Continuous woody vegetation biomass estimation based on temporal modeling of Landsat data

Abstract

Optical data records have been widely used for monitoring forest aboveground biomass (AGB) dynamics given their freely available long-term archives and high temporal and spatial resolution. However, most of the methods utilized only the spatial information of reflectance (e.g. annual mean reflectance) to estimate AGB, lack of consideration for dynamic characteristics of reflectance. These methods are susceptible to reflectance saturation and temporal noise and may cause uncertainties in their estimation. In this study, we exploited dynamic time-series information of reflectance to enhance the accuracy and robustness of AGB estimation. More specifically, we utilized the continuous change detection and classification (CCDC) algorithm for temporal modeling of Landsat reflectance, followed by estimation of forest AGB with a random forest (RF) algorithm using the temporal information extracted by the CCDC model as input. The AGB estimation method was developed using in situ AGB measurements in Australia (10304 plots, range: [0.05, 936] tons/ha, mean: 153 tons/ha) and Landsat 5/7/8 data. Plot-scale results show that the overall reflectance derived by the CCDC model outperformed the annual mean reflectance when used in RF to estimate AGB because the overall reflectance is more robust to noisy or missing data. Introducing the CCDC model parameters into the RF model further improved estimation accuracy. The use of overall reflectance plus temporal parameters in AGB estimation increased R2 to 0.60 and reduced RMSE to 72.9 tons/ha, compared to using annual mean reflectance (R2 of 0.45 and RMSE of 85.7 tons/ha). This improvement may be attributed to CCDC model better capturing temporal changes in reflectance as the solar angle changes between seasons. Those changes relate to forest canopy coverage, width, and height, which are linked to AGB. Regional analysis for a 19,000-km2 area in Western Australia showed that this approach could characterize different types of forest AGB change over a 30 years period (1990–2019), including both gradual and sudden AGB losses and gains from, e.g., forest disturbance and management activities. This AGB estimation method has the potential to mitigate noisy or missing data and could be applied to large-scale forest biomass monitoring using the rich Landsat data archive.

Keywords

• Forest aboveground biomass
• Optical reflectance
• Biomass change
• Landsat
• Reflectance dynamic information
• Temporal modeling