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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Alemayehu, B., Suarez-Minguez, J., & Rosette, J. (2024). The Implications of Plantation Forest-Driven Land Use/Land Cover Changes for Ecosystem Service Values in the Northwestern Highlands of Ethiopia. Remote Sensing, 16(22), 4159
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Alemayehu, B., Suarez-Minguez, J., & Rosette, J. (2024). Modeling the Spatial Distribution of Acacia decurrens Plantation Forests Using PlanetScope Images and Environmental Variables in the Northwestern Highlands of Ethiopia. Forests, 15(2), 277
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Alemayehu, B., Suarez-Minguez, J., Rosette, J., & Khan, S. (2023). Vegetation Trend Detection Using Time Series Satellite Data as Ecosystem Condition Indicators for Analysis in the Northwestern Highlands of Ethiopia. Remote Sensing, 15(20), 5032
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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.
-
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 -
Sitka spruce (Picea sitchensis (Bong.) Carr) drought monitoring using remotely sensed Vegetation Indices (VI) at multiple spatiotemporal scales (dyfarnwyd 2025)
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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GEG140C Dulliau Maes
(Welsh translation to follow) This module provides training in key practical skills and concepts of field data collection for geology, human and physical geography students. The module consists of two parts, each of which comprises 50% of the module mark. The first is common to all students, and the second is determined by the student¿s degree specialism (human geography, physical geography or geology). Main component - common to all students Here we learn skills and concepts that are fundamental to your geography degree and aim to begin to prepare you for fieldwork aspects of your dissertation project. These include mapwork skills, awareness of data uncertainty, and applying geographical knowledge to real world situations. Next 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. Degree specialisms 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 discussion of the links between practice and visual methodologies literature. Physical Geography Students will expand and apply their previous knowledge to natural sea defences (dunes), and learn about assessment of dynamic landscapes over time using three dimensional analysis techniques. They will also contribute to a citizen science initiative for coastal transition zones at risk from sea level change at our University Bay Campus. Using 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'. Geology The module develops geological fieldwork skills through 2 intensive day field classes and one intensive field weekend (Friday and Saturday). Field classes will introduce key aspects of geology in the field, including a variety of rock types, folds and faults, fossils and field relations, as well as developing skills such as keeping a field notebook, making a field sketch, using a compass-clinometer to measure rock surface orientations, manipulation of structural data, sediment logging and correlation and lateral variation. This collected field data will be assessed through a geological report in order to synthesize this field data.
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GEG140G Field Methods - Geology
This module provides training in key practical skills and concepts of field data collection for geology, human and physical geography students. The module consists of two parts, each of which comprises 50% of the module mark. The first is common to all students, and the second is determined by the student¿s degree specialism (human geography, physical geography or geology). Main component - common to all students Here we learn skills and concepts that are fundamental to your geography degree and aim to begin to prepare you for fieldwork aspects of your dissertation project. These include mapwork skills, awareness of data uncertainty, and applying geographical knowledge to real world situations. Next 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. Degree specialism Geology The module develops geological fieldwork skills through 2 intensive day field classes and one intensive field weekend (Friday and Saturday). Field classes will introduce key aspects of geology in the field, including a variety of rock types, folds and faults, fossils and field relations, as well as developing skills such as keeping a field notebook, making a field sketch, using a compass-clinometer to measure rock surface orientations, manipulation of structural data, sediment logging and correlation and lateral variation. This collected field data will be assessed through a geological report in order to synthesize this field data.
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GEG140H Field Methods - Human Geography
This module provides training in key practical skills and concepts of field data collection for geology, human and physical geography students. The module consists of two parts, each of which comprises 50% of the module mark. The first is common to all students, and the second is determined by the student¿s degree specialism (human geography, physical geography or geology). Main component - common to all students Here we learn skills and concepts that are fundamental to your geography degree and aim to begin to prepare you for fieldwork aspects of your dissertation project. These include mapwork skills, awareness of data uncertainty, and applying geographical knowledge to real world situations. Next 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. Degree specialisms 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 discussion of the links between practice and visual methodologies literature.
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GEG140P Field Methods - Physical and Environmental Geography
This module provides training in key practical skills and concepts of field data collection for geology, human and physical geography students. The module consists of two parts, each of which comprises 50% of the module mark. The first is common to all students, and the second is determined by the student¿s degree specialism (human geography, physical geography or geology). Main component - common to all students Here we learn skills and concepts that are fundamental to your geography degree and aim to begin to prepare you for fieldwork aspects of your dissertation project. These include mapwork skills, awareness of data uncertainty, and applying geographical knowledge to real world situations. Next 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. Degree specialism Physical Geography Students will expand and apply their previous knowledge to natural sea defences (dunes), and learn about assessment of dynamic landscapes over time using three dimensional analysis techniques. They will also contribute to a citizen science initiative for coastal transition zones at risk from sea level change at our University Bay Campus. Using 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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GEGM03 Drone Accreditation and Remote Sensing
This Module teaches the flight skills, legislative and regulatory requirements for operating a drone in the UK. Students will gain an overview of the advantages and limitations of different types of drone; opportunities offered by high resolution user-captured remote sensing data; and environmental applications for drones. Much of the teaching is practice-based and students will develop the knowledge, safety awareness and flight experience required to undertake assessment for the Civil Aviation Authority GVC (General Visual Line of Sight Certificate). Students will therefore graduate with the additional CAA GVC remote pilot accreditation, comprising theoretical training and written graded test, and flight training and assessment. This Module will be delivered in partnership with a CAA registered and authorised training provider. The externally-assessed syllabus comprises: Drone Airspace Operating Regulations; Airmanship and Aviation Safety; Air Law and Responsibilities; Meteorology; Navigation and Aviation Charts; Human Factors; Aircraft Knowledge; Operating Procedures. Students will additionally gain knowldege of drone mission planning, data capture and processing for 3D reconstruction or image mosaic creation for remote sensing applications. This Module is a prerequisite of GEGM03C Environmental Drone Remote Sensing Dissertation.
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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 Dynamics and Climate Change/Geographic Information and Climate Change/Environmental Drone Remote Sensing. Support is provided by a staff supervisor and through GEGM06P (Dissertation Preparation Module). The Dissertation will be presented in the form of a scientific paper with supporting data.
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GEGM10 Environmental Remote Sensing
This module aims to provide students with knowledge about remote sensing as a tool for capturing important information about our environment, how and where it is changing. The context of learning is for real world, operational data needs and how remote sensing can contribute to these. The principles of key types of sensor are taught, students understand how the data relate to features on the ground during field activities, and put the knowledge gained into practice during computer practicals. With a focus on satellite data, this module also includes national airborne datasets, mobile handheld sensors and field data collection techniques. Learning for this module is cumulative, with a weekly programme through which students progressively build knowledge, experience and analysis techniques which will contribute to the assessed assignment.