Bush Fire Prone Land is mapped within a local government area, which becomes the trigger for planning for bush fire protection. Bush Fire Prone Land mapping is intended to designate areas of the State that are considered to be higher bush fire risk for development control purposes. Not being designated bush fire prone is not a guarantee that losses from bush fires will not occur. The NSW Bush Fire Prone Land dataset is a map prepared in accordance with the Guide for Bush Fire Prone Land Mapping (BFPL Mapping Guide) and certified by the Commissioner of NSW RFS under purposes of Section 10.3 of the Environmental Planning and Assessment Act 1979 No 203.

Over time there has been various releases of the BFPL Mapping Guide, in which the categories and types of vegetation included in the BFPL map have changed. The version of the guide under which, each polygon or LGA was certified is contained in the data.

BFPL is an area of land that can support a bush fire or is likely to be subject to bush fire attack, as designated on a bush fire prone land map. The definition of bushfire vegetation categories under guideline version 5b: * Vegetation Category 1 consists of: > Areas of forest, woodlands, heaths (tall and short), forested wetlands and timber plantations. * Vegetation Category 2 consists of: >Rainforests. >Lower risk vegetation parcels. These vegetation parcels represent a lower bush fire risk to surrounding development and consist of: - Remnant vegetation; - Land with ongoing land management practices that actively reduces bush fire risk. * Vegetation Category 3 consists of: > Grasslands, freshwater wetlands, semi-arid woodlands, alpine complex and arid shrublands. * Buffers are created based on the bushfire vegetation, with buffering distance being 100 metres for vegetation category 1 and 30 metres for vegetation category 2 and 3.

Vegetation excluded from the bushfire vegetation categories include isolated areas of vegetation less than one hectare, managed lands and some agricultural lands. Please refer to BFPL Mapping Guide for a full list of exclusions.

The legislative context of this dataset is as follows: On 1 August 2002, the Rural Fires and Environmental Assessment Legislation Amendment Act 2002 (Amendment Act) came into effect. The Act amended both the Environmental Planning and Assessment Act 1979 and the Rural Fire Services Act 1997 to ensure that people, property and the environment are more fully protected against the dangers that may arise from bushfires. Councils are required to map bushfire prone land within their local government area, which becomes the trigger for the consideration of bushfire protection measures when developing land. BFPL Mapping Guidelines are available from www.rfs.nsw.gov.au

http://www.rfs.nsw.gov.au/_data/assets/pdf_file/0011/4412/Guideline-for-Councils-to-Bushfire-Prone-Area-Land-Mapping.pdf

Bush Fire Prone Land is mapped within a local government area, which becomes the trigger for planning for bush fire protection. Bush Fire Prone Land mapping is intended to designate areas of the State that are considered to be higher bush fire risk for development control purposes. Not being designated bush fire prone is not a guarantee that losses from bush fires will not occur. The NSW Bush Fire Prone Land dataset is a map prepared in accordance with the Guide for Bush Fire Prone Land Mapping (BFPL Mapping Guide) and certified by the Commissioner of NSW RFS under purposes of Section 10.3 of the Environmental Planning and Assessment Act 1979 No 203. \r \r Over time there has been various releases of the BFPL Mapping Guide, in which the categories and types of vegetation included in the BFPL map have changed. The version of the guide under which, each polygon or LGA was certified is contained in the data. \r \r BFPL is an area of land that can support a bush fire or is likely to be subject to bush fire attack, as designated on a bush fire prone land map. The definition of bushfire vegetation categories under guideline version 5b: * Vegetation Category 1 consists of: > Areas of forest, woodlands, heaths (tall and short), forested wetlands and timber plantations. * Vegetation Category 2 consists of: >Rainforests. >Lower risk vegetation parcels. These vegetation parcels represent a lower bush fire risk to surrounding development and consist of: - Remnant vegetation; - Land with ongoing land management practices that actively reduces bush fire risk. * Vegetation Category 3 consists of: > Grasslands, freshwater wetlands, semi-arid woodlands, alpine complex and arid shrublands. * Buffers are created based on the bushfire vegetation, with buffering distance being 100 metres for vegetation category 1 and 30 metres for vegetation category 2 and 3. \r \r Vegetation excluded from the bushfire vegetation categories include isolated areas of vegetation less than one hectare, managed lands and some agricultural lands. Please refer to BFPL Mapping Guide for a full list of exclusions.\r \r The legislative context of this dataset is as follows: On 1 August 2002, the Rural Fires and Environmental Assessment Legislation Amendment Act 2002 (Amendment Act) came into effect. The Act amended both the Environmental Planning and Assessment Act 1979 and the Rural Fire Services Act 1997 to ensure that people, property and the environment are more fully protected against the dangers that may arise from bushfires. Councils are required to map bushfire prone land within their local government area, which becomes the trigger for the consideration of bushfire protection measures when developing land. BFPL Mapping Guidelines are available from www.rfs.nsw.gov.au\r \r http://www.rfs.nsw.gov.au/_data/assets/pdf_file/0011/4412/Guideline-for-Councils-to-Bushfire-Prone-Area-Land-Mapping.pdf\r

