23.4.12.10.3 Storm Damage Assessment, Wind Throw

Chapter Contents (Back)
> Damage Assessment. Storm Damage. Forest Changes. Forest. Change Detection.

Honkavaara, E., Litkey, P., Nurminen, K.,
Automatic Storm Damage Detection in Forests Using High-Altitude Photogrammetric Imagery,
RS(5), No. 3, March 2013, pp. 1405-1424.
DOI Link 1304
BibRef

Litkey, P., Nurminen, K., Honkavaara, E.,
Automatic Detection of Storm Damages Using High-Altitude Photogrammetric Imaging,
Hannover13(195-200).
DOI Link 1308
BibRef

Negrón-Juárez, R.I.[Robinson I.], Chambers, J.Q.[Jeffrey Q.], Hurtt, G.C.[George C.], Annane, B.[Bachir], Cocke, S.[Stephen], Powell, M.[Mark], Stott, M.[Michael], Goosem, S.[Stephen], Metcalfe, D.J.[Daniel J.], Saatchi, S.S.[Sassan S.],
Remote Sensing Assessment of Forest Disturbance across Complex Mountainous Terrain: The Pattern and Severity of Impacts of Tropical Cyclone Yasi on Australian Rainforests,
RS(6), No. 6, 2014, pp. 5633-5649.
DOI Link 1407
BibRef

Polewski, P.[Przemyslaw], Yao, W.[Wei], Heurich, M.[Marco], Krzystek, P.[Peter], Stilla, U.[Uwe],
Detection of fallen trees in ALS point clouds using a Normalized Cut approach trained by simulation,
PandRS(105), No. 1, 2015, pp. 252-271.
Elsevier DOI 1506
Precision forestry BibRef

Polewski, P.[Przemyslaw], Yao, W.[Wei], Heurich, M.[Marco], Krzystek, P.[Peter], Stilla, U.[Uwe],
Learning a constrained conditional random field for enhanced segmentation of fallen trees in ALS point clouds,
PandRS(140), 2018, pp. 33-44.
Elsevier DOI 1805
CRF, Segmentation, Fallen tree detection, LIDAR BibRef

Polewski, P.[Przemyslaw], Yao, W.[Wei], Heurich, M.[Marco], Krzystek, P.[Peter], Stilla, U.[Uwe],
Active learning approach to detecting standing dead trees from ALS point clouds combined with aerial infrared imagery,
EarthObserv15(10-18)
IEEE DOI 1510
Entropy BibRef

Duan, F.Z.[Fu-Zhou], Wan, Y.C.[Yang-Chun], Deng, L.[Lei],
A Novel Approach for Coarse-to-Fine Windthrown Tree Extraction Based on Unmanned Aerial Vehicle Images,
RS(9), No. 4, 2017, pp. xx-yy.
DOI Link 1705
BibRef

Rüetschi, M.[Marius], Small, D.[David], Waser, L.T.[Lars T.],
Rapid Detection of Windthrows Using Sentinel-1 C-Band SAR Data,
RS(11), No. 2, 2019, pp. xx-yy.
DOI Link 1902
Storm damage. BibRef

Fagherazzi, S.[Sergio], Nordio, G.[Giovanna], Munz, K.[Keila], Catucci, D.[Daniele], Kearney, W.S.[William S.],
Variations in Persistence and Regenerative Zones in Coastal Forests Triggered by Sea Level Rise and Storms,
RS(11), No. 17, 2019, pp. xx-yy.
DOI Link 1909
BibRef

Yao, W.[Wutao], Ma, Y.[Yong], Chen, F.[Fu], Xiao, Z.[Zhishu], Shu, Z.[Zufei], Chen, L.J.[Li-Jun], Xiao, W.H.[Wen-Hong], Liu, J.B.[Jian-Bo], Jiang, L.Y.[Li-Yuan], Zhang, S.[Shuyan],
Analysis of Ice Storm Impact on and Post-Disaster Recovery of Typical Subtropical Forests in Southeast China,
RS(12), No. 1, 2020, pp. xx-yy.
DOI Link 2001
BibRef

