United States Department of Agriculture

Forest Service Northern Research Station General Technical Report NRS-164 November 2016

Seeing the Future Impacts of Climate 

Change and Forest Management: 

a Landscape Visualization System for 

Forest Managers

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Published by

U.S. FOREST SERVICE

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November 2016

For additional copies:

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Manuscript received for publication 28 June 2016

Abstract

Forest managers are increasingly considering how climate change 

may alter forests’ capacity to provide ecosystem goods and services. 

But identifying potential climate change effects on forests is difficult 

because interactions among forest growth and mortality, climate change, 

management, and disturbances are complex and uncertain. Although 

forest landscape models can account for most factors that structure forest 

landscapes (including climate change), the sometimes overwhelming 

amount of output from these models can make it hard for some 

managers to interpret and understand the projections. In an effort to help 

managers visualize and analyze model output, we developed an intuitive 

Web-based system: LandViz. We applied LandViz in a collaborative, 

iterative approach to conduct a Climate Change Vulnerability Assessment 

for the Chippewa National Forest in Minnesota using the LANDIS-II 

landscape model. LandViz enhanced managers’ collaboration with model 

experts and increased their understanding of the tradeoffs between 

amounts and types of various resources in a changing climate. Managers 

can use the insight gained from LandViz to inform their strategic and 

tactical planning as they manage these tradeoffs.

Cover

Photo: Harvest after a thunderstorm severely damaged a swath of the Chippewa National 

Forest, Minnesota in July 2012 (photo by Chippewa National Forest). Maps and graphs: 

Visualizations generated by LandViz to compare effects of potential future climate 

scenarios on aboveground biomass of tree species on the Chippewa National Forest.

Quality Assurance

This publication conforms to the Northern Research Station’s Quality Assurance 

Implementation Plan which requires technical and policy review for all scientific 

publications produced or funded by the Station. The process included a blind technical 

review by at least two reviewers, who were selected by the Assistant Director for Research 

and unknown to the author. This review policy promotes the Forest Service guiding 

principles of using the best scientific knowledge, striving for quality and excellence, 

maintaining high ethical and professional standards, and being responsible and 

accountable for what we do.

The use of trade or firm names in this publication is for reader information and does not 

imply endorsement by the U.S. Department of Agriculture of any product or service.
Seeing the Future Impacts of Climate 

Change and Forest Management: 

a Landscape Visualization System for 

Forest Managers

Eric Gustafson, Melissa Lucash, Johannes Liem, Helen Jenny, 

Rob Scheller, Kelly Barrett, and Brian Sturtevant

The Authors

ERIC GUSTAFSON is a research landscape ecologist and BRIAN STURTEVANT 

is a research ecologist with the U.S. Forest Service, Northern Research Station, 

Institute for Applied Ecosystem Studies, Rhinelander, WI. 

Contact information: egustafson@fs.fed.us.

MELISSA LUCASH is a member of the research faculty and ROB SCHELLER is an 

associate professor at Portland State University, Portland, OR.

JOHANNES LIEM was a graduate student at the time of writing and 

HELEN JENNY is an assistant professor at Oregon State University, Corvallis, OR.

KELLY BARRETT is a wildlife biologist with the Chippewa National Forest, 

Deer River, MN.

General Technical Report NRS-164
Top: Aerial view of the damage caused by a fast-moving thunderstorm that swept through the 

Chippewa National Forest on July 2, 2012. The storm affected an area 10 miles wide and 40 miles long 

from Cass Lake to Deer River (photo by Chippewa National Forest). Lower left: Aftermath of the storm 

(photo by Chippewa National Forest). Lower right: Mixed hardwood and softwood forest near Cass 

Lake (photo by R. Scheller, used with permission).
1

INTRODUCTION

Strategic forest management planning considers the 

ability of forests to provide multiple ecosystem goods 

and services over the long term. Forest ecosystems 

are heterogeneous in space and dynamic through 

time, being subject to highly complex interactions 

among succession and disturbance processes and 

their biotic and abiotic drivers. Strategic planning is 

increasingly taking into account the expected effects 

of climate change, with particular emphasis on how to 

manage landscapes to maintain ecosystem goods and 

services (Temperli et al. 2012). However, it is difficult 

to predict the novel (and uncertain) environmental 

conditions of the future and how a changing 

climate might interact with the myriad natural 

and anthropogenic forces that can structure forests 

(Gustafson 2013). Many tools and approaches have 

been designed to help forest managers incorporate 

climate change considerations into management and 

devise adaptation tactics (Swanston and Janowiak 

2012).

The U.S. Forest Service has mandated that each 

national forest address climate change in both strategic 

(national forest-level) and tactical (project-level) 

management planning, and assess progress with an 

annual Climate Change Scorecard (U.S. Forest Service 

2011b). To comply, national forests are developing 

plans to guide vegetation management activities 

intended to produce a sustained output of ecosystem 

goods and services as climate changes (U.S. Forest 

Service 2011a). Federal regulations require the use 

of the best available science during the planning 

process to identify the most likely outcomes of 

alternative management options. In consultation with 

stakeholders, managers decide on the mix of ecosystem 

goods and services desired for the ecosystem in 

question. Managers then choose the options that 

are expected to achieve the desired combination 

of goods and services. Although not governed by 

these regulations, industrial forest managers have an 

economic incentive to use the best available science 

to sustainably manage their forest landscapes under 

changing environmental conditions.

The long-term landscape outcomes of various 

climate and management scenarios can be projected 

with forecasting tools that incorporate the complex 

interactions among forest succession, disturbances, and 

their drivers (Gustafson 2013). State-of-the-art forest 

landscape models are powerful simulation tools that 

are based on current ecological science. They are a class 

of predictive simulators that model forest generative 

(establishment, development, aging) and degenerative 

(disturbance, senescence) processes at broad spatial 

and temporal scales (He 2008). Few alternatives to 

these models are capable of accounting for both the 

spatial and nonspatial biotic and abiotic interactions 

that structure forested ecosystems (including climate 

change). Forest landscape models have two particular 

strengths: their ability to explicitly model spatial 

processes, such as seed dispersal and disturbance spread, 

and their ability to account for interactions in both 

time and space, which are important determinants 

of future landscape composition and spatial pattern. 

