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INSPIRE and PSI directives induced major changes in the Walloon geomatics ecosystem. INSPIRE aims at establishing an infrastructure for spatial information in the European community for environmental policies. PSI is the European legislation on the re-use of public sector information, including all government-held geodatasets. For compliance purposes, Wallonia proposed to develop a Georeferential aiming at providing free, open-access and consistent reference geodatasets to the Walloon stakeholders. Some of the current data being outdated, the Georeferential proposes pilot projects for a collaborative and user-oriented design of new maps. One of the categories being identified for the Georeferential is land cover and land use. Build on the knowledge of past researches, the Walous project aims at combining very high spatial with very high temporal resolution Earth observation and thematic data in a classification scheme. To ensure that the new data covers the general interest and to ensure his appropriation by the users, gathering requirements is one of the most vital pieces. Poor gathering of user needs could lead to an unused final product. Walous carried out interviews for clarifying data requirements. This paper introduces this methodology and illustrates how the results impacted the priorities and the specifications of the land cover and land use data.

Due to its cost-effectiveness and repeatability of observations, high resolution optical satellite remote sensing has become a major technology for land use and land cover mapping. However, inventory compilers for the Land Use, Land Use Change, and Forestry (LULUCF) sector are still mostly relying on annual census and periodic surveys for such inventories. This study proposes a new approach based on per-pixel supervised classification using Sentinel-2 imagery from 2016 for mapping greenhouse gas emissions and removals associated with the LULUCF sector in Wallonia, Belgium. The Land Use/Cover Area frame statistical Survey (LUCAS) of 2015 was used as training data and reference data to validate the map produced. Then, we investigated the performance of four widely used classifiers (maximum likelihood, random forest, k-nearest neighbor, and minimum distance) on different training sample sizes. We also studied the use of the rich spectral information of Sentinel-2 data as well as single-date and multitemporal classification. Our study illustrates how open source data can be effectively used for land use and land cover classification. This classification, based on Sentinel-2 and LUCAS, offers new opportunities for LULUCF inventory of greenhouse gas on a European scale.