matthew-j-payne Goto Github PK

Dalagnol, R., Wagner, FH., Galvão, LS., Braga, D., Osborn, F., da Conceição Bispo, P., Payne, M., Junior, CS., Favrichon, S., Silgueiro, V. 2023. Mapping tropical forest degradation with deep learning and Planet NICFI data. Remote Sensing of Environment, 298, 113798, https://doi.org/10.1016/j.rse.2023.113798

Naciri, W., Boom, A., Payne, M., Browne, N., Evans, NJ., Holdship, P., Rankenburg, K., Nagarajan, R., McDonald, B., McIlwain, J. 2023, Massive corals record deforestation in Malaysian Borneo through sediments in river discharge. Biogeosciences, 20, 1587-1604, https://doi.org/10.5194/bg-20-1587-2023

Cole, B., Smith, G., de la Barreda-Bautista, B., Hamer, A., Payne, M., Codd, T., Johnson, S., Chan, LK., Balzter, H. 2022, Dynamic landscapes in the UK driven by pressures from energy production and forestry—Results of the CORINE Land Cover map 2018. Land, 11, 192, https://doi.org/10.3390/land11020192

Payne, M. 2021, Satellite remote sensing of deforestation for oil palm. Nature Reviews Earth & Environment, 2, 230-230, https://doi.org/10.1038/s43017-021-00159-7

• Mapping agriculture across Nigeria to understand the ideal locations for renewable energy units.
• Evaluating whether the spectral signature of Wheat affected by the Wheat Blast fungal pathogen, Magnaporthe oryzae, can be scaled to the remote sensing scale, enabling efficient mapping of the disease.
• Mapping the deforestation footprint of oil palm in tropical forests (South America, Central Africa and South-East Asia)

Pyeo (https://github.com/clcr/pyeo) is a near-real-time (NRT) deforestation monitoring library (some literature - https://www.mdpi.com/2072-4292/14/3/707). The library also acts as a way to query, specify Region of Interest (ROI) and download Sentinel-2 imagery using the Copernicus DataSpace Ecosystem (CDSE).

My thesis was titled - "Exploring the deforestation dynamics of oil palm expansion in the Central Peruvian Amazon".
In this thesis, I used satellite imagery to explore the promotion of deforestation by oil palm expansion, the drivers of oil palm expansion from deforestation and mapped oil palm using Convolutional Neural Networks (CNN) at an unprecedented spatial resolution.