Land Cover Database Research Articles

Extracting Impervious Surface from Aerial Imagery Using Semi-Automatic Sampling and Spectral Stability

The quantification of impervious surface through remote sensing provides critical information for urban planning and environmental management. The acquisition of quality reference data and the selection of effective predictor variables are two factors that contribute to the low accuracies of impervious surface in urban remote sensing. A hybrid method was developed to improve the extraction of impervious surface from high-resolution aerial imagery. This method integrates ancillary datasets from OpenStreetMap, National Wetland Inventory, and National Cropland Data to generate training and validation samples in a semi-automatic manner, significantly reducing the effort of visual interpretation and manual labeling. Satellite-derived surface reflectance stability is incorporated to improve the separation of impervious surface from other land cover classes. This method was applied to 1-m National Agriculture Imagery Program (NAIP) imagery of three sites with different levels of land development and data availability. Results indicate improved extractions of impervious surface with user’s accuracies ranging from 69% to 90% and producer’s accuracies from 88% to 95%. The results were compared to the 30-m percent impervious surface data of the National Land Cover Database, demonstrating the potential of this method to validate and complement satellite-derived medium-resolution datasets of urban land cover and land use.

How do natural features in the residential environment influence women's self-reported general health? Results from cross-sectional analyses of a U.S. national cohort.

BackgroundThe relationship between health and human interaction with nature is complex. Here we conduct analyses to provide insights into potential health benefits related to residential proximity to nature. ObjectivesWe aimed to examine associations between measures of residential nature and self-reported general health (SRGH), and to explore mediation roles of behavioral, social, and air quality factors, and variations in these relationships by urbanicity and regional climate. MethodsUsing residential addresses for 41,127 women from the Sister Study, a U.S.-based national cohort, we derived two nature exposure metrics, canopy and non-gray cover, using Percent Tree Canopy and Percent Developed Imperviousness from the National Land Cover Database. Residential circular buffers of 250 m and 1250 m were considered. Gradient boosted regression trees were used to model the effects of nature exposure on the odds of reporting better SRGH (Excellent/Very Good versus the referent, Good/Fair/Poor). Analyses stratified by urbanicity and regional climate (arid, continental, temperate) and mediation by physical activity, social support, and air quality were conducted. ResultsA 10% increase in canopy and non-gray cover within 1250 m buffer was associated with 1.02 (95% CI: 1.00–1.03) and 1.03 (95% CI: 1.01–1.04) times the odds of reporting better SRGH, respectively. Stronger associations were observed for the urban group and for continental climate relative to other strata. Social support and physical activity played a more significant mediation role than air quality for the full study population. DiscussionFindings from this study identified a small but important beneficial association between residential nature and general health. These findings could inform community planning and investments in neighborhood nature for targeted health improvements and potential societal and environmental co-benefits.

A tale of three cities: Mercury in urban deciduous foliage and soils across land-uses in Poughkeepsie NY, Hartford CT, and Springfield MA USA.

Mercury is a global pollutant that harms human and wildlife health through chronic exposure. The role of urban forests in Hg biogeochemistry has been understudied in cities without historical mining or current coal combustion. This study aimed to quantify total Hg concentrations and pools in urban forests to determine whether adjacent land-use impacts Hg accumulation. Three cities in the northeastern United States were studied: Hartford, Connecticut; Poughkeepsie, New York; and Springfield, Massachusetts. We identified ~20 urban forests sites in a ~10km by ~10km grid for each city and sampled foliage and soil at each site. Foliage from Populus exhibited significantly lower Hg concentrations (15.6±2.1ngg-1) than mean foliar Hg concentrations (23.7±0.6ngg-1) but most deciduous genera had comparable concentrations. Average forest floor Hg concentrations (195±21ngg-1) and Hg pools (1.9±0.5mgm-2) were similar to previous, non-urban studies in the region. Average A horizon (182±19ngg-1) and B horizon (125±14ngg-1) Hg concentrations were double those of regional forest soils. Mineral soil Hg pools for the top 30cm (49±6mgm-2) averaged two to ten times higher than rural, montane forests in the region. Soil pH, LOI, and %clay were poorly correlated with mineral soil Hg concentrations. Instead, highest foliar and soil Hg concentrations and pools were in urban forests adjacent to high and medium intensity developed areas in Springfield and Hartford. To differentiate the impact of land-uses not captured by the National Land Cover Database (NLCD) system, we implemented new land-use categories. Industrial areas had highest foliar and soil Hg concentrations and pools of any land use. Our results show increasing land-use increases Hg accumulation in urban forests.

