Thematic accuracy assessment of the 2011 National Land Cover Database (NLCD)

Wickham, James D.; Stehman, Stephen V.; Gass, Leila; Dewitz, Jon; Sorenson, Daniel G.; Granneman, Brian; Poss, Richard V.; Baer, Lori Anne

doi:10.1016/j.rse.2016.12.026

Cited by 199 publications

(147 citation statements)

References 23 publications

(47 reference statements)

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“…Figure S3. 2011 NLCD error matrix (Wickham et al 2017) with assigned p nlcd soy ( | ), the conditional probability of mapping a pixel to any NLCD class other than cultivated crops (to which soybean fields are assigned) when that pixel is incorrectly identified as soybean. Table S1.…”

Section: Discussionmentioning

confidence: 99%

“…The prior probability was adjusted based on accuracy assessment information from the 2011 NLCD dataset (Wickham et al 2017) by land-cover class using the principles of Bayes' theorem, which is used to update probabilities by incorporating additional evidence (Ott and Longnecker 2015). The prior probability was adjusted based on accuracy assessment information from the 2011 NLCD dataset (Wickham et al 2017) by land-cover class using the principles of Bayes' theorem, which is used to update probabilities by incorporating additional evidence (Ott and Longnecker 2015).…”

Section: Crop Footprint Developmentmentioning

confidence: 99%

“…The p nlcd soy ( | ) (Supplemental Data Table S1) and p nlcd soy Table S2) are derived from the published accuracy assessment information from the NLCD database (Wickham et al 2017). The p nlcd soy ( | ) (Supplemental Data Table S1) and p nlcd soy Table S2) are derived from the published accuracy assessment information from the NLCD database (Wickham et al 2017).…”

Section: Crop Footprint Developmentmentioning

confidence: 99%

See 2 more Smart Citations

A Probabilistic Co‐Occurrence Approach for Estimating Likelihood of Spatial Overlap Between Listed Species Distribution and Pesticide Use Patterns

Richardson¹,

Bang

Budreski³

et al. 2019

Integr Envir Assess & Manag

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Characterizing potential spatial overlap between federally threatened and endangered (“listed”) species distributions and registered pesticide use patterns is important for accurate risk assessment of threatened and endangered species. Because accurate range information for such rare species is often limited and agricultural pesticide use patterns are dynamic, simple spatial co‐occurrence methods may overestimate or underestimate overlap and result in decisions that benefit neither listed species nor the regulatory process. Here, we demonstrate a new method of co‐occurrence analysis that employs probability theory to estimate spatial distribution of rare species populations and areas of pesticide use to determine the likelihood of potential exposure. Specifically, we 1) describe a probabilistic method to estimate pesticide use based on crop production patterns; 2) construct species distribution models for 2 listed insect species whose ranges were previously incompletely described, the rusty‐patched bumble bee (Bombus affinis) and the Poweshiek skipperling (Oarisma poweshiek); and 3) develop a probabilistic co‐occurrence methodology and assessment framework. Using the principles of the Bayes' theorem, we constructed probabilistic spatial models of pesticide use areas by integrating information from land‐cover spatial data, agriculture statistics, and remote‐sensing data. We used maximum entropy methods to build species distribution models for 2 listed insects based on species collection and observation records and predictor variables relevant to the species' biogeography and natural history. We further developed novel methods for refinement of these models at spatial scales relevant to US Fish and Wildlife Service (FWS) regulatory priorities (e.g., critical habitat areas). Integrating both probabilistic assessments and focusing on USFWS priority management areas, we demonstrate that spatial overlap (i.e., potential for exposure) is not deterministic but instead a function of both species distribution and land use patterns. Our work serves as a framework to enhance the accuracy and efficiency of threatened and endangered species assessments using a data‐driven likelihood analysis of species co‐occurrence. Integr Environ Assess Manag 2019;00:1–12. © 2019 SETAC

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Section: Discussionmentioning

confidence: 99%

Section: Crop Footprint Developmentmentioning

confidence: 99%

Section: Crop Footprint Developmentmentioning

confidence: 99%

See 1 more Smart Citation

A Probabilistic Co‐Occurrence Approach for Estimating Likelihood of Spatial Overlap Between Listed Species Distribution and Pesticide Use Patterns

