
Our People
Professor Mathias (Mat) Disney

Divisional Director Carbon Cycle
Carbon cycle: land, atmosphere and oceans
Research interests
I am interested in vegetation – particularly trees, forests and the carbon cycle. I use EO data such as ground-based and other lidar, to make measurements of forests and carbon as well as to understand radiative transfer in these systems.
Recent publications
Tracking Aboveground Biomass Dynamics in Africa: Evidence for a Changing Role in Carbon Cycling. 2026-03-14
DOI: https://doi.org/10.5194/egusphere-egu26-21337
Quantifying forest structural attributes and aboveground carbon dynamics with terrestrial laser scanning in a temperate deciduous forest. 2026-03-01
https://app.dimensions.ai/details/publication/pub.1196358873
Tree diversity is changing across tropical Andean and Amazonian forests in response to global change (vol 10, pg 267, 2026). 2026-03
https://app.dimensions.ai/details/publication/pub.1198590734
Family imprint reveals basin-wide patterns of Amazon forest embolism resistance. 2026-02-26
https://app.dimensions.ai/details/publication/pub.1198782217
ForestScan: a unique multiscale dataset of tropical forest structure across 3 continents including terrestrial, UAV and airborne LiDAR and in-situ forest census data. 2026-02-16
https://app.dimensions.ai/details/publication/pub.1198355806
Tree diversity is changing across tropical Andean and Amazonian forests in response to global change. 2026-02
https://app.dimensions.ai/details/publication/pub.1197475899
Benchmarking tree instance segmentation of terrestrial laser scanning point clouds. 2026-01
DOI: https://doi.org/10.1016/j.isprsjprs.2025.10.033
Street Tree Characteristics from Street View Imagery: A London Case Study for Urban Heat Island Analysis. 2026
DOI: https://doi.org/10.2139/ssrn.6909263
Species functional traits affect regional and local dominance across western Amazonian forests. 2025-12-29
https://app.dimensions.ai/details/publication/pub.1195982509
Consistent and scalable monitoring of birds and habitats along a coffee production intensity gradient. 2025-12
DOI: https://doi.org/10.1002/rse2.70015
Expanding forest research with terrestrial LiDAR technology. 2025-10-06
DOI: https://doi.org/10.1038/s41467-025-63946-6
Realistic virtual forests for understanding forest disturbances and recovery from space. 2025-09
https://app.dimensions.ai/details/publication/pub.1190149313
The Fifth Phase of the Radiation Transfer Model Intercomparison Exercise (RAMI-V): Experiment Description and Results on Actual Canopy Scenarios. 2025-07-29
https://app.dimensions.ai/details/publication/pub.1188899833
Investigating the accuracy of tropical woody stem CO<sub>2</sub> efflux estimates: scaling methods, and vertical and diel variation. 2025-06
https://app.dimensions.ai/details/publication/pub.1187317555
Use and misuse of trait imputation in ecology: the problem of using out-of-context imputed values. 2025-06
https://app.dimensions.ai/details/publication/pub.1185202704
The influence of 3D canopy structure on modelled photosynthesis. 2025-05
DOI: https://doi.org/10.1016/j.agrformet.2025.110437
Variation in wood density across South American tropical forests. 2025-03-10
https://app.dimensions.ai/details/publication/pub.1186343099
Tropical forests in the Americas are changing too slowly to track climate change. 2025-03-07
https://app.dimensions.ai/details/publication/pub.1186259872
The impact of leaf-wood separation algorithms on aboveground biomass estimation from terrestrial laser scanning. 2025-03-01
https://app.dimensions.ai/details/publication/pub.1183850993
Using handheld mobile laser scanning to quantify fine-scale surface fuels and detect changes post-disturbance in northern California forests. 2025-03
https://app.dimensions.ai/details/publication/pub.1186151439
