Forest Damage Assessment Using Deep Learning on High Resolution Remote Sensing Data

• Main Authors: Zayd Mahmoud Hamdi, Melanie Brandmeier, Christoph Straub
• Format: Article
• Language: English
• Published: MDPI AG 2019-08-01
• Series: Remote Sensing
• Subjects: forest damage assessment; windthrow; convolutional neural networks; GIS; remote sensing
• Online Access: https://www.mdpi.com/2072-4292/11/17/1976

Storms can cause significant damage to forest areas, affecting biodiversity and infrastructure and leading to economic loss. Thus, rapid detection and mapping of windthrows are crucially important for forest management. Recent advances in computer vision have led to highly-accurate image classification algorithms such as Convolutional Neural Network (CNN) architectures. In this study, we tested and implemented an algorithm based on CNNs in an ArcGIS environment for automatic detection and mapping of damaged areas. The algorithm was trained and tested on a forest area in Bavaria, Germany. . It is a based on a modified U-Net architecture that was optimized for the pixelwise classification of multispectral aerial remote sensing data. The neural network was trained on labeled damaged areas from after-storm aerial orthophotos of a ca. <inline-formula> <math display="inline"> <semantics> <mrow> <mn>109</mn> <mi>k</mi> <msup> <mi>m</mi> <mn>2</mn> </msup> </mrow> </semantics> </math> </inline-formula> forest area with RGB and NIR bands and 0.2-m spatial resolution. Around <inline-formula> <math display="inline"> <semantics> <msup> <mn>10</mn> <mn>7</mn> </msup> </semantics> </math> </inline-formula> pixels of labeled data were used in the process. Once the network is trained, predictions on further datasets can be computed within seconds, depending on the size of the input raster and the computational power used. The overall accuracy on our test dataset was <inline-formula> <math display="inline"> <semantics> <mrow> <mn>92</mn> <mo>%</mo> </mrow> </semantics> </math> </inline-formula>. During visual validation, labeling errors were found in the reference data that somewhat biased the results because the algorithm in some instance performed better than the human labeling procedure, while missing areas affected by shadows. Our results are very good in terms of precision, and the methods introduced in this paper have several additional advantages compared to traditional methods: CNNs automatically detect high- and low-level features in the data, leading to high classification accuracies, while only one after-storm image is needed in comparison to two images for approaches based on change detection. Furthermore, flight parameters do not affect the results in the same way as for approaches that require DSMs and DTMs as the classification is only based on the image data themselves, and errors occurring in the computation of DSMs and DTMs do not affect the results with respect to the z component. The integration into the ArcGIS Platform allows a streamlined workflow for forest management, as the results can be accessed by mobile devices in the field to allow for high-accuracy ground-truthing and additional mapping that can be synchronized back into the database. Our results and the provided automatic workflow highlight the potential of deep learning on high-resolution imagery and GIS for fast and efficient post-disaster damage assessment as a first step of disaster management.