The Use of the CORINE Land Cover (CLC) Database for Analyzing Urban Sprawl

Urban sprawl is generally defined as the urbanization of space adjacent to a city, which results from that city’s development. The discussed phenomenon involves land development, mainly agricultural land, in the proximity of cities, the development of infrastructure, and an increase in the number of residents who rely on urban services and commute to work in the city. Urban sprawl generates numerous problems which, in the broadest sense, result from the difficulty in identifying the boundaries of the central urban unit and the participation of local inhabitants, regardless of their actual place of residence, in that unit’s functional costs. These problems are associated not only with tax collection rights but with difficulties in measuring the extent of urban sprawl in research and local governance. The aim of this study was to analyze the applicability of the CORINE Land Cover (CLC) database for monitoring urbanization processes, including the dynamic process of urban sprawl. Polish cities with county rights, i.e., cities that implement independent spatial planning policies, were analyzed in the study to determine the pattern of urban sprawl in various types of cities. Buffer zones composed of municipalities that are directly adjacent to the central urban unit were mapped around the analyzed cities. The study proposes a novel method for measuring the extent of suburbanization with the use of the CLC database and Geographic Information System (GIS) tools. The developed method relies on the overgrowth of urbanization (OU) index calculated based on CLC data. The OU index revealed differences in the rate of urbanization in three groups of differently sized Polish cities. The analysis covered two periods: 2006–2012 and 2012–2018, and it revealed that urban sprawl in the examined cities proceeded in an unstable manner over time. The results of the present study indicate that the CLC database is a reliable source of information about urbanization processes.

Tree Canopy Coverage Predicts Lower Conduct Problem Severity in Children with ASD

ABSTRACTIntroduction: Conduct problems are commonly reported among individuals with autism spectrum disorders (ASD) and children with other special health care needs (CSHCN). Environmental research indicates that exposure to natural environments can lead to decreased conduct problems; opposite effects (i.e., increased problems) are associated with built “gray” environments (e.g., roadways).Methods: This exploratory study analyzed the association between Zip-code level tree canopy coverage and severity of conduct problems in typical children, children with ASD, and CSHCN. Tree canopy data came from National Land Cover Database and ASD data came from the cross-sectional National Survey of Children’s Health (2011/2012).Results: Percent tree canopy coverage predicted a decreased risk of severe conduct problems in youth with ASD, but not CSHCN; “gray” space was unassociated with conduct problems in any children.Conclusions: Community tree canopy coverage is negatively associated with conduct problems in children with ASD. More research using individual assessments and street level metrics will help better determine the relationship between canopy coverage and conduct problems in ASD.

Land cover types and changes in land use in Republic of Srpska (Bosnia and Herzegovina) over the period 2000-2018

Republic of Srpska (RS) covers an area of 24,666 km2 or about 48.5% of Bosnia and Herzegovina’s territory. Spatial and environmental changes accelerated due to dynamic historical period upon the RS formation and the following development period. The extent of these changes can be determined by comparing satellite images obtained from different acquisition periods. By processing images, with the support of geographic information systems, it is possible to create a database that aims to analyze spatial processes in a specific area in order to determine quantitative and qualitative parameters. In this way, the trends of spatial development (e.g. excessive logging, land cover damage, water pollution, etc.) and potentially vulnerable components of the environment can be monitored. The data for Bosnia and Herzegovina were collected from CORINE Land Cover (CLC) database for 39 European countries. The CLC is a project launched by the European Environment Agency (EEA) more than thirty years ago with the aim of collecting, coordinating and ensuring the consistency of information on natural resources and the environment. The goal of the paper is to identify land cover types and determine environment changes in the territory of Republic of Srpska over the period 2000–2018 as a consequence of the land use conversion.

