National Land Cover Database Data Research Articles

The association between adverse childhood experiences, neighborhood greenspace, and body mass index: A cross-sectional study

An association between adverse childhood experiences (ACEs) and elevated body mass index (BMI) has been found in previous investigations. ACEs’ effects on BMI have been primarily considered via individual-level physiological and behavioral frameworks. Neighborhood factors, such as greenspace, are also associated with BMI and may merit consideration in studies examining ACEs-BMI associations. This exploratory study examined associations of BMI with ACEs and neighborhood greenspace and tested whether greenspace moderated ACEs-BMI associations. Methods entailed secondary analysis of cross-sectional data. ACEs and BMI were captured from 2012/2013 Philadelphia ACE Survey and 2012 Southeastern Household Heath Survey data; greenspace percentage in participants’ (n = 1,679 adults) home neighborhoods was calculated using National Land Cover Database data. Multi-level, multivariable linear regression 1) examined associations between BMI, ACEs, (0 ACEs [reference], 1–3 ACEs, 4 + ACEs), and neighborhood greenspace levels (high [reference], medium, low) and 2) tested whether greenspace moderated the ACEs-BMI association (assessed via additive interaction) before and after controlling for sociodemographic and health-related covariates. Experiencing 4 + ACEs (β = 1.21; 95 %CI: 0.26, 2.15; p = 0.01), low neighborhood greenspace (β = 1.51; 95 %CI: 0.67, 2.35; p < 0.01), and medium neighborhood greenspace (β = 1.37; 95 %CI: 0.52, 2.21; p < 0.01) were associated with BMI in unadjusted models. Only low neighborhood greenspace was associated with BMI (β = 0.95; 95 %CI: 0.14, 1.75; p = 0.02) in covariate-adjusted models. The ACEs-greenspace interaction was not significant in unadjusted (p = 0.89–0.99) or covariate-adjusted (p = 0.46–0.79) models. In conclusion, when considered simultaneously, low neighborhood greenspace, but not ACEs, was associated with BMI among urban-dwelling adults in covariate-adjusted models.

LAND COVER CHANGE ANALYSIS IN THE STATE OF CALIFORNIA USING NLCD DATA

Abstract. Land cover change is critical to be monitored as land cover change has significant impacts on flooding, ground water recharge, and urban air temperature. In this paper, key findings from a land cover change analysis study performed in the State of California are presented. National Land Cover Database (NLCD) data from the Multi-Resolution Land Characteristics Consortium (MRLC) was used for this study. Time series of NLCD data during the time period of 2001 through 2016 was used for the analysis. NLCD data processing was done in ArcMap 10.6.1. This paper includes the methodology in detail, and the results of the analysis. Results of the study indicate a significant increase in impervious surfaces, and a significant decrease in forest land cover.

Combining Multiple Geospatial Data for Estimating Aboveground Biomass in North Carolina Forests

Mapping and quantifying forest inventories are critical for the management and development of forests for natural resource conservation and for the evaluation of the aboveground forest biomass (AGFB) technically available for bioenergy production. The AGFB estimation procedures that rely on traditional, spatially sparse field inventory samples constitute a problem for geographically diverse regions such as the state of North Carolina in the southeastern U.S. We propose an alternative AGFB estimation procedure that combines multiple geospatial data. The procedure uses land cover maps to allocate forested land areas to alternative forest types; uses the light detection and ranging (LiDAR) data to evaluate tree heights; calculates the area-total AGFB using region- and tree-type-specific functions that relate the tree heights to the AGFB. We demonstrate the procedure for a selected North Carolina region, a 2.3 km2 area randomly chosen in Duplin County. The tree diameter functions are statistically estimated based on the Forest Inventory Analysis (FIA) data, and two publicly available, open source land cover maps, Crop Data Layer (CDL) and National Land Cover Database (NLCD), are compared and contrasted as a source of information on the location and typology of forests in the study area. The assessment of the consistency of forestland mapping derived from the CDL and the NLCD data lets us estimate how the disagreement between the two alternative, widely used maps affects the AGFB estimation. The methodology and the results we present are expected to complement and inform large-scale assessments of woody biomass in the region.