Peter, J.S.[Joseph St.], Anderson, C.[Chad], Drake, J.[Jason], Medley, P.[Paul],
Spatially Quantifying Forest Loss at Landscape-scale Following a Major Storm Event,
RS(12), No. 7, 2020, pp. xx-yy.
DOI Link 2004
BibRef

Kislov, D.E.[Dmitry E.], Korznikov, K.A.[Kirill A.],
Automatic Windthrow Detection Using Very-High-Resolution Satellite Imagery and Deep Learning,
RS(12), No. 7, 2020, pp. xx-yy.
DOI Link 2004
BibRef

McCarthy, M.J.[Matthew J.], Jessen, B.[Brita], Barry, M.J.[Michael J.], Figueroa, M.[Marissa], McIntosh, J.[Jessica], Murray, T.[Tylar], Schmid, J.[Jill], Muller-Karger, F.E.[Frank E.],
Automated High-Resolution Time Series Mapping of Mangrove Forests Damaged by Hurricane Irma in Southwest Florida,
RS(12), No. 11, 2020, pp. xx-yy.
DOI Link 2006
BibRef

Deigele, W.[Wolfgang], Brandmeier, M.[Melanie], Straub, C.[Christoph],
A Hierarchical Deep-Learning Approach for Rapid Windthrow Detection on PlanetScope and High-Resolution Aerial Image Data,
RS(12), No. 13, 2020, pp. xx-yy.
DOI Link 2007
BibRef

Tomppo, E.[Erkki], Ronoud, G.[Ghasem], Antropov, O.[Oleg], Hytönen, H.[Harri], Praks, J.[Jaan],
Detection of Forest Windstorm Damages with Multitemporal SAR Data: A Case Study: Finland,
RS(13), No. 3, 2021, pp. xx-yy.
DOI Link 2102
BibRef

Olmo, V.[Valentina], Tordoni, E.[Enrico], Petruzzellis, F.[Francesco], Bacaro, G.[Giovanni], Altobelli, A.[Alfredo],
Use of Sentinel-2 Satellite Data for Windthrows Monitoring and Delimiting: The Case of 'Vaia' Storm in Friuli Venezia Giulia Region (North-Eastern Italy),
RS(13), No. 8, 2021, pp. xx-yy.
DOI Link 2104
BibRef

Polewski, P.[Przemyslaw], Shelton, J.[Jacquelyn], Yao, W.[Wei], Heurich, M.[Marco],
Instance segmentation of fallen trees in aerial color infrared imagery using active multi-contour evolution with fully convolutional network-based intensity priors,
PandRS(178), 2021, pp. 297-313.
Elsevier DOI 2108
simulated annealing, U-net, sample consensus, precision forestry, energy minimization BibRef


Bernardes, S., Madden, M.,
Vegetation Disturbance And Recovery Following A Rare Windthrow Event In The Great Smoky Mountains National Park,
ISPRS16(B8: 571-575).
DOI Link 1610
BibRef

Pirotti, F., Travaglini, D., Giannetti, F., Kutchartt, E., Bottalico, F., Chirici, G.,
Kernel Feature Cross-correlation For Unsupervised Quantification Of Damage From Windthrow In Forests,
ISPRS16(B7: 17-22).
DOI Link 1610
BibRef

Saarinen, N., Vastaranta, M., Honkavaara, E., Wulder, M.A., White, J.C., Litkey, P., Holopainen, M., Hyyppä, J.,
Mapping the Risk of Forest Wind Damage Using Airborne Scanning LiDAR,
PIA15(189-196).
DOI Link 1504
BibRef

Chapter on Cartography, Aerial Images, Buildings, Roads, Terrain, Forests, Trees, ATR continues in
Eucalypt Trees, Eucalyptus .


Last update:Oct 16, 2021 at 11:54:21