LANDFIRE's (LF) Annual Disturbance products provide temporal and spatial information related to landscape change. Annual Disturbance depicts areas of 4.5 hectares (11 acres) or larger that have experienced a natural or anthropogenic landscape change (or treatment) within a given year. For the creation of the Annual Disturbance product, information sources include national fire mapping programs such as Monitoring Trends in Burn Severity (MTBS), Burned Area Reflectance Classification (BARC) and Rapid Assessment of Vegetation Condition after Wildfire (RAVG), 18 types of agency-contributed "event" perimeters (see LF Public Events Geodatabase), and remotely sensed Landsat imagery.
To create the LF Annual Disturbance products, individual Landsat scenes are stacked and made into composites representing the 50th percentile of all stacked pixels (band-by-band) to reduce data gaps caused by clouds or other anomalies. Composite imagery from the specified mapping year, the two prior years, ...

The LANDFIRE Limited Disturbance (LDist)23 product is a new product introduced with the LF 2023 Update (LF 2023). LDist23 is an early "draft" of the LANDFIRE Annual Disturbance product and includes disturbance events captured through October 31, 2023. LDist23 is the first of three Annual Disturbance products releasing in the LF 2023 Update. LDist23 releases in January 2024, then Preliminary Disturbance (PDist)23 will release mid-year 2024. PDist23 will be the second "draft" of Annual Disturbance for the LF 2023 update. Finally, in the fall of 2024 the final "draft" of Annual Disturbance (Dist23) will be released.

LANDFIRE’s (LF) Annual Disturbance products provide temporal and spatial information related to landscape change. Annual Disturbance depicts areas of 4.5 hectares (11 acres) or larger that have experienced a natural or anthropogenic landscape change (or treatment) within a given year. For the creation of the Annual Disturbance product, information sources include national fire mapping programs such as Monitoring Trends in Burn Severity (MTBS), Burned Area Reflectance Classification (BARC) and Rapid Assessment of Vegetation Condition after Wildfire (RAVG), 18 types of agency-contributed "event" perimeters (see LF Public Events Geodatabase), and remotely sensed Landsat imagery. To create the LF Annual Disturbance products, individual Landsat scenes are stacked and made into composites representing the 50th percentile of all stacked pixels (band-by-band) to reduce data gaps caused by clouds or other anomalies. Composite imagery from the specified mapping year, the two prior years, and the following year serve as the base data from which change products such as the Normalized Differenced Vegetation Index (dNDVI), the Normalized Burn Ratio (dNBR), and the Multi-Index Integrated Change Algorithm (MIICA) (Jin et al. 2013) are derived. Image analysts collectively use these datasets (separately or in combination) to isolate the true change from false change (commission errors). False changes can be attributed to many anomalies but are mostly caused by differences in annual or seasonal phenology, and/or artifacts in the image composites. Fire-caused disturbances sourced from MTBS may contain data gaps where clouds obscure the full burn scar from being mapped. Models trained from pre-fire and post-fire Landsat data are used to fill these gaps. The result is gap-free continuous severity and extent information for all MTBS fire disturbances. MTBS pixels derived from gap filling techniques, such as modeling, are noted as such in the Annual Disturbance attribute table. Smaller fires that do not meet the size criteria set forth by MTBS may be attributed using Burned Area (BA), informed from Landsat Level-3 science products and only available in the lower 48 states. Causality and severity information assigned to a disturbance are prioritized by source, with the highest priorities reserved for fire mapping programs (MTBS, BARC, and RAVG) followed by user-contributed events contained in the LF Events Geodatabase, and lastly, Landsat image-based change.

LANDFIRE’s (LF) Annual Disturbance products provide temporal and spatial information related to landscape change. Annual Disturbance depicts areas of 4.5 hectares (11 acres) or larger that have experienced a natural or anthropogenic landscape change (or treatment) within a given year. For the creation of the Annual Disturbance product, information sources include national fire mapping programs such as Monitoring Trends in Burn Severity (MTBS), Burned Area Reflectance Classification (BARC) and Rapid Assessment of Vegetation Condition after Wildfire (RAVG), 18 types of agency contributed “event” perimeters (see LF Public Events Geodatabase), and remotely sensed Landsat imagery. To create the LF Annual Disturbance products, individual Landsat scenes are stacked and made into composites representing the 50th percentile of all stacked pixels (band-by-band) to reduce data gaps caused by clouds or other anomalies. Composite imagery from the specified mapping year, the two prior years, and the following year serve as the base data from which change products such as the Normalized Differenced Vegetation Index (dNDVI), the Normalized Burn Ratio (dNBR), and the Multi-Index Integrated Change Algorithm (MIICA) (Jin et al. 2013) are derived. Image analysts collectively use these datasets (separately or in combination) to isolate the true change from false change (commission errors). False changes can be attributed to many anomalies but are mostly caused by stark differences in annual or seasonal phenology, and/or artifacts in the image composites.

