This is an ongoing dataset of fully processed 8-day average Sentinel 3A and 3B chlorophyll-a (Chla) and suspended particulate matter (SPM) imagery for coastal and offshore British Columbia (BC) and Southeast Alaska waters. Sentinel 3A and 3B are European Space Agency (ESA) oceanography satellites jointly operated with the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT). The ocean and land colour instrument (OLCI) onboard both satellites has a 300m spatial resolution, near daily temporal coverage (when 3A and 3B are combined), 21 spectral bands from 400-1200nm, high signal-to-noise ratio and an off-nadir swath centered to minimize ocean sun glint. These features make the instruments well suited to retrievals of biogeochemical products from optically complex coastal waters.

At the University of Victoria (BC, Canada), the SPECTRAL remote sensing laboratory has performed extensive evaluation of methods for the best regional Chla and SPM retrievals. Validation with in-situ data showed the best results using Level-1 imagery processed with the POLYnomial based algorithm applied to MERIS (POLYMER) processor. Following validation, the SPECTRAL laboratory and the MOD(ularity) Squad developed an automated processing system that: 1) downloads imagery from the Marine Copernicus Online Data Access (CODA) web service; 2) applies POLYMER and flagging and; 3) mosaics the imagery for fully processed Chla and SPM concentrations over the study region. Additionally, an interactive public web interface was created to view the near real time outputs at www.algaeexplorer.ca (provided in resources). Full validation details are provided in Giannini et al. (2021) and processing details in Jacoby et al. (2019) and Marchese et al. (2022) referenced in the resources.

In 2022, the Hakai Institute took responsibility of the project, added processing of 3B imagery allowing for 8-day averaging of 3A and 3B imagery, created automated submission to the Canadian Integrated Ocean Observing System (CIOOS) and updated the Algae Explorer web interface to include all products. Averaging imagery over 8 days considerably improves spatial coverage in cloudy regions such as the northeast Pacific.

This product provides the best known regional OLCI Chla and SPM retrievals shown to have low systematic biases (<1%) and follow expected seasonal and spatial trends; however, relative percent difference between validation data and satellite retrievals was high notably for Chla (~83%) due to the underestimation of high Chla concentrations and potentially an artifact of spatial-temporal mismatches between validation samples and image pixels. The SPECTRAL laboratory has performed initial radiometric comparisons of 3A and 3B imagery and shown highly comparable data; however, comparison of biogeochemical outputs is still in progress. Further uncertainties exist in high turbidity regions (Fraser River plume and fjords) where uncorrectable poor-quality pixels are sometimes observed. Additionally, an unresolved data striping issue is periodically present and observed as a narrow band of distorted pixels, which sometimes evades the land mask and, crosses images diagonally above Vancouver Island. Efforts are underway to correct this issue. Data users should consider these uncertainties and issues when using the data.

Satellite remote sensing is increasingly used to study surface ocean processes at the spatial and temporal resolutions required for understanding long term variability under a changing climate. Chlorophyll-a is the most widely used measure of phytoplankton biomass and crucial for understanding phytoplankton which are the base of the marine food web and control ocean biogeochemical cycling. Suspended particulate matter is a key water quality indicator (i.e. turbidity) with increased concentrations reducing light availability to aquatic species.

Funding was provided by the UBC/UVic Hakai Coastal Initiative postdoctoral fellowship, NSERC NCE Marine Environmental Observation Prediction and Response (MEOPAR) network, Canadian Space Agency (CSA), Canadian Foundation for Innovation (CFI) and NSERC Discover Grant awarded to Maycira Costa.

It is requested that Giannini et al. (2021), Jacoby et al. (2019) and Marchese et al. (2022) are referenced if data is used for published research and the ESA acknowledged as the data provider.

