Open AccessExtending deep learning approaches for forest disturbance segmentation on very high‐resolution satellite images
Author(s) -
Kislov Dmitry E.,
Korznikov Kirill A.,
Altman Jan,
Vozmishcheva Anna S.,
Krestov Pavel V.
Publication year - 2021
Publication title -
remote sensing in ecology and conservation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.191
H-Index - 21
ISSN - 2056-3485
DOI - 10.1002/rse2.194
Subject(s) - convolutional neural network , deep learning , windthrow , artificial intelligence , computer science , random forest , satellite imagery , remote sensing , adaboost , pattern recognition (psychology) , support vector machine , machine learning , environmental science , geography , forestry
Accurate remote detection of various forest disturbances is a challenge in global environmental monitoring. Addressing this issue is crucial for forest health assessment, planning salvage logging operations, modeling stand dynamics, and estimating forest carbon stocks and uptake. Substantial progress on this problem has been achieved owing to the rapid development of remote sensing devices that provide very high‐resolution images. Concurrently, image processing algorithms have witnessed rapid development owing to the extensive use of artificial neural networks with complex architectures and deep learning approaches. This opens new opportunities and perspectives for applying deep learning methods to solving various problems in environmental sciences. In this study, we used deep convolutional neural networks (DCNNs) to recognize forest damage induced by windthrows and bark beetles. We used satellite imagery of very high resolution in visual spectra represented as pansharpened images (RGB channels). When predicting forest damage, we obtained accuracies higher than 90% on test data for recognition of both windthrow areas and damaged trees impacted by bark beetles. A comparative analysis indicated that the DCNN‐based approach outperforms traditional pixel‐based classification methods (AdaBoost, random forest, support vector machine, quadratic discrimination) by at least several percentage points. DCNNs can learn a specific pattern of the area of interest and thus yield fewer false positive decisions than pixel‐based algorithms. The ability of DCNNs to generalize makes them a good tool for delineating smooth and ill‐defined boundaries of damaged forest areas, such as windthrow patches.