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将统计和深度学习方法进行比较,同时预测热带森林中标准水平的地面和地下生物质

Bao Huy¹, Krishna P Poudel², Hailemariam Temesgen³

¹Forest Resources and Environment Management Consultancy (FREM), 06 Nguyen Hong, Buon Ma Thuot, Dak Lak 630000, Viet Nam; Department of Forest Engineering, Resources and Management, Oregon State University (OSU), Corvallis, OR 97333, USA.

The Science of the total environment

|December 6, 2024

概括

深度学习添加模型 (DLAM) 准确地同时预测热带森林的地面生物量 (AGB),地下生物量 (BGB) 和总生物量 (ABGB). 这种创新方法显著改善了传统的森林生态管理方法.

科学领域:

林业科学林业科学
生态建模生态建模
生物质估计生物质估计

背景情况:

准确的标准水平生物质预测对于热带森林管理和生态系统服务至关重要.
同时预测地表生物量 (AGB),地下生物量 (BGB) 和总生物量 (ABGB),同时保持添加性是具有挑战性的.
现有的方法往往缺乏可靠的森林库存和碳库存评估所需的精度.

研究的目的:

开发和验证深度学习添加模型 (DLAMs),用于热带森林的同时标准级AGB,BGB和ABGB预测.
为了比较DLAM与传统方法的性能,例如加权非线性看似无关回归 (WNSUR) 和多变量自适应回归线 (MARS).
确定影响生物质成分的关键森林状况,生态和环境因素,以提高预测准确度.

主要方法:

开发多输入多输出深度神经网络 (DLAM) 以实现综合生物质预测.
应用因子分析对混合数据的应用,以选择最佳的预测共变量.
与WNSUR和MARS对DLAM进行交叉验证,使用来自越南多样化热带森林的121个地块的数据.

主要成果:

与WNSUR和MARS相比,DLAM在同时预测AGB,BGB和ABGB方面表现出更高的可靠性.
最佳的DLAM实现了较低的平均绝对百分比错误 (MAPE):AGB为6.3%,BGB为4.3%,ABGB为5.3%.
对于 BGB 预测,DLAM 的表现明显优于 WNSUR 和 MARS,而 MAPE 的表现则分别低至 14.0% 和 11.6%.

结论:

DLAM为热带森林生物质组成部分的准确和增量同时预测提供了显著的进步.
该模型的有效性突显了深度学习在生态建模和森林资源评估中的潜力.
实施DLAM可以提高森林生物质估计的精度,这对于有效管理和气候变化减缓工作至关重要.

关键词:

深度学习是一种深度学习.森林中的碳是森林中的碳.同时的添加模型.热带雨林是一个热带雨林.

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