Note: To visualize the data in the viewer, zoom into the area of interest. The National Air Photo Library (NAPL) of Natural Resources Canada archives over 6 million aerial photographs covering all of Canada, some of which date back to the 1920s. This collection includes Time Series of aerial orthophoto mosaics over a selection of major cities or targeted areas that allow the observation of various changes that occur over time in those selected regions. These mosaics are disseminated through the Data Cube Platform implemented by NRCan using geospatial big data management technologies. These technologies enable the rapid and efficient visualization of high-resolution geospatial data and allow for the rapid generation of dynamically derived products. The data is available as Cloud Optimized GeoTIFF (COG) for direct access and as Web Map Services (WMS) or Web Coverage Services (WCS) with a temporal dimension for consumption in Web or GIS applications. The NAPL mosaics are made from the best spatial resolution available for each time period, which means that the orthophotos composing a NAPL Time Series are not necessarily coregistrated. For this dataset, the spatial resolutions are: 100 cm for the year 1932 and 50 cm for the year 1950. The NAPL indexes and stores federal aerial photography for Canada, and maintains a comprehensive historical archive and public reference centre. The Earth Observation Data Management System (EODMS) online application allows clients to search and retrieve metadata for over 3 million out of 6 million air photos. The EODMS online application enables public and government users to search and order raw Government of Canada Earth Observation images and archived products managed by NRCan such as aerial photos and satellite imagery. To access air photos, you can visit the EODMS web site: https://eodms-sgdot.nrcan-rncan.gc.ca/index-en.html

Kelp Field Data for Remote Sensing - BC Central Coast - July 2014.

Ground truthing of kelp in areas along the Central Coast of British Columbia consists of collecting spatial data on kelp in order to match remote sensing information with what is observed in the field. Data collection was conducted in areas where WorldView-2 satellite coverage was planned from Calvert Island to McMullin Group. Data consists of point SHP file with field photos and species / density metrics. Field equipment: A 2 x 2 meter quadrant was used in a variety of kelp beds in order to be representative of kelp beds observed on the central coast. A 2 meter quadrant was chosen because the WorldView-2 satellite data resolution is produced at 2 meters (multispectral). Topcon GR5 survey grade GPS was used to collect spatial data. A DGPS mode was used to collect data soaking for 5-10 seconds providing < 10 cm X / Y accuracy.

Field work conducted by Keith Holmes and Luba Reshitnyk on July 9th to 13th 2014 on the research vessel "Hakai Blue"

The main goal of this information is to match up field data with remote sensing satellite imagery to accurately classify the extent / species / and densities of kelp. The information will be valuable in identifying spectral signatures to differentiate species in remote sensing platforms such as ecognition.

Hakai Institute: Keith Holmes and Luba Reshitnyk.

Green roofs are a promising mitigation tool against environmental concerns caused by increased urbanization. This study aims to address which of the University of British Columbia’s buildings are most suitable for a retrofitted green roof using active remote sensing. Through the assessment of six characteristics of buildings, a suitability score was given to all buildings within the area of interest on campus. The six suitability criteria are rooftop slope, rooftop area, building usage, building ownership, structural materials, and light intensity. The six suitability characteristics where selected based off literature on existing green roof retrofitting analyzing their structure, types and performance. This study is part of the Social Ecological Economic Development Studies (SEEDS) Sustainability Program to address sustainability policies and practices on UBC’s campus. The building suitability analysis resulted in the detection of five buildings with the greatest suitability score and their total rooftop area being 31,940.80 m^2. Each of the five buildings then was further investigated in terms of the vegetation health and density surrounding the buildings to isolate potential concerns of planting in the area. The mean NDVI of existing greenery surrounding the top five suitable buildings is 0.67, indicative of moderate to high density vegetation. Implementing green roof retrofitting on the suitable UBC buildings requires an additional accessibility analysis to maximize the positive social impacts of increasing campus greenery and make decisions on the green roof structure.