The NSW Bush Fire Prone Land dataset is a map prepared in accordance with the Guide for Bush Fire Prone Land Mapping (BFPL Mapping Guide) and certified by the Commissioner of NSW RFS under section 146(2) of the Environmental Planning and Assessment Act 1979. Over time there has been various releases of the BFPL Mapping Guide, in which the categories and types of vegetation included in the BFPL map have changed. The version of the guide under which, each polygon or LGA was certified is contained in the data. An area of land that can support a bush fire or is likely to be subject to bush fire attack, as designated on a bush fire prone land map. The definition of bushfire vegetation categories under guideline version 5b: \r Vegetation Category 1 consists of: \r

Areas of forest, woodlands, heaths (tall and short), forested wetlands and timber plantations. \r Vegetation Category 2 consists of: \r Rainforests. \r Lower risk vegetation parcels. These vegetation parcels represent a lower bush fire risk to surrounding development and consist of: - Remnant vegetation; - Land with ongoing land management practices that actively reduces bush fire risk. \r Vegetation Category 3 consists of: \r Grasslands, freshwater wetlands, semi-arid woodlands, alpine complex and arid shrublands. \r Buffers are created based on the bushfire vegetation, with buffering distance being 100 metres for vegetation category 1 and 30 metres for vegetation category 2 and 3. Vegetation excluded from the bushfire vegetation categories include isolated areas of vegetation less than one hectare, managed lands and some agricultural lands. Please refer to BFPL Mapping Guide for a full list of exclusions.The legislative context of this dataset is as follows: On 1 August 2002, the Rural Fires and Environmental Assessment Legislation Amendment Act 2002 (Amendment Act) came into effect.The Act amended both the Environmental Planning and Assessment Act 1979 and the Rural Fire Services Act 1997 to ensure that people, property and the environment are more fully protected against the dangers that may arise from bushfires. Councils are required to map bushfire prone land within their local government area, which becomes the trigger for the consideration of bushfire protection measures when developing land. BFPL Mapping Guidelines are available from www.rfs.nsw.gov.au\r This dataset is update upon certification of each LGA BFPL change or spot change.

This geodatabase contains existing vegetation map products that were developed for the Central and Northern Tongass (2024), Ketchikan Misty Fjords (2022), and Prince of Wales (2019) project areas in a collaborative effort between the United States Department of Agriculture, Forest Service Tongass National Forest (Tongass), Alaska Regional Office (Region 10), and the Field Services & Innovation Center - Geospatial Office. These map products were designed to be consistent with the standards established in the Existing Vegetation Classification and Technical Guide (Nelson et al., 2015) and be compatible with the National Vegetation Classification (NVC). Existing vegetation data provide baseline information to support project planning and land management activities across the Tongass National Forest.Map products comprise six integrated vegetation attributes related to flora and composition: 1) project map group, 2) project vegetation type, 3) Tongass NF map group, 4) Tongass NF vegetation type, 5) NVC division, and 6) NVC macrogroup. Additionally, there are twelve forest metrics that describe structure for forest vegetation types (segments that contain at least 10% tree cover), including: 1) tree canopy cover, 2) tree canopy cover class, 3) tree size, 4) biomass (Mg/ac) for trees ≥2” diameter at breast height (DBH), 5) crown competition factor (CCF), 6) gross board feet (GBF), 7) quadratic mean diameter (QMD) for trees ≥2” DBH, 8) QMD for trees ≥9” DBH, 9) rumple index, 10) stand density index (SDI) for trees ≥9” DBH, 11) trees per acre (TPA) for trees ≥1’ tall, and 12) TPA for trees ≥6” DBH. A Source Extent attribute indicates whether the forest metrics associated with a map feature (segment) were derived directly from lidar or estimated using an extrapolation model. Additionally, there are two attributes that describe changes in the landscape that haves occurred since 1985 using the Land Change Monitoring System (LCMS): 1) change percent, and 2) change year.Data products were derived using an object-based approach that relied on an semi-automated image segmentation process that generated the modeling units (segments) across the various project areas with a minimum mapping unit (MMU) size of 0.25 acres. Approximately 26 million acres were mapped, including 18.2 million terrestrial acres encompassing inland waterbodies and rivers. Each vegetation product, except the change attributes, were validated with an accuracy assessment to provide additional insight into their reliability for use in real-world resource applications.For more detailed information on the Tongass National Forest existing vegetation products refer to the following report:Bellante,G; Foss, J; Homan, K; Wittwer, D; Mohatt, K; Lund, A; Johnson, J; Caster, A; Rizzo, E; Heutte, T; Zona, D; Dangerfield, C; Johnson, T; Moody, R; James, R; Vernier, M; Achtenhagen, A; Hemingway, B; Egan, B; Goetz, W; Ryerson, D; Megown, K; Reischmann, J. 2025. Tongass National Forest Existing Vegetation Data Products. GO-10300-RPT1. Salt Lake City, UT. U.S. Department of Agriculture, Forest Service, Geospatial Office. 38 p.

