iForest - Biogeosciences and Forestry


Rapid assessment of wind storm-caused forest damage using satellite images and stand-wise forest inventory data

D Jonikavičius (1)   , G Mozgeris (2)

iForest - Biogeosciences and Forestry, Volume 6, Issue 3, Pages 150-155 (2013)
doi: https://doi.org/10.3832/ifor0715-006
Published: Apr 08, 2013 - Copyright © 2013 SISEF

Research Articles

Collection/Special Issue: IUFRO 7.01.00 - COST Action FP0903, Kaunas (Lithuania - 2012)
Biological Reactions of Forest to Climate Change and Air Pollution
Guest Editors: Elena Paoletti, Andrzej Bytnerowicz, Algirdas Augustaitis

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 wind-damaged areas was around 95-98%, based solely on difference data from satellite images gathered on two dates.


Forest Damage, Satellite Images, Change Detection, k-Nearest Neighbour

Authors’ address

D Jonikavičius
Laboratory of Geomatics, Institute of Land Management and Geomatics, Aleksandras Stulginskis University, Studentu 11, LT-53361 Akademija, Kaunas distr. (Lithuania)
G Mozgeris
Institute of Forest Management and Wood Science, Aleksandras Stulginskis University, Studentu 11, LT-53361 Akademija, Kaunas distr. (Lithuania)

Corresponding author

D Jonikavičius


Jonikavičius D, Mozgeris G (2013). Rapid assessment of wind storm-caused forest damage using satellite images and stand-wise forest inventory data. iForest 6: 150-155. - doi: 10.3832/ifor0715-006

Academic Editor

Agostino Ferrara

Paper history

Received: Jul 31, 2012
Accepted: Feb 26, 2013

First online: Apr 08, 2013
Publication Date: Jun 01, 2013
Publication Time: 1.37 months

Breakdown by View Type

(Waiting for server response...)

Article Usage

Total Article Views: 48837
(from publication date up to now)

Breakdown by View Type
HTML Page Views: 41152
Abstract Page Views: 2372
PDF Downloads: 3927
Citation/Reference Downloads: 82
XML Downloads: 1304

Web Metrics
Days since publication: 4086
Overall contacts: 48837
Avg. contacts per week: 83.67

Article Citations

Article citations are based on data periodically collected from the Clarivate Web of Science web site
(last update: Feb 2023)

Total number of cites (since 2013): 13
Average cites per year: 1.18


Publication Metrics

by Dimensions ©

Articles citing this article

List of the papers citing this article based on CrossRef Cited-by.

