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Abstract Understanding forest dynamics at the landscape scale is critical given the demands of sustainable forest management and climate change mitigation. This study proposes an approach for holistically characterising and analysing forest dynamics across large areas and long-time periods using information derived from Landsat time-series. To achieve this, we first developed a two-phase classification process to predictively map (1) disturbance and recovery levels and (2) disturbance causal agents for multiple detected disturbance events. The model explanatory data included a range of trajectory-based metrics derived from Landsat time-series, while model training and validation data were derived from a human-interpreted reference dataset. While previous studies have often described forest dynamics using some specific spectral change metrics, we demonstrated an ensemble approach to map disturbance and recovery trends (by treating them as a single metric) and to characterise not only abruptly occurring change events (e.g., clear-fell logging and wildfire) but also events of low severity (e.g., prescribed burning and selective logging). In addition, we adopted a space-time data cube concept to simultaneously report both newly detected disturbance events (detected disturbances) as well as events that have previously occurred but are ongoing (ongoing disturbances). This ongoing element of forest dynamics is often under-reported. The method was tested over 3.7 million ha of public land sclerophyll forests, where multiple disturbance events have occurred over the last 30 years (1987–2016). Our models of disturbance and recovery levels obtained overall accuracies of 78.6% and 72.3% for primary and secondary disturbance events, respectively. The overall accuracies for the models of disturbance causal agents were 80.7% and 73.0%, respectively. The data cube reported an average annual disturbance rate of 4.2% per year. This was dominated by newly detected disturbance (2.7% per year) as distinct from ongoing disturbance that was, however, considerable (1.5% per year). Our approach presented herein can improve the understanding of forest dynamics over long time periods and large areas and has potential for supporting land managers. Graphical abstract Unlabelled Image Highlights • Forest change analysis over large areas and long-time series using Landsat data • Mapping forest disturbance and recovery levels and associated casual agents • Improving reporting of forest dynamics by adopting a data cube concept [ABSTRACT FROM AUTHOR]

The success and rate of forest regeneration has consequences for sustainable forest management, climate change mitigation, and biodiversity, among others. Systematically monitoring forest regeneration over large and often remote areas is challenging. Remotely sensed data and associated analytical approaches have demonstrated consistent and transparent options for spatially-explicit characterization of vegetation return following disturbance. Moreover, time series of satellite imagery enable the establishment of spatially meaningful recovery baselines that can provide a benchmark for identifying areas that are either under- or over-performing relative to those baselines. This information allows for the investigation and/or prioritization of areas requiring some form of management intervention, including guiding tree planting initiatives. In this research, we assess recovery following stand replacing disturbances for the 650 Mha forested ecosystems of Canada for the period 1985–2017, wherein ~51 Mha of Canada's forested ecosystems were impacted by wildfire, and ~ 21 Mha were impacted by harvesting. For quantification of forest recovery, we implement the Years to Recovery or Y2R metric using Landsat time series data based on the Normalized Burn Ratio (NBR) to relate the number of years required for a pixel to return to 80% of its pre-disturbance NBR value. By the end of the analyzed period, 76% of areas impacted by wildfire were considered spectrally recovered compared to 93% of harvested areas. On average, we found that harvest areas had more rapid spectral recovery (mean Y2R = 6.1 years) than wildfire (mean Y2R = 10.6 years) and importantly, that Y2R varied by ecozone, disturbance type, pre-disturbance land cover, and latitude. We used airborne laser scanning data to assess whether pixels that were considered spectrally recovered had attained United Nations Food and Agricultural Organization benchmarks of canopy height (>5 m) and cover (>10%) across four geographic regions representing different forest types. Overall, 87% and 97% of recovered pixels sampled in harvests and wildfires, respectively, had achieved