Windthrows Detection With Satellite Remote Sensing Data: A Comparison Among Sentinel-2, Planet, And Cosmo Sky-Med Data
Wind disturbances represent a great source of damage in forests, and an assessment of such damage is very important for adequate forest management. Remote sensing is an effective tool for this purpose and can be used by considering different data sources: active vs passive sensors. While passive sen...
- Autores:
-
Dalponte, Michele
Solano-Correa, Yady Tatiana
Marinelli, Daniele
Gianelle, Damiano
- Tipo de recurso:
- Fecha de publicación:
- 2023
- Institución:
- Universidad Tecnológica de Bolívar
- Repositorio:
- Repositorio Institucional UTB
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.utb.edu.co:20.500.12585/12738
- Acceso en línea:
- https://hdl.handle.net/20.500.12585/12738
- Palabra clave:
- Windthrows
Remote Sensing
Sentinel- 2
Planet
Cosmo SkyMed
LEMB
- Rights
- closedAccess
- License
- http://purl.org/coar/access_right/c_14cb
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Windthrows Detection With Satellite Remote Sensing Data: A Comparison Among Sentinel-2, Planet, And Cosmo Sky-Med Data |
| title |
Windthrows Detection With Satellite Remote Sensing Data: A Comparison Among Sentinel-2, Planet, And Cosmo Sky-Med Data |
| spellingShingle |
Windthrows Detection With Satellite Remote Sensing Data: A Comparison Among Sentinel-2, Planet, And Cosmo Sky-Med Data Windthrows Remote Sensing Sentinel- 2 Planet Cosmo SkyMed LEMB |
| title_short |
Windthrows Detection With Satellite Remote Sensing Data: A Comparison Among Sentinel-2, Planet, And Cosmo Sky-Med Data |
| title_full |
Windthrows Detection With Satellite Remote Sensing Data: A Comparison Among Sentinel-2, Planet, And Cosmo Sky-Med Data |
| title_fullStr |
Windthrows Detection With Satellite Remote Sensing Data: A Comparison Among Sentinel-2, Planet, And Cosmo Sky-Med Data |
| title_full_unstemmed |
Windthrows Detection With Satellite Remote Sensing Data: A Comparison Among Sentinel-2, Planet, And Cosmo Sky-Med Data |
| title_sort |
Windthrows Detection With Satellite Remote Sensing Data: A Comparison Among Sentinel-2, Planet, And Cosmo Sky-Med Data |
| dc.creator.fl_str_mv |
Dalponte, Michele Solano-Correa, Yady Tatiana Marinelli, Daniele Gianelle, Damiano |
| dc.contributor.author.none.fl_str_mv |
Dalponte, Michele Solano-Correa, Yady Tatiana Marinelli, Daniele Gianelle, Damiano |
| dc.subject.keywords.spa.fl_str_mv |
Windthrows Remote Sensing Sentinel- 2 Planet Cosmo SkyMed |
| topic |
Windthrows Remote Sensing Sentinel- 2 Planet Cosmo SkyMed LEMB |
| dc.subject.armarc.none.fl_str_mv |
LEMB |
| description |
Wind disturbances represent a great source of damage in forests, and an assessment of such damage is very important for adequate forest management. Remote sensing is an effective tool for this purpose and can be used by considering different data sources: active vs passive sensors. While passive sensors can provide a direct view of windthrows, they are often affected by clouds. Active sensors have the significant advantage of not being affected by the presence of clouds which can be prevalent in certain seasons in mountain areas. The objective of this study is to compare the capability of active (Cosmo SkyMed SAR sensor) and passive (Sentinel-2 and Planet sensors) data in detecting windthrows in different seasons of image acquisition. A study site was analysed, located in the Trentino-South Tyrol region (Italy), which was affected by the Vaia storm on 27-30 October 2018, which caused significant forest damage. |
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2023 |
| dc.date.issued.none.fl_str_mv |
2023-10-20 |
