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• W2949138140 abstract "Abstract The availability of suitable habitat is a key predictor of the changing status of biodiversity. Quantifying habitat availability over large spatial scales is, however, challenging. Although remote sensing techniques have high spatial coverage, there is uncertainty associated with these estimates due to errors in classification. Alternatively, the extent of habitats can be estimated from ground‐based field survey. Financial and logistical constraints mean that on‐the‐ground surveys have much lower coverage, but they can produce much higher quality estimates of habitat extent in the areas that are surveyed. Here, we demonstrate a new combined model which uses both types of data to produce unified national estimates of the extent of four key habitats across Great Britain based on Countryside Survey and Land Cover Map. This approach considers that the true proportion of habitat per km 2 ( Z i ) is unobserved, but both ground survey and remote sensing can be used to estimate Z i . The model allows the relationship between remote sensing data and Z i to be spatially biased while ground survey is assumed to be unbiased. Taking a statistical model‐based approach to integrating field survey and remote sensing data allows for information on bias and precision to be captured and propagated such that estimates produced and parameters estimated are robust and interpretable. A simulation study shows that the combined model should perform best when error in the ground survey data is low. We use repeat surveys to parameterize the variance of ground survey data and demonstrate that error in this data source is small. The model produced revised national estimates of broadleaved woodland, arable land, bog, and fen, marsh and swamp extent across Britain in 2007." @default.
• W2949138140 created "2019-06-27" @default.
• W2949138140 creator A5002877874 @default.
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• W2949138140 date "2019-06-20" @default.
• W2949138140 modified "2023-10-14" @default.
• W2949138140 title "Integration of ground survey and remote sensing derived data: Producing robust indicators of habitat extent and condition" @default.
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• W2949138140 doi "https://doi.org/10.1002/ece3.5376" @default.
• W2949138140 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6662320" @default.
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• W2949138140 hasPublicationYear "2019" @default.
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