Impact of Preprocessing on Tree Canopy Cover Modelling: Does Gap-Filling of Landsat Time Series Improve Modelling Accuracy?

Impact of Preprocessing on Tree Canopy Cover Modelling: Does Gap-Filling of Landsat Time Series Improve Modelling Accuracy?

Preprocessing of Landsat images is a double-edged sword, transforming the raw data into a useful format but potentially introducing unwanted values with unnecessary steps. Through recovering missing data of satellite images in time series analysis, gap-filling is an important, highly developed, prep...

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Bibliographic Details
Main Authors: Zhipeng Tang, Hari Adhikari, Petri K. E. Pellikka, Janne Heiskanen
Format: Article
Language:English
Published: Frontiers Media S.A. 2022-07-01
Series:Frontiers in Remote Sensing
Subjects:
tropical forest
seasonal composite
machine learning
spectral-temporal metrics
random forest regression
harmonic regression
Online Access:https://www.frontiersin.org/articles/10.3389/frsen.2022.936194/full

Internet

https://www.frontiersin.org/articles/10.3389/frsen.2022.936194/full

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