Trosolwg
Mae Dr Jacqueline Rosette yn aelod o'r Adran Ddaearyddiaeth ym Mhrifysgol Abertawe.
Dr Jacqueline Rosette
Uwch-ddarlithydd,
Geography
Swyddfa Academaidd - 246A
Ail lawr
Adeilad Wallace
Campws Singleton
Ail lawr
Adeilad Wallace
Campws Singleton
Ar gael ar gyfer Goruchwyliaeth Ôl-raddedig
Dolenni Ymchwil
Cyhoeddiadau
Uchafbwyntiau Ymchwil
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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.
Holl Ymchwil
Erthyglau Cylchgronau
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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
Penodau Llyfrau
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Rosette, J. (2012). Lidar Remote Sensing for Biomass Assessment. Remote Sensing of Biomass: Principles and Applications InTechSU Repository: https://cronfa.swan.ac.uk/Record/cronfa15575
Goruchwyliaeth
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Detection Of Metabolic Response To Early Pathogen Infection Using Hyperspectral Reflectance (cyfredol)
PhDGoruchwyliwr arall: Prof Peter North -
Desertification in Iraq: impact of changes in hydrology and human migration using a geomatic approach (cyfredol)
PhDGoruchwyliwr arall: Prof Peter North -
Climate-related tree health and their role in the carbon balance (cyfredol)
PhDGoruchwyliwr arall: Dr Sietse Los -
Investigating the potential for detecting Oak Decline using Unmanned Aerial Vehicle (UAV) Remote Sensing (dyfarnwyd 2023)
PhDGoruchwyliwr arall: Prof Peter North
Modiwlau a Addysgir
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GEC140 Prosiect a Dulliau Ymchwil
Mae¿r modiwl hwn yn cynnwys hyfforddiant gwaith maes a sgiliau GIS i ddaearyddwyr dynol a ffisegol. Rhan 1: Mae¿r rhan yma yn cyflwyno egwyddorion a thechnegau allweddol a ddefnyddir mewn gwaith maes yn nisgyblaethau Daearyddiaeth, astudiaethau amgylcheddol ac astudiaethau defnyddio tir. Mae¿n cynnig profiad o ddefnyddio amryw o ddulliau a thechnegau gwaith maes er mwyn casglu a dadansoddi gwybodaeth mewn perthynas â Daearyddiaeth, yr amgylchedd a defnyddio tir. Dysgir y modiwl ar y cyd â staff o Brifysgol Bangor a Phrifysgol Aberystwyth a dysgir y myfyrwyr o¿r sefydliadau yma hefyd ar y modiwl. Dysgir rhan yma'r modiwl yn ystod penwythnos preswyl. Rhan 2: Sgiliau casglu data o¿r maes a dadansoddi critigol. Yn ystod rhan 2 o¿r modiwl yma, bydd myfyrwyr yn ehangu ar y wybodaeth a gasglwyd yn flaenorol. Bydd hyn yn cyfuno ymchwiliadau o'n byd mewn tri dimensiwn, a bydd myfyrwyr yn defnyddio ffotograffau a dynnwyd eu hunain i adeiladu model 3D. Bydd myfyrwyr yn dysgu am dechnegau dadansoddi 3D eraill ac yn eu cymhwyso i amcangyfrif paramedrau amgylcheddol y byddant yn eu cymharu â'u data o¿r maes. Byddant hefyd yn cyfrannu at fenter gwyddoniaeth dinasyddion (citizen science) gan ddefnyddio ap symudol ar gyfer parthau trawsnewid arfordirol sydd mewn perygl o newid yn lefel y môr ar gampysau ein Prifysgolion. Gan ddefnyddio'r data hyn a gasglwyd ac a ddadansoddwyd yn ystod y semester, bydd myfyrwyr yn cael cipolwg ar ffynonellau ansicrwydd ymhlith setiau data, gan eu galluogi i archwilio'n feirniadol y cysyniad o 'wirionedd' daear (ground ¿truth¿).
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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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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.