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Established in 1918, the Petawawa Research Forest (PRF) is the oldest, continuously operated research forest in Canada. The PRF was established to generate and transfer knowledge that could be used to improve the quality, productivity, and health of Canada’s forests. Over time, numerous sample plots and research trials have been established at the PRF. For example, the PRF is known to host approximately 500 growth and yield permanent sample plots (PSPs), 100 silvicultural field studies, 125 research plantations, 1100 intensive forest management plots, 300 genetic trials, 100 forest fire experimental sites, 13 ecological reserves, and 300 remote sensing sites (D’Eon, 2006).
The PRF is 100 km2 and is situated in Ontario, approximately two-hours northwest of Canada’s capital city, Ottawa. Located in the mixedwood forests of the Great Lakes–St. Lawrence Forest region, common tree species include white pine (Pinus strobus L.), trembling aspen (Populus tremuloides Michx.), red oak (Quercus rubra L.), red pine (P. resinosa Ait.), white birch (Betula papyrifera), maple (Acer spp.), and white spruce (Picea glauca), among others (Wetzel et al. 2011). This forest region is considered a transition between the boreal forests to the north, which are dominated by coniferous species, and the deciduous-dominated forests to the south.
The 100-year history of research at the PRF make it a unique location for the development, testing, and validation of algorithms, approaches, and applications of remotely sensed data for forest management. Over time, the PRF has been the site of numerous operational and experimental remotely sensed data acquisitions (Leckie, 1990). More recently, airborne Light Detection and Ranging (LiDAR) data have been acquired, and used in conjunction with ground plot data to develop an Enhanced Forest Inventory (EFI) (White et al. 2013, 2017). As a remote sensing supersite, open access to PRF data holdings is intended to stimulate innovation and enable further research and development. Many of the data sets available for the PRF, including LiDAR, airborne and satellite optical imagery, radar, and ground plot data, have been compiled here and are summarized in the tables below. Additional data may be added to this site as time and resources permit. To locate all of the data holdings associated with the PRF supersite, users can search for “PRF” using NFIS Catalogue Portal. Links or search terms for specific datasets are identified in the tables below.
For more information about the supersite and the Petawawa Research Forest:
White, J. C., Chen, H., Woods, M. E., Low, B., and Nasonova, S. 2019. The Petawawa Research Forest: Establishment of a remote sensing supersite. The Forestry Chronicle, 95(3): 149–156. https://doi.org/10.5558/tfc2019-024
|Airborne LiDAR 2012 and 2018 LAS files||Wall-to-wall airborne LiDAR data were acquired for the PRF in 2012 and 2018 in support of Enhanced Forest Inventory (EFI) research and development objectives. The 2012 ALS and 2018 SPL data are provided here as LAS v1.1 and v1.4, respectively.||PRF_LiDAR2012_LAS.zip (8.6G)
|Digital Terrain Model (DTM) 2012 and 2018||A Digital Terrain Model (DTM) was generated from 2012 wall-to-wall airborne LiDAR data as well as from 2018. Spatial resolution is 1 m for 2012 DTM and 0.5 m for 2018 DTM.||PRF_LiDAR2012_DTM.zip
|Canopy Height Model (CHM) 2012 and 2018||A Canopy Height Model (CHM) was generated from the 2012 wall-to-wall airborne LiDAR data as well as from 2018. Spatial resolution is 0.25 m for 2012 CHM and 0.5 m for 2018 CHM.||PRF_LiDAR2012_CHM.zip
|Field plot data 2014 and 2018||Ground plot data were acquired to support the development of 2014 and 2018 EFI at the PRF.||prf_forest_sample_plots_2014.zip
|AFRIT PRF Large Tree Tally Forms 2013 and 2018||Large tree tally forms from field measurements were acquired in 2013 and 2018. Note that not all plots were remeasured.||plot_data_large_tree.zip|
|Enhanced Forest Inventory (EFI) Predictors 2012 and 2018||Point cloud metrics were generated from the 2012 and 2018 wall-to-wall airborne LiDAR data that were used as predictors in the area-based EFI models. Note that area-based models were generated using Random Forest models.||PRF_LiDAR2012_RF_Inputs.zip