Detecting selective logging in tropical forests with optical satellite data: an experiment in Peru shows texture at 3 m gives the best results. 2025-02
DOI: https://doi.org/10.1002/rse2.414
The impact of forest canopy structure on modelled photosynthesis. 2025-01-20
DOI: https://doi.org/10.5194/egusphere-egu24-5471
Assessing Sampling Design and Voxel Size in Estimating Wheat Green Area Index With Measured and Simulated TLS Data. 2025
DOI: https://doi.org/10.1109/TGRS.2025.3589113
New Tree Height Allometries Derived From Terrestrial Laser Scanning Reveal Substantial Discrepancies With Forest Inventory Methods in Tropical Rainforests (vol 30, e17473, 2024). 2024-12
https://app.dimensions.ai/details/publication/pub.1183053849
Treegraph: tree architecture from terrestrial laser scanning point clouds. 2024-12
https://app.dimensions.ai/details/publication/pub.1172398715
Bitemporal Radiative Transfer Modeling Using Bitemporal 3D-Explicit Forest Reconstruction from Terrestrial Laser Scanning. 2024-09-29
DOI: https://doi.org/10.3390/rs16193639
Benchmarking Instance Segmentation in Terrestrial Laser Scanning Forest Point Clouds. 2024-09-05
https://app.dimensions.ai/details/publication/pub.1175489962
Quantifying Forest Dynamics with Terrestrial Laser Scanning Data. 2024-09-05
https://app.dimensions.ai/details/publication/pub.1175488329
New tree height allometries derived from terrestrial laser scanning reveal substantial discrepancies with forest inventory methods in tropical rainforests. 2024-08-19
https://app.dimensions.ai/details/publication/pub.1174873772
Multi-scale lidar measurements suggest miombo woodlands contain substantially more carbon than thought. 2024-07-10
DOI: https://doi.org/10.1038/s43247-024-01448-x
Tree Surface Area Allometry. 2024-04-28
DOI: https://doi.org/10.1101/2024.04.23.590783
Limitations of estimating branch volume from terrestrial laser scanning. 2024-04
DOI: https://doi.org/10.1007/s10342-023-01651-z
Giant sequoia ( Sequoiadendron giganteum ) in the UK: carbon storage potential and growth rates. 2024-03
DOI: https://doi.org/10.1098/rsos.230603
Consistent patterns of common species across tropical tree communities. 2024-01-10
https://app.dimensions.ai/details/publication/pub.1167787638
Consistent and scalable monitoring of birds and habitats along a coffee production intensity gradient. 2024
https://app.dimensions.ai/details/publication/pub.1173878529
Understanding different dominance patterns in western Amazonian forests. 2023-12-18
https://app.dimensions.ai/details/publication/pub.1167118505
Reconstructing the digital twin of forests from a 3D library: Quantifying trade-offs for radiative transfer modeling. 2023-12-01
https://app.dimensions.ai/details/publication/pub.1164477231
TLS2trees: A scalable tree segmentation pipeline for TLS data. 2023-12
DOI: https://doi.org/10.1111/2041-210X.14233
Monitoring canopy quality and improving equitable outcomes of urban tree planting using LiDAR and machine learning. 2023-11
https://app.dimensions.ai/details/publication/pub.1164831658
Sensitivity of South American tropical forests to an extreme climate anomaly. 2023-09-04
https://app.dimensions.ai/details/publication/pub.1163807511
Benchmarking airborne laser scanning tree segmentation algorithms in broadleaf forests shows high accuracy only for canopy trees. 2023-09
https://app.dimensions.ai/details/publication/pub.1164179403
TomoSense: A unique 3D dataset over temperate forest combining multi-frequency mono- and bi-static tomographic SAR with terrestrial, UAV and airborne lidar, and in-situ forest census. 2023-05-15
https://app.dimensions.ai/details/publication/pub.1156418122
Toward a forest biomass reference measurement system for remote sensing applications. 2023-02
DOI: https://doi.org/10.1111/gcb.16497
Basin-wide variation in tree hydraulic safety margins predicts the carbon balance of Amazon forests. 2023-01-01