Note: To visualize the data in the viewer, zoom into the area of interest. The National Air Photo Library (NAPL) of Natural Resources Canada archives over 6 million aerial photographs covering all of Canada, some of which date back to the 1920s. This collection includes Time Series of aerial orthophoto mosaics over a selection of major cities or targeted areas that allow the observation of various changes that occur over time in those selected regions. These mosaics are disseminated through the Data Cube Platform implemented by NRCan using geospatial big data management technologies. These technologies enable the rapid and efficient visualization of high-resolution geospatial data and allow for the rapid generation of dynamically derived products. The data is available as Cloud Optimized GeoTIFF (COG) files for direct access and as Web Map Services (WMS) or Web Coverage Services (WCS) with a temporal dimension for consumption in Web or GIS applications. The NAPL mosaics are made from the best spatial resolution available for each time period, which means that the orthophotos composing a NAPL Time Series are not necessarily coregistered. For this dataset, the spatial resolutions are: 25 cm for the year 1950, 50 cm for the year 1959, 50 cm for the year 1967, 50 cm for the year 1972, 50 cm for the year 1978 and 70 cm for the year 1982. The NAPL indexes and stores federal aerial photography for Canada, and maintains a comprehensive historical archive and public reference centre. The Earth Observation Data Management System (EODMS) online application allows clients to search and retrieve metadata for over 3 million out of 6 million air photos. The EODMS online application enables public and government users to search and order raw Government of Canada Earth Observation images and archived products managed by NRCan such as aerial photos and satellite imagery. To access air photos, you can visit the EODMS web site: https://eodms-sgdot.nrcan-rncan.gc.ca/index-en.html

This is an ongoing dataset of fully processed 8-day average Sentinel 3A and 3B chlorophyll-a (Chla) and suspended particulate matter (SPM) imagery for coastal and offshore British Columbia (BC) and Southeast Alaska waters. Sentinel 3A and 3B are European Space Agency (ESA) oceanography satellites jointly operated with the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT). The ocean and land colour instrument (OLCI) onboard both satellites has a 300m spatial resolution, near daily temporal coverage (when 3A and 3B are combined), 21 spectral bands from 400-1200nm, high signal-to-noise ratio and an off-nadir swath centered to minimize ocean sun glint. These features make the instruments well suited to retrievals of biogeochemical products from optically complex coastal waters.

At the University of Victoria (BC, Canada), the SPECTRAL remote sensing laboratory has performed extensive evaluation of methods for the best regional Chla and SPM retrievals. Validation with in-situ data showed the best results using Level-1 imagery processed with the POLYnomial based algorithm applied to MERIS (POLYMER) processor. Following validation, the SPECTRAL laboratory and the MOD(ularity) Squad developed an automated processing system that: 1) downloads imagery from the Marine Copernicus Online Data Access (CODA) web service; 2) applies POLYMER and flagging and; 3) mosaics the imagery for fully processed Chla and SPM concentrations over the study region. Additionally, an interactive public web interface was created to view the near real time outputs at www.algaeexplorer.ca (provided in resources). Full validation details are provided in Giannini et al. (2021) and processing details in Jacoby et al. (2019) and Marchese et al. (2022) referenced in the resources.

In 2022, the Hakai Institute took responsibility of the project, added processing of 3B imagery allowing for 8-day averaging of 3A and 3B imagery, created automated submission to the Canadian Integrated Ocean Observing System (CIOOS) and updated the Algae Explorer web interface to include all products. Averaging imagery over 8 days considerably improves spatial coverage in cloudy regions such as the northeast Pacific.

This product provides the best known regional OLCI Chla and SPM retrievals shown to have low systematic biases (<1%) and follow expected seasonal and spatial trends; however, relative percent difference between validation data and satellite retrievals was high notably for Chla (~83%) due to the underestimation of high Chla concentrations and potentially an artifact of spatial-temporal mismatches between validation samples and image pixels. The SPECTRAL laboratory has performed initial radiometric comparisons of 3A and 3B imagery and shown highly comparable data; however, comparison of biogeochemical outputs is still in progress. Further uncertainties exist in high turbidity regions (Fraser River plume and fjords) where uncorrectable poor-quality pixels are sometimes observed. Additionally, an unresolved data striping issue is periodically present and observed as a narrow band of distorted pixels, which sometimes evades the land mask and, crosses images diagonally above Vancouver Island. Efforts are underway to correct this issue. Data users should consider these uncertainties and issues when using the data.