Coppin P, Jonckheere I, Nackaerts K, Muys B, Lambin E (2004)
Digital change detection methods in ecosystem monitoring: a review. International Journal of Remote Sensing 25: 1565-1596.
CrossRef | Gscholar
Crookston NL, Moeur M, Renner D (2002)
Users guide to the most similar neighbor imputation program version 2. Gen. Tech. Rep. RMRS-GTR-96, Rocky Mountain Research Station, USDA Forest Service, Ogden, UT, USA, pp. 35.
Eastman JR, McKendry JE (1991)
Explorations in geographic information systems technology. Volume 1. Change and time series analysis. United Nations Institute for Training and Research, United Nations Environment Program Global Resource Information Database, pp. 86.
Eskelson BNI, Temesgen H, Barrett TM (2008)
Comparison of stratified and non-stratified most similar neighbour imputation for estimating stand tables. Forestry 81 (2): 125-134.
CrossRef | Gscholar
Franco-Lopez H, Ek AR, Bauer ME (2001)
Estimation and mapping of forest stand density, volume, and cover type using the k-nearest neighbors method. Remote Sensing of Environment 77: 251- 274.
CrossRef | Gscholar
Gjertsen AK, Tomter S, Tomppo E (2000)
Combined use of NFI sample plots and Landsat TM data to provide forest information on municipality level. In: Proceedings of IUFRO conference “Remote sensing and forest monitoring” (Zawila-Niedzwinski T, Brach M eds). Rogow (Poland) 1-3 Jun. 1999. Office for Official Publications of the European Communities, Luxembourg, pp. 167-174.
Gómez C, White JC, Wulder MA (2011)
Characterizing the state and processes of change in a dynamic forest environment using hierarchical spatio-temporal segmentation. Remote Sensing of Environment 115: 1665-1679.
CrossRef | Gscholar
Haapanen R, Pekkarinen A (2000)
Utilising satellite imagery and digital detection of clear cuttings for timber supply management. International Archives of Photogrammetry and Remote Sensing, vol. XXXIII, Part B7, pp. 481-488.
Häme T (1991)
Spectral interpretation of changes in forest using satellite scanner images. Acta Forestalia Fennica 222: 112.
Häme T, Heiler I, San Miguel-Ayanz J (1998)
An unsupervised change detection and recognition system for forestry. International Journal of Remote Sensing 19 (6): 1079-1099.
CrossRef | Gscholar
Holmgren J (2004)
Prediction of tree height, basal area and stem volume in forest stands using airborne laser scanning. Scandinavian Journal of Forest Research 19: 543-553.
CrossRef | Gscholar
Holmström H, Nilsson M, Stahl G (2001)
Simultaneous estimations of forest parameters using aerial photograph interpreted data and the k-nearest neighbour method. Scandinavian Journal of Forest Research 16: 67-78.
CrossRef | Gscholar
Hyyppa J, Hyyppa H, Leckie D, Gougeon F, Yu X, Maltamo M (2008)
Review of methods of small-footprint airborne laser scanning for extracting forest inventory data in boreal forests. International Journal of Remote Sensing 29 (5): 1339-1366
CrossRef | Gscholar
Jonikavičius D, Mozgeris G (2009)
Estimation of volumes for mature forests using the k-nearest neighbor technique and satellite image. In: Proccedings of the 4 International Conference “Rural development 2009”. Akademija (Lithuania) 15-17 October 2009, vol. 4 (2), pp. 235-240.
Jonikavičius D, Mozgeris G (2010)
Estimation of forest parameters using the non-parametric techniques and satellite images at compartment level. In: Proceedings of the annual 16 International Conference “Research for rural development 2010”. Jelgava (Latvia) 19-21 May 2010, pp. 194-200.
Jonikavičius D, Mozgeris G (2011)
Forest change detection using medium resolution single acquisition time satellite images. In: Proceedings of the “Biennial International Symposium Forest and Sustainable Development”. University of Brasov, Faculty of Silviculture and Forest Engineering, Brasov (Rumania) 15-16 October 2010. Transilvania University Press, pp. 421-426.
Kennedy RE, Cohen WB, Schroeder TA (2007)
Trajectory-based change detection for automated characterization of forest disturbance dynamics. Remote Sensing of Environment 110: 370-386.
CrossRef | Gscholar
Kennedy RE, Townsend PA, Gross JE, Cohen WB, Bolstad P, Wang YQ, Adams P (2009)
Remote sensing change detection tools for natural resource managers: Understanding concepts and trade offs in the design of landscape monitoring projects. Remote Sensing of Environment 113: 1382-1396.
CrossRef | Gscholar
Lillesand TM, Kiefer RW, Chipman JW (2008)
Remote sensing and image interpretation (6 edn). John Wiley and Sons Inc., USA, pp. 756.
McRoberts RE (2008)
Using satellite imagery and the k-nearest neighbors technique as a bridge between strategic and management forest inventories. Remote Sensing of Environment 112: 212-222
CrossRef | Gscholar
McRoberts RE, Cohen WB, Næsset E, Stehman SV, Tomppo E (2010)
Using remotely sensed data to construct and assess forest attribute maps and related spatial products, Scandinavian Journal of Forest Research 25 (4): 340-367.
McRoberts RE (2012)
Estimating forest attribute parameters for small areas using nearest neighbors techniques. Forest Ecology and Management 272: 3-12.
CrossRef | Gscholar
McRoberts RE, Walters BF (2012)
