed from recent satellite imagery) 

were NOT used as the initial polygon map. This is primarily because the current 

composition and structure of succession classes (today’s landscapes) are quite 

dissimilar from distributions on historical landscapes thereby extending the time 

required to achieve equilibrium behavior (Keane and others 2002c). The layer 

was then converted to a polygon layer by identifying contiguous pixels of identical 

PVTs using standard GIS techniques. A polygon list was then created from the 

polygon map using the PVT and dominant succession class as the vegetation 

attributes. The model was executed for 1,000 years with maps and files being 

printed every 50 years to demonstrate the scope of LANDSUMv4 output. All 

generated map files were brought into ARC/INFO GIS to create the digital maps. 

Tabular results, output in the ASCII files, were imported into the SAS statistical 

package to create examples of LANDSUMv4 simulation results. Simulated map 

outputs were also imported into the FRAGSTATS program (McGarigal and Marks 

1995) to describe landscape structure in terms of descriptive statistics (Keane 

and others 2002c)

The LANDSUMv4 model was then applied to the two test landscapes using the 

LANDFIRE protocols to demonstrate how the model was used to calculate an index 

of departure (Keane and Rollins, in press). The entire landscape was initialized to 

the most dominant succession class for each PVT from the LANDFIRE vegetation 

maps. All historical LANDFIRE pathways were used to simulate succession and 

LANDFIRE historical scenario files were used to simulate fire dynamics (Pratt 

and others, in press). The test landscape was stratified into landscape reporting 

units (sub-landscapes) to demonstrate the scale dependency of LANDSUMv4 

output. The resultant LANDFIRE Output File (Appendix B.1) was used as the 

historical reference file in the LANDFIRE statistical analysis program, called 

HRVSTAT, to compute an index of departure from the current conditions. The 

HRVSTAT program compares the current conditions with the range and variation 

of historical conditions using standard parametric metrics. An index between 0 

and 100 is calculated that rates the degree of departure of the current conditions 

from historical conditions using probability distributions. The current landscape 

conditions for the test landscape, quantified from the set of LANDFIRE vegetation maps that were created from satellite imagery (Zhu and others, in press), 

were summarized from the LANDFIRE vegetation data layers into a data file 

similar in structure to the LANDFIRE Output File using GIS techniques. Various 

graphs and tables were generated to illustrate this intensive LANDFIRE analysis 

process. These data were also used to compute Fire Regime Condition Class 

(FRCC) using the methods outlined in Hann (2004).
26 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-171CD. 2006

Sensitivity Analysis

A comprehensive analysis of the sensitivity of LANDSUMv4 output to several 

important input parameters was conducted to develop the simulation strategy for 

the LANDFIRE project. Keane and others (2002c), Pratt and others (in press), 

and Keane and others (2004a) had previously performed an extensive sensitivity analysis on LANDSUM version 2.0 to detect the importance of a small set of 

input parameters on patch metrics and fire regimes simulated from landscapes 

in the northern Rocky Mountains. Results from those studies allowed us to narrow our sensitivity analysis to a limited set of LANDSUMv4 parameters, called 

test parameters, that we felt were important to LANDFIRE concerns (table 1). 

The sensitivity analysis performed here differed from previous LANDSUM efforts 

in that burned area was used as the diagnostic variable rather than landscape 

structure (patch dynamics). It was important to identify if the sensitivity of certain 

input parameters was different across landscape structure and composition, and 

it was also important to detect sensitivity changes from previous studies using 

the LANDFIRE input parameters and initial data. Three values were assigned 

to each of the test parameters to encompass the full range of valid values for 

that variable.

We evaluated the effect of two fire parameters, wind speed and landscape 

size, on the behavior of LANDSUMv4 simulations using simulated burned area 

as the diagnostic variable (table 1). This detailed sensitivity analysis was performed to judge the sensitivity of altering levels of four explanatory factors on 

only one response variable - percent area burned on the simulation landscape. 

The four factors in this sensitivity analysis were: (1) average fire size (ha) which 

we termed fire size; (2) frequency scalar - a multiplier of the expected mean 

of the Weibull fire size distribution taken from the Spatial Disturbance Input File 

fire size parameters (values of the scalar variable less than 1.0 decrease fire 

frequency, whereas values greater than 1.0 increase the frequency); (3) wind 

speed (m sec-1); and (4) simulation landscape size (ha). Each factor has three 

levels as detailed in table 1.

We ran the model for each combination of sensitivity factors on six different 

landscapes that were called sites in the statistical analysis (fig. 6). Site was considered a random effect nested within the landscape size factor. Percent area 

burned was calculated for 500 time intervals where model output was printed 

every 20 years for a 10,000 year simulation (10,000 / 20 = 500 analysis units). 

To achieve independence across these time intervals, the analysis units were 

averaged for every five time periods for a total of 100 sample observations. 

To study the effects of altering the levels of each factor, a partially hierarchical 

Table 1. The levels for each factor used in the sensitivity analysis. The 

factors are: fire size - average fire size; frequency scalar - ratio of 

ignition to fire size; wind speed - wind speed at mid-flame length 

at time of fires, and landscape size - area of simulation landscape 

(ha).

Factor Level 1 Level 2 Level 3

Fire size (ha) 25.0 50 75

Frequency Scalar 0.75 1.00 1.25

Wind Speed (m sec-1) 1.0 5.0 25.0

Landscape Size (ha) 50,000 100,000 200,000

(50k) (100k) (200k)
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-171CD. 2006 27

ANACOVA was performed where the covariate was time. Interactions up to four 

ways were initially examined. Calculations were performed using S-plus 6.2 and 

SPSS 10.

Results

Model Demonstration

A set of simulation results from LANDSUMv4 generated from the two test 

landscapes are shown in figure 7. These simulation results portray the temporal 

dynamics of the three most dominant successional classes over the 1000 years 

of simulation for the Zone 16 (fig. 7A, 7B, and 7C) and Zone 19 (figs. 7D, 7E, and 

7F) demonstration landscapes (fig. 5). Graphs of cover type, structural stage, 

disturbance type, and fuel model dynamics could also have been easily created 

along with a host of other landscape characteristics (Appendices A.3 and A.4). 

These graphs illustrate the range and variation of estimated species composition 

on the historical test landscapes. The temporal dynamics of simulated burned 

area are shown in figure 7G for the Zone 16 landscape and figure 7H for the 

Zone 19 demonstration landscape. Note the large fire years are more frequent 

on the Zone 16 landscape.

Maps of estimated cover types for four simulation years during the 1,000 year 

simulation are shown in figure 8 for both demonstration landscapes. Cover types 

were used because there were fewer mapped entities than succession classes, 

which made the map legend easier to read and understand. Fire perimeters can 

be seen in the later simulation years. Fire regime maps generated by LANDSUMv4 

from all fires that occurred during the simulation, shown in figure 9, illustrate the 

Figure 6—The six landscapes used as sites to test the sensitivity of LANDSUMv4 input parameters 

across various ecosystems and topography. Each landscape has three sizes: A) 50,000 ha (50k), 

B) 100,000 ha (100k), and C) 200,000 ha (200k).
28 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-171CD. 2006

severity and frequency of fire across the simulation landscape. A fire frequency 

map (fig. 9A) depicts the mean fire interval for each pixel calculated by dividing 

the number of fires experienced by each pixel by the simulation length (1,000 

years). Three fire severity maps were generated for each demonstration landscape: percent of non-lethal surface fire (fig. 9B), mixed severity fire (fig. 9C) and 

stand-replacement fire (fig. 9D), for each pixel across all 1,000 years of simulation. The three maps can be combined to illustrate the mix of fire severities that 

occur across the simulated landscape.

The LANDFIRE prototype project used the LANDSUMv4 model to generate 

a time series of historical conditions to evaluate against the condition of current 

landscapes. A demonstration of this LANDFIRE application of LANDSUMv4 

is shown in figure 10. First, a map showing a measure of departure of current 

Figure 7—LANDSUMv4 output generated from a typical application of LANDSUMv4 for the two 

test landscapes using historical disturbance parameters. Graphs of the extent of three dominant 

succession classes on the historical landscape showing range and variation of landscape 

composition for the Zone 16 test landscape: A) Aspen-Birch HCLH, B) Douglas-fir HCHH, C) 

Lodgepole pine LCHH, and the Zone 19 test landscape: D) Douglas-fir LCHH, E) native perennial 

bunchgrass grasslands HCHH, F) big sagebrush HCLH. Graph of area burned across the historical 

simulation era for the Zone 16 landscape (G) and the Zone 19 landscape (H). Structural stage 

names are defined as: LCLH-low cover low height early succession stage, HCLH-high cover low 

height mid-seral stage, HCHH-high cover high height late succession stage, LCHH-low cover 

high height disturbance maintained late succession stage
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-171CD. 2006 29

Figure 8—Digital maps of cover type at four simulation years: A-200 year, B-400 year, C-600 year, 

D-800 year for the Zone 16 demonstration landscape and E-200 year, F-400 year, G-600 year, 

4-800 year for the Zone 19 demonstration landscape.

landscape composition from simulated historical ranges and variability of landscape 

composition (figs. 7A to 7F) for 1 km2 landscapes within the Zone 16 and Zone 19 

(fig. 10A). This departure was calculated using the computer program HRVSTAT 

detailed in Holsinger and others (in press). Next, an observed significance level 
30 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-171CD. 2006

is shown in figure 10B for each of the 1 km2 landscapes where values are probability values that assess if the departure from simulated historical conditions in 

figure 10A is statistically significant. Figure 10C depicts the FRCC (Fire Regime 

Condition Class) calculated from methods presented in Hann (2004) (Holsinger 

and others, in press). This FRCC index will be used to guide the prioritization 

and implementation of fuel treatments across the nation.

Figure 9—Digital maps of fire regime characteristics generated for Zone 16 and Zone 19: A) mean 

fire interval in years; B) percent of non-lethal surface fire; C) percent of mixed-severity fire; and 

D) percent of stand replacement fire.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-171CD. 2006 31

Figure 10—Demonstration of the use of LANDSUMv4 for calculating departure measures using 

the HRVSTAT program for Zone 16 and Zone 19: A) departure index; B) observed significance 

level; and C) Fire Regime Condition Class.

Sensitivity Analysis

Results of the sensitivity analysis show the complex influences of input parameters on area burned. Results of the partially hierarchical ANACOVA (table 

2) reveal that there is only one highly significant (p-value<0.0005) interaction, 

wind speed and fire size. The interaction between fire size and frequency scalar 

was marginally significant (p-value=.049). Due to the insignificance of most of 
32 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-171CD. 2006

the interactions and the desire to examine the main effects, the interactions were 

dropped from the model.

The results of the main effects ANACOVA in table 3 show that the effect of 

landscape size is insignificant (p-value=0.882) for percent area burned. The 

remaining main effects and time are all highly significant (p-value<.0005). The 

most significant effect is from the frequency scalar factor where changes in the 

frequency scalar had the largest effect on percent area burned. Wind speed was 

also highly significant; therefore varying the levels of wind speed will also have 

a large effect on percent area burned. Although fire size is highly significant, the 

Table 2. The analysis of covariance (ANACOVA) of the four sensitivity analysis factors and their 

interactions. The abbreviations mean: df-degrees of freedom, MS-mean sum of squares, 

F-ratio, p-value-probability value for significance. Most of the interactions are insignificant 

while the main effects are highly significant except for Landscape size (bold numbers signify significance p<0.05). The term (Site) indicates the interaction of simulation landscape. 

See table 1 for description of factors.

Source df MS F p-value

Analysis Factors

Landscape size 2 4065 0.185 0.833

Fire size 2 3871 143.4 <0.0005

Frequency scalar 2 34466 1148.89 <0.0005

Wind speed 2 12197 369.6 <0.0005

Factor Interactions

Landscape size x Fire size 4 1 0.056 0.994

Landscape size x Freq scalar 4 10 0.33 0.856

Landscape size x Wind speed 4 4 0.114 0.977

Fire size x Freq scalar 4 7 2.54 0.049

Fire size x Wind speed 4 87 34.5 <0.0005

Freq scalar x Wind speed 4 1 0.14 0.967

Landscape size x Fire size x Freq scalar 8 1 0.44 0.890

Landscape size x Fire size x Wind speed 8 2 0.89 0.531

Landscape size x Freq scalar x Wind speed 8 1 0.21 0.988

Fire size x Freq scalar x Wind speed 8 3 1.35 0.226

Fire size x Freq scalar x Wind speed x 16 2 1.47 0.1221

Landscape size

Time 1 124 494.0 <0.0005

Landscape size (Site) 15 21939

Fire size x Landscape size (Site) 30 27

Frequency scalar x Landscape size (Site) 30 33

Wind speed x Landscape size (Site) 30 33

Fire size x Freq scalar x Landscape 60 3

size (Site)

Fire size x Wind speed x Landscape 60 3

size (Site)

Freq scalar x Wind speed x Landscape 60 4

size (Site)

Fire size x Freq scalar x Wind speed x 120 1

Landscape size (Site)

Error 48113 .252

Total 48599
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-171CD. 2006 33

F ratio for this factor is much smaller. Therefore, the effects of varying the levels 

of fire size do not have as large of an effect on percent area burned as do the 

frequency scalar and wind speed.

Multiple comparisons were performed for fire size, frequency-scalar and 

wind speed using Bonferroni tests (table 4). All of the multiple comparisons 

were highly significant implying that all of the treatment levels were significantly 

different from one another. The largest differences occurred for the frequency 

scalar factor. Within this factor, the difference between the scalars 0.75 and 1.25 

was the greatest. Based on the multiple comparisons and the ANACOVA table 

for the main effects, percent area burned is most sensitive to varying levels of 

the frequency scalar variable. The differences between the 0.75 and 1.25 levels were the most significant, and, therefore the model will be most sensitive 

to varying these levels of the factor. The wind speed factor appears to have 

the second highest effect on percent area burned (-1.58 in table 4) based on 

the significance of the ANACOVA model (tables 2 and 3). However, the mean 

Table 3. Results of the ANACOVA main effects only. The factor with the largest effect on 

percent area burned was frequency scalar followed by wind speed. Although the effect 

of scale was significant, the F ratio is much smaller. There does not appear to be a 

significant effect from landscape size. Bold numbers represent significance (p<0.05)

Source df MS F p-value

Landscape size 2 4065 0.127 0.882

Fire size 2 3871 18.98 <0.0005

Frequency scalar 2 34466 565.0 <0.0005

Wind speed 2 12197 370.0 <0.0005

Time 1 124 452.0 <0.0005

Landscape size (Site) 15 31939

Fire size x Landscape size (Site) 30 204

Frequency scalar x Landscape size (Site) 30 61

Wind speed x Landscape size (Site) 30 33

Error 48576 .247

Total 48600

Table 4. Results of the multiple comparisons for the factors fire size, frequency scalar, 

and wind speed. All of the differences for each level were highly significant (p<0.05).