Correlations between soil organic carbon, land use and soil type in Serbia

Correlation between soil organic carbon (SOC) and land use and soil type were investigated in the soils of the Republic of Serbia. The database included a total of 1,140 soil profiles. To establish the correlation between organic carbon content and soil type, a soil map of Serbia was adapted to the WRB classification and divided into 15,437 polygons (map units). The SOC stock values were calculated for each reference soil group based on mean values of SOC at 0-30 and 0-100 cm and their areas. The largest SOC stocks for the soil layers 0-30 cm were found in Cambisol 194.76 x 1012 g and Leptosol 186.43 x 1012 g and for the soil layers 0-100 cm in Cambisol 274.87 x 1012 g and Chernozem 230.43 x 1012 g. Using the Corine Land Cover (CLC) database, the major categories of land use were defined. Based on the obtained mean values of organic carbon content for the soil layers 0-30 and 0-100 cm and the areas indicated by Corine Land Cover categories of land use, the organic carbon stocks in agricultural soil, forest soil, semi-natural areas, and artificial areas were calculated. The correlation of organic carbon stocks and the different land use categories, soil reference group, and soil depth was studied for reference groups that occupy the major part of central Serbia, such as Cambisol (taking up 37.76% of the territory) and Leptosol (22.22% of the territory), and have a sufficient number of sites that were required for this type of analysis.

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.

Understanding the Environmental Background of an Invasive Plant Species (Asclepias syriaca) for the Future: An Application of LUCAS Field Photographs and Machine Learning Algorithm Methods.

For developing global strategies against the dramatic spread of invasive species, we need to identify the geographical, environmental, and socioeconomic factors determining the spatial distribution of invasive species. In our study, we investigated these factors influencing the occurrences of common milkweed (Asclepias syriaca L.), an invasive plant species that is of great concern to the European Union (EU). In a Hungarian study area, we used country-scale soil and climate databases, as well as an EU-scale land cover databases (CORINE) for the analyses. For the abundance data of A. syriaca, we applied the field survey photos from the Land Use and Coverage Area Frame Survey (LUCAS) Land Cover database for the European Union. With machine learning algorithm methods, we quantified the relative weight of the environmental variables on the abundance of common milkweed. According to our findings, soil texture and soil type (sandy soils) were the most important variables determining the occurrence of this species. We could exactly identify the actual land cover types and the recent land cover changes that have a significant role in the occurrence the common milkweed in Europe. We could also show the role of climatic conditions of the study area in the occurrence of this species, and we could prepare the potential distribution map of common milkweed for the study area.

Overall Methodology Design for the United States National Land Cover Database 2016 Products

The National Land Cover Database (NLCD) 2016 provides a suite of data products, including land cover and land cover change of the conterminous United States from 2001 to 2016, at two- to three-year intervals. The development of this product is part of an effort to meet the growing demand for longer temporal duration and more frequent, accurate, and consistent land cover and change information. To accomplish this, we designed a new land cover strategy and developed comprehensive methods, models, and procedures for NLCD 2016 implementation. Major steps in the new procedures consist of data preparation, land cover change detection and classification, theme-based postprocessing, and final integration. Data preparation includes Landsat imagery selection, cloud detection, and cloud filling, as well as compilation and creation of more than 30 national-scale ancillary datasets. Land cover change detection includes single-date water and snow/ice detection algorithms and models, two-date multi-index integrated change detection models, and long-term multi-date change algorithms and models. The land cover classification includes seven-date training data creation and 14-run classifications. Pools of training data for change and no-change areas were created before classification based on integrated information from ancillary data, change-detection results, Landsat spectral and temporal information, and knowledge-based trajectory analysis. In postprocessing, comprehensive models for each land cover theme were developed in a hierarchical order to ensure the spatial and temporal coherence of land cover and land cover changes over 15 years. An initial accuracy assessment on four selected Landsat path/rows classified with this method indicates an overall accuracy of 82.0% at an Anderson Level II classification and 86.6% at the Anderson Level I classification after combining the primary and alternate reference labels. This methodology was used for the operational production of NLCD 2016 for the Conterminous United States, with final produced products available for free download.

June Temperature Trends in the Southwest Deserts of the USA (1950–2018) and Implications for Our Urban Areas

Within the United States, the Southwest USA deserts show the largest temperature changes (1901–2010) besides Alaska, according to the most recent USA National Climate Assessment report. The report does not discuss urban effects vs. regional effects that might be evident in trends. Twenty-five temperature stations with ca. 68-year records (1950 to 2018) have been accessed from US Global Historical Climate Network archives. Land cover data are accessed from a National Land Cover Database. June results considering both urban and rural sites show an astounding rate per year change among sites ranging from −0.01 to 0.05 °C for maximum temperatures and 0.01 to 0.11 °C for minimum temperatures (−0.8 to 3.2 °C, and 0.8 to 8.0 °C for the entire period). For maximum temperatures, almost half of the sites showed no significant trends at a stringent 0.01 level of statistical significance, but 20 of 25 were significant at the 0.05 level. For minimum temperatures, over 75% of sites were significant at the 0.01 level (92% at 0.05 level of significance). The urban-dominated stations in Las Vegas, Phoenix, Tucson, and Yuma show large minimum temperature trends, indicating emerging heat island effects. Rural sites, by comparison, show much smaller trends. Addressing heat in our urban areas by local actions, through collaborations with stakeholders and political resolve, will aid in meeting future urban challenges in this era of projected global climate change and continued warming.