https://app.dimensions.ai/details/publication/pub.1157512341
Analysing individual 3D tree structure using the R package ITSMe. 2023-01
DOI: https://doi.org/10.1111/2041-210X.14026
Using terrestrial laser scanning to evaluate non-destructive aboveground biomass allometries in diverse Northern California forests. 2023
https://app.dimensions.ai/details/publication/pub.1157910384
TLS2trees: a scalable tree segmentation pipeline for TLS data. 2022-12-11
DOI: https://doi.org/10.1101/2022.12.07.518693
Laser scanning reveals potential underestimation of biomass carbon in temperate forest. 2022-10
DOI: https://doi.org/10.1002/2688-8319.12197
Reliably mapping low-intensity forest disturbance using satellite radar data. 2022-09-26
https://app.dimensions.ai/details/publication/pub.1151326196
Sentinel-1 Shadows Used to Quantify Canopy Loss from Selective Logging in Gabon. 2022-08-27
DOI: https://doi.org/10.3390/rs14174233
Water table depth modulates productivity and biomass across Amazonian forests. 2022-08-01
https://app.dimensions.ai/details/publication/pub.1148072538
Estimating forest above‐ground biomass with terrestrial laser scanning: Current status and future directions. 2022-08
DOI: https://doi.org/10.1111/2041-210X.13906
Implications of 3D Forest Stand Reconstruction Methods for Radiative Transfer Modeling: A Case Study in the Temperate Deciduous Forest. 2022-07-27
DOI: https://doi.org/10.1029/2021JD036175
Gaining insight into the allometric scaling of trees by utilizing 3d reconstructed tree models – a SimpleForest study. 2022-05-05
DOI: https://doi.org/10.1101/2022.05.05.490069
Comparing Remote Sensing and Field-Based Approaches to Estimate Ladder Fuels and Predict Wildfire Burn Severity. 2022-04-06
https://app.dimensions.ai/details/publication/pub.1146929525
Quantifying tropical forest structure through terrestrial and UAV laser scanning fusion in Australian rainforests. 2022-03-15
https://app.dimensions.ai/details/publication/pub.1145094224
Detecting Tropical Forest Degradation Using Optical Satellite Data: An Experiment in Peru Show Texture at 3 M Gives Best Results. 2022-02-09
DOI: https://doi.org/10.20944/preprints202202.0141.v1
An Effective Method for InSAR Mapping of Tropical Forest Degradation in Hilly Areas. 2022-01-18
DOI: https://doi.org/10.3390/rs14030452
Tree segmentation in airborne laser scanning data is only accurate for canopy trees. 2022
https://app.dimensions.ai/details/publication/pub.1153332261
Using terrestrial laser scanning to constrain forest ecosystem structure and functions in the Ecosystem Demography model (ED2.2). 2022
https://app.dimensions.ai/details/publication/pub.1148856851
Terrestrial laser scanning to reconstruct branch architecture from harvested branches. 2021-12
DOI: https://doi.org/10.1111/2041-210X.13709
To What Extent Can UAV Photogrammetry Replicate UAV LiDAR to Determine Forest Structure? A Test in Two Contrasting Tropical Forests. 2021-12
DOI: https://doi.org/10.1029/2021JG006586
Remote sensing and the UN Ocean Decade: high expectations, big opportunities. 2021-11-26
https://app.dimensions.ai/details/publication/pub.1143203004
An Effective Method for InSAR Mapping of Tropical Forest Degradation in Hilly Areas. 2021-11-09
DOI: https://doi.org/10.20944/preprints202111.0189.v1
How can we know what we don't know? A Commentary on: Sampling forests with terrestrial laser scanning. 2021-11-02
https://app.dimensions.ai/details/publication/pub.1141039064
Taking the pulse of Earth's tropical forests using networks of highly distributed plots. 2021-08-01
https://app.dimensions.ai/details/publication/pub.1138302141
SimpleForest – a comprehensive tool for 3d reconstruction of trees from forest plot point clouds. 2021-07-30
DOI: https://doi.org/10.1101/2021.07.29.454344