These models are especially useful for projecting 

future ecosystem dynamics and objectively comparing 

landscape attributes under different management 

alternatives (Gustafson and Keane 2014).

Many managers find it difficult, however, both to 

run forest landscape models and to use the output 

to answer specialized management questions. Model 

parameterization is a complicated step. The volume of 

data generated by the models can be overwhelming, 

particularly when multiple climate change or 

management scenarios are of interest. Managers may be 

unable to extract the information necessary to inform 

their decisions—much less interpret it. Managers 

often lack the time or expertise to learn how to run 

and use forest landscape models. New approaches that 

facilitate improved collaboration between managers 

and scientists are needed. Specifically, software tools are 

needed to increase managers’ ability to independently 

access model results in a way that increases their 

understanding of the results and facilitates more 

productive interactions with scientists. This paper 

describes our recent work to create such a tool and 

illustrates its relevance to forestry practice through an 

example of its application.
2

In the project described here, we 1) implement an 

existing framework that allows managers and scientists 

to collaborate in the application of a forest landscape 

model to a specific management problem; 2) develop a 

software tool that enables managers to visualize model 

outputs without other experts’ assistance, thus better 

preparing them for collaboration with scientists during 

the decisionmaking process; and 3) describe a case 

study where these elements were applied on a national 

forest to meet specific forest management information 

needs.

COLLABORATIVE, ITERATIVE 

APPROACH

Forest managers’ successful use of complex modeling 

technology is hampered by formidable challenges. 

Managers may not have modeling expertise, and 

even technically trained managers seldom have the 

time to learn and apply modeling technologies. 

Proper interpretation of model results requires a 

clear understanding of model assumptions and 

the limitations of the results. There must be a clear 

link between the decisions to be made and the 

information that the model delivers. Model experts 

can help managers develop such links and select the 

most appropriate model. But model experts may be 

unfamiliar with the specific land base that is being 

managed, and may therefore be poorly equipped to 

parameterize the model to represent local ecological 

or management dynamics. These challenges can be 

overcome only through a substantive partnership 

between model experts and managers. A collaborative, 

iterative approach (Fall et al. 2001, Gustafson et al. 

2006) is a form of participatory modeling that has 

proven helpful when using forest landscape models in 

support of forest management planning (Doyon et al. 

2011; Sturtevant et al. 2007, 2009a).

The conceptual framework of the collaborative, 

iterative approach is best represented as an interaction 

among model experts, decisionmakers, and local 

resource experts (Fig. 1). The interaction takes the 

form of iterative communication that is focused on 

applying the model to support the decisionmaking 

process for a particular management decision. During 

these stepwise interactions, the model experts, 

decisionmakers, and resource experts better frame the 

management question, refine the parameterization of 

the model, and gain confidence in its behavior and 

in the interpretation of the results. Thus, the model 

serves as a communication vehicle that enables all 

parties to conceptualize and formalize their concerns 

about the management decision in question. The result 

is equal access to information, better understanding 

of the management decisions to be made, improved 

transparency in decisionmaking, and a clear transfer of 

modeling technology from a research environment to 

a management environment (sensu Moss et al. 2014). 

This approach also fosters a shared understanding 

of the interactions among ecological and human 

processes, the capabilities and assumptions of the forest 

landscape model, the requirements for the model, and 

appropriate interpretation of model outputs.

A key strength of the collaborative approach is that 

each partner provides expertise that is critical to the 

success of the project. The model experts can determine 

the feasibility of applying existing models and may 

recommend building new models. They know the 

assumptions underlying application of a particular 

model, are familiar with the algorithms that drive the 

model, know how to estimate model parameters and 

develop the input data, have the technical expertise 

to run the model, and can guide interpretation of 

the results. The decisionmakers understand the 

management decisions that must be made, and 

can readily identify the information gaps that limit 

their ability to make defensible decisions. Without 

the decisionmakers’ guidance, model experts may 

unwittingly develop sophisticated answers to irrelevant 

Model 

experts 

Local 

resource 

experts 

Decisionmakers Forest 

landscape 

model

Figure 1.—A conceptual framework of the collaborative, iterative 

technology transfer approach. The arrows represent iterative 

communication. Adapted from Gustafson et al. (2006).
3

questions. The local resource experts make sure that the 

model application incorporates current knowledge about 

the local ecosystem, and they help the model experts 

estimate realistic model parameters. They can readily 

identify model behaviors that incorrectly simulate onthe-ground conditions. They also assist managers in 

developing ecologically feasible management options. 

This collaborative, iterative approach helps generate 

results that are well-informed and useful for decision 

support (e.g., Sturtevant et al. 2009a).

VISUALIZATION SYSTEM 

(LandViz)

Even with the collaborative, iterative approach, 

managers may struggle to integrate model results into 

their planning process because forest landscape models 

produce huge amounts of output. The sheer volume of 

output makes it difficult for managers to extract the 

pertinent results. For example, a simple simulation 

with 15 tree species that considers only wind and fire 

disturbance can produce more than 200 maps when 

maps of species’ biomass, forest type, and disturbance 

location are generated for every decade of a single 

century. Managers or modelers may have to spend a 

good deal of time on data processing to synthesize 

data and compare landscape attributes over time for 

multiple scenarios. Furthermore, the technical expertise 

needed to conduct these analyses rarely resides within 

a manager’s office, and providing technical support to 

managers on an ongoing basis is outside the mission 

of most researchers. Compounding the problem, 

simulation models usually do not provide metadata, 

which are information on how the data were derived 

and how specific datasets may be correctly used and 

related with other data. Yet this supporting information 

is necessary both to avoid interpretive and inferential 

errors, and to meaningfully visualize and explore the 

results. Thus, the barriers are very high for managers 

who wish to fully understand the nuances of forest 

dynamics as projected by a forest landscape model for 

alternative management or climate scenarios. Modelers 

can verbally describe the logical relationships between 

simulation output datasets. But new tools are required 

if these relationships are to be automatically translated 

into easily accessible and comparable visualizations that 

managers can interactively explore to answer specific 

questions.

