Wildfires cause severe modifications in the matter and energy budgets of ecosystems. Enhanced methods are needed to assess the consequences of those disturbances and the recovery of ecosystems. In this regard, evaluation and monitoring based on ecosystem functioning have advantages over the traditional use of structural features, since functional attributes have shorter time responses to disturbances, and are more directly connected to provision of ecosystem services. Remote sensing allows for quantify ecosystem vulnerability to disturbance as well as spatiotemporal heterogeneity of wildfire disturbance effects on ecosystems. In this study, we propose and test a framework to assess and monitor changes related to wildfire disturbances, using remotely sensed proxy indicators of critical descriptors of ecosystem functioning (e.g. temperature, albedo, photosynthetic activity, soil moisture), extracted from time-series of MODIS observations. The proposed framework showed an overall accuracy of 98.7% for detection of fire disturbance events, with moderate to good agreement when compared to reference data, while also allowing for simple, fast computation of indicators of fire severity and post-fire recovery. We also discuss the added value of our approach for surveillance of fire occurrence, assessment of fire severity, and monitoring of post-fire recovery.