Forest Storm Damage Assessment, Wind Throw

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> Damage Assessment. Storm Damage. Forest Changes. Forest. Tree Fall. 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

Litkey, P., Nurminen, K., Honkavaara, E.,
Automatic Detection of Storm Damages Using High-Altitude Photogrammetric Imaging,
DOI Link 1308

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

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,
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

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

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.Y.[Shu-Yan],
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

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

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

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

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

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

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

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

Rodríguez, A.C.[Andrés C.], Caye Daudt, R.[Rodrigo], d'Aronco, S.[Stefano], Schindler, K.[Konrad], Wegner, J.D.[Jan D.],
Robust Damage Estimation of Typhoon Goni on Coconut Crops with Sentinel-2 Imagery,
RS(13), No. 21, 2021, pp. xx-yy.
DOI Link 2112

Laurin, G.V.[Gaia Vaglio], Puletti, N.[Nicola], Tattoni, C.[Clara], Ferrara, C.[Carlotta], Pirotti, F.[Francesco],
Estimated Biomass Loss Caused by the Vaia Windthrow in Northern Italy: Evaluation of Active and Passive Remote Sensing Options,
RS(13), No. 23, 2021, pp. xx-yy.
DOI Link 2112

Reder, S.[Stefan], Mund, J.P.[Jan-Peter], Albert, N.[Nicole], Waßermann, L.[Lilli], Miranda, L.[Luis],
Detection of Windthrown Tree Stems on UAV-Orthomosaics Using U-Net Convolutional Networks,
RS(14), No. 1, 2022, pp. xx-yy.
DOI Link 2201

Delaporte, B.[Baptiste], Ibanez, T.[Thomas], Despinoy, M.[Marc], Mangeas, M.[Morgan], Menkes, C.[Christophe],
Tropical Cyclone Impact and Forest Resilience in the Southwestern Pacific,
RS(14), No. 5, 2022, pp. xx-yy.
DOI Link 2203

Furukawa, F.[Flavio], Morimoto, J.[Junko], Yoshimura, N.[Nobuhiko], Koi, T.[Takashi], Shibata, H.[Hideaki], Kaneko, M.[Masami],
UAV Video-Based Approach to Identify Damaged Trees in Windthrow Areas,
RS(14), No. 13, 2022, pp. xx-yy.
DOI Link 2208

Zhang, X.[Xu], Jiao, H.B.[Hong-Bo], Chen, G.S.[Guang-Sheng], Shen, J.N.[Jia-Ning], Huang, Z.[Zihao], Luo, H.Y.[Hai-Yan],
Forest Damage by Super Typhoon Rammasun and Post-Disturbance Recovery Using Landsat Imagery and the Machine-Learning Method,
RS(14), No. 15, 2022, pp. xx-yy.
DOI Link 2208

Li, Z.C.[Zong-Chen], Yang, R.[Ruoli], Cai, W.W.[Wei-Wei], Xue, Y.F.[Yong-Fei], Hu, Y.W.[Yao-Wen], Li, L.J.[Liu-Jun],
LLAM-MDCNet for Detecting Remote Sensing Images of Dead Tree Clusters,
RS(14), No. 15, 2022, pp. xx-yy.
DOI Link 2208

Heinaro, E.[Einari], Tanhuanpää, T.[Topi], Vastaranta, M.[Mikko], Yrttimaa, T.[Tuomas], Kukko, A.[Antero], Hakala, T.[Teemu], Mattsson, T.[Teppo], Holopainen, M.[Markus],
Evaluating Factors Impacting Fallen Tree Detection from Airborne Laser Scanning Point Clouds,
RS(15), No. 2, 2023, pp. xx-yy.
DOI Link 2301

Klauberg, C.[Carine], Vogel, J.[Jason], Dalagnol, R.[Ricardo], Ferreira, M.P.[Matheus Pinheiro], Hamamura, C.[Caio], Broadbent, E.[Eben], Silva, C.A.[Carlos Alberto],
Post-Hurricane Damage Severity Classification at the Individual Tree Level Using Terrestrial Laser Scanning and Deep Learning,
RS(15), No. 4, 2023, pp. xx-yy.
DOI Link 2303

Turner, H.C.[Hannah C.], Galford, G.L.[Gillian L.], Lopez, N.H.[Norgis Hernandez], Méndez, A.F.[Armando Falcón], Borroto-Escuela, D.Y.[Daily Yanetsy], Ramos, I.H.[Idania Hernández], González-Díaz, P.[Patricia],
Extent, Severity, and Temporal Patterns of Damage to Cuba's Ecosystems following Hurricane Irma: MODIS and Sentinel-2 Hurricane Disturbance Vegetation Anomaly (HDVA),
RS(15), No. 10, 2023, pp. xx-yy.
DOI Link 2306

Emmert, L.[Luciano], Negrón-Juárez, R.I.[Robinson Isaac], Chambers, J.Q.[Jeffrey Quintin], dos Santos, J.[Joaquim], Lima, A.J.N.[Adriano José Nogueira], Trumbore, S.[Susan], Marra, D.M.[Daniel Magnabosco],
Sensitivity of Optical Satellites to Estimate Windthrow Tree-Mortality in a Central Amazon Forest,
RS(15), No. 16, 2023, pp. 4027.
DOI Link 2309

Nasimi, M.[Mitra], Wood, R.L.[Richard L.],
Using Deep Learning and Advanced Image Processing for the Automated Estimation of Tornado-Induced Treefall,
RS(16), No. 7, 2024, pp. 1130.
DOI Link 2404

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

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

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,
DOI Link 1504

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

Last update:Apr 18, 2024 at 11:38:49