利用遥感和深度学习技术了解中国北方详细森林和湿地类型的生态系统服务。

IF 8 2区 环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES
Ye Ma , Yuetong Liu , Jiayao Wang , Zhen Zhen , Fengri Li , Fujuan Feng , Yinghui Zhao
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引用次数: 0

摘要

中国东北地区横跨温带和亚寒带,拥有典型的北方森林和丰富的湿地资源。由于这些特点,该地区对全球气候调节、碳封存和生物多样性保护具有至关重要的意义。虽然现有研究探讨了不同土地覆被类型的生态系统服务(ESs)功能,但对详细的森林和湿地类型的生态系统服务功能进行全面深入的调查至关重要。针对这一不足,本研究结合遥感和深度学习技术,采用轻量级卷积神经网络(CNN)模型和决策树对森林和湿地进行大规模分类。在两个时期(2008 年和 2018 年)对黑龙江省各种森林和湿地类型的生态系统服务进行了评估,包括栖息地质量、碳储量和土壤保持力。利用 Geodetector 工具确定了决定生态系统服务的关键因素。结果表明,2008 年森林类型划分的总体准确度为 0.77,2018 年为 0.78;2008 年湿地类型划分的总体准确度为 0.88,2018 年为 0.87。其中,从 2008 年到 2018 年,阔叶混交林向针阔混交林的过渡主导了森林类型的变化,这可能是由于自然演替所致。在各种森林类型中,蒙古栎森林因其生长迅速和根系深厚而表现出最高的碳储量和土壤保持能力。阔叶混交林的栖息地质量上乘,表明干扰最小。研究发现,栖息地质量、碳储量和土壤保持力分别受到人类活动、大气质量和地形因素的显著影响。通过利用遥感和深度学习方法,本研究对森林和湿地进行了全面分析,阐明了特定森林和湿地类型在生态系统中的细微作用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Understanding ecosystem services of detailed forest and wetland types using remote sensing and deep learning techniques in Northern China
Spanning both temperate and sub-frigid zones, Northeast China boasts typical boreal forests and abundant wetland resources. Because of these attributes, the region is critically significant for global climate regulation, carbon sequestration, and biodiversity preservation. While existing research explores the ecosystem service (ESs) functions of different land cover types, a thoroughly in-depth investigation into the ESs of detailed forest and wetland types is essential. This study addresses this deficiency by combining remote sensing and deep learning techniques, employing a lightweight convolutional neural network (CNN) model and a decision tree for the large-scale classification of forests and wetlands. The ESs of various forest and wetland types—encompassing habitat quality, carbon stock, and soil retention—were assessed during two periods (2008 and 2018) in Heilongjiang Province. Key factors determinants of ESs were identified using the Geodetector tool. The results indicated an overall accuracy of 0.77 in 2008 and 0.78 in 2018 for forest type classification, and 0.88 in 2008 and 0.87 in 2018 for wetland type classification. In particular, the transition from mixed broadleaf forests to mixed coniferous-broadleaf forests dominated changes from 2008 to 2018, probably due to natural succession. Among forest types, Mongolian oak forests exhibited the highest carbon stock and soil retention capacity owing to their rapid growth and deep root systems. Mixed broadleaf forests exhibited superior habitat quality, suggesting minimal disturbance. Habitat quality, carbon stock, and soil retention were found to be significantly influenced by human activity, atmospheric quality, and topographic factors, respectively. By leveraging remote sensing and deep learning methodologies, this study offers a comprehensive analysis of forests and wetlands, elucidating the nuanced ecosystem roles of specific forest and wetland types.
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来源期刊
Journal of Environmental Management
Journal of Environmental Management 环境科学-环境科学
CiteScore
13.70
自引率
5.70%
发文量
2477
审稿时长
84 days
期刊介绍: The Journal of Environmental Management is a journal for the publication of peer reviewed, original research for all aspects of management and the managed use of the environment, both natural and man-made.Critical review articles are also welcome; submission of these is strongly encouraged.
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