Extraction of Land Cover Units from Land Cover Components Based on Geometric Rules

Land cover units are aggregations of land cover components that are obtained by using criteria of homogeneity and proximity of basic components. For example, residential urban settlements can be defined as aggregations of single buildings, neighboring green spaces, paved surfaces and small roads, which are separated by more prominent land cover components, such as main roads or rivers. Land cover components belong to standard classes typically obtained by an automated classification process applied to aerial or satellite images, such as buildings, constructed areas, bare soil, water, vegetation, and the like. Land cover units belong to more general classes, obtained by a combination of land cover components, such as residential areas, industrial areas, road networks, river systems, and agricultural units. In this paper, we describe an approach based on the application of geometric rules and semantic constraints to extract land cover units from land cover components. We use spatial operators to extract composite land cover units from land cover databases, where spatial operators are taken from standards of the Open Geospatial Consortium. Expert knowledge needs to be translated into specific automatic procedures, called complex object definitions or CODs. Finally, we build a prototype system, where the user can choose among a set of available CODs to build a sequence of actions that lead to the discovery of knowledge. We discuss several study cases, such as the recognition of urban settlements, agricultural land units, and road networks.

Lichen species as element bioindicators for air pollution in the eastern United States of America

AbstractLichen element (N, S, metals) indicators of local air pollution load (a widely used technique) are recommended for five predefined regions covering central and southern parts of the eastern United States. The final recommendations integrate the advice of regional lichenologists, information from regional floras, and species abundance data from a United States Forest Service Forest Inventory and Analysis Program (FIA) lichen database for 11 of the 21 covered eastern states. Recommended species were frequent in their region, easy for nonspecialists to distinguish in the field after training, and easy to handle using clean protocols. Regression models of species abundance in FIA plots from five southeastern states vs. climate, air pollution (both from a regional lichen response model) and type of nearby landcover (from the National Land Cover Database) identified species’ environmental limitations. Punctelia rudecta is recommended for cooler forested uplands of all regions, with three Physcia species combined and Punctelia missouriensis for isolated woodlands or urban areas of three regions. Parmotrema hypotropum and P. hypoleucinum combined (weak environmental limitation) or P. perforatum. and P. subrigidum combined (limited in more polluted areas) are recommended for warmer Coastal Plains in two regions each. Additional species are recommended for single regions. Each species must be quantitatively evaluated in each region, to demonstrate indication reliability in practice and to calculate element data conversions between species for region-wide bioindication.

Estimating tree canopy cover using harmonic regression coefficients derived from multitemporal Landsat data

The goal of this study was to evaluate whether harmonic regression coefficients derived using all available cloud-free observations in a given Landsat pixel for a three-year period can be used to estimate tree canopy cover (TCC), and whether models developed using harmonic regression coefficients as predictor variables are better than models developed using median composite predictor variables, the previous operational standard for the National Land Cover Database (NLCD). The two study areas in the conterminous USA were as follows: West (Oregon), bounded by Landsat Worldwide Reference System 2 (WRS-2) paths/rows 43/30, 44/30, and 45/30; and South (Georgia/South Carolina), bounded by WRS-2 paths/rows 16/37, 17/37, and 18/37. Plot-specific tree canopy cover (the response variable) was collected by experienced interpreters using a dot grid overlaid on 1 m spatial resolution National Agricultural Imagery Program (NAIP) images at two different times per region, circa 2010 and circa 2014. Random forest model comparisons (using 500 independent model runs for each comparison) revealed the following (1) harmonic regression coefficients (one harmonic) are better predictors for every time/region of TCC than median composite focal means and standard deviations (across times/regions, mean increase in pseudo R2 of 6.7% and mean decrease in RMSE of 1.7% TCC) and (2) harmonic regression coefficients (one harmonic, from NDVI, SWIR1, and SWIR2), when added to the full suite of median composite and terrain variables used for the NLCD 2011 product, improve the quality of TCC models for every time/region (mean increase in pseudo R2 of 3.6% and mean decrease in RMSE of 1.0% TCC). The harmonic regression NDVI constant was always one of the top four most important predictors across times/regions, and is more correlated with TCC than the NDVI median composite focal mean. Eigen analysis revealed that there is little to no additional information in the full suite of predictor variables (47 bands) when compared to the harmonic regression coefficients alone (using NDVI, SWIR1, and SWIR2; 9 bands), a finding echoed by both model fit statistics and the resulting maps. We conclude that harmonic regression coefficients derived from Landsat (or, by extension, other comparable earth resource satellite data) can be used to map TCC, either alone or in combination with other TCC-related variables.