Satellite remote sensing is increasingly used to study surface ocean processes at the spatial and temporal resolutions required for understanding long term variability under a changing climate. Chlorophyll-a is the most widely used measure of phytoplankton biomass and crucial for understanding phytoplankton which are the base of the marine food web and control ocean biogeochemical cycling. Suspended particulate matter is a key water quality indicator (i.e. turbidity) with increased concentrations reducing light availability to aquatic species.

Funding was provided by the UBC/UVic Hakai Coastal Initiative postdoctoral fellowship, NSERC NCE Marine Environmental Observation Prediction and Response (MEOPAR) network, Canadian Space Agency (CSA), Canadian Foundation for Innovation (CFI) and NSERC Discover Grant awarded to Maycira Costa.

It is requested that Giannini et al. (2021), Jacoby et al. (2019) and Marchese et al. (2022) are referenced if data is used for published research and the ESA acknowledged as the data provider. _NCProperties=version=2,netcdf=4.7.3,hdf5=1.8.14, cdm_data_type=Grid citation=Costa, M., & Hakai Institute. (2023). Sentinel-3A OLCI Imagery - Automated daily POLYMER processed satellite chlorophyll concentrations for coastal British Columbia and southeast Alaska (Version v1). Hakai Institute comment=##Limitations: Product is the best regionally evaluated output, but methods may evolve. Satellite derived chlorophyll and SPM concentrations contain error. contributor_name=Costa, Maycira;Hakai Institute contributor_role=originator,collaborator,principalInvestigator;custodian,owner,pointOfContact,resourceProvider,processor,publisher,distributor,funder Conventions=CF-1.6, COARDS, ACDD-1.3 defaultGraphQuery=chl_conc_mean[last][0][0:last][0:last]&.draw=surface&.vars=longitude|latitude|temp doi=https://doi.org/10.21966/175j-8k96 Easternmost_Easting=-121.50242544017246 geospatial_lat_max=59.5000898311175 geospatial_lat_min=47.00098814229249 geospatial_lat_resolution=0.0026949335249730503 geospatial_lat_units=degrees_north geospatial_lon_max=-121.50242544017246 geospatial_lon_min=-139.00062881782247 geospatial_lon_resolution=0.0026949335249730503 geospatial_lon_units=degrees_east id=91107fce-93a4-4bc9-bce4-e7d9e1cf02a0 infoUrl=https://catalogue.hakai.org/dataset/ca-cioos_91107fce-93a4-4bc9-bce4-e7d9e1cf02a0 institution=Hakai Institute keywords_vocabulary=CIOOS: CIOOS Essential Ocean Variables Vocabulary metadata_form=https://hakaiinstitute.github.io/hakai-metadata-entry-form#/en/hakai/7U7b8oPpeTN6gjvXlUCTGJr5pga2/-Nueis-TZEnz_78f8PYf metadata_link=https://catalogue.hakai.org/dataset/ca-cioos_91107fce-93a4-4bc9-bce4-e7d9e1cf02a0 metadata_profile=beam metadata_version=0.5 naming_authority=ca.cioos Northernmost_Northing=59.5000898311175 platform=satellite platform_vocabulary=https://vocab.nerc.ac.uk/collection/L06/current/ product_type=BINNED-L3 progress=onGoing project=Oceanography references=https://doi.org/ sourceUrl=(local files) Southernmost_Northing=47.00098814229249 standard_name_vocabulary=CF Standard Name Table v70 TileSize=64:64 time_coverage_end=2025-07-02T00:00:00Z time_coverage_start=2020-06-27T00:00:00Z Westernmost_Easting=-139.00062881782247

