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RePlant Alfa: Integrating Google Earth Engine and R Coding to Support the Identification of Priority Areas for Ecological Restoration

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  • Narkis S. Morales

    (Departamento de Ecosistemas y Medio Ambiente, Facultad de Agronomía e Ingeniería Forestal, Pontificia Universidad Católica de Chile, Santiago 7820436, Chile)

  • Ignacio C. Fernández

    (Centro de Modelación y Monitoreo de Ecosistemas, Facultad de Ciencias, Ingeniería y Tecnología, Universidad Mayor, Santiago 7500994, Chile)

  • Leonardo P. Durán

    (Escuela de Ingeniería Forestal, Facultad de Ciencias, Ingeniería y Tecnología, Universidad Mayor, Santiago 7500994, Chile
    Hémera Centro de Observación de la Tierra, Facultad de Ciencias, Ingeniería y Tecnología, Universidad Mayor, Santiago 7500994, Chile)

  • Waldo A. Pérez-Martínez

    (Escuela de Ingeniería Forestal, Facultad de Ciencias, Ingeniería y Tecnología, Universidad Mayor, Santiago 7500994, Chile
    Hémera Centro de Observación de la Tierra, Facultad de Ciencias, Ingeniería y Tecnología, Universidad Mayor, Santiago 7500994, Chile)

Abstract

Land degradation and climate change are among the main threats to the sustainability of ecosystems worldwide. As a result, the restoration of degraded landscapes is essential to maintaining the functionality of ecosystems, especially those with greater social, economic, and environmental vulnerability. Nevertheless, policymakers are frequently challenged by deciding where to prioritize restoration actions, which usually includes dealing with multiple and complex needs under an always limited budget. If these decisions are not taken based on proper data and processes, restoration implementation can easily fail. In order to help decision-makers take 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 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.

Suggested Citation

  • Narkis S. Morales & Ignacio C. Fernández & Leonardo P. Durán & Waldo A. Pérez-Martínez, 2023. "RePlant Alfa: Integrating Google Earth Engine and R Coding to Support the Identification of Priority Areas for Ecological Restoration," Land, MDPI, vol. 12(2), pages 1-13, January.
    • Handle: RePEc:gam:jlands:v:12:y:2023:i:2:p:303-:d:1043699
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    2. Lanfredi, Maria & Egidi, Gianluca & Bianchini, Leonardo & Salvati, Luca, 2022. "One size does not fit all: A tale of polycentric development and land degradation in Italy," Ecological Economics, Elsevier, vol. 192(C).
    3. Purushothaman Chirakkuzhyil Abhilash, 2021. "Restoring the Unrestored: Strategies for Restoring Global Land during the UN Decade on Ecosystem Restoration (UN-DER)," Land, MDPI, vol. 10(2), pages 1-19, February.
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    1. Ziyan Li & Libang Ma & Xianfei Chen & Xiang Wang & Jing Bai, 2023. "Zoning and Management of Ecological Restoration from the Perspective of Ecosystem Service Supply and Demand: A Case Study of Yuzhong County in Longzhong Loess Hilly Region, China," Land, MDPI, vol. 12(5), pages 1-21, April.

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