Open Data Science Europe Workshop 2021

Environmental Impact Assessments (EIA) aim at assessing a project’s impacts on the environment, including biodiversity, and proposing mitigation measures to tackle negative impacts. However, these assessments often underestimate the impacts on biodiversity due to lack of continuous biological information covering the whole targeted area. Knowing where a species occurs, and the distribution of biologically diverse and unique areas are key in successfully assessing the impacts of infrastructure projects on biodiversity, and proposing adequate biodiversity management plans; however, collecting biological information is time-consuming and costly. A powerful tool to overcome the challenges of lacking continuous biodiversity information over larger areas is to combine existing biological field-data with satellite images, such as Landsat. Satellite images provide continuous spectral information, which have already been used to model the distribution of species and predict biodiversity patterns over large areas. Therefore, the combination of available field-data, freely available satellite imagery and machine learning offers a cost-effective way to map continuous biodiversity patterns for EIAs. By combining biological data with remote sensing layers, it is possible to predict biodiversity patterns in areas far beyond the location of the field-data, nevertheless such predictions are only reliable within the area of applicability. Even though this approach has been used in different regions, it has not been used for Environmental Impact Assessments (EIAs) at local scales. Having maps of biodiversity patterns, such as beta-diversity, throughout a project’s study area allow: (i) properly quantifying a project footprint’s impact on biodiversity and identifying suitable areas for “like-for-like” compensation schemes.