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Service Description: <div style='text-align:Left;font-size:12pt'><p><span>- Metric Name: Wildfire Hazard Potential</span></p><p><span /></p><p><span>- Tier: 1</span></p><p><span /></p><p><span>- Data Vintage: 08/2022. Includes disturbances through the end of 2021.</span></p><p><span /></p><p><span>- Unit Of Measure: Relative index</span></p><p><span /></p><p><span>- Metric Definition and Relevance: Wildfire Hazard Potential (WHP) is an index that quantifies the relative potential for wildfire that may be difficult to control. WHP can be used as a measure to help prioritize where fuel treatments may be needed.</span></p><p><span /></p><p><span>- Creation Method: Pyrologix calculated WHP following the methods established by Dillon et al. (2015) and Dillon (2018). The original methods utilize lower-resolution FSim inputs, while our approach uses higher-resolution inputs including 30-m CAL vegetation inputs (derived from LANDFIRE 2016), 30-m CAL fuel model outputs, 30-m CAL burn probability results, and 30-m CAL fire-effects flame-length probabilities from the WildEST wildfire behavior results.</span></p><p><span /></p></div>

Map Name: WildfireHazardPotential

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Description: - Metric Name: Wildfire Hazard Potential- Tier: 1- Data Vintage: 08/2022. Includes disturbances through the end of 2021.- Unit Of Measure: Relative index- Metric Definition and Relevance: Wildfire Hazard Potential (WHP) is an index that quantifies the relative potential for wildfire that may be difficult to control. WHP can be used as a measure to help prioritize where fuel treatments may be needed.- Creation Method: Pyrologix calculated WHP following the methods established by Dillon et al. (2015) and Dillon (2018). The original methods utilize lower-resolution FSim inputs, while our approach uses higher-resolution inputs including 30-m CAL vegetation inputs (derived from LANDFIRE 2016), 30-m CAL fuel model outputs, 30-m CAL burn probability results, and 30-m CAL fire-effects flame-length probabilities from the WildEST wildfire behavior results.

Service Item Id: 27d256ac4995421daa7c85f28674b566

Copyright Text: Pyrologix, LLC --James Newman (California State BLM Office) [email protected]

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Title: WildfireHazardPotential_202208.tif
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Comments: - Metric Name: Wildfire Hazard Potential- Tier: 1- Data Vintage: 08/2022. Includes disturbances through the end of 2021.- Unit Of Measure: Relative index- Metric Definition and Relevance: Wildfire Hazard Potential (WHP) is an index that quantifies the relative potential for wildfire that may be difficult to control. WHP can be used as a measure to help prioritize where fuel treatments may be needed.- Creation Method: Pyrologix calculated WHP following the methods established by Dillon et al. (2015) and Dillon (2018). The original methods utilize lower-resolution FSim inputs, while our approach uses higher-resolution inputs including 30-m CAL vegetation inputs (derived from LANDFIRE 2016), 30-m CAL fuel model outputs, 30-m CAL burn probability results, and 30-m CAL fire-effects flame-length probabilities from the WildEST wildfire behavior results.
Subject: The data layers included in this Northern California Regional Resource Kit were assembled/developed by a partnership that includes the Pacific Southwest Research Station of the U.S. Forest Service, the Fire and Resource Assessment Program (FRAP) of CALFIRE, the Climate and Wildfire Institute, and faculty from the University of California Berkeley and Irvine. This science team is working together at the behest of the California Wildfire and Forest Resilience Task Force. As we continue to develop geospatial data for landscape assessment and planning throughout the state, this partnership has now taken the lead in the creation of the Regional Resource Kits for the four regions of California. The RRK has adopted the Framework for Resilience to provide a structure for assessing landscape conditions, setting objectives, designing projects, and measuring progress towards social-ecological resilience. There are ten pillars that represent the desired outcomes of landscape resilience. Each of the pillars provides a series of elements and under the elements, metrics (the data layers) for assessing landscape conditions and verifying that actions meet resilience objectives. The metrics are organized by the ten pillars of resilience in the Framework for Resilience. The Metrics describe the characteristics of the elements (key characteristics) of each pillar in quantitative or, in a few cases, qualitative terms. Metrics are used to assess, plan for, measure, and monitor progress toward desired outcomes and greater resilience. Metrics are selected to be informative, meaningful, and actionable to meet the needs of management. The metrics are also divided into three “tiers.” Among all these metrics, some are created and relevant statewide. Other metrics are more suited to conditions within a given region. The “Tiers” for metrics included in each RRK: Tier 1 – metrics that are a single, consistent data layer, developed statewide; they can also be clipped to the boundary of the region so values within that region are the only ones included for calculations or regional statistics. Example: Annual Burn Probability. Tier 2 – metrics relevant to a single region or relevant to multiple regions but data layers differ among regions because of varied data availability (sources) across regions. Example: California gnatcatcher habitat suitability. Tier 3 - metrics are those that would be of interest to some land managers for specific applications but not included as a core metric in an RRK. Example: Distribution of the Quino checkerspot butterfly. Each RRK will contain all Tier 1 and Tier 2 data together to comprise the kit. Tier 3 data will be pointed to for reference and use, as needed. Within each Tier, the data layers are available in two forms: 1) data values native to the metric (raw), and 2) translated data values. The raw data values are in the units of the metric, so for example the species richness map will show an estimated number of terrestrial vertebrate species per acre that can range from 0 to any number for each 30-m pixel, and the departure from historical fire return interval (FRID) map will have values that range from -100% to +100% departure. The translated data values represent each metric using a common unit of measure with the same range of values from -1 to +1 that represent values that are generally considered favorable (+1) and unfavorable (-1). In the case of species richness, higher species counts are considered more favorable and lower species counts are considered less favorable. In the case of FRID, values within the historical fire return interval are considered favorable, and high departure from the historical fire return interval is considered less favorable. In both cases, more and less favorable conditions for each metric are represented by values that range from +1 to -1 (respectively) so that multiple metrics can be evaluated together, including summarizing overall conditions at element and pillar levels to characterize socio-ecological resilience. Some data layers within this kit contain null values. We point this out here so users of the data will be aware and take whatever measures appropriate as they use and analyze the data. For some raster datasets in the RRK, areas have been masked (blanked) out and have a cell value of NoData (also referred to as null, NaN or missing). We, as producers and users of the data, cannot ignore NoData or fill them with zeros, since zero is often a valid value for some datasets. Removing NoData cells is not an option, a raster is a continuous grid. For users of the data performing further analyses and combining or "stacking" rasters, these NoData cells will mask out all values in that location in the output. To avoid this issue, the user must create values for the cells before combining them (i.e. 999 or any numeric value that is not real and clearly out of the range of the other values). Reasons for masking (blanking) out cells in RRK data: · Cells are lakes or reservoirs · Cells are urban or agriculture · Cells contain no information relevant to the dataset (i.e. streams, habitat) · Area (cells) subject to fire or other disturbance but the post disturbance condition or value is unknown
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Keywords: environment,geoscientific Information,planning Cadastre,land management,landscape restoration
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