Integration of generative artificial intelligence and Google Earth Engine for mangrove land cover mapping

¹ Department of Fisheries Resources Utilization, Faculty of Fisheries and Marine Science, University of Riau, Kampus Bina Widya, km. 12.5, Simpang Panam, Pekanbaru 28293, Indonesia
² Mangrove Research Institute, Perumahan OPV, Jalan Sekuntum Raya Blok Orchid No 3 Kelurahan Delima, Kecamatan Binawidya, Pekanbaru, Riau, Indonesia
³ Departement of Forestry, Faculty of Agriculture, University of Riau, Kampus Bina Widya, km. 12.5, Simpang Panam, Pekanbaru 28293, Indonesia
⁴ Departement of Aquatic Resources Management, Faculty of Fisheries and Marine Science, University of Riau, Kampus Bina Widya, km. 12.5, Simpang Panam, Pekanbaru 28293, Indonesia
⁵ Departemen of Biology Education, Universitas Riau Kepulauan, Jl. Pahlawan No.99, Bukit Tempayan, Kec. Batu Aji, Batam, Kepulauan Riau Province, 29425, Indonesia
⁶ Doctor of Environmental Science, Postgraduate Program, Universitas Riau
⁷ Abata Karya Nusa Consultant. Komplek Nangkasari 3rd Floor Blok D No. 7, Jalan Tuanku Tambusai Pekanbaru, Indonesia, +62 761 571013

^* Corresponding author: romie.jhonnerie@lecturer.unri.ac.id

Abstract

Mangrove ecosystems, crucial for coastal sustainability, are threatened by human activities, underscoring the need for accurate mapping for effective conservation. This research explores the novel integration of generative artificial intelligence, specifically Microsoft Copilot, with Google Earth Engine (GEE) for mapping mangrove land cover in Kembung River, Bengkalis Island, Indonesia. The methodology leverages Copilot’s natural language processing capabilities to generate GEE JavaScript code, streamlining the process of Sentinel-2 imagery processing and land cover classification using the Random Forest algorithm. Copilot assists in automating complex coding tasks, reducing development time and potential human errors. However, challenges emerge in hyperparameter tuning within GEE’s computational constraints. The results demonstrate an overall accuracy of 84.4% (Kappa = 0.794) in identifying nine land cover classes, with mangroves covering 46.6% of the study area. This innovative approach enhances mangrove mapping efficiency and accuracy, paving the way for improved monitoring and conservation. The study also highlights the potential of AI in environmental science applications, particularly in conservation. Future research should optimize Copilot’s performance for advanced geospatial tasks, address spectral variability challenges, and explore its applicability across diverse ecosystems. This study contributes to mangrove conservation efforts and demonstrates the potential of AI-assisted coding in environmental science applications.