Optimizing afforestation and reforestation strategies to enhance ecosystem services in critically degraded regions

  • Fahrudin
  • , Anjar Dimara Sakti*
  • , Hazel Yordan Komara
  • , Elham Sumarga
  • , Achmad Choiruddin
  • , Vempi Satriya Adi Hendrawan
  • , Therissia Hati
  • , Zuzy Anna
  • , Ketut Wikantika
  • *Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)

Abstract

Human activity has caused massive forest ecosystem damage, threatening the global environmental balance. Afforestation and reforestation are crucial strategies for the restoration of forest ecosystem functions. This study was conducted on Belitung Island, Indonesia, which has experienced forest degradation due to mining activity and is currently undergoing forest restoration efforts. This study aimed to identify priority areas for afforestation and reforestation using an innovative approach that integrates multi-criteria analysis (MCA) and machine-learning techniques based on ecosystem service (ES) indicators, wildfire susceptibility, and environmental pressure. This study is the first to combine long-term remote sensing data with machine learning to develop priority scenarios for afforestation and reforestation. Results show that low-priority afforestation areas cover 24,479.66 ha (20.45 %), medium-priority areas 58,703.30 ha (49.04 %), and high-priority areas 36,521.98 ha (30.51 %). For reforestation, low-priority areas cover 23,123.45 ha (30.45 %), medium-priority areas 38,197.36 ha (50.3 %), and high-priority areas 14,618.27 ha (19.25 %). This study is expected to serve as a reference for sustainable forest ecosystem restoration efforts in various regions by leveraging ES approaches and environmental conditions using remote-sensing technology.

Original languageEnglish
Article number100700
JournalTrees, Forests and People
Volume18
DOIs
Publication statusPublished - Dec 2024

Keywords

  • Aforestation reforestation
  • Belitung
  • Ecosystem services
  • Remote-sensing
  • Spatial modeling

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