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2024-09-12T14:05:32Z |
| dc.date.available.none.fl_str_mv |
2024-09-12T14:05:32Z |
| dc.date.submitted.none.fl_str_mv |
2024-09-11 |
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http://purl.org/coar/version/c_970fb48d4fbd8a85 |
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http://purl.org/coar/resource_type/c_c94f |
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publishedVersion |
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M. Dalponte; Y. T. Solano-Correa; D. Marinelli; D. Gianelle, "Windthrows detection with satellite remote sensing data: a comparison among Sentinel-2, Planet, and COSMO Sky-Med data," in 2023 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Pasadena, United States of America, Jul. 2023. DOI: 10.1109/IGARSS52108.2023.10282036. |
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https://hdl.handle.net/20.500.12585/12738 |
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10.1109/IGARSS52108.2023.10282036 |
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Universidad Tecnológica de Bolívar |
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Repositorio Universidad Tecnológica de Bolívar |
| identifier_str_mv |
M. Dalponte; Y. T. Solano-Correa; D. Marinelli; D. Gianelle, "Windthrows detection with satellite remote sensing data: a comparison among Sentinel-2, Planet, and COSMO Sky-Med data," in 2023 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Pasadena, United States of America, Jul. 2023. DOI: 10.1109/IGARSS52108.2023.10282036. 10.1109/IGARSS52108.2023.10282036 Universidad Tecnológica de Bolívar Repositorio Universidad Tecnológica de Bolívar |
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https://hdl.handle.net/20.500.12585/12738 |
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eng |
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eng |
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info:eu-repo/semantics/closedAccess |
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4 páginas |
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Cartagena de Indias |
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Ciencias Básicas |
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IEEE International Geoscience and Remote Sensing Symposium (IGARSS) |
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Universidad Tecnológica de Bolívar |
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Dalponte, Michele88d5773a-4fd2-49c7-bee0-e4387e64652dSolano-Correa, Yady Tatianac3d85b81-c6f5-4ad0-80dc-65e4cf4283b1Marinelli, Danieleccf36ce0-0cda-4238-9154-d709a8249f49Gianelle, Damiano90026ddd-ac1f-4553-b51b-100fd4a69bff2024-09-12T14:05:32Z2024-09-12T14:05:32Z2023-10-202024-09-11M. Dalponte; Y. T. Solano-Correa; D. Marinelli; D. Gianelle, "Windthrows detection with satellite remote sensing data: a comparison among Sentinel-2, Planet, and COSMO Sky-Med data," in 2023 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Pasadena, United States of America, Jul. 2023. DOI: 10.1109/IGARSS52108.2023.10282036.https://hdl.handle.net/20.500.12585/1273810.1109/IGARSS52108.2023.10282036Universidad Tecnológica de BolívarRepositorio Universidad Tecnológica de BolívarWind disturbances represent a great source of damage in forests, and an assessment of such damage is very important for adequate forest management. Remote sensing is an effective tool for this purpose and can be used by considering different data sources: active vs passive sensors. While passive sensors can provide a direct view of windthrows, they are often affected by clouds. Active sensors have the significant advantage of not being affected by the presence of clouds which