To this end, we developed a generic, Web-based 

landscape visualization system (LandViz) to 

view simulated landscape management scenarios 

(Appendix). The system has three components: 

1) metadata created by the model (or manually by 

modelers), 2) a preprocessing system run by the 

modeler, and 3) a Web-based visualization tool that 

modelers and managers can use to visualize key 

outputs associated with different management or 

climate scenarios. The system reads the metadata, 

which define the relations between datasets and the 

scenarios to be visualized. These metadata are a bridge 

between the simulation output and visualization of 

the output. The preprocessing component analyzes the 

statistical distributions of values in the maps, within 

and across scenarios, and then assigns default settings 

for the visualization. Default settings determine the 

initial classification and color schemes, and the spatial 

and temporal scales appropriate for comparative 

viewing of maps, graphs, and time-series animations. 

Data are optimized for loading into a Web browser. 

Modelers need only run the model, run the 

preprocessing tool, copy the folder to a Web server, 

and send managers a link to access the viewer in their 

Web browser. Modelers can easily adapt the tool for 

specific audiences (e.g., the set of scenarios available 

and the landscape attributes that can be viewed).

The viewer was specifically designed to make it easier 

for managers to compare outcomes for different 

management and climate scenarios. With a few clicks, 

managers can choose from a list of climate change 

or management scenarios, attributes of interest (e.g., 

biomass of various tree species, abundance of various 

forest types, wildlife habitat), and maps of disturbed 

areas (e.g., areas disturbed by fire or insects). Maps of 

these attributes or disturbance events are overlaid on 

current geographic backgrounds (via OpenStreetMap; 

Haklay and Weber 2008) in side-by-side windows 

that are synchronized for zoom, pan, and animation 

(Fig. 2). Users can synchronize map color legends 

and add or remove breakpoints at any time. Charts 

(graphs) of quantitative map attributes through 

time can also be selected, and these are displayed in 

separate windows. By manipulating play and pause 

buttons, users can animate and freeze the maps and 

charts and watch the landscape and its attributes 

change over time.
4

Figure 2.—Visualization of model results in LandViz. Maps show comparison of the aboveground biomass of quaking aspen, in g/m2, 

at year 100, under baseline (left) and potential warm/wet future (right) climates and current management practices (BAU) on the 

Chippewa National Forest. Colors other than gray represent amount of biomass. Dark gray is public land or roads, medium gray is 

water, and light gray is the background map. Charts below the maps compare aboveground biomass of quaking aspen, in g/m2

(left), and total projected number of 2.5-acre (1.0-ha) sites on which quaking aspen would be harvested (right), through year 100 for 

the same two climate scenarios.
5

CASE STUDY: CLIMATE CHANGE 

VULNERABILITY ASSESSMENT 

FOR THE CHIPPEWA NATIONAL 

FOREST

The Chippewa National Forest (CNF) is a 266,726-

ha (659,000-acre) national forest in north-central 

Minnesota managed by the U.S. Forest Service. The 

CNF is interspersed with lands managed by the 

Minnesota Department of Natural Resources (MNDNR), the Leech Lake Band of the Ojibwe Nation, 

and many nonindustrial private forest landowners, as 

well as UPM Blandin industrial forest land (Fig. 3). 

The current CNF Forest Management Plan (U.S. 

Forest Service 2004) reflects an ecosystem management 

philosophy that seeks to shift the relative abundance of 

ecological communities closer to their range of natural 

variability (Grumbine 1994). It is silent, however, 

on how to plan for climate change, and under its 

current management direction the ecosystem goods 

and services that the public desires may not persist in 

the face of changing conditions. Accordingly, CNF 

managers are beginning to incorporate climate change 

considerations into their current management activities, 

and are developing methods to predict climate impacts 

on the CNF in preparation for the next revision of the 

plan.

We applied the collaborative, iterative approach 

to conduct forest landscape modeling to support a 

Climate Change Vulnerability Assessment for the 

CNF that will help the Forest meet its responsibilities 

under the Climate Change Scorecard (U.S. Forest 

Service 2011b). We used the Northwoods Climate 

Change Response Framework (NCCRF) (Handler et 

al. 2014a, 2014b; U.S. Forest Service 2012), which helps 

national forests, state and local agencies, private and 

industrial landowners, and conservation organizations 

assess the vulnerability of different forest types to 

climate change to guide future management decisions. 

A variety of modeling approaches—forest landscape 

models, ecosystem-level models of productivity, species 

distribution models—are compared to discern where 

there is greatest agreement (and disagreement) about 

Figure 3.—Ownership pattern within the Chippewa National Forest boundary (map by M. Lucash).
6

climate change effects on forest types. A recently 

completed regional assessment using the NCCRF for 

northern Minnesota (Handler et al. 2014a) suggests 

that some of the forest species valued by stakeholders 

in the region will decline under climate change.

Modeling for the Climate Change Vulnerability 

Assessment for the CNF was conducted by an informal 

team of researchers (model experts) from the U.S. 

Forest Service, Northern Research Station; managers 

from the CNF; and local resource experts from the 

CNF, the MN-DNR, and academia. We used the 

LANDIS-II forest landscape model (Scheller et al. 

2007) to simulate climate change impacts because 

it was one of the models used in the NCCRF’s 

vulnerability assessments in Minnesota (Handler et 

al. 2014a) and in Wisconsin and Michigan ( Janowiak 

et al. 2014). A particular strength of LANDIS-II is 

its ability to generate easy-to-compare outputs for 

alternative management and climate scenarios. For 

analytical purposes, the model can output an almost 

unlimited variety of future forest characteristics at 

user-defined temporal and spatial scales, and these 

outputs may be further processed to estimate landscape 

conditions (e.g., wildlife habitat) at scales that are 

relevant to forest management.

LANDIS-II simulates individual tree species’ 

establishment, growth, competition, and senescence, 

and disturbances such as wildfire, wind events, insect 

outbreaks, and timber harvesting at large spatial 

(>100,000 ha, or >250,000 acres) and long temporal 

(centuries) scales on a grid of spatially interacting cells. 

