Accuracy assessment of NLCD 2006 land cover and impervious surface

Wickham, James D.; Stehman, Stephen V.; Gass, Leila; Dewitz, Jon; Fry, Joyce; Wade, Timothy G.

doi:10.1016/j.rse.2012.12.001

Cited by 319 publications

(253 citation statements)

References 27 publications

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“…Similarly (Stehman et al, 2008; Wickham et al, 2010, 2013), OA(Reference NLCD 2006, NLCD 16 classes; “Ultimate” GroundTruth 2006, NLCD 16 classes) = 78% = 100% - Mismatch(Reference NLCD 2006, NLCD 16 classes; “Ultimate” GroundTruth 2006, NLCD 16 classes) = 100% - 22%. …”

Section: Methodsmentioning

confidence: 82%

“…When this experimental work was conducted the USGS NLCD 2006 map was the most recent release of the U.S. NLCD map series developed by the USGS EDC (Vogelmann et al, 1998, 2001; Wickham et al, 2010, 2013; Xian & Homer, 2010; EPA, 2007; Homer et al, 2004); see Figure 4. By now, the U.S. NLCD map series comprises the USGS NLCD 1992, 2001, 2001 Version 2.0, 2006 (released in 2011) and 2011 (released in 2015) editions.…”

Section: Methodsmentioning

confidence: 99%

“…Its legend consists of 16 LC classes defined according to the Level II LC classification system; refer to Table 1 (EPA, 2007). Validation of the USGS NLCD 2006 map provided an overall accuracy (OA) of 78%, which increased to 84% when the 16 LC classes were aggregated into 9 LC classes (Stehman, Wickham, Wade, & Smith, 2008; Wickham et al, 2010, 2013). Noteworthy, these 9 LC classes are conceptually equivalent to an “augmented” 3-level 9-class FAO LCCS-DP taxonomy; refer to previous Chapter 1.…”

Section: Methodsmentioning

confidence: 99%

“…Let us identify with OA(Test SIAM-WELD; “Ultimate” GroundTruth) ∈ [0, 100%] = 100%—Mismatch(Test SIAM-WELD; “Ultimate” GroundTruth) the OA of an EO data-derived SIAM test map with respect to an “ultimate” (ideal) ground truth and with OA(Test SIAM-WELD; Reference NLCD) ± 0% = 100%—Mismatch(Test SIAM-WELD; Reference NLCD) ± 0% the overall degree of agreement provided with its confidence interval of a test SIAM-WELD map compared wall-to-wall without sampling with a reference USGS NLCD map at the same spatial resolution and spatial extent. It is known that (Stehman et al, 2008; Wickham et al, 2010, 2013) OA(Reference NLCD 2006, “augmented” 3-level 9-class FAO LCCS-DP; “Ultimate” GroundTruth 2006, “augmented” 3-level 9-class FAO LCCS-DP) = 84% = 100% - Mismatch(Reference NLCD 2006, “augmented” 3-level 9-class FAO LCCS-DP; “Ultimate” GroundTruth 2006, “augmented” 3-level 9-class FAO LCCS-DP) = 100% - 16%. …”

Section: Methodsmentioning

confidence: 99%

“…The open-access U.S. Geological Survey (USGS) 30 m resolution annual Web Enabled Landsat Data (WELD) image composite of the conterminous U.S. (CONUS) for the years 2006 to 2009, radiometrically calibrated into TOARF values (Homer, Huang, Yang, Wylie, & Coan, 2004; Roy et al, 2010; WELD, 2016), was identified as a viable input dataset. The 30 m resolution 16-class U.S. National Land Cover Data (NLCD) 2006 map, delivered in 2011 by the USGS Earth Resources Observation Systems (EROS) Data Center (EDC) (Environmental Protection Agency (EPA), 2007; Vogelmann et al, 2001; Vogelmann, Sohl, Campbell, & Shaw, 1998; Wickham, Stehman, Fry, Smith, & Homer, 2010; Wickham et al, 2013; Xian & Homer, 2010), was selected as the reference thematic map at the CONUS spatial extent. The 16-class NLCD map legend is described in Table 1.…”