Fire-caused disturbances sourced from MTBS may contain data gaps where clouds obscure the full burn scar from being mapped. Models trained from pre-fire and post-fire Landsat data are used to fill these gaps. The result is continuous severity and extent information for all MTBS fire disturbances. MTBS pixels derived from gap filling techniques, such as modeling, are noted as such in the Annual Disturbance attribute table. Smaller fires that do not meet the size criteria set forth by MTBS may be attributed using Burned Area (BA), informed from Landsat Level-3 science products and only available in the lower 48 states. Causality and severity information assigned to a disturbance are prioritized by source, with the highest priorities reserved for fire mapping programs (MTBS, BARC, and RAVG) followed by user-contributed events contained in the LF Events Geodatabase, and lastly, Landsat image-based change. Individual metadata files:CONUS (LF 2020 2.2.0) [XML]Download options:LANDFIRE Mosaic Downloads LANDFIRE Map Viewer

LANDFIRE’s (LF) Annual Disturbance products provide temporal and spatial information related to landscape change. Annual Disturbance depicts areas of 4.5 hectares (11 acres) or larger that have experienced a natural or anthropogenic landscape change (or treatment) within a given year. For the creation of the Annual Disturbance product, information sources include national fire mapping programs such as Monitoring Trends in Burn Severity (MTBS), Burned Area Reflectance Classification (BARC) and Rapid Assessment of Vegetation Condition after Wildfire (RAVG), 18 types of agency contributed “event” perimeters (see LF Public Events Geodatabase), and remotely sensed Landsat imagery.To create the LF Annual Disturbance products, individual Landsat scenes are stacked and made into composites representing the 50th percentile of all stacked pixels (band-by-band) to reduce data gaps caused by clouds or other anomalies. Composite imagery from the specified mapping year, the two prior years, and the following year serve as the base data from which change products such as the Normalized Differenced Vegetation Index (dNDVI), the Normalized Burn Ratio (dNBR), and the Multi-Index Integrated Change Algorithm (MIICA) (Jin et al. 2013) are derived. Image analysts collectively use these datasets (separately or in combination) to isolate the true change from false change (commission errors). False changes can be attributed to many anomalies but are mostly caused by stark differences in annual or seasonal phenology, and/or artifacts in the image composites.Fire-caused disturbances sourced from MTBS may contain data gaps where clouds obscure the full burn scar from being mapped. Models trained from pre-fire and post-fire Landsat data are used to fill these gaps. The result is continuous severity and extent information for all MTBS fire disturbances. MTBS pixels derived from gap filling techniques, such as modeling, are noted as such in the Annual Disturbance attribute table. Smaller fires that do not meet the size criteria set forth by MTBS may be attributed using Burned Area (BA), informed from Landsat Level-3 science products and only available in the lower 48 states. Causality and severity information assigned to a disturbance are prioritized by source, with the highest priorities reserved for fire mapping programs (MTBS, BARC, and RAVG) followed by user-contributed events contained in the LF Events Geodatabase, and lastly, Landsat image-based change.Individual metadata files:CONUS (LF 2020 2.2.0) [XML]Download options:LANDFIRE Mosaic Downloads LANDFIRE Map Viewer

LANDFIRE’s (LF) Annual Disturbance products provide temporal and spatial information related to landscape change. Annual Disturbance depicts areas of 4.5 hectares (11 acres) or larger that have experienced a natural or anthropogenic landscape change (or treatment) within a given year. For the creation of the Annual Disturbance product, information sources include national fire mapping programs such as Monitoring Trends in Burn Severity (MTBS), Burned Area Reflectance Classification (BARC) and Rapid Assessment of Vegetation Condition after Wildfire (RAVG), 18 types of agency contributed “event” perimeters (see LF Public Events Geodatabase), and remotely sensed Landsat imagery.To create the LF Annual Disturbance products, individual Landsat scenes are stacked and made into composites representing the 50th percentile of all stacked pixels (band-by-band) to reduce data gaps caused by clouds or other anomalies. Composite imagery from the specified mapping year, the two prior years, and the following year serve as the base data from which change products such as the Normalized Differenced Vegetation Index (dNDVI), the Normalized Burn Ratio (dNBR), and the Multi-Index Integrated Change Algorithm (MIICA) (Jin et al. 2013) are derived. Image analysts collectively use these datasets (separately or in combination) to isolate the true change from false change (commission errors). False changes can be attributed to many anomalies but are mostly caused by stark differences in annual or seasonal phenology, and/or artifacts in the image composites.Fire-caused disturbances sourced from MTBS may contain data gaps where clouds obscure the full burn scar from being mapped. Models trained from pre-fire and post-fire Landsat data are used to fill these gaps. The result is continuous severity and extent information for all MTBS fire disturbances. MTBS pixels derived from gap filling techniques, such as modeling, are noted as such in the Annual Disturbance attribute table. Smaller fires that do not meet the size criteria set forth by MTBS may be attributed using Burned Area (BA), informed from Landsat Level-3 science products and only available in the lower 48 states. Causality and severity information assigned to a disturbance are prioritized by source, with the highest priorities reserved for fire mapping programs (MTBS, BARC, and RAVG) followed by user-contributed events contained in the LF Events Geodatabase, and lastly, Landsat image-based change. Individual metadata files:CONUS (LF 2020 2.2.0) [XML]Download options:LANDFIRE Mosaic Downloads LANDFIRE Map Viewer

