Understanding ecosystem services of detailed forest and wetland types using remote sensing and deep learning techniques in Northern China

被引：3

作者：

Ma, Ye ^{[1
,2
,4
]}

Liu, Yuetong ^{[1
,2
]}

Wang, Jiayao ^{[1
,2
]}

Zhen, Zhen ^{[1
]}

Li, Fengri ^{[1
]}

Feng, Fujuan ^{[2
,3
]}

Zhao, Yinghui ^{[1
,2
]}

机构：

[1] Northeast Forestry Univ, Sch Forestry, Key Lab Sustainable Forest Ecosyst Management, Minist Educ, Harbin 150040, Peoples R China

[2] Northeast Forestry Univ, Northeast Asia Biodivers Res Ctr, 26 Hexing Rd, Harbin 150040, Peoples R China

[3] Northeast Forestry Univ, Coll Life Sci, 26 Hexing Rd, Harbin 150040, Peoples R China

[4] Harbin Normal Univ, Coll Geog Sci, Heilongjiang Prov Key Lab Geog Environm Monitoring, Harbin 150025, Peoples R China

来源：

JOURNAL OF ENVIRONMENTAL MANAGEMENT | 2024年 / 372卷

关键词：

Forest and wetland classification; Ecosystem services; Remote sensing; Deep learning; NET PRIMARY PRODUCTIVITY; HABITAT QUALITY; LAND-USE; MODEL; URBANIZATION; WUHAN; BASIN;

D O I：

10.1016/j.jenvman.2024.123410

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

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 largescale 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.

引用

页数：12

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