A Comprehensive Review of Land Use and Land Cover Change Based on Knowledge Graph and Bibliometric Analyses
A Comprehensive Review of Land Use and Land Cover Change Based on Knowledge Graph and Bibliometric Analyses
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DOI：
10.3390/land12081573
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发表时间：
2023-08
期刊：
影响因子：
3.9
通讯作者：
Caixia Rong;W. Fu
中科院分区：
文献类型：
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作者：
Caixia Rong;W. Fu
土地利用/土地覆被变化在环境影响评价、自然灾害监测等领域具有重要意义。这项研究通过对过去十年中1432篇论文的分析，采用定量、定性、文献计量学分析和知识图谱技术，旨在评估LULC中深度学习（DL）的演变和现状。重点领域是：（1）对已发表论文的数量和年度引文进行趋势分析;（2）确定主要机构、国家/地区和出版物来源;（3）探索主要机构和国家/地区之间的科学合作;以及（4）审查关键研究主题及其发展趋势。从2013年到2023年，土地利用/土地利用变化中的土地使用权应用大幅增加，中国是主要贡献者。值得注意的是，国际合作，特别是中国和美国之间的合作，有了显著的增长。此外，该研究阐明了在DL应用于LULC时样本数据和模型所面临的挑战，提供了可以指导未来研究方向的见解，以加快这一领域的进展。
Land use and land cover (LULC) changes are of vital significance in fields such as environmental impact assessment and natural disaster monitoring. This study, through an analysis of 1432 papers over the past decade employing quantitative, qualitative, bibliometric analysis, and knowledge graph techniques, aims to assess the evolution and current landscape of deep learning (DL) in LULC. The focus areas are: (1) trend analysis of the number and annual citations of published articles, (2) identification of leading institutions, countries/regions, and publication sources, (3) exploration of scientific collaborations among major institutions and countries/regions, and (4) examination of key research themes and their development trends. From 2013 to 2023 there was a substantial surge in the application of DL in LULC, with China standing out as the principal contributor. Notably, international cooperation, particularly between China and the USA, saw a significant increase. Furthermore, the study elucidates the challenges concerning sample data and models in the application of DL to LULC, providing insights that could guide future research directions to accelerate progress in this domain.