The Natural Disturbance Type map is based on the Provincial Biodiversity Guidebook (1995) and the current and most detailed version of the approved corporate provincial Biogeoclimatic Ecosystem Classification (BEC) Zone/Subzone/Variant/Phase map (version 12, September 2, 2021) (Data Catalog record: https://catalogue.data.gov.bc.ca/dataset/bec-map). The natural disturbance type classification code is used to designate a period process or event such as insect outbreaks, fire, disease, flooding, windstorms and avalanches that cause ecosystem change and renewal. Natural disturbance type classification and mapping is used for a wide variety of applications in British Columbia. A few examples include: delineation of Natural Disturbance Types for Landscape Unit Planning; delineation of Seed Planning Zones; as an input for Predictive Ecosystem Mapping; reporting on the ecological representation of the Protected Areas Strategy; and as a level in the classification hierarchy for Broad Ecosystem Units. Note that this mapping is deliberately extended across the ocean, lakes, glaciers, etc to facilitate intersection with a terrestrial landcover layer of your choice

An opening's disturbance and silviculture activities reported into RESULTS. Most activities are within opening boundaries with the exception of broadcast treatments. An opening may have more than one activities associated with it. Activities may also overlap each other. Reporting of disturbance and silviculture attribute information is a mandatory requirement while the map is optional. This is part of the Silviculture and Land status Tracking dataset, which includes tracking harvesting and silviculture obligations on Crown Land

The area of Crown land disturbance for applications falling within a Ministry of Transportation and Infrastructure (MOTI) road allowance. The BC Energy Regulator issues cutting permits for any new Crown land disturbance within MOTI unconstructed road allowances. The Regulator does not issue land tenure over MOTI right of ways. This dataset contains polygon features for proposed applications collected through the Regulator's Application Management System (AMS). This dataset is updated nightly.

This data shows anthropogenic polygon disturbance features. Features were digitized using high resolution satellite imagery and orthophotos. The following data was not included in the dataset: proposed features.Table 1. A list of attributes, associated domains, and descriptions.AttributeData TypeDomainsDescriptionREF_IDText (20) Unique feature reference IDDATABASEText (20)Historic, Most Recent, RetiredSub-database to which the feature belongsTYPE_INDUSTRYText (50)Table 2.3.2Major classification of disturbance feature by industryTYPE_DISTURBANCEText (50)Table 2.3.2Sub classification of disturbance featureSCALE_CAPTUREDLong Scale at which the feature was digitizedDATA_SOURCEText (10)Imagery, GPS, OtherData source: digitized from imagery, captured by GPS, or obtained by other meansIMAGE_NAMEText (100) Filename of source imageryIMAGE_DATEDate Date that imagery was captured (YYYYMMDD)IMAGE_RESOLUTIONDouble Resolution of source imagery in metersIMAGE_SENSORText (35) Name of sensor that captured source imageryTable 2. A list of disturbance feature types and their descriptions.TYPE_INDUSTRYTYPE_DISTURBANCEDESCRIPTIONAgricultureAgricultureFarms, ranches, or other agricultural areasForestryForestryCut blocks or other forestry related activitiesMiningBuildingA building footprint or the building and the surrounding land related to mining activities.Drill PadDrill pad features related to mineral exploration activitiesFuel CacheRemote caches of fuel allowing for mineral exploration activities (will often have fuel tanks and barrels)Gravel Pit / QuarryPit or quarry for mining gravel or aggregateLaydown areaAreas used to store materials and equipment for mining operationsMiningMiscellaneous or unknown mining activitiesPlacer Mining - MinorPlacer mining area with little disturbancePlacer Mining - SignificantPlacer mining area with greater disturbanceQuartz Mining - MinorQuartz mining area with little disturbanceQuartz Mining - SignificantQuartz mining area with greater disturbanceTailing PondTailing pond associated with mining activityCampMining campOil and GasWell PadCleared area surrounding oil or gas wellRuralCampAny camp outside of mining areas, including fishing/hunting camps, ENV conservation officer cabins/camps, outfitters, etc.HomesteadRural dwelling and associated landTransportationAirstripAirport or AirstripClearingClearings that are related to transportation but could not be clearly attributed as a turn area, pullout, road cut and fill, etc.Gravel Pit / QuarryGravel pits related to transportationPullout / Turn AreaAn area associated with transportation and is intended as a vehicle pullout or turn areaRoad Cut and FillCut slopes and moved earth for road construction purposesUnknownClearingA tract of land devoid (or nearly devoid) of natural land cover and suspected to be anthropogenic in natureGravel Pit / QuarryA gravel pit with unknown related industryUnknownUnable to identify from imagery, but suspected to be anthropogenicUrbanBuildingVisible building or structureCemeteryCemeteryClearingMiscellaneous urban clearingsCul-de-sac / Turn AreaA turn area associated with transportation or road cul-de-sacDamBarrier impounding water or streamGolf CourseRecreational golfing areaIndustrialAreas that are designated for industrial uses: factories, tank farm, transportation areaInstitutionalAny institutional buildings and immediate cleared area: School, government, etc.LandfillSite used for disposal of waste materialsPondStanding body of water, created anthropogenically; includes sewage lagoons, wastewater facilities, and artificial bodies of water.Recreation AreaVisible disturbance in Urban / Rural parks and recreation areasRural ResidentialLand use in which housing predominates in an urban or community settingTowerA tall structure, possibly used for communications or forestryUrbanMiscellaneous or unknown urban features Distributed from [GeoYukon](https://yukon.ca/en/statistics-and-data/mapping/explore-map-data-using-geoyukon) by the [Government of Yukon](https://yukon.ca/) . Discover more digital map data and interactive maps from Yukon's digital [map](https://yukon.ca:443/en/maps) data collection.For more information: [geomatics.help@yukon.ca](mailto:geomatics.help@yukon.ca)      