can be prevalent in certain seasons in mountain areas. The objective of this study is to compare the capability of active (Cosmo SkyMed SAR sensor) and passive (Sentinel-2 and Planet sensors) data in detecting windthrows in different seasons of image acquisition. A study site was analysed, located in the Trentino-South Tyrol region (Italy), which was affected by the Vaia storm on 27-30 October 2018, which caused significant forest damage.4 páginasapplication/pdfengIEEE International Geoscience and Remote Sensing Symposium (IGARSS)Windthrows Detection With Satellite Remote Sensing Data: A Comparison Among Sentinel-2, Planet, And Cosmo Sky-Med Datainfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_c94fhttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_2df8fbb1WindthrowsRemote SensingSentinel- 2PlanetCosmo SkyMedLEMBinfo:eu-repo/semantics/closedAccesshttp://purl.org/coar/access_right/c_14cbCartagena de IndiasCiencias BásicasInvestigadoresM.-J. Schelhaas, "Impacts of natural disturbances on the development of European forest resources: application of model approaches from tree and stand levels to large-scale scenarios", Dissertationes Forestales, vol. 2008, no. 56, 2008.R. L. Rich, L. Frelich, P. B. Reich and M. E. Bauer, "Detecting wind disturbance severity and canopy heterogeneity in boreal forest by coupling high-spatial resolution satellite imagery and field data", Remote Sensing of Environment, vol. 114, no. 2, pp. 299-308, Feb. 2010.K. Einzmann, M. Immitzer, S. Böck, O. Bauer, A. Schmitt and C. Atzberger, "Windthrow Detection in European Forests with Very High-Resolution Optical Data", Forests, vol. 8, no. 1, pp. 21, Jan. 2017.D. Jonikavičius and G. Mozgeris, "Rapid assessment of wind storm-caused forest damage using satellite images and stand-wise forest inventory data", iForest - Biogeosciences and Forestry, vol. 6, no. 3, pp. 150-155, Jun. 2013.M. Rüetschi, D. Small and L. Waser, "Rapid Detection of Windthrows Using Sentinel-1 C-Band SAR Data", Remote Sensing, vol. 11, no. 2, pp. 115, Jan. 2019.M. Schwarz, C. Steinmeier, F. Holecz, O. Stebler and H. Wagner, "Detection of Windthrow in Mountainous Regions with Different Remote Sensing Data and Classification Methods", Scandinavian Journal of Forest Research, vol. 18, no. 6, pp. 525-536, Dec. 2003.M. Dalponte, S. Marzini, Y. T. Solano-Correa, G. Tonon, L. Vescovo and D. Gianelle, "Mapping forest windthrows using high spatial resolution multispectral satellite images", International Journal of Applied Earth Observation and Geoinformation, vol. 93, pp. 102206, Dec. 2020.I. Vorovencii, "Detection of environmental changes due to windthrows using Landsat 7 ETM+ satellite images", Environmental Engineering and Management Journal, vol. 13, no. 3, pp. 565-576, 2014.F. Wang and Y. J. Xu, "Comparison of remote sensing change detection techniques for assessing hurricane damage to forests", Environmental Monitoring and Assessment, vol. 162, no. 1–4, pp. 311-326, Mar. 2010.G. Chirici et al., "Forest damage inventory after the ‘Vaia’ storm in Italy", Forest@ - Rivista di Selvicoltura ed Ecologia Forestale, vol. 16, no. 1, pp. 3-9, Feb. 2019.M. Nyström, J. Holmgren, J. E. S. Fransson and H. Olsson, "Detection of windthrown trees using airborne laser scanning", International Journal of Applied Earth Observation and Geoinformation, vol. 30, pp. 21-29, Aug. 2014.F. Pirotti, D. Travaglini, F. Giannetti, E. Kutchartt, F. Bottalico and G. Chirici, "Kernel feature cross-correlation for unsupervised quantification of damage from windthrow in forests", ISPRS - International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences, vol. XLI-B7, pp. 17-22, Jun. 2016.D. E. Kislov and