About
Dr Jacqueline Rosette is a member of the Geography Department at Swansea University.
Dr Jacqueline Rosette
Senior Lecturer,
Geography
Available For Postgraduate Supervision
Research Links
Publications
Research Highlights
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Gorgens, E., Nunes, M., Jackson, T., Coomes, D., Keller, M., Reis, C., Valbuena, R., Rosette, J., Almeida, D., Gimenez, B., Cantinho, R., Motta, A., Assis, M., Pereira, F., Spanner, G., Higuchi, N., & Ometto, J. (2021). Resource availability and disturbance shape maximum tree height across the Amazon. Global Change Biology, 27(1), 177-189.
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North, P., Rosette, J., Suárez, J., Los, S., North, P., Los, S., & Rosette, J. (2010). A Monte Carlo radiative transfer model of satellite waveform LiDAR. International Journal of Remote Sensing, 31(5), 1343
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Rosette, J., North, P., Suárez, J., Armston, J., North, P., & Rosette, J. (2009). A comparison of biophysical parameter retrieval for forestry using airborne and satellite LiDAR. International Journal of Remote Sensing, 30(19), 5229
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Rosette, J., North, P., & Suárez, J. (2008). Vegetation height estimates for a mixed temperate forest using satellite laser altimetry. International Journal of Remote Sensing, 29(5), 1475
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Rosette, J., North, P., Suárez, J., & Los, S. (2010). Uncertainty within satellite LiDAR estimations of vegetation and topography. International Journal of Remote Sensing, 31(5), 1325
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Rosette, J., North, P., & J.C., S. (2008). Satellite lidar estimation of stemwood volume: a method using waveform decomposition. Photogrammetric Journal of Finland, 21(1), 76-85.SU Repository: https://cronfa.swan.ac.uk/Record/cronfa8034
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Los, S., Rosette, J., Kljun, N., North, P., Chasmer, L., Suárez, J., Hopkinson, C., Hill, R., Gorsel, E., Mahoney, C., Berni, J., North, P., Los, S., Rosette, J., & Kljun, N. (2012). Vegetation height and cover fraction between 60° S and 60° N from ICESat GLAS data. Geoscientific Model Development, 5, 413-432.
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Rosette, J., Suárez, J., North, P., & Los, S. (2011). Forestry Applications for Satellite Lidar Remote Sensing. Photogrammetric Engineering & Remote Sensing, 77(3), 271-279.
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Morton, D., Nagol, J., Carabajal, C., Rosette, J., Palace, M., Cook, B., Vermote, E., Harding, D., & North, P. (2014). Amazon forests maintain consistent canopy structure and greenness during the dry season. Nature, 506(7487), 221-224.
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Mahoney, C., Kljun, N., Los, S., Chasmer, L., Hacker, J., Hopkinson, C., North, P., Rosette, J., & van Gorsel, E. (2014). Slope Estimation from ICESat/GLAS. Remote Sensing, 6(10), 10051-10069.
All Research
Journal Articles
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Gorgens, E., Nunes, M., Jackson, T., Coomes, D., Keller, M., Reis, C., Valbuena, R., Rosette, J., Almeida, D., Gimenez, B., Cantinho, R., Motta, A., Assis, M., Pereira, F., Spanner, G., Higuchi, N., & Ometto, J. (2021). Resource availability and disturbance shape maximum tree height across the Amazon. Global Change Biology, 27(1), 177-189.
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García, M., North, P., Viana-Soto, A., Stavros, N., Rosette, J., Martín, M., Franquesa, M., González-Cascón, R., Riaño, D., Becerra, J., & Zhao, K. (2020). Evaluating the potential of LiDAR data for fire damage assessment: A radiative transfer model approach. Remote Sensing of Environment, 247, 111893
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Chen, B., Pang, Y., Li, Z., Lu, H., North, P., Rosette, J., & Yan, M. (2020). Forest signal detection for photon counting LiDAR using Random Forest. Remote Sensing Letters, 11(1), 37-46.
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Chen, B., Pang, Y., Li, Z., Lu, H., Liu, L., North, P., Rosette, J., & Rosette, J. (2019). Ground and Top of Canopy Extraction From Photon-Counting LiDAR Data Using Local Outlier Factor With Ellipse Searching Area. IEEE Geoscience and Remote Sensing Letters, 16(9), 1447-1451.
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Gorgens, E., Motta, A., Assis, M., Nunes, M., Jackson, T., Coomes, D., Rosette, J., Aragão, L., Ometto, J., & Rosette, J. (2019). The giant trees of the Amazon basin. Frontiers in Ecology and the Environment, 17(7), 373-374.
