National Land Cover Data Research Articles

Regional variation in the landscape ecology of West Nile virus sentinel chicken seroconversion in Florida.

How landscape composition and configuration impact the distribution of multi-vector and multi-host mosquito vector-borne disease systems, such as West Nile virus (WNV), remains challenging because of complex habitat and resource requirements by hosts and vectors that affect transmission opportunities. We examined correlations between landscape composition and configuration and 2018 WNV sentinel chicken seroconversion in Florida, USA across the state and within five National Oceanic Atmospheric Administration (NOAA) bioclimatic regions to understand strength and variation of landscape effects during an elevated transmission year. Although few landscape studies have examined WNV in Florida, we expected higher percentages of residential or medium-developed landscapes and more fragmented landscapes would be positively correlated with WNV seroconversion owing to the main mosquito vector habitats and avian host distributions. However, we expected to find variation in the importance of forest, wetland, and agriculture landscapes across bioclimatic regions in the state. WNV seroconversion rates were calculated using Florida 2018 Department of Health WNV sentinel chicken seroconversion data from 187 flocks maintained by mosquito control programs. Percent land cover and edge density metrics were calculated for multiple land cover classes and within multiple buffer distances from chicken coops using 2019 National Land Cover Data. We used binomial generalized linear mixed effects models to calculate the importance of landscape metrics to WNV seroconversion. We found no statewide predictors of seroconversion, but as expected, the importance of landscape varied across regions. In the north-central part of the state, we found higher seroconversion in less populated suburban areas while higher seroconversion in south-central Florida was correlated with fragmented forested areas within 0.5 km of coops and intact woody wetland areas within 2 km of coops. This work corroborates previous findings that consistent landscape predictors of WNV are difficult to identify across broader geographic areas and sets the stage for additional work that incorporates climate and landscapes interactions for a greater understanding of WNV ecology in this geographic region.

Monitoring Sustainable Development Goal Indicator 15.3.1 on Land Degradation Using SEPAL: Examples, Challenges and Prospects

A third of the world’s ecosystems are considered degraded, and there is an urgent need for protection and restoration to make the planet healthier. The Sustainable Development Goals (SDGs) target 15.3 aims at protecting and restoring the terrestrial ecosystem to achieve a land degradation-neutral world by 2030. Land restoration through inclusive and productive growth is indispensable to promote sustainable development by fostering climate change-resistant, poverty-alleviating, and environmentally protective economic growth. The SDG Indicator 15.3.1 is used to measure progress towards a land degradation-neutral world. Earth observation datasets are the primary data sources for deriving the three sub-indicators of indicator 15.3.1. It requires selecting, querying, and processing a substantial historical archive of data. To reduce the complexities, make the calculation user-friendly, and adapt it to in-country applications, a module on the FAO’s SEPAL platform has been developed in compliance with the UNCCD Good Practice Guidance (GPG v2) to derive the necessary statistics and maps for monitoring and reporting land degradation. The module uses satellite data from Landsat, Sentinel 2, and MODIS sensors for primary productivity assessment, along with other datasets enabling high-resolution to large-scale assessment of land degradation. The use of an in-country land cover transition matrix along with in-country land cover data enables a more accurate assessment of land cover changes over time. Four different case studies from Bangladesh, Nigeria, Uruguay, and Angola are presented to highlight the prospect and challenges of monitoring land degradation using various datasets, including LCML-based national land cover legend and land cover data.

Glucocorticoids and land cover: a largescale comparative approach to assess a physiological biomarker for avian conservation.

As humans alter landscapes worldwide, land and wildlife managers need reliable tools to assess and monitor responses of wildlife populations. Glucocorticoid (GC) hormone levels are one common physiological metric used to quantify how populations are coping in the context of their environments. Understanding whether GC levels can reflect broad landscape characteristics, using data that are free and commonplace to diverse stakeholders, is an important step towards physiological biomarkers having practical application in management and conservation. We conducted a phylogenetic comparative analysis using publicly available datasets to test the efficacy of GCs as a biomarker for large spatial-scale avian population monitoring. We used hormone data from HormoneBase (51 species), natural history information and US national land cover data to determine if baseline or stress-induced corticosterone varies with the amount of usable land cover types within each species' home range. We found that stress-induced levels, but not baseline, positively correlated with per cent usable land cover both within and across species. Our results indicate that GC concentrations may be a useful biomarker for characterizing populations across a range of habitat availability, and we advocate for more physiological studies on non-traditional species in less studied populations to build on this framework. This article is part of the theme issue 'Endocrine responses to environmental variation: conceptual approaches and recent developments'.