LANDFIRE’s (LF) Annual Disturbance products provide temporal and spatial information related to landscape change. Annual Disturbance depicts areas of 4.5 hectares (11 acres) or larger that have experienced a natural or anthropogenic landscape change (or treatment) within a given year. For the creation of the Annual Disturbance product, information sources include national fire mapping programs such as Monitoring Trends in Burn Severity (MTBS), Burned Area Reflectance Classification (BARC) and Rapid Assessment of Vegetation Condition after Wildfire (RAVG), 18 types of agency-contributed "event" perimeters (see LF Public Events Geodatabase), and remotely sensed Landsat imagery. To create the LF Annual Disturbance products, individual Landsat scenes are stacked and made into composites representing the 50th percentile of all stacked pixels (band-by-band) to reduce data gaps caused by clouds or other anomalies. Composite imagery from the specified mapping year, the two prior years, and the following year serve as the base data from which change products such as the Normalized Differenced Vegetation Index (dNDVI), the Normalized Burn Ratio (dNBR), and the Multi-Index Integrated Change Algorithm (MIICA) (Jin et al. 2013) are derived. Image analysts collectively use these datasets (separately or in combination) to isolate the true change from false change (commission errors). False changes can be attributed to many anomalies but are mostly caused by differences in annual or seasonal phenology, and/or artifacts in the image composites. Fire-caused disturbances sourced from MTBS may contain data gaps where clouds obscure the full burn scar from being mapped. Models trained from pre-fire and post-fire Landsat data are used to fill these gaps. The result is gap-free continuous severity and extent information for all MTBS fire disturbances. MTBS pixels derived from gap filling techniques, such as modeling, are noted as such in the Annual Disturbance attribute table. Smaller fires that do not meet the size criteria set forth by MTBS may be attributed using Burned Area (BA), informed from Landsat Level-3 science products and only available in the lower 48 states. Causality and severity information assigned to a disturbance are prioritized by source, with the highest priorities reserved for fire mapping programs (MTBS, BARC, and RAVG) followed by user-contributed events contained in the LF Events Geodatabase, and lastly, Landsat image-based change.

LANDFIRE's Annual Disturbance products track how landscapes change across space and time on an annual basis. The Annual Disturbance (Dist) product identifies satellite-detected areas larger than 4.5 hectares (11 acres) that underwent natural or human-caused changes within a specific year (for Dist23, October 1, 2022 – September 30, 2023), or represent fire activity/field treatments as small as 80 square meters. While creating the Annual Disturbance product a variety of data sources are leveraged. 1) National fire mapping programs: This includes information from Monitoring Trends in Burn Severity (MTBS), Burned Area Reflectance Classification (BARC), and Rapid Assessment of Vegetation Condition after Wildfire (RAVG), which offer severity information for fire-caused disturbances. 2) Agency-reported events: There are 18 designated classes for contributed polygon "Event" types such as disease, insects, development, harvest, etc. that are reported by government agencies for inclusion into the disturbance product. 3) Remotely sensed imagery: Harmonized Landsat Sentinel (HLS) satellite images offer a comprehensive-uninterrupted view of the landscape covering all lands, public and private, to fill in the gaps inherent in the previous data sources. These data are reviewed and edited by a team of image analysts to ensure and maintain high quality standards. To create the LF Annual Disturbance product, individual Landsat scenes are stacked and made into composites representing the 15th, 50th, and 90th percentiles of all stacked pixels (band-by-band) to reduce data gaps caused by clouds or other anomalies. Composite imagery from the specified mapping year and the two prior years serves as the base data from which change products such as the Normalized Differenced Vegetation Index (dNDVI), the Normalized Burn Ratio (dNBR), and the Multi-Index Integrated Change Algorithm (MIICA) (Jin et al. 2013) are derived. Image analysts collectively use these datasets (separately or in combination) to isolate the true change from false change (commission errors). False changes can be attributed to many anomalies but are most commonly caused by differences in annual or seasonal phenology, artifacts in the image composites, or difficult to map classes such as wetlands and grasses. Fire-caused disturbances sourced from MTBS may contain data gaps where clouds obscure the full burn scar from being mapped. Models trained from pre-fire and post-fire Landsat data are used to fill these gaps. The result is gap-free continuous severity and extent information for all MTBS fire disturbances. MTBS pixels derived from modeling are noted as such in the Annual Disturbance attribute table. Smaller fires that do not meet the size criteria set forth by MTBS may be attributed as fire by using Burned Area (BA) Level-3 science products derived from Landsat 8 and 9. BA data is only available in the lower 48 states (CONUS). Causality information assigned to annual disturbance products are prioritized by source, with the highest priorities reserved for fire mapping program data (MTBS, BARC, and RAVG) followed by user-contributed events contained in the LF Events Geodatabase, and lastly, satellite image-based change. Severity is assigned directly from fire program data. For events and satellite-detected change, severity is derived from pre- and post-burn standard deviation values of the differenced Normalized Burn Ratio (dNBR). When mapping the LF Annual Disturbance product, the start date is utilized for disturbances from fire program data whereas all other disturbances utilize the end date.

