文献类型：期刊文献

英文题名：Research Progress and Hotspot Evolution in Remote Sensing Monitoring of Mangrove Forests: A CiteSpace-Based Analysis

作者：Liu, Yonghua[1];Zhang, Qi[2];Liu, Dazhao[2,3]

机构：[1]Guangdong Ocean Univ, Univ Lib, Zhanjiang 524088, Peoples R China;[2]Guangdong Ocean Univ, Coll Elect & Informat Engn, Zhanjiang 524088, Peoples R China;[3]Guangdong Engn Technol Res Ctr Ocean Remote Sensin, Zhanjiang 524088, Peoples R China

年份：2026

卷号：17

期号：8

外文期刊名：FORESTS

收录：SCI-EXPANDED(收录号：WOS:001860017000001)、、EI(收录号：20263521370493)、Scopus(收录号：2-s2.0-105048233576)、WOS

语种：英文

外文关键词：coastal wetlands; knowledge mapping; publication trends; author collaboration; keyword bursts; blue carbon; biomass estimation; machine learning

外文摘要：Under the combined impacts of climate change and intensified human activities in coastal zones, mangrove ecosystems are increasingly exposed to degradation, fragmentation, and declines in ecological functions. It is therefore important to systematically examine the progress and evolution of the research hotspots in remote sensing monitoring of mangroves. In this study, 942 publications on mangrove remote sensing monitoring from 2000 to 2025 were retrieved from the China National Knowledge Infrastructure (CNKI) and the Web of Science Core Collection, comprising 485 CNKI records and 457 Web of Science records. CiteSpace 6.4.R2 was used to conduct bibliometric and knowledge-mapping analyses of publication trends, geographic distribution, author collaboration networks, keyword co-occurrence, keyword cluster timelines, and burst keywords. The results show that research on mangrove remote sensing monitoring generally increased over time, with marked growth after 2015. Research topics gradually shifted from early studies on mangrove distribution mapping, land-use change, and image classification to multi-source remote sensing applications, change detection, biomass estimation, blue carbon assessment, and machine learning- and deep learning-based methods. Author collaboration networks provide a descriptive overview of collaboration patterns and suggest that cross-team and cross-regional collaboration still needs to be strengthened. Keyword co-occurrence, cluster timeline, and burst keyword results indicate that remote sensing monitoring, machine learning, deep learning, random forest, support vector machine, object-based image analysis, ALOS PALSAR, ALOS-2 PALSAR-2, blue carbon, carbon stock, aboveground biomass, ecosystem services, and forest degradation are important themes in this field. Future research should further strengthen multi-source remote sensing data integration, cross-regional validation of intelligent algorithms, degradation monitoring indicator systems, uncertainty assessment, and long-term time-series analysis. These efforts will improve the accuracy, comparability, and management applicability of mangrove remote sensing monitoring and provide scientific support for coastal ecological conservation, mangrove restoration, and blue carbon governance.