For the simulations described here, we used LANDISII v6.0 with the Century Succession extension v.4.0.2 

(Scheller et al. 2011) to simulate forest establishment, 

growth, and competition. In the model, aboveground 

and belowground live and dead biomass and age of 

cohorts are tracked for each cell, along with detritus 

accumulation, soil processes, and carbon and nitrogen 

cycling. The following extensions were used: the 

Biomass Harvest extension (Gustafson et al. 2000) to 

simulate timber harvesting, the Dynamic Fire and Fuel 

System v2.0.5 extension (Sturtevant et al. 2009b) to 

simulate wildfire, the Base Wind v2.1.2 extension to 

simulate wind disturbance, and BDA v3.0 extension 

(Sturtevant et al. 2004) to simulate jack pine (Pinus 

banksiana L.) mortality from jack pine budworm 

(Choristoneura pinus). The Century Succession and 

disturbance extensions independently act on individual 

cohorts within cells, so together they simulate forest 

dynamics that are under the combined influences 

of succession, disturbance, and forest management 

processes. We simulated 100 years into the future 

using a 10-year time step, beginning at 2010, a time 

period consistent with the length of climate change 

projections.

Model inputs and procedures are described in detail 

in Lucash et al. (in review), but we briefly outline 

them here. Initial forest conditions were derived for 

each ownership type by using actual forest stand data 

when available (e.g., CNF, MN-DNR) and otherwise 

imputing conditions by using U.S. Forest Service Forest 

Inventory and Analysis data (Handler et al. 2014a) at a 

cell resolution of 4 ha (10 acres). Species-specific vital 

attributes for the succession extension were derived 

from the literature and the local resource experts. The 

landscape was subdivided into 25 ecoregions (areas of 

homogeneous soils and climate). Five climate scenarios 

(including baseline) representing combinations of 

temperature and precipitation trends were used as 

inputs to produce a wide range of climate futures. 

The following monthly climate projections (CMIP5; 

Intergovernmental Panel on Climate Change 2013) 

were downloaded from the U.S. Geological Survey’s 

online climate data portal (U.S. Geological Survey 

2016): the University of Idaho Gridded Surface 

Meteorological Dataset (baseline), CanESM2-45 RCP 

4.5 (warm/dry scenario), ACCESS1_0 RCP 4.5 (hot/

wet), GFDL-ESM2M RCP 8.5 (warm/wet), and 

MIROC ESM RCP 8.5 (hot/dry). Forest management 

prescriptions representing the current practices of 

each landowner were developed in consultation with 

managers and resource experts, and applied within the 

boundaries for each ownership. We collaborated with 

local resource experts to estimate model parameters for 

natural disturbance using published and unpublished 

empirical data.

We held face-to-face meetings to build a mutual 

understanding of the conceptual framework of the 

LANDIS-II model, collaboratively develop model 

inputs, develop heuristic management scenarios, and 

produce specifications for the visualization system. 

The process was iterative, so meetings were held at 

about 6-month intervals, with details worked out 

collaboratively between meetings by phone and email. 
7

At each meeting, progress was presented to the team 

and adjustments were made based on feedback by 

managers, resource experts, and model experts. The 

team developed several scenarios for simulation and 

analysis. Here we present outputs from a baseline 

“business as usual” (BAU) management scenario for 

illustrative purposes. When this scenario was simulated 

with and without climate change, the consequences 

of not considering climate change in management 

strategies could be evaluated. To best illustrate the 

visualization capabilities of the new LandViz tool, we 

present results for selected species for which climate 

change produced relatively divergent outcomes.

Chippewa National Forest managers currently use 

LandViz to explore model output to understand how 

the scenarios affect the simulated dynamics of specific 

forest characteristics. Use of LandViz allows them 

to engage in ongoing collaborative interactions with 

scientists on a much deeper and more insightful level. 

For example, managers wondering how the abundance 

of quaking aspen (Populus tremuloides Michx.) might 

be affected by future climate and disturbance regimes 

can quickly view maps of projected aspen biomass over 

100 years under present and future climate and then 

graph how biomass varies over time (Fig. 2). They will 

see a projected decline in aspen, which may prompt a 

question about which species will be gaining in biomass 

as aspen declines. Managers can quickly create graphs 

of the biomass of other species through time for each 

climate scenario (Fig. 4). The insights from this exercise 

may result in creative interactions with scientists to 

develop novel management approaches that can be 

initially tested with the forest landscape model.

In another example, an interdisciplinary team working 

on an environmental analysis for a future vegetation 

management project could examine how various species 

are projected to fare within a specific Ranger District. 

Because the CNF has experienced several recent major 

windthrow events, the team might also use LandViz 

to view the maps of wind disturbance events across the 

Figure 4.—Charts comparing the projected effect of various climates and current management practices (BAU) on the aboveground 

biomass of sugar maple (Acer saccharum Marsh.; upper left), quaking aspen (Populus tremuloides Michx.; upper right), eastern white 

pine (Pinus strobus L., lower left), and yellow birch (Betula alleghaniensis Britt.; lower right), in g/m2, on the Chippewa National Forest, 

through year 100.
8

CNF for each climate scenario and analyze the impact 

of those simulated events on forest composition and 

biomass (Fig. 5).

As a final example, one of the goals of the current 

CNF Plan involves increasing the percentage area of 

conifers on the landscape. Achieving this goal could 

be very expensive, involving site preparation, planting 

or seeding or a combination, and control of competing 

vegetation and damage by animals. Model outputs 

will help CNF managers determine which coniferous 

species would be wiser investments, given that some 

species may fare poorly in a changing climate. For 

example, objectives in several CNF landscapes include 

increasing jack pine, eastern white pine (P. strobus L.), 

red pine (P. resinosa Ait.), white spruce (Picea glauca

(Moench) Voss), and balsam fir (Abies balsamea L.). 

In some locations, eastern white pine has not been 

favored because of concerns about browsing by deer 

(Odocoileus virginiana) and susceptibility to blister 

rust (Cronartium ribicola J.C. Fisch). But if white 

pine is projected to do better under future climate 

than other currently favored species—which may 

not fare as well in the future (e.g., jack pine)—then 

managers may be persuaded to revise their tactics. 

