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Alex Schotman; Alterra - Animal Ecology; Friso van der Zee; Gerard Hazeu; Jaap Bloem; Jeroen Sluijsmans; Marian Vittek; Alterra - Biodiversity And Policy; Alterra - Spatial Knowledge Systems; Pe&rc; Alterra - Regional development and spatial use. Verkenning van bodem en vegetatie in 25 zonneparken in Nederland : Eerste overzicht van de ligging van zonneparken in Nederland en stand van de kennis over het effect van zonneparken op de bodemkwaliteit. Verkenning van bodem en vegetatie in 25 zonneparken in Nederland : Eerste overzicht van de ligging van zonneparken in Nederland en stand van de kennis over het effect van zonneparken op de bodemkwaliteit 2021, 1 .
AMA StyleAlex Schotman, Alterra - Animal Ecology, Friso van der Zee, Gerard Hazeu, Jaap Bloem, Jeroen Sluijsmans, Marian Vittek, Alterra - Biodiversity And Policy, Alterra - Spatial Knowledge Systems, Pe&rc, Alterra - Regional development and spatial use. Verkenning van bodem en vegetatie in 25 zonneparken in Nederland : Eerste overzicht van de ligging van zonneparken in Nederland en stand van de kennis over het effect van zonneparken op de bodemkwaliteit. Verkenning van bodem en vegetatie in 25 zonneparken in Nederland : Eerste overzicht van de ligging van zonneparken in Nederland en stand van de kennis over het effect van zonneparken op de bodemkwaliteit. 2021; ():1.
Chicago/Turabian StyleAlex Schotman; Alterra - Animal Ecology; Friso van der Zee; Gerard Hazeu; Jaap Bloem; Jeroen Sluijsmans; Marian Vittek; Alterra - Biodiversity And Policy; Alterra - Spatial Knowledge Systems; Pe&rc; Alterra - Regional development and spatial use. 2021. "Verkenning van bodem en vegetatie in 25 zonneparken in Nederland : Eerste overzicht van de ligging van zonneparken in Nederland en stand van de kennis over het effect van zonneparken op de bodemkwaliteit." Verkenning van bodem en vegetatie in 25 zonneparken in Nederland : Eerste overzicht van de ligging van zonneparken in Nederland en stand van de kennis over het effect van zonneparken op de bodemkwaliteit , no. : 1.
G.W. Hazeu; Alterra - Spatial Knowledge Systems; M. Vittek; R. Schuiling; J.D. Bulens; M.H. Storm; G.J. Roerink; W.M.L. Meijninger; Alterra - Earth Informatics. LGN2018: een nieuwe weergave van het grondgebruik in Nederland. LGN2018: een nieuwe weergave van het grondgebruik in Nederland 2020, 1 .
AMA StyleG.W. Hazeu, Alterra - Spatial Knowledge Systems, M. Vittek, R. Schuiling, J.D. Bulens, M.H. Storm, G.J. Roerink, W.M.L. Meijninger, Alterra - Earth Informatics. LGN2018: een nieuwe weergave van het grondgebruik in Nederland. LGN2018: een nieuwe weergave van het grondgebruik in Nederland. 2020; ():1.
Chicago/Turabian StyleG.W. Hazeu; Alterra - Spatial Knowledge Systems; M. Vittek; R. Schuiling; J.D. Bulens; M.H. Storm; G.J. Roerink; W.M.L. Meijninger; Alterra - Earth Informatics. 2020. "LGN2018: een nieuwe weergave van het grondgebruik in Nederland." LGN2018: een nieuwe weergave van het grondgebruik in Nederland , no. : 1.
With the unprecedented availability of satellite data and the rise of global binary maps, the collection of shared reference data sets should be fostered to allow systematic product benchmarking and validation. Authoritative global reference data are generally collected by experts with regional knowledge through photo-interpretation. During the last decade, crowdsourcing has emerged as an attractive alternative for rapid and relatively cheap data collection, beckoning the increasingly relevant question: can these two data sources be combined to validate thematic maps? In this article, we compared expert and crowd data and assessed their relative agreement for cropland identification, a land cover class often reported as difficult to map. Results indicate that observations from experts and volunteers could be partially conflated provided that several consistency checks are performed. We propose that conflation, i.e., replacement and augmentation of expert observations by crowdsourced observations, should be carried out both at the sampling and data analytics levels. The latter allows to evaluate the reliability of crowdsourced observations and to decide whether they should be conflated or discarded. We demonstrate that the standard deviation of crowdsourced contributions is a simple yet robust indicator of reliability which can effectively inform conflation. Following this criterion, we found that 70% of the expert observations could be crowdsourced with little to no effect on accuracy estimates, allowing a strategic reallocation of the spared expert effort to increase the reliability of the remaining 30% at no additional cost. Finally, we provide a collection of evidence-based recommendations for future hybrid reference data collection campaigns.
