The land cover change modeling based Artificial Neural Network (ANN) of mangroves in Teluk Bintuni Regency

Awalludin Ramdhan; Obed N Lense; Renny Purnawaty

doi:10.35724/ag.v15i1.6572

Awalludin Ramdhan Universitas Negeri Papua
Obed N Lense Universitas Negeri Papua
Renny Purnawaty Universitas Negeri Papua

DOI: https://doi.org/10.35724/ag.v15i1.6572

Keywords: Artificial Neural Network, Mangrove, Carbone storage, Land cover change modeling

Abstract

This study discusses mangrove forest land cover change in Teluk Bintuni Regency using Artificial Neural Network (ANN) modeling. Bintuni Bay has one of the largest mangrove ecosystems in the world, covering more than 220,000 hectares, making it critical for carbon storage and climate change mitigation. This study used geospatial data from the Indonesian Ministry of Environment and Forestry, combined with drivers such as distance from roads, settlements, rivers, and topography. The ANN model achieved high accuracy with a kappa index of 0.94 and an overall accuracy of 95.85%. The model predicted changes in mangrove cover between 2022 and 2030. The area of primary mangrove forest is projected to decrease by 10.29%, while secondary mangrove forest increases by 5.36%. The analysis reveals alarming impacts on carbon stocks. Total carbon stocks are expected to decrease by 6.51% or by 2,206,344.54 Mg C from 2022 to 2030. These findings highlight the need for targeted conservation strategies in the Bintuni Bay mangrove ecosystem. Projected changes not only affect carbon storage but also impact biodiversity, habitat quality, and ecosystem services, emphasizing the importance of effective management practices to sustain these globally important mangrove forests.

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