|Enhanced Forest Inventory (EFI) Output Layers 2012 and 2018||Area-based estimates in 2012 and 2018 for a suite of forest inventory attributes including height, basal area, volume, etcetera.||PRF_LiDAR2012_RF_Surfaces.zip
|Historic Forest Inventory Data||Conventional forest inventory data were generated using air photo interpretation and ground plot data. Inventories were completed in 2000 and 2007.||prf_forest_inventory_2007.zip
|Wet Areas Mapping||A suite of products was derived from the 2012 LiDAR DTM that characterize streams, depth-to-water, etcetera.||PRF_WAM.zip|
|Digital Aerial Photogrammetry (DAP)||Leica ADS100 imagery was acquired for the PRF in 2009. The imagery and the derived SGM image point clouds in (LAS format) are also available.||FCIR_SGM_2009.zip
|Additional datasets for the PRF supersite are available through NFIS Catalogue Portal, operated by the Canadian Forest Service, Natural Resources Canada. Suggested search terms for each data collection are indicated in the table below.|
|Landsat||Archived Landsat Collection 1 data (1972–2018). Includes Landsat Multispectral Scanner (MSS), Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and the Operational Land Imager (OLI). With the exception of MSS, all data is corrected to surface reflectance. Search terms: “PRF” and “Landsat”, “Landsat4”, “Landsat5”, “MSS”, “TM”, “ETM+”, “OLI”, etc.|
|Sentinel-2||Archived Sentinel-2 data (2016–2018), corrected to surface reflectance. Search terms: “PRF” and “Sen2Cor”|
|Harmonized Landsat and Sentinel-2||Harmonized Landsat and Sentinel-2 surface reflectance data generated by NASA/USGS (2013–2018). Search terms: “PRF” and “HLS”|
|Sentinel-1||Processed Sentinel-1 backscatter data (2016–2018) for the PRF. Search terms: “PRF” and “Sentinel1”|
|Various airborne imagery collections||Various digital airborne image datasets have been acquired for the PRF. Search terms: “PRF” and “optical” or "airborne" or “CIR”; etc.|
|Various base data||Base data includes PRF boundary, lakes, wetlands, and various digital elevation models. Search terms: “PRF” and “vector” or "raster"; “PRF” and “DEM”; etc.|
|Various LiDAR data||Various acquisitions of LiDAR data for research are available for the PRF. Search terms: “PRF” and “LiDAR” or "CSGYM"|
D’Eon, S. 2006. “20 Year results, you’re just getting started” Long term experimentation at the Petawawa Research Forest: A brief introduction to a living laboratory, In: Irland, L.C., Camp, A.E., Brissette, J.C., Donohew, Z.R. (Eds.), Long-term Silvicultural and Ecological studies: Results for Science and Management. Yale University, School of Forestry and Environmental Studies, Global Institute of Sustainable Forestry Research Paper 005, pp. 128–135.
Leckie, D.G. 1990. Advances in remote sensing technologies for forest surveys and management. Canadian Journal of Forest Research, 20: 464–483.
Wetzel, S., Swift, D.E., Burgess, D., Robinson, C. 2011. Research in Canada’s National Research Forests–Past, present, and future. Forest Ecology and Management, 261: 893–899.
White, J.C., Wulder, M.A., Varhola, A., Vastaranta, M., Coops, N.C., Cook, B.D., Pitt, D., Woods, M. 2013. A best practices guide for generating forest inventory attributes from airborne laser scanning data using an area-based approach. Natural Resources Canada, Canadian Forest Service, Canadian Wood Fibre Centre, Victoria, British Columbia, Canada. Information Report FI-X-010, 39 pp.
White, J.C., Tompalski, P., Vastaranta, M., Wulder, M.A., Saarinen, S., Stepper, C., Coops, N.C. 2017. A model development and application guide for generating an enhanced forest inventory using airborne laser scanning data and an area-based approach. Natural Resources Canada, Canadian Forest Service, Canadian Wood Fibre Centre, Victoria, British Columbia, Canada. Information Report FI-X-018, 38 pp.
Contact: Joanne White, PhD, Research Scientist, Canadian Forest Service, Natural Resources Canada (Email: joanne.white@NRCan-RNCan.gc.ca)
Last update: October 5, 2022