| dc.description.abstract | The availability of cloud computing and open access satellite data provides new opportunities for the application of earth observation (EO) based environmental monitoring. Mapping the environmental change resulting from resource extraction at the source of energy transition supply chains represents a particularly interesting application for these services. The energy transition fosters a high resource demand, and the extraction of resources goes hand in hand with the appropriation and exploitation of land, often conducted with little or no environmental concerns. Using Google Earth Engine (GEE), Landsat and Sentinel imagery, I developed an example of a workflow model to create remote sensing time series to measure land cover and vegetation change. The model is based on scripts using the programming language JavaScript. To test the workflow, it was applied to two case studies, mapping change resulting from graphite and natural gas extraction in Cabo Delgado, Mozambique. The case studies were investigated in time series between 2005 and 2021. The analyses shows that extraction related facilities and infrastructure replace, to a large areal extent, natural areas and cause a significant increase in unvegetated areas. Thereby, the workflow model yielded high classification accuracies of > 90 %. Testing the model in both case studies proved it reproducible and scalable under the requisite that specific parameters, such as the region of interest and time series intervals are provided as inputs to the scripts. The use of open access satellite imagery and the GEE platform make the model applicable in circumstances of low financial and technical means, as data is free and no costly hard- and software is required. Therefore, the proposed workflow can be particularly beneficial to monitor change in areas of resource extraction which commonly occur in vulnerable, low-income regions, where Environmental Impact Assessments (EIAs) and their follow ups are often weak or being neglected. Hence, my workflow model provides a reliable solution to map change at the source of energy transition supply chains in an effort for a more sustainable energy transition. By identifying and quantifying the change, private and non-profit decision makers can develop and enhance plans to preserve, manage and restore adjacent land and nature and protect local communities. | |