LANDFIRE’s (LF) 2022 Vegetation Condition Class (VCC) is a reclassification and categorization of the LF 2022 Vegetation Departure (VDep) product. VCC indicates the general level to which current vegetation is different from the simulated historical reference condition. Therefore, VCC is a derivative of VDep; the VDep product indicates how different current vegetation is compared to the estimated historical reference condition, and is based on change to species composition, structure, and canopy closure. To learn more about VCC and VDep go to https://www.landfire.gov/fireregime.php. Condition classes for VCC are defined in two ways; the original 3 category system from Fire Regime Condition Class Guidebook (FRCC Guidebook), and a newer 6 category system that provides additional thematic detail. For the original 3-category system, the VDep value is reclassified as: Condition Class I: VDep value from 0 to 33 (Low Departure), Class II: VDep value between 34 - 66 (Moderate Departure), and Condition Class III: VDep value from 67 to 100 (High Departure). The 6-category system provides more detail and is collapsible to the 3-category system. The 6 VCC categories are defined as: Condition Class I.A: VDep between 0 and 16 (Very Low Departure), Condition Class I.B: VDep between 17 and 33 (Low to Moderate Departure); Condition Class II.A: VDep between 34 and 50 (Moderate to Low Departure); Condition Class II.B: VDep between 51 and 66 (Moderate to High Departure); Condition Class III.A: VDep between 67 and 83 (High to Moderate Departure), and Condition Class III.B: VDep between 84 and 100 (High Departure).

RECOVER supports assessment of vegetation community structure and landscape pattern via various means: ground truthing and related mapping of field morphometrics, community and species identification along elevation gradients (marl prairie to ridge and slough gradients), and community typing via photogrammetry. The focus of this initiative is on methods development using new satellite imagery (with greater spectral and spatial resolution). New imagery and new methods (including technological advancements) make remote sensing using the Digital Globe WorldView 2 imagery a strong candidate to track landscape and vegetative change within the Everglades CERP footprint. Information gained from this and related efforts is expected to provide future guidance relevant to CERP project implementation and structure operations. This scope is written as part of the RECOVER Monitoring and Assessment Plan (MAP) section 3.1.3 and 3.1.4. The objectives include: • Develop vegetation/landscape community maps and assess effectiveness of mapping using WorldView 2 imagery. 1) Acquire needed imagery (in consultation with RECOVER GE working group to determine most relevant areas of expected change given expected CERP project sequencing and implementation. As well as considering the location of available or expected ground truthing information- GRTS panels, marl prairie- slough transects and photogrammetric mapping in ENP). 2) Create map above and below Tamiami trail upstream and downstream of 1 mile bridge 3) Create map of Ridge and Slough, Tree Island, wet prairie habitat in WCA3A (using the same panels as the RECOVER R&S landscape contract- Heffernan)

• Investigate and characterize the spectral and metric qualities of the Worldview-2 data as related to specific aspects of the sensor, data acquisition, and level of processing (including available band combinations, angle of acquisition, sun elevation angle, method of geo-referencing, etc). • Investigate and characterize the outcome of spatial re-sampling on the spectral integrity of the imagery and on the type of landscape information that can be derived at various spatial scales with the intent of comparing the results to similar work of this type (e.g. 30 m Landsat classification of the Everglades.

Vegetation surveys included in this dataset have been collected through various vegetation classification and mapping projects, and some were also collected independently from these kinds of projects. Reports for these projects can be found in CDFWs document library see https://nrm.dfg.ca.gov/documents/ContextDocs.aspx?cat=VegCAMP.

LANDFIRE’s (LF) 2023 Vegetation Condition Class (VCC) is a reclassification and categorization of the LF 2023 Vegetation Departure (VDep) product. VCC indicates the general level to which current vegetation is different from the simulated historical reference condition. Therefore, VCC is a derivative of VDep; the VDep product indicates how different current vegetation is compared to the estimated historical reference condition, and is based on change to species composition, structure, and canopy closure. Condition classes for VCC are defined in two ways; the original 3 category system from Fire Regime Condition Class Guidebook (FRCC Guidebook), and a newer 6 category system that provides additional thematic detail. For the original 3-category system, the VDep value is reclassified as: Condition Class I: VDep value from 0 to 33 (Low Departure), Class II: VDep value between 34 - 66 (Moderate Departure), and Condition Class III: VDep value from 67 to 100 (High Departure). The 6-category system provides more detail and is collapsible to the 3-category system. The 6 VCC categories are defined as: Condition Class I.A: VDep between 0 and 16 (Very Low Departure), Condition Class I.B: VDep between 17 and 33 (Low to Moderate Departure); Condition Class II.A: VDep between 34 and 50 (Moderate to Low Departure); Condition Class II.B: VDep between 51 and 66 (Moderate to High Departure); Condition Class III.A: VDep between 67 and 83 (High to Moderate Departure), and Condition Class III.B: VDep between 84 and 100 (High Departure).

