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Integration of ground survey and remote sensing derived data: Producing robust indicators of habitat extent and condition
Author(s) -
Henrys Peter A.,
Jarvis Susan G.
Publication year - 2019
Publication title -
ecology and evolution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.17
H-Index - 63
ISSN - 2045-7758
DOI - 10.1002/ece3.5376
Subject(s) - habitat , survey data collection , variance (accounting) , remote sensing , field (mathematics) , key (lock) , land cover , data quality , computer science , survey methodology , environmental science , geography , statistics , ecology , land use , mathematics , metric (unit) , operations management , accounting , computer security , economics , pure mathematics , business , biology
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.

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