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Intertidal vegetation provides important ecological functions, such as food and shelter for wildlife and ecological services with increased coastline protection from erosion. In cold temperate and subarctic environments, the short growing season has a significant impact on the phenological response of the different vegetation types, which must be considered for their mapping using satellite remote sensing technologies. This study focuses on the effect of the phenology of vegetation in the intertidal ecosystems on remote sensing outputs. The studied sites were dominated by eelgrass (Zostera marina L.), saltmarsh cordgrass (Spartina alterniflora), creeping saltbush (Atriplex prostrata), macroalgae (Ascophyllum nodosum, and Fucus vesiculosus) attached to scattered boulders. In situ data were collected on ten occasions from May through October 2019 and included biophysical properties (e.g., leaf area index) and hyperspectral reflectance spectra (Rrs(λ)). The results indicate that even when substantial vegetation growth is observed, the variation in Rrs(λ) is not significant at the beginning of the growing season, limiting the spectral separability using multispectral imagery. The spectral separability between vegetation types was maximum at the beginning of the season (early June) when the vegetation had not reached its maximum growth. Seasonal time series of the normalized difference vegetation index (NDVI) values were derived from multispectral sensors (Sentinel-2 multispectral instrument (MSI) and PlanetScope) and were validated using in situ-derived NDVI. The results indicate that the phenology of intertidal vegetation can be monitored by satellite if the number of observations obtained at a low tide is sufficient, which helps to discriminate plant species and, therefore, the mapping of vegetation. The optimal period for vegetation mapping was September for the study area.

Background Chronic conditions such as diabetes and chronic obstructive pulmonary disease (COPD) are common and burdensome diseases primarily managed in primary care. Yet, evidence points to suboptimal quality of care for these conditions in primary care settings. Quality improvement collaboratives (QICs) are organized, multifaceted interventions that can be effective in improving chronic disease care processes and outcomes. In Quebec, Canada, the Institut national d’excellence en santé et en services sociaux (INESSS) has developed a large-scale QIC province-wide program called COMPAS+ that aims to improve the prevention and management of chronic diseases in primary care. This paper describes the protocol for our study, which aims to evaluate implementation and impact of COMPAS+ QICs on the prevention and management of targeted chronic diseases like diabetes and COPD. Methods This is a mixed-methods, integrated knowledge translation study. The quantitative component involves a controlled interrupted time series involving nine large integrated health centres in the province. Study sites will receive one of two interventions: the multifaceted COMPAS+ intervention (experimental condition) or a feedback only intervention (control condition). For the qualitative component, a multiple case study approach will be used to achieve an in-depth understanding of individual, team, organizational and contextual factors influencing implementation and effectiveness of the COMPAS+ QICs. Discussion COMPAS+ is a QI program that is unique in Canada due to its integration within the governance of the Quebec healthcare system and its capacity to reach many primary care providers and people living with chronic diseases across the province. We anticipate that this study will address several important gaps in knowledge related to large-scale QIC projects and generate strong and useful evidence (e.g., on leadership, organizational capacity, patient involvement, and implementation) having the potential to influence the design and optimisation of future QICs in Canada and internationally.