95% Confidence

Interval

Variable Treatment Levels Mean Diff for Difference p-value

Fire size 25 vs 50 0.621 (.606,.637) <.0001

25 vs 75 0.964 (.952,.977) <.0001

50 vs 75 0.343 (.331,.356) <.0001

Frequency 0.75 vs 1.00 1.54 (1.52,1.55) <.0001

Scalar 0.75 vs 1.25 2.92 (2.90,2.93) <.0001

1.00 vs 1.25 1.38 (1.36,1.39) <.0001

Wind speed 1 vs 5 -0.158 (-.173,-.142) <.0001

1 vs 25 -1.58 (-1.59,-1.56) <.0001

5 vs 25 -1.42 (-1.43,-1.40) <.0001
34 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-171CD. 2006

difference, although significant, between 1 and 5 was quite small compared to 

the differences between 1 and 25 and 5 and 25. The factor that has the least 

effect, although highly significant, on percent area burned is the fire size factor. 

Figure 11 shows side by side box plots of the factors fire size, frequency scalar 

and wind speed at three different simulation sites where each site had three different-sized landscapes. The box plots reveal that varying levels of frequency 

scalar has the largest effect on the response variable—percent area burned. 

Also evident is the large difference in the percent area burned for the wind speed 

level 25 versus wind speed levels 1 or 5.

Discussion

The LANDSUMv4 model seems to be a viable vehicle for simulating historical fire and vegetation time series to use as reference to compare with current 

conditions as a means of locating, prioritizing and implementing future fuel treatments. We feel the model is a good compromise between simplicity and realism 

for generating historical chronosequences. Other models with more detailed 

simulations of fire spread and vegetation development require prohibitively long 

simulation times and difficult parameterization. For instance, simulating fire spread 

at hourly time steps using weather, wind, and fuels dramatically increases simulation times especially if the simulating time span were 5,000 years. LANDSUMv4 

Figure 11—Side by side box plots of the sensitivity analysis factors of fire size, frequency scalar and 

wind speed. The remaining variables not in the box plot are held at level 2. The box plots reveal 

that varying the level of the frequency scalar has the largest effect on percent area burned. The 

box plots also reveal the large difference between the 25 level of wind speed versus the 1 and 

5 level of wind speed.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-171CD. 2006 35

has a simple state-and-transition pathway model to simulate succession, and 

a simple cell percolation model to simulate fire spread. This reduces execution 

time and allows the simulation of large landscapes over long time spans to get 

temporally deep simulated chronosequences.

Even with it’s simplicity of design, the LANDSUMv4 model, in its current form, is 

quite difficult to initialize, parameterize, and execute because it is a command-line 

program linked to a set of 14 ASCII input files (fig. 2). The C program is available 

on this CD, or it can be downloaded from a number of web sites including www.

frames.nbii.gov, www.landfire.gov, and www.fire.org in the near future. Current 

efforts are underway to simplify the input and output structure of LANDSUMv4 

and to provide a user-friendly front-end to easily specify simulation options. 

Moreover, the model is constantly being improved and revised to incorporate 

the latest simulation technology and research findings.

Sensitivity Analysis

The effects of altering levels of fire size, frequency scalar, and wind speed 

are highly significant but the effect of landscape size on percent area burned 

appears insignificant in our analysis (table 2, fig. 11). Most of the interactions 

between the factors proved to be insignificant, implying that the effect of one of 

the factors is not dependent on the level of another factor. This means it is very 

important that the input fire return intervals (in other words, fire probabilities) reflect 

realistic conditions and that realistic wind speeds and fire size parameters be 

used since they have a large impact on the area burned. We suggest that these 

parameters be initially estimated using any available data, and then iteratively 

adjusted as the results from the model (area burned) are compared with actual 

field measurements or expert opinion. Fortunately, there does not appear to be 

a significant effect of landscape size so the size of the simulation landscape can 

be selected to match computer resources for increasing efficiency and reducing 

simulation time.

Other sensitivity analyses on previous versions of LANDSUM also emphasize 

the importance of selecting important landscape characteristics for simulation. 

Keane and others (2002c) note that the shape and orientation of the landscape is 

critical for determining realistic fire regimes given the input values of wind speed 

and direction. They also found that there should be a simulation buffer around the 

target simulation landscape to ensure that immigrating fires are allowed to burn 

onto the target simulation landscape thereby reducing edge effect. Moreover, 

they found that output interval should be long enough to minimize temporal autocorrelation but large enough so that there is not an overabundance of redundant 

simulation years in the record. The LANDFIRE prototype project also performed 

a variety of sensitivity analyses on different landscapes and found roughly the 

same results (Pratt and others, in press). Fire size and probability input parameters remain the most sensitive and most important in a LANDSUMv4 simulation, 

whereas succession class time span is less important.

Model Parameterization

The quantification of succession and disturbance parameters in LANDSUMv4 

is a difficult task. Because LANDSUMv4 is a spatial model that simulates spatial dynamics on landscapes composed of multiple PVTs, it is often hard for the 

user to determine the effect of a parameter or parameter set on model results. 

Moreover, previously measured disturbance and succession parameters are rare 
36 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-171CD. 2006

in the literature and there are scant field data for the many PVTs and landscapes 

across the United States, so these parameters are often quantified through an 

iterative process where the results of the model are used to set the parameter 

value. The user selects a starting value for the parameter, often taken from the 

next most similar succession class, and runs the model with this first estimate. 

Model results are evaluated and the parameter is modified if results disagree with 

experience. What is needed is a simplistic modeling platform that allows the testing 

and revision of model parameters without all the complexity of LANDSUMv4.

Model parameterization tool—One product of the Columbia River Basin 

Project (Hann and others 1998) was the VDDT (Vegetation Dynamics Development 

Tool) model that simulates succession and fire for one PVT in a non-spatial domain 

(Beukema and Kurz 1998). The VDDT model was built so that users could quickly 

prototype succession pathways for simulating succession and disturbance to 

determine the effect of management scenarios across the Columbia River Basin. 

VDDT serves as an excellent model building tool for LANDSUMv4 pathways.

VDDT allows the user to easily build and validate fire and successional parameters by comparing predictions against professional opinions and real historical 

data (Long and others, in press). It has an extensive graphical user interface 

that allows the quick and simple modification of parameters to determine their 

effect on PVT simulations. VDDT can also be used as a primary modeling tool, 

but since spatial relationships, such as fire spread, are NOT included in VDDT 

simulations, the results cannot be summarized to spatial units and the variability 

simulated by VDDT will tend to be underestimated over time. A spatial version of 

VDDT, called TELSA, simulates fire spread across the landscape but at resolutions coarser than LANDSUMv4 (Kurz and others 1999).

Fire occurrence probabilities—Both VDDT and LANDSUMv4 use fire probabilities to simulate fire processes. The input fire probabilities are point estimates 

of fire return interval. They do NOT describe the spatial aspect of fire regimes 

such as fire rotation or cycle. They only describe the probability that a fire will 

burn at a point on the landscape over long time spans. An example would be the 

number of times Rip Van Winkle got burned during his 20 year sleep. These fire 

probabilities are not fire ignition probabilities. They represent a one-dimensional 

statistic of fire occurrence over time and they do not infer other fire regime characteristics such as fire size, pattern, severity, and seasonality.

It is best if the fire probabilities are stratified by the three main fire severity 

types: non-lethal surface fires, mixed severity fires, and stand-replacement fires. 

Non-lethal surface fires burn surface fuels at low intensities but do not kill many 

overstory trees. Stand-replacement burns kill the majority of the dominant vegetation, often trees and shrubs (greater than 90 percent mortality). These fires 

include both lethal surface fires and active crown fires. Mixed severity burns 

contain elements of both non-lethal surface and stand-replacement fires mixed 

in time and space and include passive crown fires, patchy stand-replacement 

burns, and mixed severity underburns. Other types of severity classes exist, such 

as ground fires (in other words, smoldering fire burning extensive duff layers), 

but their effects must be manifest by changes in the successional pathways. 

The sum of the probabilities for these three fire types would equal the fire return 

probability, and the inverse would be the fire interval.

The two models use fire probabilities quite differently during simulation. VDDT 

uses the fire probabilities to determine how many points burn each year using 

the probability as a proportion. For example, a 0.1 fire return probability would 

translate into 10 percent of the points on the simulation landscape burning each 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-171CD. 2006 37

year, but the size of the landscape is unknown. Since VDDT does not integrate 

the complex processes of spread and ignition with climate and fuels, much of 

the annual variation in burn size and severity is lost so the simulated time series 

will often underestimate landscape variability in fire. LANDSUMv4 uses the fire 

return probabilities to calculate an ignition probability for each landscape pixel 

by multiplying the probability by the pixel size divided by mean fire size. Once 

an ignition is simulated, the fire is spread across the landscape based on wind, 

slope, and climate. This approach is better able to describe the variability of fire 

effects and pattern because of the spatial implementation, but it is still a crude 

approximation of actual fire dynamics because it does not include fuels, weather, 

and moisture conditions.

Estimating fire occurrence probabilities—Since very few studies have 

measured fire probabilities on the landscape as they are defined and used in this 

model, we must find a way to approximate these parameters from other sources. 

These sources may measure the fire probabilities using methods that may limit 

the use of probabilities as model parameters. We suggest that the user always 

compare the simulation results with observed conditions or expert opinion to 

ensure these parameters are acceptable. If not, parameters should be modified 

so that results fit with expectations.

Fire history studies remain our major source for estimating fire parameters, 

especially fire return interval, in the modeling efforts. A review and summary of 

the fire history literature is an excellent method for approximating these elusive 

model parameters. But, there are some problems of scale and analysis in the 

data collected in fire history studies that the user must account for when estimating fire probabilities. First, many fire history studies are usually conducted in 

small areas within a highly complex landscape. Topographical features and their 

orientation, coupled with predominant wind patterns, can influence fire history 

within small study areas. For example, study areas on the lee side of large lakes 

or rock fields will tend to underestimate fire frequency while study areas adjacent 

to highly flammable ecosystems, such as grasslands on Native American travel 

routes, may tend to overestimate fire frequency. Second, fire intervals are often 

determined from dating fire scarred trees and the scar years are compared across 

a certain number of trees to determine if that year was a fire year. This threshold 

number of trees is very important to the detection and identification of fire years. 

It is difficult to determine if a tree did NOT scar because the fire did not visit the 

tree or because the fuels were insufficient for generating a scarring fire. So, the 

computed fire return interval is dependent not only on the number of trees used 

to identify a fire year, but also the number of fire scarred trees sampled within 

the study area. Again, fire scars are point measures of fire history and do not 

integrate spatial interactions of fire spread. Therefore, when using fire history 

studies to estimate fire occurrence probabilities, the user should adjust fire return 

intervals to reflect point level data using one of the following techniques.

Most fire history studies report mean fire intervals (MFI) from the fire dates 

recorded on scarred trees. Again, reported MFIs are estimated from a cluster of 

trees at a stand level and mostly describe non-lethal surface fire, and sometimes 

mixed severity fire, frequencies. Fire history studies in stand-replacement fire 

ecosystems determine the fire rotation from the extent of successional communities on the landscape. The assumption is that the proportional distribution of age 

classes on the landscape can be used to approximate a probability distribution 

that can then be used to approximate MFI. The MFI derived from each type of 

fire history study is quite different in its interpretation but either can be used as 
38 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-171CD. 2006

a starting point in the quantification of fire model parameters in the LANDFIRE 

and FRCC efforts. MFIs from most field studies are estimated using clusters 

of trees at a stand level (frequency often called a Composite Fire Interval), not 

“point” data from a single tree. Second, most data come from relatively small 

sample sites (sites less than an acre in size, which increases the likelihood that 

a given fire burned most of the sample site). Third, the stand MFI for a given 

PVT is assumed to be representative for the PVT as a whole. Together, these 

assumptions suggest that stand MFIs can be used to estimate fire parameters 

in the models, regardless of landscape size.

Fire history studies are missing for many ecosystems and regions of the United 

States. Often, the only way to estimate fire and succession parameters in these 

regions is by expert opinion, sometimes euphemistically called the “Delphi” approach. This remains a viable method of approximating fire probabilities, but it 

should be augmented with a feedback process to ensure realistic model results. 

In this feedback process, the local experts estimate fire and succession parameters to the best of their abilities. These parameters are then fed into computer 

models, such as VDDT, and simulation results are compared with the expert’s 

expectations. Parameters are adjusted if needed and new results are generated. 

The process would stop once results agree with the expert’s experience. If fire 

probabilities are unknown for an ecosystem, the local experts can estimate a set 

of starting fire probabilities, and the run the models continually adjusting the fire 

probabilities until results concurred with observed landscapes.

Most fire history studies document fire events over a very short time interval 

of about three to five centuries. The climate and human land use during this time 

was quite unique and therefore the fire regime documented by these fire history 

studies may be only applicable to this period. However, we often use the documented fire regime parameters and simulate fire over longer time spans than 

the fire parameters capture. This has obvious limitations and results generated 

from thousand year simulation runs must recognize that the parameters used to 

describe the fire regime were static and quantified from data taken from a small 

slice of history.