Grasslands in British Columbia (BC) play a pivotal role in biodiversity, supporting over 30% of the region's endangered species. However, rapid urbanization and forest encroachment threaten these habitats. This study addresses the urgent need for an accurate, automated method for delineating and monitoring BC's grasslands by employing Object-Based Image Analysis (GEOBIA) within the Google Earth Engine platform, utilizing high-resolution Sentinel-2 satellite imagery. The approach innovates by integrating Superpixel Segmentation Based on Simple Non-Iterative Clustering (SNIC) with Random Forest classification, aimed at overcoming the mixed pixel effect prevalent in pixel-based methods. The methodology demonstrates a significant improvement in the accuracy of grassland delineation, achieving an overall classification accuracy of 96%. Specifically, the accuracy for grassland identification increased by 26.6% compared to the previous study, underscoring the effectiveness of GEOBIA for environmental monitoring. This advancement offers a promising tool for the conservation and management of grassland ecosystems in BC, suggesting a scalable model for similar ecological studies worldwide. The findings advocate for the adoption of GEOBIA in remote sensing practices, potentially transforming how grasslands are monitored and conserved, thereby contributing to the preservation of biodiversity.

Wildfire is a significant threat to ecosystems and human safety, exacerbated by climate warming. The Penticton region of British-Columbia, Canada is an area which is experiencing increasingly worsening wildfire events. These natural disturbance events represent a significant threat to local ecosystems, property and human life and wellbeing. As fire conditions worsen, and the population density of this region increases, landscape analysis of fire hazard levels is necessary to direct emergency service management prior to and during wildfire events and to inform policy on how to manage these natural disasters. To assess fire hazard levels, a GIS-based multi-criteria analysis was performed to understand fire hazard spatially, subdivided into low, moderate, high, and severe hazard areas. Two models were built to achieve this, taking into account commonly used variables employed to assess fire hazard severity around the world. To identify potential differences in hazard assessment, the models weighted these variables differently from one another. Fire location points from the year 2000 to 2021 were overlayed with each respective model output. Model 1 spatially overlapped with 73.88% of these fires, while model 2 spatially overlapped with 74.35%. These results can help identify areas of elevated hazard under ideal burning conditions, inform deployment of emergency services and resources, and provide a framework for using a GIS to conduct a fire hazard landscape assessment. Datasets associated and created to complete analysis employed in this research project.

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.

Marine debris poses significant threats to coastal ecosystems and infrastructure, especially in semi-enclosed regions where monitoring is limited by inaccessibility and uneven population distribution. In Howe Sound, British Columbia, this study integrates remote sensing and environmental modeling to predict debris accumulation zones. Sentinel-2 satellite imagery was selected due to its high spatial resolution, broad spectral range, and frequent revisit time, which make it well-suited for capturing detailed coastal features. A neural network algorithm was used to classify six landcover types with an overall accuracy of 0.98. The classification results showed that many known debris hotspots are located near urban shorelines and within semi-enclosed bays. To simulate debris transport, river discharge and seasonal wind direction were modeled as surface movement drivers. The study area was divided into three sections to account for spatial variation in debris driving forces contribution. Hourly wind data from four weather stations were used to construct wind rose diagrams that captured seasonal changes in wind direction. The simulation identified 49 predicted debris hotspot locations. Of these, 20 overlapped with known hotspots, while 10 of the 29 newly identified hotspots are in less populated and previously underreported areas, particularly along the western shoreline. These findings demonstrate that remote sensing, when combined with physically based modeling, can overcome limitations of traditional monitoring methods and improve the identification of marine debris accumulation. This approach provides a scalable and transferable framework for supporting more targeted and proactive coastal management strategies.

Lidar data collection parameters*.

Il s'agit de l'enregistrement de métadonnées pour les ensembles de données LiDAR acquis pour la région de l'île Calvert, en Colombie-Britannique, au Canada, en 2012 et 2014. Il fournit des détails sur les deux missions LiDAR et le traitement initial pour fusionner les deux ensembles de données ensemble.

L'ensemble de données a été créé à partir de deux missions, l'une en 2012 et l'autre en 2014. Les deux ensembles de données ont été fusionnés et utilisés pour générer de nombreux produits de données dérivés (voir la section des liens ci-dessous). La mission de 2012 a recueilli des hyperspectrales et des orthophotographies afin que les métadonnées de mission pour l'ensemble de données LiDAR s'appliquent également à ces actifs.

