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利用哨兵-2 时间序列和谷歌地球引擎中的机器学习改进高原竹林制图

IF 3.3 4区 地球科学 Q2 ENVIRONMENTAL SCIENCES
Geocarto International Pub Date : 2024-07-01 DOI:10.1080/10106049.2024.2364680
Dagnew Yebeyen, Binyam Tesfaw Hailu, Worku Zewdie, Temesgen Abera, Gudeta W. Sileshi, Melaku Getachew, Sileshi Nemomissa
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引用次数: 0

摘要

在谷歌地球引擎（GEE）云计算平台上应用卫星观测光谱波段和机器学习算法（MLA）的最新进展已经得到了证实。
本文章由计算机程序翻译，如有差异，请以英文原文为准。
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Improved mapping of highland bamboo forests using Sentinel-2 time series and machine learning in Google Earth Engine
Recent advances in the application of spectral bands from satellite observations and machine learning algorithms (MLA) in the Google Earth Engine (GEE) cloud-computing platform have been demonstrat...
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来源期刊
Geocarto International
Geocarto International ENVIRONMENTAL SCIENCES-GEOSCIENCES, MULTIDISCIPLINARY
CiteScore
6.30
自引率
13.20%
发文量
407
审稿时长
>12 weeks
期刊介绍： Geocarto International is a professional academic journal serving the world-wide scientific and user community in the fields of remote sensing, GIS, geoscience and environmental sciences. The journal is designed: to promote multidisciplinary research in and application of remote sensing and GIS in geosciences and environmental sciences; to enhance international exchange of information on new developments and applications in the field of remote sensing and GIS and related disciplines; to foster interest in and understanding of science and applications on remote sensing and GIS technologies; and to encourage the publication of timely papers and research results on remote sensing and GIS applications in geosciences and environmental sciences from the world-wide science community. The journal welcomes contributions on the following: precise, illustrated papers on new developments, technologies and applications of remote sensing; research results in remote sensing, GISciences and related disciplines; Reports on new and innovative applications and projects in these areas; and assessment and evaluation of new remote sensing and GIS equipment, software and hardware.
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