Deep neural networks with transfer learning for forest variable estimation using sentinel-2 imagery in boreal forest

Estimation of forest structural variables is essential to provide relevant insights for public and private stakeholders in forestry and environmental sectors. Airborne light detection and ranging (LiDAR) enables accurate forest inventory, but it is expensive for large area analyses. Continuously increasing volume of open Earth Observation (EO) imagery from high-resolution (|BIAS%| = 0.8%). We found 3×3 pixels to be the optimal size for the sampling window, and two to three hidden layer DNNs to produce the best results with relatively small improvement to single hidden layer networks. Including CHM features with S2 data and additional features led to reduced relative RMSE (RMSE% = 28.6–30.7%) but increased the absolute value of relative bias (|BIAS%| = 0.9–4.0%). Transfer learning was found to be beneficial mainly with training data sets containing less than 250 field plots. The performance differences of DNN and random forest models were marginal. Our results contribute to improved structural variable estimation performance in boreal forests with the proposed image sampling and input feature concept

A methodology for implementing a digital twin of the earth’s forests to match the requirements of different user groups

Publisher Copyright: © 2021 GI_Forum.Europe has acknowledged the need to develop a very high precision digital model of the Earth, a Digital Twin Earth, running on cloud infrastructure to bring data and end-users closer together. We present results of an investigation of a proposed submodel of the digital twin, simulating the worlds’ forests. We focus on the architecture of the system and the key user needs on data content and access. The results are based on a user survey showing that the forest-related communities in Europe require information on contrasting forest variables and processes, with common interest in the status and forecast of forest carbon stock. We discuss the required spatial resolution, accuracies, and modelling tools required to match the needs of the different communities in data availability and simulation of the forest ecosystem. This, together with the knowledge on existing and projected future capabilities, allows us to specify a data architecture to implement the proposed system regionally, with the outlook to expand to continental and global scales. Ultimately, a system simulating the behaviour of forests, a digital twin, would connect the bottom-up and top-down approaches of computing the forest carbon balance: from tree-based accounting of forest growth to atmospheric measurements, respectively.Peer reviewe

Deep Neural Networks with Transfer Learning for Forest Variable Estimation Using Sentinel-2 Imagery in Boreal Forest

Estimation of forest structural variables is essential to provide relevant insights for public and private stakeholders in forestry and environmental sectors. Airborne light detection and ranging (LiDAR) enables accurate forest inventory, but it is expensive for large area analyses. Continuously increasing volume of open Earth Observation (EO) imagery from high-resolution (<30 m) satellites together with modern machine learning algorithms provide new prospects for spaceborne large area forest inventory. In this study, we investigated the capability of Sentinel-2 (S2) image and metadata, topography data, and canopy height model (CHM), as well as their combinations, to predict growing stock volume with deep neural networks (DNN) in four forestry districts in Central Finland. We focused on investigating the relevance of different input features, the effect of DNN depth, the amount of training data, and the size of image data sampling window to model prediction performance. We also studied model transfer between different silvicultural districts in Finland, with the objective to minimize the amount of new field data needed. We used forest inventory data provided by the Finnish Forest Centre for model training and performance evaluation. Leaving out CHM features, the model using RGB and NIR bands, the imaging and sun angles, and topography features as additional predictive variables obtained the best plot level accuracy (RMSE% = 42.6%, |BIAS%| = 0.8%). We found 3×3 pixels to be the optimal size for the sampling window, and two to three hidden layer DNNs to produce the best results with relatively small improvement to single hidden layer networks. Including CHM features with S2 data and additional features led to reduced relative RMSE (RMSE% = 28.6–30.7%) but increased the absolute value of relative bias (|BIAS%| = 0.9–4.0%). Transfer learning was found to be beneficial mainly with training data sets containing less than 250 field plots. The performance differences of DNN and random forest models were marginal. Our results contribute to improved structural variable estimation performance in boreal forests with the proposed image sampling and input feature concept