Forestry digital twin with machine learning in Landsat 7 data

TL;DR

This paper proposes an LSTM-based digital twin model for forest analysis using Landsat 7 remote sensing images over 20 years, enabling effective future image prediction for forestry studies.

Contribution

It introduces a novel digital twin approach utilizing LSTM and remote sensing data for forest modeling, which is less explored compared to traditional statistical methods.

Findings

Effective future image prediction demonstrated

LSTM-based model outperforms traditional methods

Applicable to long-term forest monitoring

Abstract

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.