Estimating Afforestation Area Using Landsat Time Series and Photointerpreted Datasets

Afforestation processes, natural and anthropogenic, involve the conversion of other land uses to forest, they represent one most important use transformations, influencing numerous ecosystem services. Although remotely sensed data are commonly used monitor forest disturbance, only a few reported studies have these afforestation. The objectives this study were two fold: (1) develop illustrate method that exploits 1985–2019 Landsat time series for predicting afforestation areas at 30 m resolution national scale, (2) estimate statistically rigorously within Italian administrative regions elevation classes. We best-available-pixel (1985–2019) calculate set temporal predictors that, together with random forests prediction technique, facilitated construction map afforested in Italy. Then, was guide selection an estimation sample dataset which, after complex photointerpretation phase, associated confidence intervals. classification approach achieved accuracy 87%. At level, area between 1985 2019 covered 2.8 ± 0.2 million ha, corresponding potential C-sequestration 200 t. region largest Sardinia, 260,670 58,522 while smallest 28,644 12,114 ha Valle d’Aosta. Considering classes m, greatest 400 600 above sea where it 549,497 84,979 ha. Our results help understand process Italy relation geographical location altitude, could be basis further on species composition management conditions.