Les altitudes du terrain mesurées par LiDAR aéroporté ont été recueillies en août 2012 pour l'île Calvert, l'île Hécate et trois autres zones de relevé situées au nord-est (c.-à-d. l'embouchure de la rivière Koeye, la ville portuaire de Namu et le coin sud-ouest de l'île King) par Terra Remote Sensing Inc. (TRSI), Sidney, Colombie-Britannique (figure 1). La couverture aérienne totale de l'acquisition était de 477,2 km2. La densité de points (nombre de retours par unité de surface) de l'acquisition était en moyenne d'environ 2,34 pts/m2 pour toutes les déclarations affichées (maximum de trois déclarations discrètes par impulsion), 2,11 pts/m2 pour les premières déclarations et 0,51 pts/m2 pour les déclarations classées au sol. Informations plus détaillées sur le capteur LiDAR TRSI et les paramètres de levé, le prétraitement des données, les contrôles de qualité et la vérification de la précision. Une deuxième acquisition de données LiDAR aéroportées a été entreprise par le LiDAR Research Group de l'Université du Nord de la Colombie-Britannique (UNBC) en août 2014, dans le but exprès de recueillir des données LiDAR à résolution spatiale plus élevée (3,4 à 22,3 pts/m2) sur certains domaines de recherche d'intérêt sur les îles Hécate et Calvert, ainsi que remplir et éliminer plusieurs vides de données trouvés dans la couverture LiDAR TRSI initiale.

Mission 2012 : Dates : 5, 15 et 17 août Dirigé par : Terra Remote Sensing Surface couverte : 477,22 km^2 Hauteur de vol : 1150 mètres au-dessus du sol Projection : UTM Zone 9 Date : NAD 83 Géoïde : HT 2.0 Points LiDAR : une précision relative de +/-15 cm et une précision absolue de 1 m Données hyperspectrales : +/- 2 pixels Densité des nuages de points : En prélevant des échantillons aléatoires dans toute la zone du projet, la densité de points moyenne pour une seule ligne de vol est de 1 pts/m2

Mission 2014 : Dates : 7 août 2014 Dirigé par : UNBC LiDAR Research Group Surface couverte : 34,9 km^2 Hauteur de vol : ~475 m et 650 m +/- 50 m Projection : UTM Zone 9 Date : CSRS NAD 83 Géoïde : hauteurs d'ellipsoïdes La densité de points variait de 22,3 pts/m2 à 3,4 pts/m2

Currently, light detection and ranging (LiDAR) scans have been used to produce 1m & 5m digital elevation models (DEMs) throughout many important drainages across the province and are ideal candidates to effectively inform vegetation classification within floodplain and riparian ecosystems. These ecosystems are highly influenced by fluctuating freshwater regimes and are invaluable in providing, regulating, and supporting various ecosystem functions. This paper explored the extent that various floodplain characteristics, including bench heights and overall floodplain extent, can be modelled using a readily available 1m DEM within the Date Creek Research Forest (DCRF) in northwestern British Columbia (BC), Canada. Stream paths were derived from a flow accumulation analysis of the DEM. These paths highlighted how the pre-existing river feature network, the Freshwater Atlas, differed up to 230.8m, with a median distance between segments being 23.4m. Bench height transition areas were easily identifiable considering slope and curvature, yet further research is needed to classify areas of the varying benches across the landscape. The Height Above Nearest Drainage (HAND) method was used to assess floodplain extent using peak water depth values observed at nearby water survey of Canada hydrometric stations. Overall, the HAND method is limited to generating water height along flow paths that drain into the main stream, and such are inaccurate in demonstrating any spill over into adjacent braided river channels. This paper demonstrates the inadequacies of using the HAND method to define floodplain extent within braided river channels common throughout BC floodplains and therefore concludes that alternative methods should be employed when expanding floodplain prediction to other rivers systems within the province.