This data shows anthropogenic polyline disturbance features. Features were digitized using high resolution satellite imagery and orthophotos. Features from the National Road Network (NRN) and the National Railway Network (NRWN) were adapted and included. The following data was not included in the dataset: proposed features.Table 1. A list of attributes, associated domains, and descriptions.AttributeData TypeDomainsDescriptionREF_IDText (20) Unique feature reference IDDATABASEText (20)Historic, Most Recent, RetiredSub-database to which the feature belongsTYPE_INDUSTRYText (50)Table 2.3.2Major classification of disturbance feature by industryTYPE_DISTURBANCEText (50)Table 2.3.2Sub classification of disturbance featureWIDTH_M*Double Width of feature in metersWIDTH_CLASS**Text (5)HIGH, MED, LOWWidth of feature by classificationSCALE_CAPTUREDLong Scale at which the feature was digitizedDATA_SOURCEText (10)Imagery, GPS, OtherData source: digitized from imagery, captured by GPS, or obtained by other meansIMAGE_NAMEText (100) Filename of source imageryIMAGE_DATEDate Date that imagery was captured (YYYYMMDD)IMAGE_RESOLUTIONDouble Resolution of source imagery in metersIMAGE_SENSORText (35) Name of sensor that captured source imagery\*WIDTH_M: Linear features must be attributed with a width measurement. The width of the feature can be estimated in meters, rounded to the nearest whole number.\*\*WIDTH_CLASS: This field employs a classification scheme used by previous contractors. This classification scheme was discussed and agreed upon by Mammoth Mapping and the Project Manager in 2011-2013. The width values are the following.Table 2. Width classification breakdown.WIDTH_CLASSAnticipated Value Range (meters)LOW<4MED4-8HIGH>8Table 3. A list of disturbance feature types and their descriptions.TYPE_INDUSTRYTYPE_DISTURBANCEDESCRIPTIONMiningSurvey / CutlineA linear cleared area through undeveloped land, used for line-of-sight surveying; impossible to distinguish whether associated with quartz or placer mining (overlapping or unclear claims information)Survey / Cutline - PlacerA linear cleared area through undeveloped land, used for line-of-sight surveying; associated with placer mining (identified using claims information and/or other indicators)Survey / Cutline - QuartzA linear cleared area through undeveloped land, used for line-of-sight surveying; associated with quartz mining (identified using claims information and/or other indicators)TrenchA long, narrow excavation dug to expose vein or ore structureUnknownUnknown linear mining disturbanceOil and GasPipelineVisible pipeline or pipeline Right-of-Way (above- or below-ground)Seismic LineSeismic linesRuralDrivewayA driveway in a rural areaFenceA fence in a rural areaTransportationAccess AssumedA linear feature that is assumed to be an access road, but could also be a trailAccess RoadA road or narrow passage whose primary function is to provide access for resource extraction (i.e. mining, forestry) and may also have served in providing public access to the backcountry.Arterial RoadA major thoroughfare with medium to large traffic capacityLocal RoadA low-speed thoroughfare, provides access to front of properties, including those with potential public restrictions such as trailer parks, First Nations land, private estate, seasonal residences, gravel pits (NRN definition for Local Street/Local Strata/Local Unknown). Shows signs of regular use.Right of WayFor Road Rights as attributed in the land parcels ancillary dataTrailPath or track (typically <1.5 m wide) used for walking, cycling, ORV, or other backcountry activities. (Note: trails used for mining activities are Access Roads.)Unpaved RoadDirt or gravel road (typically >1.5 m wide) that does not necessarily access remote resourcesUnknownRight of WayA right of way with unknown industry typeSurvey / CutlineA linear cleared area through undeveloped land, used for line-of-sight surveying. A cutline may not always be associated with mineral exploration, therefore, Type: Unknown was used to differentiate all cutlines that were outside of mineral exploration.UnknownUnclassified, or unable to identify type based on imagery, but suspected to be anthropogenicUtilityElectric Utility CorridorCorridor usually running parallel to highway, where transmission lines or other utilities are visibleUnknownUnknown linear feature assumed to be a utility corridor; ancillary data is unclear.Distributed from [GeoYukon](https://yukon.ca/en/statistics-and-data/mapping/explore-map-data-using-geoyukon) by the [Government of Yukon](https://yukon.ca/) . Discover more digital map data and interactive maps from Yukon's digital [map](https://yukon.ca/en/maps) data collection.For more information: [geomatics.help@yukon.ca](mailto:geomatics.help@yukon.ca)       

