journal article
Open Access
Feb 13, 2019
Boreal Forest Snow Damage Mapping Using Multi-Temporal Sentinel-1 Data
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.
Topics
No keywords indexed for this article. Browse by subject →
References
48
[1]
Suvanto "Forest susceptibility to storm damage is affected by similar factors regardless of storm type: Comparison of thunder storms and autumn extra-tropical cyclones in Finland" For. Ecol. Manag. (2016) 10.1016/j.foreco.2016.09.005
[2]
Suvi Jylhänlehto, S. (2019, February 12). Metsäkeskus Kokosi Lumituhot Kartalle—Tykkytuhohakkuita Jopa Miljoona Mottia. Available online: https://bit.ly/2Hu6Bx1.
[3]
Torres "GMES Sentinel-1 mission" Remote Sens. Environ. (2012) 10.1016/j.rse.2011.05.028
[4]
Vastaranta "Area-Based Snow Damage Classification of Forest Canopies Using—Temporal LIDAR Data" ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. (2011)
[5]
Honkavaara "Automatic Storm Damage Detection in Forests Using High? Altitude Photogrammetric Imagery" Remote Sens. (2013) 10.3390/rs5031405
[6]
Nelson "Combining satellite imagery with forest inventory data to assess damage severity following a major blowdown event in northern Minnesota, USA" Int. J. Remote Sens. (2009) 10.1080/01431160903022951
[7]
Einzmann, K., Immitzer, M., Böck, S., Bauer, O., Schmitt, A., and Atzberger, C. (2017). Windthrow Detection in European Forests with Very High-Resolution Optical Data. Forests, 8.
10.3390/f8010021
[8]
Joshi "Mapping dynamics of deforestation and forest degradation in tropical forests using radar satellite data" Environ. Res. Lett. (2015) 10.1088/1748-9326/10/3/034014
[9]
Thiele, A., Boldt, M., and Hinz, S. (2012, January 22–27). Automated detection of storm damage in forest areas by analyzing TerraSAR-X data. Proceedings of the 2012 IEEE International Geoscience and Remote Sensing Symposium, Munich, Germany.
10.1109/igarss.2012.6351205
[10]
Rosa, R.A.S., Fernandes, D., Nogueira, J.B., and Wimmer, C. (2015, January 26–31). Automatic change detection in multitemporal X- and P-band SAR images using Gram-Schmidt process. Proceedings of the 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Milan, Italy.
10.1109/igarss.2015.7326395
[11]
Antropov, O., Rauste, Y., Väänänen, A., Mutanen, T., and Häme, T. (2016, January 10–15). Mapping forest disturbance using long time series of Sentinel-1 data: Case studies over boreal and tropical forests. Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China.
10.1109/igarss.2016.7730014
[12]
Santoro "Nation-Wide Clear-Cut Mapping in Sweden Using ALOS PALSAR Strip Images" Remote Sens. (2012) 10.3390/rs4061693
[13]
Rignot "Change detection techniques for ERS-1 SAR data" IEEE Trans. Geosci. Remote Sens. (1993) 10.1109/36.239913
[14]
Quegan "Multitemporal ERS SAR analysis applied to forest mapping" IEEE Trans. Geosci. Remote Sens. (2000) 10.1109/36.842003
[15]
Ranson "Disturbance recognition in the boreal forest using radar and Landsat-7" Can. J. Remote Sens. (2003) 10.5589/m02-096
[16]
Tanase "Radar Burn Ratio for fire severity estimation at canopy level: An example for temperate forests" Remote Sens. Environ. (2015) 10.1016/j.rse.2015.08.025
[17]
Pantze "Change detection of boreal forest using bi-temporal ALOS PALSAR backscatter data" Remote Sens. Environ. (2014) 10.1016/j.rse.2013.08.050
[18]
Antropov "Land cover and soil type mapping from spaceborne PolSAR data at L-Band With probabilistic neural network" IEEE Trans. Geosci. Remote Sens. (2014) 10.1109/tgrs.2013.2287712
[19]
Fransson, J.E.S., Pantze, A., Eriksson, L.E.B., Soja, M.J., and Santoro, M. (2010, January 25–30). Mapping of wind-thrown forests using satellite SAR images. Proceedings of the 2010 IEEE International Geoscience and Remote Sensing Symposium, Honolulu, HI, USA.
10.1109/igarss.2010.5654183
[20]
Eriksson, L.E.B., Fransson, J.E.S., Soja, M.J., and Santoro, M. (2012, January 22–27). Backscatter signatures of wind-thrown forest in satellite SAR images. Proceedings of the 2012 IEEE International Geoscience and Remote Sensing Symposium, Munich, Germany.
10.1109/igarss.2012.6352732
[21]
Tanase "Detection of windthrows and insect outbreaks by L-band SAR: A case study in the Bavarian Forest National Park" Remote Sens. Environ. (2018) 10.1016/j.rse.2018.03.009
[22]
Rüetschi, M., Small, D., and Waser, L.T. (2019). Rapid Detection of Windthrows Using Sentinel-1 C-Band SAR Data. Remote Sens., 11.
10.3390/rs11020115
[23]
Katila "The Multi-source National Forest Inventory of Finland—Methods and results 2013" Nat. Resour. Bioecon. Stud. (2016)
[24]
Tomppo "Using coarse scale forest variables as ancillary information and weighting of variables in k-NN estimation: A genetic algorithm approach" Remote Sens. Environ. (2004) 10.1016/j.rse.2004.04.003
[25]
Tomppo, E., and Haakana, M.K.M.P.J. (2008). Multi-Source National Forest Inventory—Methods and Applications. Managing Forest Ecosystems, Springer.