K. A. Korznikov, "Automatic Windthrow Detection Using Very-High-Resolution Satellite Imagery and Deep Learning", Remote Sensing, vol. 12, no. 7, pp. 1145, Apr. 2020.Z. M. Hamdi, M. Brandmeier and C. Straub, "Forest Damage Assessment Using Deep Learning on High Resolution Remote Sensing Data", Remote Sensing, vol. 11, no. 17, pp. 1976, Aug. 2019.W. Deigele, M. Brandmeier and C. Straub, "A Hierarchical Deep-Learning Approach for Rapid Windthrow Detection on PlanetScope and High-Resolution Aerial Image Data", Remote Sensing, vol. 12, no. 13, pp. 2121, Jul. 2020.F. Duan, Y. Wan and L. Deng, "A Novel Approach for Coarse-to-Fine Windthrown Tree Extraction Based on Unmanned Aerial Vehicle Images", Remote Sensing, vol. 9, no. 4, pp. 306, Mar. 2017.R. M. Green, "The sensitivity of SAR backscatter to forest windthrow gaps", International Journal of Remote Sensing, vol. 19, no. 12, pp. 2419-2425, Jan. 1998.J. E. S. Fransson, F. Walter, K. Blennow, A. Gustavsson and L. M. H. Ulander, "Detection of storm-damaged forested areas using airborne CARABAS-II VHF SAR image data", IEEE Trans. Geosci. Remote Sensing, vol. 40, no. 10, pp. 2170-2175, Jan. 2002.L. M. H. Ulander et al., "Mapping of wind-thrown forests in Southern Sweden using space- and airborne SAR", Proceedings. 2005 IEEE International Geoscience and Remote Sensing Symposium 2005. IGARSS ’05, pp. 3619-3622, 2005.L. E. B. Eriksson, J. E. S. Fransson, M. J. Soja and M. Santoro, "Backscatter signatures of wind-thrown forest in satellite SAR images", 2012 IEEE International Geoscience and Remote Sensing Symposium, pp. 6435-6438, Jul. 2012.A. Thiele, M. Boldt and S. Hinz, "Automated detection of storm damage in forest areas by analyzing TerraSAR-X data", 2012 IEEE International Geoscience and Remote Sensing Symposium, pp. 1672-1675, Jul. 2012.M. A. Tanase, C. Aponte, S. Mermoz, A. Bouvet, T. Le Toan and M. Heurich, "Detection of windthrows and insect outbreaks by L-band SAR: A case study in the Bavarian Forest National Park", Remote Sensing of Environment, vol. 209, pp. 700-711, May 2018.G. Vaglio Laurin, N. Puletti, C. Tattoni, C. Ferrara and F. Pirotti, "Estimated Biomass Loss Caused by the Vaia Windthrow in Northern Italy: Evaluation of Active and Passive Remote Sensing Options", Remote Sensing, vol. 13, no. 23, pp. 4924, Dec. 2021.M. Lazecky, S. Wadhwa, M. Mlcousek and J. J. Sousa, "Simple method for identification of forest windthrows from Sentinel-1 SAR data incorporating PCA", Procedia Computer Science, vol. 181, pp. 1154-1161, 2021.F. Bovolo and L. Bruzzone, "A detail-preserving scale-driven approach to change detection in multitemporal SAR images", IEEE Trans. Geosci. Remote Sensing, vol. 43, no. 12, pp. 2963-2972, Dec. 2005.http://purl.org/coar/resource_type/c_c94fORIGINAL2023-C-Windthrows Detection With Satellite Remote Sensing Data_ A Comparison Among Sentinel-2, Planet, And Cosmo Sky-Med Data.pdf2023-C-Windthrows Detection With Satellite Remote Sensing Data_ A Comparison Among Sentinel-2, Planet, And Cosmo Sky-Med Data.pdfapplication/pdf1302370https://repositorio.utb.edu.co/bitstream/20.500.12585/12738/1/2023-C-Windthrows%20Detection%20With%20Satellite%20Remote%20Sensing%20Data_%20A%20Comparison%20Among%20Sentinel-2%2c%20Planet%2c%20And%20Cosmo%20Sky-Med%20Data.pdf3747dbf3de0e99d14415bfcb908e9265MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-83182https://repositorio.utb.edu.co/bitstream/20.500.12585/12738/2/license.txte20ad307a1c5f3f25af9304a7a7c86b6MD52TEXT2023-C-Windthrows Detection With Satellite Remote Sensing Data_ A Comparison Among Sentinel-2, Planet, And Cosmo Sky-Med Data.pdf.txt2023-C-Windthrows Detection With Satellite 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