Using Atmospheric Inverse Modelling of Methane Budgets with Copernicus Land Water and Wetness Data to Detect Land Use-Related Emissions

Climate change mitigation requires countries to report their annual greenhouse gas (GHG) emissions and sinks, including those from land use, land use change, and forestry (LULUCF). In Finland, the LULUCF sector plays a crucial role in achieving net-zero GHG emissions, as the sector is expected to be a net sink. However, accurate estimates of LULUCF-related GHG emissions, such as methane (CH4), remain challenging. We estimated LULUCF-related CH4 emissions in Finland in 2013–2020 by combining national land cover and remote-sensed surface wetness data with CH4 emissions estimated by an inversion model. According to our inversion model, most of Finland’s CH4 emissions were attributed to natural sources such as open pristine peatlands. However, our research indicated that forests with thin tree cover surrounding open peatlands may also be a significant source of CH4. Unlike open pristine peatlands and pristine peatlands with thin tree cover, surrounding transient forests are included in the Finnish GHG inventory if they meet the criteria used for forest land. The current Finnish national GHG inventory may therefore underestimate CH4 emissions from forested organic soils surrounding open peatlands, although more precise methods and data are needed to verify this. Given the potential impact on net GHG emissions, CH4 emissions from transitional forests on organic soils should be further investigated. Furthermore, the results demonstrate the potential of combining atmospheric inversion modelling of GHGs with diverse data sources and highlight the need for methods to more easily combine atmospheric inversions with national GHG inventories.

Neighborhood greenness and participation in specific types of recreational physical activities in the Sister Study

BackgroundUrban green spaces have been consistently shown to have important human health benefits across a range of outcomes. These benefits are thought to be achieved, in part, because urban greenness provides opportunities for participation in recreational activity. However, the findings from studies that have assessed links between exposure to greenness and physical activity have been mixed. To date, few studies have examined association between greenness and specific types of recreational physical activities. ObjectiveWe evaluated associations between measures of greenness and specific types of recreational physical activities. Moreover, we explored the extent to which these associations were modified by socioeconomic conditions, and regionally. MethodsWe analyzed cross-sectional data from 49,649 women in the Sister Study and assigned three residentially-based measures of greenness based on national land cover data at buffer distances of 250 m and 500 m. Data on participation in up to ten specific recreational physical activities, including time spent in each activity were collected. Logistic regression was used to estimate odds ratios (OR) and their 95% confidence intervals (CI) controlling for confounders. ResultsCompared to those in the lowest tertile of greenness, participants in the upper tertile of greenness within a 500 m buffer, were more likely to garden (OR = 1.46, 95% CI = 1.25,1.69), participate in sports (OR = 1.28, 95% CI = 1.19,1.38), run (OR = 1.15, 95% CI = 1.04,1.27), walk (OR = 1.11, 95% CI = 1.06,1.16), and engage in conditioning exercises (OR = 1.10, 95% CI = 1.05,1.16) at least once a week for at least one month over the past year. These associations were modified by household income and US region. DiscussionOur findings suggest a beneficial effect of greenness on physical activity and provide additional information to inform planning of green environments that contribute to better health and wellbeing.

Comparative assessment of homogeneity differences in multi-temporal NDVI strata and the currently used agricultural area frames in Rwanda

This study compared two methods used for agricultural statistics generation in Rwanda. The first method is area frame sampling, which is also the currently used method in Rwandan seasonal agricultural surveys; while the second method is the application of remote sensing technique using multi-temporal Normalised Difference Vegetation Index (NDVI) classes to stratify land into homogenous agriculture land classes. The analysis of the methodological flow of Rwanda area frames and the estimated homogeneity in the resulting frames was mainly based on literature review. For the delineation of homogeneous NDVI classes, the study used 10 years data from Moderate Resolution Imaging Spectroradiometer (MODIS) sensor (2004 – 2014). The NDVI data were classified using ISODATA clustering technique, and the focus was put on agriculture-dominated classes, obtained through the intersection with 2010 national land use and land cover data. Analysis of Variance (ANOVA) and Fisher’s Least Significant Difference (LSD) statistical methods were applied to investigate significant differences between and within NDVI classes and the currently used Rwanda strata in terms of area coverage of four (4) dominant crops in Rwanda – banana, maize, cassava, and beans. The results of the analysis revealed homogeneity of 85% within NDVI classes, and 69% within the current Rwanda strata, at p = 0.05. The NDVI classes were also used to improve the Rwanda strata, and the homogeneity has increased by 5%; reaching 74% after NDVI-based reclassification.