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Iglhaut, J., Cabo, C., Puliti, S., Piermattei, L., O’Connor, J., & Rosette, J. (2019). Structure from Motion Photogrammetry in Forestry: a Review. Current Forestry Reports, 5(3), 155-168.
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Chen, B., Pang, Y., Li, Z., North, P., Rosette, J., Sun, G., Suarez-Minguez, J., Bye, I., & Lu, H. (2019). Potential of Forest Parameter Estimation Using Metrics from Photon Counting LiDAR Data in Howland Research Forest. Remote Sensing, 11(7), 856
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Montesano, P., Rosette, J., Sun, G., North, P., Nelson, R., Dubayah, R., Ranson, K., & Kharuk, V. (2015). The uncertainty of biomass estimates from modeled ICESat-2 returns across a boreal forest gradient. Remote Sensing of Environment, 158, 95-109.
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Rosette, J., Cook, B., Nelson, R., Chengquan Huang, ., Masek, J., Tucker, C., Guoqing Sun, ., Wenli Huang, ., Montesano, P., Rubio-Gil, J., & Ranson, J. (2015). Sensor Compatibility for Biomass Change Estimation Using Remote Sensing Data Sets: Part of NASA's Carbon Monitoring System Initiative. IEEE Geoscience and Remote Sensing Letters, 12(7), 1511-1515.
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Duncanson, L., Dubayah, R., Cook, B., Rosette, J., Parker, G., & Rosette, J. (2015). The importance of spatial detail: Assessing the utility of individual crown information and scaling approaches for lidar-based biomass density estimation. Remote Sensing of Environment, 168, 102-112.
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Mahoney, C., Kljun, N., Los, S., Chasmer, L., Hacker, J., Hopkinson, C., North, P., Rosette, J., & van Gorsel, E. (2014). Slope Estimation from ICESat/GLAS. Remote Sensing, 6(10), 10051-10069.
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Mahoney, C., Kljun, N., Los, S., Chasmer, L., Hacker, J., Hopkinson, C., North, P., Rosette, J., & van Gorsel, E. (2014). Slope Estimation from ICESat/GLAS. Remote Sensing, 6(10), 10051-10069.
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Morton, D., Nagol, J., Carabajal, C., Rosette, J., Palace, M., Cook, B., Vermote, E., Harding, D., & North, P. (2014). Amazon forests maintain consistent canopy structure and greenness during the dry season. Nature, 506(7487), 221-224.
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Rosette, J., North, P., Rubio-Gil, J., Cook, B., Los, S., Suárez, J., Sun, G., Ranson, J., & Blair, J. (2013). Evaluating Prospects for Improved Forest Parameter Retrieval From Satellite LiDAR Using a Physically-Based Radiative Transfer Model. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 6(1), 45-53.
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Los, S., Rosette, J., Kljun, N., North, P., Chasmer, L., Suárez, J., Hopkinson, C., Hill, R., Gorsel, E., Mahoney, C., Berni, J., North, P., Los, S., Rosette, J., & Kljun, N. (2012). Vegetation height and cover fraction between 60° S and 60° N from ICESat GLAS data. Geoscientific Model Development, 5, 413-432.
-
Rosette, J., Suárez, J., North, P., & Los, S. (2011). Forestry Applications for Satellite Lidar Remote Sensing. Photogrammetric Engineering & Remote Sensing, 77(3), 271-279.
-
North, P., Rosette, J., Suárez, J., Los, S., North, P., Los, S., & Rosette, J. (2010). A Monte Carlo radiative transfer model of satellite waveform LiDAR. International Journal of Remote Sensing, 31(5), 1343
-
Rosette, J., North, P., Suárez, J., & Los, S. (2010). Uncertainty within satellite LiDAR estimations of vegetation and topography. International Journal of Remote Sensing, 31(5), 1325
-
Rosette, J., North, P., Suárez, J., Armston, J., North, P., & Rosette, J. (2009). A comparison of biophysical parameter retrieval for forestry using airborne and satellite LiDAR. International Journal of Remote Sensing, 30(19), 5229
-
Rosette, J., North, P., & Suárez, J. (2008). Vegetation height estimates for a mixed temperate forest using satellite laser altimetry. International Journal of Remote Sensing, 29(5), 1475
-
Rosette, J., North, P., & J.C., S. (2008). Satellite lidar estimation of stemwood volume: a method using waveform decomposition. Photogrammetric Journal of Finland, 21(1), 76-85.SU Repository: https://cronfa.swan.ac.uk/Record/cronfa8034
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Rosette, J. (2008). Stemwood Volume Estimates for a Mixed Temperate Forest using Satellite LiDAR. Journal of Forest Planning, 13-214.SU Repository: https://cronfa.swan.ac.uk/Record/cronfa15583
Book Chapters
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Rosette, J. (2012). Lidar Remote Sensing for Biomass Assessment. Remote Sensing of Biomass: Principles and ApplicationsInTechSU Repository: https://cronfa.swan.ac.uk/Record/cronfa15575
Supervision
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Desertification in Iraq: impact of changes in hydrology and human migration using a geomatic approach (current)
PhDOther supervisor: Prof Peter North -
Climate-related tree health and their role in the carbon balance (current)
PhDOther supervisor: Dr Sietse Los -
Investigating the potential for detecting Oak Decline using Unmanned Aerial Vehicle (UAV) Remote Sensing (awarded 2023)
PhDOther supervisor: Prof Peter North
Taught Modules
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GEG140H Project and Methods (Human Geography)