One problem with the design of LANDSUMv4 fire simulation is that the pointlevel estimate of fire occurrence probabilities (Scenario Input File) are not closely 

linked to the simulated process of fire spread. As a result, the final fire frequency 

map created by a LANDSUMv4 simulation may reflect more fire than the map 

of the input fire probabilities (see figure 9A for an example). This is primarily 

because more fire starts are simulated in high fire frequency environments, typically at lower, drier elevations, and these starts will spread uphill and down-wind 

to burn what are typically low fire frequency environments. Subtle differences in 

landscape orientation, shape, topography, and prevailing wind will dictate spatial 

fire regimes. Because the fire probabilities are estimated from studies that do 

not quantify landscape relationships, it is difficult, at this time, to fully integrate 

fire spread with ignition and get realistic results.

Fire size parameterization—Good historical data may be even more difficult 

to obtain for quantifying the fire size distribution in the Spatial Distribution File 

(average fire size and fire size parameter, Appendix A.6). While fire scars or 

pollen records may be used to reconstruct historical fire frequencies, they have 

limited use for reconstructing historic fire perimeters or fire sizes. Fire atlases 

have been compiled for some ecosystems, but the temporal record is relatively 

short (see Rollins and others 2001 for example). Fire size data for the last one 

or two decades remain our only source of estimating historical fire size (Hardy 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-171CD. 2006 39

and others 2001, Schmidt and others 2002). The NIFMID database is probably 

the most important data source for fire size descriptions. However, these data 

reflect fire size distributions during the active fire suppression era where fire size 

distributions are probably not representative of historical conditions. Moreover, 

the NIFMID data have many problems, including double reporting of fires and 

the absence of many small fires (Schmidt and others 2002).

The two fire size parameters strongly influence fire and vegetation dynamics. 

A consistent methodology for setting these parameters to achieve appropriate 

fire frequencies is important. As we discussed, the historical data available for 

estimating these parameters is limited. We recommend using these estimates as 

a starting point for the average fire size parameter. While this estimate is likely 

to be larger than the historical average fire size, the average fire size modeled 

in LANDSUMv4 is also likely to be larger. Due to scale and model efficiency, no 

fires smaller than 1 ha are modeled. This removes a portion of the left end of 

the fire size distribution curve, which is where the largest numbers of fires occur, 

and thus increases the mean fire size. With this in mind, we recommend starting with the NIFMID estimate for the average fire size and setting the fire-size 

distribution parameter to 1/2 to 1/3 of this value and then running the model on 

several test landscapes and varying both parameters until the simulated average 

fire size approaches the NIFMID estimate and the fire frequencies simulated for 

each PVT approach the probabilities set in the pathways.

Another possible source of information regarding historic average fire sizes 

may be the expert opinion of the vegetation modelers who develop the succession pathways (Long and others, in press). Presumably the people developing the 

pathways have good familiarity with the ecosystem and may have some estimate 

of fire size based on their experience and the literature review they conducted 

to develop the pathways that could be used along with the NIFMID information 

to establish a starting point for the average fire size parameter. Simulated fire 

return intervals should always be compared with the probabilities entered in the 

Scenario Input File to ensure that they are within 10 to 20 percent; the simulated fire frequencies should approach the fire frequencies that parameterize 

the model.

Recommendations—The following are some recommendations for estimating the fire probability and size parameters in the LANDSUMv4 and VDDT 

models.

Use fire history studies as guidelines or starting points for estimating fire 

probabilities. Incrementally adjust these parameters to agree with observed 

conditions.

Use the model as the estimation tool when no fire history data are available. 

Guess approximate values of fire probabilities and run the model to determine if landscape composition agrees with expectations. Repeat process 

until results are realistic.

Separate fire probabilities into the three fire severity types if possible. Use 

the effects of that fire type as guidelines for deciding if a fire severity type 

is warranted.

Document sources of all parameters used in the model pathways. Define the 

temporal and spatial scales and confine the interpretation of model results 

to that scale.

•

•

•

•
40 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-171CD. 2006

Interpret the results from VDDT differently than LANDSUMv4 in that VDDT 

will always have significantly less variation than that observed on the landscape because of the lack of spatial dynamics.

Simulation Issues and Limitations

Issues of fire simulation—The most important limitation of the LANDSUMv4 

model is absence of the close linkage between fuels, weather, and topography 

when estimating fire effects and pattern. Since fuel moistures are not computed, 

it is difficult to stop simulated fires from spreading into those stands that tend to 

be moist for many parts of the year, such as spruce fir forests. As a result, the 

model tends to overestimate the number of fires in mesic communities because 

fire is constantly spreading into these areas from surrounding flammable communities. Because LANDSUMv4 does not integrate the spatial distribution of fuel 

loading, fuel moisture, and daily weather, the pattern and severity of fire may not 

be entirely accurate. Use of LANDSUMv4 results to generate spatial statistics 

or metrics for describing HRV of landscape structure may not be as realistic as 

using the annual succession class distributions for describing HRV of landscape 

composition. Limitations of the LANDSUMv4 model must be recognized while 

interpreting simulated time series of landscape pattern metrics.

Even if the there were good historical data to estimate the fire parameters, there 

would still be differences between simulated fire regimes and actual historical 

fire regimes. It is difficult to simulate fire regimes from two sets of independent 

fire parameters (fire occurrence probabilities and fire size distributions). Fire, 

like many natural processes, is complex and any attempt to model it is necessarily a simplification of the actual processes. Fire operates at many different 

spatial and temporal scales and its occurrence is influenced by many factors, 

such as vegetation, weather, wind, and topography, which also operate at different spatial and temporal scales. As a result of this complexity, it is difficult 

to realistically simulate fire without building overly complex models that would 

be difficult to parameterize and inefficient to run for large landscapes over long 

simulation periods. Fire simulation in LANDSUMv4 incorporates mainly coarse 

scale processes. The weather parameters in the model function at a yearly time 

step and are generalized for the entire simulation landscape. The model does 

not incorporate daily or localized weather information. Wind speed and direction are also parameterized for the entire zone and then varied by time step, 

and not locally for each fire event. Integrating fine scale processes of weather 

or fuel into the model would make the model computational intensive and would 

dramatically reduce the efficiency of the model runs. Therefore, results of each 

LANDSUMv4 simulation should be compared with expert experience and any 

available data to determine if output is reasonable because of the overly simplistic model algorithms. If not, then the parameters should be adjusted to more 

closely approximate reality.

Mixed fire regimes present a special problem in the LANDSUMv4 modeling 

efforts. Mixed severity fire regimes have elements of both non-lethal surface fires 

and stand-replacement fires mixed in space and time. Technically, mixed severity 

fires should only be included as a separate fire type with associated probability 

in these models if their effects are significantly different from the other two severity types. This is difficult to assess since mixed severity regimes can create 

many small patches of tree mortality inside a simulated polygon. Therefore, the 

spatial grain of the mixed severity fire should always be more than the minimum 

polygon size to properly simulate mixed severity fire effects, especially in the 

LANDSUMv4 modeling effort.

•
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-171CD. 2006 41

The relationship between the average fire size and fire size distribution parameters can cause some confusion and problems in parameterization and 

interpretation of model results. The average fire size is the average size of the 

fire on the simulation landscape and it is used to scale the point estimate of fire 

probability to the polygon level. The fire size distribution parameter is a constant 

in the fire size distribution equation that determines the ultimate size of a simulated fire. This parameter is often approximated from the average fire size, but in 

LANDSUMv4 simulations, it often turns out to be about a third of the average fire 

size to match observed fire regimes. We recommend that the average fire size 

be determined from available data, and then the fire size distribution parameter 

be estimated using calibration techniques. The model should be executed and 

fire statistics from the Fire Output File (Appendix B.1) should be inspected to 

determine if the fire size distribution parameter should be altered to obtain more 

realistic fire regime simulations.

Issues of landscape simulation—Much of the LANDSUMv4 modeling is 

about balancing realistic simulations of fire and vegetation dynamics with the often 

opposing goal of computational and logistical efficiency. This struggle becomes 

more important as simulation landscapes increase in size and complexity. We 

found simulation times tend to increase exponentially with increasing landscape 

size but the use of larger simulation landscapes are logistically simpler and 

may produce better simulation results. There is probably some size landscape 

at which the model becomes efficient and overall processing time is optimized. 

We found the 100,000 to 250,000 ha landscapes work well but this may change 

with topographic and succession pathway complexity and as computer technology improves.

One of the problems with defining simulation landscapes is that the landscape 

edges create artificial boundaries across which fire cannot burn. In real landscapes, water, rock or topography may create real boundaries and influence 

fire spread, but our simulation landscapes did not follow natural boundaries and 

may cut right through areas of constant vegetation or topography through which 

the fire would naturally spread. Another problem is that areas near the edge of 

the landscape have a limited number of surrounding pixels from which a fire can 

spread into them. This problem is exacerbated by wind direction, because fire is 

only spread by wind and slope, and wind direction is at a constant direction and 

speed for the entire simulation. This means, all things being equal, pixels near 

the direction where the wind is originating (south and west edges, for example) 

have the lowest probability of burning while those near direction where the wind 

is blowing (the north and east for example) have the highest probability of burning (fig. 12A).

The best way to mitigate the edge effects in a LANDSUMv4 simulation is to 

surround the simulation landscape with a buffer (fig. 12). A buffer is created by 

making the simulation landscape larger and then stratifying the results into two 

categories – the buffer and the context area where both comprise the simulation 

landscape. The LANDFIRE prototype project used a 3-km buffer area around 

each 20,000 ha context area to create the final simulation landscape. If the buffer area is large enough, the relative position of pixels within the landscape will 

not influence the chance of burning and fire frequency will be determined by fire 

probabilities, adjacency, and topography. Areas of overlap, that is, those areas 

that were simulated twice (once as part of the buffer and once as part of the 

context area), are useful for examining how the buffers are functioning because 

fire probabilities and topography are constant between the two runs but the 
42 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-171CD. 2006

Figure 12—An example of fire regime simulation results for the Zone 16 test landscape showing 

the effect of edges on fire frequency across the landscape. A) fire frequency without a landscape 

buffer and B) fire regime for the same landscape with a 3 km buffer.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-171CD. 2006 43

relative position of the pixels within the larger simulation landscape changes. Fire 

should not be underestimated in the leeward buffer region, as most fires burn in 

from the windward regions. If the buffer is large enough, fire should not be underestimated in the context area because the buffer should provide an adequate 

source for fires to burn in from the windward side. So if the buffer is adequate, 

the fire frequency in the leeward buffer region should not be substantially different from the fire frequency for this same region when it is part of the context 

area. Each landscape is unique, so buffers will be different for each setting. The 

user should always inspect fire regime maps to determine if the buffer is large 

enough to minimize edge effects in the context landscape, keeping in mind that 

simulation time increases exponentially as landscapes get larger.

Another key to efficient and accurate simulations is a consistent methodology 

for testing the pathways and parameters established for the simulation landscape. 

LANDSUMv4 has an extensive error checking routine that scans the input data for 

inconsistencies between the various input files that could cause problems during 

simulation. However, there may be problems with the input data that the model 

does not recognize as an inconsistency but may cause unexpected results. For 

example, differences in the way the VDDT and LANDSUMv4 simulate succession can lead to problems where the pathways did not function in LANDSUMv4 

the same way they functioned in VDDT. And while the LANDSUMv4 model has 

undergone an extensive debugging process, it is always possible that some new, 

unique circumstance will arise in a new map zone that will cause unexpected 

results in LANDSUMv4. We recommend a thorough quality control process for 

the pathways once they are completed by the vegetation modelers. Ideally this 

could be done as a series of queries in a database environment that would check 

for consistencies between the ending age of one class and the beginning age 

of the subsequent class, as well as check that the next age or age increment 

applied by a disturbance results in an age that is within the age range of the 

resulting class. Once the pathways have passed this process, they should then 

be simulated and vegetation and fire regime information should be summarized 

for these test simulations and reviewed for any unexpected results. If there are 

any suspicious results, further analysis should be done to determine the reason 

for such results and whether they make sense ecologically.

It appears that a significant number of simulation years are required before 

the trends in succession classes stabilize and the annual simulations achieve 

some level of equilibrium. This stabilization time may take upwards of two to three 

centuries on simple landscapes and over 500 years on complex landscapes. We 

found that using the current landscape to initialize the historical landscape was 

inefficient on two levels. First, the current landscape contained too many polygons, 

and the simulation times increased as these numerous polygons were added by 

the continual division of polygons by fire. Second, the current landscape is so 

departed from historical conditions that it takes an excessively long time for this 

landscape to reach equilibrium. We found that initializing the model by assigning one succession class to each PVT polygon was the most efficient. The one 

succession class was selected as the most dominant in the VDDT simulations.

Model validation and verification are difficult tasks with landscape models that 

simulate vegetation and fire dynamics over millennia time spans (Keane and Finney 

2003, Keane and others 2004a). There is a lack of spatially explicit historical time 

series data that are in the right context to compare with model results. Validation 

data must have many characteristics to be useful for model validation. First, the 

data must be described in the same format as model output; the mapped data 
44 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-171CD. 2006

must have the same categories as those simulated by LANDSUMv4. Next, the 

historical data must be from many time periods. The comparison of one historical 

map with the simulation results provides only a qualitative or descriptive means 

of testing and verifying the model. Multiple maps would provide the basis for 

an objective statistical comparison. And, since LANDSUMv4 does not replicate 

historical fire sequences because it is a stochastic model, it would be difficult 

to compare past fire patterns with corresponding simulated patterns. Last, the 

data must be incorporated into maps that describe historical conditions across 

the entire landscape. Because of these limitations, we could find no historical 

data sets to comprehensively validate LANDSUMv4. This leaves validation of 

the pathway and disturbance parameters as the only way to verify the model 

is producing realistic results. Stage and others (1995) performed an extensive 

validation of succession parameters for the pathways in the Interior Columbia 

River Basin Ecosystem Management Project using the FVS stand growth model 

and found greater than 80 percent accuracy (Stage 1997). Keane and others 

(2002c) compared simulated fire area and pattern statistics from a 1,000 year 

LANDSUM run to the historical fire atlas created by Rollins and others (2001) 

and found excellent agreement between the distributions of fire size and patch 

shape.

