Time-series simulation of alpine grassland cover using transferable stacking deep learning and multisource remote sensing data in the Google Earth Engine

Time-series simulation of alpine grassland cover using transferable stacking deep learning and multisource remote sensing data in the Google Earth Engine

The growth of vegetation on the Qinghai Tibet Plateau (QTP) is experiencing significant changes due to climate change. There is still a lack of high-precision simulation methods for alpine grassland cover (AGC), and the climate feedback mechanisms of AGC remain unclear, which poses challenges for th...

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Bibliographic Details
Main Authors: Xingchen Lin, Jianjun Chen, Tonghua Wu, Shuhua Yi, Jie Chen, Xiaowen Han
Format: Article
Language:English
Published: Elsevier 2024-07-01
Series:International Journal of Applied Earth Observations and Geoinformation
Subjects:
Alpine grassland cover
Stacking deep learning (Stacking-DL)
MODIS
Unmanned aerial vehicle (UAV)
Google Earth Engine
Yellow River source area (YRSR)
Online Access:http://www.sciencedirect.com/science/article/pii/S1569843224003182

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http://www.sciencedirect.com/science/article/pii/S1569843224003182

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