Quantifying the impact of ecosystem services for landscape management under wildfire hazard

Moudio, Pelagie Elimbi; Pais, Cristóbal; Shen, Zuo‐Jun Max

doi:10.1007/s11069-020-04474-y

Cited by 4 publications

(8 citation statements)

References 30 publications

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“…These management plans are flexible, meaning that decision makers can easily modify their main input parameters and add specific constraints to obtain realistic plans. This includes budgetary or maximum area treated constraints, satisfy adjacency/connectivity constraints (selecting a set of continuous or fragmented set of cells to treat), prioritize specific areas (e.g., protected habitats), as well as custom ones provided by the landscape manager (see Moudio et al, 2021;Pais et al, 2021 for an application of this framework).…”

Section: Discussionmentioning

confidence: 99%

“…One way to mitigate the impact of fire and protect our communities and natural resources is through Forest Fuel Management (FFM). Activities such as harvesting, prescribed burning, the construction of firebreaks, commercial harvesting, and thinning can reduce the detrimental impact of fire (Loehle, 2004;Agee and Skinner, 2005;Moudio et al, 2021;Pais et al, 2021). In addition, these actions could also benefit the diversity of species (Shinneman et al, 2019) and improve the health of the forest ecosystem, among many other benefits (Finney, 2001;Hirsch et al, 2001;Moghaddas and Craggs, 2008).…”

Section: Wildfire Managementmentioning

confidence: 99%

“…Software such as Burn-P3, FSPro, and FlamMap (Parisien et al, 2005b(Parisien et al, , 2011Finney et al, 2011b) can calculate these values using fire growth algorithms to produce high-resolution spatial estimates. Burn probability maps of managed landscapes (e.g., landscapes on which simulated fuel treatments have been implemented) can be used to generate baseline estimates of the impact of such treatments that can be compared with fuel treatment plans generated by using more complex approaches such as network-based prioritization metrics and simulationoptimization techniques (Acuna et al, 2010;Russo et al, 2016;Moudio et al, 2021;Pais et al, 2021). We describe how Cell2Fire can be used for such purposes.…”

Section: Burn Probability Mapsmentioning

confidence: 99%

“…It helps them understand and generates relevant managerial insights regarding which areas of the landscape are more/less prone to future wildfires, the most likely areas to which the fire can propagate after ignition in the treated-landscape, the expected rate of spread/burned area across the available cells, among other relevant information. Perhaps more important, is the support that Cell2Fire can provide to researchers studying methods for forest management planning [practical examples for the interested reader can be found in Moudio et al (2021) and Pais et al (2021)]. All this, to support and improve the whole decision-making process, providing an opportunity to analyze the complex and significant trade-offs involved in landscape management under wildfire uncertainty.…”

Section: Managing the Landscapementioning

confidence: 99%

See 3 more Smart Citations

Cell2Fire: A Cell-Based Forest Fire Growth Model to Support Strategic Landscape Management Planning

Pais

Carrasco

Martell

et al. 2021

Front. For. Glob. Change

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Cell2Fire is a new cell-based wildland fire growth simulator designed to integrate data-driven landscape management planning models. The fire environment is modeled by partitioning the landscape into cells characterized by fuel, weather, moisture content, and topographic attributes. The model can use existing fire spread models such as the Canadian Forest Fire Behavior Prediction System to model fire growth. Cell2Fire is structured to facilitate its use for predicting the growth of individual fires or by embedding it in landscape management simulation models. Decision-making models such as fuel treatment/harvesting plans can be easily integrated and evaluated. It incorporates a series of out-of-the-box planning heuristics that provide benchmarks for comparison. We illustrate their use by applying and evaluating a series of harvesting plans for forest landscapes in Canada. We validated Cell2Fire by using it to predict the growth of both real and hypothetical fires, comparing our predictions with the fire scars produced by a validated fire growth simulator (Prometheus). Cell2Fire is implemented as an open-source project that exploits parallelism to efficiently support the modeling of fire growth across large spatial and temporal scales. Our experiments indicate that Cell2Fire is able to efficiently simulate wildfires (up to 30x faster) under different conditions with similar accuracy as state-of-the-art simulators (above 90% of accuracy). We demonstrate its effectiveness as part of a harvest planning optimization framework, identifying relevant metrics to capture and actions to mitigate the impact of wildfire uncertainty.

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Section: Discussionmentioning

confidence: 99%