Abstract
Natural disturbances significantly influence forest ecosystem services and biodiversity. Accurate delineation and early detection of areas affected by disturbances are critical for estimating extent of damage, assessing economical influence and guiding forest management activities. In this study we focus on snow load damage detection from C-Band SAR images. Snow damage is one of the least studied forest damages, which is getting more common due to current climate trends. The study site was located in the southern part of Northern Finland and the SAR data were represented by the time series of C-band SAR scenes acquired by the Sentinel-1 sensor. Methods used in the study included improved k nearest neighbour method, logistic regression analysis and support vector machine classification. Snow damage recordings from a large snow damage event that took place in Finland during late 2018 were used as reference data. Our results showed an overall detection accuracy of 90%, indicating potential of C-band SAR for operational use in snow damage mapping. Additionally, potential of multitemporal Sentinel-1 data in estimating growing stock volume in damaged forest areas were carried out, with obtained results indicating strong potential for estimating the overall volume of timber within the affected areas. The results and research questions for further studies are discussed.
Highlights
Forest ecosystems worldwide are still undergoing significant changes caused by several factors, both natural and anthropogenic
support vector machine (SVM) are based on statistical learning theory and have the aim of determining the location of decision boundaries that produce the optimal separation of classes [37]
We have studied the applicability of Sentinel-1 C-band synthetic aperture radar (SAR) time series for a rapid localization of forest crown snow-load damage
Summary
Forest ecosystems worldwide are still undergoing significant changes caused by several factors, both natural and anthropogenic. It has been suspected that climate change is one reason, e.g., for more frequent wind storm and snow damage [1]. Poor management and over use of resources in some regions and under use in other regions, e.g., in many European countries are examples of anthropogenic factors. We concentrate on the evaluation of special case of forest structural disturbance, caused by severe snow-load damage that took place between December 2017 and early January 2018 in Finland. A possible effect of the ongoing climate change on the heavy snow-fall in a connection of heavy wind may make the damage more common in the future
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