Fine Resolution Imagery and LIDAR-Derived Canopy Heights Accurately Classify Land Cover with a Focus on Shrub/Sapling Cover in a Mountainous Landscape

Publicly available land cover maps do not accurately represent shrubs and saplings, an uncommon but ecologically relevant cover type represented by woody vegetation <4 m tall. This omission likely occurs because (1) the resolution is too coarse, (2) poor training data are available, and/or (3) shrub/saplings are difficult to discriminate from spectrally similar classes. We present a framework for classifying land cover, including shrub/saplings, by combining open-source fine-resolution (1 m) spectral and structural data across a large (>6000 km2) mountainous region. We hypothesized that the combination of spectral (imagery) and structural (LIDAR) data would allow for discrimination of shrub/sapling cover from other cover types. Specifically, we created training data using segmented four-band imagery from the National Agricultural Imagery Program (NAIP). In addition to spectral information from imagery, we used topographic information (elevation, slope, and aspect) and a LIDAR-derived canopy height model to classify land cover within a pixel-based random forests framework. To assess model accuracy, we used image interpretation and an independent sample of validation points. Due to the fine resolution of predictor rasters across such a large geographic region, we classified five subregions (counties) separately. We also compared the landscape metrics calculated for our custom classification at fine (1 m) and coarse resolution (resampled to 30 m) to metrics calculated with National Land Cover Data (NLCD). We achieved an overall accuracy of 89% and >80% accuracy for each land cover class. The LIDAR-derived canopy height model was consistently ranked as the most important predictor of vegetative land cover classes. Compared with our custom classification, NLCD underrepresented pasture/grassland by up to 10% and overrepresented forest up to 30%. There was no correlation between percent shrub/sapling cover in our custom classification and NLCD, suggesting that NLCD is not reliable for applications concerned with this ecologically relevant cover type.

Landscape-Scale Effects of Habitat and Weather on Scaled Quail Populations

Scaled quail (Callipepla squamata) have declined over the last half century; however, there is spatial variation within their geographic distribution. Interior populations have increased and peripheral populations have generally decreased. Declines have been attributed to habitat loss and degradation. Scaled quail populations also show interannual fluctuations related to precipitation. Our objective was to determine the relative impact of habitat and weather (i.e., precipitation and temperature) on scaled quail population dynamics. Our hypothesis was that habitat metrics would be more important for decreasing populations whereas weather metrics would be more important for increasing populations. We used publicly available datasets for scaled quail abundance measures (Breeding Bird Survey, Christmas Bird Count), weather (PRISM), and land cover (National Land Cover Data) collected over 3 5-year time periods (1990–1994, 1999–2003, 2009–2013). Data were collected at 2 scales: a route scale (5-km route buffer) and region scale (25-km circular buffer). We developed 25 a priori models that fit into 4 “model classes” (habitat amount, habitat fragmentation, matrix quality, weather). Model selection followed a 2-stage approach, where models were initially evaluated within each individual model class, then top models from each class were evaluated in combination to determine a global model. We used mixed-effects models with a negative binomial response distribution, treating route as a random effect. Weather variables were the primary explanatory factor for increasing populations at both scales. Similarly following our hypothesis, habitat variables were generally the most important for decreasing populations, but only at the route scale; weather variables dominated at the region scale. Both abundance datasets provided similar results and explanatory power (R2 ≈ 0.10 for route scale; R2 ≈ 0.27 for region scale), for both increasing and decreasing populations. Comparisons of land cover variables showed increasing populations to have higher amounts of habitat (p = 0.0028), higher mean patch area of habitat (p = 0.0446), and lower urban cover (p = 0.0287). Our hypothesis that weather variables account for more variation of increasing scaled quail populations was generally supported, likely because of increased amounts of habitat in these areas. However, given the low overall explanatory power of our models, it is likely that other factors such as habitat quality may be more important to scaled quail. Increasing temperature and reduced precipitation associated with climate change are likely to exacerbate scaled quail declines both directly and through continued habitat degradation, even within areas with increasing populations.

