Analysis and application of forest fuels data

Recent advances in fire modeling permit quantitative estimations of fire behavior from quantitative inputs that describe the fuel array and conditions, such as weather and site data, under which it will burn. This paper describes the collection, analysis, and stratification of flammable forest fuels data for coniferous forest ecosystems in Montana and then illustrates the resource management application of these data in three areas: the development of the fire behavior model, a determination of the model's sensitivity to input errors as reflected by fire behavior prediction errors, and the development of a fire hazard simulator (TAROT). A new integrated stand simulator, GANDALF, is highlighted.Conclusions center on the need to integrate fire management into the land management planning decision-making process.This work was supported by a USDI National Park Service contract to Gradient Modeling, Inc., a nonprofit research foundation devoted to ecologic research and resource management applications, and by cooperative aid agreements between Gradient Modeling, Inc. and the USDA Forest Service, Intermountain Forest and Range Experiment Station, Northern Forest Fire Laboratory (Fire in Multiple Use Management, R, D, and A Program).     

Implementation of two new resource management information systems in Australia

This paper describes the development and implementation ofPREPLAN, A Pristine Environment Planning Language and Simulator, for two conservation areas in Australia, Kosciusko National Park (New South Wales) and Tutanning Nature Reserve (Western Australia).PREPLAN was derived from the North American gradient modeling systems and theForest Planning Language and Simulator (FORPLAN), but includes unique characteristics not previously available.PREPLAN includes an integrated resource management data base, modules for predicting site-specific vegetation, fuels, animals, fire behavior, and fire effects, and an English language instruction set.PREPLAN was developed specifically to provide available information and understanding of ecosystems to managers in a readily accessible and usable form, and to provide the motivation to conduct additional required research projects. An evaluation of the system's advantages and limitations is presented, and the way the utilization of such systems is improving natural area decision making throughout Australia is discussed.     

Remote sensing for land management and planning

The primary role of remote sensing in land management and planning has been to provide information concerning the physical characteristics of the land which influence the management of individual land parcels or the allocation of lands to various uses These physical characteristics have typically been assessed through aerial photography, which is used to develop resource maps and to monitor changing environmental conditions These uses are well developed and currently well integrated into the planning infrastructure at local, state, and federal levels in the United States.Many newly emerging uses of remote sensing involve digital images which are collected, stored, and processed automatically by electromechanical scanning devices and electronic computers Some scanning devices operate from aircraft or spacecraft to scan ground scenes directly; others scan conventional aerial transparencies to yield digital images. Digital imagery offers the potential for computer-based automated map production, a process that can significantly increase the amount and timeliness of information available to land managers and planners.Future uses of remote sensing in land planning and management will involve geographic information systems, which store resource information in a geocoded format. Geographic information systems allow the automated integration of disparate types of resource data through various types of spatial models so that with accompanying sample ground data, information in the form of thematic maps and/ or aerially aggregated statistics can be produced Key issues confronting the development and integration of geographic information systems into planning pathways are restoration and rectification of digital images, automated techniques for combining both quantitative and qualitative types of data in information-extracting procedures, and the compatibility of alternative data storage modes     

Display and interpretation of fire behavior probabilities for long-term planning

Fire management planning for wildlands traditionally uses fire behavior estimated on the basis of worst-case weather at a specific site, but more realistic estimates can be obtained by considering the entire distribution of possible sites and weather conditions. Probability distributions of four widely used fire behavior variables were derived for four test cases in the Northern Rockies and Northern Intermountain Zone. The variables were rate of spread, fireline intensity, fire perimeter length-to-width ratio, and scorch height. Results were depicted in simple line graphs, three-dimensional pin graphs, and tables; they ranged from the cumulative probability of one variable to joint probabilities of four variables. Increasing the number of variables depicted increased the amount and scope of information available. Examples of interpreting the graphs and tables show how these techniques can be used in long-term fire program planning, fire suppression, management of various resources affected by fire, and interdisciplinary resource planning.     

