Forestry digital twin with machine learning in Landsat 7 data
This provides a tool for forestry analysis by predicting forest evolution, but it is incremental as it applies an existing method (LSTM) to a new domain (remote sensing images for forests).
The paper tackled forest modeling by proposing an LSTM-based digital twin approach using 20 years of Landsat 7 remote sensing images, and the results show it can effectively predict future images of the study area.
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.