VDDT vs LANDSUMv4—VDDT does not simulate spatial relationships so 

none of the output should be interpreted in that context. There are options in 

the model that infer that spatial results can indeed be simulated into “number 

of pixels” or “area in PVT”. In fact, these are misleading and contrary to model 

assumptions. It is suggested that the user interpret the number of pixels to be 

points on the landscape, and assume that the landscape can be any size. Most 

VDDT results are summarized in graphs or tables of percent of the landscape 

by modeled units (for example, cover type, structural stage, and disturbance 

type). These results should be interpreted as percent of points on the landscape 

rather than percent of area occupied by modeled elements. This is because most 

disturbances, especially fire, have a critical spread spatial component, and if 

spread isn’t modeled, simulation results really shouldn’t be summarized by area 

measures. That doesn’t mean VDDT simulations are “wrong,” rather, the VDDT 

simulations are coarser approximations of what is happening in the real world. 

These VDDT results help us understand the importance of fire parameters to 

landscape and vegetation dynamics. This approximation can be refined and improved by inserting the VDDT pathways and parameters into the spatial model 

LANDSUMv4.

There are subtle differences in the way LANDSUMv4 and VDDT simulate 

succession. The succession age in LANDSUMv4 is an index of vegetation development where polygons with old succession ages are more successionally 

advanced. VDDT, on the other hand, treats succession age as the age of the 

oldest plants in the simulated polygon. This difference is important when developing the succession transition ages because LANDSUMv4 defines a succession 

class with a beginning and ending age whereas VDDT specifies the longevity of 

that succession class. Another difference is that VDDT has an option that does 

not allow disturbances, primarily fire, to occur until a user-specified number of 

years has elapsed. Future versions of LANDSUMv4 will integrate these VDDT 

characteristics as options in the simulation.

Simulating HRV Issues

Many people feel it may be inappropriate to simulate fire and landscape dynamics over millennial simulation spans while holding climate and fire regimes 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-171CD. 2006 45

constant. This would be true if the objective of the fire modeling were to replicate 

historical fire events. However, the primary purpose of the LANDFIRE modeling 

efforts is to describe variability in historical landscape dynamics. It is important 

that the entire range and variation of landscape conditions and processes be 

documented so that current condition can be compared against a realistic and 

comprehensive reference database. To accomplish this, field-based fire history 

study findings were used to parameterize the models even though these results 

represent a relatively small slice of time (300 to 400 years). The assumption here 

is that this small temporal span is a good proxy for the creation of reference conditions used in HRV simulation. Since the time slice represents only four or five 

centuries, it may seem that only 500 years of simulation are needed. However, 

the sampled fire events that occurred during this time represent only one unique 

sequence of fire start locations and subsequent fire spread that created the 

unique landscapes observed today. If these events had happened on a different 

timetable or in different areas, an entirely new set of landscape conditions would 

have resulted. It follows then that the documentation of landscape conditions from 

only historical records would tend to underestimate the variability of conditions 

that that landscape could have experienced in the past and will experience in 

the future. As a result, we attempted to approximate the entire range of conditions by simulating the static historical fire regime for thousands of years. We 

assumed that 3,000 to 5,000 years are long enough to contain enough fires to 

approximate the variability of conditions that the historical test landscape (fig. 5) 

would have experienced, but this time span is likely to be longer for landscapes 

with infrequent fires (we used 10,000 years in LANDFIRE Zone 19). We believe 

this is the best way to simulate historical reference conditions because it allows 

future landscapes to have variable fire ignitions and fire patterns. If only the last 

300 years were used as reference conditions, then the future fire regimes would 

have to replicate the same ignition pattern and timing for the landscapes to be 

within bounds.

The complexity of PVT pathways can influence the comparison of historical 

dynamics to current conditions and ultimately affect the computation of departure. 

Pathways with a large number of succession classes have more elements to 

compare with current conditions and, as a result, the departure may be underestimated. Departure from a five succession class pathway, for example, would be 

greater than departure from a 40 class pathway because the large number of near 

zero values for the majority of succession class pairs tends to lower departure 

estimates. Departure estimation is best when succession pathway complexity 

is somewhat equal across all simulation landscape PVTs.

The size of the landscape unit for reporting simulated historical time sequences 

and computing departure within the simulation landscape is a critical question 

that still needs to be answered with further research. The LANDFIRE prototype 

effort divided the simulation landscape into a 900 meter grid to create 81 hectare 

reporting units. This size was selected so that it could be matched to a similar 1 

km coarse scale effort (Hardy and others 2001) and not because it made ecological sense (fig. 10A). It is difficult to determine the optimum size of a simulation 

landscape reporting unit because of subtle differences in topography, climate, 

and vegetation across large regions. Current efforts are underway to develop 

guidelines for determining the appropriate reporting unit size and shape and preliminary results indicate that an appropriate landscape size may be between 10 

and 20 km2. In the meantime, it is probably best to match the reporting unit size 

with the management objectives. For example, watershed boundaries might be 
46 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-171CD. 2006

appropriate for the selection of fuels treatment prioritization if the management 

plans are stratified by watersheds.

Historical time series are autocorrelated in space and time. Any pixel on the 

landscape is ultimately dependent on the status of the surrounding pixels as fire 

spreads across the landscape. The instantaneous status of any landscape is 

dependent on the landscape composition and structure of the previous year. In 

addition, the extent of any succession class is related to the extent of all other 

classes; the increase in one succession class must result in the corresponding 

decrease of one or more succession classes on the landscape. It is important to 

minimize the mentioned autocorrelation in the historical time series by selecting 

an output reporting interval that is long enough to reduce the interdependencies of 

time, space, and succession class. This reporting interval will vary by landscape 

depending on fire frequency and succession transition times. The LANDFIRE 

prototype effort used 50 year reporting intervals to minimize autocorrelation for 

their simulations (Pratt and others, in press).

Many types of LANDSUMv4 output can be used to describe the historical 

variability for a multitude of management objectives. For example, simulated 

historical maps of fire frequency and severity can be compared with current fire 

atlases to estimate departure in terms of fire regime. Fuel models can be assigned 

to the PVT-succession class combinations in the Attribute Input File (Appendix 

A.12) to generate historical chronosequences of fuels to compare with current 

fuel maps. This comparison would be especially valuable if reduction of fire hazard were an important management objective. Similarly, hiding cover, thermal 

cover, or forage value can be assigned to PVT-succession class combinations 

to estimate historical variation in wildlife habitat. Mountain pine beetle and other 

insect and disease disturbance fluctuations can be simulated for comparison to 

current epidemics.

Historical scenarios might not be the only scenario used to compute departure 

and summarize HRV for simulation landscapes. Other scenarios can be developed 

and implemented into the Scenario Input File (Appendix A.11) to generate a time 

series for a completely different set of reference conditions. For example, the 

invasion of exotics may be so extensive that most of the landscape is departed 

from historical conditions. Therefore, the exotics can be included as disturbances 

in the Scenario Input File so that a more contemporary reference condition can 

be used. Or, historical reference conditions may not be a viable target for some 

heavily managed areas, so the fire probabilities in the Scenario Input File can 

be modified to reflect a fire suppression program.

Conclusions

The landscape fire succession model LANDSUMv4 appears to be a useful 

tool to simulate the range and variation of historical landscape dynamics. These 

landscape dynamics can be used as reference to evaluate the departure of current landscapes to historical conditions to prioritize and design treatment areas 

for land management.

Although simulation modeling in general, and the LANDSUMv4 model in 

particular, has several important limitations, it is still a useful and perhaps the 

only viable approach for generating historical reference conditions of vegetation 

and fire across large areas and ultimately the nation. First, LANDSUMv4 can 

generate this information in a consistent manner across all areas in the nation. 

While data coverage on actual historical conditions is limited in spatial extent, 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-171CD. 2006 47

simulation modeling can estimate the reference conditions everywhere. This is 

not to say that the simulated conditions are intended to replace actual historical 

data where it exists, but rather to provide information where it is currently lacking. 

In addition to increasing the spatial coverage of historical reference conditions, 

simulation modeling can increase the temporal depth. Historical data often represents a narrow slice of time. This has obvious limitations and results generated 

from thousand year simulation runs must recognize that the parameters used to 

describe the fire regime and climate were static and quantified from data taken 

from a small slice of history.

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Appendix A.1 – LANDSUMv4 Model Execution

This appendix A describes the methods and procedures needed to develop 

the input data files for executing the weather summary model LANDSUMV4. The 

LANDSUMV4 model requires several tabular input files and two map files for its 

execution. The tabular files must be in ASCII format. All tabular input files should 

have an extension of “in” (for example, driver.in) to signify input files.

The program is executed via a command on the command line:

landsumv4 <DriverFile>

Where landsumv4 is the program name and DriverFile is the main file that 

drives the execution (see Appendix A.2, Figures 2, 3).

The LANDSUMv4 model can be found on this CD or downloaded from three 

web sites. The LANDFIRE project web site (www.landfire.gov) contains the source 

code and the executable for LANDSUMv4. It is compiled for a PC to work in a 

DOS window. The user can take the source code and compile LANDSUMv4 on 

any platform since the code is ANSI C++. The FRAMES website (www.frames.

nbii.gov) also has the executable file but not the source code. Last, the program is 

available on the Missoula Fire Sciences Laboratory website (www.firelab.org).

The LANDSUMv4 program was primarily designed for execution on a personal 

computer within a DOS window.

The remaining sub-appendices in Appendix A describe the input files to 

LANDSUMv4. All input and output files in LANDSUMv4 are in ASCII format and 

are space and column delimited. This is often referred to as text format. Each 

input or output ASCII file is configured as a vertical or horizontal file. Vertical files 

contain only one input value per line with a descriptor of that input value following 

the number. Horizontal files contain more than one value per line and resemble 

a table. In general, LANDSUMv4 input files are both vertical and horizontal, 

while all output files are only horizontal. The horizontal file structure is used for 

efficiency while the vertical file structure is used for ease of development, greater 

understanding, and error management. Files in LANDSUMv4 are also cycling 

or discrete. Cycling files contain the same format that is cycled downward or 

repeated for each similar instance on the next line or set of lines. For example, 

site parameters are cycled vertically for each site on the landscape. Discrete files 

do not cycle and always contain a discrete number of vertical records. The first 

line of every LANDSUMv4 input file is called a title line and is used to describe 

the file and its origins. There are 256 characters available to craft a descriptive 

title that can uniquely identify the file while it is being viewed within a text editor. The title line should describe who, what, when, where, and why the file was 

developed. For example, one header might have the project for which the file 

was developed, who developed the file, when the file was developed, where the 

data were taken (data sources), and why this file was used for this particular 

simulation scenario.
54 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-171CD. 2006

Appendix A.2 – The Driver Input File

This Driver Input File is a vertical discrete scenario file that contains the names 

of all input and output files involved in LANDSUMV4 execution. Each file is listed 

as an entire pathname. The order of these files is absolutely critical. The structure of the file is as follows where VAR is variable name, LINE is line number in 

file, SIZE is the size of the field in characters or digits, DESCRIPTION is a short 

description of the variable and UNITS is the units of this variable.

VAR	 LINE	 SIZE	 DESCRIPTION	 UNITS

title	 1 	 256 	 Title line for identifying landsum driver file	 None

echo	 2 	 256 	 Echo file: contains summary of all input data	 None

error	 3 	 256 	 Error file: contains errors, warnings encountered during init	 None

sim	 4 	 256 	 Simulation file: contains specific simulation criteria	 None

mapfile	 5 	 256 	 Map file: contains all input,output parms for generating a map	 None

polyfile	 6 	 256 	 Polygon file:list of polygons on landscape with attributes	 None

spatdist	 7 	 256 	 Spatial disturbance file: parameters to simulate spatial dist	 None

vegfile	 8 	 256 	 Veg file: Contains all vegetation succession parameters	 None

distfile	 9 	 256 	 Disturbance file: contains disturbance parameters	 None

vegfix	 10 	 256 	 Vegfix file: fixes in inconsistencies between maps and files	 None

mgtfile	 11 	 256 	 Management file: instructions for assigning scenarios to zones	 None

scenfile	 12 	 256 	 Scenario file: probability parms by disturbance for scenario	 None

attfile	 13 	 256 	 Attribute file: a set attributes for each PVT, ct, ss combo	 None

firefile	 14 	 256 	 Fire year file:fire year severity assignments for sim years	 None

ostand	 15 	 256 	 Output stand file: same as the input polygon file	 None

oland	 16 	 256 	 Output Landscape file: summarizes landscape by PVT, CT, SS	 None

odist	 17 	 256 	 Output Disturbance file: summarizes disturb by PVT, CT, SS	 None

ofire	 18 	 256 	 Output Fire file: output of individual fires on landscape	 None

olandfire	 19 	 256 	 Output LANDFIRE file: contains output for HRV computataion	 None

The first line in this file is a title or file header for reference. The next 19 lines 

specify filenames that are entered as full pathnames. All pathnames in this file 

should start in column one and proceed left to right without blank spaces or 

commas (valid file names). It is recommended that a space be placed after the 

file name and then a descriptive statement be written to describe the filename 

such as who created it and so on. There are 256 characters allowed on these 

records. Be sure to identify the entire pathname including the directory structure 

up to the root directory (for example, C: is the root directory on a personal computer). There are some important options in driver file development. First, if you 

put “NONE” or “none” for any output filename, then no output will be written. The 

word NONE means that no output need be written for that topic.

The next two lines in this file specify important output filenames (also called 

pathnames) that are printed from LANDSUMv4 and allow the user to check if the 

input data are entered correctly into LANDSUMv4. The first pathname (echo) is 

used to identify the filename containing a dump of all the input values from all 

other input files. This file is important because it allows the user to check if the 

data were read into LANDSUMv4 correctly. The second pathname represents 

the error file that contains any error or warning messages that LANDSUMv4 

found when it compared the input data across files and across the maps.