Ecosystem modeling with GIS

Management of natural resources is becoming a complex problem. To ensure sustainability of the resources, the manager must have better tools with which to make decisions. The development of simulation models that make use of GIS data bases is an emerging area of resource management. This paper examines several grid-based models and addresses the use of GIS programs to construct spatial dynamic models. Some considerations for the implementation of modeling using GIS data bases are provided. The capability to simulate ecosystem processes such as fire, erosion, and other factors will allow the resource manager to make more informed decisions by evaluating potential consequences on the computer.     

Phytosociological inference and resource management

The application of vegetation information to resource management problems is reviewed in four areas: (1) the stratification of vegetation data by classification and gradient analysis, (2) the storage and retrieval of these data, (3) the sources of vegetation information, and (4) the accuracy and resolution requirements posed by different management applications. Conclusions indicate that the successful application of inference methods to resource management problems requires an appropriate integration of these four components.     

Development of Customized Fire Behavior Fuel Models for Boreal Forests of Northeastern China

Knowledge of forest fuels and their potential fire behavior across a landscape is essential in fire management. Four customized fire behavior fuel models that differed significantly in fuels characteristics and environmental conditions were identified using hierarchical cluster analysis based on fuels data collected across a boreal forest landscape in northeastern China. Fuel model I represented the dense and heavily branched Pinus pumila shrubland which has significant fine live woody fuels. These forests occur mainly at higher mountain elevations. Fuel model II is applicable to forests dominated by Betula platyphylla and Populus davidiana occurring in native forests on hill slopes or at low mountain elevations. This fuel model was differentiated from other fuel models by higher herbaceous cover and lower fine live woody loading. The primary coniferous forests dominated by Larix gmelini and Pinus sylvestris L. var. mongolica were classified as fuel model III and fuel model IV. Those fuel models differed from one another in average cover and height of understory shrub and herbaceous layers as well as in aspect. The potential fire behavior for each fuel model was simulated with the BehavePlus5.0 fire behavior prediction system. The simulation results indicated that the Pinus pumila shrubland fuels had the most severe fire behavior for the 97th percentile weather condition, and had the least severe fire behavior under 90th percentile weather condition. Fuel model II presented the least severe fire potential across weather conditions. Fuel model IV resulted in greater fire severity than Fuel model III across the two weather scenarios that were examined.     

Planning future fuelbreak strategies using mathematical modeling techniques

Major drainage basins within the Angeles National Forest in southern California USA are aggregated into zones of homogeneous wildland fire damage-potential using multivariate statistical techniques. Mathematical models are utilized to examine the policy implications of ongoing and projected fuel management strategies in different Forest zones based on simulated impacts of fuelbreak construction and maintenance upon two proxies for future damage-potential: the expected area burned and an index of relative value used by Forest planners. Alternative strategies for future fuelbreak investments are evaluated based on model outcomes.     

Integrating fire management into land-use planning: A multiple-use management research,development, and applications program

The traditional view of fire as a destructive agent requiring immediate suppression is giving way to the view that fire can and should be used to meet land management goals. Thus,fire control is being replaced by the more general concept offire management, which is based on the need to integrate fire policy with land management objectives. The social, economic, and ecologic effects of fire must be evaluated in the selection of land management alternatives.The activities of fire management organizations—fire prevention, control, and use of fire—must respond to needs of land management. Many agencies have developed fire organizations as separate entities that set their own objectives. The many land and resource managers who have recognized the need to incorporate fire considerations into land-use planning have so far lacked the techniques to do so.As a natural process, fire has an important function in forest and range ecosystems. Fire can greatly influence the quantity and quality of resource outputs; it is a two-edged sword that can either harm or benefit our goals, depending upon the complex effects of fire and the nature of our wants.The Fire in Multiple-Use Management Research, Development, and Applications (RD&A) Program was initiated by the U.S. Department of Agriculture, Forest Service, at the Northern Forest Fire Lab in Missoula to assist land managers. This profile explains what an RD&A program is; discusses its mission, goals, and approach to the problem; and tells why the approach involves federal laboratories, universities, and private research foundations.     

