Geospatial forest inventory dataset updated for depletions, such as harvesting, and projected annually for growth. Sample attributes in this dataset include: age, species, volume, height. The Vegetation Resources Inventory (VRI) spatial datasets describe both where a vegetation resource (ie timber volume, tree species) is located and how much of a given resource is within an inventory unit. Suggested citation: Forest Analysis and Inventory Branch (2024). VRI - 2023 - Forest Vegetation Composite Rank 1 Layer (R1). British Columbia Data Catalogue. https://catalogue.data.gov.bc.ca/dataset/2ebb35d8-c82f-4a17-9c96-612ac3532d55

Published by the Ministry of Forests, Lands, Natural Resource Operations and Rural Development - Forest Analysis and Inventory Geospatial forest inventory dataset updated for depletions, such as harvesting, and projected annually for growth. Sample attributes in this dataset include: age, species, volume, height. The Vegetation Resources Inventory (VRI) spatial datasets describe both where a vegetation resource (ie timber volume, tree species) is located and how much of a given resource is within an inventory unit.

The Resources box to the right includes links to an Interactive Mapping App, an FTP site and a Data Dictionary that together provide access to compiled data from the primary ground-sampling programs managed by the Forest Analysis and Inventory Branch (FAIB). The following is a summary of what’s available in the two links: 1) The Interactive Mapping App provides a spatial view of FAIB ground plots with custom filters to enable selection of areas, BEC zones, species, TSA or plot types of interest. Once plots of interest are selected or filtered, an ‘export data’ button is available to download a plot summary file with limited attributes. 2) The Compiled Ground Plot FTP site contains tree- and plot-level compiled mensurational attributes for each ground plot across a series of repeated measurements. Both the PSP and non-PSP compilation outputs include a Data Dictionary that describes all the tables and attributes found in the downloadable files. FAIB ground-sampling programs include the Permanent Sample Plots (PSPs) that provide long term growth and yield information to support development and testing of growth-and-yield models. Active PSPs are the only plot type protected from harvesting. The Provincial Change Monitoring Inventory (CMI), Provincial Young Stand Monitoring (YSM) and National Forest Inventory (NFI) programs monitor the changes in growth, mortality, and forest health from statistically valid populations. Vegetation Resource Inventory (VRI) plots are used to audit and verify key spatial inventory attributes estimated during photo interpretation.

Dominant Species Map 2015 The data represent dominant tree species for British Columbia forests in 2015, are based upon Landsat data and modeling, with results mapped at 30 m spatial resolution. The map was generated with the Random Forests classifier that used predictor variables derived from Landsat time series including surface reflectance, land cover, forest disturbance, and forest structure, and ancillary variables describing the topography and position. Training and validation samples were derived from the Vegetation Resources Inventory (VRI), from a pool of polygons with homogeneous internal conditions and with low discrepancies with the remotely sensed predictions. Local models were applied over 100x100 km tiles that considered training samples from the 5x5 neighbouring tiles to avoid edge effects. An overall accuracy of 72% was found for the species which occupy 80% of the forested areas. Satellite data and modeling have demonstrated the capacity for up-to-date, wall-to-wall, forest attribute maps at sub-stand level for British Columbia, Canada. BC Species Likelihood 2015 The tree species class membership likelihood distribution data included in this product focused on the province of British Columbia, based upon Landsat data and modeling, with results mapped at 30 m spatial resolution. The data represent tree species class membership likelihood in 2015. The map was generated with the Random Forests classifier that used predictor variables derived from Landsat time series including surface reflectance, land cover, forest disturbance, and forest structure, and ancillary variables describing the topography and position. Training and validation samples were derived from the Vegetation Resources Inventory (VRI) selecting from a stratified pool of polygons with homogeneous internal conditions and with low discrepancies when related to remotely sensed information. Local models were applied over 100x100 km tiles that, to avoid edge effects, considered training samples from the 5x5 neighbouring tiles. An overall accuracy of 72% was found for the species which occupy 80% of the forested areas. As an element of the mapping process, we also obtain the votes received for each class by the Random Forest models. The votes can be understood as analogous to class membership likelihoods, providing enriched information on land cover class uncertainty for use in modeling. Tree species class membership likelihoods lower than 5% have been masked and converted to zero. When using this data, please cite as: Shang, C., Coops, N.C., Wulder, M.A., White, J.C., Hermosilla, T., 2020. Update and spatial extension of strategic forest inventories using time series remote sensing and modeling. International Journal of Applied Earth Observation and Geoinformation 84, 101956. DOI: 10.1016/j.jag.2019.101956 ( Shang et al. 2020).

