Description: Analysis of forest disturbance patterns in relation to precipitation seasonality is important for understanding African tropical forest dynamics under changing climate conditions and different levels of human activities. Newly available radar-based forest disturbance information now enables an investigation of the intra-annual relationship between precipitation and forest disturbance in a spatially and temporally explicit manner, especially in the tropics, where frequent cloud cover hinders the use of optical-based remote sensing products. In this study, we applied cross-correlation on monthly precipitation and forest disturbance time series for 2019 and 2020 at a 0.5° grid in the African rainforest. We used the magnitude of the correlation and time lag to assess the intra-annual relationship between precipitation and forest disturbance, and introduced accessibility proxies to analyse the spatial variation of the relationship. Results revealed that a significant negative correlation between forest disturbance and precipitation dominates the study region. We found that significant negative correlations appear on average closer to settlements with overall smaller variations in travel time to settlements compared to grid cells with non-significant and significant positive correlation. The magnitude of the negative correlation increases as the travel time to settlements increases, implying that forest disturbances in less accessible areas are more affected by precipitation seasonality and that in particular human-induced disturbance activities are predominantly carried out in the drier months. Few areas showed a significant positive correlation, mainly resulting from natural causes such as flooding. These new insights in the interaction between forest disturbance, precipitation and accessibility provide a step forward in understanding the complex interactions that underlie the complexity of forest loss patterns that we can increasingly capture with Earth Observation approaches. As such, they can support forest conservation and management in coping with climate change induced changes of precipitation patterns in African rainforest countries.

Description: Two novel satellite LiDAR missions —GEDI and ICESat-2— are currently operational and combined provide near-global measurements of forest height and structure. Such data underpin a new era of large-area approaches for measuring forest height in regrowing forests of different ages and assessing associated regrowth rates. Two LiDAR missions further allow for comparing independently derived forest heights and regrowth rates. This study utilized both GEDI and ICESat-2 measurements to assess regrowth rates in regrowing forests of different ages for the Brazilian state Rondônia. We considered 19 data subgroups stratified by beam strength, light condition, beam sensitivity, and waveform processing algorithm to assess the retrieval uncertainty and identify data subgroups associated with the most reliable regrowth estimates. The quality assessment of GEDI and ICESat-2 forest heights over four 50 km long airborne LiDAR strips determined a root mean square error of 4.14 m (CV = 17%) and 5.91 m (CV = 19%) and a mean error of 0.04 m and −2.81 m, respectively. A linear calibration model between satellite- and airborne-LiDAR heights was then derived for each data subgroup and used to calibrate satellite heights. Forest regrowth rates were subsequently estimated for each satellite mission using a space-for-time imputation with forest heights’ medians per stand age class. The total growth of GEDI and ICESat-2 median forest heights after 33 years was 20.17 m (SE = 1.3 m) and 20.13 m (SE = 2.8 m), respectively. However, when growth was approximated with different non-linear models, the total growth differed by up to 6%, and the average regrowth rate even by up to 23%. The study revealed that omitting either the calibration step or the removal of secondary-forest-border pixels would result in an underestimation of the regrowth rate by more than 20%. Furthermore, the ICESat-2 weak beams were found unreliable for regrowth retrieval. The study showed that the novel satellite LiDAR data and the proposed methods could assess median forest height growth over large areas. However, forest age errors should also be accounted for in the retrieval uncertainty before comparing the growth estimates across different regions. Code and data necessary to reproduce the results are freely available on GitHub and Zenodo.

Description: An increase in the frequency and severity of disturbances (such as forest fires) is putting pressure on the resilience of the Amazon tropical forest; potentially leading to reduced ability to recover and to maintain a functioning forest ecosystem. Dense and long-term satellite time series approaches provide a largely untapped data source for characterizing disturbance- recovery forest dynamics across large areas and varying types of forests and conditions. Although large-scale forest recovery capacity metrics have been derived from optical satellite image time series and validated over various ecosystems, their sensitivity to disturbance (e.g. disturbance magnitude, disturbance timing, and recovery time) and environmental data characteristics (e.g. noise magnitude, seasonality, and missing values) are largely unknown. This study proposes an open source simulation framework based on the characteristics of sampled original satellite image time series to (i) compare the reliability of recovery metrics, (ii) evaluate their sensitivity with respect to environmental and disturbance characteristics, and (iii) evaluate the effect of pre-processing techniques on the reliability of the recovery metrics for abrupt disturbances, such as fires, in the Amazon basin forests. The effect of three pre-processing techniques were evaluated: changing the temporal resolution, noise removal techniques (such as time series smoothing and segmenting), and using a varying time span after the disturbance to calculate recovery metrics. Here, reliability is quantified by comparing derived and theoretical values of the recovery metrics (RMSE and R2). From the three recovery metrics evaluated, the Year on Year Average (YrYr) and the Ratio of Eighty Percent (R80p) are more reliable than the Relative Recovery Index (RRI). Time series segmentation tends to improve the reliability of recovery metrics. Recovery metrics derived from temporal dense Landsat time series tend to show a higher reliability than those derived from time series aggregated to quarterly or annual values. Although the framework is demonstrated on Landsat time series of the Amazon tropical forest, it can be used to perform such test on other datasets and ecosystems.