Insights into forest vegetation changes and landscape fragmentation in Southeastern China: From a perspective of spatial coupling and machine learning

被引：3

作者：

Lin, Yuying ^{[1
,2
,3
]}

Jin, Yidong ^{[1
]}

Ge, Yang ^{[1
]}

Hu, Xisheng ^{[5
]}

Weng, Aifang ^{[3
,4
]}

Wen, Linsheng ^{[3
,4
]}

Zhou, Yunrui ^{[3
,4
]}

Li, Baoyin ^{[3
,4
]}

机构：

[1] Fujian Normal Univ, Sch Culture Tourism & Publ Adm, Fuzhou 350117, Peoples R China

[2] Fujian Normal Univ, Postdoctoral Res Stn Ecol, Fuzhou 350117, Peoples R China

[3] Fujian Normal Univ, Sch Geog Sci, Sch Carbon Neutral Future Technol, Fuzhou 350117, Peoples R China

[4] Fujian Normal Univ, Minist Sci & Technol & Fujian Prov, State Key Lab Subtrop Mt Ecol, Fuzhou 350117, Peoples R China

[5] Fujian Agr & Forestry Univ, Coll Transportat & Civil Engn, Fuzhou 350002, Peoples R China

来源：

ECOLOGICAL INDICATORS | 2024年 / 166卷

基金：

中国国家自然科学基金;

关键词：

Forest vegetation changes; Forest landscape fragmentation; Random forest; Landscape pattern; Machine learning algorithm; ECOLOGICAL QUALITY; LEVEL ASSESSMENT; PATTERN ANALYSIS; ROAD NETWORK; FUZHOU CITY; COVER; CONNECTIVITY; AREAS;

D O I：

10.1016/j.ecolind.2024.112479

中图分类号：

X176 [生物多样性保护];

学科分类号：

090705 ;

摘要：

A comprehensive quantification of the relationships between forest vegetation changes and forest landscape fragmentation is urgently required to provide valuable insights for informed forest management decisions. Previous efforts often focused on these two aspects separately, overlooking their intricate relationships, restricts the formulation of precise forest protection and recovery measures. To address this gap, taking Southeast China as a case, this study employed the Google Earth Engine (GEE) platform to calculate the annual Normalized Difference Vegetation Index (NDVI) from 2000 to 2020, a forest fragmentation comprehensive index (FFCI) was constructed to evaluate the static and dynamic forest landscape fragmentation over the study period, then the Theil-Sen and Mann-Kendall, the two-dimensional framework and the bivariate spatial autocorrelation, and a machine learning algorithm (random forest, RF) were employed to explore the forest vegetation change and forest landscape fragmentation, and their spatial coupling relationships and driving patterns, respectively. The results showed that the temporal trend of the forest vegetation can be categorized into three distinct phases, with the pattern of "decreasing-rising-decreasing", 78.2% of the area improved in forest vegetation, while 11.0% degraded. Approximately 6.0% of forest landscape showed a decline in fragmentation, while 9.3% experienced increased fragmentation trends, including 3.8% of forest landscape with moderate to high static fragmentation and 5.5% of forest landscape with low to very low static fragmentation. Throughout the study period, approximately 66.8% of forest landscape remained stability with the improved forest vegetation; however, there were cases (more than 7% of forests) with improved forest vegetation, but experienced severe landscape fragmentation. The RF outcome revealed that the forest vegetation change dynamics were influenced by the multiple factors, including distance to forest edge, distance to road, and elevation; while distance to forest edge emerged as the overwhelming dominant factor driving the forest landscape fragmentation. This research offers significant insights into the intricate interconnections between the forest vegetation changes and the forest landscape fragmentation dynamics.

引用

页数：13