LANDFIRE’s (LF) Annual Disturbance products provide temporal and spatial information related to landscape change. Annual Disturbance depicts areas of 4.5 hectares (11 acres) or larger that have experienced a natural or anthropogenic landscape change (or treatment) within a given year. For the creation of the Annual Disturbance product, information sources include national fire mapping programs such as Monitoring Trends in Burn Severity (MTBS), Burned Area Reflectance Classification (BARC) and Rapid Assessment of Vegetation Condition after Wildfire (RAVG), 18 types of agency contributed “event” perimeters (see LF Public Events Geodatabase), and remotely sensed Landsat imagery. To create the LF Annual Disturbance products, individual Landsat scenes are stacked and made into composites representing the 50th percentile of all stacked pixels (band-by-band) to reduce data gaps caused by clouds or other anomalies. Composite imagery from the specified mapping year, the two prior years, and the following year serve as the base data from which change products such as the Normalized Differenced Vegetation Index (dNDVI), the Normalized Burn Ratio (dNBR), and the Multi-Index Integrated Change Algorithm (MIICA) (Jin et al. 2013) are derived. Image analysts collectively use these datasets (separately or in combination) to isolate the true change from false change (commission errors). False changes can be attributed to many anomalies but are mostly caused by stark differences in annual or seasonal phenology, and/or artifacts in the image composites. Fire-caused disturbances sourced from MTBS may contain data gaps where clouds obscure the full burn scar from being mapped. Models trained from pre-fire and post-fire Landsat data are used to fill these gaps. The result is continuous severity and extent information for all MTBS fire disturbances. MTBS pixels derived from gap filling techniques, such as modeling, are noted as such in the Annual Disturbance attribute table. Smaller fires that do not meet the size criteria set forth by MTBS may be attributed using Burned Area (BA), informed from Landsat Level-3 science products and only available in the lower 48 states. Causality and severity information assigned to a disturbance are prioritized by source, with the highest priorities reserved for fire mapping programs (MTBS, BARC, and RAVG) followed by user-contributed events contained in the LF Events Geodatabase, and lastly, Landsat image-based change. Individual metadata files:Hawaii (LF 2020 2.2.0) [XML]Download options:LANDFIRE Mosaic Downloads LANDFIRE Map Viewer

