Permanent grasslands (meadows and pastures) are the most common agricultural land use type covering 34% (0.65 million hectares) of agricultural land in Latvia. The Common Agriculture policy (CAP) stipulates that the EU Member States have to designate permanent grasslands, ensure that farmers do not convert or plough them and that the ratio of permanent grasslands to the total agricultural area does not decrease by more than 5% in order to receive support payments. However, semi-natural grassland habitats require appropriate management activities to ensure their long-term conservation. The European Commission report (2015) required by the Birds and Habitats directives concludes that ‘grasslands and wetlands have the highest proportion of habitats with an unfavourable-bad and deteriorating status’ in the EU, while the midterm review of EU biodiversity strategy 2010-2020 highlighted that grassland habitat change presents a high risk to biodiversity. Latvia’s rural development programme (2014-2020) has identified only 47 thousand hectares of biologically valuable grasslands. These grasslands are semi-natural meadows and pastures that include species and habitat types of EU importance. 70-90% of EU importance grassland habitats in Natura 2000 sites were in poor condition in Latvia during 2012. There is a clear interest from a number of end-users (e.g. the Nature Conservation Agency, the Rural Support Service,) for grassland mapping and management practice monitoring solutions. In order to prevent loss of high nature value grasslands and increase sustainability of semi-natural grassland management, the Integrated Planning tool was developed in frames of LIFE+ project “Integrated planning tool to ensure viability of grasslands” (LIFE Viva Grass ENV/LT/00018). Spectral remote sensing technique was used for preparation of necessary inputs for the tool from Cesis Municipality in Latvia - mapping of grasslands, detection of overgrowth with shrubs/trees and spread of invasive species (Sosnowsky’s hogweed) as well as assessment of grass biomass. High spatial and spectral resolution of hyperspectral airborne data obtained with flying laboratory ARSENAL was complemented with temporal dimension of Sentinel-2 satellite data in order to achieve the best result reaching classification accuracy >90%.