Towards the identification and mapping of traditional agricultural landscapes at the national scale: an inventory approach from Italy

ABSTRACT This paper presents an original approach for identifying and mapping agricultural landscapes that are highly representative of different biogeographical and natural settings and likely to be traditional, at the national scale. In the case study of Italy, we used national land cover data of different dates to examine composition of agricultural types and their persistence over time while we employed national stratifications into ecoregions and potential natural vegetation (PNV) to include environmental representativeness. Implementing the procedure returned 120 landscapes, which are distributed across all ecoregions and 51% of the PNV types. Most landscapes match areas with certified and traditional produce (93%) and relevant potential vegetation (86%), whereas 30% overlap acknowledged historical rural landscapes. These results show that our approach highlights the coevolutionary process between traditional crops and the underlying environmental framework and, therefore, provides an ecologically sound coarse filter for selecting and mapping traditional agricultural landscapes.

Mapping the morphology of sprawl and blight: A note on entropy

Abstract The urban expansion from the city center to the suburb and beyond is indicated by Shannon entropy, a robust and versatile measure of sprawl. However, the metropolitan regionwide entropy masks the morphology of land cover and land use consequential to urban expansion within the city-region. To surmount the limitation, we focus on the block-group, which is a US census defined socio-spatial unit that identifies the metropolitan region’s development pattern structurally, forming tracts that comprise neighborhoods. The concentration and dispersion of land use and land cover by block-group reveals a North American metropolitan region’s commonly known but rarely measured spatial structure of its urban and suburban sprawl. We use parcel data from county assessor of property (GIS) and land cover pixel data from the National Land Cover Data (NLCD) to compute block-group land-use and land-cover entropy. The change in block group entropy over a decade indicates whether the city- region’s land use and land cover transition to a concentrated or dispersed pattern. Furthermore, we test a hypothesis that blight correlates with sprawl. Blight and sprawl are among the key factors that plague the metropolitan region. We determine the correlations with household income as well as (block group) distance from the city center. It turns out, blight is among the universally held distance-decay phenomena. The share of the block group’s blighted properties decays (nonlinearly) with distance from the city center. Highlights for public administration, management and planning: • The metropolitan region’s outward growth is highlighted by mapping the changing morphology of the block group within the city-region. • The block group entropy is computed with land use (parcel) and land cover (pixel) data. • The block group entropy change indicates the pattern of the land use and land cover transition with concentration or dispersion. • We test the hypothesis that blight correlates with sprawl with statistical models. • The block group’s blighted properties decrease (nonlinearly) with distance from the city center.

Mapping urban land use by combining multi-source social sensing data and remote sensing images

Knowledge of detailed urban land-use patterns is essential in urban management, economic analysis, and policy-making aimed at sustainable urban development. To extract this information, previous studies relied on either the physical features extracted from remote sensing images or human activity patterns analyzed from social sensing data, but seldom on both of them. In this study, we proposed a framework to map the land-use patterns of New York City by combining multiple-source social sensing data and remote sensing images. We started by generating urban land use parcels using the transportation network from the Open street map and grouping them into built-up and non-built-up categories. Then, the random forest method was applied to classify built-up parcels and the National Land Cover Data was used to determine the land use type for non-built-up parcels. Results indicate that a satisfying overall testing accuracy with 77.31% was achieved for the level I classification (residential, commercial, and institutional regions) and 66.53% for level II classification (house, apartment, public service, transportation, office building, health service, education, and retails). Among the Level II classes, the residential land use has achieved the highest accuracy in built-up parcels with the user’s accuracy at 74.19% and producer’s accuracy at 80.99%. In addition, the classified map indicates that most commercial areas are concentrated in the Manhattan, residential land uses are distributed in the boroughs of Staten Island, Bronx, Queens, and Brooklyn, and institutional areas are evenly distributed in Manhattan, Brooklyn, Queens, Bronx, and Staten Island. The classified land use and functional information could further be used in other studies, such as urban planning and urban building energy use modeling.

