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Tracking small-scale tropical forest disturbances: Fusing the Landsat and Sentinel-2 data record

Zhang, Yihang; Ling, Feng; Wang, Xia; Foody, Giles M.; Boyd, Doreen S.; Li, Xiaodong; Du, Yun; Atkinson, Peter M.

Tracking small-scale tropical forest disturbances: Fusing the Landsat and Sentinel-2 data record Thumbnail


Authors

Yihang Zhang

Feng Ling

Xia Wang

GILES FOODY giles.foody@nottingham.ac.uk
Professor of Geographical Information

DOREEN BOYD doreen.boyd@nottingham.ac.uk
Professor of Earth Observation

Xiaodong Li

Yun Du

Peter M. Atkinson



Abstract

Information on forest disturbance is crucial for tropical forest management and global carbon cycle analysis. The long-term collection of data from the Landsat missions provides some of the most valuable information for understanding the processes of global tropical forest disturbance. However, there are substantial uncertainties in the estimation of non-mechanized, small-scale (i.e., small area) clearings in tropical forests with Landsat series images. Because the appearance of small-scale openings in a tropical tree canopy are often ephemeral due to fast-growing vegetation, and because clouds are frequent in tropical regions, it is challenging for Landsat images to capture the logging signal. Moreover, the spatial resolution of Landsat images is typically too coarse to represent spatial details about small-scale clearings. In this paper, by fusing all available Landsat and Sentinel-2 images, we proposed a method to improve the tracking of small-scale tropical forest disturbance history with both fine spatial and temporal resolutions. First, yearly composited Landsat and Sentinel-2 self-referenced normalized burn ratio (rNBR) vegetation index images were calculated from all available Landsat-7/8 and Sentinel-2 scenes during 2016–2019. Second, a deep-learning based downscaling method was used to predict fine resolution (10 m) rNBR images from the annual coarse resolution (30 m) Landsat rNBR images. Third, given the baseline Landsat forest map in 2015, the generated fine-resolution Landsat rNBR images and original Sentinel-2 rNBR images were fused to produce the 10 m forest disturbance map for the period 2016–2019. From data comparison and evaluation, it was demonstrated that the deep-learning based downscaling method can produce fine-resolution Landsat rNBR images and forest disturbance maps that contain substantial spatial detail. In addition, by fusing downscaled fine-resolution Landsat rNBR images and original Sentinel-2 rNBR images, it was possible to produce state-of-the-art forest disturbance maps with OA values more than 87% and 96% for the small and large study areas, and detected 11% to 21% more disturbed areas than either the Sentinel-2 or Landsat-7/8 time-series alone. We found that 1.42% of the disturbed areas indentified during 2016–2019 experienced multiple forest disturbances. The method has great potential to enhance work undertaken in relation to major policies such as the reducing emissions from deforestation and forest degradation (REDD+) programmes.

Citation

Zhang, Y., Ling, F., Wang, X., Foody, G. M., Boyd, D. S., Li, X., …Atkinson, P. M. (2021). Tracking small-scale tropical forest disturbances: Fusing the Landsat and Sentinel-2 data record. Remote Sensing of Environment, 261, Article 112470. https://doi.org/10.1016/j.rse.2021.112470

Journal Article Type Article
Acceptance Date Apr 21, 2021
Online Publication Date May 3, 2021
Publication Date Aug 1, 2021
Deposit Date Aug 31, 2022
Publicly Available Date Sep 7, 2022
Journal Remote Sensing of Environment
Print ISSN 0034-4257
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 261
Article Number 112470
DOI https://doi.org/10.1016/j.rse.2021.112470
Keywords Computers in Earth Sciences; Geology; Soil Science
Public URL https://nottingham-repository.worktribe.com/output/5530819
Publisher URL https://www.sciencedirect.com/science/article/abs/pii/S0034425721001887?via%3Dihub

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