The Ontario Ministry of Natural Resources (OMNR) and Ducks Unlimited Canada (DUC) have been engaged in developing an efficient and accurate methodology for inventorying wetlands. Their progress in this area has demonstrated that Digital Elevation Models (DEMs) are crucial input for wetland identification and boundary delineation. The provincial DEM, however, has known precision limitations in areas of minimal topographic relief that cause considerable mapping error. This study explored whether wetland mapping derived from bare-earth light detection and ranging (LiDAR) data would overcome the limitations of the provincial DEM. An automated wetland mapping approach was applied to the 2 elevation datasets and the results were compared using 2 methods of validation. One hundred aerial-photo-interpreted sample plots were used to quantitatively measure the ability of each source to separate upland from wetland. An overlay of wetland maps created from the 2 DEM sources was then qualitatively assessed to further clarify the magnitude of discrepancy between the 2 mapping sources. The study concluded that LiDAR showed a significant improvement at p = 0.05 over the provincial DEM for mapping wetlands, improving overall mapping accuracy from 76% to 84%. However, an overlay analysis and qualitative assessment showed the magnitude of this reported improvement is greater than was quantified by the accuracy assessment and that an assessment scheme with different sample units may further elucidate this discrepancy.Key words: LiDAR, DEM, wetland, mapping RÉSUMÉLe Ministère des Richesses naturelles de l'Ontario (MRNO) et Canards illimités Canada (CIC) ont entrepris le développe-ment d'une méthodologie efficace et précise d'inventaire des milieux humides. Les progrès enregistrés dans ce domaine indiquent que les modèles de numérisation de l'altitude (MNA) constituent un élément clé de l'identification des milieux humides et de leur cartographie. Le MNA provincial, cependant, a révélé des limites dans la précision des zones à relief peu accidenté qui ont entraîné d'importantes erreurs cartographiques. Cette étude a cherché à savoir si la cartographie des milieux humides dérivée des données de détection de la lumière et de calcul de la distance (LiDAR) sur zone dénudée pouvait permettre de pallier les limites du MNA provincial. Une approche automatisée de cartographie des milieux humides a été utilisée pour deux ensembles de données altimétriques et les résultats ont été comparés au moyen de deux méthodes de validation. Cent parcelles-échantillons interprétées à partir de photos aériennes ont été utilisées pour mesurer quantativement la capacité de chaque source à séparer les zones sèches des milieux humides. Une superposition des cartes de milieux humides produites à partir de deux sources de MNA a été par la suite évaluée qualitativement pour identifier plus en détail l'importance des différences entre les deux sources de cartographie. L' étude permet de conclure que le LiDAR a démontré une amélioration significative, p =. ...
Habitat loss and fragmentation are major drivers of global pollinator declines, yet even after recent unprecedented periods of anthropogenic land-use intensification the amount of habitat needed to support pollinators remains unknown. Here we use comprehensive datasets to determine the extent and amount of habitat needed. Safeguarding wild bee communities in a Canadian landscape requires 11.6-16.7% land-cover from a diverse range of habitats (~1.8-3.6x current policy guidelines), irrespective of whether conservation aims are enhancing species richness or abundance. Sensitive habitats, like tallgrass woodlands and wetlands, were important predictors of bee biodiversity. Conservation strategies that under-estimate the extent of habitat, spatial scale and specific habitat needs of functional guilds are unlikely to protect bee communities and the essential pollination services they provide to crops and wild plants.
Canada's forests have frequently been characterized using binary classifications such as intact/non-intact or managed/unmanaged. A more nuanced classification approach is needed to better understand the geography of forest management in Canada. The best way to represent Canada's complex diversity of forest management regimes with a simple classification is to categorize according to ownership, protection status and tenure. We gathered federal, provincial and territorial geospatial datasets and used a binary decision tree approach in GIS to classify land into nine classes: (i) Protected, (ii) Restricted, (iii) Federal Reserve, (iv) Indian Reserve, (v) Treaty/Settlement, (vi) Private, (vii) Long-Term Tenure, (viii) Short-Term Tenure, and (ix) Other. These classes are broad; management intensity may vary considerably within classes. Not all forests in Long-Term Tenure or Short-Term Tenure areas are available for timber supply. Government regulations establish considerable reserve areas within forest management units where harvesting is not permitted. The resulting map dataset is current to 2017 and will need to be updated as land designations change.
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