The content and accuracy of the CORINE Land Cover dataset for Norway

The CORINE Land Cover dataset for Norway for the reference year 2018 (CLC2018) was compared to detailed national land cover and land use data. This allowed us to describe the thematic composition of the CLC-polygons and aggregate the information into statistical profiles for each CLC-class. We compared the results to the class definitions found in the CLC mapping instructions, while considering the generalization and minimal mapping units required for CLC. The study showed that CLC2018 in general complied with the definitions. Nonconformities were mainly found for detailed and (in a Norwegian context) marginal classes. The classification can still be improved by complementing visual interpretation with classification based on the statistical profile of each polygon when detailed land use and land cover information is available. The use of auxiliary information at the polygon level can thus provide a better, thematically more accurate CLC dataset for use in European land monitoring.

Valuing Environmental Amenities across Space: A Geographically Weighted Regression of Housing Preferences in Greenville County, SC

As global consumption and development rates continue to grow, there will be persistent stress placed on public goods, namely environmental amenities. Urban sprawl and development places pressure on forested areas, as they are often displaced or degraded in the name of economic development. This is problematic because environmental amenities are valued by the public, but traditional market analysis typically obscures the value of these goods and services that are not explicitly traded in a market setting. This research examines the non-market value of environmental amenities in Greenville County, SC, by utilizing a hedonic price model of home sale data in 2011. We overlaid home sale data with 2011 National Land Cover Data to estimate the value of a forest view, proximity to a forest, and proximity to agriculture on the value of homes. We then ran two regression models, an ordinary least squares (OLS) and a geographically weighted regression to compare the impact of space on the hedonic model variables. Results show that citizens in Greenville County are willing to pay for environmental amenities, particularly views of a forest and proximity to forested and agricultural areas. However, the impact and directionality of these variables differ greatly across space. These findings suggest the need for an integration of spatial dynamics into environmental valuation estimates to inform conservation policy and intentional city planning.

Valuing Our National Parks: An Ecological Economics Perspective

The annual budget for the United States National Park Service was roughly $3 billion in 2016. This is distributed amongst 405 National Parks, 23 national scenic and historic trails, and 60 wild and scenic rivers. Entrance fees and concessions generate millions of dollars in income for the National Park Service; however, this metric fails to account for the total value of the National Parks. In failing to consider the value of the ecosystem services provided by the National Parks, we fail to quantify and appreciate the contributions our parks make to society. This oversight allows us to continue to underfund a valuable part of our natural capital and consequently damage our supporting environment, national heritage, monetary economy, and many of our diverse cultures. We explore a simple benefits transfer valuation of the United States’ national parks using National Land Cover Data from 2011 and ecosystem service values determined by Costanza et al. This produces an estimate suggesting the parks provide $98 billion/year in ecosystem service value. If the natural infrastructure ‘asset’ that is our national park system had a budget comparable to a piece of commercial real estate of this value, the annual budget of the National Park Service would be roughly an order of magnitude larger at something closer to $30 billion rather than $3 billion.

Extraction of urban built-up areas from nighttime lights using artificial neural network

The spatial distribution of urban areas at the national and regional scales is critical for urban planners and governments to design sustainable and environment-friendly future development plans. The nighttime lights (NTL) data provide an effective way to monitor the urban at different scales however is usually achieved by using empirical threshold-based algorithms. This study proposed a novel Artificial Neural Network (ANN) approach, using moderate resolution imageries as NTL, MODIS NDVI and land surface temperature data, to map urban areas. Both random and maximum dissimilarity distance algorithm sampling methods were considered and compared. The validation of the urban areas extracted from MDA-based ANN against the 2011 US national land cover data showed a reasonable quality (overall accuracy = 97.84; Kappa = 0.74) and achieved more accurate result than the threshold method. This study demonstrates that ANN can provide an effective, rapid, and accurate alternative in extracting urban built-up areas from NTL.