Simulation modeling and resource management

Simulation models are becoming increasingly important as tools for synthesizing and applying information in almost all aspects of land management. They are particulary valuable for predicting and comparing outcomes of alternative decisions and assumptions. Models also permit managers to consider and integrate the potential influences of a large number of variables.     

Assessing Land-Use Impacts on Natural Resources

/ Much information is available on changes that occur in natural resources from both spatially-explicit data on environmental conditions and models of the interactions of these conditions and resources with human activities. The strategy for assessing land-use impacts on natural resources developed in this paper provides a framework for using relevant data and models to address questions of how management practices can promote both use and protection of resources. This assessment strategy integrates spatially explicit environmental data using geographic information systems (GIS) with computer models that simulate changes in land cover in response to land-use impacts. The computer models also simulate susceptibility of species to changes in habitat suitability and landscape patterns. The approach is applied to management of limestone barrens on the Oak Ridge Reservation in East Tennessee. Potential limestone barrens habitats are identified by overlaying appropriate soils, geology, slope, and land-use/land-cover conditions. Their validity is tested against known sites containing rare species that occur in these habitats. The location of habitats at risk in the aftermath of human activities is determined by using an available area model that identifies the size and proximity of sites that particular types of species can no longer use as habitat. The resulting risk map can be used in land management planning. The approach uses readily available in situ and remotely sensed data and is applicable to a wide range of locations and land-use scenarios. This approach can be refined based on needs identified by land managers and on the sensitivity of the results to the resolution of available resource information.KEY WORDS: Land management; Assessment; Habitat characterization; Limestone barrens; Ecological modeling; Geographic information systems     

Cluster analysis of structural stage classes to map wildland fuels in a Madrean ecosystem

Geospatial information technology is changing the nature of fire mapping science and management. Geographic information systems (GIS) and global positioning system technology coupled with remotely sensed data provide powerful tools for mapping, assessing, and understanding the complex spatial phenomena of wildland fuels and fire hazard. The effectiveness of these technologies for fire management still depends on good baseline fuels data since techniques have yet to be developed to directly interrogate understory fuels with remotely sensed data. We couple field data collections with GIS, remote sensing, and hierarchical clustering to characterize and map the variability of wildland fuels within and across vegetation types. One hundred fifty six fuel plots were sampled in eight vegetation types ranging in elevation from 1150 to 2600 m surrounding a Madrean 'sky island' mountain range in the southwestern US. Fuel plots within individual vegetation types were divided into classes representing various stages of structural development with unique fuel load characteristics using a hierarchical clustering method. Two Landsat satellite images were then classified into vegetation/fuel classes using a hybrid unsupervised/supervised approach. A back-classification accuracy assessment, which uses the same pixels to test as used to train the classifier, produced an overall Kappa of 50% for the vegetation/fuels map. The map with fuel classes within vegetation type collapsed into single classes was verified with an independent dataset, yielding an overall Kappa of 80%.     

Fuel Reduction Management Practices in Riparian Areas of the Western USA

Two decades of uncharacteristically severe wildfires have caused government and private land managers to actively reduce hazardous fuels to lessen wildfire severity in western forests, including riparian areas. Because riparian fuel treatments are a fairly new management strategy, we set out to document their frequency and extent on federal lands in the western U.S. Seventy-four USDA Forest Service Fire Management Officers (FMOs) in 11 states were interviewed to collect information on the number and characteristics of riparian fuel reduction treatments in their management district. Just under half of the FMOs surveyed (43%) indicated that they were conducting fuel reduction treatments in riparian areas. The primary management objective listed for these projects was either fuel reduction (81%) or ecological restoration and habitat improvement (41%), though multiple management goals were common (56%). Most projects were of small extent (93% < 300 acres), occurred in the wildland-urban interface (75%), and were conducted in ways to minimize negative impacts on species and habitats. The results of this survey suggest that managers are proceeding cautiously with treatments. To facilitate project planning and implementation, managers recommended early coordination with resource specialists, such as hydrologists and fish and wildlife biologists. Well-designed monitoring of the consequences of riparian fuel treatments on fuel loads, fire risk, and ecological effects is needed to provide a scientifically-defensible basis for the continued and growing implementation of these treatments.     