Amazon tree dominance across forest strata. 2021-06-01
https://app.dimensions.ai/details/publication/pub.1136851403
Using terrestrial laser scanning to constrain forest ecosystem structure and functions in the Ecosystem Demography model (ED2.2). 2021-04-12
https://app.dimensions.ai/details/publication/pub.1137160243
New insights into large tropical tree mass and structure from direct harvest and terrestrial lidar. 2021-02
DOI: https://doi.org/10.1098/rsos.201458
Terrestrial laser scanning in forest ecology: Expanding the horizon. 2020-12-15
https://app.dimensions.ai/details/publication/pub.1131196207
Canopy wetness in the Eastern Amazon. 2020-11-24
https://app.dimensions.ai/details/publication/pub.1132897684
New 3D measurements of large redwood trees for biomass and structure. 2020-10-15
https://app.dimensions.ai/details/publication/pub.1131738799
The mechanical stability of the world’s tallest broadleaf trees. 2020-10-05
https://app.dimensions.ai/details/publication/pub.1131427684
Old growth Afrotropical forests critical for maintaining forest carbon. 2020-10-01
https://app.dimensions.ai/details/publication/pub.1129411251
Tree Species Classification Using Structural Features Derived From Terrestrial Laser Scanning. 2020-10
https://app.dimensions.ai/details/publication/pub.1130236679
Quantifying urban forest structure with open-access remote sensing data sets. 2020-04
DOI: https://doi.org/10.1016/j.ufug.2020.126653
Transpiration from subarctic deciduous woodlands: Environmental controls and contribution to ecosystem evapotranspiration. 2020-04
DOI: https://doi.org/10.1002/eco.2190
3D forest model-assisted validation of the Sentinel-2 SNAP fAPAR product. 2020-03-23
DOI: https://doi.org/10.5194/egusphere-egu2020-7124
Time for a Plant Structural Economics Spectrum. 2020-03-23
DOI: https://doi.org/10.5194/egusphere-egu2020-8670
Assessment of bias in pan-tropical biomass predictions. 2020-02-20
https://app.dimensions.ai/details/publication/pub.1125022558
3D Imaging Insights into Forests and Coral Reefs. 2020-01-01
https://app.dimensions.ai/details/publication/pub.1122309401
New insights into large tropical tree mass and structure from direct harvest and terrestrial lidar. 2020
https://app.dimensions.ai/details/publication/pub.1131374856
Tree height in tropical forest as measured by different ground, proximal, and remote sensing instruments, and impacts on above ground biomass estimates. 2019-10-01
https://app.dimensions.ai/details/publication/pub.1117324129
Theoretical uncertainties for global satellite-derived burned area estimates. 2019-08-23
DOI: https://doi.org/10.5194/bg-16-3147-2019
Time for a Plant Structural Economics Spectrum. 2019-08-06
https://app.dimensions.ai/details/publication/pub.1120138169
Innovations in Ground and Airborne Technologies as Reference and for Training and Validation: Terrestrial Laser Scanning (TLS). 2019-07-29
DOI: https://doi.org/10.1007/s10712-019-09527-x
Ground Data are Essential for Biomass Remote Sensing Missions. 2019-07
https://app.dimensions.ai/details/publication/pub.1113785796
The Importance of Consistent Global Forest Aboveground Biomass Product Validation. 2019-07
https://app.dimensions.ai/details/publication/pub.1116113249
The World's Tallest Tropical Tree in Three Dimensions. 2019-06-18
https://app.dimensions.ai/details/publication/pub.1117336195
An architectural understanding of natural sway frequencies in trees. 2019-06
https://app.dimensions.ai/details/publication/pub.1116675289
Terrestrial LiDAR: a three‐dimensional revolution in how we look at trees. 2019-06
DOI: https://doi.org/10.1111/nph.15517
Leaf and wood classification framework for terrestrial LiDAR point clouds. 2019-05
DOI: https://doi.org/10.1111/2041-210X.13144
Theoretical uncertainties for global satellite-derived burned area estimates. 2019-04-03
DOI: https://doi.org/10.5194/bg-2019-115
