LANDFIRE’s (LF) 2023 Succession Class (SClass) categorizes current vegetation composition and structure into up to five successional classes, with successional classes defined in the appropriate Biophysical Settings (BpS) Model. There are two additional categories for uncharacteristic species (exotic or invasive vegetation), and uncharacteristic native vegetation cover, structure, or composition. Current successional classes and their historical reference conditions are compared to assess departure of vegetation characteristics. The classification schemes used to produce BpS and SClass may vary slightly between adjacent map zones, and reference conditions may be simulated independently in different map zones for the same BpS.

LANDFIRE’s (LF) 2022 Succession Class (SClass) categorizes current vegetation composition and structure into up to five successional classes, with successional classes defined in the appropriate Biophysical Settings (BpS) Model. There are two additional categories for uncharacteristic species (exotic or invasive vegetation), and uncharacteristic native vegetation cover, structure, or composition. Current successional classes and their historical reference conditions are compared to assess departure of vegetation characteristics. The classification schemes used to produce BpS and SClass may vary slightly between adjacent map zones, and reference conditions may be simulated independently in different map zones for the same BpS.

LANDFIRE’s (LF) 2022 Succession Class (SClass) categorizes current vegetation composition and structure into up to five successional classes, with successional classes defined in the appropriate Biophysical Settings (BpS) Model. There are two additional categories for uncharacteristic species (exotic or invasive vegetation), and uncharacteristic native vegetation cover, structure, or composition. Current successional classes and their historical reference conditions are compared to assess departure of vegetation characteristics. The classification schemes used to produce BpS and SClass may vary slightly between adjacent map zones, and reference conditions may be simulated independently in different map zones for the same BpS.

LANDFIRE's (LF) Remap Succession Class (SClass) categorizes current vegetation composition and structure into up to five successional classes, with successional classes defined in the appropriate Biophysical Settings (BpS) Model. There are two additional categories for uncharacteristic species (exotic or invasive vegetation), and uncharacteristic native vegetation cover, structure, or composition. Current successional classes and their historical reference conditions are compared to assess departure of vegetation characteristics. To calculate vegetation departure from historical reference conditions, SClass is combined with BpS and LF map zone data to create LF Historical Reference Condition tables. The classification schemes used to produce BpS and SClass may vary slightly between adjacent map zones, and reference conditions are simulated independently for each map zone. Departure can be further quantified by using the Interagency Fire Regime Condition Class Guidebook (FRCC Guidebook) methods (Hann et al. 2010).

LANDFIRE’s (LF) 2023 Succession Class (SClass) categorizes current vegetation composition and structure into up to five successional classes, with successional classes defined in the appropriate Biophysical Settings (BpS) Model. There are two additional categories for uncharacteristic species (exotic or invasive vegetation), and uncharacteristic native vegetation cover, structure, or composition. Current successional classes and their historical reference conditions are compared to assess departure of vegetation characteristics. The classification schemes used to produce BpS and SClass may vary slightly between adjacent map zones, and reference conditions may be simulated independently in different map zones for the same BpS.

LANDFIRE’s (LF) 2023 Succession Class (SClass) categorizes current vegetation composition and structure into up to five successional classes, with successional classes defined in the appropriate Biophysical Settings (BpS) Model. There are two additional categories for uncharacteristic species (exotic or invasive vegetation), and uncharacteristic native vegetation cover, structure, or composition. Current successional classes and their historical reference conditions are compared to assess departure of vegetation characteristics. The classification schemes used to produce BpS and SClass may vary slightly between adjacent map zones, and reference conditions may be simulated independently in different map zones for the same BpS.

LANDFIRE’s (LF) 2023 Succession Class (SClass) categorizes current vegetation composition and structure into up to five successional classes, with successional classes defined in the appropriate Biophysical Settings (BpS) Model. There are two additional categories for uncharacteristic species (exotic or invasive vegetation), and uncharacteristic native vegetation cover, structure, or composition. Current successional classes and their historical reference conditions are compared to assess departure of vegetation characteristics. The classification schemes used to produce BpS and SClass may vary slightly between adjacent map zones, and reference conditions may be simulated independently in different map zones for the same BpS.

LANDFIRE’s (LF) 2022 Succession Class (SClass) categorizes current vegetation composition and structure into up to five successional classes, with successional classes defined in the appropriate Biophysical Settings (BpS) Model. There are two additional categories for uncharacteristic species (exotic or invasive vegetation), and uncharacteristic native vegetation cover, structure, or composition. Current successional classes and their historical reference conditions are compared to assess departure of vegetation characteristics. The classification schemes used to produce BpS and SClass may vary slightly between adjacent map zones, and reference conditions may be simulated independently in different map zones for the same BpS.

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.