Satellite earth observation is being increasingly used to monitor forests across the world. Freely available Landsat data stretching back four decades, coupled with advances in computer processing capabilities, has enabled new time-series techniques for analyzing forest change. Typically, these methods track individual pixel values over time, through the use of various spectral indices. This study examines the utility of eight spectral indices for characterizing fire disturbance and recovery in sclerophyll forests, in order to determine their relative merits in the context of Landsat time-series. Although existing research into Landsat indices is comprehensive, this study presents a new approach, by comparing the distributions of pre and post-fire pixels using Glass’s delta, for evaluating indices without the need of detailed field information. Our results show that in the sclerophyll forests of southeast Australia, common indices, such as the Normalized Difference Vegetation Index (NDVI) and the Normalized Burn Ratio (NBR), both accurately capture wildfire disturbance in a pixel-based time-series approach, especially if images from soon after the disturbance are available. However, for tracking forest regrowth and recovery, indices, such as NDVI, which typically capture chlorophyll concentration or canopy ‘greenness’, are not as reliable, with values returning to pre-fire levels in 3–5 years. In comparison, indices that are more sensitive to forest moisture and structure, such as NBR, indicate much longer (8–10 years) recovery timeframes. This finding is consistent with studies that were conducted in other forest types. We also demonstrate that additional information regarding forest condition, particularly in relation to recovery, can be extracted from less well known indices, such as NBR2, as well as textural indices incorporating spatial variance. With Landsat time-series gaining in popularity in recent years, it is critical to understand the advantages and limitations of the various indices that these methods rely on.

The free open access data policy instituted for the Landsat archive since 2008 has revolutionised the use of Landsat data for forest monitoring, especially for estimating forest aboveground biomass (AGB). This paper provides a comprehensive review of recent approaches utilising Landsat time-series (LTS) for estimating AGB and its dynamics across space and time. In particular, we focus on reviewing: (1) how LTS has been utilised to improve the estimation of AGB (for both single-date and over time) and (2) recent LTS-based approaches used for estimating AGB and its dynamics across space and time. In contrast to using single-date images, the use of LTS can benefit forest AGB estimation in two broad areas. First, using LTS allows for the filling of spatial and temporal data gaps in AGB predictions, improving the quality of AGB products and enabling the estimation of AGB across large areas and long time-periods. Second, studies have demonstrated that spectral information extracted from LTS analysis, including forest disturbance and recovery metrics, can significantly improve the accuracy of AGB models. Throughout the last decade, many innovative LTS-based approaches for estimating forest AGB dynamics across space and time have been demonstrated. A general trend is that methods have evolved as demonstrated through recent studies, becoming more advanced and robust. However, most of these methods have been developed and tested in areas that are either supported by established forest inventory programs and/or can rely on Lidar data across large forest areas. Further investigations should focus on tropical forest areas where inventory data are often not systematically available and/or out-of-date.

The wildfires in south-eastern Australia in the 2019–20 fire season were some of the worst in recent memory. The effectiveness of fuel-reduction burning as a risk mitigation strategy is, once again, being scrutinised. Some argue that more fuel-reduction burning is needed, while others suggest that it is of limited use in such extreme fires. In this study, we tested the effectiveness of fuel-reduction burning at a landscape scale in terms of its ability to reduce the severity of subsequent wildfire. To achieve this, we selected all the recent (2015–2019) fuel-reduction burns undertaken in New South Wales and Victoria that intersected with the extent of the 2019–20 wildfires and evaluated whether the fire severity was significantly different in the recently treated areas to that of similar untreated areas in the vicinity. To determine fire severity, Sentinel 2 satellite imagery and the change in normalised burn ratio (a common metric used for rapid and broadscale fire severity mapping) was used. Our results showed that 48% of the 307 recent fuel-reduction burns resulted in statistically significant decreased fire severity. Our results also indicated that more recent fuel-reduction burns had a greater impact, with 66% of burns undertaken in 2019 significantly reducing severity, compared with 42% from 2015. We also analysed each fuel-reduction burn in the context of a range of metrics, including location, elevation, slope, aspect and forest heterogeneity, to assess whether these factors influenced the likelihood that a burn would be effective. Location and, to a lesser degree, forest heterogeneity were found to be significant factors. Our results support the view that recent fuel-reduction burns reduce fire severity. It is unclear, however, whether the differences would be operationally significant under extreme conditions, when wildfires are driven largely by weather, irrespective of fuel loads.

The prediction of forest biomass at the landscape scale can be achieved by integrating data from field plots with satellite imagery, in particular data from the Landsat archive, using k-nearest neighbour (kNN) imputation models. While studies have demonstrated different kNN imputation approaches for estimating forest biomass from remote sensing data and forest inventory plots, there is no general agreement on which approach is most appropriate for biomass estimation across large areas. In this study, we compared several imputation approaches for estimating forest biomass using Landsat time-series and inventory plot data. We evaluated 18 kNN models to impute three aboveground biomass (AGB) variables (total AGB, AGB of live trees and AGB of dead trees). These models were developed using different distance techniques (Random Forest or RF, Gradient Nearest Neighbour or GNN, and Most Similar Neighbour or MSN) and different combinations of response variables (model scenarios). Direct biomass imputation models were trained according to the biomass variables while indirect biomass imputation models were trained according to combinations of forest structure variables (e.g., basal area, stem density and stem volume of live and dead-standing trees). We also assessed the ability of our imputation method to spatially predict biomass variables across large areas in relation to a forest disturbance history over a 30-year period (1987–2016). Our results show that RF consistently outperformed MSN and GNN distance techniques across different model scenarios and biomass variables. The lowest error rates were achieved by RF-based models with generalized root mean squared difference (gRMSD, RMSE divided by the standard deviation of the observed values) ranging from 0.74 to 1.24. Whereas gRMSD associated with MSN-based and GNN-based models ranged from 0.92 to 1.36 and from 1.04 to 1.42, respectively. The indirect imputation method generally achieved better biomass predictions than the direct imputation method. In particular, the kNN model trained with the combination of basal area and stem density variables was the most robust for estimating forest biomass. This model reported a gRMSD of 0.89, 0.95 and 1.08 for total AGB, AGB of live trees and AGB of dead trees, respectively. In addition, spatial predictions of biomass showed relatively consistent trends with disturbance severity and time since disturbance across the time-series. As the kNN imputation method is increasingly being used by land managers and researchers to map forest biomass, this work helps those using these methods ensure their modelling and mapping practices are optimized.