LANDFIRE's Annual Disturbance products track how landscapes change across space and time on an annual basis. The Annual Disturbance (Dist) product identifies satellite-detected areas larger than 4.5 hectares (11 acres) that underwent natural or human-caused changes within a specific year (for Dist23, October 1, 2022 – September 30, 2023), or represent fire activity/field treatments as small as 80 square meters. While creating the Annual Disturbance product a variety of data sources are leveraged. 1) National fire mapping programs: This includes information from Monitoring Trends in Burn Severity (MTBS), Burned Area Reflectance Classification (BARC), and Rapid Assessment of Vegetation Condition after Wildfire (RAVG), which offer severity information for fire-caused disturbances. 2) Agency-reported events: There are 18 designated classes for contributed polygon "Event" types such as disease, insects, development, harvest, etc. that are reported by government agencies for inclusion into the disturbance product. 3) Remotely sensed imagery: Harmonized Landsat Sentinel (HLS) satellite images offer a comprehensive-uninterrupted view of the landscape covering all lands, public and private, to fill in the gaps inherent in the previous data sources. These data are reviewed and edited by a team of image analysts to ensure and maintain high quality standards. To create the LF Annual Disturbance product, individual Landsat scenes are stacked and made into composites representing the 15th, 50th, and 90th percentiles of all stacked pixels (band-by-band) to reduce data gaps caused by clouds or other anomalies. Composite imagery from the specified mapping year and the two prior years serves as the base data from which change products such as the Normalized Differenced Vegetation Index (dNDVI), the Normalized Burn Ratio (dNBR), and the Multi-Index Integrated Change Algorithm (MIICA) (Jin et al. 2013) are derived. Image analysts collectively use these datasets (separately or in combination) to isolate the true change from false change (commission errors). False changes can be attributed to many anomalies but are most commonly caused by differences in annual or seasonal phenology, artifacts in the image composites, or difficult to map classes such as wetlands and grasses. Fire-caused disturbances sourced from MTBS may contain data gaps where clouds obscure the full burn scar from being mapped. Models trained from pre-fire and post-fire Landsat data are used to fill these gaps. The result is gap-free continuous severity and extent information for all MTBS fire disturbances. MTBS pixels derived from modeling are noted as such in the Annual Disturbance attribute table. Smaller fires that do not meet the size criteria set forth by MTBS may be attributed as fire by using Burned Area (BA) Level-3 science products derived from Landsat 8 and 9. BA data is only available in the lower 48 states (CONUS). Causality information assigned to annual disturbance products are prioritized by source, with the highest priorities reserved for fire mapping program data (MTBS, BARC, and RAVG) followed by user-contributed events contained in the LF Events Geodatabase, and lastly, satellite image-based change. Severity is assigned directly from fire program data. For events and satellite-detected change, severity is derived from pre- and post-burn standard deviation values of the differenced Normalized Burn Ratio (dNBR). When mapping the LF Annual Disturbance product, the start date is utilized for disturbances from fire program data whereas all other disturbances utilize the end date.

LANDFIRE’s (LF) Annual Disturbance products provide temporal and spatial information related to landscape change. Annual Disturbance depicts areas of 4.5 hectares (11 acres) or larger that have experienced a natural or anthropogenic landscape change (or treatment) within a given year. For the creation of the Annual Disturbance product, information sources include national fire mapping programs such as Monitoring Trends in Burn Severity (MTBS), Burned Area Reflectance Classification (BARC) and Rapid Assessment of Vegetation Condition after Wildfire (RAVG), 18 types of agency-contributed “event” perimeters (see LF Public Events Geodatabase), and remotely sensed Landsat imagery. To create the LF Annual Disturbance products, individual Landsat scenes are stacked and made into composites representing the 50th percentile of all stacked pixels (band-by-band) to reduce data gaps caused by clouds or other anomalies. Composite imagery from the specified mapping year, the two prior years, and the following year serve as the base data from which change products such as the Normalized Differenced Vegetation Index (dNDVI), the Normalized Burn Ratio (dNBR), and the Multi-Index Integrated Change Algorithm (MIICA) (Jin et al. 2013) are derived. Image analysts collectively use these datasets (separately or in combination) to isolate the true change from false change (commission errors). False changes can be attributed to many anomalies but are mostly caused by differences in annual or seasonal phenology, and/or artifacts in the image composites. Fire-caused disturbances sourced from MTBS may contain data gaps where clouds obscure the full burn scar from being mapped. Models trained from pre-fire and post-fire Landsat data are used to fill these gaps. The result is gap-free continuous severity and extent information for all MTBS fire disturbances. MTBS pixels derived from gap filling techniques, such as modeling, are noted as such in the Annual Disturbance attribute table. Smaller fires that do not meet the size criteria set forth by MTBS may be attributed using Burned Area (BA), informed from Landsat Level-3 science products and only available in the lower 48 states. Causality and severity information assigned to a disturbance are prioritized by source, with the highest priorities reserved for fire mapping programs (MTBS, BARC, and RAVG) followed by user-contributed events contained in the LF Events Geodatabase, and lastly, Landsat image-based change.

