Please use this identifier to cite or link to this item:https://hdl.handle.net/20.500.12259/86292
Type of publication: Straipsnis Clarivate Analytics Web of Science ar/ir Scopus / Article in Clarivate Analytics Web of Science or / and Scopus (S1)
Field of Science: Miškotyra / Forestry (A004)
Author(s): Jonikavičius, Donatas;Mozgeris, Gintautas
Title: Rapid assessment of wind storm-caused forest damage using satellite images and stand-wise forest inventory data
Is part of: iForest-Biogeosciences and Forestry. Potenza : Societa Italiana di Selvicoltura ed Ecologia Forestale, 2013, vol. 6
Extent: p. 150-155
Date: 2013
Keywords: Forest Damage;Satellite Images;Change Detection;k-Nearest Neighbour
Abstract: This paper introduces a method for rapid forest damage assessment using satellite images and stand-wise forest inventory data. Two Landsat 5 Thematic Mapper (TM) images from June and September 2010 and data from a forest stand register developed within the frameworks of conventional stand-wise forest inventories in Lithuania were used to assess the forest damage caused by wind storms that occurred on August 8, 2010. Satellite images were geometrically and radiometrically corrected. The percentage of damage in terms of wind-fallen or broken tree volume was then predicted for each forest compartment within the zone potentially affected by the wind storm, using the non-parametric k-nearest neighbor technique. Satellite imagery-based difference images and general forest stand characteristics from the stand register were used as the auxiliary data sets for prediction. All auxiliary data were available from existing databases, and therefore did not involve any added data acquisition costs. Simultaneously, aerial photography of the area damaged by the wind storm was carried-out and color infrared (CIR) orthophotos with a resolution of 0.5 x 0.5 m were produced. A precise manual interpretation of the effects of the wind storm was used to validate satellite image-based estimates. The total wind damaged volume in pine dominating forest (~1.180.000 m3) was underestimated by 2.2%, in predominantly spruce stands (~233.000 m3) by 2.6% and in predominantly deciduous stands (~195.000 m3) by 4.2%, compared to validation data. The overall accuracy of identification of winddamaged areas was around 95-98%, based solely on difference data from satellite images gathered on two dates
Internet: http://www.sisef.it/iforest/pdf/?id=ifor0715-006
Affiliation(s): Vytauto Didžiojo universitetas
Žemės ūkio akademija
Appears in Collections:Universiteto mokslo publikacijos / University Research Publications

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