This dataset shows the locations of ignition points for forest fires less than 40 hectares in size. Fires that grow larger than 40 hectares are mapped in the [Fire Disturbance Area](https://data.ontario.ca/dataset/fire-disturbance-area-firedstb) dataset. The [Forest Fire Info Map](https://www.gisapplication.lrc.gov.on.ca/ForestFireInformationMap/index.html?viewer=FFIM.FFIM&locale=en-US) shows active fires, current fire danger and restricted fire zones in place due to high fire danger.

Landsat-derived forest age for Canada 2022Satellite-based forest age map for 2022 across Canada's forested ecozones at a 30-m spatial resolution, developed within the framework of Canada’s National Terrestrial Ecosystem Monitoring System (NTEMS). Remotely sensed data from Landsat (disturbances, surface reflectance composites, forest structure) and MODIS (Gross Primary Production) are utilized to determine age. Age can be determined where disturbance can be identified directly (disturbance approach) or inferred using spectral information (recovery approach) or using inverted allometric equations to model age where there is no evidence of disturbance (allometric approach). The disturbance approach is based upon satellite data and mapped changes and is the most accurate. The recovery approach also avails upon satellite data plus logic regarding forest succession, with an accuracy that is greater than pure modeling. Given the lack of widespread recent disturbance over Canada's forests, the allometric approach is required over the greatest area (86.6%). Using information regarding realized heights and growth and yield modeling, ages are estimated where none are otherwise possible. Trees of all ages are mapped, with trees >150 years old combined in an - old tree - category.See Maltman et al. (2023) for an overview of the methods, data, image processing, as well as information on agreement assessment using Canada's National Inventory (NFI). Maltman, J.C., Hermosilla, T., Wulder, M.A., Coops, N.C., White, J.C., 2023. Estimating and mapping forest age across Canada's forested ecosystems. Remote Sensing of Environment 290, 113529. ( Maltman et al. 2023).

