A novel approach for environmental monitoring based on the integration of multi-temporal multi-source Earth Observation data and field surveys in a spatio-temporal framework

To perform specific environmental analyses with high accuracy and spatial resolution, typically dedicated Earth Observation (EO) data are acquired via aircraft or drones. Although valuable, these data can be: (i) limited and sparse in time and space due to their acquisition cost, and (ii) asynchronous to field data collection. To consistently ingest asynchronous EO data and field surveys, this paper generates a spatio-temporal framework by exploiting the ability of Sentinel-1 satellites to provide frequent EO data with global coverage. Experiments, conducted in Indonesia to estimate changes in forest Above-Ground Biomass (AGB) between 2017 and 2019, demonstrate the ability of the spatio-temporal frame-work to integrate Light Detection and Ranging (LIDAR) data acquired in 2020. The method achieved a R2 of 0.76 and a RMSE of 21.24 compared to 0.50 and 0.57 and 28.65 and 23.93 for the standard bi-temporal approach (using field data and Sentinel-1 data) and the bi-temporal approach including the LIDAR data without any adaptation, respectively.