Statistical inference for remote sensing-based estimates of net deforestation. Remote Sensing of Environment 124: 394-401.
CrossRef | Gscholar
Means JE, Acker SA, Harding DJ, Blair JB, Lefsky MA, Cohen WB, Harmon ME, McKee WA (1999)
Use of large-footprint scanning airborne lidar to estimate forest stand characteristics in the Western Cascades of Oregon. Remote Sensing of Environment 67: 298-308.
CrossRef | Gscholar
Moeur M, Stage AR (1995)
Most similar neighbor: an improved sampling inference procedure for natural resource planning. Forest Science 41: 337-359.
Mozgeris G (2008)
Estimation and use of continuous surfeces of forest parameters: options for Lithuanian forest inventory. Baltic Forestry 14 (2): 176-184.
Mozgeris G, Galaune A, Palicinas M (2008)
Systemy informacji geograficznej w urzdzaniu lasu na Litwie - dekada praktycznego stosowania. Sylwan 1: 58-63.
Muinonen E, Maltamo M, Hyppanen H, Vainikainen V (2001)
Forest stand characteristics estimation using a most similar neighbor approach and image spatial structure information. Remote Sensing of Environment 78: 223-228.
CrossRef | Gscholar
Næsset E (1997)
Estimating timber volume of forest stands using airborne laser scanner data. Remote Sensing of Environment 61: 246-253.
CrossRef | Gscholar
Næsset E (2004)
Effects of different flying altitudes on biophysical stand properties estimated from canopy height and density measured with a small-footprint airborne scanning laser. Remote Sensing of Environment 91: 243- 255.
CrossRef | Gscholar
Næsset E (2007)
Airborne laser scanning as a method in operational forest inventory: status of accuracy assessments accomplished in Scandinavia. Scandinavian Journal of Forest Research 22: 433-442.
CrossRef | Gscholar
Nilsson M (1997)
Estimation of forest variables using satellite image data and airborne LiDAR. PhD thesis, Department of Forest Resource Management and Geomatics, Swedish University of Agricultural Sciences, Umea, Sweden. Acta Universitatis Agriculturae Sueciae, Silvestrias, pp. 17.
Olsson H (1994)
Monitoring of local reflectance changes in boreal forests using satellite data, Dissertation, Biometry and Forest Management Department, Swedish University of Agricultural Sciences, Umea, Sweden.
Reese H, Nilsson M, Granqvist Pahlén T, Hagner O, Joyce S, Tingelöf U, Egberth M, Olsson H (2003)
Countrywide estimates of forest variables using satellite data and field data from the National Forest Inventory. Ambio 32 (8): 542-548.
CrossRef | Gscholar
Singh A (1989)
Digital change detection techniques using remotely-sensed data. International Journal of Remote Sensing 10 (6): 989-1003.
CrossRef | Gscholar
Temesgen H, LeMay VM, Froese KL, Marshall PL (2003)
Imputing tree-lists from aerial attributes for complex stands of south-eastern British Columbia. Forest Ecology and Management 177: 277-285.
CrossRef | Gscholar
Tomppo E (1993)
Multi-source national forest inventory of Finland. In: Proceedings of the IUFRO S4.02 “Ilvessalo Symposium on National Forest Inventories”. Finnish Forest Research Institute, University of Helsinki, Helsinki, Finland, pp. 52-60.
Tomppo E, Goulding C, Katila M (1999)
Adapting Finnish multisource forest inventory techniques to the New Zealand preharvest inventory. Scandinavian Journal of Forest Research 14: 182-192.
CrossRef | Gscholar
Tomppo E (2005)
The Finnish multisource national forest inventory - small area estimation and map production. In: “Forest inventory: methodology and applications” (Kangas A, Maltamo M eds). Springer, Berlin, Germany, pp. 191-220.
Tomppo E, Olsson H, Ståhl G, Nilsson M, Hagner O, Katila M (2008)
Combining national forest inventory field plots and remote sensing data for forest databases. Remote Sensing of Environment 112: 1982-1999.
CrossRef | Gscholar
Townsend PA, Lookingbill TR, Kingdon CC, Gardner RH (2009)
Spatial pattern analysis for monitoring protected areas. Remote Sensing of Environment 113:1410-1420.
CrossRef | Gscholar
Varjo J (1996)
A nonparametric method for controlling stand register by satellite data. The International Journal of Remote Sensing 17: 43-67.
CrossRef | Gscholar
Varjo J (1997)
Change detection and controlling forest information using multi-temporal Landsat TM imagery. Acta Forestalia Fennica 258: 64.
Varjo J, Folving S (1997)
Monitoring of forest changes using unsupervised methods: a case study from boreal forest on mineral soils. Scandinavian Journal of Forest Research 12: 362-369.
CrossRef | Gscholar
Vogelmann JE, Tolk B, Zhu Zc (2009)
Monitoring forest changes in the south western United States using multitemporal Landsat data. Remote Sensing of Environment 113: 1739-1748.
CrossRef | Gscholar
Wang F, Xu YJ (2010)
Comparison of remote sensing change detection techniques for assessing hurricane damage to forests. Environmental Monitoring and Assessment 162:311-326.
CrossRef | Gscholar
Wulder MA, White JC, Goward SN, Masek JG, Irons JR, Herold M, Cohen WB, Loveland TR, Woodcock CE (2008)
Landsat continuity: Issues and opportunities for land cover monitoring. Remote Sensing of Environment 112: 955-969.
CrossRef | Gscholar

This website uses cookies to ensure you get the best experience on our website. More info