Characterizing tropical evergreen forest disturbances and post-disturbance recovery using time-series Landsat canopy openings

Zhang, Yihang and Wang, Xia and Li, Xiaodong and Zhao, Wenqiong and Zhong, Xinyan and Yu, Bingjie and Du, Yun and Atkinson, Peter M. (2025) Characterizing tropical evergreen forest disturbances and post-disturbance recovery using time-series Landsat canopy openings. IEEE Transactions on Geoscience and Remote Sensing, 63: 4420823. pp. 1-23. ISSN 0196-2892

Text (Y Zhang et al IEEE TGRS 2025)
Y_Zhang_et_al_IEEE_TGRS_2025.docx - Accepted Version
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Text (Zhang et al IEEE TGRS 2025)
Zhang_et_al_IEEE_TGRS_2025.docx - Accepted Version
Available under License Creative Commons Attribution.
Download (21MB)

Abstract

Tropical evergreen forests, among the most diverse and complex forest ecosystem, host an extraordinary variety of plants and animal species, underscoring the importance of monitoring their spatio-temporal dynamics. Despite substantial advances in tracking tropical forest disturbances with Landsat images, understanding of post-disturbances recovery, particularly small-scale disturbances, remains limited. Here, by enhancing the subtle signal caused by small-scale disturbances, we propose a method (i.e., framework) to simultaneously monitor tropical evergreen forest disturbances and post-disturbance recovery using annual maximal Landsat canopy openings. A baseline evergreen forest cover map was automatically generated by integrating median Normalized Difference Fraction Index (NDFI) and maximal self-referenced NDFI (rNDFI) images. Utilizing the baseline map and long-term spatio-temporally filtered annual maximal rNDFI images, three additional thematic maps were produced: first forest disturbance year, inter-annual forest disturbance frequency, and post-disturbance forest recovery. For the study areas of Brazil Mato Grosso, the Democratic Republic of the Congo Kasaï and Indonesia Kalimantan Tengah, the results demonstrated that the proposed framework not only detected more forest disturbance events, particularly enhancing the small-scale disturbances, but also predicted forest disturbances more accurately than the Global Forest Change (GFC) forest loss and Joint Research Centre (JRC) Tropical Moist Forest (TMF) deforestation and degradation products. The generated inter-annual forest disturbance frequency and post-disturbance forest recovery maps exhibit high accuracies, with the R2, MAE and RMSE in the ranges 0.82-0.95, 0.47-1.15 and 1.33-2.88, respectively, which are more accurate than the benchmark results extracted from JRC TMF product. Our results are more sensitive to the inter-annual canopy opening and recovery of small-scale disturbances due to smallholder clearing and selective logging than JRC TMF. This research offers an effective framework for addressing the existing knowledge gap on post-change dynamics after forest disturbances and the net carbon change of tropical evergreen forests.