Section: Introductionmentioning

confidence: 99%

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GEO-CEOS stage 4 validation of the Satellite Image Automatic Mapper lightweight computer program for ESA Earth observation level 2 product generation – Part 2: Validation

et al. 2018

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ESA defines as Earth Observation (EO) Level 2 information product a multi-spectral (MS) image corrected for atmospheric, adjacency, and topographic effects, stacked with its data-derived scene classification map (SCM), whose legend includes quality layers cloud and cloud-shadow. No ESA EO Level 2 product has ever been systematically generated at the ground segment. To fill the information gap from EO big data to ESA EO Level 2 product in compliance with the GEO-CEOS stage 4 validation (Val) guidelines, an off-the-shelf Satellite Image Automatic Mapper (SIAM) lightweight computer program was selected to be validated by independent means on an annual 30 m resolution Web-Enabled Landsat Data (WELD) image composite time-series of the conterminous U.S. (CONUS) for the years 2006 to 2009. The SIAM core is a prior knowledge-based decision tree for MS reflectance space hyperpolyhedralization into static (non-adaptive to data) color names. For the sake of readability, this paper was split into two. The present Part 2—Validation—accomplishes a GEO-CEOS stage 4 Val of the test SIAM-WELD annual map time-series in comparison with a reference 30 m resolution 16-class USGS National Land Cover Data (NLCD) 2006 map. These test and reference map pairs feature the same spatial resolution and spatial extent, but their legends differ and must be harmonized, in agreement with the previous Part 1 - Theory. Conclusions are that SIAM systematically delivers an ESA EO Level 2 SCM product instantiation whose legend complies with the standard 2-level 4-class FAO Land Cover Classification System (LCCS) Dichotomous Phase (DP) taxonomy.

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Section: Methodsmentioning

confidence: 82%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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GEO-CEOS stage 4 validation of the Satellite Image Automatic Mapper lightweight computer program for ESA Earth observation level 2 product generation – Part 2: Validation

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Holidays in lights: Tracking cultural patterns in demand for energy services

2015

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Successful climate change mitigation will involve not only technological innovation, but also innovation in how we understand the societal and individual behaviors that shape the demand for energy services. Traditionally, individual energy behaviors have been described as a function of utility optimization and behavioral economics, with price restructuring as the dominant policy lever. Previous research at the macro-level has identified economic activity, power generation and technology, and economic role as significant factors that shape energy use. However, most demand models lack basic contextual information on how dominant social phenomenon, the changing demographics of cities, and the sociocultural setting within which people operate, affect energy decisions and use patterns. Here we use high-quality Suomi-NPP VIIRS nighttime environmental products to: (1) observe aggregate human behavior through variations in energy service demand patterns during the Christmas and New Year's season and the Holy Month of Ramadan and (2) demonstrate that patterns in energy behaviors closely track sociocultural boundaries at the country, city, and district level. These findings indicate that energy decision making and demand is a sociocultural process as well as an economic process, often involving a combination of individual price-based incentives and societal-level factors. While nighttime satellite imagery has been used to map regional energy infrastructure distribution, tracking daily dynamic lighting demand at three major scales of urbanization is novel. This methodology can enrich research on the relative importance of drivers of energy demand and conservation behaviors at fine scales. Our initial results demonstrate the importance of seating energy demand frameworks in a social context.

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Urban irrigation effects on WRF‐UCM summertime forecast skill over the Los Angeles metropolitan area

Vahmani

Hogue

2015

JGR Atmospheres

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In the current study, we explicitly address the impacts of urban irrigation on the local hydrological cycle by integrating a previously developed irrigation scheme within the coupled framework of the Weather Research and Forecasting-Urban Canopy Models (WRF-UCM) over the semiarid Los Angeles metropolitan area. We focus on the impacts of irrigation on the urban water cycle and atmospheric feedback. Our results demonstrate a significant sensitivity of WRF-UCM simulated surface turbulent fluxes to the incorporation of urban irrigation. Introducing anthropogenic moisture, vegetated pixels show a shift in the energy partitioning toward elevated latent heat fluxes. The cooling effects of irrigation on daily peak air temperatures are evident over all three urban types, with the largest influence over low-intensity residential areas (average cooling of 1.64°C). The evaluation of model performance via comparison against CIMIS (California Irrigation Management Information System) evapotranspiration (ET) estimates indicates that WRF-UCM, after adding irrigation, performs reasonably during the course of the month of July, tracking day-to-day variability of ET with notable consistency. In the nonirrigated case, CIMIS-based ET fluctuations are significantly underestimated by the model. Our analysis shows the importance of accurate representation of urban irrigation in modeling studies, especially over water-scarce regions such as the Los Angeles metropolitan area. We also illustrate that the impacts of irrigation on simulated energy and water cycles are more critical for longer-term simulations due to the interactions between irrigation and soil moisture fluctuations.

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Accuracy assessment of NLCD 2006 land cover and impervious surface

Cited by 319 publications

References 27 publications

GEO-CEOS stage 4 validation of the Satellite Image Automatic Mapper lightweight computer program for ESA Earth observation level 2 product generation – Part 2: Validation

GEO-CEOS stage 4 validation of the Satellite Image Automatic Mapper lightweight computer program for ESA Earth observation level 2 product generation – Part 2: Validation

Holidays in lights: Tracking cultural patterns in demand for energy services

Urban irrigation effects on WRF‐UCM summertime forecast skill over the Los Angeles metropolitan area

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