CLRA Alberta is now accepting abstracts for our online webinar series!
Have a great idea for a session? We want to hear it!
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In 2014, the Government of Alberta instated the Roadway Watercourse Crossings Remediation Directive (RWCRD) to address the issue of declining fish populations. Since then, many natural resource companies were unaware of the existence of the RWCRD until watercourse crossings under their tenure were inspected and the company penalized for non-compliance. These opaque regulatory changes, both
provincially and federally, to watercourse crossing managements that are then enforced without industry knowledge, lead to unexpected fines which then impact the environmental budgets of resource road operators. Therefore, to clarify the process, workflow, and considerations to be made regarding watercourse crossings, Woodlands North Inc. presents an optimized regulatory workflow tool for watercourse crossings to simplify the regulatory framework surrounding these projects.
People are familiar with seeing drones flying around taking pictures and video; but, drones can also be used to help combat invasive species populations. During this presentation, we will dive into the different uses of drone technology for invasive species management - this can include both scouting as well as precision spot treatments and introducing competitive species. We will also touch on the current regulatory environment surrounding drone spraying in Canada and when you might be able to expect widespread adoption of the technology near you.
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This presentation will provide a broad overview of the challenges posed by current and future soil preparations. With various testing procedures, an exact model can be completed to determine what source of nutrients will need to be naturally regenerated into the soil. Native planting strategies, encouraging ideas on how to mitigate issues with non-habituated zones for restoration planning will be discussed. Strategies will include diversity in site preparation techniques with the consideration of organic mineral supplementation to the soil organics.
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A presentation celebrating mine reclamation achievement in Ontario.
Itinerary
Research and Reclamation at Detour Lake Mine, presented by Veronika Raizman, Manager, Reclamation & Geochemistry, Kirkland Lake Gold (KL).
The effects of restoration on carbon storage in smelter-impacted industrial barrens, presented by Robyn Rumney, Research Project Manager, Laurentian University/Université Laurentienne.
Constraints on Northern Aggregate Pit Reclamation and Novel Reclamation Strategies for Enhancing Biodiversity and Ecosystem Functioning.
Vegetation health assessment is used in a wide range of environmental assessment programs such as spill clean-up verification and annual vegetation health assessment at risk-managed contaminated sites.
Timely cost-effective assessments using traditional (boots-on-the-ground) field assessment year after year can be difficult (and subjective) due to high variances in vegetation growth rates and species influenced by meteorological conditions, diverse soil types, and topography. This presentation provides a summary of a proof-of-concept demonstration project that uses the power of artificial intelligence to complete vegetation health assessments. The demonstration project exploits the abilities of machine learning computer scripts to independently recognize, identify, sort, and classify complex patterns.
For this initiative, a supervised Machine Learning System (MLS) is trained with a known dataset of vegetation, examples of photos that correspond to vegetation feature classes that require identification and classification. The training dataset is created by subject matter experts who use ground-truth vegetation assessment data to ensure that the training dataset inputted to MLS is accurate. In addition, unsupervised MLS classification analyses are completed and compared against the supervised MLS classification outputs to minimize the introduction of human bias.
MLS scans ultra-high-resolution visible wavelengths of georeferenced air-photos obtained from low altitude Unmanned Aerial Vehicle (UAV) flights to identify and classify the desired vegetation features by matching the vegetation patterns in the UAV photo with the training dataset. MLS is programmed to automatically scan and classify billions of pinpoint locations before it finalizes its prediction. The MLS automation requires little human intervention, enabling it to review air-photo(s) covering areas ranging from thousands of square metres (UAV photo) to millions of square kilometres (satellite photo).
This presentation will also cover how artificial neural network technology can be used to leverage the information from the MLS and site-specific environmental investigation data to solve perplexing real-world problems.
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Vegetation health assessment is used in a wide range of environmental assessment programs such as spill clean-up verification and annual vegetation health assessment at risk-managed contaminated sites.
Timely cost-effective assessments using traditional (boots-on-the-ground) field assessment year after year can be difficult (and subjective) due to high variances in vegetation growth rates and species influenced by meteorological conditions, diverse soil types, and topography. This presentation provides a summary of a proof-of-concept demonstration project that uses the power of artificial intelligence to complete vegetation health assessments. The demonstration project exploits the abilities of machine learning computer scripts to independently recognize, identify, sort, and classify complex patterns.
For this initiative, a supervised Machine Learning System (MLS) is trained with a known dataset of vegetation, examples of photos that correspond to vegetation feature classes that require identification and classification. The training dataset is created by subject matter experts who use ground-truth vegetation assessment data to ensure that the training dataset inputted to MLS is accurate. In addition, unsupervised MLS classification analyses are completed and compared against the supervised MLS classification outputs to minimize the introduction of human bias.
MLS scans ultra-high-resolution visible wavelengths of georeferenced air-photos obtained from low altitude Unmanned Aerial Vehicle (UAV) flights to identify and classify the desired vegetation features by matching the vegetation patterns in the UAV photo with the training dataset. MLS is programmed to automatically scan and classify billions of pinpoint locations before it finalizes its prediction. The MLS automation requires little human intervention, enabling it to review air-photo(s) covering areas ranging from thousands of square metres (UAV photo) to millions of square kilometres (satellite photo).
This presentation will also cover how artificial neural network technology can be used to leverage the information from the MLS and site-specific environmental investigation data to solve perplexing real-world problems.
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During these unprecedented times, the ability to maintain project efficiency and ongoing team collaboration, while continuing to regulate costs can single-handedly determine the longevity of many environmental services organizations.
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The Canadian Ambient Air Quality Standards (CAAQS) are part of a collaborative national Air Quality Management System (AQMS), both of these organizations are working towards continued protection of human health and the environment in Canada. The Canadian Council of Ministers of the Environment (CCME) agreed to new CAAQS which are currently now effective as of 2020.
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To support continued improvements in decision making, we’ll provide an update to our Petroleum Technology Alliance Canada (PTAC) initiative: Review of Phase 2 ESA data from past drilling waste disposal locations to better understand the effectiveness of the Alberta Energy Regulator (AER) document “Assessing Drilling Waste Disposal Areas: Compliance Options for Reclamation Certification” (Compliance Options, AER 2014). North Shore and Waterline collaborated to determine if the Compliance Options: are appropriate as currently written; require adjustment to reduce false positive or negative triggers for Phase II ESAs; or are in need of other changes. Stage 1 (Data Collection), Stage 2 (Data Analysis) and Stage 3 (Draft Report) have been completed.
The intent of this presentation is to provide insight and guidance on improving decision making with respect to evaluating drilling waste disposal risk. We’ll provide an overview of the results and suggested recommendations to make the Drilling Waste Compliance Options more effective.
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