Morales, N.S.; Fernández, I.C.; Durán, L.P.; Pérez-Martínez, W.A. RePlant Alfa: Integrating Google Earth Engine and R Coding to Support the Identification of Priority Areas for Ecological Restoration. Land 2023, 12, 303. https://doi.org/10.3390/land12020303
Morales, N.S.; Fernández, I.C.; Durán, L.P.; Pérez-Martínez, W.A. RePlant Alfa: Integrating Google Earth Engine and R Coding to Support the Identification of Priority Areas for Ecological Restoration. Land 2023, 12, 303. https://doi.org/10.3390/land12020303
Morales, N.S.; Fernández, I.C.; Durán, L.P.; Pérez-Martínez, W.A. RePlant Alfa: Integrating Google Earth Engine and R Coding to Support the Identification of Priority Areas for Ecological Restoration. Land 2023, 12, 303. https://doi.org/10.3390/land12020303
Morales, N.S.; Fernández, I.C.; Durán, L.P.; Pérez-Martínez, W.A. RePlant Alfa: Integrating Google Earth Engine and R Coding to Support the Identification of Priority Areas for Ecological Restoration. Land 2023, 12, 303. https://doi.org/10.3390/land12020303
Abstract
Land degradation and climate change are among the main threats to the sustainability of ecosystems worldwide. Therefore, the restoration of degraded landscapes is essential to maintain the functionality of ecosystems, especially those with greater social, economic and environmental vulnerability. Nevertheless, policy-makers are frequently challenged by deciding on where to prioritize restoration actions, which usually includes to deal with multiple and complex needs under an always short budget. If these decisions are not taken based on proper data and processes, restoration implementation can easily fail. To help decision-makers taking informed decisions on where to implement restoration activities, we have developed a semiautomatic geospatial platform to prioritize areas for restoration activities based on ecological, social and economic variables. This platform takes advantage of the potential to integrate R coding, Google Earth Engine cloud computing and GIS visualization services to generate an interactive geospatial decision-maker tool for restoration. Here, we present a prototype version called "RePlant alpha" which was tested with data from the Central Zone of Chile. This exercise proved that integrating R and GEE was feasible, and that the analysis, with at least six indicators and for a specific region was also feasible to implement even from a personal computer. Therefore, the use of a virtual machine in the cloud with a large number of indicators over large areas is both possible and practical.
Keywords
Google Earth Engine; R coding; GIS, Restoration, Decision-Making
Subject
Environmental and Earth Sciences, Environmental Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