This data publication includes three map datasets:1. Detailed annual disturbance maps – Forty raster layers mapping Canadian forest disturbance types from 1985 to 2024 at a 30m resolution.2. Latest disturbance type and latest disturbance year maps – A simplified version for enhanced usability, consisting of three rasters that represent the most recent disturbance type and year (starting and ending).3. Latest Landsat time series Julian day map – A raster capturing the latest Julian day for each pixel in the time series, where the Julian day represents the number of days elapsed since January 1, 1970.The forest disturbance types include wildfire, harvesting, pest outbreaks, windthrow, and new water bodies.The method is based on the summer composite Landsat time series (Guindon et al 2024). Disturbance breaks in the time series are first detected using the LandTrendr approach (Kennedy et al., 2010). Next, a one-dimensional convolutional neural network model (TempCNN; Pelletier et al., 2019) is applied with a 10-year window to classify disturbance types. The resulting maps achieve an overall accuracy of 81%. For aggregated pest/no-pest classes, the overall accuracy is 88.0% ±1.2%, with a commission error of 25.4% ±5.7% and an omission error of 63.2% ±4.3%.## Data use constraints:1. For pest-related disturbances, the proposed defoliation severity classes do not directly correspond to annual aerial survey classifications. Instead, they represent the intensity of cumulative spectral change at the end of the epidemic or, for ongoing outbreaks, the most recent observed year. Contrary to aerial survey classifications, only moderate to severe cumulative defoliation levels are detected, representing a good compromise between omission error and commission. 2. The models are aimed at insect pests that primarily affect conifers. However, they may also capture severe defoliation in mixed or deciduous forests.3. The analysis is based on a 10-year time window to adequately capture the effects of progressive defoliation. Therefore, to properly detect a pest causing this type of defoliation, such as the spruce budworm, historical data only becomes truly relevant starting around 1995. For insects with faster defoliation, the 1990s might be considered a good starting point. 4. The wood harvesting class refers to the removal of trees, regardless of the underlying intention. It primarily includes areas intended to remain forested or to be reforested but may also encompass certain sectors converted to other uses, such as road construction, mining, or various infrastructure projects.6. The windthrow class is effective at detecting large-scale events but has a high false detection rate, particularly along the edges of harvested areas, where mixed pixels create spectral similarities with windthrow. Among all disturbance classes, windthrow has the highest error rate. 7. The new water body class was not formally validated in this study, though visual assessments were conducted. 8. Since the summer composite considers only July and August imagery, disturbances occurring in the fall are detected the following year. For example, a wildfire that occurred in August 2023 might only become visible in the 2024 composite if the 2023 composite used images from early July, before the disturbance occurred. Additionally, cloud or shadow masking can create gaps in the time series, causing some disturbance events to appear delayed by one or two years. Users can use the national fire database (NBAC Canadian Wildland Fire Information System) for validation and year adjustments of wildfires. Moreover, the last Julian days raster could be used to better interpret the predicted year of disturbance. 9. The most recent years in the time series may have a higher commission error. These errors will be addressed in future annual updates.10. The minimum detectable disturbance size is 1.08 hectares (12 pixels), which may limit the detection of linear disturbances such as roads. ## More details will be available in the future scientific publication:Perbet, P., ## Please cite this dataset as:Perbet, P., Guindon, L., Correia D.L.P., P. Villemaire, O., Reisi Gahrouei R. St-Amant, Canada Landsat Disturbance with pest (CanLaD): a Canada-wide Landsat-based 30-m resolution product of fire, harvest and pest outbreak detection and attribution since 1987. https://doi.org/10.23687/902801fd-4d9d-4df4-9e95-319e429545cc## Cited references:Guindon, Luc, Francis Manka, David L.P. Correia, Philippe Villemaire, Byron Smiley, Pierre Bernier, Sylvie Gauthier, Andre Beaudoin, Jonathan Boucher, et Yan Boulanger. 2024. « A New Approach for Spatializing the CAnadian National Forest Inventory (SCANFI) Using Landsat Dense Time Series ». Canadian Journal of Forest Research, février, cjfr-2023-0118. https://doi.org/10.1139/cjfr-2023-0118.Kennedy, Robert E., Zhiqiang Yang, et Warren B. Cohen. 2010. « Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr — Temporal segmentation algorithms ». Remote Sensing of Environment 114 (12): 2897 2910. https://doi.org/10.1016/j.rse.2010.07.008.Pelletier, Charlotte, Geoffrey I. Webb, et François Petitjean. 2019. « Temporal Convolutional Neural Network for the Classification of Satellite Image Time Series ». Remote Sensing 11 (5): 523. https://doi.org/10.3390/rs11050523.