François Waldner; Anne Schucknecht; Myroslava Lesiv; Javier Gallego; Linda See; Ana Pérez-Hoyos; Raphaël D'Andrimont; Thomas de Maet; Juan Carlos Laso Bayas; Steffen Fritz; Olivier Leo; Hervé Kerdiles; Mónica Díez; Kristof Van Tricht; Sven Gilliams; Andrii Shelestov; Mykola Lavreniuk; Margareth Simões; Rodrigo Ferraz; Beatriz Bellón; Agnès Bégué; Gerard Hazeu; Vaclav Stonacek; Jan Kolomaznik; Jan Misurec; Santiago R. Verón; Diego de Abelleyra; Dmitry Plotnikov; Li Mingyong; Mrinal Singha; Prashant Patil; Miao Zhang; Pierre Defourny. Conflation of expert and crowd reference data to validate global binary thematic maps. Remote Sensing of Environment 2018, 221, 235 -246.
AMA StyleFrançois Waldner, Anne Schucknecht, Myroslava Lesiv, Javier Gallego, Linda See, Ana Pérez-Hoyos, Raphaël D'Andrimont, Thomas de Maet, Juan Carlos Laso Bayas, Steffen Fritz, Olivier Leo, Hervé Kerdiles, Mónica Díez, Kristof Van Tricht, Sven Gilliams, Andrii Shelestov, Mykola Lavreniuk, Margareth Simões, Rodrigo Ferraz, Beatriz Bellón, Agnès Bégué, Gerard Hazeu, Vaclav Stonacek, Jan Kolomaznik, Jan Misurec, Santiago R. Verón, Diego de Abelleyra, Dmitry Plotnikov, Li Mingyong, Mrinal Singha, Prashant Patil, Miao Zhang, Pierre Defourny. Conflation of expert and crowd reference data to validate global binary thematic maps. Remote Sensing of Environment. 2018; 221 ():235-246.
Chicago/Turabian StyleFrançois Waldner; Anne Schucknecht; Myroslava Lesiv; Javier Gallego; Linda See; Ana Pérez-Hoyos; Raphaël D'Andrimont; Thomas de Maet; Juan Carlos Laso Bayas; Steffen Fritz; Olivier Leo; Hervé Kerdiles; Mónica Díez; Kristof Van Tricht; Sven Gilliams; Andrii Shelestov; Mykola Lavreniuk; Margareth Simões; Rodrigo Ferraz; Beatriz Bellón; Agnès Bégué; Gerard Hazeu; Vaclav Stonacek; Jan Kolomaznik; Jan Misurec; Santiago R. Verón; Diego de Abelleyra; Dmitry Plotnikov; Li Mingyong; Mrinal Singha; Prashant Patil; Miao Zhang; Pierre Defourny. 2018. "Conflation of expert and crowd reference data to validate global binary thematic maps." Remote Sensing of Environment 221, no. : 235-246.
Accurate and timely information on the global cropland extent is critical for food security monitoring, water management and earth system modeling. Principally, it allows for analyzing satellite image time-series to assess the crop conditions and permits isolation of the agricultural component to focus on food security and impacts of various climatic scenarios. However, despite its critical importance, accurate information on the spatial extent, cropland mapping with remote sensing imagery remains a major challenge. Following an exhaustive identification and collection of existing land cover maps, a multi-criteria analysis was designed at the country level to evaluate the fitness of a cropland map with regards to four dimensions: its timeliness, its legend, its resolution adequacy and its confidence level. As a result, a Unified Cropland Layer that combines the fittest products into a 250 m global cropland map was assembled. With an evaluated accuracy ranging from 82% to 95%, the Unified Cropland Layer successfully improved the accuracy compared to single global products.
François Waldner; Steffen Fritz; Antonio Di Gregorio; Dmitry Plotnikov; Sergey Bartalev; Nataliia Kussul; Peng Gong; Prasad Thenkabail; Gerard Hazeu; Igor Klein; Fabian Löw; Jukka Miettinen; Vinay Kumar Dadhwal; Céline Lamarche; Sophie Bontemps; Pierre Defourny. A Unified Cropland Layer at 250 m for Global Agriculture Monitoring. Data 2016, 1, 3 .
AMA StyleFrançois Waldner, Steffen Fritz, Antonio Di Gregorio, Dmitry Plotnikov, Sergey Bartalev, Nataliia Kussul, Peng Gong, Prasad Thenkabail, Gerard Hazeu, Igor Klein, Fabian Löw, Jukka Miettinen, Vinay Kumar Dadhwal, Céline Lamarche, Sophie Bontemps, Pierre Defourny. A Unified Cropland Layer at 250 m for Global Agriculture Monitoring. Data. 2016; 1 (1):3.
Chicago/Turabian StyleFrançois Waldner; Steffen Fritz; Antonio Di Gregorio; Dmitry Plotnikov; Sergey Bartalev; Nataliia Kussul; Peng Gong; Prasad Thenkabail; Gerard Hazeu; Igor Klein; Fabian Löw; Jukka Miettinen; Vinay Kumar Dadhwal; Céline Lamarche; Sophie Bontemps; Pierre Defourny. 2016. "A Unified Cropland Layer at 250 m for Global Agriculture Monitoring." Data 1, no. 1: 3.