The next eleven lines specify pathnames that describe LANDSUMv4 input files 

that contain the input parameters and conditions needed to run the model. The 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-171CD. 2006 55

formats for all these files are discussed in this Appendix A. The simfile contains 

all general simulation parameters that are used to govern the LANDSUMv4 execution such as number of years, output intervals, and output formats. The mapfile

contains the list of pathnames that specify the input and output map filenames 

and the general mapping attributes for those output map files such as pixel size, 

starting coordinates, and number of rows and columns. The polygonfile is a 

pathname that contains all the polygons, and their associated attributes, that 

comprise the simulation landscape. The spatdistfile identifies the pathname 

where spatial parameters are stored to simulate the spread of fire across a 

landscape. The vegfile and the distfile are pathnames of files that contain the 

vegetation succession parameters and the disturbance pathway parameters, 

respectively. The vegfix pathname specifies a file that contains all the temporary fixes for inconsistencies between the vegetation succession parameters, 

polygon attributes, and the input polygon map. The mgtfile pathname identifies 

a file that contains all the time and space specifications for the disturbance occurrence parameters that are in the following scenfile (the pathname for all the 

management scenario disturbance probabilities). This scenario file contains the 

probabilities of disturbances by succession class and PVT (Potential Vegetation 

Type). The attfile pathname is a file where the user specifies up to five attributes, 

such as fuel model and cover type, for all PVT-succession class combinations. 

The firefile is where the user can specify the weather years (dry, moist, wet) for 

each year of simulation.

The last five lines are pathnames for LANDSUMv4 output. Output from 

LANDSUMv4 is the area (m2) by region, zone, strata, PVT, succession class, 

then disturbance. The ostand file is a list of the polygons and their attributes 

by each simulation year output interval. The oland file summarizes this polygon 

list by region, zone, strata, PVT, and succession class and prints it every output year. The odist file is the same as the oland file but includes an additional 

stratification of disturbance for that year. The ofire file contains a list of all fires 

by year, PVT, and map coordinate. The last file, olandfire, contains output that is 

specifically formatted for the LANDFIRE project to compute the historical range 

and variation of landscape composition.

An example of the Driver file is shown below:

LANDSUMv4 Driver file: driver_112.in 20 Jan 04 11:12:04 Tuesday lh

c:\landsum\landfire\test2\outfiles\echo_112.out 	 Echo file: contains summary of all input data

c:\landsum\landfire\test2\outfiles\error_112.out 	 Error file: contains all errors, warnings during initialization

c:\landsum\landfire\test2\infiles\sim_112.in 	 Simulation file: contains specific simulation criteria

c:\landsum\landfire\test2\infiles\map_p20kha.in 	 Map file: contains all map input output parameters a map

c:\landsum\landfire\test2\infiles\poly_p20kha.in 	 Polygon file: contains list of polygons

c:\landsum\landfire\test2\infiles\spatdist_112.in 	 Spatial disturbance file: contains spatial disturb parms

c:\landsum\landfire\test2\infiles\veg_z16.in 	 Veg file: Contains all vegetation succession parameters

c:\landsum\landfire\test2\infiles\dist_z16.in 	 Disturbance file: contains disturbance parameter

c:\landsum\landfire\test2\infiles\vegfix.in 	 Vegfix file: contains all fixes between maps and files

c:\landsum\landfire\test2\infiles\plan.in Management plan file: instructions for assigning scenarios

c:\landsum\landfire\test2\infiles\scen_z16.in 	 Scenario file: contains probability of occurrence parms

c:\landsum\landfire\test2\infiles\attrib_z16.in 	 Attribute file: contains a set of attributes for PVT, ct, ss

NONE

NONE Output stand file: same as the input polygon file except it is output

NONE Output Landscape file: summarizes landscape by PVT, CT, SS stratification

NONE Output Disturbance file: summarizes all disturbances by PVT, CT, SS, and zone

NONE Output Fire file: contains output of individual fires on landscape

c:\landsum\landfire\test2\\outfiles\landfire_112.stat Output LANDFIRE file: contains output for HRV computataion in

LANDFIRE
56 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-171CD. 2006

Appendix A.3 – The Simulation Input File

This Simulation Input File is also a vertical discrete scenario file that contains 

general simulation information about a LANDSUMv4 run. Again, the order of these 

parameters is important and the user must document the file origin in the titleline. 

The structure of the file is as follows where VAR is variable name, LINE is line 

number in file, SIZE is the size of the field in characters or digits, DESCRIPTION 

is a short description of the variable, and UNITS is the units of this variable.

VAR 	 LINE 	 SIZE 	 DESCRIPTION UNITS

title 	 1 	 256 	 Title line for reference None

verbose 	 2 	 1 	 Verbose flag:(0-no messages, 1-limited messages, 2-full messages) None

simyear 	 3 	 5 	 Number of years to simulate succession years

attrib 	 4 	 1 	 Number of attributes in the Attribute file None

projID 	 5 	 10 	 Project ID code for specific simulation parameters None

ageinit 	 6 	 1 	 Age init option (0-use age, 1-Random, 2-midpoint , 3-beg age) None

outint 	 7 	 5 	 Interval (yrs) to print results to output maps and tabular files Years

outbegyr 	 8 	 5 	 Year to start printing output results to maps and tabular files Years

firebeg 	 9 	 5 	 Starting code for fire disturbances Code

fireend 	 10 	 5 	 Ending code for fire disturbances Code

ostand 	 11 	 1 	 Tabular results: Stand Summary (0=none, 1=partial, 2=full) None

oland 	 12 	 1 	 Tabular results: Landscape Summary (0=none, 1=partial, 2=full) None

odist 	 13 	 1 	 Tabular results: Disturbance Summary (0=none, 1=partial, 2=full) None

ofire 	 14 	 1 	 Tabular results: FIRE Summary (0=none, 1=partial, 2=full) None

olandfire 	 15 	 1 	 Tabular results: LANDFIRE (0=none,1=sclass,2=ct 3=ss,4=att1,... None

distopt 	 16 	 1 	 Disturb option(0-include all dist, 1-exclude all dist, 2-exclude all but fire)

ranscheme 	 17 	 1 	 Random number scheme (0 = different every time, 1 = repeatable) None

rengen 	 18 	 1 	 Random number generator (0 = system, 1 = Ran1, 2 = Ran2) None

The variable title is the line that uniquely identifies this file and should contain information for user reference. The verbose flag is set to zero if the user 

doesn’t want any messages to be printed to the command line window during 

program execution; set to one (1) if a limited set of messages are desired so 

the user knows how long the program has been running; and two (2) if a full 

set of descriptive messages are to be printed so the user can track the details 

of a simulation run. The simyear is the number of years that the user wants to 

run the model. The attrib variable signifies the number of attributes that the 

user has entered in the attfile for each PVT-succession class combination. The 

projID is the identification number for the current simulation project to match 

the parameters in the vegetation succession file (vegfile) and the disturbance 

file (distfile). The ageint variable identifies how the user wishes to initialize the 

age of each polygon on the landscape: 0-use the age that is specified in the 

polygon file (polyfile); 1-randomly assign the age between the range specified 

for that succession class; 2-use the midpoint of that succession class; or 3-use 

the beginning age for the succession class.

The next set of variables identifies output specifications. The outint is a variable that specifies the number of years or output interval for printing simulation 

results for that simulation year. For example, the number 50 would specify that 

output be printed every 50 years on year 1, 51, 101, and so on. The variable outbegyr allows the user to specify the year to begin printing output. In LANDFIRE 

it took about 200-500 years for the model to equilibrate so the number 200 was 

entered here.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-171CD. 2006 57

The firebeg and fireend variables signify the range of codes used to identify fire disturbances within LANDSUMv4. For example, all fire disturbances in 

LANDFIRE spanned from 1,000 to 2,000.

The remaining variables allow the user to tailor the output format for greater 

efficiency in storage and execution. The variables ostand, oland, odist, and

ofire signify the output formats for the polygon, landscape, disturbance, and 

fire files (Driver Input File) using the following convention: 0-do not print output; 

1-print a summary of the output; or 2-provide the full details in the output. The 

olandfire file option allows the user to print the area summary by region, zone, 

strata, and pvt by the following variables: 1-none; 2-succession class; 2-cover 

type; 3-structural stage; 4-attribute1; 5-attribute2; 6-attribute 3; 7-attribute4; 8-

attribute5; or 9-all possible output variables.

The distopt variable allows the user to run the model with only succession 

and no disturbances (option 1), run the model with all disturbances (option 0), and 

run the model with only fire disturbances (option 2). The random number scheme 

(ranscheme) is a variable for the user to specify that the random numbers are 

the same across each execution (option 1) or different across all executions (option 0). And last, the rangen variable specifies the random number generator to 

use in LANDSUMv4: 0-system random number, 1-random number generator 1, 

2-random number generator 2. The system random number generator seems 

to perform adequately.

An example of the Simulation Input File is show below:

LANDSUMv4 -- Test watershed p20kha-112 for Zone 16 -- General simulation parameters -- Keane Fall 2003

2	 Verbose flag: (0-no messages, 1-limited messages, 2-full messages during execution)

555	 Number of years to simulate succession

0	 Number of attributes in the Attribute file

LANDFIRE	 Project ID code for specific simulation parameters

1	 Age initialization option (0-use age, 1-Random, 2-midpoint, 3-entered beg age in pvt)

3	 Interval (yrs) to print results to output maps and tabular files

3	 Year to start printing output results to maps and tabular files

1100	 Starting code for fire disturbances

1140	 Ending code for fire disturbances

0	 Tabular results: Stand Summary (0=none, 1=partial, 2=full)

0	 Tabular results: Landscape Summary (0=none, 1=partial, 2=full)

0	 Tabular results: Disturbance Summary (0=none, 1=partial, 2=full)

0	 Tabular results: FIRE Summary (0=none, 1=partial, 2=full)

11	 Tabular results: LANDFIRE Summary

0	 Disturbance exclusion option (0-include all dist, 1-exclude all dist, 2-exclude all but fire)

0	 Random number scheme (0 = different every time, 1 = repeatable random)

0	 Random number generator (0 = system, 1 = Ran1, 2 = Ran2)
58 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-171CD. 2006

Appendix A.4 – The Map Input File

This Map Input File is another vertical discrete scenario file that contains 

general spatial information about the input and output maps that are generated 

from LANDSUMv4. Again, the order of these parameters is important and the 

user should document the file origin in the titleline. The structure of the file is as 

follows where VAR is variable name, LINE is line number in file, SIZE is the size 

of the field in characters or digits, DESCRIPTION is a short description of the 

variable, and UNITS is the units of this variable:

VAR 	 LINE 	 SIZE 	 DESCRIPTION UNITS

title 	 1 	 256 	 Title line for reference None

pixsize 	 2 	 10 	 Pixel width size of all maps in meters None

cols 	 3 	 10 	 Number columns (i.e., pixels in a column) in all maps None

rows 	 4 	 10 	 Number rows (i.e., pixels in a row) in all maps None

xllcoor 	 5 	 15.5 	 Lower left corner UTM East coordinate of all maps meters

yllcoor 	 6 	 15.5 	 Lower left corner UTM north coordinate of all maps meters

nodata 	 7 	 10 	 NODATA value in all maps None

ulr,ulc 	 8 	 10,10 	 Upper left row,col coordinates for output submap (-999 entire map) None

lrr,lrc 	 9 	 10,10 	 Lower right row,col coordinates for output submap (-999 entire map) None

NONE 	 10 	 	 This line is put in for dividine the map names from above None

demmap 	 11 	 256 	 Input Digital elevation model for landscape filename

ipolymap 	 12 	 256 	 Input Initial polygon map in pixel or raster form filename

opolymap 	 13 	 256 	 Output Dynamic polygon output map name (Enter NONE if no output) filename

ofiremap 	 14 	 256 	 Output cumulative fire map (Enter NONE if no output desired) filename

oscmap 	 15 	 256 	 Output succession class map(Enter NONE if no output desired) filename

odistmap 	 16 	 256 	 Output disturbance map (Enter NONE if no output desired) filename

octmap 	 17 	 256 	 Output Cover type map (Enter NONE if no output desired) filename

ossmap 	 18 	 256 	 Output structural stage map (Enter NONE if no output desired) filename

ofyrmap 	 19 	 256 	 Output fire year map (Enter NONE if no output desired) filename

oagemap 	 20 	 256 	 Output age map (Enter NONE if no output desired) filename

oatt1map 	 21 	 256 	 Output attribute number 1 map (Enter NONE if no output desired) filename

oatt2map 	 22 	 256 	 Output attribute number 2 map (Enter NONE if no output desired) filename

oatt3map 	 23 	 256 	 Output attribute number 3 map (Enter NONE if no output desired) filename

oatt4map 	 24 	 256 	 Output attribute number 4 map (Enter NONE if no output desired) filename

oatt5map 	 25 	 256 	 Output attribute number 5 map (Enter NONE if no output desired) filename

ofrmap 	 26 	 256 	 Output fire regime map prefix (Enter NONE if no output desired) filename

where pixsize is the width of the pixel in meters, cols and rows are the columns 

and rows that make up the rectangular input and output map structure. The xllcoor

and yllcoor are the lower left (southwest) corner coordinates for the easting and 

northing using a UTM projection. The nodata variable is the value given in the 

input maps for missing or no data value available. The ulr, ulc, lrr, and lrc variables are the upper left (ul, or northwest) and lower right (lr, or southeast) output 

map coordinates in rows (r) and columns (c). These row and column coordinates 

allow the user to output only a small section of the simulation landscape. There 

is another line in this input file but it only serves to separate the map information 

from the map filename specifications.

