This is a map of the Pocket Fire that burned during the North Bay Fire Outbreak in October 2017. According to the California Department of Forestry and Fire Protection (CalFire), the Pocket Fire burned 17,357 acres; destroyed 6 structures; damaged 2 structures; and caused 0 fatalities. The fire was contained on October 31, 2017. The fire burned in portions of Sonoma County, and burned to the east of the cities of Cloverdale and Geyserville, California, mostly in rugged terrain of the North Bay Mountains.The red outline on this map represents the fire perimeter.*Data and statistics updated as of March 2019.

This dataset provides estimates of wildfire progression represented by date of burning (DoB) within fire scars across Alaska and Canada for the period 2001-2019. Burn scar locations were obtained from two datasets: the Alaskan Interagency Coordination Center (AICC) and the Natural Resources Canada (NRC) databases. All scars within these databases were used in this study. The estimated DoB was derived using an algorithm for identifying the first fire occurrence from the Moderate Resolution Imaging Spectroradiometer (MODIS) active fire detection product (MCD14ML, Collection 6) and to subsequently determine all dates of burning within fire scars. The DoB data are provided as polygons and map the daily progression of a fire within each burn scar. As a result, there is one polygon for each DoB detected within an identified burn scar boundary. The MODIS active fire points associated with the burn scar data are also provided. Data for the period 2001-2015 were first published in 2017 and data for the period 2016-2019 were added in January 2021.

North Bay - Post Fire Near Infra-Red Imagery

This data set provides mapped estimates of the stand age of young (less than 25 years old) larch forests across Siberia from 1989-2012 at 30-m resolution. The age estimates were derived from Landsat-based composites and tree cover for years 2000 and 2012 developed by the Global Forest Change (GFC) project and the stand-replacing fire mapping (SRFM) data set. This approach is based on the assumption that the relationship between the spectral signature of a burned or unburned forest stand acquired by Landsat ETM+ and TM sensors and stand age before and after the year 2000 is similar, thus allowing for training an algorithm on the data from the post-2000 era and applying the algorithm to infer stand age for the pre-2000 era. The output map combines the modeled forest disturbances before 2000 and direct observations of forest loss after 2000 to deliver a 24-year stand age distribution map.

The Project Entry Portal is where groups, agencies or landowners can enter Wildfire Risk Reduction Projects to be considered for inclusion on the Sonoma County CWPP Project list. The county-wide Project List and Project Map provides a central place where wildfire risk reduction projects can be seen so that trends and gaps, and opportunities for collaboration, can be identified. This will ultimately help prioritize treatment areas and project types, and encourage the implementation of high value projects. Additionally, many grant programs require that projects are listed in a CWPP to be considered for funding. Here’s your opportunity to list your project in the Sonoma County CWPP!The portal has been redesigned to make it easier and faster for you to enter your wildfire risk reduction project. We estimate that it will take 15 to 30 minutes for you to enter your project.

This raster format data set covers the province of Manitoba. The data were gridded into the AEAC projection from the original vector data. The original vector data were produced by Forestry Canada from hand-drawn boundaries of fires on photocopies of 1:250,000-scale maps. The locational accuracy of the data is considered fair to poor. When the locations of some fire boundaries were compared to Landsat TM images, they were found to be off by as much as a few kilometers. This problem should be kept in mind when using these data.

The State of California has experienced a series of persistent and destructive wildfires from 2014 to 2024, which have impacted the State’s sub-county population trends and housing. These wildfires have caused significant damage to many structures, resulting in displacement of residents and the demolition of homes. The map provides a detailed visual representation of the extent of housing units lost due to wildfires from 2014 to 2024 across California.From 2014 to 2024, there were 94,397 total wildfires in California resulting in the loss of 36,404 housing structures. Single Family Homes accounted for the most losses with 27,598 structures, followed by Mobile Homes with the loss of 8,289 structures, and Multi-Family Homes with 517 structures.2018 was the most devastating fire year with the loss of 16,596 homes. The total percent loss of California’s housing stock in 2018 was -0.118% of the 14,157,502 homes in the state. The Camp Fire in Paradise accounted for 13,972 of the total loss homes in 2018.While wildfires affected 48 out of the 58 California counties, three counties suffered the most significant housing losses. Butte County experienced the most substantial loss of homes due to wildfires, with a total of 15,899 structures destroyed. Sonoma County ranked second with the loss of 5,824 homes, followed by Lake County, which lost 1,903 homes.

NOTE: This map only includes Single Family, Mobile, and Multi-Family housing structures. This map does not include any housing structures with attached commercial business use. Data for the Fire Perimeter layer was last updated December 21, 2023.For more information about the wildfire data, please visit the CalFire webpage.

To view more data from the State of California Department of Finance, visit the Demographic Research Unit Data Hub.

This vector format data set covers the province of Manitoba and was produced by Forestry Canada from hand-drawn boundaries of fires on photocopies of 1:250,000 scale maps. The locational accuracy of the data is considered fair to poor. When the locations of some fire boundaries were compared to Landsat TM images, they were found to be off by as much as a few kilometers.

This dataset provides annual estimates of tree mortality due to fires and bark beetles from 2003 to 2012 on forestland in the continental western United States. Tree mortality was estimated at 1-km spatial resolution by combining tree aboveground carbon (AGC) and disturbance datasets derived largely from remote sensing. Tree mortality is expressed as the amount of AGC stored in trees killed by disturbance (Mg carbon per km2). The dataset also includes annual uncertainty maps that were generated using a Monte Carlo approach in which tree biomass, biomass carbon content, and disturbance severity were iteratively varied by their uncertainty.

This data set is a condensed forest cover type digital map of Saskatchewan and is a product of the Saskatchewan Environment and Resource Management, Forestry Branch - Inventory Unit (SERM-FBIU). This map was generalized from SERM township maps of vegetation cover at an approximate scale of 1:63,000 (1 in. = 1 mile). The cover information was iteratively generalized until it was compiled on a 1:1,000,000 scale map base. This data set was prepared by SERM-FBIU. The data is a condensed forest cover type map of Saskatchewan at a scale of 1:1,000,000.