nerc.ac.uk

Integration of ground survey and remote sensing derived data: producing robust indicators of habitat extent and condition

Henrys, Peter A. ORCID: https://orcid.org/0000-0003-4758-1482; Jarvis, Susan G.. 2019 Integration of ground survey and remote sensing derived data: producing robust indicators of habitat extent and condition. Ecology and Evolution, 9 (14). 8104-8112. https://doi.org/10.1002/ece3.5376

Before downloading, please read NORA policies.
[img]
Preview
Text
N523842JA.pdf - Published Version
Available under License Creative Commons Attribution 4.0.

Download (856kB) | Preview

Abstract/Summary

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 km2 (Zi) is unobserved, but both ground survey and remote sensing can be used to estimate Zi. The model allows the relationship between remote sensing data and Zi 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.

Item Type: Publication - Article
Digital Object Identifier (DOI): https://doi.org/10.1002/ece3.5376
UKCEH and CEH Sections/Science Areas: Soils and Land Use (Science Area 2017-)
ISSN: 2045-7758
Additional Information. Not used in RCUK Gateway to Research.: Open Access paper - full text available via Official URL link.
Additional Keywords: Bayesian model calibration, data integration, field survey, Great Britain, peatland, remote sensing
NORA Subject Terms: Ecology and Environment
Mathematics
Data and Information
Date made live: 03 Jul 2019 10:59 +0 (UTC)
URI: http://nora.nerc.ac.uk/id/eprint/523842

Actions (login required)

View Item View Item

Document Downloads

Downloads for past 30 days

Downloads per month over past year

More statistics for this item...