The next set of fields are input filenames for the model. There are two input 

files where the full pathnames should be specified. The demmap is the digital 

elevation model map with the above map specifications (pixsize, cols, rows, 

xllcoor, yllcoor). The ipolymap is the input polygon map where all pixels are 

assigned a polygon number. These numbers correspond to the list of polygons 

in the Polygon Input File (Appendix A.5).
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-171CD. 2006 59

The last set of map filenames are all output filenames. When the user enters 

a pathname in these lines, the model will write a map to that pathname every 

time there is an output year (the output year interval is specified in the Simulation 

Input File in Appendix A.3). The program will append a year number at the end 

of the pathname to denote the output year that the map represents. If the user 

does NOT want any of these output maps, the word NONE is specified in columns 1 through 4 and the model will not print this map. The opolymap is the 

map containing polygon numbers that can be cross referenced to the output 

polygon file specified in the Driver Input File (Appendix A.1). The ofiremap is 

a map where each pixel is assigned the cumulative number of fires that have 

occurred up to that simulation year. The oscmap is an output map that displays 

current succession classes for that output year. The odistmap is a map that 

shows the disturbance ID value for any disturbance that occurred during that 

output simulation year. The octmap and ossmap are maps where the pixels are 

assigned the cover type ID and structural stage ID values, respectively, and are 

referenced in the Attribute Input File (Appendix A.12). The ofyrmap is a map that 

shows the fires that burned during that output simulation year. The oagemap is a 

map where each pixel is assigned a succession age. The oatt1map, oatt2map, 

oatt3map, oatt4map, and oatt5map variables signify five maps that display the 

values assigned to the succession classes in the Attribute Input File (Appendix 

A.12). The last output map, ofrmap, is perhaps the most important because it 

is a prefix that is used for four fire regime maps. The prefix is then assigned a 

suffix of .freq, .sev1, .sev2, or .sev3 for each of the four maps. The first map is 

a map of fire frequency where each pixel is assigned a value for the cumulative 

number of fires across the entire simulation. There are three maps of severity. 

The .sev1 map represents non-lethal surface fires, the .sev2 map represents 

mixed severity fires, and the sev3 map represents stand replacement fires. The 

probability of each of these fire severity types is assigned to the pixels in each 

of the maps.

An example of the Map Input File is as follows:

LANDSUMv4 MAP infile: map_p20kha.in 20 Jan 04 11:12:04 Tuesday lh

30 	 Pixel size of all maps in meters

397 	 Number columns (i.e., pixels in a column) in all maps

397 	 Number rows (i.e., pixels in a row) in all maps

-1448116.625 Xllcorner of all maps in meters

1757702.5 Yllcorner of all maps in meters

-9999 	 NODATA value in all maps

-999 -999 Upper left row and col coordinates for output submap (-999 means entire map output)

-999 -999 Lower right row and col coordinates for output submap (-999 means entire map output)

Input and output map files -- Enter NONE if you do not want that map

c:\keane\applications\landsum\landfire\test2\inmaps\dem_p20kha.asc 	 INPUT: Digital elevation model for landscape

c:\keane\applications\landsum\landfire\test2\inmaps\poly_p20kha.asc 	 INPUT: Initial polygon map (NOT VECTOR)

NONE 	 OUTPUT: Dynamic polygon output map name in pixel form (NOT VECTOR)

NONE 	 OUTPUT: Dynamic fire output map

NONE 	 OUTPUT: Succession class map

NONE 	 OUTPUT: Disturbance map

NONE 	 OUTPUT: Cover type map

NONE 	 OUTPUT: Structural stage map

NONE 	 OUTPUT: pvtxssxct map

NONE 	 OUTPUT: Age map

NONE 	 OUTPUT: Attribute number one

NONE 	 OUTPUT: Attribute number two

NONE 	 OUTPUT: Attribute number three

NONE 	 OUTPUT: Attribute number two

NONE 	 OUTPUT: Attribute number five

c:\keane\applications\landsum\landfire\test2\outmaps\firereg OUTPUT: Fire regime map prefix
60 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-171CD. 2006

Appendix A.5 – The Polygon Input File

The polygon file is a horizontal, cycling, descriptive file that contains a list of 

all polygons on the simulation landscape defined in the map specified in the Map 

Input File (Appendix A.4). A set of critical attributes are assigned to each polygon 

in the list. The polygons in this list must perfectly match all the polygons in the 

polygon input map specified in the Map Input File. This file is cycling because 

each line is another polygon.

The first line of the file is the title or reference line where anything can be 

written to describe this file. The second line is a column header line (colhead) 

for ease of entering data. The structure of the file is as follows where VAR is 

variable name, LINE is line number in file, SIZE is the size of the file in characters 

or digits, DESCRIPTION is a short description of the variable, and UNITS is the 

units of this variable:

VAR 	 LINE 	 SIZE 	 DESCRIPTION	 UNITS

title 	 1 	 256 	 Title line for reference	 None

colhead 	 2 	 256 	 Column header information for user only-not used in program	 None

polyID 	 3 	 10 	 Code that uniquely identifies the polygon	 None

regionID 	 4 	 10 	 Unique code for a broad region ID number	 None

zoneID 	 5 	 10 	 Stratification code for a zone within a region	 None

strataID 	 6 	 10 	 Stratification code for a reporting area within a zone and region	 None

pvtID 	 7 	 10 	 Potential vegetation type ID number	 None

sclassID 	 8 	 10 	 Succession class ID number	 None

age 9 	 10 	 Succession age of this polygon	 None

area 	 10 	 10.2 	 Area of this polygon in initial polygon map	 meters

where the polyID is a unique identification number assigned to this polygon. 

Polygon numbers need not be in order or sequential, but, there should not be 

any repeat ID numbers. Each polygon ID number should exactly cross-reference 

the polygons mapped in the polygon input map specified in the Map Input File 

(Appendix A.4).

The next three fields allow the user to hierarchically spatially stratify the polygons 

by regions, zones, and strata. Regions (regionID) are broad stratifications that 

could represent ownership, ecological sections, or political boundaries. Nested 

inside regions are zones (zoneID) that subdivide regions. Zones might be management zones within ownerships such as wilderness and non-wilderness lands. 

Last, strata (strataID) represent nested divisions of zones. The LANDFIRE effort 

used regions to represent broad mapping zones (68 zones divided the contiguous United States), then zones represented simulation landscapes within the 

mapping zones, and last, strata represented the reporting landscapes or units 

for the calculation of historical departure and fire regime condition class.

The pvtID identifies the code for the PVT ID number for this polygon as referenced in the Vegetation Input File (Appendix A.7). The sclassID represents 

the unique code for the succession class of the polygon within the PVT as 

defined in the Veg Input File. The age is the succession age of that polygon. If 

this attribute is unknown or difficult to estimate, then the user would enter zero 

and the LANDSUMv4 program will calculate the succession age based on the 

option specified in the Simulation Input File (Appendix A.3). Last, the area is a 

variable that represents the area this polygon contains and should exactly match 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-171CD. 2006 61

the area in the Polygon Input Map. This variable is used as a logic check from 

this polygon list to the polygon input map.

An example of the polygon file is shown below:

Polygon input file for LANDSUMv4 demostration Prepared Jan 2 2002

PolyID 	 RegID	 ZID	 StrID	 PVTID	 SClID	 Age	 Area(m2)

1	 16	 1	 2	 1611	 131206	 0	 900

2	 16	 1	 2	 1601	 141211	 0	 16200

3	 16	 1	 2	 1603	 131211	 0	 900

4	 16	 1	 2	 1663	 323101	 0	 900

5	 16	 1	 2	 1601	 141211	 0	 900

6	 16	 1	 2	 1663	 323101	 0	 3600

7	 16	 1	 2	 1601	 141211	 0	 900

8	 16	 1	 2	 1663	 323101	 0	 2700

9	 16	 1	 2	 1601	 141211	 0	 1800

10	 16	 1	 3	 1601	 141211	 0	 900

11	 16	 1	 4	 1601	 141211	 0	 2700

12	 16	 1	 5	 1601	 141211	 0	 900

13	 16	 1	 5	 1601	 141211	 0	 1800

14	 16	 1	 5	 1601	 141211	 0	 900

15	 16	 1	 5	 1603	 131211	 0	 15619500
62 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-171CD. 2006

Appendix A.6 – The Spatial Disturbance Input File

The Spatial Disturbance File is a vertical, cycling, descriptive and scenario file 

that contains all spatial disturbances that are to be simulated in LANDSUM and 

the parameters that are needed to simulate these spatial disturbances. Only fire 

can be simulated with this version of LANDSUMv4, the others are listed and their 

spatial simulation is included but they haven’t been tested. The first two lines of 

the file define the file. The first line of the file is the title or reference line where 

anything can be written to describe this file. The second line specifies how many 

disturbances are cycled in this file (ndist). Again, only fire is available so the 

number one is entered. The remaining lines describe each spatial disturbance 

and they are cycled if the other spatial disturbances were available. The structure of the file is as follows where VAR is variable name, LINE is line number in 

file, SIZE is the size of the field in characters or digits, DESCRIPTION is a short 

description of the variable, and UNITS is the units of this variable:

VAR LINE 	 SIZE 	 DESCRIPTION UNITS

title 	 1 	 256 	 Title line for reference None

ndist 	 2 	 10 	 Number of disturbances that are cycled in this file None

sdistID 	 3 	 10 	 Spatial disturbance ID number(1-fire, 2-beetle, 3-harvest) None

modID 	 4 	 10 	 Spatial spread model (1-cell automata, 2-Cookie, 3-percolation) 	 None

nys,nyn,nyw 	 5 3 	 10 	 Number of years that a severe, normal and wet year in a decade 	 None

fsize 	 6 	 10.2 	 Average fire size for this landscape Hectares

wsim 	 7 	 10 	 Wind simulation:0-same every year, 1-year, 2-fire, 3-timestep, 4-cell)

wind 	 8 	 10.2 	 Average wind speed m/sec

wdir 	 9 	 10.2 	 Average wind direction for a fire event azimuths

freqequ 	 10 	 10 	 Equation number for frequency probablity equation for occurrence 	 None

freqa 	 11 	 10.2 	 Parameter 1 for freq equation (Year till it can burn ) Year

freqb 	 12 	 10.2 	 Parameter 2 for freq equation (Approximate fire return interval yrs) 	 Year

freqc 	 13 	 10.2 	 Parameter 3 for freq equation (shape parm) None

fsizeequ 	 14 	 10 	 Equation number fire size None

fsizea 	 15 	 10.2 	 Parameter 1 for fire size equation m2

fsizeb 	 16 	 10.2 	 Parameter 2 for fire size equation (shape of curve parm) None

fsizeb 	 17 	 10.2 	 Parameter 3 for fire size equation (shape of curve parm) None

shape 	 18 	 10 	 Shape pattern (1-triangle, 2-parabolic, 3-circular, 4-rectangle) 	 None

ecc 19 	 10.2 	 Eccentricity or elongation parameter (0.0-10.0) None

cycle for each disturbance of beetle or harvest

where sdistID is the spatial disturbance ID number and the number 1 is wildland 

fire. Options 2 (beetles) and 3 (timber harvest) have not been fully tested. The 

variable modID indicates the spatial spread model to use to simulate the spread 

of this disturbance. Option 1 is a cell automata model that is very simplistic; option 2 is a cookie cutter method where shapes (specified in following lines) are 

cut from the landscape to mimic disturbance patches; and option 3 is the fully 

tested cell percolation approach that uses a mechanistic means to spread fire. 

Option 3 is available only for fire.

The next three fields identify the distribution of fires over 10 decades and provide 

a means for the user to impose variation in burned areas over simulation years. 

When the user specified the number of severe, normal, and wet years (nys, nyn, 

nyw) in a decade, the model will compute multipliers that increase or decrease 

fire probabilities by an order of magnitude to generate variability across years. 

For example, if the user specifies 1,1,8 for these variables, then there would be 

a very low number of severe fire years, but when these years occur, large areas 

would be burned. A triplet of 3,4,3 would generate the same number of fires each 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-171CD. 2006 63

year. Even though the probabilities of fire are modified each year, the annual fire 

probabilities averaged across the simulation run will be exactly as specified in 

the Scenario Input File (Appendix A.11).

The variable fsize identifies the average size (ha) of all fires on the landscape. 

This value is used to scale the point estimates of fire probabilities in the Scenario 

Input File to an area basis.

The next three fields concern the simulation of wind. First, the user specifies 

how wind will be simulated. Option 0 specifies that the wind will be in the same 

direction and at the same speed each year; option 1 varies wind speed and 

direction each year; option 2 varies the speed and direction for each fire; option 

3 varies wind by simulation time step; and option 4 varies wind speed and direction for each cell. This allows a fully stochastic treatment of wind. Wind speed is 

varied with the range of half and double wind speed specified in the wind variable, while wind direction is varied by 45 degrees from the direction specified in 

the wdir variable.

The next four fields concern the fire frequency probability computation. The 

freqequ is a variable where the ID number of the frequency probability equation 

is stored. The available probability equations are only 1-Weibull. We have others in LANDSUMv4 but only allow the Weibull in this version. Then, the freqa, 

freqb, and freqc are the parameters for the Weibull equation quantified for 

the simulation landscape. In the Weibull equation, the freqa parameter is the 

number of years before the pixel can burn again; freqb is the fire return interval 

(inverse of fire probability); and freqc is the shape parameter (usually 2.0 for 

most applications).

The fire size equation is described in the next four fields. The fsizeequ is the 

equation ID number where options are 1-Pareto, 2-lognormal, 3-exponential, 4-

uniform, 5-normal, 6-extreme, 7-Weibull, and 8-logistic. Again, we recommend 

the Weibull and the parameters that describe that equation: fsizea is a shape 

parameter usually set at 3.0; fsizeb is the inflection of the exponential Weibull 

equation which is roughly about a third of the average fire size; and fsizec is not 

used for this LANDFIRE application.

The last two parameters are used only if the spread model is specified by 

the user as the cookie cutter approach. The shape variable is the shape of the 

patch where options are: 1-triangle, 2-parabolic, 3-circular, 4-rectangle, and ecc

is the eccentricity or elongation parameter that varies from zero (not stretched) 

to 10 (fully elongated).

