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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Al-Qaraghuli, A., North, P., Los, S., Bye, I., & Rosette, J. (2026). The impact of climate factors and surface water management on the Mesopotamian Marshes for the period 2000–2023. Scientific Reports, 0
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Al-Qaraghuli, A., North, P., Bye, I., Rosette, J., & Los, S. (2026). Comparative assessment of land use and land cover classification from Planet, Landsat 8, and Sentinel-2A data in the semi-arid region of Najaf, Iraq. International Journal of Remote Sensing, 47(13), 5772-5801.
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Al-Qaraghuli, A., North, P., Bye, I., Rosette, J., & Los, S. (2026). Environmental Degradation in Iraq: Attribution of Climatic Change and Human Influences Through Multi-Factor Analysis. Remote Sensing, 18(4), 640
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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
-
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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Original title: Detection Of Metabolic Response To Early Pathogen Infection Using Hyperspectral Reflectance New title: From Leaf Biochemistry to Canopy Spectra: Hyperspectral Signals of Stress and the Challenge of Scaling in Temperate Forest Trees (current)
PhDOther supervisor: Prof Peter North -
Evaluating the socio-environmental benefits and perception of urban nature based solutions in the UK (current)
PhDOther supervisor: Dr Iain Robertson -
Land Degradation in Iraq: Impact of Changes in Climate and Human Management 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 (awarded 2025)
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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GEG140C Dulliau Maes
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 focuses on visual methods. Students use photography to study Swansea High Street. Two visual methodologies - Photo-Documentation and Photo-Essays ¿ are taught. Following lectures and workshops students undertake fieldwork on Swansea High Street to evidence their research question. Following group discussion to select concept, theme, whether analytical or evocative photographs (or both), and discussion of the links between practice and visual methodologies literature students complete a photo essay as the assessment.
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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 habitat characteristics and field measurements. They will also contribute to a citizen science initiative for coastal transition zones at risk from sea level change. Using data collected and analysed during the semester, students will gain experience in three-dimensional analysis techniques and insight into sources of uncertainty among datasets, enabling them to critically examine the concept of ground 'truth'.
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GEG140PC Gwaith Maes - Daearyddiaeth Ffisegol ac Amgylcheddol
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 habitat characteristics and field measurements. They will also contribute to a citizen science initiative for coastal transition zones at risk from sea level change. Using data collected and analysed during the semester, students will gain experience in three-dimensional analysis techniques and insight into sources of uncertainty among datasets, enabling them to critically examine the concept of ground 'truth'.
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GEG252ICE Geographical Fieldwork skills : Iceland
The module is concerned with identifying and defining geographical questions while on location in Iceland and applying the relevant geographical skills, knowledge and techniques to these questions. The general aims are to observe, analyse and achieve an understanding of the varied geographical landscapes and inherent features of Iceland, with themes including volcanoes, glaciers, geothermal energy and geotourism..The module comprises a week's fieldwork in south-west Iceland with preparatory lectures and other classes held at Singleton Campus before the field trip.
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GEG252ICEC Sgiliau Gwaith Maes Daearyddol: Gwlad yr Iâ
Mae'r modiwl hwn yn ymwneud â nodi a diffinio cwestiynau daearyddol pan ar leoliad yng Ngwlad yr Iâ, a chymhwyso'r sgiliau daearyddol, yr wybodaeth a'r technegau perthnasol i'r cwestiynau hyn. Y nodau cyffredinol yw arsylwi, dadansoddi a meithrin dealltwriaeth o dirweddau daearyddol amrywiol a nodweddion cynhenid Gwlad yr Iâ, gyda themâu¿n cynnwys llosgfynyddoedd, rhewlifoedd, ynni geothermol a geodwristiaeth. Mae'r modiwl yn cynnwys wythnos o waith maes yn ne-orllewin Gwlad yr Iâ, a chynhelir darlithoedd paratoadol a dosbarthiadau eraill ar Gampws Singleton cyn y daith faes.
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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 characteristics 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 theoretical and legislative knowledge, safety awareness and flight experience required to undertake assessment for Civil Aviation Authority (CAA) remote pilot accreditation. This comprises theoretical training and written graded test, flight training and practical competency test. This Module will be delivered in partnership with a CAA RAE (Recognised Assessment Entity). 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 knowledege of drone mission planning, data capture and processing for 3D reconstruction or orthomosaic creation for remote sensing applications using RGB imagery for photogrammetry (Structure from Motion), multispectral and thermal data. This Module is a prerequisite of GEGM06D 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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GEGM07 Environmental Dynamics
This module explores past, present and potential future changes in the Earth's climate and environment. It provides a broad approach to the study of environmental processes and dynamics across land, the oceans and the atmosphere at both global and regional scales. The module highlights the evidence used to reconstruct past environmental dynamics and examines what they reveal about current processes, future predictions and likely impacts.
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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.