Land management monitoring of near-natural areas through an integrated analysis of multi-temporal satellite data in a model framework

A method to derive products for a sustainable management of the land surface is developed in the frame of the M⁴Land project (“Model based, Multi-temporal, Multi-scale and Multi-sensoral retrieval of continuous land management information”). The system relies on a model-supervised dynamic classification of land cover from multi-temporal satellite data that works automatically without the need for training data or manual data processing. This approach is tested for the first time in a mesoscale setting (300m resolution). The performed model-supervised land cover classification of ENVISAT MERIS data at a test site in Southern Germany in 2010 is promising with an overall accuracy of 84.7%. After the consolidation of the method on this scale further land management products can be developed that are based on the underlying land surface model data.