Extracting individual trees from lidar point clouds using treeseg. 2019-03
DOI: https://doi.org/10.1111/2041-210X.13121
Performance of Laser-Based Electronic Devices for Structural Analysis of Amazonian Terra-Firme Forests. 2019-03
DOI: https://doi.org/10.3390/rs11050510
Finite element analysis of trees in the wind based on terrestrial laser scanning data. 2019-02
DOI: https://doi.org/10.1016/j.agrformet.2018.11.014
A New Architectural Perspective on Wind Damage in a Natural Forest. 2019-01-07
https://app.dimensions.ai/details/publication/pub.1111211242
New estimates of leaf angle distribution from terrestrial LiDAR: Comparison with measured and modelled estimates from nine broadleaf tree species. 2019-01
DOI: https://doi.org/10.1016/j.agrformet.2018.10.021
The mechanical stability of the world’s tallest broadleaf trees. 2019
https://app.dimensions.ai/details/publication/pub.1117020591
Decoupling Canopy Structure and Leaf Biochemistry: Testing the Utility of Directional Area Scattering Factor (DASF). 2018-11-29
DOI: https://doi.org/10.3390/rs10121911
Detecting Human Presence and Influence on Neotropical Forests with Remote Sensing. 2018-10-05
DOI: https://doi.org/10.3390/rs10101593
Simulating arbitrary hyperspectral bandsets from multispectral observations via a generic Earth Observation-Land Data Assimilation System (EO-LDAS). 2018-10-01
https://app.dimensions.ai/details/publication/pub.1105772110
Author Correction: Strong constraint on modelled global carbon uptake using solar-induced chlorophyll fluorescence data. 2018-07-05
https://app.dimensions.ai/details/publication/pub.1105306003
Estimating urban above ground biomass with multi-scale LiDAR. 2018-06-26
https://app.dimensions.ai/details/publication/pub.1104690863
Realistic Forest Stand Reconstruction from Terrestrial LiDAR for Radiative Transfer Modelling. 2018-06
DOI: https://doi.org/10.3390/rs10060933
Variability and bias in active and passive ground-based measurements of effective plant, wood and leaf area index. 2018-04-15
https://app.dimensions.ai/details/publication/pub.1101111317
New perspectives on the ecology of tree structure and tree communities through terrestrial laser scanning. 2018-04-06
DOI: https://doi.org/10.1098/rsfs.2017.0052
Non-intersecting leaf insertion algorithm for tree structure models. 2018-04-06
DOI: https://doi.org/10.1098/rsfs.2017.0045
The terrestrial laser scanning revolution in forest ecology. 2018-04-06
DOI: https://doi.org/10.1098/rsfs.2018.0001
Weighing trees with lasers: advances, challenges and opportunities. 2018-04-06
DOI: https://doi.org/10.1098/rsfs.2017.0048
Plant Structure-Function Relationships and Woody Tissue Respiration: Upscaling to Forests from Laser-Derived Measurements.. 2018-03-05
https://app.dimensions.ai/details/publication/pub.1101126682
Estimation of above‐ground biomass of large tropical trees with terrestrial LiDAR. 2018-02
DOI: https://doi.org/10.1111/2041-210X.12904
Strong constraint on modelled global carbon uptake using solar-induced chlorophyll fluorescence data. 2018-01-31
https://app.dimensions.ai/details/publication/pub.1100658003
Validating canopy clumping retrieval methods using hemispherical photography in a simulated Eucalypt forest. 2017-12-15
https://app.dimensions.ai/details/publication/pub.1092114550
Data acquisition considerations for Terrestrial Laser Scanning of forest plots. 2017-07
https://app.dimensions.ai/details/publication/pub.1085393398
Remote Sensing in Ecology and Conservation: three years on. 2017-06
https://app.dimensions.ai/details/publication/pub.1090287673
Influence of levelling technique on the retrieval of canopy structural parameters from digital hemispherical photography. 2017-05-01
https://app.dimensions.ai/details/publication/pub.1083893750