Expansion of the built-up areas in Finnish city regions – The approach of travel-related urban zones

Finnish city regions have expanded rapidly due to urbanisation and urban sprawl, most recently in the 2000s. In this study, an approach of travel-related urban zones is introduced to enhance the interpretation of urban expansion in relation to previous land cover and urban form. The urban expansion of 34 Finnish city regions was studied by combining national CORINE Land Cover (CLC) data with socio-economic data using GIS. The regions were analysed by dividing them into zones: pedestrian, public transport, and car zones. The approach is applied from the theory of three urban fabrics, where the fabrics have distinct spatial characteristics and have been shaped by the evolution of transport systems. The aim was to determine which land cover classes were the most exposed to urban expansion during 2000–2012, and how large a share of the new residential floor space was located in previously developed areas in each zone. All the regions expanded the most into former forest areas in the fringe of the city, i.e., in car zones. On average, 77% of the area of urban expansion was previously classified as green spaces suitable for recreation. In the majority of the regions, the largest share of new residential floor space was concentrated in car-oriented locations that were not previously developed. The built-up areas grew even within regions with a declining population, exceeding the population growth altogether by a factor of 1.6. The study supports the previous findings on the sprawl-type urban expansion of Finnish cities, and implies insufficient steering of the urban development with negative environmental impacts in the 2000s. However, for the largest cities, the analyses also show signs of shifting towards infill development policies in public transport zones.

Accuracy assessment of the global TanDEM-X Digital Elevation Model with GPS data

The primary goal of the German TanDEM-X mission is the generation of a highly accurate and global Digital Elevation Model (DEM) with global accuracies of at least 10 m absolute height error (linear 90% error). The global TanDEM-X DEM acquired with single-pass SAR interferometry was finished in September 2016. This paper provides a unique accuracy assessment of the final TanDEM-X global DEM using two different GPS point reference data sets, which are distributed across all continents, to fully characterize the absolute height error. Firstly, the absolute vertical accuracy is examined by about three million globally distributed kinematic GPS (KGPS) points derived from 19 KGPS tracks covering a total length of about 66,000 km. Secondly, a comparison is performed with more than 23,000 “GPS on Bench Marks” (GPS-on-BM) points provided by the US National Geodetic Survey (NGS) scattered across 14 different land cover types of the US National Land Cover Data base (NLCD). Both GPS comparisons prove an absolute vertical mean error of TanDEM-X DEM smaller than ±0.20 m, a Root Means Square Error (RMSE) smaller than 1.4 m and an excellent absolute 90% linear height error below 2 m. The RMSE values are sensitive to land cover types. For low vegetation the RMSE is ±1.1 m, whereas it is slightly higher for developed areas (±1.4 m) and for forests (±1.8 m). This validation confirms an outstanding absolute height error at 90% confidence level of the global TanDEM-X DEM outperforming the requirement by a factor of five. Due to its extensive and globally distributed reference data sets, this study is of considerable interests for scientific and commercial applications.

Mapping ecosystem service value in Germany

ABSTRACTCurrently, a national TEEB study is being conducted in Germany. However, it lacks an overall estimation of ecosystem service value (ESV) at the national level. In this paper, we estimate the ESV in Germany based on the national land cover data and the unit value transfer method and examine the relationships between ESV and gross domestic product (GDP). The results indicate that the total ESV amounts to 248,895 million US$/yr, accounting for 7.91% of the sum of ESV and GDP. Cropland with 111,704 million US$/yr makes the largest contribution to the total ESV, and the second largest state, Lower Saxony, has the greatest ESV of 60,346 million US$/yr. In addition, a spatial pattern that distinguishes four regions in terms of the relationship between ESV and GDP at the district level is identified. This study illustrates the overall status of ecosystem services at the current point of time in Germany, raises public awareness about the magnitude of these services relative to other services captured in GDP, and implies suggestions for future research and ecological policy.

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.

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.

GEO-CEOS stage 4 validation of the Satellite Image Automatic Mapper lightweight computer program for ESA Earth observation level 2 product generation - Part 1: Theory.

ESA defines as Earth Observation (EO) Level 2 information product a single-date 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 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 2006–2009. The SIAM core is a prior knowledge-based decision tree for MS reflectance space hyperpolyhedralization into static color names. Typically, a vocabulary of MS color names in a MS data (hyper)cube and a dictionary of land cover (LC) class names in the scene-domain do not coincide and must be harmonized (reconciled). The present Part 1—Theory provides the multidisciplinary background of a priori color naming. The subsequent 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 2006 map, based on an original protocol for wall-to-wall thematic map quality assessment without sampling, where the test and reference maps feature the same spatial resolution and spatial extent, but whose legends differ and must be harmonized.