The ultimate goal of this project is to create an updated fine‐scale vegetation map for about 58,000 acres of Orange County, consisting of the 37,000‐acre Orange County Central and Coastal Subregions Natural Community Conservation Plan (NCCP)/Habitat Conservation Plan (HCP) Habitat Reserve System; approximately 9,500 acres of associated NCCP/HCP Special Linkages, Existing Use Areas, and Non‐Reserve Open Space; and approximately 11,000 acres of adjoining conserved open space (study area). The project consisted of three phases.Phase 1: To update vegetation mapping, the Natural Reserve of Orange County (NROC) proposes to use Manual of California Vegetation (MCV) methods (2009), which will be implemented in two stages: Stage 1 – Development of a vegetation classification system for the Central and Coastal Subregions of Orange County that is consistent with the MCV. Stage 2 – Application of the vegetation classification system to create a vegetation map through photointerpretation of available aerial imagery and ground reconnaissance. The MCV methods were developed by the California Department of Fish and Game (CDFG) Vegetation Classification and Mapping Program in collaboration with the California Native Plant Society (CNPS). This approach relies on the collection of quantifiable environmental data to identify and classify biological associations that repeat across the landscape. For areas where documentation is lacking to effectively define all of the vegetation patterns found in California, CDFG and CNPS developed the Vegetation Rapid Assessment Protocol. This protocol guides data collection and analysis to refine vegetation classifications that are consistent with CDFG and MCV standards. Based on an earlier classification by Gray and Bramlet (1992), Orange County is expected to have vegetation types not yet described in the MCV. Using the MCV approach, Rapid Assessment (RA) data was collected throughout the study area and analyzed to characterize these new vegetation types or show concurrence with existing MCV types.Phase 2: Aerial Information Systems, Inc. (AIS) was contracted by the Nature Reserve of Orange County (NROC) to create an updated fine-scale regional vegetation map consistent with the California Department of Fish & Wildlife (CDFW) classification methodology and mapping standards. The mapping area covers approximately 86,000 acres of open space and adjacent urban and agricultural lands including habitat located in both the Central and Coastal Subregions of Orange County. The map was prepared over a baseline digital image created in 2012 by the US Department of Agriculture – Farm Service Agency’s National Agricultural Imagery Program (NAIP). Vegetation units were mapped using the National Vegetation Classification System (NVCS) to the Alliance level as depicted in the second edition of the Manual of California Vegetation (MCV2). One of the most important data layers used to guide the conservation planning process for the 1996 Orange County Central & Coastal Subregion Natural Community Conservation Plan/Habitat Conservation Plan (NCCP/HCP) was the regional vegetation map created in the early 1990s by Dave Bramlett & Jones & Stokes Associates, Inc. (Jones & Stokes Associates, Inc. 1993). Up until now, this same map continues to be used to direct monitoring and management efforts in the NCCP/HCP Habitat Reserve. An updated map is necessary in order to address changes in vegetation makeup due to widespread and multiple burns in the mapping area, urban expansion, and broadly occurring vegetation succession that has occurred over the past 20 years since the original map was created. This update is further necessary in order to conform to the current NVCS, which is supported by the extensive acquisition of ground based field data and subsequent analysis that has ensued in those same 20 years over the region and adjacent similar habitats in the coastal and mountain foothills of Southern California. Vegetative and cartographic comparisons between the newly created 2012 image-based map and the original 1990s era vegetation map are documented in a separate report produced by the California Native Plant Society at the end of 2014.Phase 3: The California Native Plant Society (CNPS) Vegetation Program conducted an independent accuracy assessment of a new vegetation map completed for the natural lands of Orange County in collaboration with Aerial Information Systems (AIS), the California Department of Fish and Wildlife (CDFW), and the Nature Reserve of Orange County (NROC). This report provides a summary of the accuracy assessment allocation, field sampling methods, and analysis results; it also provides an in-depth crosswalk and comparison between the new map and the existing 1992 vegetation map. California state standards (CDFW 2007) require that a vegetation map should achieve an overall accuracy of 80%. After final scoring, the new Orange County vegetation map received an overall user’s accuracy of 87%. The new fine-scale vegetation map and supporting field survey data provide baseline information for long-term land management and conservation within the remaining natural lands of Orange County.Data made available in the OC Data Portal in partnership with UCI Libraries. Methods The project consisted of three phases, each with its own methodology.Phase 1: To update vegetation mapping, the Natural Reserve of Orange County (NROC) usedManual of California Vegetation (MCV) methods (2009), which will be implemented in two stages: Stage 1 – Development of a vegetation classification system for the Central and Coastal Subregions of Orange County that is consistent with the MCV. Stage 2 – Application of the vegetation classification system to create a vegetation map through photointerpretation of available aerial imagery and ground reconnaissance.Phase 2: Aerial Information Systems, Inc. (AIS) was contracted by the Nature Reserve of Orange County (NROC) to create an updated fine-scale regional vegetation map consistent with the California Department of Fish & Wildlife (CDFW) classification methodology and mapping standards.Phase 3: The California Native Plant Society (CNPS) Vegetation Program conducted an independent accuracy assessment of a new vegetation map completed for the natural lands of Orange County in collaboration with Aerial Information Systems (AIS), the California Department of Fish and Wildlife (CDFW), and the Nature Reserve of Orange County (NROC).For more detailed methodology information please consult the README.txt file included with dataset.

