Landsat remote sensing of forest windfall disturbance

• Published in: Remote sensing of environment 2014-03, Vol.143, p.171-179
• Main Authors: Baumann, Matthias, Ozdogan, Mutlu, Wolter, Peter T., Krylov, Alexander, Vladimirova, Nadezda, Radeloff, Volker C.
• Format: Article
• Language: English
• Subjects: Algorithms; Classifications; Disturbance Index; Disturbances; Forestness Index; Forests; Harvesting; Landsat; Remote sensing; Russia; Tasseled Cap transformation; Timber; Windfall
• ISSN: 0034-4257; 1879-0704
• DOI: 10.1016/j.rse.2013.12.020

Knowing if a forest disturbance is caused by timber harvest or a natural event is crucial for carbon cycle assessments, econometric analyses of timber harvesting, and other research questions. However, while remote sensing of forest disturbance in general is very well developed, discerning between different types of forest disturbances remains challenging. In this work, we developed an algorithm to separate windfall disturbance from clear-cut harvesting using Landsat data. The method first extracts training data primarily based on Tasseled Cap transformed bands and histogram thresholds with minimal user input. We then used a support-vector machine classifier to separate disturbed areas into ‘windfall’ and ‘clear-cut harvests’. We tested our algorithm in the temperate forest zone of European Russia and the southern boreal forest zone of the United States. The forest-cover change classifications were highly accurate (~90%) and windfall classification accuracies were greater than 75% in both study areas. Accuracies were generally higher for larger disturbance patches. At the Russia study site about 60% of all disturbances were caused by windfall, versus 40% at the U.S. study site. Given the similar levels of accuracy in both locations and the ease of application, the algorithm has the potential to fill a research gap in mapping wind disturbance using Landsat data in both temperate and boreal forests that are subject to frequent wind events. •We present an approach to classify windfall disturbance with Landsat data.•We tested the algorithm in both temperate and southern boreal forests.•Classifications accuracies exceeded 75% in both study areas.•Larger disturbance sites were classified with higher accuracies than smaller sites.