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Tropical Forest Degradation Experiment

CORDIS provides links to public deliverables and publications of HORIZON projects.

Links to deliverables and publications from FP7 projects, as well as links to some specific result types such as dataset and software, are dynamically retrieved from OpenAIRE .

Deliverables

Data Management Plan

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Publications

Detecting Tropical Forest Degradation Using Optical Satellite Data: An Experiment in Peru Show Texture at 3 M Gives Best Results

Author(s): Chiara Aquino; Edward T. A. Mitchard; Iain M. McNicol; Harry Carstairs; Andrew Burt; Beisit Luz Puma Vilca; Sylvia Mayta; Mathias Disney
Published in: Preprints, Issue 1, 2022, ISSN 2310-287X
Publisher: MDPI
DOI: 10.20944/preprints202202.0141.v1

An Effective Method for InSAR Mapping of Tropical Forest Degradation in Hilly Areas

Author(s): Harry Carstairs; Edward T. A. Mitchard; Iain McNicol; Chiara Aquino; Andrew Burt; Medard Obiang Ebanega; Anaick Modinga Dikongo; Jose-Luis Bueso-Bello; Mathias Disney
Published in: Remote Sensing, Issue 1, 2022, ISSN 2072-4292
Publisher: Multidisciplinary Digital Publishing Institute (MDPI)
DOI: 10.3390/rs14030452

To What Extent Can UAV Photogrammetry Replicate UAV LiDAR to Determine Forest Structure? A Test in Two Contrasting Tropical Forests

Author(s): Iain M. McNicol; Edward T.A. Mitchard; Chiara Aquino; Andrew Burt; Harry Carstairs; Creck Dassi; Anaick Modinga Dikongo; Mathias I. Disney
Published in: JGR Biogeosciences, Issue 1, 2021, ISSN 2169-8961
Publisher: AGU
DOI: 10.1029/2021jg006586

Improving Estimates and Change Detection of Forest Above-Ground Biomass Using Statistical Methods

Author(s): Amber E. Turton; Nicole H. Augustin; Edward T. A. Mitchard
Published in: Remote Sensing, Vol 14, Iss 19, p 4911 (2022), Issue 3, 2022, ISSN 2072-4292
Publisher: Multidisciplinary Digital Publishing Institute (MDPI)
DOI: 10.3390/rs14194911

Estimating forest above‐ground biomass with terrestrial laser scanning: Current status and future directions

Author(s): Miro Demol; Hans Verbeeck; Bert Gielen; John Armston; Andrew Burt; Mathias Disney; Laura Duncanson; Jan Hackenberg; Daniel Kükenbrink; Alvaro Lau; Pierre Ploton; Artie Sewdien; Atticus Stovall; Stéphane Momo Takoudjou; Liubov Volkova; Christopher Weston; Verginia Wortel; Kim Calders
Published in: Methods in Ecology and Evolution , 13 (8) pp. . (2022), Issue 3, 2022, ISSN 2041-210X
Publisher: John Wiley and Sons Inc.
DOI: 10.1111/2041-210x.13906

Monitoring Mega-Crown Leaf Turnover from Space

Author(s): Bush, Emma R; Mitchard, Edward T A; Silva, Thiago S F; Dimoto, Edmond; Dimbonda, Pacôme; Makaga, Loïc; Abernethy, Katharine
Published in: Remote Sensing, Issue 1, 2020, ISSN 2072-4292
Publisher: Multidisciplinary Digital Publishing Institute (MDPI)
DOI: 10.3390/rs12030429

Reliably mapping low-intensity forest disturbance using satellite radar data

Author(s): Chiara Aquino; Edward T. A. Mitchard; Iain M. McNicol; Harry Carstairs; Andrew Burt; Beisit Luz Puma Vilca; Médard Obiang Ebanéga; Anaick Modinga Dikongo; Creck Dassi; Sylvia Mayta; Mario Tamayo; Pedro Grijalba; Fernando Miranda; Mathias Disney; Mathias Disney
Published in: Frontiers in Forests and Global Change, Issue 1, 2022, ISSN 2624-893X
Publisher: Frontiers
DOI: 10.3389/ffgc.2022.1018762

Sentinel-1 Shadows Used to Quantify Canopy Loss from Selective Logging in Gabon

Author(s): Harry Carstairs; Edward T. A. Mitchard; Iain McNicol; Chiara Aquino; Eric Chezeaux; Médard Obiang Ebanega; Anaick Modinga Dikongo; Mathias Disney
Published in: Remote Sensing; Volume 14; Issue 17; Pages: 4233, Issue 3, 2022, ISSN 2072-4292
Publisher: Multidisciplinary Digital Publishing Institute (MDPI)
DOI: 10.3390/rs14174233

Using Experimental Sites in Tropical Forests to Test the Ability of Optical Remote Sensing to Detect Forest Degradation at 0.3 - 30 M Resolutions

Author(s): Chiara Aquino; Edward T. A. Mitchard; Iain McNicol; Harry Carstairs; Andrew Burt; Beisit L. Puma Vilca; Mathias Disney
Published in: 2021 IEEE International GeoScience and Remote Sensing Symposium IGARSS, Issue 1, 2021
Publisher: IEEE
DOI: 10.1109/igarss47720.2021.9553389

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