An example of the Spatial Disturbance File is:

LANDSUM v2.0 - Spatial disturbance parameter file for zone 16, January 2004

1 	 Number of Disturbances represented in this file

1 	 Wildand_Fire (1-fire, 2-beetle, 3-harvest)

3 	 Model of spatial spread simulation (1-cell automata, 2-Cookie cut shapes, 3-cell spread percolation)

0.0 0.0 10.0 Number of years that a severe, normal and wet year occurs in a decade (must add to 10)

50.0 	 Average fire size for this landscape (ha)

3 	 Wind simulation: 0-same every year, 1-vary by year, 2-vary by fire, 3-vary by timestep, 4-very by cell)

5 	 Average wind speed in meters per sec

60.0 	 Average wind direction for a fire event (azimuths true north)

1 	 Equation number for frequency probablity equation for occurrence (1-Weibull )

3.000 	 Value of first parameter for above equation (Year till it can burn )

40.000 	 Value of second parameter for above equation (Approximate fire return interval yrs)

2.0000 	 Value of third parameter for above equation (Shape parameter )

7 	 Equation fire size (1-Pareto, 2-lognormal,3-exponentil, 4-uniform,5-normal,6-extreme,7-Weibull,8-logistic)

1.000 	 Value first parameter fire size equation (ave fire size ha)

3.000 	 Value second parameter fire size equation (shape of curve parm)

0.000 	 Value third parameter fire size equation

2 	 Shape pattern (not for cell automata) (1-triangle, 2-parabolic, 3-circular, 4-rectangle)

2.0 	 Eccentricity or elongation parameter (0.0-10.0)
64 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-171CD. 2006

Appendix A.7 – The Vegetation Input File

The Vegetation Input File is a horizontal, cycling, descriptive file that contains 

all the succession parameters for every succession class within a potential vegetation type (PVT). The first line of the file is the title or reference line where 

anything can be written to describe this file. The second line is a column header 

line (colhead) for ease of entering data. The structure of the file is as follows 

where VAR is variable name, LINE is line number in file, SIZE is the size of the 

field in characters or digits, DESCRIPTION is a short description of the variable, 

and UNITS is the units of this variable:

VAR 	 LINE 	 SIZE 	 DESCRIPTION	 UNITS

title 1 	 256 	 Title line for reference	 None

colhead 	 2 	 256 	 Column head line not used in program	

None

regionID 	 3 	 10 	 Unique code for a broad region ID number	 None

pvtID 4 	 10 	 Potential vegetation type ID number	 None

sclassID 	 5 	 10 	 Succession class ID number	 None

begyear 	 6 	 10 	 Beginning year of the Succession class	 Year

endyear 	 7 	 10 	 Ending year of the Succession Class	 Year

gotosclass 	 8 	 10 	 Succession class to go to after the current	 None

prob 9 	 10.2 	 Prob that sclass will transition to gotosclass	 Prob

where regionID is a variable that allows the stratification of succession parameters by PVT for regions of the country, pvtID is the number of the PVT for the 

succession parameters, and sclassID is the number of the succession class.

The next set of fields are the succession parameters. The begyear and endyear are the beginning and ending year of the succession class. The difference is 

the lifespan, in years, of that succession class. After a succession class is aged 

beyond its lifespan, it would transition to the next succession class (gotosclass

) with a probability specified in the prob variable. Most probabilities are set at 

1.0 for many succession classes, but occasionally succession will result in a 

transition to more than one class and the program selects the next succession 

class based on that probability. An error is printed if the probabilities do not add 

up to 1.0 across the succession class.

An example of the Vegetation Input File is:

Succession parameters for zone 16 July 2004

Project 	 Region 	 Pvt 	 SCLASS 	 BegYear 	 EndYear 	 SCLASS2 	 Prob

LANDFIRE 	 16 	 1601 	 111201 	 15 	 28 	 121201 	 1

LANDFIRE 	 16 	 1601 	 121201 	 28 	 45 	 131201 	 1

LANDFIRE 	 16 	 1601 	 131201 	 45 	 515 	 131211 	 1

LANDFIRE 	 16 	 1601 	 141201 	 280 	 315 	 131201 	 1

LANDFIRE 	 16 	 1601 	 111205 	 15 	 30 	 121205 	 1

LANDFIRE 	 16 	 1601 	 121205 	 30 	 60 	 131205 	 1

LANDFIRE 	 16 	 1601 	 131205 	 60 	 315 	 131211 	 1

LANDFIRE 	 16 	 1601 	 141205 	 187 	 232 	 131205 	 1

LANDFIRE 	 16 	 1601 	 111206 	 15 	 30 	 121206 	 1

LANDFIRE 	 16 	 1601 	 121206 	 30 	 65 	 131206 	 1

LANDFIRE 	 16 	 1601 	 131206 	 65 	 315 	 131211 	 1

LANDFIRE 	 16 	 1601 	 141206 	 190 	 240 	 131206 	 1

LANDFIRE 	 16 	 1601 	 111211 	 15 	 40 	 121211 	 1

LANDFIRE 	 16 	 1601 	 121211 	 40 	 70 	 131211 	 1

LANDFIRE 	 16 	 1601 	 131211 	 70 	 9999 	 131211 	 1

LANDFIRE 	 16 	 1601 	 141211 	 267 	 322 	 131211 	 1

LANDFIRE 	 16 	 1601 	 111405 	 15 	 25 	 121405 	 1
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-171CD. 2006 65

LANDFIRE 	 16 	 1601 	 121405 	 25 	 50 	 131405 	 1

LANDFIRE 	 16 	 1601 	 131405 	 50 	 165 	 131211 	 1

LANDFIRE 	 16 	 1601 	 141405 	 107 	 147 	 131405 	 1

LANDFIRE 	 16 	 1601 	 111801 	 15 	 45 	 121801 	 1

LANDFIRE 	 16 	 1601 	 121801 	 45 	 90 	 131801 	 1

LANDFIRE 	 16 	 1601 	 131801 	 90 	 315 	 131211 	 1

LANDFIRE 	 16 	 1601 	 141801 	 203 	 248 	 131801 	 1

LANDFIRE 	 16 	 1601 	 313301 	 1 	 15 	 111201 	 0.2

LANDFIRE 	 16 	 1601 	 313301 	 1 	 15 	 111205 	 0.17

LANDFIRE 	 16 	 1601 	 313301 	 1 	 15 	 111206 	 0.12

LANDFIRE 	 16 	 1601 	 313301 	 1 	 15 	 111211 	 0.17

LANDFIRE 	 16 	 1601 	 313301 	 1 	 15 	 111405 	 0.21

LANDFIRE 	 16 	 1601 	 313301 	 1 	 15 	 111801 	 0.13

LANDFIRE 	 16 	 1601 	 343704 	 1 	 15 	 111201 	 0.05

LANDFIRE 	 16 	 1601 	 343704 	 1 	 15 	 111205 	 0.29

LANDFIRE 	 16 	 1601 	 343704 	 1 	 15 	 111206 	 0.1

LANDFIRE 	 16 	 1601 	 343704 	 1 	 15 	 111211 	 0.19

LANDFIRE 	 16 	 1601 	 343704 	 1 	 15 	 111405 	 0.29

LANDFIRE 	 16 	 1601 	 343704 	 1 	 15 	 111801 	 0.08

LANDFIRE 	 16 	 1601 	 524102 	 1 	 15 	 111205 	 0.3

LANDFIRE 	 16 	 1601 	 524102 	 1 	 15 	 111211 	 0.3

LANDFIRE 	 16 	 1601 	 524102 	 1 	 15 	 111405 	 0.4
66 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-171CD. 2006

Appendix A.8 – The Disturbance Input File

The Disturbance Input File is a horizontal, cycling, descriptive file that contains all the succession parameters for every succession class within a potential 

vegetation type (PVT). The first line of the file is the title or reference line where 

anything can be written to describe this file. The second line is a column header 

line (colhead) for ease of entering data. The structure of the file is as follows 

where VAR is variable name, LINE is line number in file, SIZE is the size of the 

field in characters or digits, DESCRIPTION is a short description of the variable, 

and UNITS is the units of this variable:

VAR 	 LINE 	 SIZE 	 DESCRIPTION UNITS

title 1 	 256 	 Title line for reference None

colhead 	 2 	 256 	 Column head line not used in program None

regionID 	 3 	 10 	 Unique code for a broad region ID number None

pvtID 4 	 10 	 Potential vegetation type ID number None

sclassID 	 5 	 10 	 Succession class ID number None

distID 	 6 	 10 	 Disturbance ID number None

gotosclass 	 7 	 10 	 Transition succession class ID number None

prob 8 	 10.2 	 Probability that this disturbance will occur Prob

nextage 	 9 	 10 	 Age to assign to the new transition sclass Year

ageinc 	 10 	 10 	 Age increment or decrement to add to the current age Year

where regionID is a variable that allows the stratification of disturbance parameters by PVT for regions of the country, pvtID is the number of the PVT for the 

succession parameters, and sclassID is the number of the succession class. 

The distID number is the number that uniquely identifies this disturbance. If a 

fire disturbance is specified, the ID number should be referenced to the range 

specified in the Simulation Input File (Appendix A.3).

The next fields are the disturbance parameters. The gotosclass is the succession class that the polygon transitions if this disturbance is simulated. The 

probability of this disturbance occurring is specified in the prob variable. This 

is a point estimate of disturbance return interval (inverse of probability). The 

nextage variable allows the user to set the succession age of the disturbed 

polygon to a specific succession age that represents the development of that 

polygon. Alternatively, the user can increment or decrement (ageinc) the disturbed 

polygon’s succession age.

An example of the Disturbance Input File is as follows:

Disturbance parameters for zone 16 July 2004

PROJECT 	 REGION 	 PVT 	 SCLASS 	 DIST 	 SCLASS2 	 PROB 	 NEXT_AGE 	 AGE_INC

LANDFIRE 	 16 	 1601 	 131201 	 1130 	 131201 	 1 	 315 	 0

LANDFIRE 	 16 	 1601 	 141201 	 1120 	 141201 	 1 	 280 	 0

LANDFIRE 	 16 	 1601 	 131201 	 1120 	 141201 	 1 	 280 	 0

LANDFIRE 	 16 	 1601 	 121201 	 1130 	 141201 	 1 	 280 	 0

LANDFIRE 	 16 	 1601 	 141201 	 1130 	 141201 	 1 	 280 	 0

LANDFIRE 	 16 	 1601 	 131201 	 1210 	 141201 	 1 	 280 	 0

LANDFIRE 	 16 	 1601 	 131205 	 1130 	 131205 	 1 	 232 	 0

LANDFIRE 	 16 	 1601 	 131205 	 1220 	 131205 	 0.75 	 187 	 0

LANDFIRE 	 16 	 1601 	 131205 	 1120 	 141205 	 1 	 187 	 0

LANDFIRE 	 16 	 1601 	 141205 	 1120 	 141205 	 1 	 187 	 0

LANDFIRE 	 16 	 1601 	 131206 	 1120 	 141205 	 0.7143 	 187 	 0

LANDFIRE 	 16 	 1601 	 141206 	 1120 	 141205 	 0.4925 	 187 	 0

LANDFIRE 	 16 	 1601 	 131211 	 1120 	 141205 	 0.5634 	 187 	 0

LANDFIRE 	 16 	 1601 	 141211 	 1120 	 141205 	 0.5634 	 187 	 0
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-171CD. 2006 67

Appendix A.9 – The Vegetation Fix Input File

The Vegetation Fix Input File is a horizontal, cycling, descriptive file that contains all fixes for any errors that are encountered when the succession logic and 

parameters are checked against the Polygon Input File and maps. The user can 

specify the fixes in this file so that the Input polygon maps need not be changed. 

The first line of the file is the title or reference line where anything can be written 

to describe this file. The second line is a column header line (colhead) for ease 

of entering data. The structure of the file is as follows where VAR is variable 

name, LINE is line number in file, SIZE is the size of the field in characters or 

digits, DESCRIPTION is a short description of the variable, and UNITS is the 

units of this variable:

VAR 	 LINE 	 SIZE 	 DESCRIPTION UNITS

title 	 1 	 256 	 Title line for reference None

colhead 	 2 	 256 	 Column head line not used in program None

projectID 	 3 	 10 	 Project ID number or alphanumeric None

regionID 	 4 	 10 	 Unique code for a broad region ID number None

pvtID 	 5 	 10 	 Potential vegetation type ID number None

sclassID 	 6 	 10 	 Succession class ID number None

fixclass 	 7 	 10 	 Succession class to reassign to the pvt for the sclassID None

prob 8 	 10.2 	 Probability that this sclass gets assigned to pvt for the sclassID Prob

where projectID represents a name for a project for this fix file. This project would 

be matched to the project in the Scenario Input File. The regionID is a variable 

that allows the stratification of disturbance parameters by PVT for regions of the 

country. The pvtID is the number of the PVT for the succession parameters and 

sclassID is the number of the succession class that is wrong. The fixclass is 

the succession class ID number to assign for the wrong sclassID for this pvtID. 

Then, the user can enter a probability of this assignment (prob) where a set of 

assignments can be made for one wrong sclassID based on these probabilities 

which must add to one.

An example of the Vegetation Fix Input File is as follows:

vegfix.in -- Vegetation correction file for map mismatches -- Keane spring 2003

Project 	 Region 	 PVT 	 SCLASS 	 TOSCLAS 	 PROB

LANDFIRE 	 160000 	 75 	 42203 	 42009 	 0.5

LANDFIRE 	 160000 	 10 	 264087 	 264087 	 0.25

LANDFIRE 	 160000 	 10 	 264087 	 264087 	 0.25

LANDFIRE 	 160000 	 10 	 264087 	 254079 	 0.25

LANDFIRE 	 160000 	 10 	 264087 	 264087 	 0.25

LANDFIRE 	 160000 	 10 	 14080 	 34081 	 0.10

LANDFIRE 	 160000 	 10 	 14080 	 254079 	 0.20

LANDFIRE 	 160000 	 10 	 14080 	 54081 	 0.30

LANDFIRE 	 160000 	 10 	 14080 	 14080 	 0.30

LANDFIRE 	 160000 	 10 	 14080 	 14080 	 0.10

LANDFIRE 	 160000 	 10 	 34081 	 34081 	 1.0

LANDFIRE 	 160000 	 10 	 44081 	 44081 	 1.0

LANDFIRE 	 160000 	 10 	 54081 	 254079 	 1.0
68 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-171CD. 2006

Appendix A.10 – The Management Plan Input File

The Management Plan Input File is a horizontal, cycling, scenario file that 

specifies which scenario is used for each portion of the simulation landscape 

during a specific span of years. The first line of the file is the title or reference 

line where anything can be written to describe this file. The second line is a column header line (colhead) for ease of entering data. The structure of the file is 

as follows where VAR is variable name, LINE is line number in file, SIZE is the 

size of the field in characters or digits, DESCRIPTION is a short description of 

the variable, and UNITS is the units of this variable:

VAR 	 LINE 	 SIZE 	 DESCRIPTION UNITS

title 	 1 	 256 	 Title line for reference None

colhead 	 2 	 256 	 Column head line not used in program None

projectID 	 3 	 10 	 Project ID number or alphanumeric None

regionID 	 4 	 10 	 Unique code for a broad region ID number None

zoneID 	 5 	 10 	 Stratification code for a zone within a region None

scenarioID 	 6 	 10 	 Scenario ID number referenced in the Scenario input file None

startyr 	 7 	 10 	 Starting year of this management plan Year

endyear 	 8 	 10 	 Ending year of this management plan Year

The landscape is stratified by region (regionID) and zone (zoneID), and a 

scenario (scenarioID) that is defined in the Scenario Input File by project (projectID) is assigned to each region and zone. This scenario is then used for the 

span of simulation years specified by startyr and endyr. For example, lets say 

the landscape was divided into two regions (public and private lands) and all 

public lands are further divided into two zones (wilderness and non-wilderness). 