The Visible and Infrared Imager/Radiometer Suite (VIIRS) is a multi-disciplinary instrument that is being flown on the Joint Polar Satellite System (JPSS) series of spacecraft, including the Suomi National Polar-orbiting Partnership (S-NPP) that launched in October 2011. JPSS is a multi-platform, multi-agency program that consolidates the polar orbiting spacecraft of NASA and the National Oceanic and Atmospheric Administration (NOAA). S-NPP is the initial spacecraft in this series, and VIIRS is the successor to MODIS for Earth science data product generation. VIIRS has 22 spectral bands ranging from 412 nm to 12 nm. There are 16 moderate-resolution bands (750m at nadir), 5 image-resolution bands (375m), and one day-night band (DNB).

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles.

In 2009, Kass Green & Associates (KGA) was chosen by the NPS to map the vegetation of Grand Canyon National Park and the Lake Mead National Recreation Area – administered portions of the Grand Canyon Parashant National Monument using a National Vegetation Classification Standard (NVCS) compliant classification. Mapping tools and techniques used included remotely sensed digital airborne NAIP imagery, image segmentation, ancillary data, GIS biophysical modeling, photo interpretation, and field visits. This section of the report summarizes the methods used by KGA to map the vegetation of the project area. The vegetation was mapped in three phases based on floristic similarity and difficulties dealing with the logistical barriers presented by extreme terrain. Phase 1 included the high elevation forests and woodlands on rims of the eastern portion of the mapping area; Phase 2 was the inner canyon areas of the eastern and central mapping area; Phase 3 was most of the rim- and canyon areas west of Parashant Canyon. Each phase was mapped and accuracy assessed as a unit. The final mapping activities involved reconciling map classes, boundaries and accuracy assessment among all phases. The final map contained 87 map classes with a minimum mapping unit of 0.5 hectares across more than 560,000 hectares. The classes included 41 NVC Associations, 36 NVC Alliances, seven NVC Group-level classes and three classes of unvegetated surfaces (built-up, water, and bare soil/rock). Accuracy assessment (AA) was done for 1847 map segments, distributed based on map class abundances. Accuracy by map class varied between 13% (Pinyon – Juniper / Talus or Canyon Slope Scrub) and 100% (Douglas Fir / Snowberry Forest and 5 others); project-wide accuracy was 77%. Roughly one-third of the mis-identified samples were among closely– related vegetation types. Others were among classes which were found in similar habitats (e.g., constrained tributary beds) and had very similar spectral signatures.

LANDFIRE's (LF) National Vegetation Classification (NVC) represents the current distribution of vegetation groups within the U.S. National Vegetation Classification System ([version 2.0] http://usnvc.org/). Groups within the NVC hierarchy are defined as combinations of relatively narrow sets of diagnostic plant species, including dominants and co-dominants, broadly similar composition, and diagnostic growth forms. NVC groups are mapped using decision tree models informed by field reference data, Landsat imagery, elevation data, and biophysical gradient inputs. NVC models are developed separately for each lifeform, including sparse vegetation, and for each Environmental Protection Agency (EPA) Level III Ecoregion (https://www.epa.gov/eco-research/ecoregions). Riparian, alpine, sparse and other site-specific EVTs are constrained by predetermined masks. Urban and developed areas are derived from the National Land Cover Database (NLCD), whereas agricultural lands originate from the Cropland Data Layer (CDL) and Common Land Unit (CLU) database. Developed ruderal classes are identified by combining wildland-urban-interface (WUI) data with population density information from the US Census Bureau. Annual Disturbance products are included to describe areas that have experienced landscape change within the previous 10-year period. NVC is reconciled through QA/QC measures to ensure lifeform is synchronized with both Existing Vegetation Cover (EVC) and Height (EVH) products.

