Continuous monitoring of land change activities and post-disturbance dynamics from Landsat time series: A test methodology for REDD plus reporting

The REDD+ mechanism of UNFCCC was established to reduce greenhouse gases emissions by means of financial incentives. Of importance to the success of REDD+ and similar initiatives is the provision of credible evidence of reductions in the extent of land change activities that release carbon to the atmosphere (e.g. deforestation). The criteria for reporting land change areas and associated emissions within REDD+ stipulate the use of sampling-based approaches, which allow for unbiased estimation and uncertainty quantification. But for economic compensation for emission reductions to be feasible, agreements between participating countries and donors often require reporting every year or every second year. With the rates of land change typically being very small relative to the total study area, sampling-based approaches for estimation of annual or bi-annual areas have proven problematic, especially when comparing area estimates over time. In this paper, we present a methodology for monitoring and estimating areas of land change activity at high temporal resolution that is compliant with international guidelines. The methodology is based on a break detection algorithm applied to time series of Landsat data in the Colombian Amazon between 2001 and 2016. A biennial stratified sampling approach was implemented to (1) remove the bias introduced by the change detection and classification algorithm in mapped areas derived from pixel-counting; and (2) provide confidence intervals for area estimates obtained from the reference data collected for the sample. Our results show that estimating the area of land change, like deforestation, at annual or bi-annual resolution is inherently challenging and associated with high degrees of uncertainty. We found that better precision was achieved if independent sample datasets of reference observations were collected for each time interval for which area estimates are required. The alternative of selecting one sample of continuous reference observations analyzed for inference of area for each time interval did not yield area estimates significantly different from zero. Also, when large stable land covers (primary forest in this case, occupying almost 90% of the study area) are present in the study area in combination with small rates of land change activity, the impact of omission errors in the map used for stratifying the study area will be substantial and potentially detrimental to usefulness of land change studies. The introduction of a buffer stratum around areas of mapped land change reduced the uncertainty in area estimates by up to 98%. Results indicate that the Colombian Amazon has experienced a small but steady decrease in primary forest due to establishment of pastures, with forest-to-pasture conversion reaching 103 +/- 30 kha (95% confidence interval) in the period between 2013 and 2015, corresponding to 0.22% of the study area. Around 29 +/- 17 kha (95% CI) of pastureland that had been abandoned shortly after establishment reverted to secondary forest within the same period. Other gains of secondary forest from more permanent pastures averaged about 12 +/- 11 kha (95% CI), while losses of secondary forest averaged 20 +/- 12 kha (95% CI).