LANDFIRE's (LF) Annual Disturbance (Dist) product provides temporal and spatial information related to landscape change. Dist depicts areas that have experienced a disturbance within a given year of 4.5 hectares (11 acres) or larger, along with cause and severity. Information sources include national fire mapping programs such as Monitoring Trends in Burn Severity (MTBS), Burned Area Reflectance Classification (BARC), and Rapid Assessment of Vegetation Condition after Wildfire (RAVG), local user/agency contributed data (LF Events Geodatabase), and remotely sensed Landsat imagery. Composite Landsat image pairs from the current year, prior year, and following year are spectrally compared to determine where change occurred and its corresponding severity. Additionally, vegetation indices (Normalized Differenced Vegetation Index [NDVI] and Normalized Burn Ratio [NBR]) serve as inputs into the Multi-Index Integrated Change Algorithm (MIICA) (Jin et al. 2013); MIICA outputs and differenced products (e.g., dNDVI and dNBR) are used to locate change. Predictive modeling based on the previous 10 years of disturbance data provides an additional dataset useful for locating disturbance. Image analysts use the aforementioned datasets separately or in combination to isolate true change from false change (e.g., change caused by stark differences in phenology rather than a true disturbance event). The accuracy of the final product is often related to the quality of the Landsat image composite. Areas with persistent cloud cover are particularly challenging (e.g., the northeast US). Fire caused disturbances sourced from MTBS may contain data gaps where clouds, smoke, water or Landsat7 SLC-off stripes exist. Models trained from pre-fire and post-fire Landsat data are used to fill the gaps. The result is continuous severity and extent information for all MTBS fire disturbances. MTBS pixels derived from gap filling techniques, such as modeling, are noted as such in their corresponding attribute table. Smaller fires that do not meet the size criteria set forth by MTBS) may be attributed as a Burned Area Essential Climate Variable (BAECV), which are only produced for the lower 48 states. Causality and severity information assigned to a disturbance are prioritized by source, with the highest priorities reserved for fire mapping programs (MTBS, BARC and RAVG) followed by user-contributed events contained in the LF Events Geodatabase, and lastly, Landsat image based change.

LANDFIRE's (LF) Annual Disturbance (Dist) product provides temporal and spatial information related to landscape change. Dist depicts areas that have experienced a disturbance within a given year of 4.5 hectares (11 acres) or larger, along with cause and severity. Information sources include national fire mapping programs such as Monitoring Trends in Burn Severity (MTBS), Burned Area Reflectance Classification (BARC), and Rapid Assessment of Vegetation Condition after Wildfire (RAVG), local user/agency contributed data (LF Events Geodatabase), and remotely sensed Landsat imagery. Composite Landsat image pairs from the current year, prior year, and following year are spectrally compared to determine where change occurred and its corresponding severity. Additionally, vegetation indices (Normalized Differenced Vegetation Index [NDVI] and Normalized Burn Ratio [NBR]) serve as inputs into the Multi-Index Integrated Change Algorithm (MIICA) (Jin et al. 2013); MIICA outputs and differenced products (e.g., dNDVI and dNBR) are used to locate change. Predictive modeling based on the previous 10 years of disturbance data provides an additional dataset useful for locating disturbance. Image analysts use the aforementioned datasets separately or in combination to isolate true change from false change (e.g., change caused by stark differences in phenology rather than a true disturbance event). The accuracy of the final product is often related to the quality of the Landsat image composite. Areas with persistent cloud cover are particularly challenging (e.g., the northeast US). Fire caused disturbances sourced from MTBS may contain data gaps where clouds, smoke, water or Landsat Seven SLC-off stripes exist. Models trained from pre-fire and post-fire Landsat data are used to fill the gaps. The result is continuous severity and extent information for all MTBS fire disturbances. MTBS pixels derived from gap filling techniques, such as modeling, are noted as such in their corresponding attribute table. Smaller fires that do not meet the size criteria set forth by MTBS) may be attributed as a Burned Area Essential Climate Variable (BAECV), which are only produced for the lower 48 states. Causality and severity information assigned to a disturbance are prioritized by source, with the Highest priorities reserved for fire mapping programs (MTBS, BARC and RAVG) followed by user-contributed events contained in the LF Events Geodatabase, and lastly, Landsat image based change.

The LANDFIRE Limited Disturbance (LDist)23 product is a new product introduced with the LF 2023 Update (LF 2023). LDist23 is an early "draft" of the LANDFIRE Annual Disturbance product and includes disturbance events captured through October 31, 2023. LDist23 is the first of three Annual Disturbance products releasing in the LF 2023 Update. LDist23 releases in January 2024, then Preliminary Disturbance (PDist)23 will release mid-year 2024. PDist23 will be the second "draft" of Annual Disturbance for the LF 2023 update. Finally, in the fall of 2024 the final "draft" of Annual Disturbance (Dist23) will be released.