LiDAR (Light Detection And Ranging) is a modern survey method that produces three-dimensional spatial information in the form of a data point cloud. LiDAR is an active remote sensing system; it produces its own energy to acquire information, versus passive systems, like cameras, that only receive energy. LiDAR systems are made up of a scanner, which is a laser transmitter and receiver; a GNSS (GPS) receiver; and an inertial navigation system (INS). These instruments are mounted to an aircraft. The laser scanner transmits near-infrared light to the ground. The light reflects off the ground and returns to the scanner. The scanner measures the time interval and intensity of the reflected signals. This information is integrated with the positional information provided by the GNSS and INS to create a three-dimensional point cloud representing the surface. A LiDAR system can record millions of points per second, resulting in high spatial resolution, which allows for differentiation of many fine terrain features. Point clouds collected with LiDAR can be used to create three-dimensional representations of the Earth’s surface, such as Digital Elevation Models (DEMs) and Digital Surface Models (DSMs). DEMs model the elevation of the ground without objects on the surface, and DSMs model ground elevations as well as surface objects such as trees and buildings. LidarBC's Open LiDAR Data Portal (see link under Resources) is an initiative to provide open public access to LiDAR and associated datasets collected by the Government of British Columbia. The data in the portal is released as Open Data under the Open Government Licence – British Columbia (OGL-BC). Four Government of British Columbia business areas and one department of the Government of Canada make LiDAR data available through the portal: * GeoBC * Emergency Management and Climate Readiness (EMCR) * BC Timber Sales (BCTS) * Forest Analysis and Inventory Branch (FAIB) * Natural Resources Canada (NRCan) GeoBC is the provincial branch that oversees and manages LidarBC’s Open LiDAR Data Portal, including storage, distribution, maintenance, and updates. Please direct questions to LiDAR@gov.bc.ca.

This data set provides GIS shapefiles and Google Earth kmz files containing polygons delineating slow-moving (0.5-6 cm/year in the radar line-of-sight direction) landslides and subsiding fan deltas in the Glacier Bay region of Alaska and British Columbia. Landslides and fan deltas were identified from displacement signals captured by Interferometric Synthetic Aperture Radar (InSAR) interferograms of Sentinel-1 C-band Synthetic Aperture Radar images. The images were acquired at 12-day intervals from June to October from 2018 to 2020. We applied the persistent scatterer InSAR (PSInSAR) methods to images from both descending (scene P145) and ascending (scene P50) satellite tracks. We used PSInSAR results from the descending track as a primary means to identify ground movement and then used results from the ascending track to confirm the ground movement. The overlapping area covered by both images is 14,780 sq. km. Each polygon in the shapefile and .kmz file outlines an area of moving ground from 2018 to 2020. We categorized each area of moving ground into one of three categories: 1) slow-moving landslides on steep rocky slopes not near (> 2 km away from) present-day glacier termini, 2) slow moving landslides directly adjacent to (< 2 km away from) and associated with glacier thinning and retreat; and 3) subsidence of outwash fan deltas near glacier termini. These three categories are differentiated in the shapefile attribute table and in an explanation box in the kmz file. The attribute table also provides the area of each polygon in sq. meters. Overall, we detected 4 landslides distal to glacier termini, 22 adjacent to termini, and 5 subsiding fan deltas. We have also included shapefiles for the boundary of Glacier Bay National Park and Preserve; the coverage area for scenes P145 and P50, and the overlap between the two; and points and labels for each polygon of moving ground. These data were used in the following interpretive paper: Kim, J., Coe, J.A., Lu, Z., Avdievitch, N.N., and Hults, C.P., in review, Spaceborne InSAR mapping of landslides and subsidence in rapidly deglaciating terrain, Glacier Bay National Park and Preserve and vicinity, Alaska and British Columbia: Remote Sensing of Environment.

Lidar point cloud statistics.