[26]
Tomppo "Combining national forest inventory field plots and remote sensing data for forest databases" Remote Sens. Environ. (2008) 10.1016/j.rse.2007.03.032
[27]
National Land Survey of Finland (2018). Elevation Model 10 m. Maps and Spatial Data, National Land Survey of Finland.
[28]
Tomppo "Predicting categorical forest variables using an improved k-Nearest Neighbour estimator and Landsat imagery" Remote Sens. Environ. (2009) 10.1016/j.rse.2008.05.021
[29]
[30]
McRoberts "Post-classification approaches to estimating change in forest area using remotely sensed auxiliary data" Remote Sens. Environ. (2014) 10.1016/j.rse.2013.03.036
[31]
Chirici "A meta-analysis and review of the literature on the k-Nearest Neighbors technique for forestry applications that use remotely sensed data" Remote Sens. Environ. (2016) 10.1016/j.rse.2016.02.001
[32]
Mountrakis "Support vector machines in remote sensing: A review" ISPRS J. Photogramm. Remote Sens. (2011) 10.1016/j.isprsjprs.2010.11.001
[33]
Huang "An assessment of support vector machines for land cover classification" Int. J. Remote Sens. (2002) 10.1080/01431160110040323
[34]
"Kernel-Based Framework for Multitemporal and Multisource Remote Sensing Data Classification and Change Detection" IEEE Trans. Geosci. Remote Sens. (2008) 10.1109/tgrs.2008.916201
[35]
Pal "Support vector machines for classification in remote sensing" Int. J. Remote Sens. (2005) 10.1080/01431160512331314083
[36]
Kuo "A Kernel-Based Feature Selection Method for SVM With RBF Kernel for Hyperspectral Image Classification" IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. (2014) 10.1109/jstars.2013.2262926
[37]
[38]
Regression Shrinkage and Selection Via the Lasso
Robert Tibshirani
Journal of the Royal Statistical Society Series B:...
1994
10.1111/j.2517-6161.1996.tb02080.x
[39]
Kim "Model-based prediction error uncertainty estimation for k-nn method" Remote Sens. Environ. (2006) 10.1016/j.rse.2006.04.009
[40]
McRoberts "Estimating areal means and variances of forest attributes using the k-Nearest Neighbors technique and satellite imagery" Remote Sens. Environ. (2007) 10.1016/j.rse.2007.04.002
[41]
Antropov, O., Rauste, Y., Häme, T., and Praks, J. (2017). Polarimetric ALOS PALSAR Time Series in Mapping Biomass of Boreal Forests. Remote Sens., 9.
10.3390/rs9100999
[42]
Santoro "Assessment of stand-wise stem volume retrieval in boreal forest from JERS-1 L-band SAR backscatter" Int. J. Remote Sens. (2006) 10.1080/01431160600646037
[43]
Antropov "Stand-Level Stem Volume of Boreal Forests From Spaceborne SAR Imagery at L-Band" IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. (2013) 10.1109/jstars.2013.2241018
[44]
Kurvonen "Retrieval of biomass in boreal forests from multitemporal ERS-1 and JERS-1 SAR images" IEEE Trans. Geosci. Remote Sens. (1999) 10.1109/36.739154
[45]
Sinha "A review of radar remote sensing for biomass estimation" Int. J. Environ. Sci. Technol. (2015) 10.1007/s13762-015-0750-0
[46]
"Airborne laser scanning as a method in operational forest inventory: Status of accuracy assessments accomplished in Scandinavia" Scand. J. For. Res. (2007) 10.1080/02827580701672147
[47]
Buonaccorsi "Prediction in the Presence of Measurement Error: General Discussion and an Example Predicting Defoliation" Biometrics (1995) 10.2307/2533288
[48]
Olesk, A., Praks, J., Antropov, O., Zalite, K., Arumae, T., and Voormansik, K. (2016). Interferometric SAR Coherence Models for Characterization of Hemiboreal Forests Using TanDEM-X Data. Remote Sens., 8.
10.3390/rs8090700
Cited By
29
Application of Sentinel-1 radar data for mapping ice disturbance in a forested area
European Journal of Remote Sensing
László Zoltán, Zoltán Friedl · 2021
Metrics
29
Citations
48
References
Details
- Published
- Feb 13, 2019
- Vol/Issue
- 11(4)
- Pages
- 384
- License
- View
Authors
Cite This Article
Erkki Tomppo, Oleg Antropov, Jaan Praks (2019). Boreal Forest Snow Damage Mapping Using Multi-Temporal Sentinel-1 Data. Remote Sensing, 11(4), 384. https://doi.org/10.3390/rs11040384
Related
You May Also Like
A Spatial-Temporal Attention-Based Method and a New Dataset for Remote Sensing Image Change Detection
Hao Chen, Zhenwei Shi · 2020
1,650 citations
An Easy-to-Use Airborne LiDAR Data Filtering Method Based on Cloth Simulation
Wuming Zhang, Jianbo Qi · 2016
1,320 citations
Reconstructing Three Decades of Land Use and Land Cover Changes in Brazilian Biomes with Landsat Archive and Earth Engine
Carlos SOUZA, Julia Z. Shimbo · 2020
1,198 citations
Hyperspectral Imaging: A Review on UAV-Based Sensors, Data Processing and Applications for Agriculture and Forestry
Telmo Adão, Jonáš Hruška · 2017
1,101 citations
Transferring Deep Convolutional Neural Networks for the Scene Classification of High-Resolution Remote Sensing Imagery
Fan Hu, Gui-Song Xia · 2015
1,058 citations