LANDFIRE's (LF) Annual Disturbance (Dist) product provides temporal and spatial information related to landscape change. Dist depicts areas that have experienced a disturbance within a given year of 4.5 hectares (11 acres) or larger, along with cause and severity. Information sources include national fire mapping programs such as Monitoring Trends in Burn Severity (MTBS), Burned Area Reflectance Classification (BARC), and Rapid Assessment of Vegetation Condition after Wildfire (RAVG), local user/agency contributed data (LF Events Geodatabase), and remotely sensed Landsat imagery. Composite Landsat image pairs from the current year, prior year, and following year are spectrally compared to determine where change occurred and its corresponding severity. Additionally, vegetation indices (Normalized Differenced Vegetation Index [NDVI] and Normalized Burn Ratio [NBR]) serve as inputs into the Multi-Index Integrated Change Algorithm (MIICA) (Jin et al. 2013); MIICA outputs and differenced products (e.g., dNDVI and dNBR) are used to locate change. Predictive modeling based on the previous 10 years of disturbance data provides an additional dataset useful for locating disturbance. Image analysts use the aforementioned datasets separately or in combination to isolate true change from false change (e.g., change caused by stark differences in phenology rather than a true disturbance event). The accuracy of the final product is often related to the quality of the Landsat image composite. Areas with persistent cloud cover are particularly challenging (e.g., the northeast US). Fire caused disturbances sourced from MTBS may contain data gaps where clouds, smoke, water or Landsat7 SLC-off stripes exist. Models trained from pre-fire and post-fire Landsat data are used to fill the gaps. The result is continuous severity and extent information for all MTBS fire disturbances. MTBS pixels derived from gap filling techniques, such as modeling, are noted as such in their corresponding attribute table. Smaller fires that do not meet the size criteria set forth by MTBS) may be attributed as a Burned Area Essential Climate Variable (BAECV), which are only produced for the lower 48 states. Causality and severity information assigned to a disturbance are prioritized by source, with the highest priorities reserved for fire mapping programs (MTBS, BARC and RAVG) followed by user-contributed events contained in the LF Events Geodatabase, and lastly, Landsat image based change.

LANDFIRE's (LF) Annual Disturbance (Dist) product provides temporal and spatial information related to landscape change. Dist depicts areas that have experienced a disturbance within a given year of 4.5 hectares (11 acres) or larger, along with cause and severity. Information sources include national fire mapping programs such as Monitoring Trends in Burn Severity (MTBS), Burned Area Reflectance Classification (BARC), and Rapid Assessment of Vegetation Condition after Wildfire (RAVG), local user/agency contributed data (LF Events Geodatabase), and remotely sensed Landsat imagery. Composite Landsat image pairs from the current year, prior year, and following year are spectrally compared to determine where change occurred and its corresponding severity. Additionally, vegetation indices (Normalized Differenced Vegetation Index [NDVI] and Normalized Burn Ratio [NBR]) serve as inputs into the Multi-Index Integrated Change Algorithm (MIICA) (Jin et al. 2013); MIICA outputs and differenced products (e.g., dNDVI and dNBR) are used to locate change. Predictive modeling based on the previous 10 years of disturbance data provides an additional dataset useful for locating disturbance. Image analysts use the aforementioned datasets separately or in combination to isolate true change from false change (e.g., change caused by stark differences in phenology rather than a true disturbance event). The accuracy of the final product is often related to the quality of the Landsat image composite. Areas with persistent cloud cover are particularly challenging (e.g., the northeast US). Fire caused disturbances sourced from MTBS may contain data gaps where clouds, smoke, water or Landsat Seven SLC-off stripes exist. Models trained from pre-fire and post-fire Landsat data are used to fill the gaps. The result is continuous severity and extent information for all MTBS fire disturbances. MTBS pixels derived from gap filling techniques, such as modeling, are noted as such in their corresponding attribute table. Smaller fires that do not meet the size criteria set forth by MTBS) may be attributed as a Burned Area Essential Climate Variable (BAECV), which are only produced for the lower 48 states. Causality and severity information assigned to a disturbance are prioritized by source, with the Highest priorities reserved for fire mapping programs (MTBS, BARC and RAVG) followed by user-contributed events contained in the LF Events Geodatabase, and lastly, Landsat image based change.

LANDFIRE (LF) disturbance products are developed to provide temporal and spatial information related to landscape change. LF 2022 Fuel Disturbance (FDist) uses the latest Annual Disturbance products from the effective disturbance years of 2013 to 2022. FDist is created from LF 2022 Historical Disturbance (HDist) which in turn aggregates the Annual Disturbance products. FDist groups similar disturbance types, severities and time since disturbance categories which represent disturbance scenarios within the fuel environment. FDist is used in conjunction with Fuel Vegetation Type (FVT), Cover (FVC), and Height (FVH) to calculate Canopy Cover (CC), Canopy Height (CH), Canopy Bulk Density (CBD), Canopy Base Height (CBH), Fire Behavior Fuel Models 13 Anderson (FBFM13) and 40 Scott and Burgan (FBFM40). FDist is developed using the aggregated Annual Disturbance products from 2013 to 2022. All existing disturbances between 2013-2022 are represented in the LF 2022 update, and the products are intended to be used in 2023 (the year of release). The "capable" year terminology used in LF 2020 and LF 2016 Remap is no longer specified, due to reduction in latency from when a disturbance occurs to the release date of fuel products accounting for that disturbance. However, users should still consider adjusting fuel layers for disturbances that occurred after the end of the 2022 fiscal year (after October 1st, 2022) when using the LF 2022 fuel products. Because those changes would not be accounted for. Learn more about LF 2022.Individual metadata files:AK (LF 2022 2.3.0) [XML]Download options:LANDFIRE Mosaic Downloads LANDFIRE Map Viewer