Beginning with the 2011 grow season, the Earth Observation Team of the Science and Technology Branch (STB) at Agriculture and Agri-Food Canada (AAFC) started collecting ground truth data via windshield surveys. This observation data is collected in support of the generation of an annual crop inventory digital map. These windshield surveys take place in provinces where AAFC does not have access to crop insurance data. The collection routes driven attempt to maximize not only the geographical distribution of observations but also to target unique or specialty crop types within a given region. Windshield surveys are mainly collected by the AAFC Earth Observation team (Ottawa) with the support of regional AAFC Research Centres (St John’s NL; Kentville NS; Charlottetown PE; Moncton NB; Guelph ON; Summerland BC). Support is also provided by provincial agencies in British Columbia, Ontario, and Prince Edward Island, and by contractors when needed.

USHAP (USHighAirPollutants) is one of the series of long-term, full-coverage, high-resolution, and high-quality datasets of ground-level air pollutants for the United States. It is generated from the big data (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence by considering the spatiotemporal heterogeneity of air pollution. This is the big data-derived seamless (spatial coverage = 100%) daily, monthly, and yearly 1 km (i.e., D1K, M1K, and Y1K) ground-level Black Carbon (BC) dataset in the United States from 2000 to 2020. Our daily BC estimates agree well with ground measurements with an average cross-validation coefficient of determination (CV-R2) of 0.80 and normalized root-mean-square error (NRMSE) of 0.60, respectively. All the data will be made public online once our paper is accepted, and if you want to use the USHighBC dataset for related scientific research, please contact us (Email: weijing_rs@163.com; weijing@umd.edu).

Wei, J., Wang, J., Li, Z., Kondragunta, S., Anenberg, S., Wang, Y., Zhang, H., Diner, D., Hand, J., Lyapustin, A., Kahn, R., Colarco, P., da Silva, A., and Ichoku, C. Long-term mortality burden trends attributed to black carbon and PM2.5 from wildfire emissions across the continental USA from 2000 to 2020: a deep learning modelling study. The Lancet Planetary Health, 2023, 7, e963–e975. https://doi.org/10.1016/S2542-5196(23)00235-8 More air quality datasets of different air pollutants can be found at: https://weijing-rs.github.io/product.html

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).

The U.S. Geological Survey (USGS) has developed and implemented an algorithm that identifies burned areas in temporally-dense time series of Landsat Analysis Ready Data (ARD) scenes to produce the Landsat Burned Area Products. The algorithm makes use of predictors derived from individual ARD Landsat scenes, lagged reference conditions, and change metrics between the scene and reference conditions. Scene-level products include pixel-level burn probability (BP) and burn classification (BC) images, corresponding to each Landsat image in the ARD time series. Annual composite products are also available by summarizing the scene level products. Prior to generating annual composites, individual scenes that had > 0.010 burned proportion were visually assessed as part of a quality assurance check. Scenes with obvious commission errors were removed. The annual products include the maximum burn probability (BP), burn classification count (BC) or the number of scenes a pixel was classified as burned, filtered burn classification (BF) with burned areas persistent from the previous year removed, and the burn date (BD) or the Julian date of the first Landsat scene a burned areas was observed in. Vectorized versions of the BF raster are also provided as shapefiles (BF_labeled) with attributes including summary statistics of BC, BD, BP, as well as the majority level 3 ecoregion (Omernik and Griffith, 2014) and count of pixels by each National Land Cover Database Category (Vogelmann et al., 2001; Yang et al., 2018) for each burned area polygon. These products were generated for the conterminous United States for 1984 through 2019 individually for Landsat TM (5), Landsat ETM+ (7), OLI/TIRS (8), and for all sensors combined. The products for each sensor combination and year are contained in a compressed tar file are available through the USGS Science Base Catalog (https://doi.org/10.5066/P9QKHKTQ) and also at https://gec.cr.usgs.gov/outgoing/baecv/LBA/LBA_CU_C01_V01/ Additional details about the algorithm used to generate these products are described in Hawbaker, T.J., Vanderhoof, M.K., Schmidt, G.L., Beal, Y, Takacs, J.D., Falgout, J.T., Picotte, J.J., and Dwyer, J.L. 2020. The Landsat Burned Area algorithm and products for the conterminous United States. Remote Sensing of Environment, Vol. 244, https://doi.org/10.1016/j.rse.2020.111801