预测新几内亚的森林砍伐和碳损失,使用机器学习和蜂自动机
Christoph Parsch1, Benjamin Wagner2, Jayden E Engert3
1Biodiversity, Macroecology and Biogeography, University of Goettingen, Buesgenweg 1, 37077 Goettingen, Germany.
The Science of the total environment
|March 8, 2025
概括
新几内亚的森林砍伐威胁着生物多样性和碳库存. 机器学习模型预测低地地区的森林砍伐风险很高,需要有针对性的保护工作来保护脆弱的生态系统.
科学领域:
- 生态生态学 生态生态学
- 环境科学 环境科学
- 遥感 遥感 遥感 遥感
背景情况:
- 新几内亚的生态系统具有高度的生物多样性和特有性.
- 土地利用变化对生物多样性和碳库存构成重大威胁.
- 了解森林砍伐的驱动因素和未来的风险对于保护至关重要.
研究的目的:
- 比较印尼新几内亚和巴布亚新几内亚之间的森林砍伐驱动因素.
- 根据各种发展情景,确定高风险的森林砍伐地区.
- 评估森林砍伐对碳池的影响.
主要方法:
- 集成机器学习和蜂自动机用于森林砍伐建模.
- 预测的森林砍伐和估计的碳库存损失四种情景 (4.8%28%森林损失).
- 分析了不同高度和不同地区的森林砍伐风险.
主要成果:
- 在低地地区持续预测高森林砍伐风险.
- 印尼新几内亚的森林砍伐主要在海拔380米以下,在巴布亚新几内亚更高 (海拔750米以下). ) 的情况.
- 土地利用变化可能导致印尼新几内亚的碳损失156918万,巴布亚新几内亚的碳损失2231082万.
结论:
- 机器学习和蜂自动机有效地预测高分辨率的森林砍伐.
- 新几内亚的低地对未来的森林砍伐非常脆弱.
- 优先保护森林砍伐与碳,生态系统和生物多样性保护相冲突的关键地区.
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