The VLI identifies and delineates areas of visual sensitivity near communities and along travel corridors throughout the province. It includes information about the visual condition, characteristics and sensitivity to alteration. It also houses scenic area and established Visual Quality Objective ( VQO ) attributes.

A direction one looks from a viewpoint towards a visual landscape. When a view is panoramic, it is to the middle of that panoramic view

Vegetative or non-vegetative objects alongside major roads and highways preventing passers by from seeing the surrounding landscape

Cultural heritage of the revised urban and development plan of the City of Laval**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

Well capture zones are intended to identify potential areas of risk to aquifers where the release of contaminants could affect the water quality of community wells. The information was compiled as a discreet project under the 'Yukon Water Strategy' and represents a 'snapshot in time' of the Drinking Water Systems. Well capture zones were identified using a combination of buffers, analytical methods, and groundwater flow modelling using the Waterloo Hydrogeologic Inc. Visual MODFLOW.Distributed from [GeoYukon](https://yukon.ca/geoyukon) by the [Government of Yukon](https://yukon.ca/maps) . Discover more digital map data and interactive maps from Yukon's digital map data collection.For more information: [geomatics.help@yukon.ca](mailto:geomatics.help@yukon.ca)

This dataset contains observations of species occurrences from seafloor imagery collected by the autonomous underwater vehicle (AUV) during the 2012 Expedition to Cobb Seamount. The National Oceanographic and Atmospheric Administration-operated SeaBED-class AUV which collected photographic images from 4 transects ranging from 436 m to 1154 m in depth.

This dataset contains observations of species occurrences from seafloor imagery collected by the remotely operated underwater vehicle (ROV) during the 2012 Expedition to Cobb Seamount. The ROV operated by Fisheries and Oceans Canada was a customized Deep Ocean Engineering Phantom HD2+2 which collected photographic images from 12 transects ranging from 35 m to 211 m in depth.

This dataset was created to give a regional overview of Yukon and the surrounding jurisdictions for visual representation only. The borders are not intended to provide a legal representation of the Yukon border. The data is referenced at approximately 1:1,000,000.Distributed from [GeoYukon](https://yukon.ca/geoyukon) by the [Government of Yukon](https://yukon.ca/maps). Discover more digital map data and interactive maps from Yukon's digital map data collection.For more information: [geomatics.help@yukon.ca](mailto:geomatics.help@yukon.ca)

The 2010 AAFC Land Use is a culmination and curated metaanalysis of several high-quality spatial datasets produced between 1990 and 2021 using a variety of methods by teams of researchers as techniques and capabilities have evolved. The information from the input datasets was consolidated and embedded within each 30m x 30m pixel to create consolidated pixel histories, resulting in thousands of unique combinations of evidence ready for careful consideration. Informed by many sources of high-quality evidence and visual observation of imagery in Google Earth, we apply an incremental strategy to develop a coherent best current understanding of what has happened in each pixel through the time series.

The 2005 AAFC Land Use is a culmination and curated metaanalysis of several high-quality spatial datasets produced between 1990 and 2021 using a variety of methods by teams of researchers as techniques and capabilities have evolved. The information from the input datasets was consolidated and embedded within each 30m x 30m pixel to create consolidated pixel histories, resulting in thousands of unique combinations of evidence ready for careful consideration. Informed by many sources of high-quality evidence and visual observation of imagery in Google Earth, we apply an incremental strategy to develop a coherent best current understanding of what has happened in each pixel through the time series.