Evaluation of the Range Accuracy and the Radiometric Calibration of Multiple Terrestrial Laser Scanning Instruments for Data Interoperability. 2017-02-17
https://app.dimensions.ai/details/publication/pub.1083936851
Vegetation structure (LiDAR). 2017-01-01
https://app.dimensions.ai/details/publication/pub.1086225756
Measurement of fine-spatial-resolution 3D vegetation structure with airborne waveform lidar: Calibration and validation with voxelised terrestrial lidar. 2016-11-11
https://app.dimensions.ai/details/publication/pub.1034201465
Large-area virtual forests from terrestrial laser scanning data. 2016-11-03
https://app.dimensions.ai/details/publication/pub.1093362603
Terrestrial Laser Scanning for Plot-Scale Forest Measurement (vol 1, pg 239, 2015). 2016-09-01
https://app.dimensions.ai/details/publication/pub.1041243450
African Savanna-Forest Boundary Dynamics: A 20-Year Study. 2016-06-23
https://app.dimensions.ai/details/publication/pub.1048593540
Quantifying the impact of woody material on leaf area index estimation from hemispherical photography using 3D canopy simulations. 2016-05-25
https://app.dimensions.ai/details/publication/pub.1040406978
A New Global fAPAR and LAI Dataset Derived from Optimal Albedo Estimates: Comparison with MODIS Products. 2016-03
http://www.mdpi.com/2072-4292/8/4/275
Efficient Emulation of Radiative Transfer Codes Using Gaussian Processes and Application to Land Surface Parameter Inferences. 2016-02-01
https://app.dimensions.ai/details/publication/pub.1000798196
Is waveform worth it? A comparison of LiDAR approaches for vegetation and landscape characterisation. 2016-02-01
https://app.dimensions.ai/details/publication/pub.1030161072
An improved theoretical model of canopy gap probability for Leaf Area Index estimation in woody ecosystems. 2015-12-15
https://app.dimensions.ai/details/publication/pub.1023474933
Terrestrial Laser Scanning for Plot-Scale Forest Measurement. 2015-12
https://app.dimensions.ai/details/publication/pub.1038307186
SimpleTree-An Efficient Open Source Tool to Build Tree Models from TLS Clouds. 2015-11
https://app.dimensions.ai/details/publication/pub.1051307023
The fourth phase of the radiative transfer model intercomparison (RAMI) exercise: Actual canopy scenarios and conformity testing. 2015-11
https://app.dimensions.ai/details/publication/pub.1020039139
Waveform lidar over vegetation: An evaluation of inversion methods for estimating return energy. 2015-07
https://app.dimensions.ai/details/publication/pub.1000230654
Nondestructive estimates of above-ground biomass using terrestrial laser scanning. 2015-02
https://app.dimensions.ai/details/publication/pub.1019027068
Sensitivity of direct canopy gap fraction retrieval from airborne waveform lidar to topography and survey characteristics. 2014-03-05
https://app.dimensions.ai/details/publication/pub.1051197041
Developing a dual-wavelength full-waveform terrestrial laser scanner to characterize forest canopy structure. 2014-01-01
https://app.dimensions.ai/details/publication/pub.1025405643
Highly accurate tree models derived from terrestrial laser scan data: A method description. 2014-01-01
https://app.dimensions.ai/details/publication/pub.1016519329
Rapid characterisation of forest structure from TLS and 3D modelling. 2013-12-01
https://app.dimensions.ai/details/publication/pub.1094319667
The impact of sensor characteristics for obtaining accurate ground-based measurements of LAI. 2013-12-01
https://app.dimensions.ai/details/publication/pub.1094892314
The fourth radiation transfer model intercomparison (RAMI‐IV): Proficiency testing of canopy reflectance models with ISO‐13528. 2013-07-16
https://app.dimensions.ai/details/publication/pub.1027139450
Reply to Ollinger et al.: Remote sensing of leaf nitrogen and emergent ecosystem properties. 2013-07-02
https://app.dimensions.ai/details/publication/pub.1006174625
