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

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 esti...

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Main Authors: Peter A. Henrys, Susan G. Jarvis
Format: Article
Language:English
Published: Wiley 2019-07-01
Series:Ecology and Evolution
Subjects:
Online Access:https://doi.org/10.1002/ece3.5376
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spelling doaj-2f5021526b804b3599234ae077ec593e2021-03-02T06:56:47ZengWileyEcology and Evolution2045-77582019-07-019148104811210.1002/ece3.5376Integration of ground survey and remote sensing derived data: Producing robust indicators of habitat extent and conditionPeter A. Henrys0Susan G. Jarvis1NERC Centre for Ecology and Hydrology Lancaster Environment Centre Lancaster UKNERC Centre for Ecology and Hydrology Lancaster Environment Centre Lancaster UKAbstract 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.https://doi.org/10.1002/ece3.5376Bayesian model calibrationdata integrationfield surveyGreat Britainpeatlandremote sensing
collection DOAJ
language English
format Article
sources DOAJ
author Peter A. Henrys
Susan G. Jarvis
spellingShingle Peter A. Henrys
Susan G. Jarvis
Integration of ground survey and remote sensing derived data: Producing robust indicators of habitat extent and condition
Ecology and Evolution
Bayesian model calibration
data integration
field survey
Great Britain
peatland
remote sensing
author_facet Peter A. Henrys
Susan G. Jarvis
author_sort Peter A. Henrys
title Integration of ground survey and remote sensing derived data: Producing robust indicators of habitat extent and condition
title_short Integration of ground survey and remote sensing derived data: Producing robust indicators of habitat extent and condition
title_full Integration of ground survey and remote sensing derived data: Producing robust indicators of habitat extent and condition
title_fullStr Integration of ground survey and remote sensing derived data: Producing robust indicators of habitat extent and condition
title_full_unstemmed Integration of ground survey and remote sensing derived data: Producing robust indicators of habitat extent and condition
title_sort integration of ground survey and remote sensing derived data: producing robust indicators of habitat extent and condition
publisher Wiley
series Ecology and Evolution
issn 2045-7758
publishDate 2019-07-01
description 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 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.
topic Bayesian model calibration
data integration
field survey
Great Britain
peatland
remote sensing
url https://doi.org/10.1002/ece3.5376
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AT susangjarvis integrationofgroundsurveyandremotesensingderiveddataproducingrobustindicatorsofhabitatextentandcondition
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