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Abstract

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.