About
Dr Jacqueline Rosette is a member of the Geography Department at Swansea University.
Dr Jacqueline Rosette
Senior Lecturer
Geography
Academic Office - 246A
Second Floor
Wallace Building
Singleton Campus
Second Floor
Wallace Building
Singleton Campus
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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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.
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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.
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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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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
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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
-
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 Applications InTechSU Repository: https://cronfa.swan.ac.uk/Record/cronfa15575
Supervision
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Detection Of Metabolic Response To Early Pathogen Infection Using Hyperspectral Reflectance (current)
PhDOther supervisor: Prof Peter North -
Desertification in Iraq: impact of changes in hydrology and human migration using a geomatic approach (current)
PhDOther supervisor: Prof Peter North -
Sitka spruce (Picea sitchensis (Bong.) Carr) drought monitoring using remotely sensed Vegetation Indices (VI) at multiple spatiotemporal scales (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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GEC140 Prosiect a Dulliau Ymchwil
Welsh Translation to follow This module aims to provide diverse skills in undertaking research. Students will gain experience in methods of data collection in the field for a geographical research question, analysing and interpreting the data obtained, and understanding the limitations of the work. In part 1 of this module, students follow a physical or human geography project option, depending on their geography degree specialism: Part 1: Physical Geography Students following a physical geography-based degree 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 will undertake local visits to explore the potential impacts of sea level on our locality and on our coastal university. We will consider built-up and rural environments and different mitigation policies that may be used. You will apply a technique of Stakeholder Analysis to consider economic and social impacts of sea-level change. 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 discussion of the links between practice and visual methodologies in literature. Part 2: Field data collection and critical analysis skills. Part 2 of this module is common to all geography degree specialisms. Using data collected by yourselves and other undergraduate students for the field sites, you will learn and apply analysis techniques to interpret the datasets, and become aware of sources of uncertainty and limitations of the data, equipment or field techniques. In doing so, we use the field data and build on skills that students developed earlier in the module, collect new datasets and apply statistical analysis techniques that were learnt in the previous semester to your own data.
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GEG140C Dulliau Maes
Welsh Translation to follow This module aims to provide diverse skills in undertaking research. Students will gain experience in methods of data collection in the field for a geographical research question, analysing and interpreting the data obtained, and understanding the limitations of the work. In part 1 of this module, students follow a physical or human geography project option, depending on their geography degree specialism: Part 1: Physical Geography Students following a physical geography-based degree 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 will undertake local visits to explore the potential impacts of sea level on our locality and on our coastal university. We will consider built-up and rural environments and different mitigation policies that may be used. You will apply a technique of Stakeholder Analysis to consider economic and social impacts of sea-level change. 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 discussion of the links between practice and visual methodologies in literature. Part 2: Field data collection and critical analysis skills. Part 2 of this module is common to all geography degree specialisms. Using data collected by yourselves and other undergraduate students for the field sites, you will learn and apply analysis techniques to interpret the datasets, and become aware of sources of uncertainty and limitations of the data, equipment or field techniques. In doing so, we use the field data and build on skills that students developed earlier in the module, collect new datasets and apply statistical analysis techniques that were learnt in the previous semester to your own data.
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GEG140H Project and Methods (Human Geography)
This module aims to provide diverse skills in undertaking research. Students will gain experience in methods of data collection in the field for a geographical research question, analysing and interpreting the data obtained, and understanding the limitations of the work. In part 1 of this module, students follow a physical or human geography project option, depending on their geography degree specialism: Part 1: Physical Geography Students following a physical geography-based degree 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 will undertake local visits to explore the potential impacts of sea level on our locality and on our coastal university. We will consider built-up and rural environments and different mitigation policies that may be used. You will apply a technique of Stakeholder Analysis to consider economic and social impacts of sea-level change. 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 discussion of the links between practice and visual methodologies in literature. Part 2: Field data collection and critical analysis skills. Part 2 of this module is common to all geography degree specialisms. Using data collected by yourselves and other undergraduate students for the field sites, you will learn and apply analysis techniques to interpret the datasets, and become aware of sources of uncertainty and limitations of the data, equipment or field techniques. In doing so, we use the field data and build on skills that students developed earlier in the module, collect new datasets and apply statistical analysis techniques that were learnt in the previous semester to your own data.
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GEG140P Project and Methods (Physical Geography)
This module aims to provide diverse skills in undertaking research. Students will gain experience in methods of data collection in the field for a geographical research question, analysing and interpreting the data obtained, and understanding the limitations of the work. In part 1 of this module, students follow a physical or human geography project option, depending on their geography degree specialism: Part 1: Physical Geography Students following a physical geography-based degree 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 will undertake local visits to explore the potential impacts of sea level on our locality and on our coastal university. We will consider built-up and rural environments and different mitigation policies that may be used. You will apply a technique of Stakeholder Analysis to consider economic and social impacts of sea-level change. 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 discussion of the links between practice and visual methodologies in literature. Part 2: Field data collection and critical analysis skills. Part 2 of this module is common to all geography degree specialisms. Using data collected by yourselves and other undergraduate students for the field sites, you will learn and apply analysis techniques to interpret the datasets, and become aware of sources of uncertainty and limitations of the data, equipment or field techniques. In doing so, we use the field data and build on skills that students developed earlier in the module, collect new datasets and apply statistical analysis techniques that were learnt in the previous semester to your own data.
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GEG331 Dissertation Report: Geography
The dissertation is an original, substantive and independent research project in an aspect of Geography. The dissertation research project is based on 20 - 25 days of primary research (e.g fieldwork, lab work, archive work) and several months of analysis and write-up. The end result must be less than 10,000 words of text. The dissertation offers you the chance to follow your personal interests and to demonstrate your capabilities as a Geographer. During the course of your dissertation, you will be supported by a peer-led discussion group and a staff supervisor. Lectures and guidance are delivered via this module and peer and staff led Dissertation Support Groups are delivered via GEG332. Participating in Dissertation Support Groups is vital, and is assessed and, in these groups, students will provide constructive criticism to fellow students undertaking related research projects, learning from their research problems and subsequent solutions. This support and supervision is delivered through GEG332, which is a co- requisite.
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GEG332 Dissertation Support: Geography
This module provides structured, student-led peer-group support and academic staff group supervision for students undertaking the 30-credit 'Dissertation Report: Geography' module. This support and supervision is assessed through the submission of the Dissertation Outline and the Dissertation Support Group Reflection and Attendance Log. Working within a supervised Student Peer Group, students have the opportunity to provide constructive criticism to fellow students undertaking related research projects, learning from their research problems and subsequent solutions. Group sessions are the main support provision as student¿s work through their Dissertation. This module complements the 'Dissertation Report: Geography' module, which is a co-requisite.
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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.