Machine Learning-driven Fusion of NASA’s ICESat-2 and Landsat 8 OLI Data for Assessing Forest Recovery Following Hurricane Disturbance in Southern Forests
- Forest Biometrics, Remote Sensing and Artificial Intelligence Laboratory (Silva Lab), School of Forest, Fisheries, and Geomatics Sciences, University of Florida, PO Box 110410, Gainesville, FL 32611,USA
The Southern U.S. hosts some of the most productive forests globally, playing a crucial role in the U.S. terrestrial carbon sink and contributing significantly to timber production (over 60% in commercial terms). Despite their importance, these forests face frequent hurricanes, leading to substantial harm to the forest structure and related ecosystem functions. This damage extends to the loss of timber supply, heightened wildfire risk, and diminished recreational opportunities. With an increasing frequency of hurricanes in the region, accurately gauging the harm inflicted on these forests becomes imperative for formulating effective protective measures and comprehending the dynamics of forest recovery.
To address this issue, the study aimed to create a data fusion framework utilizing NASA's ICESat-2 and Landsat 8 OLI for mapping large-scale canopy height. This mapping would then be employed to assess the severity of post-hurricane disturbance damage and monitor forest recovery. The research utilized ICESat-2-derived canopy height estimates to calibrate a Random Forest model, predicting and mapping canopy height both before and after Hurricane Michael. The findings revealed that a combination of spectral bands and vegetation indices from Landsat 8 explained a significant portion of the canopy height variation. In areas heavily affected by Hurricane Michael in 2018, the average canopy height dropped from approximately 18 meters to 12 meters in 2019, experienced a slight increase to around 12.5 meters in 2021, and reached about 13 meters in 2022. Despite three years post-Hurricane Michael, the canopy height did not fully recover to pre-disturbance conditions.
This research introduces an innovative approach to enhance forest structure mapping by integrating ICESat-2 and Landsat 8 data streams. The advancement in data fusion methodology provides an opportunity for more precise and detailed assessments of the impacts of natural disasters, such as hurricanes, on forest ecosystems in the Southern U.S
How to cite: Silva, C. A., Hamamura, C., and Klauberg, C.: Machine Learning-driven Fusion of NASA’s ICESat-2 and Landsat 8 OLI Data for Assessing Forest Recovery Following Hurricane Disturbance in Southern Forests, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6928, https://doi.org/10.5194/egusphere-egu24-6928, 2024.
