Fusion of hyperspectral and ALS remote sensing data for the analysis of forest areas
Event details
| Date | 18.11.2014 |
| Hour | 16:15 › 17:15 |
| Speaker | Dr Michele Dalponte, Department of Sustainable Agroecosystems and Bioresources, Research and Innovation Centre, Fondazione Edmund Mach |
| Location | |
| Category | Conferences - Seminars |
Abstract:
Nowadays, the use of remote sensing data for forest inventory purposes is increasing. Remote sensing data represent a very useful tool for the estimation of forest attributes needed in forest inventories: i.e. tree species, stem volume, diameter at breast height (DBH), and tree heights. A remote sensing based forest inventory can be carried out at two different spatial scales: plot level and individual tree crown (ITC) level. In plot level forest inventories, forest attributes are estimated for plots of a given size (usually circular areas of a given radius). Differently for ITC level forest inventories forest attributes are estimated for each ITC delineated using specific algorithms. These kind of methods provides a more spatially detailed inventory as theoretically for each tree in the forest the volume, DBH and species are estimated.
Many kinds of remote sensing data can be considered for a forest inventory. In the last years large attention have been devoted to the use of hyperspectral and airborne laser scanning (ALS) data. Hyperspectral data showed to be very useful for the fine characterization of tree species distribution., while ALS data showed to be very useful for the characterization of structural forest attributes (height, volume, DBH) in many kinds of forest environments. Moreover with ALS data it is possible to have accurate tree crowns delineations that are a key step in ITC level forest inventories.ALS and hyperspectral data can be considered complementary, and their combination can be very useful as they provide a detailed spectral and spatial information. Their fusion have been studied in the literature for forestry application, showing the benefits of their combined use.
In this seminar after a general approach on the use of remote sensing for forestry applications some case studies will be presented in which both hyperspectral and LIDAR data are used for forest attributes estimation at both plot and ITC level.
Short biography:
Michele Dalponte received the M.Sc. degree in Telecommunications Engineering, and the PhD in Information and Communication Technologies from the University of Trento, Italy in 2006 and 2010, respectively. He is currently working within the Forests and Biogeochemical Cycles Group at the Research and Innovation Center of the Edmund Mach Foundation (Italy). His research interests are in the field of remote sensing, in particular the analysis of hyperspectral, multispectral and LIDAR data for forest monitoring. His work has been published in international journals and presented at international conferences. He is a reviewer for many remote sensing journals.
Nowadays, the use of remote sensing data for forest inventory purposes is increasing. Remote sensing data represent a very useful tool for the estimation of forest attributes needed in forest inventories: i.e. tree species, stem volume, diameter at breast height (DBH), and tree heights. A remote sensing based forest inventory can be carried out at two different spatial scales: plot level and individual tree crown (ITC) level. In plot level forest inventories, forest attributes are estimated for plots of a given size (usually circular areas of a given radius). Differently for ITC level forest inventories forest attributes are estimated for each ITC delineated using specific algorithms. These kind of methods provides a more spatially detailed inventory as theoretically for each tree in the forest the volume, DBH and species are estimated.
Many kinds of remote sensing data can be considered for a forest inventory. In the last years large attention have been devoted to the use of hyperspectral and airborne laser scanning (ALS) data. Hyperspectral data showed to be very useful for the fine characterization of tree species distribution., while ALS data showed to be very useful for the characterization of structural forest attributes (height, volume, DBH) in many kinds of forest environments. Moreover with ALS data it is possible to have accurate tree crowns delineations that are a key step in ITC level forest inventories.ALS and hyperspectral data can be considered complementary, and their combination can be very useful as they provide a detailed spectral and spatial information. Their fusion have been studied in the literature for forestry application, showing the benefits of their combined use.
In this seminar after a general approach on the use of remote sensing for forestry applications some case studies will be presented in which both hyperspectral and LIDAR data are used for forest attributes estimation at both plot and ITC level.
Short biography:
Michele Dalponte received the M.Sc. degree in Telecommunications Engineering, and the PhD in Information and Communication Technologies from the University of Trento, Italy in 2006 and 2010, respectively. He is currently working within the Forests and Biogeochemical Cycles Group at the Research and Innovation Center of the Edmund Mach Foundation (Italy). His research interests are in the field of remote sensing, in particular the analysis of hyperspectral, multispectral and LIDAR data for forest monitoring. His work has been published in international journals and presented at international conferences. He is a reviewer for many remote sensing journals.
Practical information
- General public
- Free
- This event is internal
Organizer
- EESS - IIE
Contact
- Dr Devis Tuia, LASIG