This record is now superseded. The current record for ‘Transitional – Native Vegetation Regulatory Map' can be viewed here. \r \r The Native Vegetation Regulatory Map (NVR Map) was prepared by Office of Environment and Heritage under Part 5A of the amended Local Land Services Act 2013 (LLS Act) and supporting regulation. The NVR Map has been developed to underpin the new land management framework. The NVR Map shows rural land where clearing of native vegetation can occur without approval and rural land where clearing requires approval.\r Please refer to the Method Statement for more details https://iar.environment.nsw.gov.au/dataset/asset_details/native-vegetation-regulatory-map \r Broadly, category 1 is land that was cleared of native vegetation as at 1 January 1990, or land that was lawfully cleared between 1 January 1990 and 25 August 2017. Category 2 is land that was not cleared as at 1 January 1990, was unlawfully cleared after 1 January 1990, or is a prescribed area with an identified environmental value. Land is mapped to each category on the basis of past clearing or disturbance events, as detected by satellite and aerial imagery, and updated land use data. Prescribed areas with an identified environmental value are mapped as category 2, overriding a category 1 designation based on the mapping.\r Native Vegetation Regulatory Map – Land Categories and map the 6 colour code\r Category\tDefinition\r 1. Category 1 - Blue\r Unrestricted Management (Exempt)\tRural lands where clearing of Native Vegetation is not regulated by Part 5A of the LLS Act 2013.This includes land cleared or significantly disturbed as at 1 January 1990 or lawfully cleared between that date and commencement of Part 5A of the LLS Act 2013. Other legislation may apply to Exempt land. \r 2. Category 2 - Yellow\r Code Based Management (Regulated)\tRural lands where clearing is regulated and can be carried out in accordance with Part 5A of the LLS Act 2013 or other legislation. This includes complying with the Codes and Allowable Activities. Land not cleared as at 1 January 1990, land unlawfully cleared since 1 January 1990, and land subject to existing conservation obligations including remedial directions.\r 3. Category 2 - Orange\r Regulated (Vulnerable)\tRural land where clearing of native vegetation is more restricted than on other Category 2 land. This includes steep and highly erodible lands, riparian land and special category land (as declared). \r 4. Category 2 - Pink\r Regulated (Sensitive)\tRural land where clearing of native vegetation is more restricted than other Category 2 land. This includes lands that are Sensitive Lands due to factors such as the presence of coastal wetlands, certain rainforests, core koala habitat, high conservation grasslands, critically endangered entities, land subject to conservation or incentive agreements or covenants and others. \r 5. Category 2 - Brown\r Is to depict land where Category 2 Regulated [(Vulnerable)Orange] and Category 2 Regulated [(Sensitive)Pink] overlap.\r 6. Excluded Land - Grey\r Land not regulated by Part 5A of the LLS Act 2013. This land includes urban zones, environmental conservation zones and R5 large lot residential as gazetted under a Local Environment Plan (LEP). It also includes public conservation lands such as National parks and State Forests. \r