Thinking Like a Grassland: Challenges and Opportunities for Biodiversity Conservation in the Great Plains of North America

Fauna of North America’s Great Plains evolved strategies to contend with the region’s extreme spatiotemporal variability in weather and low annual primary productivity. The capacity for large-scale movement (migration and/or nomadism) enables many species, from bison to lark buntings, to track pulses of productivity at broad spatial scales (> 1 000 km2). Furthermore, even sedentary species often rely on metapopulation dynamics over extensive landscapes for long-term population viability. The current complex pattern of land ownership and use of Great Plains grasslands challenges native species conservation. Approaches to managing both public and private grasslands, frequently focused at the scale of individual pastures or ranches, limit opportunities to conserve landscape-scale processes such as fire, animal movement, and metapopulation dynamics. Using the US National Land Cover Database and Cropland Data Layers for 2011−2017, we analyzed land cover patterns for 12 historical grassland and savanna communities (regions) within the US Great Plains. On the basis of the results of these analyses, we highlight the critical contribution of restored grasslands to the future conservation of Great Plains biodiversity, such as those enrolled in the Conservation Reserve Program. Managing disturbance regimes at larger spatial scales will require acknowledging that, where native large herbivores are absent, domestic livestock grazing can function as a central component of Great Plains disturbance regimes if they are able move at large spatial scales and coexist with a diverse array of native flora and fauna. Opportunities to increase the scale of grassland management include 1) spatial prioritization of grassland restoration and reintroduction of grazing and fire, 2) finding creative approaches to increase the spatial scale at which fire and grazing can be applied to address watershed to landscape-scale objectives, and 3) developing partnerships among government agencies, landowners, businesses, and conservation organizations that enhance cross-jurisdiction management and address biodiversity conservation in grassland landscapes, rather than pastures.

Measuring continuous landscape patterns with Gray-Level Co-Occurrence Matrix (GLCM) indices: An alternative to patch metrics?

Abstract Characterizing landscape patterns is an important analytical step towards understanding the effects of physical layouts on ecological and social processes. While a continuous representation of landscape structure has great potential as a realistic alternative to traditional patch-based models, its empirical merits have been limited by the lack of measures for quantifying patterns from such continuous surface. This paper examines the utility of Gray-Level Co-Occurrence Matrix (GLCM) indices as spatial metrics applied to the landscape level for measuring underlying spatial properties. Eight GLCM indices (contrast, dissimilarity, homogeneity, energy, entropy, mean, variance, correlation) are compared to most commonly used 18 landscape metrics (LMs) featuring landscape composition, aggregation, dominance, dispersion, and shape complexity, with an application to urban tree canopy landscape. Two different types of map, sub-pixel tree canopy cover percentage map versus binary tree-pixel map, are used to compute GLCM indices and class-level LMs with a moving window approach across 4556 focal points. The data, extracted from the National Land Cover Database (NLCD) and the National Agricultural Imagery Program (NAIP), characterize the city of Columbus and Franklin County, Ohio. Correlation and regression analyses demonstrate that there is a strong and robust analogy between textural traits implied by GLCM indices and patch-based characteristics measured by LMs. Four LM components generated by principal component analysis contribute differently to individual GLCM indices, enabling more nuanced interpretation of GLCM indices in terms of LMs. The identified meanings consist of a unique mix of patch abundance, aggregation, dispersion, large patch dominance, patch size variability, and landscape homogeneity. The prediction of landscape patterns by GLCM indices increases in accuracy with landscape size, to a scale comparable to census tracts, while staying robust to the variation in GLCM bin width. GLCM indices can serve as reliable indicators of spatial configuration, and therefore provide an effective tool for researchers to better utilize continuous landscape models.