This module involves training in fieldwork and GIS skills for both human and physical geographers. In part 1, students can choose from a physical or human geography project option: Part 1: Physical Geography In the physical geography component we will look at sea-level change and its impacts on communities and ecosystems. During classroom sessions we will consider the causes of sea-level change and how it is measured. We use the technique of Stakeholder Analysis to look at the economic and social impacts of sea-level change in different regions. We will then undergo local visits to explore the potential impacts of sea level to our locality and on our coastal university. We¿ll look at both urban and rural environments and different mitigation policies that may be used. Part 1: Human Geography The human geography project focusses on Cities and Photography. Students will investigate the use of photography through three Visual Methodologies: Photo-Documentation, Photo-Elicitation, and Photo-Essays. Students will take part in a photo documentation workshop and group photography fieldwork in Swansea City Centre. They will also complete a photo essay aided by group discussion to select concept, theme, whether analytical or evocative photographs (or both), and dicussion of the links between practice and visual methodologies literature. Part 2: Field data collection and critical analysis skills. During part 2 of this module, students will expand on the knowledge gained previously. This will combine investigations of our world in three dimensions, for which students will use photographs captured themselves to construct a 3D model. Students will learn about and apply other 3D analysis techniques to estimate environmental parameters that they will compare with their field data. They will also contribute to a citizen science initiative using a mobile app for coastal transition zones at risk from sea level change at our University campuses. Using these data collected and analysed during the semester, students will gain insight into sources of uncertainty among datasets, enabling them to critically examine the concept of ground 'truth'.
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GEG140P Project and Methods (Physical Geography)
This module involves training in fieldwork and GIS skills for both human and physical geographers. In part 1, students can choose from a physical or human geography project option: Part 1: Physical Geography In the physical geography component we will look at sea-level change and its impacts on communities and ecosystems. During classroom sessions we will consider the causes of sea-level change and how it is measured. We use the technique of Stakeholder Analysis to look at the economic and social impacts of sea-level change in different regions. We will then undergo local visits to explore the potential impacts of sea level to our locality and on our coastal university. We¿ll look at both urban and rural environments and different mitigation policies that may be used. Part 1: Human Geography The human geography project focusses on Cities and Photography. Students will investigate the use of photography through three Visual Methodologies: Photo-Documentation, Photo-Elicitation, and Photo-Essays. Students will take part in a photo documentation workshop and group photography fieldwork in Swansea City Centre. They will also complete a photo essay aided by group discussion to select concept, theme, whether analytical or evocative photographs (or both), and dicussion of the links between practice and visual methodologies literature. Part 2: Field data collection and critical analysis skills. During part 2 of this module, students will expand on the knowledge gained previously. This will combine investigations of our world in three dimensions, for which students will use photographs captured themselves to construct a 3D model. Students will learn about and apply other 3D analysis techniques to estimate environmental parameters that they will compare with their field data. They will also contribute to a citizen science initiative using a mobile app for coastal transition zones at risk from sea level change at our University campuses. Using these data collected and analysed during the semester, students will gain insight into sources of uncertainty among datasets, enabling them to critically examine the concept of ground 'truth'.
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GEGM06C Environmental Drone Remote Sensing Dissertation
This module offers the opportunity to undertake a major individual research project in the field of Environmental Drone Remote Sensing. Support is provided by a staff supervisor and through student-led discussions. There will also be an opportunity to give constructive feedback to other students undertaking related research projects, learning from their research problems and their subsequent solutions. Provisional research results will be communicated verbally (in July and August). The final results of the thesis will be presented as the scientific paper of a leading international journal in the same field of research.
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GEGM10 Satellite Remote Sensing
This module explains the use of remote sensing as a tool for gathering and analyzing information about human resources and the natural environment. It is appropriate for students who would find it valuable to understand how information about human activity and environmental change is retrieved from images of the Earth acquired by satelite or aircraft instruments. Emphasis is placed on the role of ongoing missions in providing operational information for science and society. Lecture material is supported by hands-on experience exploring satellite images in a computer environment.