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草地景观中的土地覆被变化：将增强型大地遥感卫星数据组成、LandTrendr 和谷歌地球引擎中的机器学习分类与 MLP-ANN 场景预测相结合

IF 6 2区 地球科学 Q1 GEOGRAPHY, PHYSICAL
GIScience & Remote Sensing Pub Date : 2024-01-16 DOI:10.1080/15481603.2024.2302221
Cecilia Parracciani, Daniela Gigante, Onisimo Mutanga, Stefania Bonafoni, Marco Vizzari
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引用次数: 0

摘要

了解草原生境在空间和时间上的动态对于评估保护措施的有效性和制定可持续的管理方法至关重要，特别是在草原生态系统中。
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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Land cover changes in grassland landscapes: combining enhanced Landsat data composition, LandTrendr, and machine learning classification in google earth engine with MLP-ANN scenario forecasting
Understanding grassland habitat dynamics in space and time is crucial for evaluating the effectiveness of protection measures and developing sustainable management practices, specifically within th...
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来源期刊
GIScience & Remote Sensing
GIScience & Remote Sensing 地学-遥感
CiteScore
11.20
自引率
9.00%
发文量
84
审稿时长
6 months
期刊介绍： GIScience & Remote Sensing publishes original, peer-reviewed articles associated with geographic information systems (GIS), remote sensing of the environment (including digital image processing), geocomputation, spatial data mining, and geographic environmental modelling. Papers reflecting both basic and applied research are published.
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