Detecting shrub recovery in sagebrush steppe: comparing Landsat-derived maps with field data on historical wildfires

BackgroundThe need for basic information on spatial distribution and abundance of plant species for research and management in semiarid ecosystems is frequently unmet. This need is particularly acute in the large areas impacted by megafires in sagebrush steppe ecosystems, which require frequently updated information about increases in exotic annual invaders or recovery of desirable perennials. Remote sensing provides one avenue for obtaining this information. We considered how a vegetation model based on Landsat satellite imagery (30 m pixel resolution; annual images from 1985 to 2018) known as the National Land Cover Database (NLCD) “Back-in-Time” fractional component time-series, compared with field-based vegetation measurements. The comparisons focused on detection thresholds of post-fire emergence of fire-intolerant Artemisia L. species, primarily A. tridentata Nutt. (big sagebrush). Sagebrushes are scarce after fire and their paucity over vast burn areas creates challenges for detection by remote sensing. Measurements were made extensively across the Great Basin, USA, on eight burn scars encompassing ~500 000 ha with 80 plots sampled, and intensively on a single 113 000 ha burned area where we sampled 1454 plots.ResultsEstimates of sagebrush cover from the NLCD were, as a mean, 6.5% greater than field-based estimates, and variance around this mean was high. The contrast between sagebrush cover measurements in field data and NLCD data in burned landscapes was considerable given that maximum cover values of sagebrush were ~35% in the field. It took approximately four to six years after the fire for NLCD to detect consistent, reliable signs of sagebrush recovery, and sagebrush cover estimated by NLCD ranged from 3 to 13% (equating to 0 to 7% in field estimates) at these times. The stabilization of cover and presence four to six years after fire contrasted with previous field-based studies that observed fluctuations over longer time periods.ConclusionsWhile results of this study indicated that further improvement of remote sensing applications would be necessary to assess initial sagebrush recovery patterns, they also showed that Landsat satellite imagery detects the influence of burns and that the NLCD data tend to show faster rates of recovery relative to field observations.

A Cloud-Based Evaluation of the National Land Cover Database to Support New Mexico’s Food–Energy–Water Systems

Accurate estimation of land use/land cover (LULC) areas is critical, especially over the semi-arid environments of the southwestern United States where water shortage and loss of rangelands and croplands are affecting the food production systems. This study was conducted within the context of providing an improved understanding of New Mexico’s (NM’s) Food–Energy–Water Systems (FEWS) at the county level. The main goal of this analysis was to evaluate the most important LULC classes for NM’s FEWS by implementing standardized protocols of accuracy assessment and providing bias-corrected area estimates of these classes. The LULC data used in the study was based on National Land Cover Database (NLCD) legacy maps of 1992, 2001, 2006, 2011, and 2016. The analysis was conducted using the cloud-based geospatial processing and modeling tools available from System for Earth Observation Data Access, Processing, and Analysis for Land Monitoring (SEPAL) of the Food and Agricultural Organization. Accuracy assessment, uncertainty analysis, and bias-adjusted area estimates were evaluated by collecting a total of 11,428 reference samples using the Open Foris Collect Earth tool that provided access to high spatial and temporal resolution images available in Google Earth. The reference samples were allocated using a stratified random sampling approach. The results showed an overall accuracy that ranged from 71%–100% in all six study counties. The user’s and producer’s accuracy of most LULC classes were about or above 80%. The obtained bias-adjusted area estimates were higher than those based on pixel counting. The bias-adjusted area estimates simultaneously showed decreasing and increasing trends in grassland and shrubland, respectively in four counties that include Curry, Roosevelt, Lea, and Eddy during the 1992–2016 period. Doña Ana county experienced increasing and decreasing trends in grassland and shrubland areas, respectively. San Juan county experienced decreasing trends in both grassland and shrubland areas. Cultivated cropland areas showed decreasing trends in three counties in southeast NM that rely on groundwater resources including Curry, Roosevelt, and Lea. Similarly, cultivated cropland areas showed increasing trends in the other three counties that rely on surface water or conjunctive use of surface and groundwater resources including San Juan, Doña Ana, and Eddy. The use of SEPAL allowed for efficient assessment and production of more accurate bias-adjusted area estimates compared to using pixel counting. Providing such information can help in understanding the behavior of NM’s food production systems including rangelands and croplands, better monitoring and characterizing NM’s FEWS, and evaluating their behavior under changing environmental and climatic conditions. More effort is needed to evaluate the ability of the NLCD data and other similar products to provide more accurate LULC area estimates at local scales.

Short-Term, Large-Area Survey of Container Aedes spp. (Diptera: Culicidae): Presence and Abundance is Associated with Fine-scale Landscape Factors in North Carolina, USA.

