Monitoring aboveground forest biomass dynamics over three decades using Landsat time-series and single-date inventory data

• Published in: International journal of applied earth observation and geoinformation Vol. 84; p. 101952
• Main Authors: Nguyen, Trung H., Jones, Simon D., Soto-Berelov, Mariela, Haywood, Andrew, Hislop, Samuel
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01-02-2020; Elsevier
• Subjects: Aboveground biomass; Australia; Forest disturbance; Forest recovery; Landsat time-series; Lidar; Single-date inventory; Forest recovery; Landsat time-series; Single-date inventory; Lidar; Australia; Forest disturbance; Aboveground biomass
• ISSN: 1569-8432; 1872-826X
• DOI: 10.1016/j.jag.2019.101952

•A robust framework for monitoring forest AGB dynamics across space and time.•Estimating annual forest AGB using Landsat time-series and inventory data.•Assessing predictions of forest AGB dynamics using multi-temporal Lidar data.•Analysing biomass dynamics according to forest disturbance and recovery histories.
Understanding forest biomass dynamics is crucial for carbon and environmental monitoring, especially in the context of climate change. In this study, we propose a robust approach for monitoring aboveground forest biomass (AGB) dynamics by combining Landsat time-series with single-date inventory data. We developed a Random Forest (RF) based kNN model to produce annual maps of AGB from 1988 to 2017 over 7.2 million ha of forests in Victoria, Australia. The model was internally evaluated using a bootstrapping technique. Predictions of AGB and its change were then independently evaluated using multi-temporal Lidar data (2008 and 2016). To understand how natural and anthropogenic processes impact forest AGB, we analysed trends in relation to the history of disturbance and recovery. Specifically, change metrics (e.g., AGB loss and gain, Years to Recovery - Y2R) were calculated at the pixel level to characterise the patterns of AGB change resulting from forest dynamics. The imputation model achieved a RMSE value of 132.9 Mg ha−1 (RMSE% = 46.3%) and R2 value of 0.56. Independent assessments of prediction maps in 2008 and 2016 using Lidar-based AGB data achieved relatively high accuracies, with a RMSE of 108.6 Mg ha−1 and 135.9 Mg ha−1 for 2008 and 2016, respectively. Annual validations of AGB maps using un-changed, homogenous Lidar plots suggest that our model is transferable through time (RMSE ranging from 109.65 Mg ha−1 to 112.27 Mg ha−1 and RMSE% ranging from 25.38% to 25.99%). In addition, changes in AGB values associated with forest disturbance and recovery (decrease and increase, respectively) were captured by predicted maps. AGB change metrics indicate that AGB loss and Y2R varied across bioregions and were highly dependent on levels of disturbance severity (i.e., a greater loss and longer recovery time were associated with a higher severity disturbance). On average, high severity fire burnt from 200 Mg ha−1 to 550 Mg ha−1 of AGB and required up to 15 years to recover while clear-fell logging caused a reduction in 250 Mg ha−1 to 600 Mg ha−1 of AGB and required nearly 20 years to recover. In addition, AGB within un-disturbed forests showed statistically significant but monotonic trends, suggesting a mild gradual drop over time across most bioregions. Our methods are designed to support forest managers and researchers in developing forest monitoring systems, especially in developing regions, where only a single date forestry inventory exists.