{"refrec":{"BRefID":360325,"RR":"<b>Radoux, J.; Bourdouxhe, A.; Coppée, T.; De Vroey, M.; Dufrêne, M.; Defourny, P.</b> (2023). A consistent land cover map time series at 2 m spatial resolution—the LifeWatch 2006-2015-2018-2019 Dataset for Wallonia. <i>Data 8(1)</i>: 13. <a href=\"https://dx.doi.org/10.3390/data8010013\" target=\"_blank\">https://dx.doi.org/10.3390/data8010013</a>","BEntID":358040,"PublicFlag":1,"CheckedFlag":0,"wosflag":0,"vabbflag":0,"RefStringPartII":". <i>Data 8(1)</i>: 13. <a href=\"https://dx.doi.org/10.3390/data8010013\" target=\"_blank\">https://dx.doi.org/10.3390/data8010013</a>","DocTypID":8,"DocType":"Journal article","MarineFlag":0,"FreshFlag":0,"BrackishFlag":0,"TerrestrialFlag":0,"Authorstring":"Radoux, J.; Bourdouxhe, A.; Coppée, T.; De Vroey, M.; Dufrêne, M.; Defourny, P.","OrigTitleTranslFlag":0,"Authorstringtrunc":"Radoux, J. <i>et al.</i>","Englishabstract":"Ecosystem accounting is based on the definition of the extent and the status of an ecosystem. Land cover maps extents are representative of several ecosystems and can therefore be used to support ecosystem accounting if reliable change information is available. The dataset described in this paper aims to provide land cover information (13 classes) for biodiversity monitoring, which has driven two key features. On one hand, open areas were described in more details (5 classes) than in the other maps available in the study area in order to increase their relevance for biodiversity models. On the other hand, monitoring means that the time series must consist of comparable layers. The time series integrate information from existing high quality land cover maps that are not fully comparable, as well as thematic products (crop type, road network and forest type) and remote sensing data (25 cm orthophotos, 0.8 pts/m2 LIDAR and Sentinel-1&2 data). Because of the high spatial resolution of the data and the fragmented landscape, boundary errors could cause a large proportion of false change detection if the maps are classified independently. Buildings and forests were therefore consolidated across time in order to build a time series where these changes can be trusted. Based on an independent validation, the overall accuracy was 93.1%, 92.6%, 94.8% and 93.9% +/− 1.3% for the years 2006, 2015, 2018 and 2019, respectively. The specific assessment of forest patch change highlighted a 98% +/− 2.7% user accuracy across the 4 years and 85% of forest cut detection. This time series will be completed and further consolidated with other dates using the same protocol and legend. 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