Adjusting Emergent Herbaceous Wetland Elevation with Object-Based Image Analysis, Random Forest and the 2016 NLCD

Emergent herbaceous wetlands are characterized by complex salt marsh ecosystems that play a key role in diverse coastal processes including carbon storage, nutrient cycling, flood attenuation and shoreline protection. Surface elevation characterization and spatiotemporal distribution of these ecosystems are commonly obtained from LiDAR measurements as this low-cost airborne technique has a wide range of applicability and usefulness in coastal environments. LiDAR techniques, despite significant advantages, show poor performance in generation of digital elevation models (DEMs) in tidal salt marshes due to large vertical errors. In this study, we present a methodology to (i) update emergent herbaceous wetlands (i.e., the ones delineated in the 2016 National Land Cover Database) to present-day conditions; and (ii) automate salt marsh elevation correction in estuarine systems. We integrate object-based image analysis and random forest technique with surface reflectance Landsat imagery to map three emergent U.S. wetlands in Weeks Bay, Alabama, Savannah Estuary, Georgia and Fire Island, New York. Conducting a hyperparameter tuning of random forest and following a hierarchical approach with three nomenclature levels for land cover classification, we are able to better map wetlands and improve overall accuracies in Weeks Bay (0.91), Savannah Estuary (0.97) and Fire Island (0.95). We then develop a tool in ArcGIS to automate salt marsh elevation correction. We use this ‘DEM-correction’ tool to modify an existing DEM (model input) with the calculated elevation correction over salt marsh regions. Our method and tool are validated with real-time kinematic elevation data and helps correct overestimated salt marsh elevation up to 0.50 m in the studied estuaries. The proposed tool can be easily adapted to different vegetation species in wetlands, and thus help provide accurate DEMs for flood inundation mapping in estuarine systems.

Mapping Annual Urban Change Using Time Series Landsat and NLCD

Annual urban change information is important for an improved understanding of urban dynamics and continuous modeling of urban ecosystem processes. This study examined Landsat-derived Normalized Difference Vegetation Index (NDVI) time series for characterizing annual urban change. To reduce impacts from cloud contamination and missing data, United States Geological Survey (USGS) Landsat Analysis Ready Data were processed to derive annual NDVI layers using a maximum value composite algorithm. National Land Cover Database land cover products from 2001 and 2011 were used as references for generating a decadal urban change mask. Within the decadal urban change mask and using annual NDVI as input, we examined three time-series change detection methods to pinpoint specific year of urban change: (a) minimum-value method, (b) break-point detection, and (c) simple-threshold identification. For accuracy assessment, we divided change pixels into urbanization and urban-intensification pixel groups, defined by initial land cover types. We used Google Earth's High-Resolution Imagery Archive as primary reference data for detailed accuracy assessment. Overall, the urbanization pixel group has good change detection accuracies of above 82% for all three change detection algorithms. The break-point detection method resulted in the highest overall accuracy of 88%. Overall accuracies for urban intensification pixel group were in the range of 35%–76%, depending on choice of change detection algorithm, length of input time-series, and further division of pixel subgroups.

Impacts of spatial clustering of urban land cover on land surface temperature across Köppen climate zones in the contiguous United States

This study examines the effects of spatial clustering of urban land cover types on land surface temperature (LST). The potential impact of the background regional climate is also taken into consideration. To study this relationship, multiple cities, each representing a major Köppen climate region in the U.S., namely Portland, Los Angeles, Chicago, Denver, Kansas City, Orlando, and Phoenix, were selected. Urban land cover types were derived from the 2011 National Land Cover Database (NLCD); summer mean LST from 2011 was calculated using the Moderate Resolution Imaging Spectroradiometer (MODIS) LST products. Spatial clustering was quantified using Moran’s I, and was analyzed against LST using correlation and multivariate regression analyses. The results indicate that in most climate regions, clustered impervious surfaces can elevate LST for both daytime and nighttime. The cooling effect of clustered vegetation cover was only found significant in regions with dry and warm summers, such as in Phoenix and Portland. Clustered water bodies have a strong cooling effect during the daytime but have a warming effect at night, except for cities such as Los Angeles and Phoenix, which have scant large water bodies. Furthermore, policy recommendations were put forward to suggest that reducing the spatial clustering of impervious surfaces, having more spatially clustered greenspaces, and having spatially dispersed water bodies with clustered greenspaces nearby are potential strategies to reduce urban warming in most cities in the contiguous U.S.