at least one of the benchmarks, with benchmarks of height more readily achieved than benchmarks of cover. By analyzing spatial patterns of Y2R, we identified areas that had significant positive or negative spatial clustering in their rate of spectral recovery. Approximately 3.5–4% of areas disturbed by wildfire or harvest had significant positive spatial clustering, indicative of slower spectral recovery rates; these areas were also less likely to have attained benchmarks of height and cover. Conversely, we identified significant negative spatial clustering for 0.94% of areas recovering from harvest and 1.93% of areas recovering from wildfire, indicative of spectral recovery that was more rapid than the ecozonal baseline. Herein, we demonstrated that remote sensing can provide spatial intelligence on the nature of disturbance-recovery dynamics in forested ecosystems over large areas and moreover, can retrospectively quantify and characterize historic forest recovery trends within the past three decades that have implications for forest management, climate change mitigation, and restoration initiatives in the near term. • Landsat time series monitored forest disturbance and recovery for 32 years across 650 Mha. • Harvests spectrally recovered more rapidly (6.1 years) than wildfire (10.6 years). • Lidar-derived benchmarks of canopy cover (>10%) and height (>5 m) used for validation. • 87% and 97% of recovered harvest and wildfire samples achieved one of the benchmarks. • Nationally, 3.5%–4% of wildfire and harvest areas had slower than expected recovery. [ABSTRACT FROM AUTHOR]

Estimates of the magnitude and distribution of aboveground carbon in Earth's forests remain uncertain, yet knowledge of forest carbon content at a global scale is critical for forest management in support of climate mitigation. In light of this knowledge gap, several upcoming spaceborne missions aim to map forest aboveground biomass, and many new biomass products are expected from these datasets. As these new missions host different technologies, each with relative strengths and weaknesses for biomass retrieval, as well as different spatial resolutions, consistently comparing or combining biomass estimates from these new datasets will be challenging. This paper presents a demonstration of an inter-comparison of biomass estimates from simulations of three NASA missions (GEDI, ICESat-2 and NISAR) over Sonoma county in California, USA. We use a high resolution, locally calibrated airborne lidar map as our reference dataset, and emphasize the importance of considering uncertainties in both reference maps and spaceborne estimates when conducting biomass product validation. GEDI and ICESat-2 were simulated from airborne lidar point clouds, while UAVSAR's L-band backscatter was used as a proxy for NISAR. To estimate biomass for the lidar missions we used GEDI's footprint-level biomass algorithms, and also adapted these for application to ICESat-2. For UAVSAR, we developed a locally trained biomass model, calibrated against the ALS reference map. Each mission simulation was evaluated in comparison to the local reference map at its native product resolution (25 m, 100 m transect, and 1 ha) yielding RMSEs of 57%, 75%, and 89% for GEDI, NISAR, and ICESat-2 respectively. RMSE values increased for GEDI's power beam during simulated daytime conditions (64%), coverage beam during nighttime conditions (72%), and coverage beam daytime conditions (87%). We also test the application of GEDI's biomass modeling framework for estimation of biomass from ICESat-2, and find that ICESat-2 yields reasonable biomass estimates, particularly in relatively short, open canopies. Results suggest that while all three missions will produce datasets useful for biomass mapping, tall, dense canopies such as those found in Sonoma County present the greatest challenges for all three missions, while steep slopes also prove challenging for single-date SAR-based biomass retrievals. Our methods provide guidance for the inter-comparison and validation of spaceborne biomass estimates through the use of airborne lidar reference maps, and could be repeated with on-orbit estimates in any area with high quality field plot and ALS data. These methods allow for regional interpretations and filtering of multi-mission biomass estimates toward improved wall-to-wall biomass maps through data fusion. • GEDI, ICESat-2 and NISAR will collect useful data for estimating forest biomass. • All three missions have increased errors with canopy cover and slope. • Airborne Lidar biomass maps allow consistent multi-mission accuracy assessment. • These missions will produce naturally synergistic datasets. • GEDI models can be applied to ICESat-2 data. [ABSTRACT FROM AUTHOR]