Exploring the Influence of Forest Tenure and Protection Status on Post-Fire Recovery in Southeast Australia

Research Highlights: We used Landsat time series data to investigate the role forest tenure and protection status play in the recovery of a forest after a fire. Background and Objectives: Changing fire regimes put forests in southeast Australia under increasing pressure. Our investigation aimed to explore the impact of different forest management structures on a forest’s resilience to fire by looking at the post-fire recovery duration. Materials and Methods: The analysis included a total of 60.6 Mha of land containing 25.4 Mha of forest in southeast Australia. Multispectral time series data from Landsat satellites and a local reference dataset were used to model attributes of disturbance and recovery over a period of 33 years. Results: Protected public forest spectrally recovered 0.4 years faster than protected private forest. No other significant effects in relation to different tenure and protection status were found. Climatic and topographic variables were found to have much greater influence on post-fire spectral recovery. Conclusions: Protected area status in public forests resulted in slightly faster recovery, compared with the private protected forest estate. However, factors outside the control of land managers and policy makers, i.e., climatic and topographic variables, appear to have a much greater impact on post-fire recovery

Intercomparison of Real and Simulated GEDI Observations across Sclerophyll Forests

Forest structure is an important variable in ecology, fire behaviour, and carbon management. New spaceborne lidar sensors, such as the Global Ecosystem Dynamics Investigation (GEDI), enable forest structure to be mapped at a global scale. Virtual GEDI-like observations can be derived from airborne laser scanning (ALS) data for given locations using the GEDI simulator, which was a tool initially developed for GEDI’s pre-launch calibration. This study compares the relative height (RH) and ground elevation metrics of real and simulated GEDI observations against ALS-derived benchmarks in southeast Australia. A total of 15,616 footprint locations were examined, covering a large range of forest types and topographic conditions. The impacts of canopy cover and height, terrain slope, and ALS point cloud density were assessed. The results indicate that the simulator produces more accurate canopy height (RH95) metrics (RMSE: 4.2 m, Bias: −1.3 m) than the actual GEDI sensor (RMSE: 9.6 m, Bias: −1.6 m). Similarly, the simulator outperforms GEDI in ground detection accuracy. In contrast to other studies, which favour the Gaussian algorithm for ground detection, we found that the Maximum algorithm performed better in most settings. Despite the determined differences between real and simulated GEDI observations, this study indicates the compatibility of both data sources, which may enable their combined use in multitemporal forest structure monitoring