Uncertainty and risk in wildland fire management: a review

Wildland fire management is subject to manifold sources of uncertainty. Beyond the unpredictability of wildfire behavior, uncertainty stems from inaccurate/missing data, limited resource value measures to guide prioritization across fires and resources at risk, and an incomplete scientific understanding of ecological response to fire, of fire behavior response to treatments, and of spatiotemporal dynamics involving disturbance regimes and climate change. This work attempts to systematically align sources of uncertainty with the most appropriate decision support methodologies, in order to facilitate cost-effective, risk-based wildfire planning efforts. We review the state of wildfire risk assessment and management, with a specific focus on uncertainties challenging implementation of integrated risk assessments that consider a suite of human and ecological values. Recent advances in wildfire simulation and geospatial mapping of highly valued resources have enabled robust risk-based analyses to inform planning across a variety of scales, although improvements are needed in fire behavior and ignition occurrence models. A key remaining challenge is a better characterization of non-market resources at risk, both in terms of their response to fire and how society values those resources. Our findings echo earlier literature identifying wildfire effects analysis and value uncertainty as the primary challenges to integrated wildfire risk assessment and wildfire management. We stress the importance of identifying and characterizing uncertainties in order to better quantify and manage them. Leveraging the most appropriate decision support tools can facilitate wildfire risk assessment and ideally improve decision-making.     

AN INTERACTWE MODELING ENVIRONMENT FOR NON-POINT SOURCE POLLUTION CONTROL1

ABSTRACT: Non-point source pollution cuntinues to be an important environmental and water quality management problem. For the moat part, analysis of non-point source pollution in watersheds has depended on the use of distributed models to identify potential problem areas and to assess the effectiveness of alternative management practices. To effectively use these models for watershed water quality management, users depend on integrated geographic information systems (GIS)-based interfaces for input/output data management. However, existing interfaces are ad-hoc and the utility of GIS is limited to organization of input data and display of output data. A highly interactive water quality modeling interface that utilizes the functional components and analytical capability of GIS is highly desirable. This paper describes the tight coupling of the Agricultural Non-point Source (AGNPS) water quality model and ARC/INFO GIS software to provide an interactive hybrid modeling environment for evaluation of non-point source pollution in a watershed. The modeling environment is designed to generate AGNPS input parameters from user-specified GIS coverages, create AGNPS input data files, control AGNPS model simulations, and extract and organize AGNPS model output data for display. An example application involving the estimation of pesticide loading in a southern Iowa agricultural watershed demonstrates the capability of the modeling environment. Compared with traditional methods of watershed water quality modeling using the AGNPS model or other ad-hoc interfaces between a distributed model and GIS, the interactive modeling environment system is efficient and significantly reduces the task of watershed analysis using tightly coupled GIS databases and distributed models.     

Modeling Fire Susceptibility to Delineate Wildland–Urban Interface for Municipal-Scale Fire Risk Management

The wildland–urban interface (WUI) is the region where development meets and intermingles with wildlands. The WUI has an elevated fire risk due to the proximity of development and residents to wildlands with natural wildfire regimes. Existing methods of delineating WUI are typically applied over a large region, use proxies for risk, and do not consider site-specific fire hazard drivers. While these models are appropriate for federal and provincial risk management, municipal managers require models intended for smaller regions. The model developed here uses the Burn-P3 fire behavior model to model WUI from local fire susceptibility (FS) in two study communities. Forest fuel code (FFC) maps for the study communities were modified using remote sensing data to produce detailed forest edges, including ladder fuels, update data currency, and add buildings and roads. The modified FFC maps used in Burn-P3 produced bimodal FS distributions for each community. The WUI in these communities was delineated as areas within community bounds where FS was greater than or equal to ?1 SD from the mean FS value ( ${\text{WUI}} = {\text{FS}} \geqslant - 1 \, [\bar \chi - \sigma ]$ ), which fell in the trough of the bimodal distribution. The WUI so delineated conformed to the definition of WUI. This model extends WUI modeling for broader risk management initiatives for municipal management of risk, as it (a) considers site-specific drivers of fire behavior; (b) models risk, represented by WUI, specific to a community; and, (c) does not use proxies for risk.     

