Accuracy Assessment of NOAA Coastal Change Analysis Program 2006-2010 Land Cover and Land Cover Change Data

McCombs, John; Herold, Nate; Burkhalter, Shan G.; Robinson, Christopher

doi:10.14358/pers.82.9.711

Cited by 17 publications

(10 citation statements)

References 31 publications

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“…Our LC error assessment found 15% error between CCAP and DSL data; this value is the same as the published 15% error for the CCAP dataset (Table S2 and McCombs et al, 2016). Overall, error was highest in bare land and marsh categories.…”

Section: Land Cover Errorsupporting

confidence: 85%

Relationships between regional coastal land cover distributions and elevation reveal data uncertainty in a sea-level rise impacts model

Lentz¹,

Plant²,

Thieler³

2018

Preprint

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Abstract. Understanding land loss or resilience in response to sea-level rise (SLR) requires spatially extensive and continuous datasets to capture landscape variability. We investigate sensitivity and skill of a model that predicts dynamic response likelihood to SLR across the northeastern U.S. by exploring several data inputs and outcomes. Using elevation and land cover datasets, we determine where data error is likely, quantify its effect on predictions, and evaluate its influence on prediction confidence. Results show data error is concentrated in low-lying areas with little impact on prediction skill, as the inherent correlation between the datasets can be exploited to reduce data uncertainty using Bayesian inference. This suggests the approach may be extended to regions with limited data availability and/or poor quality. Furthermore, we verify that model sensitivity in these first-order landscape change assessments is well-matched to larger coastal process uncertainties, for which process-based models are important complements to further reduce uncertainty.

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Section: Land Cover Errorsupporting

confidence: 85%

Relationships between regional coastal land cover distributions and elevation reveal data uncertainty in a sea-level rise impacts model

Lentz¹,

Plant²,

Thieler³

2018

Preprint

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show abstract

“…, McCombs et al. ). In this time frame, satellite instruments have also routinely measured ocean currents, surface winds, precipitation, and color and temperature of the ocean surface (Muller‐Karger et al.…”

Section: Essential Biodiversity Variables In the Coastal Zonementioning

confidence: 99%

“…Satellite sensors can cover large areas quickly and repeatedly. Estimates of wetland extent have been periodically generated from Landsat since the early 1970s (Tiner et al 2015, McCombs et al 2016. In this time frame, satellite instruments have also routinely measured ocean currents, surface winds, precipitation, and color and temperature of the ocean surface includes open ocean detection of diatoms and their phenology (Racault et al 2012, IOCCG 2014, Soppa et al 2016), tracking of harmful algal blooms (e.g., Soto et al 2016), and testing of algorithms for phytoplankton size distribution and functional group detection (Uitz et al 2010, Mouw et al 2012, Brotas et al 2013, Bracher et al 2017.…”

Section: Essential Biodiversity Variables In the Coastal Zonementioning

confidence: 99%

Satellite sensor requirements for monitoring essential biodiversity variables of coastal ecosystems

Muller‐Karger

Hestir

Ade

et al. 2018

Ecological Applications

119

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The biodiversity and high productivity of coastal terrestrial and aquatic habitats are the foundation for important benefits to human societies around the world. These globally distributed habitats need frequent and broad systematic assessments, but field surveys only cover a small fraction of these areas. Satellite‐based sensors can repeatedly record the visible and near‐infrared reflectance spectra that contain the absorption, scattering, and fluorescence signatures of functional phytoplankton groups, colored dissolved matter, and particulate matter near the surface ocean, and of biologically structured habitats (floating and emergent vegetation, benthic habitats like coral, seagrass, and algae). These measures can be incorporated into Essential Biodiversity Variables (EBVs), including the distribution, abundance, and traits of groups of species populations, and used to evaluate habitat fragmentation. However, current and planned satellites are not designed to observe the EBVs that change rapidly with extreme tides, salinity, temperatures, storms, pollution, or physical habitat destruction over scales relevant to human activity. Making these observations requires a new generation of satellite sensors able to sample with these combined characteristics: (1) spatial resolution on the order of 30 to 100‐m pixels or smaller; (2) spectral resolution on the order of 5 nm in the visible and 10 nm in the short‐wave infrared spectrum (or at least two or more bands at 1,030, 1,240, 1,630, 2,125, and/or 2,260 nm) for atmospheric correction and aquatic and vegetation assessments; (3) radiometric quality with signal to noise ratios (SNR) above 800 (relative to signal levels typical of the open ocean), 14‐bit digitization, absolute radiometric calibration <2%, relative calibration of 0.2%, polarization sensitivity <1%, high radiometric stability and linearity, and operations designed to minimize sunglint; and (4) temporal resolution of hours to days. We refer to these combined specifications as H4 imaging. Enabling H4 imaging is vital for the conservation and management of global biodiversity and ecosystem services, including food provisioning and water security. An agile satellite in a 3‐d repeat low‐Earth orbit could sample 30‐km swath images of several hundred coastal habitats daily. Nine H4 satellites would provide weekly coverage of global coastal zones. Such satellite constellations are now feasible and are used in various applications.

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“…Although imperfect, the LULC mapping products provided previously unavailable information on LULC change from 1997 to 2010. We acknowledge that the sample intensity used in the accuracy assessment of LULC mapping products in this study is lower than some studies, such as those by McCombs et al (2016) and Yang et al (2018). However, our study, as is, enabled quantification of the percent overall accuracy of the LULC change map and the individual LULC maps used to compute the change map.…”

Section: Implications Of Resultsmentioning

confidence: 90%

Mapping Land Use Land Cover Change in the Lower Mekong Basin From 1997 to 2010

Spruce

Bolten²,

Mohammed

et al. 2020

Front. Environ. Sci.

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Accuracy Assessment of NOAA Coastal Change Analysis Program 2006-2010 Land Cover and Land Cover Change Data

Cited by 17 publications

References 31 publications

Relationships between regional coastal land cover distributions and elevation reveal data uncertainty in a sea-level rise impacts model

Relationships between regional coastal land cover distributions and elevation reveal data uncertainty in a sea-level rise impacts model

Satellite sensor requirements for monitoring essential biodiversity variables of coastal ecosystems

Mapping Land Use Land Cover Change in the Lower Mekong Basin From 1997 to 2010

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