These lines represent routes within Motor Vehicle Prohibition Regulation Areas where motor vehicles are prohibited or restricted year round or seasonally. These lines were created as a visual representation of the Wildlife Act Motor Vehicle Prohibition Regulations. Under the Motor Vehicle Prohibition Regulation of the provincial Wildlife Act, motor vehicle use on crown land in B.C. may be prohibited or restricted. This data is a summary of the Motor Vehicle Prohibition Regulations, and is intended for general information purposes only. Where there is a discrepancy between these maps and the Regulations, the Motor Vehicle Prohibition Regulations are the final authority. Motor Vehicle Prohibitions (MVP) are put in place for a variety of reasons. MVPs can be used to: * protect habitat * reduce disturbance and displacement of wildlife * provide areas for hunters on foot, bike or horseback to hunt without motorized vehicles present * reduce hunter harvest while maintaining hunting opportunity   Motor vehicles include but are not limited to: * vehicles * ATVs/UTVs * snowmobiles * motorcycles * electric bikes   For full definitions and detailed regulations, visit the Motor Vehicle Prohibition Regulations website (available under Related Links). There are 2 types of motor vehicle restrictions under the Wildlife Act, and all types can be seasonal or year-round. 1. **Motor Vehicle Closed Area** (formerly referred to as Access Management Areas (AMAs): Prohibits the use or operation of a motor vehicle and e-bike. These prohibitions can be for all motor vehicles, or specific to ATVs/e-bikes or snowmobiles. 2. **Motor Vehicle Hunting Closed Area**: The operation of motor vehicles and e-bikes to hunt, transport wildlife, transport equipment and supplies which are intended for or in support of hunting, or transport hunters to and from the location of wildlife is prohibited. These prohibitions can apply to all motor vehicles, or be specific to ATVs/e-bikes or snowmobiles.

Landsat-derived forest age for Canada 2022Satellite-based forest age map for 2022 across Canada's forested ecozones at a 30-m spatial resolution. Remotely sensed data from Landsat (disturbances, surface reflectance composites, forest structure) and MODIS (Gross Primary Production) are utilized to determine age. Age can be determined where disturbance can be identified directly (disturbance approach) or inferred using spectral information (recovery approach) or using inverted allometric equations to model age where there is no evidence of disturbance (allometric approach). The disturbance approach is based upon satellite data and mapped changes and is the most accurate. The recovery approach also avails upon satellite data plus logic regarding forest succession, with an accuracy that is greater than pure modeling. Given the lack of widespread recent disturbance over Canada's forests, the allometric approach is required over the greatest area (86.6%). Using information regarding realized heights and growth and yield modeling, ages are estimated where none are otherwise possible. Trees of all ages are mapped, with trees >150 years old combined in an - old tree - category. This product was developed within the framework of Canada’s National Terrestrial Ecosystem Monitoring System (NTEMS).See Maltman et al. (2023) for an overview of the methods, data, image processing, as well as information on agreement assessment using Canada's National Inventory (NFI). Maltman, J.C., Hermosilla, T., Wulder, M.A., Coops, N.C., White, J.C., 2023. Estimating and mapping forest age across Canada's forested ecosystems. Remote Sensing of Environment 290, 113529. ( Maltman et al. 2023).