Direct retrieval of canopy gap probability using airborne waveform lidar. 2013-07-01
https://app.dimensions.ai/details/publication/pub.1023402133
Investigating assumptions of crown archetypes for modelling LiDAR returns. 2013-07
https://app.dimensions.ai/details/publication/pub.1021116848
Reply to Townsend et al.: Decoupling contributions from canopy structure and leaf optics is critical for remote sensing leaf biochemistry. 2013-03-19
https://app.dimensions.ai/details/publication/pub.1016887862
Fast Automatic Precision Tree Models from Terrestrial Laser Scanner Data. 2013-02
https://app.dimensions.ai/details/publication/pub.1004552166
Measuring forests with dual wavelength lidar: A simulation study over topography. 2012-08-15
https://app.dimensions.ai/details/publication/pub.1035832448
Terrestrial ecosystems from space: A review of earth observation products for macroecology applications. 2012-06-01
https://app.dimensions.ai/details/publication/pub.1031516621
<b>SAGE Handbook of Remote Sensing</b>, 1st edn, edited by G. Foody, T. Warner and D. Nellis. 2012-01-10
https://app.dimensions.ai/details/publication/pub.1048040636
Effects of clumping on modelling LiDAR waveforms in forest canopies.. 2012
https://app.dimensions.ai/details/publication/pub.1095460995
A threshold insensitive method for locating the forest canopy top with waveform lidar. 2011-12-15
https://app.dimensions.ai/details/publication/pub.1043822365
3D radiative transfer modelling of fire impacts on a two-layer savanna system. 2011-08-15
https://app.dimensions.ai/details/publication/pub.1007190054
An assessment of the MODIS collection 5 leaf area index product for a region of mixed coniferous forest. 2011-02-15
https://app.dimensions.ai/details/publication/pub.1021946225
On canopy spectral invariants and hyperspectral ray tracing. 2010-11-29
https://app.dimensions.ai/details/publication/pub.1095389791
Simulating the impact of discrete-return lidar system and survey characteristics over young conifer and broadleaf forests. 2010-03-16
https://app.dimensions.ai/details/publication/pub.1012916502
Modelling the impact of wildfire on spectral reflectance. 2009-12-01
https://app.dimensions.ai/details/publication/pub.1094242283
Satellite monitoring of disturbances in Arctic ecosystems. 2009-12-01
https://app.dimensions.ai/details/publication/pub.1094926378
Quantifying Surface Reflectivity for Spaceborne Lidar via Two Independent Methods. 2009-09
https://app.dimensions.ai/details/publication/pub.1061611025
Upscaling as ecological information transfer: a simple framework with application to Arctic ecosystem carbon exchange. 2009-08
https://app.dimensions.ai/details/publication/pub.1003428896
Impact of land cover uncertainties on estimates of biospheric carbon fluxes. 2008-12-04
https://app.dimensions.ai/details/publication/pub.1005290691
Estimating the spatial exchange of carbon through the assimilation of earth observation derived products using an ensemble kalman filter. 2008-12-01
https://app.dimensions.ai/details/publication/pub.1095185983
Extracting tree heights over topography with multi-spectral spaceborne waveform lidar. 2008-12-01
https://app.dimensions.ai/details/publication/pub.1094166544
Quantifying surface reflectivity for spaceborne lidar missions. 2008-12-01
https://app.dimensions.ai/details/publication/pub.1095312821
Using remote sensing data to quantify changes in vegetation over peatland areas. 2008-12-01
https://app.dimensions.ai/details/publication/pub.1094532659
Assimilating canopy reflectance data into an Ecosystem Model with an Ensemble Kalman Filter. 2008-09-10
https://app.dimensions.ai/details/publication/pub.1039013541
The RAMI On-line model checker (ROMC): A web-based benchmarking facility for canopy reflectance models. 2008
https://app.dimensions.ai/details/publication/pub.1016778136