The Water Conservation Area 1 (WCA-1) (Located within the Arthur R. Marshall Loxahatchee National Wildlife Refuge) vegetation map was compiled from color infrared photography that was collected in December 2003 and January 2004. A quarter hectare grid (50 x 50 meter) was generated and superimposed over the aerial photography, resulting in 227,429 individual grid cells covering all of WCA-1. Vegetation within each individual grid cell was photointerpreted utilizing a Leica SD2000 stereo-plotter and labeled with the majority vegetation category observed. Vegetation was classified using the Vegetation Classification for South Florida Natural Areas (Rutchey et al. 2006). For ground-truthing, 775 locations within WCA-1 were visited using differential GPS navigation by airboat or helicopter. These points were determined to be areas in question or "unknown" during the photointerpretation process. The final overall map accuracy was determined to be 93.2%. This map represents an overall generalization of vegetation classes found in WCA-1, with the dominant vegetation within a grid cell being depicted. Exotics, cattail and tree islands were also labeled as secondary vegetation classes within individual grid cells but are not depicted in this map.The Water Conservation Area 2A (WCA-2A) vegetation map was compiled from 1:24,000 scale color infrared aerial photography that was collected in January and February 2003. A quarter hectare grid (50 x 50 meter) was generated and superimposed over the 2003 aerial photography, resulting in 170,429 individual grid cells covering all of WCA-2A. Vegetation within each individual grid cell was photointerpreted utilizing a Leica SD2000 stereo-plotter and labeled with the majority vegetation category observed. Vegetation was classified using A Vegetation Classification System for Southern Florida's National Parks and Preserves (Jones et al. 1999). For ground-truthing, 1332 locations within WCA-2A were visited using differential GPS navigation by airboat or helicopter. These points were determined to be areas in question or "unknown" during the photointerpretation process. A separate effort was completed in August of 2003 that concentrated on cattail within this area and was found to have an overall map accuracy of 92.9%. The results of that effort along with new ground-truthing data were utilized to improve on the cattail areas and also to create a vegetation map, which not only includes cattail but all other vegetation within the impoundment. This new complete vegetation map was found to have an overall map accuracy of 90.7%.The Water Conservation Area 3 (WCA-3) vegetation map was compiled from the collection of approximately 320 color infrared 1:24000 scale photographs collected in 2004. The aerial photographs were digitally scanned and the photointerpretation was conducted using customized DAT/EM Summit Evolution digital photogrammetric workstations, which provided sufficient magnification capability to accurately identify and delineate the vegetation within a virtual three-dimensional (3-D) rendered landscape. Vegetation mapping was conducted by overlaying a one-quarter hectare (50 x 50 meter) grid within the boundaries of WCA-3A/WCA-3B, resulting in 939,415 grid cells. Advantages of grid system mapping include greater time and cost efficiency, and the unique ability to classify vegetation within the same quarter hectare grid cells from this analysis and during past and future mapping efforts. In addition, the grid system more accurately depicts the overall heterogeneity of Everglades vegetation than using a vector approach (Rutchey et al., 2008; Rutchey and Godin, 2009). Each grid cell was labeled according to the majority vegetation community as described in the Vegetation Classification System for South Florida Natural Areas (Rutchey et al., 2006). WCA-3 was divided into six nearly equal sections to make certain that the requirements of overall map accuracy of 90 percent or better was being met as the project was progressing. Two-hundred-and-twenty random sampling points were selected for each section and used to calculate an overall map accuracy assessment. Overall individual map accuracies ranged from 85.9-95.5 percent for the six sections. These accuracies reflect the relative difficulties of mapping the various sections. The average for the six sections was 90.8 percent, which compared favorably to the overall map accuracy of 90.9 which was calculated for the entire project area. These data establish a trend from which future vegetation mapping products can be compared and to help to ascertain if the implementation of restoration efforts are successful in preserving and restoring predrainage landscape features. The most significant finding may be that cattail expanded approximately 12,500 ha in comparison to a previous vegetation map done of WCA-3 nine years earlier in 1995 (Rutchey et al., 2005). Further evaluation and study are needed to ascertain the driving mechanisms that resulted in this expansion. This map represents an overall generalization of vegetation classes found in WCA-3, with the dominant vegetation within a grid cell being depicted. Exotics, cattail and tree islands were also labeled as secondary vegetation classes within individual grid cells but are not depicted in this map.Advantages of the grid system mapping are a greater time and cost efficiency and the unique ability to classify vegetation within the same quarter hectare grid cells from this analysis and during future mapping efforts. In addition, the grid system more accurately depicts the overall heterogeneity of Everglades' vegetation than using a vector approach (Rutchey et al., in press; Rutchey and Godin, submitted). See the Vegetation Classification document.The Comprehensive Everglades Restoration Plan (CERP), authorized as part of the Water Resources and Development Act (WRDA) of 2000 (U.S. Congress 2000), is an $US8-10 billion hydrologic restoration project for south Florida. CERP includes 68 separate projects to be managed over the next 30 years by the South Florida Water Management District (SFWMD) and the U. S. Army Corps of Engineers (USACE). Restoration Coordination and Verification (RECOVER) is a system-wide program within the CERP to organize and provide scientific and technical support for design, implementation, and assessment of the restoration program. It is the role of RECOVER to develop a system-wide monitoring and assessment plan that will document how well the CERP is meeting its objectives for ecosystem restoration. Vegetation mapping will be used to document changes in the spatial extent, pattern, and proportion of plant communities within the landscape.

LANDFIRE's (LF) Remap Vegetation Condition Class (VCC) is a reclassification and categorization of the LF Remap Vegetation Departure (VDep) product. VCC indicates the general level to which current vegetation is different from the simulated historical reference condition. Therefore, VCC is a derivative of VDep; the VDep product indicates how different current vegetation is compared to the estimated historical reference condition, and is based on change to species composition, structure, and canopy closure. To learn more about VCC and VDep go to https://www.landfire.gov/fireregime.php. Condition classes for VCC are defined in two ways; the original 3 category system from Fire Regime Condition Class Guidebook (FRCC Guidebook), and a newer 6 category system that provides additional precision. For the original 3 category system, the VDep value is reclassified as: Condition Class I: VDep value from 0 to 33 (Low Departure), Class II: VDep value between 34 - 66 (Moderate Departure), and Condition Class III: VDep value from 67 to 100 (High Departure). The 6 category system provides more resolution to VCC and is collapsible to the 3 category system. The 6 VCC categories are defined as: Condition Class I.A: VDep between 0 and 16 (Very Low Departure), Condition Class I.B: VDep between 17 and 33 (Low to Moderate Departure); Condition Class II.A: VDep between 34 and 50 (Moderate to Low Departure); Condition Class II.B: VDep between 51 and 66 (Moderate to High Departure); Condition Class III.A: VDep between 67 and 83 (High to Moderate Departure), and Condition Class III.B: VDep between 84 and 100 (High Departure).

