Improved LSTM Model for Boreal Forest Height Mapping Using Sentinel-1 Time Series

Abstract

Time series of SAR imagery combined with reference ground data can be suitable for producing forest inventories. Copernicus Sentinel-1 imagery is particularly interesting for forest mapping because of its free availability to data users; however, temporal dependencies within SAR time series that can potentially improve mapping accuracy are rarely explored. In this study, we introduce a novel semi-supervised Long Short-Term Memory (LSTM) model, CrsHelix-LSTM, and demonstrate its utility for predicting forest tree height using time series of Sentinel-1 images. The model brings three important modifications to the conventional LSTM model. Firstly, it uses a Helix-Elapse (HE) projection to capture the relationship between forest temporal patterns and Sentinel-1 time series, when time intervals between datatakes are irregular. A skip-link based LSTM block is introduced and a novel backbone network, Helix-LSTM, is proposed to retrieve temporal features at different receptive scales. Finally, a novel semisupervised strategy, Cross-Pseudo Regression, is employed to achieve better model performance when reference training data are limited. CrsHelix-LSTM model is demonstrated over a representative boreal forest site located in Central Finland. A time series of 96 Sentinel-1 images are used in the study. The developed model is compared with basic LSTM model, attention-based bidirectional LSTM and several other established regression approaches used in forest variable mapping, demonstrating consistent improvement of forest height prediction accuracy. At best, the achieved accuracy of forest height mapping was 28.3% relative root mean squared error (rRMSE) for pixel-level predictions and 18.0% rRMSE on stand level. We expect that the developed model can also be used for modeling relationships between other forest variables and satellite image time series.

Deep Learning Models in Forest Mapping Using Multitemporal SAR and Optical Satellite Data

“…Regarding coupling of ALS data with satellite EO data in our pretraining in Lapland, our results are in line with other similar studies [41]. Use of recurrent and fully convolutional neural networks with fully segmented labels and Sentinel-1 time series or combined SAR and optical data provided accuracies on the order of 17-30% rRMSE that are similar to results in our work over pretraining site in Lapland [15], [16], [20], [42]. Inversion of TanDEM-X images acquired over Estonian hemiboreal and Canadian boreal forests provided accuracies with RMSE in range of 3-4 m and correlation coefficients R 2 larger than 0.5 [40], [43]- [46].…”

Current Status and De Novo Synthesis of Anti-Tumor Alkaloids in Nicotiana: A Mini-Review

“…Second, the tanh layer generates new data Xt. They can be included in the state of the cell [43,[49][50].…”

Assessing the Feasibility of Wave Energy Harvesting through Coastal Wave Modeling and Forecasting with Long Short-Term Memory Optimization using Univariate Time Series Approach

“…LSTM (Long Short-Term Memory) is a type of recurrent neural network (RNN) designed explicitly for processing sequential data, such as time series. LSTM has been shown to be very effective in univariate time series forecasting and, in many cases, outperforms traditional statistical methods such as ARIMA and exponential smoothing [43,[49][50][51].…”

“…The power of LSTM in univariate time series forecasting lies in its ability to capture complex patterns and relationships within the data [50]. Unlike traditional statistical methods, LSTM can capture non-linear dependencies and long-term dependencies within the data.…”