Container Aedes mosquitoes are responsible for the transmission of anthroponotic and zoonotic viruses to people. The surveillance and control of these mosquitoes is an important part of public health protection and prevention of mosquito-borne disease. In this study, we surveyed 327 sites over 2 weeks in late June and early July in 2017 in North Carolina, USA for the presence and abundance of Aedes spp. eggs in an effort to better target potential Ae. aegypti collections. We examined the ability of 2 types of landscape data, Light Detection And Ranging (LIDAR) and National Land Cover Database (NLCD) to explain the presence and abundance of eggs using principal component analysis to deal with collinearity, followed by generalized linear regression. We explained variation of both egg presence and abundance for Aedes albopictus (Skuse) and Aedes triseriatus (Say) using both NLCD and LIDAR data. However, the ability to make robust predictions was limited by variation in the data. Increased sampling time and better landscape data would likely improve the predictive ability of our models, as would a better understanding of oviposition behavior.

Accuracy Assessment of National Land Cover Database Shrubland Products on the Sagebrush Steppe Fringe

Remotely sensed data products depicting physical and ecological attributes of a landscape are becoming invaluable tools in wildlife and rangeland management. However, if such geospatial tools and data layers are to be used in management, their accuracy and appropriateness for such use needs to be vetted and validated. We assessed accuracy of two National Land Cover Database (NLCD) shrubland products for use in western South Dakota—percent sagebrush and sagebrush height—by comparing them to ground-truthed data. Western South Dakota sagebrush communities are an ecotone between sagebrush (Artemisia spp.) and grassland. This ecotone is typified by shorter- and lower-density sagebrush than interior sagebrush steppe ecosystem. This distinction could make it difficult to remotely detect and map sagebrush in this area. We determined NLCD correlations with ground estimates of sagebrush canopy cover (r = 0.17) and sagebrush height (r = 0.40). The NLCD percent sagebrush accurately predicted sagebrush presence ∼73‒76% of the time once resampled to 100-m pixels and 50-m mean values, respectively. Cohen’s kappa values were estimated to determine if the ground-truthed and remoted-sensed data were in agreement when determining sagebrush presence. Kappa values were 0.26 ± 0.06 and 0.28 ± 0.06 for mean values within 50-m and resampled 100-m pixels, respectively, indicating a “fair” level of agreement between the ground-truthed and remote-sensed data types when determining presence of sagebrush. The NLCD data sufficiently described the presence of sagebrush in South Dakota, which is useful for estimating geographic distributions of sagebrush obligate species, species distribution models in which presence or absence of sagebrush is of interest, or mapping the occurrence of sagebrush in South Dakota. Inaccuracies of the NLCD shrubland products in predicting sagebrush height and sagebrush canopy cover may limit their utility as continuous variables in species distribution models, habitat selection, and suitability models or when assessing rangeland quality in South Dakota.

Zonal lacunarity analysis: a new spatial analysis tool for geographic information systems

Lacunarity as a scale-dependent measure of spatial heterogeneity has received great attention in landscape ecology. Most lacunarity measures have been obtained from greyscale or binary (0 and 1) data for an entire study area or fixed rectangular windows, and a zonal lacunarity tool for discrete raster data is still lacking in current geographic information systems. This short communication presents the development of a free zonal lacunarity analysis tool for ArcGIS to support applications involving scale-dependent analysis of spatial heterogeneity, including landscape ecology. The application of the tool is also demonstrated using 2001 and 2011 land cover data from the National Land Cover Database (NLCD). Based on the gliding-box algorithm for lacunarity estimation, a tool for zonal lacunarity analysis of discrete raster data is developed using ArcPy and the Python programming language. The tool uses discrete raster data as input, an optional zone feature class as zone data to partition the input raster data into different zones, and a spreadsheet with zonal lacunarity values as output. As a demonstration, lacunarity measurements of grasslands in Corinth and Lake Dallas, Texas were calculated from the 2001 and 2011 NLCD data using box sizes (scales) of 2, 3, 4, 5, 6, 7, 8, 9, and 10. The results show that measures of grassland lacunarity in Lake Dallas were higher than Corinth at all scales, and the measures of grassland lacunarity in 2011 were higher than 2001 for both cities because of the increasing gap sizes in grasslands. The increasing gap sizes in grasslands were caused by converting the grasslands into developed areas. The results suggest that the zonal lacunarity analysis tool can provide important information on the spatial distribution of gaps in the input discrete raster data at different scales. It is hoped that the zonal lacunarity analysis tool can be further evaluated using different datasets in landscape ecology.

The managed clearing: An overlooked land-cover type in urbanizing regions?

