Mapping the spatial distribution of stand age and aboveground biomass from Landsat time series analyses of forest cover loss in tropical dry forests. Issue 3 (7th December 2021) - George‐Chacón, Stephanie P.; Mas, Jean François; Dupuy, Juan Manuel; Castillo‐Santiago, Miguel Angel; Hernández‐Stefanoni, José Luis
Abstract: Spatial information on the timing of forest cover loss is important to identify and map stand age, which is a key factor driving the recovery of carbon pools and can also be used to estimate aboveground biomass (AGB) based on its relationship with stand age. Here, we estimated the spatial distribution of stand age and AGB of young forest (<20 years) in three types of tropical dry forest in the Yucatan peninsula using Landsat NDVI (normalized difference vegetation index) time series from 2000 to 2020. We estimated AGB based on chronosequence data and compared these results to reference field data and estimations obtained from remote‐sensing studies. The overall and user accuracy of the age map was high (95.7–99.9% and 87.35–98.5% respectively). However, lower producer accuracy values (from 31.2 to 67.2%) suggest an underestimation of the extension of young forests. We found a greater extent of young forests in the semi‐deciduous and deciduous forests compared to the semi‐evergreen ones. Mean AGB estimated from stand age (53.1 Mg ha −1 ) was lower than that estimated from remote‐sensing studies (67.5 to 95.2 Mg ha −1 ). These results indicate that spatial information of forest age can be accurately assessed from Landsat time series, and that the combination of stand age with chronosequence data can reduce the overestimation of AGB of recovering forests commonly found in remotely sensed data. Abstract : We estimated the spatial distribution of stand age and AGB (aboveground biomass) of young forest (< 20 years) in three types of tropical dry forest in the Yucatan peninsula using Landsat NDVI (normalized difference vegetation index) time‐series from 2000 to 2020. The results of this study indicate that spatial information of forest age can be accurately assessed from Landsat time series, and that the combination of stand age with chronosequence data can reduce the overestimation of AGB of recovering forests commonly found in remotely sensed data.