Vegetation maps are important for characterizing many important features of a landscape such as wildlife habitat, fuels conditions, forest composition, and carbon. Such data are most useful if they can depict vegetation type, cover, and tree size class. This version was created to capture current conditions as best as possible through a variety of existing and current sources. Cross-walks were used to compile the various sources into the common classification scheme, the California Wildlife Habitat Relationships (CWHR) system. The California Department of Forestry and Fire Protections CALFIRE Fire and Resource Assessment Program (FRAP) in cooperation with California Department of Fish and Wildlife VegCamp program and extensive use of USDA Forest Service Region 5 Remote Sensing Laboratory (RSL) (now known as Mapping and Remote Sensing Team [MARS]). has compiled the 'best available' land cover data available for California into a single comprehensive statewide data set. The data span a period from approximately 1900 to 2014. Cross-walks were used to compile the various sources into the common classification scheme, the California Wildlife Habitat Relationships (CWHR) system. There are three separate rasters provided; one for CWHR Vegetation Type, one for CWHR Tree Size Class, and one for CWHR Veg Canopy Cover (Density) Class. FVEG's WHRtype was updated with the LANDFIRE Existing Vegetation Type (EVT) data product version 2.2.0 (LANDFIRE 2020) and the Rangeland Analysis Platform (RAP) fractional ground cover data product version 3.0 (Jones et al. 2018, Allred et al. 2021). Pixels were considered for update where high severity wildfire occurred after the FVEG mapping date. high severity was defined as wildfire burned areas that experienced ≥75% loss in basal area (Parks et al. 2018, Young-Hart et al. 2022) following the wildfire event. The type of update that occurred in each 'high severity' pixel was dependent upon a lifeform conversion comparison (FVEG-to-LANDFIRE EVT) vegetation height (SALO 2020), and percent ground cover by annual and perennial grasses (RAP). Following the WHRtype update, pixels that had lifeform 'tree' then had the FVEG attributes 'WHRdensity' and 'WHRsize' updated using the SALO Forest Observatory canopy height and canopy cover data products (SALO 2020, SALO data were available for past years 2016-2020, values of canopy height and canopy cover were averaged across years for the update). To update WHRdensity, SALO canopy cover was converted to WHRdensity canopy closure class per the Wildlife Habitat Relationships, Standards for Canopy Closure Table 114C. To update WHRsize, we developed allometric equations that predict tree DBH (diameter at breast height, breast height = 4.5 ft) as a function of tree height (HT, ft). We used data from the USDA Forest Inventory and Analysis program (FIA) for California (FIA DataMart 2023, California 2022 database ver: 5.0.1). For this analysis, we included live trees ≥ 4.5 ft tall with a crown class code of dominant, co-dominant, or open grown [N = 165,224 tree measurements between 19.91 and 2019). Trees were grouped by region based on the 'fuzzed' location of the plot. Regions were defined by the original Regional Resource Kits (2023, 4 regions) and separated into softwoods and hardwoods as defined by FIA (2 categories). For each analysis, three functions were evaluated: linear, saturating, and power: Linear: DBH = a + (bHT); saturating (Michaelis-Menten) DBH = (VrmHT) /(K+HT); Power: DBH = aHTb. For the most informative model (i.e., lowest AIC), we report both the root mean squared error (RMSE) and the pseudo R1. In this case, pseudo R* was calculated as the coefficient of determination between the observed and predicted DBH. We used the most informative HT-to-DBH function for the region and tree category to convert SALO canopy height data to DBH that was then converted to WHRsize class per the Wildlife Habitat Relationships, Standards for Tree Size Table 114B.

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for Mount Rainier National Park. The vegetation map is a geotiff raster, and at 67MB may be difficult to download. An ArcGIS file geodatabase contains plot data and lookup tables that relate map class units to mapping associations. The geodatabase includes a vegetation Feature dataset with the park boundary and project boundary used in the map. The map development process was organized around the random forests machine learning algorithm. The modeling used 1,900 plots representing 124 vegetation associations and 37 map classes. Imagery from the National Agriculture Imagery Program and the Sentinel-2 and Landsat 8 satellites, airborne lidar bare earth and canopy height data, elevation data from the U.S. Geological Survey 3D Elevation Program, and climate normals from the PRISM Climate Group were used to develop a variety of predictor metrics. The predictors and the map class calls at each plot were input to a process in which each map class was modeled against every other map class in a factorial random forests scheme. We used the plot-level modeling outcomes and species composition data to adjust the crosswalk between association and map class so that floristic consistency and model accuracy were jointly optimized across all classes. The map was produced by predicting the factorial models and selecting the overall best-performing class at each 3-meter pixel. The final vegetation map, including a buffer surrounding the park, contains 33 natural vegetated classes, five mostly unvegetated natural classes, and four classes representing burned areas or anthropogenic disturbance

This data set provides (1) soil maps for Brazil that are digital versions of the MAPA DE SOLOS DO BRASIL (EMBRAPA, 1981) classified at three levels of detail, 19-class, 70-class and 249-class; (2) vegetation maps for Brazil that are digital versions of the MAPA DE VEGETACAO DO BRASIL (IBGE, 1988) classified at three levels of detail, 13-class, 59-class, and an overprint (combination) class; and (3) a land cover map for all of South America that was derived from National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) data over the time period 1987 through 1991 (Stone et al., 1994).The seven soil, vegetation, and general land cover classification maps are provided as GeoTIFF files (*.tif) files. There are also three companion files (.pdf), one each, for the soil, vegetation, and land cover maps, with information on map units, class values, codes, and descriptions.