Urban ecosystem assessments increasingly rely on widely available map products, such as the U.S. Geological Service (USGS) National Land Cover Database (NLCD), and datasets that use generic classification schemes to detect and model large-scale impacts of land-cover change. However, utilizing existing map products or schemes without identifying relevant urban class types such as semi-natural, yet managed land areas that account for differences in ecological functions due to their pervious surfaces may severely constrain assessments. To address this gap, we introduce the managed clearings land-cover type–semi-natural, vegetated land surfaces with varying degrees of management practices–for urbanizing landscapes. We explore the extent to which managed clearings are common and spatially distributed in three rapidly urbanizing areas of the Charlanta megaregion, USA. We visually interpreted and mapped fine-scale land cover with special attention to managed clearings using 2012 U.S. Department of Agriculture (USDA) National Agriculture Imagery Program (NAIP) images within 150 randomly selected 1-km2 blocks in the cities of Atlanta, Charlotte, and Raleigh, and compared our maps with National Land Cover Database (NLCD) data. We estimated the abundance of managed clearings relative to other land use and land cover types, and the proportion of land-cover types in the NLCD that are similar to managed clearings. Our study reveals that managed clearings are the most common land cover type in these cities, covering 28% of the total sampled land area– 6.2% higher than the total area of impervious surfaces. Managed clearings, when combined with forest cover, constitutes 69% of pervious surfaces in the sampled region. We observed variability in area estimates of managed clearings between the NAIP-derived and NLCD data. This suggests using high-resolution remote sensing imagery (e.g., NAIP) instead of modifying NLCD data for improved representation of spatial heterogeneity and mapping of managed clearings in urbanizing landscapes. Our findings also demonstrate the need to more carefully consider managed clearings and their critical ecological functions in landscape- to regional-scale studies of urbanizing ecosystems.

A fishnet-constrained land use mix index derived from remotely sensed data

ABSTRACTResearch works related to public health, transportation and urban planning have called for indices of land use mix (LUM) to support their spatial models. We propose a fishnet-based LUM calculation algorithm that works with the National Land Cover Database (NLCD) land cover data, a high-level product of Landsat satellite images. Comparing to the traditional LUM calculation, the fishnet structure can work at various spatial scales if aggregating to the administrative boundaries. Test results from regression models showed that our method was able to solve the scale problem identified as modifiable area unit problem that caused an unexpected positive correlation of obesity rate with LUM at the county scale. This is due to the fact that the existing methods do not limit the distance of LUM. The fishnet method provides a feasible way to calculate LUM indices across multiple scales. The NLCD data are the state-of-the art land use and land cover data for the contiguous United States. Our research provides a working example of the application of NLCD data or similar remote sensing products in public health-related research.

Quantifying Urban Watershed Stressor Gradients and Evaluating How Different Land Cover Datasets Affect Stream Management.

Watershed management and policies affecting downstream ecosystems benefit from identifying relationships between land cover and water quality. However, different data sources can create dissimilarities in land cover estimates and models that characterize ecosystem responses. We used a spatially balanced stream study (1) to effectively sample development and urban stressor gradients while representing the extent of a large coastal watershed (>4400km(2)), (2) to document differences between estimates of watershed land cover using 30-m resolution national land cover database (NLCD) and <1-m resolution land cover data, and (3) to determine if predictive models and relationships between water quality and land cover differed when using these two land cover datasets. Increased concentrations of nutrients, anions, and cations had similarly significant correlations with increased watershed percent impervious cover (IC), regardless of data resolution. The NLCD underestimated percent forest for 71/76 sites by a mean of 11% and overestimated percent wetlands for 71/76 sites by a mean of 8%. The NLCD almost always underestimated IC at low development intensities and overestimated IC at high development intensities. As a result of underestimated IC, regression models using NLCD data predicted mean background concentrations of NO3 (-) and Cl(-) that were 475 and 177%, respectively, of those predicted when using finer resolution land cover data. Our sampling design could help states and other agencies seeking to create monitoring programs and indicators responsive to anthropogenic impacts. Differences between land cover datasets could affect resource protection due to misguided management targets, watershed development and conservation practices, or water quality criteria.

Assessment of 2001 NLCD Percent Tree and Impervious Cover Estimates

The 2001 National Land Cover Database (NLCD) tree and impervious cover maps provide an opportunity to extract basic land-cover information helpful for natural resource assessments. To determine the potential utility and limitations of the 2001 NLCD data, this exploratory study compared 2001 NLCD-derived values of overall percent tree and impervious cover within geopolitical boundaries with aerial photo interpretation-derived values for the same areas. Results of the comparison reveal that NLCD underestimates tree cover and to a lesser extent, underestimates impervious cover. The underestimate appears to be consistent across the conterminous United States with no statistical differences among regions. However, there were statistical differences in the degree of underestimation of tree cover among mapping zones and of impervious cover by population density class.