This chapter deals with long-term Dutch national land cover and land use mapping activities. Land cover and land use are often difficult to separate 1:1. For practical reasons in this chapter only the term land cover will be used with the exception of direct translations of the names of database. Four main databases can be discerned:
Gerard W. Hazeu. Operational Land Cover and Land Use Mapping in the Netherlands. Remote Sensing and Digital Image Processing 2014, 18, 283 -296.
AMA StyleGerard W. Hazeu. Operational Land Cover and Land Use Mapping in the Netherlands. Remote Sensing and Digital Image Processing. 2014; 18 ():283-296.
Chicago/Turabian StyleGerard W. Hazeu. 2014. "Operational Land Cover and Land Use Mapping in the Netherlands." Remote Sensing and Digital Image Processing 18, no. : 283-296.
A range of new spatial datasets classifying the European environment has been constructed over the last few years. These datasets share the common objective of dividing European environmental gradients into convenient units, within which objects and variables of interest have relatively homogeneous characteristics. The stratifications and typologies can be used as a basis for up-scaling, for stratified random sampling of ecological resources, for the representative selection of sites for studies across the continent and for the provision of frameworks for modeling exercises and reporting at the European scale. This paper provides an overview of five recent European stratifications and typologies, constructed for contrasting objectives, and differing in spatial and thematic detail. These datasets are: the Environmental Stratification (EnS), the European Landscape Classification (LANMAP), the Spatial Regional Reference Framework (SRRF), the Agri-Environmental Zonation (SEAMzones), and the Foresight Analysis for Rural Areas Of Europe (FARO-EU) Rural Typology. For each classification the objective, background, and construction of the dataset are described, followed by a discussion of its robustness. Finally, applications of each dataset are summarized. The five stratifications and typologies presented here give an overview of different research objectives for constructing such classifications. In addition they illustrate the most up to date methods for classifying the European environment, including their limitations and challenges. As such, they provide a sound basis for describing the factors affecting the robustness of such datasets. The latter is especially relevant, since there is likely to be further interest in European environmental assessment. In addition, advances in data availability and analysis techniques, will probably lead to the construction of other typologies in the future.
G.W. Hazeu; M.J. Metzger; Sander Mucher; M. Perez-Soba; Ch. Renetzeder; Erling Andersen. European environmental stratifications and typologies: An overview. Agriculture, Ecosystems & Environment 2011, 142, 29 -39.
AMA StyleG.W. Hazeu, M.J. Metzger, Sander Mucher, M. Perez-Soba, Ch. Renetzeder, Erling Andersen. European environmental stratifications and typologies: An overview. Agriculture, Ecosystems & Environment. 2011; 142 (1-2):29-39.
Chicago/Turabian StyleG.W. Hazeu; M.J. Metzger; Sander Mucher; M. Perez-Soba; Ch. Renetzeder; Erling Andersen. 2011. "European environmental stratifications and typologies: An overview." Agriculture, Ecosystems & Environment 142, no. 1-2: 29-39.
The Agri-Environmental Zonation (AEnZ) is a biophysical typology based on a recently available detailed database on organic carbon content of the topsoil of Europe, the Environmental Stratification (EnS) and an Agri-mask. The AEnZ is used within the integrated assessment framework of SEAMLESS. The basis for this typology is the Environmental Stratification of Europe (EnS) building mainly on climate and altitude characteristics. The 84 environmental strata were aggregated into 13 environmental zones (EnZs). The environmental zones were then combined with organic carbon topsoil data (OCTOP) to cover the wide range of agri-environmental diversity of Europe. The OCTOP content was selected as soil variable as it explained most of the variation in soils in Europe. The EnZs/OCTOP land units were combined with an Agri-mask representing major obstacles for farming resulting in the final AEnZ typology. The Agri-mask, which is based on CORINE Land Cover, soil, altitude and slope data, divides Europe into three zones with different agricultural potential (suited, unsuited and marginally suited). The AEnZ consists of 238 land types of which 82 classes are referred as suitable for agriculture (75.8% of EU27+). For the SEAMLESS framework two of the three dimensions of the Agri-Environmental Zonation have been used to build the spatial framework to link information on farming and biophysics. The spatial building block of SEAMLESS is thus the Seamzones, that is an overlay of the 13 environmental zones, the seven OCTOP classes and 270 administrative (NUTS2) regions. In total this results in 3,513 Seamzones that are used to structure the biophysical data as well as the data on farming across the EU27+.
Gerard Hazeu; Berien Elbersen; Erling Andersen; Bettina Baruth; Kees Van Diepen; Marc Metzger. A Biophysical Typology in Agri-environmental Modelling. Environmental and Agricultural Modeling: 2009, 159 -187.
AMA StyleGerard Hazeu, Berien Elbersen, Erling Andersen, Bettina Baruth, Kees Van Diepen, Marc Metzger. A Biophysical Typology in Agri-environmental Modelling. Environmental and Agricultural Modeling:. 2009; ():159-187.
Chicago/Turabian StyleGerard Hazeu; Berien Elbersen; Erling Andersen; Bettina Baruth; Kees Van Diepen; Marc Metzger. 2009. "A Biophysical Typology in Agri-environmental Modelling." Environmental and Agricultural Modeling: , no. : 159-187.