  Modeling forests using historical data allows for more accurately evolution
analysis, thus providing an important basis for other studies. As a recognized
and effective tool, remote sensing plays an important role in forestry
analysis. We can use it to derive information about the forest, including tree
type, coverage and canopy density. There are many forest time series modeling
studies using statistic values, but few using remote sensing images. Image
prediction digital twin is an implementation of digital twin, which aims to
predict future images bases on historical data. In this paper, we propose an
LSTM-based digital twin approach for forest modeling, using Landsat 7 remote
sensing image within 20 years. The experimental results show that the
prediction twin method in this paper can effectively predict the future images
of study area.
