AUTHOR=Jiang Xuetao , Jiang Meiyu , Gou YuChun , Li Qian , Zhou Qingguo 

TITLE=Forestry Digital Twin With Machine Learning in Landsat 7 Data

JOURNAL=Frontiers in Plant Science

VOLUME=Volume 13 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2022.916900

DOI=10.3389/fpls.2022.916900

ISSN=1664-462X

ABSTRACT=Modeling forests using historical data allows for a more accurate evolution analysis, thus providing an important basis for other studies.
Remote sensing plays a vital role As a recognized and effective tool in forestry analysis.
We can use it to obtain information about the forest, including tree type, coverage, and canopy density.
Serveral forest time series modeling studies use statistic values, but only a few using remote sensing images. 
The image prediction digital twin is an implementation of the digital twin, which aims at predicting future images based on historical data.
In this study, we propose an long short-term memory network (LSTM) based digital twin approach.
It use Landsat 7 remote sensing images for forest modeling, where twenty years are used as training and one year as testing.
The experimental results show that this study's prediction twin method can effectively predict the future images of the study area.