Such model projections can also be used to evaluate the 

consequences of climate change on conifer-dependent 

birds, which make up one-third of bird species on the 

CNF (Fig. 6).

We envision that LANDIS-II outputs and LandViz 

will be routinely used to inform planning at both 

tactical and strategic levels. Outputs can inform timber 

harvest practices that encourage some species and 

discourage others, depending on key climatic influences. 

Because LandViz shows the spatial interaction of 

climate effects, ecoregion, and management across the 

landscape, tactical practices can be spatially targeted. 

We must note that cell-level projections of landscape 

models are highly uncertain, but general trends and 

spatial patterns at landscape scales can be relied upon 

when answering project-level questions. Analysis 

teams can use LandViz interactively to collectively 

view model outputs and explore management 

issues within a common framework. Perhaps most 

importantly, LandViz allows for transparent analysis 

of the influences of climate change and management 

and enables each stakeholder to evaluate tradeoffs 

associated with alternative management and climate 

scenarios on a common footing.

Figure 5.—Visualization of the location and intensity of wind disturbances between years 30 and 40 for microbursts (left) and 

linear wind events such as derechos and tornadoes (right) on the Chippewa National Forest. Colors other than gray represent 

relative intensity of wind events. Dark gray is public land or roads, medium gray is water, and light gray is the background map. 
9

Strategic planners for the CNF anticipate our approach 

will be extremely useful during the next revision of 

their management plan because 1) LANDIS-II is 

well-suited to project the outcome of alternative 

management scenarios while accounting for other 

factors that structure forested landscapes, 2) the CNF 

planners were involved in the modeling process and 

therefore guided the questions to be addressed, and 

3) LandViz makes the outputs readily accessible to 

a wide audience, from local experts to stakeholders. 

Model outputs can be used to quantify the effects of 

alternatives on forest age, species composition, and 

overall landscape pattern, including wildlife habitat. 

Pairing forest landscape models with visualization tools 

will allow managers to become even more sophisticated 

partners in collaborations with scientists to support 

planning efforts.

LandViz is also an effective communication tool 

for managers, partners, stakeholders, and the public. 

Results from our LANDIS-II Climate Change 

Vulnerability Assessment were recently shared with 

a regional landscape committee of the Minnesota 

Figure 6.—Visualization of relative habitat quality for Blackburnian warbler (Setophaga fusca; left) and pine warbler (S. pinus; 

right) on the Chippewa National Forest under the baseline (current) climate scenario and current management practices (BAU) 

at year 100. Colors other than gray represent relative habitat quality. Dark gray is public land or roads, medium gray is water, and 

light gray is the background map.

Forest Resource Council. This committee is 

responsible for revising a landscape plan for northcentral Minnesota that considers potential climate 

change impacts. Committee members represent a 

variety of organizations and ownerships, including 

the MN-DNR, the U.S. Forest Service, five county 

land departments, three soil and water conservation 

districts, conservation organizations such as The 

Nature Conservancy, the forest products industry, 

Minnesota Indian Affairs Council, universities, and 

nonindustrial private forest landowners. A key benefit 

of visualizing model results is that committee members 

were encouraged to think more explicitly about climate 

change and the viability of current management 

strategies into the future from an “all-lands” perspective 

across ownerships. They are now more likely to explore 

other tools for climate change analysis and to develop 

management options for comparison. We saw that 

LandViz catalyzed a process by which partners and 

stakeholders began to better appreciate what a future 

under climate change may look like in terms of forest 

dynamics and forestry practice.
10

CONCLUSIONS

One of the greatest benefits of LandViz for managers 

is its ability to paint a picture of the realm of future 

possibilities. The ability to visualize how climate and 

various management scenarios may change a landscape 

through time is very powerful and compelling. 

Managers have complex stories to tell about the work 

that they are contemplating for a forested ecosystem 

or landscape. Being able to show various alternative 

futures facilitates clearer communication and focuses 

attention and dialogue on goods, services, and habitat 

values that matter to stakeholders. It helps make the 

case for the critical need to manage in the context 

of change, rather than just attempting to maintain 

the status quo. For tactical planning, LandViz helps 

managers decide where and how to invest time, 

energy, and financial resources to most efficiently 

and effectively achieve functional ecosystems—and 

perhaps more importantly, to identify where efforts to 

resist change are likely to be futile. Strategic planning 

involves managing the tradeoffs between amounts and 

types of various resources, and we expect that models 

paired with visualization will increase understanding 

of these tradeoffs in a changing climate. The most 

important benefit of visualization tools designed 

for managers may be the level of independence that 

they give managers. Tools such as LandViz support 

exploration and reflection, which in turn strengthen 

the value of managers’ collaboration with scientists. 

Such independence is difficult to achieve with forest 

landscape models because of their complexity. But it is 

essential if managers are to understand model outputs 

enough to apply them in routine strategic and tactical 

planning.

Although LandViz has powerful potential to enhance 

manager–scientist collaboration, it is important to 

note that is not a “silver-bullet” decision-support tool. 

It has no optimization capabilities, it does not make 

management recommendations, it does not compute 

levels of uncertainty, and it is limited to the scenarios 

defined in advance. Nevertheless, its ability to help 

managers to directly explore outputs from a forest 

landscape model increases their understanding of how 

alternative scenarios affect specific forest characteristics 

and dynamics and provides valuable insight for decision 

support that is difficult to obtain elsewhere. It is also 

important to recognize that LANDIS-II projections 

have limitations as well. LANDIS-II is designed 

to simulate the effect of each ecological process on 

vegetation independently, which allows the vegetation 

outcome to emerge as an interaction of the processes. 

This is a robust approach for novel combinations of 

processes and environmental conditions (e.g., climate 

change). However, values used for input maps and 

estimated model parameters do have a degree of 

uncertainty. There are also competing scientific views 

about how some ecological processes operate, and 

LANDIS-II was constructed by making assumptions 

about which view is correct. Therefore this model 

accurately computes outputs based on the inputs and 

its formalization of ecological theory, but the output 

must be interpreted in terms of these various sources 

of uncertainty (Xu et al. 2004). LANDIS-II predicts 

expected behavior given the inputs, but cannot predict 

what specifically will happen in the future (Thompson 

et al. 2012).