Richardson¹,

Bang

Budreski³

et al. 2019

Integr Envir Assess & Manag

View full text Add to dashboard Cite

show abstract

“…The product uses Landsat imagery as the primary data resource and has a spatial resolution of 30 m. The database classifies the national surface into 20 land cover types, and deciduous forest, evergreen forest, and mixed forest constitute the American forest. The overall accuracies of NLCD 2011 was 88% [63]. The NLCD 2011 has been recognized as an important source to analyze carbon cycle problems within certain land cover types at both the regional and national scales [64].…”

Section: National Land Cover Database 2011mentioning

confidence: 99%

Estimation of Forest Canopy Height and Aboveground Biomass from Spaceborne LiDAR and Landsat Imageries in Maryland

2018

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Abstract:Mapping the regional distribution of forest canopy height and aboveground biomass is worthwhile and necessary for estimating the carbon stocks on Earth and assessing the terrestrial carbon flux. In this study, we produced maps of forest canopy height and the aboveground biomass at a 30 m spatial resolution in Maryland by combining Geoscience Laser Altimeter System (GLAS) data and Landsat spectral imageries. The processes for calculating the forest biomass included the following: (i) processing the GLAS waveform and calculating spatially discrete forest canopy heights; (ii) developing canopy height models from Landsat imagery and extrapolating them to spatially contiguous canopy heights in Maryland; and, (iii) estimating forest aboveground biomass according to the relationship between canopy height and biomass. In our study, we explore the ability to use the GLAS waveform to calculate canopy height without ground-measured forest metrics (R 2 = 0.669, RMSE = 4.82 m, MRE = 15.4%). The machine learning models performed better than the principal component model when mapping the regional forest canopy height and aboveground biomass. The total forest aboveground biomass in Maryland reached approximately 160 Tg. When compared with the existing Biomass_CMS map, our biomass estimates presented a similar distribution where higher values were in the Western Shore Uplands region and Folded Application Mountain section, while lower values were located in the Delmarva Peninsula and Allegheny Mountain regions.

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“…The urban, agriculture, water, barren, and perennial ice and snow classes were reclassified as background. The producer's accuracies for the NLCD 2011 forest, shrubland, grassland, and wetland classes were 88, 90, 88, and 91%, respectively (Wickham et al 2017). MSPA identifies the structural elements of foreground from the binary map of foreground and background (Soille & Vogt 2009).…”

Section: Data and Processingmentioning

confidence: 99%

An inventory of continental U.S. terrestrial candidate ecological restoration areas based on landscape context

Wickham

Riitters

Vogt

et al. 2017

Restoration Ecology

Self Cite

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Landscape context is an important factor in restoration ecology, but the use of landscape context for site prioritization has not been as fully developed. We used morphological image processing to identify candidate ecological restoration areas based on their proximity to existing natural vegetation. We identified 1,102,720 candidate ecological restoration areas across the continental United States. Candidate ecological restoration areas were concentrated in the Great Plains and eastern United States. We populated the database of candidate ecological restoration areas with 17 attributes related to site content and context, including factors such as soil fertility and roads (site content), and number and area of potentially conjoined vegetated regions (site context) to facilitate its use for site prioritization. We demonstrate the utility of the database in the state of North Carolina, U.S.A. for a restoration objective related to restoration of water quality (mandated by the U.S. Clean Water Act), wetlands, and forest. The database will be made publicly available on the U.S. Environmental Protection Agency's EnviroAtlas website (http://enviroatlas.epa.gov) for stakeholders interested in ecological restoration.

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Thematic accuracy assessment of the 2011 National Land Cover Database (NLCD)

Cited by 199 publications

References 23 publications

A Probabilistic Co‐Occurrence Approach for Estimating Likelihood of Spatial Overlap Between Listed Species Distribution and Pesticide Use Patterns

A Probabilistic Co‐Occurrence Approach for Estimating Likelihood of Spatial Overlap Between Listed Species Distribution and Pesticide Use Patterns

Estimation of Forest Canopy Height and Aboveground Biomass from Spaceborne LiDAR and Landsat Imageries in Maryland

An inventory of continental U.S. terrestrial candidate ecological restoration areas based on landscape context

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