Then, a let-burn scenario is assigned for the first 100 years of simulation for the 

public and wilderness lands, and a full fire suppression scenario is assigned to 

both public non-wilderness and private lands. This file allows the user to target 

unique scenarios in space and time.

An example of the Management Plan Input File is as follows:

LANDSUMv4 Management plan file -- Z16 Test landscape - August 2003

Project 	 Region 	 Zone 	 Scenario 	 StartYear 	 EndYear

LANDFIRE 	 16 	 1 	 1 0 	 10000

LANDFIRE 	 16 	 2 	 2 0 	 10000
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-171CD. 2006 69

Appendix A.11 – The Scenario Input File

The Scenario Input File is another horizontal, cycling, scenario file that specifies the frequency, cost, and benefit of implementing or allowing a disturbance to 

happen on the landscape. The scenarios are stratified by a project and region. 

The first line of the file is the title or reference line where anything can be written to describe this file. The second line is a column header line (colhead) for 

ease of entering data. The structure of the file is as follows where VAR is variable name, LINE is line number in file, SIZE is the size of the field in characters 

or digits, DESCRIPTION is a short description of the variable, and UNITS is the 

units of this variable.

VAR 	 LINE 	 SIZE 	 DESCRIPTION UNITS

title 	 1 	 256 	 Title line for reference None

colhead 	 2 	 256 	 Column head line not used in program None

projectID 	 3 	 10 	 Project ID number or alphanumeric None

regionID 	 3 	 10 	 Unique code for a broad region ID number None

scenarioID 	 3 	 10 	 Scenario ID number referenced in the Scenario input file None

pvtID 	 3 	 10 	 Potential vegetation type ID number None

sclassID 	 3 	 10 	 Succession class ID number None

distID 	 3 	 10 	 Disturbance ID number None

prob 	 3 	 10.2 	 Probability that this sclass gets assigned to pvt for the sclassID Prob

cost 	 3 	 10 	 Cost of implementing the proposed disturbance action $/ha

benefit 	 3 	 10 	 Benefit of implement the proposed disturbance action $/ha

Each scenario is assigned a project (projectID) and area (regionID) so that 

multiple scenarios can be built across space and projects. Scenarios can be 

stratified by time using the Management Plan Input File. The scenario is given 

a unique identification number (scenarioID) and then each disturbance that 

can happen on that landscape (distID) is given a probability (prob) by potential 

vegetation type (pvtID) and succession class (sclassID). Disturbances need not 

be assigned to every combination of PVT and succession class.

The cost of the disturbance (cost) and the estimated benefits (benefit) are 

given values in this file (dollars per hectare). Currently, LANDSUMv4 doesn’t 

output any results for the cost-benefit analysis but future versions of the model 

will have the capability of a full investigation into the financial repercussions of 

the proposed scenario.

An example of the Scenario Input File is as follows:

Scenario Parameters for Zone 16 July 2004

PROJECT 	 REGION 	 SCENARIO 	 PVT 	 SCLASS 	 DISTURB 	 PROB 	 COST 	 BENEFITS

LANDFIRE 	 16 	 1 	 1601 	 111201 	 1110 	 0.013 	 0 	 0

LANDFIRE 	 16 	 1 	 1601 	 111205 	 1110 	 0.013 	 0 	 0

LANDFIRE 	 16 	 1 	 1601 	 111206 	 1110 	 0.01 	 0	 0

LANDFIRE 	 16 	 1 	 1601 	 111211 	 1110 	 0.0025 	 0 	 0

LANDFIRE 	 16 	 1 	 1601 	 111405 	 1110 	 0.004 	 0 	 0

LANDFIRE 	 16 	 1 	 1601 	 111801 	 1110 	 0.003 	 0 	 0

LANDFIRE 	 16 	 1 	 1601 	 121201 	 1110 	 0.0067 	 0 	 0

LANDFIRE 	 16 	 1 	 1601 	 121201 	 1130 	 0.02 	 0 	 0

LANDFIRE 	 16 	 1 	 1601 	 121205 	 1110 	 0.0067 	 0 	 0

LANDFIRE 	 16 	 1 	 1601 	 121205 	 1130 	 0.02 	 0 	 0

LANDFIRE 	 16 	 1 	 1601 	 121206 	 1130 	 0.02 	 0 	 0

LANDFIRE 	 16 	 1 	 1601 	 121206 	 1110 	 0.0067 	 0 	 0

LANDFIRE 	 16 	 1 	 1601 	 121211 	 1110 	 0.003 	 0 	 0

LANDFIRE 	 16 	 1 	 1601 	 121405 	 1110 	 0.005 	 0 	 0
70 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-171CD. 2006

Appendix A.12 – The Attribute Input File

The Attribute Input File is yet another horizontal, cycling, scenario file that 

specifies up to five attributes for each combination of potential vegetation type 

and succession class. The first line of the file is the title or reference line where 

anything can be written to describe this file. The second line is a column header 

line (colhead) for ease of entering data. The structure of the file is as follows 

where VAR is variable name, LINE is line number in file, SIZE is the size of the 

field in characters or digits, DESCRIPTION is a short description of the variable, 

and UNITS is the units of this variable.

VAR 	 LINE 	 SIZE 	 DESCRIPTION UNITS

title 	 1 	 256 	 Title line for reference None

colhead 	 2 	 256 	 Column head line not used in program None

projectID 	 3 	 10 	 Project ID number or alphanumeric None

regionID 	 3 	 10 	 Unique code for a broad region ID number None

pvtID 	 3 	 10 	 Potential vegetation type ID number None

sclassID 	 3 	 10 	 Succession class ID number None

ctID 	 3 	 10 	 Cover type ID number None

ssID 	 3 	 10 	 Structural stage ID number None

att1 	 3 	 10 	 Unique ID number of attribute 1 describing the PVT-ct-ss combo None

att2 	 3 	 10 	 Unique ID number of attribute 2 describing the PVT-ct-ss combo None

att3 	 3 	 10 	 Unique ID number of attribute 3 describing the PVT-ct-ss combo None

att4 	 3 	 10 	 Unique ID number of attribute 4 describing the PVT-ct-ss combo None

att5 	 3 	 10 	 Unique ID number of attribute 5 describing the PVT-ct-ss combo None

The attributes can be stratified by simulation project (projectID) and by simulation area (regionID). But all attributes must be assigned by potential vegetation 

type (pvtID) and succession class (sclassID) combinations. If combinations are 

missed, the program will print an error. The next two fields, cover type (ctID) and 

structural stage (ssID), should be entered for most applications, especially if the 

user desires maps of these variables. The list of five attributes (att1, att2, att3, 

att4, att5) is specified next where each attribute is the quantification of some 

ecosystem parameter that is sensitive to PVT-succession class pairs. For example, 

the first two attributes can be the fire behavior fuel model and the NFDRS fuel 

model, then the next attribute could be an elk habitat value (1-low, 2-moderate, 

3-high), the fourth attribute could be snag density, and the last category may be 

canopy cover class.

An example of the Attribute Input File is:

Attribute.in --File for assigning attributes to sclass -- Z16 Landscape -- July 04

Project	 Reg	 Pvt	 SCLASS	 Ctype	 SS	 Fmod 	 FCC	 NFDRS	 Nothing	 Nothing

LANDFIRE	 16	 1601	 111201	 1201	 11	 10	 10	 10	 10	 10

LANDFIRE	 16	 1601	 111205	 1205	 11	 10	 10	 10	 10	 10

LANDFIRE	 16	 1601	 111206	 1206	 11	 10 	 10	 10	 10	 10

LANDFIRE	 16	 1601	 111211	 1211	 11	 10	 10	 10	 10	 10

LANDFIRE	 16	 1601	 111405	 1405	 11	 10	 10	 10	 10	 10

LANDFIRE	 16	 1601	 111801	 1801	 11	 10	 10	 10	 10	 10

LANDFIRE	 16	 1601	 121201	 1201	 12	 10	 10	 10	 10	 10

LANDFIRE	 16	 1601	 121205	 1205	 12	 10	 10	 10	 10	 10

LANDFIRE	 16	 1601	 121206	 1206	 12	 10	 10	 10	 10	 10

LANDFIRE	 16	 1601	 121211	 1211	 12	 10	 10	 10	 10	 10

LANDFIRE	 16	 1601	 121405	 1405	 12	 10	 10	 10	 10	 10

LANDFIRE	 16	 1601	 121801	 1801	 12	 10	 10	 10	 10	 10

LANDFIRE	 16	 1601	 131201	 1201	 13	 10	 10	 10	 10	 10
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-171CD. 2006 71

Appendix A.13 – The Fire Year Input File

The Fire Year Input File is a horizontal, scenario file that specifies the fire year 

(severe, normal, wet) for every year of the simulation. The first line of the file 

is the title or reference line where anything can be written to describe this file. 

The second line is a column header line (colhead) for ease of entering data. 

The structure of the file is as follows where VAR is variable name, LINE is line 

number in file, SIZE is the size of the field in characters or digits, DESCRIPTION 

is a short description of the variable, and UNITS is the units of this variable.

VAR 	 LINE 	 SIZE 	 DESCRIPTION UNITS

title 	 1 	 256 	 Title line for reference None

fmult 	 2 	 5.2 	 Fire multiplier (number from 0 to 1.0; 0.0 is no fires, 1.0 all fire None

An example of the Fire Year Input File is:

Fire multiplier year file -- Z16 Landscape -- July 04

0.1

0.2

0.01

0.9

0.01

0.1

0.1
72 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-171CD. 2006

Appendix B.1 – LANDSUMv4 Output File Formats

LANDSUMv4 prints output to five different files as specified in the Simulation 

Input File (Appendix A.3). Shown here are the formats for those five files.

Output STAND.STAT file structure is:

VARIABLE 	 COLUMN 	 SIZE 	 DESCRIPTION UNITS

standID 	 1 	 10 	 Polygon keyID none

regionID 	 2 	 10 	 Landfire Region None

zoneID 	 3 	 10 	 Management zone None

strataID 	 4 	 10 	 Polygon stratification code None

pvtID 	 5 	 10 	 Potential vegetation type None

sclassID 	 6 	 10 	 Succession class None

age 7 	 5 	 Successional age Years

area 	 8 	 10.1 	 Area of polygon m2

year 9 	 5 	 Simulation year Years

lastdistID 	 10 	 10 	 ID of last disturbance None

ysd 11 	 5 	 Years since last disturbance Years

Missing values are coded with a -1.00

Output FIRE.STAT file structure is:

VARIABLE 	 COLUMN 	 SIZE 	 DESCRIPTION UNITS

year 1 	 5 	 Simulation year Years

fmult 	 2 	 5.2 	 fire year multiplier fraction

fireID 	 3 	 5 	 Fire ID number None

x 4 	 5 	 Fire start x coordinate None

y 5 	 5 	 Fire start y coordinate None

pvtID 	 6 	 10 	 PVT ID code None

area 	 7 	 10.1 	 Area burned m2

Missing values are coded with a -1.00

Output LANDSCAPE.STAT file structure is:

VARIABLE 	 COLUMN 	 SIZE 	 DESCRIPTION UNITS

year 1 	 5 	 Simulation year Years

regionID 	 2 	 10	 LANDFIRE region None

zoneID 	 3 	 10 	 Management zone None

strataID 	 4 	 10 	 Landscape strata None

pvtID 	 5 	 10 	 Potential vegetation type None

sclassID 	 6 	 10 	 Succession class (ssxct) None

coverID 	 7 	 5 	 Cover type ID None

strucID 	 8 	 5 	 Structural stage ID None

area 	 9 	 10.1 	 Area in this combination m2

Missing values are coded with a -1.00
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-171CD. 2006 73

Output ACTION.STAT file structure is:

VARIABLE C	 OLUMN 	 SIZE 	 DESCRIPTION UNITS

year 1 	 5 	 Simulation year Years

regionID 	 2 	 10 	 LANDFIRE region None

pvtID 	 3 	 10 	 Potential vegetation type None

sclassID 	 4 	 10 	 Succession class (ctxss) None

actionID 	 5 	 10 	 ID number of disturbance None

area 	 6 	 10.1 	 Cumulative area over interval m2

Missing values are coded with a -1.00

Output LANDFIRE.STAT file structure is:

VARIABLE 	 FIELD 	 SIZE 	 DESCRIPTION UNITS

project 	 1 	 10 	 Name of simulation project Code

region 	 2 	 10 	 Region of country of simulation landscape Code

year 	 3 	 5 	 Simulation year year

zone 	 4 	 10 	 Management zone of simulation landscape Code

strata 	 5 	 10 	 Landscape strata Code

pvt 	 6 	 10 	 Potential vegetation type Code

vset 	 7 	 5 	 Variable set: there are eight variable sets

	 	 1-succession classes, 2-fire severity types, 3-cover types,

	 	 4-structural stages, 5-first user-spec attribute

	 	 (an example would be fuel model), 6-8-second thru

	 	 fifth user-specified attributes

varID 	 8 	 10 	 Code of variables in the specified variable set.

	 	 For example, a cover type code of 2003 is used in the

	 	 cover type variable set.

area 	 9 	 10.1 	 Area occupied on simulation landscape m2

Missing values are coded with a -1.00
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