{"refrec":{"BRefID":368352,"RR":"<b>De Vroey, M.</b> (2023). Measuring grassland use intensity by remote sensing for agroecological monitoring. PhD Thesis. Université Catholique de Louvain: Louvain-la-Neuve. x, 172 pp.","BEntID":366078,"PublicFlag":1,"CheckedFlag":0,"wosflag":0,"vabbflag":0,"RefStringPartII":". PhD Thesis. Université Catholique de Louvain: Louvain-la-Neuve.  x, 172 pp.","DocTypID":5,"DocType":"Book/Monograph","MarineFlag":0,"FreshFlag":0,"BrackishFlag":0,"TerrestrialFlag":0,"Authorstring":"De Vroey, M.","OrigTitleTranslFlag":0,"Authorstringtrunc":"De Vroey, M.","Englishabstract":"Grasslands cover about one-third of the global ice-free land surface and deliver crucial ecosystem services. The state of grasslands and the balance between provisioning and regulating ecosystem services are largely determined by grassland use intensity. A better characterization of grassland production systems is essential to evolve toward more sustainable grassland management. Therefore, temporally and spatially explicit data on each aspect of grassland use intensity are crucial. The overarching objective of this thesis is to measure grassland use intensity over large areas thanks to satellite remote sensing. To that end, we developed and evaluated methods, based on Sentinel-1 and Sentinel-2 time series, (i) to classify grassland management practices (i.e. grazing and mowing), (ii) to delineate management units, (iii) to detect the timing and frequency of mowing events, and (iv) to estimate forage yield and quality. Each method was thoroughly evaluated in terms of robustness, versatility, and transferability, with particular attention to reference data quality and quantitative validation. Grasslands were characterized exhaustively and with high thematic precision compared to existing datasets. 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