Dominant Species Map 2015 The data represent dominant tree species for British Columbia forests in 2015, are based upon Landsat data and modeling, with results mapped at 30 m spatial resolution. The map was generated with the Random Forests classifier that used predictor variables derived from Landsat time series including surface reflectance, land cover, forest disturbance, and forest structure, and ancillary variables describing the topography and position. Training and validation samples were derived from the Vegetation Resources Inventory (VRI), from a pool of polygons with homogeneous internal conditions and with low discrepancies with the remotely sensed predictions. Local models were applied over 100x100 km tiles that considered training samples from the 5x5 neighbouring tiles to avoid edge effects. An overall accuracy of 72% was found for the species which occupy 80% of the forested areas. Satellite data and modeling have demonstrated the capacity for up-to-date, wall-to-wall, forest attribute maps at sub-stand level for British Columbia, Canada. BC Species Likelihood 2015 The tree species class membership likelihood distribution data included in this product focused on the province of British Columbia, based upon Landsat data and modeling, with results mapped at 30 m spatial resolution. The data represent tree species class membership likelihood in 2015. The map was generated with the Random Forests classifier that used predictor variables derived from Landsat time series including surface reflectance, land cover, forest disturbance, and forest structure, and ancillary variables describing the topography and position. Training and validation samples were derived from the Vegetation Resources Inventory (VRI) selecting from a stratified pool of polygons with homogeneous internal conditions and with low discrepancies when related to remotely sensed information. Local models were applied over 100x100 km tiles that, to avoid edge effects, considered training samples from the 5x5 neighbouring tiles. An overall accuracy of 72% was found for the species which occupy 80% of the forested areas. As an element of the mapping process, we also obtain the votes received for each class by the Random Forest models. The votes can be understood as analogous to class membership likelihoods, providing enriched information on land cover class uncertainty for use in modeling. Tree species class membership likelihoods lower than 5% have been masked and converted to zero. When using this data, please cite as: Shang, C., Coops, N.C., Wulder, M.A., White, J.C., Hermosilla, T., 2020. Update and spatial extension of strategic forest inventories using time series remote sensing and modeling. International Journal of Applied Earth Observation and Geoinformation 84, 101956. DOI: 10.1016/j.jag.2019.101956 ( Shang et al. 2020).

Seral stage assessment for the Cariboo Region. Based on Vegetated Resource Information (VRI) data. Last update 2019-06-06, based on 2018 VRI. Previous seral stage assessments can be found here: ftp://ftp.geobc.gov.bc.ca/publish/Regional/WilliamsLake/forest/seral/

Old-growth forests have complex structure variability that provides critical habitat for endangered species, enhancing biodiversity and ecosystem services, but old-growth has become rare due to historical forest harvesting. In order to support the conservation of Old-Growth forests in B.C., it is necessary to identify old-growth forests from other non-old-growth forests. However, the traditional age measurement methods are costly and intractable at landscape scales, also the structural characteristics of old growth are not included. This study attempts to predict the distribution of old-growth attributes in the Coastal Western Hemlock (CWH) zone in British Columbia using area-based lidar metrics. Lidar point clouds of 61 forestry inventory plots are extracted to generate liDAR metrics to create multilinear regression models for four old-growth attributes: standard deviation of diameter at breast height (DBH), maximum tree DBH, average live crown percentage, and the sum of understory plants percentage. The results show that multilinear regression and LiDAR data can be used to estimate the distribution of old-growth attributes except for the average live crown percentage. An old-growth index is derived from four old-growth attributes for mapping the potential locations of old-growth. However, the validation results of 11.28% from vegetation resource inventory (VRI) illustrate that the old-growth index does not successfully identify old growth. Despite the challenges encountered, the prediction results can still be used to identify old-growth attributes and enhance knowledge of old-growth landscapes. Also, this study has potential applications in old-growth forest restoration in the Western Hemlock Ecosystem and supports the old-growth management plan of the government.

Seral stage assessment for the Cariboo Region. Based on Vegetated Resource Information (VRI) data. Last update 2023-11-15, based on 2022 VRI (Vegetation Resource Inventory) data. PDF reports for this and previous seral stage assessments are available for download under "Data and Resources" on the right side of this page. Previous seral stage assessment datasets are currently available via FTP download here: ftp://ftp.geobc.gov.bc.ca/publish/Regional/WilliamsLake/forest/seral/.

Machine learning algorithms have been widely adopted in the monitoring ecosystem. British Columbia suffers from grassland degradation but the province does not have an accurate spatial database for effective grassland management. Moreover, computational power and storage space remain two of the limiting factors in developing the database. In this study, we leverage supervised machine learning algorithms using the Google Earth Engine to better annual grassland inventory through an automated process. The pilot study was conducted over the Rocky Mountain district. We compared two different classification algorithms: the Random forest, and the Support vector machine. Training data was sampled through stratified and grided sampling. 19 predictor variables were chosen from Sentinel-1 and Sentinel-2 imageries and relevant topological derivatives, spectral indices, and textural indices using a wrapper-based feature selection method. The resultant map was post-processed to remove land features that were confounded with grasslands. Random forest was chosen as the prototype because the algorithm predicted features relevant to the project’s scope at relatively higher accuracy (67% - 86%) than its counterparts (50% - 76%). The prototype was good at delineating the boundaries between treed and non-treed areas and ferreting out opened patches among closed forests. These opened patches are usually disregarded by the VRI but they are deemed essential to grassland stewardship and wildlife ecologists. The prototype demonstrated the feasibility of automating grassland delineation by a Random forest classifier using the Google Earth Engine. Furthermore, grassland stewards can use the product to identify monitoring and restoration areas strategically in the future.