We believe that the collaborative, iterative modeling 

approach coupled with targeted visualization (e.g., 

LandViz) has potential to benefit not only U.S. 

Forest Service managers but also managers in other 

agencies and organizations responsible for large 

forest landholdings. The combination of collaborative, 

iterative modeling and visualization can assist land 

managers in harnessing the powerful predictive 

capabilities of forest landscape models. All land 

management organizations face similar issues related 

to climate change and strategic planning that must be 

addressed at the scale of ecosystems and landscapes, 

and forest landscape models are arguably the most 

comprehensive and integrative predictive tools available 

to answer management questions at that scale. A 

collaborative, iterative approach lowers the barriers to 

the use of forest landscape models by forest managers 

and increases their confidence in model outputs.

If the collaborative, iterative approach for applications 

of forest landscape models is widely adopted to support 

management decisions, the demand for modeling 

expertise could quickly outpace the supply. However, 

our experience suggests that model developers need 

not serve as the model experts. Instead, entrepreneurial 

model experts with experience in applications of forest 

landscape models (e.g., former postdoctoral scientists) 

could provide contractual modeling services as the 

demand for such services increases.
11

LandViz allows managers to extract information from 

model outputs independently of the model experts. 

This capability is expected to greatly increase the 

accessibility and usefulness of model results. Thus, 

we expect managers to be more likely to use the 

model outputs for decision support. Further, LandViz 

was designed for flexibility. It is a general platform 

that links to a time-series of maps and tabular data 

contained within a predictable hierarchical folder 

structure via metadata. Any spatial model that projects 

change over time and produces such data could be 

linked to LandViz to display its outputs. Regardless 

of the model used, decisionmakers can benefit from 

the ability to visualize how management options will 

change landscape dynamics through time as they 

consider options for providing future ecosystem goods 

and services.

ACKNOWLEDGMENTS

This project was funded by USDA AFRI grant 

#226280. We thank the many staff members of 

the CNF who provided expertise and input for the 

implementation of this project, particularly Audrey 

Gustafson, Gary Swanson, Barb Knight, and Sharon 

Klinkhammer. We also thank Jerry Niemi, Hannah 

Panci, and Alexis Grinde at the University of 

Minnesota Duluth for their contributions to our bird 

habitat outputs. Thanks also to Paul Hessburg with the 

U.S. Forest Service’s Pacific Northwest Station, Maritza 

Mallek with the U.S. Fish and Wildlife Service, and 

two anonymous reviewers for critical reviews of the 

manuscript.

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14

APPENDIX

LandViz Development and Details

(3) Scenario comparison: The comparison of 

multiple scenarios is essential. The visualizations 

should be tailored to this requirement by 

automatically synchronizing data and visual 

elements (e.g., color ramps) across scenarios.

(4) Web-enabled: Data transfer, storage, and 

organization contribute to the overall data 

bottleneck. Therefore the system should store 

data remotely and provide a unified interface 

to model outputs and tools for scenario 

comparison. In addition, the system should be 

suitably reactive to user inputs with minimal 

load times and fast refresh rates.

(5) Extensibility and generic system architecture: 

The software should be designed with a 

modular and generic architecture that is 

not limited to a single model or model 

configuration, but allows the visualization of 

any landscape simulation model output.

We developed a system to meet these requirements. 

LandViz has a generic software architecture, initially 

designed to display LANDIS-II model output 

(Scheller et al. 2007). Although approaches for 

visualizing LANDIS-II simulation output exist (Birt 

et al. 2009, Jenny et al. 2014), they do not facilitate 

manager–scientist communication and do not readily 

enable side-by-side comparison of scenarios.

2. Implementation of LandViz

The software requirements outlined earlier were 

implemented as follows:

2.1. Ease of Use

We removed as many steps as possible between tool 

activation by an end user, such as a forest manager, 

and meaningful engagement with the data. Therefore 

it was imperative that the requested menus, maps, and 

tables load with reasonable default settings and without 

user input. Creating such preferences for visualization 

required additional metadata about the model output, 

which would otherwise be cumbersome for the model 

user to collect.

1. LandViz: System Requirements and 

Objectives

Our goal was to develop a generic Web-based system 

for geovisual analysis of forest landscape simulation 

scenarios. Therefore we developed LandViz, a 

configurable, extendable data visualization system for 

LANDIS-II forest simulation output. The system is 

intended to facilitate communication between forest 

scientists and forest managers. It serves these two user 

groups’ different needs for data exploration and analysis 

by affording temporal and multi-map side-by-side 

comparison across scenarios. Key features of the system 

are out-of-the box Web deployment, customization of 

data access and functionality for different user groups, 

easy integration of modules in a generic architecture, 

data-driven default map symbolization, and interactive 

on-the-fly reclassification, filtering, and highlighting in 

maps and diagrams across space, time, and scenarios. 

Based on our experience working with the forest 

managers of the Chippewa National Forest, we 

identified the following five software requirements 

for a visualization system that would facilitate the 

exchange of information between forest researchers 

(who parameterize and run landscape change 

models) and stakeholders (who use simulation data 

to inform management decisions and policy choices). 

Requirements are listed in order of importance.

(1) Ease of use: The software should not require 

any familiarity with geographic information 

systems, Web programming, cartographic 

design, or complicated software installation. 

The approach should allow users to concentrate 

solely on analyzing simulation output data.

(2) Customization: Stakeholders are interested 

only in specific model output datasets. The 

system should allow customized dataset access 

and visualization based on specific needs. 

Typically stakeholders need access to only a 

small fraction of the total outputs generated by 

a landscape model; additional data detract from 

their focus and can cause cognitive overload 

(Bunch and Lloyd 2006).
15

Therefore LandViz includes a preprocessing 

component, a Python command-line script using 

GDAL (Geospatial Data Abstraction Library; http://

gdal.org/) and NumPy (http://www.numpy.org/) that 

extracts this meta-information from model outputs 

automatically. Using XML metadata files (provided 

by the LANDIS-II model for each output map or 

table), the script populates a data model used for the 

visualization process. It runs analyses on the temporal 

map outputs and stores additional statistics like data 

type (e.g., nominal, ordinal), data range, and a measure 

of central tendency over the whole time period.

