2014
DOI: 10.1111/geb.12182
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A global 1‐km consensus land‐cover product for biodiversity and ecosystem modelling

Abstract: Aim For many applications in biodiversity and ecology, existing remote sensing‐derived land‐cover products have limitations due to among‐product inconsistency and their typically non‐continuous nature. Here we aim to help address these shortcomings by generating a 1‐km resolution global product that provides scale‐integrated and accuracy‐weighted consensus land‐cover information on an approximately continuous scale. Location Global. Methods Using a generalized classification scheme and an accuracy‐based integr… Show more

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Cited by 411 publications
(375 citation statements)
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“…Following the requests of the user survey, the land cover data set is available across multiple spatial domains, conforms to standard file formats used in numerical models, and includes information on classification confidence levels for the land cover classes and resulting PFT fractions. The standardized conversion tool provides users with a consistent documented approach for aggregating land cover classes and thus overcomes limitations associated with consensus approaches (e.g., Tuanmu and Jetz 2014). Of particular importance is that the multitemporal LC_CCI mapping approach facilitated more accurate mapping leading to improved remote sensing observations of deforested areas in the tropics, the tree line-tundra boundary in the high latitudes, and better distinctions between managed and non-managed grasslands in Africa.…”
Section: Advantages Of the Lc_cci For Esm Modelingmentioning
confidence: 99%
“…Following the requests of the user survey, the land cover data set is available across multiple spatial domains, conforms to standard file formats used in numerical models, and includes information on classification confidence levels for the land cover classes and resulting PFT fractions. The standardized conversion tool provides users with a consistent documented approach for aggregating land cover classes and thus overcomes limitations associated with consensus approaches (e.g., Tuanmu and Jetz 2014). Of particular importance is that the multitemporal LC_CCI mapping approach facilitated more accurate mapping leading to improved remote sensing observations of deforested areas in the tropics, the tree line-tundra boundary in the high latitudes, and better distinctions between managed and non-managed grasslands in Africa.…”
Section: Advantages Of the Lc_cci For Esm Modelingmentioning
confidence: 99%
“…Moreover, the MODIS Collection 5 maps was developed with five different legends to address the thematic requirements of different users [5]. Map accuracy assessment, as well as map integration, has also been conducted by taking the perspectives and needs of specific users into account [37][38][39].…”
Section: User Engagement In Glc Mapping and Validationmentioning
confidence: 99%
“…The second activity, which we here call 'predictive modelling' (Shmueli 2010), instead presumes that a model describing the relationship of interest is already known, as are observed or estimated values of the relevant predictor variables, and therefore combines these to predict previously unknown values of the biodiversity response variable. The model used to make such predictions can be either an 'inductive model' derived through data analysis, in which case the activities of data analysis and prediction are integrally linked, or a 'deductive model' built directly from existing expert knowledge of the relationship between response and predictor variables (Corsi et al 2000;Overmars et al 2007;Tuanmu and Jetz 2014).…”
Section: Broad Roles Of Modelling In Biodiversity Assessmentmentioning
confidence: 99%