Using Satellite Observations in Regional Scale Calculations of Carbon Exchange.. 2008
https://app.dimensions.ai/details/publication/pub.1026619865
Spectral invariants and scattering across multiple scales from within-leaf to canopy. 2007-07-30
https://app.dimensions.ai/details/publication/pub.1000804940
Third Radiation transfer Model Intercomparison (RAMI) exercise: Documenting progress in canopy reflectance models. 2007-05-08
https://app.dimensions.ai/details/publication/pub.1044068937
Assimilating reflectance data into an ecosystem model to improve estimates of terrestrial carbon fluxes.. 2007
https://app.dimensions.ai/details/publication/pub.1093558577
Canopy spectral invariants for remote sensing and model applications. 2007
https://app.dimensions.ai/details/publication/pub.1011001016
3D modelling of forest canopy structure for remote sensing simulations in the optical and microwave domains. 2006-01-15
https://app.dimensions.ai/details/publication/pub.1035442265
Fluorescence explorer (FLEX): An optimised payload to map vegetation photosynthesis from space. 2006-01-01
https://app.dimensions.ai/details/publication/pub.1049552915
Comparison of MODIS broadband albedo over an agricultural site with ground measurements and values derived from Earth observation data at a range of spatial scales.. 2004-12-01
https://app.dimensions.ai/details/publication/pub.1032963256
Coupling a canopy reflectance model with a global vegetation model. 2004-12-01
https://app.dimensions.ai/details/publication/pub.1095430270
Radiation Transfer Model Intercomparison (RAMI) exercise: Results from the second phase – art. no. D06210. 2004-03-25
https://app.dimensions.ai/details/publication/pub.1039603748
Inter-comparison of phenological measures derived from coarse resolution earth observation and implications for assimilation into dynamic vegetation models. 2003-11-24
https://app.dimensions.ai/details/publication/pub.1095476489
Modelling the radiometric response of a dynamic, 3D structural model of wheat in the optical and microwave domains. 2003-11-24
https://app.dimensions.ai/details/publication/pub.1093233200
Biophysical parameter retrieval from forest and crop canopies in the optical and microwave domains using 3D models of canopy structure. 2003
https://app.dimensions.ai/details/publication/pub.1094174310
First operational BRDF, albedo nadir reflectance products from MODIS. 2002-11-01
https://app.dimensions.ai/details/publication/pub.1011527589
On the potential of CHRIS/PROBA for estimating vegetation canopy properties from space. 2000-01-01
https://app.dimensions.ai/details/publication/pub.1011609312
Monte Carlo ray tracing in optical canopy reflectance modelling. 2000
https://app.dimensions.ai/details/publication/pub.1025753754
The Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF and albedo product: preliminary results. 2000
https://app.dimensions.ai/details/publication/pub.1093228974
Validation of a manual measurement method for deriving 3D canopy structure using the BPMS. 1998-01-01
https://app.dimensions.ai/details/publication/pub.1094907128
An investigation of how linear BRDF models deal with the complex scattering processes encountered in a real canopy. 1998
https://app.dimensions.ai/details/publication/pub.1095593935
The Botanical Plant Modelling System (BPMS): a case study of multiple scattering in a barley canopy. 1998
https://app.dimensions.ai/details/publication/pub.1094926789
A radar backscatter simulation of a forest canopy using 3D physical structures derived from LiDAR scanning.
https://app.dimensions.ai/details/publication/pub.1181117020
Carbon storage in peatlands: A case study on the Isle of Man.
https://app.dimensions.ai/details/publication/pub.1052922038
Upscaling tundra CO2 exchange from chamber to eddy covariance tower.
https://app.dimensions.ai/details/publication/pub.1020123027