Using these statistics, a default classification for 

visualization is generated for every time-series map. 

Separate distinct colors are used for qualitative data 

values; two map classification modes are provided 

for quantitative data: classified and unclassified. 

Classified data result in symbolization with distinct 

class boundaries (Fig. 7, lower image); unclassified 

data are represented with a gradient color ramp (Fig. 

7, upper image). Unclassified maps are particularly 

useful for animations and comparison tasks over time, 

which made them very suitable for use in LandViz. 

According to an empirical survey, users perceive 

unclassified map animations as smoother and slower; 

those surveyed indicated that unclassified data tend 

to show change better than classified data (Harrower 

2007).

Similarly, menus of options are automatically 

populated with the list of scenarios and model outputs 

associated with a given project and stakeholder group. 

The same XML metadata files used to record data 

types also record variable names and units; these are 

used to construct intuitive menu lists with associated 

units.

2.2. Customization

LandViz can be customized for a particular project 

and stakeholder group by running the preprocessing 

component that creates a ready-to-deploy Web 

application. Scenario and data selections are made 

by editing an XML template file. The scientists who 

run the model can create interactive Web sites with 

visualizations geared for different audiences; they can 

highlight different parameters or scenarios without any 

background knowledge of Web programming. 

This customizable XML template specifies metadata 

about the scenarios and their outputs as well as 

configuration information for the automatic generation 

of the Web application’s visualization and user 

interface (see following). This information is the bridge 

between the landscape change model (LANDIS-II) 

and LandViz. 

End users can easily modify the default map 

symbolization to make certain characteristics or details 

of a dataset more apparent. For this reason, the map 

legends (Fig. 8d) can be interactively adapted: users 

can edit the number of classes, change class breaks and 

colors, and restrict the data range, which acts like a 

filtering function. 

The color gradient defined by the user through 

manipulating the interactive legend is converted to 

a texture. The texture is then made accessible to the 

self-developed WebGL fragment shader on the client 

graphics processing unit (see Section 2.4) to assign a 

color to each raster value in the map. Line charts and 

maps are synchronized for animation. When a user is 

animating over time, maps are exchanged at each time 

step and a vertical line moves over the chart to indicate 

the chart values corresponding to this time step. Users 

Figure 7.—Map tiles (see Figure 2) resymbolized in the Web 

application using the customizable classification tools powered 

by WebGL.
16

can easily make temporal and spatial connections 

between datasets. For example, if a chart indicates a 

sharp drop in biomass of a forest at a certain point in 

time, the user may pay special attention to the map of 

fires at that time step. Using map and playback controls 

(Fig. 8f ), users can jump to certain points in simulation 

time, change animation speed, and zoom and pan in 

the maps during animation.

2.3. Scenario Comparison

After accessing LandViz, the end user selects a 

simulation scenario for exploration or two scenarios 

for comparison using a drop-down menu (Fig. 8a). 

Two additional drop-down menus provide a selection 

of map and diagram parameters for display (Fig. 8b). 

The user-interface allows for a maximum of four open 

map windows (Fig. 8c); thus, two parameters can be 

shown as maps for a two-scenario comparison or up to 

four maps for exploring a single scenario. Side-by-side 

comparison instead of overlay was chosen to allow for 

temporal side-by-side animation and to better discern 

differences between maps with parameters symbolized 

by using multiple classes. Interactive zooming and 

panning in one map window is applied to all displayed 

maps to facilitate comparison between parameters and 

scenarios.

To compare scenarios across time and space, maps 

need to be standardized by colors and data range 

across scenarios. Class breaks and ranges used for 

symbolization of a parameter are calculated based on 

the range of all displayed parameter sets over space and 

time. When a scenario is added, the symbolization is 

automatically adjusted to allow valid comparison.

The Web-based visualization client can also display 

tabular data over time as line charts (Fig. 8e). A single 

chart can show datasets from different scenarios; 

a maximum of four charts, each with multiple 

parameters, can be viewed at the same time. When 

the user is comparing scenarios, selecting a specific 

parameter automatically leads to the addition of the 

datasets of both scenarios. Depending on the data, 

the user can filter line charts by predefined landscape 

classes. Flot (http://www.flotcharts.org/), a plotting 

library that builds on the jQuery JavaScript library, was 

used to implement the line charts.

2.4. Web-enabled with Rapid Response Rate

The LandViz preprocessing component creates a 

customized, ready-to-use Web application with 

graphical user-interface that runs in the end user’s 

Web browser. Access through a browser is especially 

useful for organizations that require security clearance 

for local software installation; security clearance is not 

required to use the LandViz visualization client. The 

Web visualization can easily be made accessible to the 

end user by providing the corresponding URL. 

The Web-based visualization client is an HTML5/

JavaScript application that uses the jQuery framework, 

a popular client-side JavaScript library, to implement 

the logical application structure and interface 

elements. It uses OpenLayers 3 (OpenGIS Web map 

tile service implementation standard; http://www.

opengeospatial.org/standards/wmts), a free and opensource Web mapping framework, to load map tiles and 

display them in the client’s map view using WebGL 

for hardware accelerated rendering. WebGL (Web 

Graphics Library) is a JavaScript API for rendering 

two-dimensional and three-dimensional graphics 

(WebGL – OpenGL ES 2.0 for the Web; https://

www.khronos.org/webgl/).

WebGL was designed to render images on standard 

computer displays and thus can access data in standard 

image formats with three or four 8-bit color channels. 

Because LANDIS-II model outputs are encoded 

as single band rasters with 8, 16, or 32 bits per cell, 

Figure 8.—Web client interface showing (a, b) selection menus, 

(c) maps, (d) filter and interactive legend, (e) charts, and (f) 

temporal navigation.
17

depending on the measurement scale of the grid 

parameter (nominal, ordinal, interval, or ratio scale), the 

preprocessing component must convert the data into 

image pixel values.