LANDFIRE disturbance products are developed to provide temporal and spatial information related to landscape change. In the LF 2023 Update (LF 2023) Fuel Disturbance (FDist) uses the latest Annual Disturbance products from the effective disturbance years of 2014 to 2023. FDist is created from LF 2023 Historical Disturbance (HDist) which in turn aggregates the Annual Disturbance products. FDist groups similar disturbance types, severities, and time since disturbance categories which represent disturbance scenarios within the fuel environment. FDist is used in conjunction with Fuel Vegetation Type (FVT), Cover (FVC), and Height (FVH) to calculate Forest Canopy Cover (CC), Height (CH), Bulk Density (CBD), Base Height (CBH), 13 Anderson Fire Behavior Fuel Models (FBFM13) and 40 Scott and Burgan (FBFM40). FDist is developed using the most current aggregated Annual Disturbance products from 2014 to 2023. All existing disturbances between 2014-2023 are represented in LF 2023, and the products are intended to be used in 2024 (the year of release). When using any product from the LF 2023 fuel product suite, users should consider adjusting fuel layers for disturbances that occurred after the end of the 2023 fiscal year (after October 1st, 2023). Disturbances that occurred after the end of the 2023 fiscal year are not accounted for within LF 2023 fuel products. Learn more about LF 2023 at https://landfire.gov/data/lf2023.

LANDFIRE's (LF) Annual Disturbance (Dist) product provides temporal and spatial information related to landscape change. Dist depicts areas that have experienced a disturbance within a given year of 4.5 hectares (11 acres) or larger, along with cause and severity. Information sources include national fire mapping programs such as Monitoring Trends in Burn Severity (MTBS), Burned Area Reflectance Classification (BARC), and Rapid Assessment of Vegetation Condition after Wildfire (RAVG), local user/agency contributed data (LF Events Geodatabase), and remotely sensed Landsat imagery. Composite Landsat image pairs from the current year, prior year, and following year are spectrally compared to determine where change occurred and its corresponding severity. Additionally, vegetation indices (Normalized Differenced Vegetation Index [NDVI] and Normalized Burn Ratio [NBR]) serve as inputs into the Multi-Index Integrated Change Algorithm (MIICA) (Jin et al. 2013); MIICA outputs and differenced products (e.g., dNDVI and dNBR) are used to locate change. Predictive modeling based on the previous 10 years of disturbance data provides an additional dataset useful for locating disturbance. Image analysts use the aforementioned datasets separately or in combination to isolate true change from false change (e.g., change caused by stark differences in phenology rather than a true disturbance event). The accuracy of the final product is often related to the quality of the Landsat image composite. Areas with persistent cloud cover are particularly challenging (e.g., the northeast US). Fire caused disturbances sourced from MTBS may contain data gaps where clouds, smoke, water or Landsat Seven SLC-off stripes exist. Models trained from pre-fire and post-fire Landsat data are used to fill the gaps. The result is continuous severity and extent information for all MTBS fire disturbances. MTBS pixels derived from gap filling techniques, such as modeling, are noted as such in their corresponding attribute table. Smaller fires that do not meet the size criteria set forth by MTBS) may be attributed as a Burned Area Essential Climate Variable (BAECV), which are only produced for the lower 48 states. Causality and severity information assigned to a disturbance are prioritized by source, with the Highest priorities reserved for fire mapping programs (MTBS, BARC and RAVG) followed by user-contributed events contained in the LF Events Geodatabase, and lastly, Landsat image based change.

LANDFIRE (LF) disturbance products are developed to provide temporal and spatial information related to landscape change. LF Remap Fuel Disturbance (FDist) uses the latest 10 years of Annual Disturbance products from the effective disturbance year. FDist is a refinement of past LF versions and is created from Remap historical Disturbance (HDist) by grouping similar disturbances types, which represent disturbance scenarios within the fuels environment. LF Remap contains refinements to the FDist process enabling the creation of pre-disturbance fuel vegetation within disturbed areas. the new approach facilitates the production of capable fuels products, such as FDist. The capable fuels process calculates Time Since Disturbance (TSD) assignments for disturbed areas using an "effective year". Year 2020 fuels, for example, may be calculated for the year 2020. the new process considers all the existing disturbances included in LF Remap and assigns fuels to the pre-disturbance vegetation based on the adjusted TSD, making the products "2020 capable fuels." By synchronizing TSDs for surface and canopy fuels, these refinements improve performance of fire behavior modeling. LF Remap fuel products in areas mapped as disturbed within the past 10 years are delivered with the capable fuels functionality. More information about capable fuels can be found at https://www.landfire.gov/lf_remap.php.