Predicting Sustained Fire Spread in Tasmanian Native Grasslands

Fire is widely used in conservation management of native grasslands. Burning is often carried out under conditions that are marginal for sustained fire spread, and therefore it would be useful to be able to predict fire sustainability. There is currently no model allowing such prediction in temperate grasslands. This study aims to identify the environmental variables that determine whether fires will sustain in native grasslands in Tasmania, Australia, and develop a model for predicting fire sustainability in this vegetation. Fuel characteristics and weather conditions were recorded for 111 test fires. Logistic regression modeling identified dead fuel moisture content, fuel load, and percentage dead fuel as predictors of fire sustainability. Classification tree modeling identified dead fuel moisture and fuel load threshold values for sustaining fires. There was also evidence indicating a percentage dead fuel threshold. The logistic regression model and a model combining the results of the classification tree and the percentage dead fuel threshold accurately predicted the outcomes of a small set of experimental fires. These models are likely to have utility in predicting fire sustainability in Tasmanian grasslands and are also likely to be applicable to similar grasslands elsewhere.     

Evaluating Pre‐ and Post‐Fire Peak Discharge Predictions across Western U.S. Watersheds

This study reviews five models commonly used in post‐fire hydrologic assessments: the Rowe Countryman and Storey (RCS), United States Geological Survey (USGS) Linear Regression Equations, USDA Windows Technical Release 55 (USDA TR‐55), Wildcat5, and U.S. Army Corps of Engineers (USACE) Hydrologic Modeling System (HEC‐HMS). The models are applied to eight diverse basins in the western United States (U.S.) (Arizona, California, Colorado, Montana, and Washington) affected by wildfires and assessed by input parameters, calibration methods, model constraints, and performance. No one model is versatile enough for application to all study sites. Results show inconsistency between model predictions for events across the sites and less confidence with larger return periods (25‐ and 50‐year events) and post‐fire predictions. The RCS method performs well, but application is limited to southern California. The USGS linear regression model has wider regional application, but performance is less reliable at the large recurrence intervals and post‐fire predictions are reliant on a subjective modifier. Of the three curve number‐based models, Wildcat5 performs best overall without calibration, whereas the calibrated TR‐55 and HEC‐HMS models show significant improvement in pre‐fire predictions. Results from our study provide information and guidance to ultimately improve model selection for post‐fire prediction and encourage uniform parameter acquisition and calibration across the western U.S.     

Emerging Tools for Continuous Nutrient Monitoring Networks: Sensors Advancing Science and Water Resources Protection

Sensors and enabling technologies are becoming increasingly important tools for water quality monitoring and associated water resource management decisions. In particular, nutrient sensors are of interest because of the well‐known adverse effects of nutrient enrichment on coastal hypoxia, harmful algal blooms, and impacts to human health. Accurate and timely information on nutrient concentrations and loads is integral to strategies designed to minimize risk to humans and manage the underlying drivers of water quality impairment. Using nitrate sensors as the primary example, we highlight the types of applications in freshwater and coastal environments that are likely to benefit from continuous, real‐time nutrient data. The concurrent emergence of new tools to integrate, manage, and share large datasets is critical to the successful use of nutrient sensors and has made it possible for the field of continuous monitoring to rapidly move forward. We highlight several near‐term opportunities for federal agencies, as well as the broader scientific and management community, that will help accelerate sensor development, build and leverage sites within a national network, and develop open data standards and data management protocols that are key to realizing the benefits of a large‐scale, integrated monitoring network. Investing in these opportunities will provide new information to guide management and policies designed to protect and restore our nation's water resources.