Single channel images are split up into four channel 

images; bit shifting is used to pack 16- or 32-bit 

bands into two or four 8-bit channels (which results 

in a coloring as seen in Figure 9). This method was 

inspired by Auer’s (2012) approach to visualize 

terrain with WebGL based on 16-bit grayscale digital 

elevation models. After the bit-shifted grids are 

loaded into the Web-based visualization client, the 

original grid value can be accessed again by inverting 

the process, which makes interactive reclassification 

and thus resymbolization possible (Fig. 7). Changing 

the symbolization of a map interactively thus does 

not require additional loading of Web tiles as long as 

spatial extent and zoom level are not altered. To decode 

the bit-shifted grids and symbolize them on the client, 

the OpenLayers 3 WebGL renderer was extended with 

a self-developed fragment shader. 

Output grids can be too large to be loaded by the 

client at interactive frame rates. To get around this 

problem, the preprocessing component creates 

different resolution levels and tiles the grids by using 

Tilers-Tools (scripts for raster tile sets from digital 

maps; http://tilers-tools.sourceforge.net/) following 

an OpenGIS standard. It creates PNG image tiles 

composed of four 8-bit channels with a size of 256 

pixels × 256 pixels (Fig. 9).

Flickering between animated time steps may break 

the flow of the animation (Garlandini and Fabrikant 

2009). Often time-step patterns in model output 

differed among parameters. To avoid flickering, a 

map from a previous time step is displayed until the 

animation reaches the next time step where a dataset is 

available. Maps showing different parameters may thus 

be updated at different moments during an animation. 

Raster tiles for the next time step are preloaded, 

rendered in the background, and then swapped to 

create a fluid animation.

2.5. An Extendable System Architecture

The architecture of LandViz was designed to 

accommodate different combinations of outputs 

depending on location, model selection, model 

configuration, and stakeholder needs. For example, 

LANDIS-II has a core framework and all ecological 

processes are represented within model extensions that 

can be substituted, removed, and added as necessary. 

The flexibility required a generic design, which makes 

it easy to adapt the system to different models, projects, 

spatial areas, and datasets. The configurable and 

modular architecture also allows use of the visualization 

tool with any cell-based landscape model.

LITERATURE CITED

Auer, M. 2012. Real-time Web GIS analysis using 

WebGL. International Journal of 3-D Information 

Modeling (IJ3DIM). 1(3): 49-61. http://dx.doi.

org/10.4018/ij3dim.2012.070105.

Birt, A.G.; Xi, W.; Coulson, R.N. 2009. 

LANDISVIEW: a visualization tool for landscape 

modelling. Environmental Modelling and Software. 

24(11): 1339-1341. http://dx.doi.org/10.1016/j.

envsoft.2009.04.007.

Bunch, R.L.; Lloyd, R.E. 2006. The cognitive load 

of geographic information. The Professional 

Geographer. 58(2): 209-220. http://dx.doi.

org/10.1111/j.1467-9272.2006.00527.x.

Garlandini, S.; Fabrikant, S.I. 2009. Evaluating the 

effectiveness and efficiency of visual variables for 

geographic information visualization. In: Stewart 

Hornsby, K.; Claramunt, C.; Denis, M.; Ligozat, G., 

eds. Spatial information theory. Berlin, Germany: 

Springer: 195-211. Chapter 12.

Figure 9.—Result of preprocessing LANDIS-II raster maps into 

subsets (tiles) at relevant resolution to speed the display of the 

map: PNG map tiles storing the original 16-bit values within two 

8-bit channels with level of detail appropriate for user pan and 

zoom activity.
18

Harrower, M. 2007. Unclassed animated choropleth 

maps. The Cartographic Journal. 44(4): 313-320. 

http://dx.doi.org/10.1179/000870407X241863.

Jenny, H.; Liem, J.; Lucash, M.S.; Scheller, R.M. 

2014. 4-D statistical surface method for visual 

change detection in forest ecosystem simulation 

time series. IEEE Journal of Selected Topics in 

Applied Earth Observations and Remote Sensing. 

7(11): 4505-4511. http://dx.doi.org/10.1109/

JSTARS.2014.2324972.

Scheller, R.M.; Domingo, J.B.; Sturtevant, B.R.; 

Williams, J.S.; Rudy, A.; Gustafson, E.J.; Mladenoff, 

D.J. 2007. Design, development, and application of 

LANDIS-II, a spatial landscape simulation model 

with flexible temporal and spatial resolution. 

Ecological Modelling. 201(3): 409-419. http://

dx.doi.org/10.1016/j.ecolmodel.2006.10.009.
Printed on Recycled Paper

Gustafson, Eric; Lucash, Melissa; Liem, Johannes; Jenny, Helen; Scheller, Rob; Barrett, 

Kelly; Sturtevant, Brian. 2016. Seeing the future impacts of climate change and 

forest management: a landscape visualization system for forest managers. Gen. 

Tech. Rep. NRS-164. Newtown Square, PA: U.S. Department of Agriculture, Forest 

Service, Northern Research Station. 18 p.

Forest managers are increasingly considering how climate change may alter forests’ 

capacity to provide ecosystem goods and services. But identifying potential climate 

change effects on forests is difficult because interactions among forest growth and 

mortality, climate change, management, and disturbances are complex and uncertain. 

Although forest landscape models can account for most factors that structure forest 

landscapes (including climate change), the sometimes overwhelming amount of 

output from these models can make it hard for some managers to interpret and 

understand the projections. In an effort to help managers visualize and analyze model 

output, we developed an intuitive Web-based system: LandViz. We applied LandViz in a 

collaborative, iterative approach to conduct a Climate Change Vulnerability Assessment 

for the Chippewa National Forest in Minnesota using the LANDIS-II landscape model. 

LandViz enhanced managers’ collaboration with model experts and increased their 

understanding of the tradeoffs between amounts and types of various resources in a 

changing climate. Managers can use the insight gained from LandViz to inform their 

strategic and tactical planning as they manage these tradeoffs.

KEY WORDS: forest management planning, decision support, forest landscape model, 

model visualization, climate change assessment, LANDIS-II model

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