National Land Cover Dataset Research Articles

New Cropland on Former Rangeland and Lost Cropland from Urban Development: The “Replacement Land” Debate

In this study, a land use/land cover change analysis method was developed to examine patterns of land use/land cover conversions of cropland to urban uses and conversions of rangeland to cropland uses in the United States (US) Midwest region. We used the US 2001 and 2006 National Land Cover Datasets (NLCD) for our spatial analyses of these conversion trends. Our analysis showed that the eastern part of the Midwest, like prior periods, continued to experience losses of cropland to urban expansion but at a much more rapid rate, as this was during an expansion phase of the US real estate construction cycle. The period showed a very small net loss of cropland as the loss was being balanced by gains in cropland at the expense of rangeland lost in the western part of the Midwest. We refer to this rangeland to cropland conversion as “replacement land”. We do not suggest by replacement that there is a signal in the system that interconnects the loss of a hectare of cropland to urban land by converting a hectare of rangeland to cropland, rather we highlight this spatial trend as it raises concerns about the environmental sustainability of agriculture in the western part of the region, as production is dependent on the use of irrigation and the already stressed High Plains aquifer.

Analysis and expert assessment of the semantic similarity between land cover classes

Products of CORINE Land Cover (CLC), the National Land Cover Dataset (NLCD), the FAO/UNEP Land Cover Classification System (LCCS), etc. currently provide an important source of information used for the assessment of issues such as landscape change, landscape fragmentation and the planning of urbanization. Assuming that the data from these various databases are often used in searching for solutions to environmental problems, it is necessary to know which classes of different databases exist and to what extent they are similar, i.e. their possible compatibility and interchangeability. An expert assessment of the similarity between the CLC and NLCD 1992 nomenclatures is presented. Such a similarity assessment in comparison with the ‘geometric model’, the ‘feature model’ and the ‘network model’ is not frequently used. The results obtained show the similarity of assessments completed by four experts who marked the degree of similarity between the compared land cover classes by 1 (almost similar classes), 0.5 (partially similar classes) and 0 (not similar classes). Four experts agreed on assigning 1 in only three cases; 0.5 was given 33 times. A single expert assigned 0.5 a total of 17 times. Results confirmed that the CLC and NLCD nomenclatures are not very similar.

Uncertainty in hurricane surge simulation due to land cover specification

Hurricane storm surge is one of the most costly natural hazards in the United States. Numerical modeling to predict and estimate hurricane surge flooding is currently widely used for research, planning, decision making, and emergency response. Land cover plays an important role in hurricane surge numerical modeling because of its impacts on the forcing (changes in wind momentum transfer to water column) and dissipation (bottom friction) mechanisms of storm surge. In this study, the hydrodynamic model ADCIRC was used to investigate predicted surge response in bays on the central and lower Texas coast using different land cover data sets: (1) Coastal Change Analysis Program for 1996, 2001, and 2006; (2) the National Land Cover Dataset for 1992, 2001, and 2006; and (3) the National Wetlands Inventory for 1993. Hypothetical storms were simulated with varying the storm track, forward speed, central pressure, and radius to maximum wind, totaling 140 simulations. Data set choice impacts the mean of maximum surges throughout the study area, and variability in the surge prediction due to land cover data set choice strongly depends on storm characteristics and geographical location of the bay in relation to storm track. Errors in surge estimation due to land cover choice are approximately 7% of the surge value, with change in surge prediction varying by as much as 1 m, depending on location and storm condition. Finally, the impact of land cover choice on the accuracy of simulating surges for Hurricane Bret in 1999 is evaluated.

High-resolution landcover classification using Random Forest

Potential data sets for landcover classification, such as Landsat (or pre-processed data such as the National Land Cover Dataset (NLCD)), are often too coarse for fine-scale research needs or are cost-prohibitive (Quickbird, Ikonos and Geoeye). Repeated attempts at classifying high spatial resolution data, National Agricultural Imagery Program (NAIP) imagery, based on traditional techniques, such as a maximum likelihood supervised classification, have failed to produce a product with sufficient accuracy. We used the ensemble Random Forests (RFs) classifier to classify landcover at 1 m resolution using 2009 NAIP imagery in south-eastern Wyoming. We classified riparian areas within a 225 km2 study area, at 1 m spatial resolution, using RFs with emphasis on riparian corridors that yielded a land cover map with overall accuracy of 81% and a kappa coefficient of 79%. Users’ accuracy of important riparian vegetation species, aspen, riparian grasses and willow were 79%, 81% and 83%, respectively. Techniques presented in this paper, which exploit free NAIP imagery for landcover classification, represent an inexpensive and reliable alternative to purchasing commercial imagery when high spatial resolution landcover data is needed.

Numerical study of the sensitivity of mangroves in reducing storm surge and flooding to hurricane characteristics in southern Florida

The sensitivity of the mangrove effect on reducing storm surge flooding to hurricane characteristics is investigated by using the numerical model Coastal and Estuarine Storm Tide (CEST). First, the attenuation of storm surge by mangroves is incorporated into the model by updating Manning’s coefficient based on the National Land Cover Dataset (NLCD) 2001. Then CEST is verified by comparing the model results with field observations in South Florida for Hurricane Wilma. Secondly, a set of numerical experiments using synthetic hurricanes with different intensity, forward speed, radius of maximum wind speed and travel direction are conducted for the sensitivity study. Results indicate that storm surge magnitudes and flooding areas are reduced by the mangrove zone more for fast moving hurricanes than slow moving hurricanes in the west coast of South Florida. In addition, increasing hurricane intensity and hurricane size lower the effect of mangroves on attenuating storm surge and reducing the flooding area. The mangrove zone plays a more effective role in reducing flooding areas from hurricanes that travel from east to west than from hurricanes that travel from west to east. The mangrove reduction effect is most sensitive to changes in hurricane forward speed. A 6.7m/s to 2.2m/s decrease in forward speed can result in a decrease in flood area reduction by mangroves that is equivalent to the decrease in flood area reduction by mangroves from Category 3 to 5 hurricanes.

Validating Satellite-Derived Land Surface Temperature with in Situ Measurements: A Public Health Perspective

Background: Land surface temperature (LST) and percent surface imperviousness (SI), both derived from satellite imagery, have been used to characterize the urban heat island effect, a phenomenon in which urban areas are warmer than non-urban areas.Objectives: We aimed to assess the correlations between LSTs and SI images with actual temperature readings from a ground-based network of outdoor monitors.Methods: We evaluated the relationships among a) LST calculated from a 2009 summertime satellite image of the Detroit metropolitan region, Michigan; b) SI from the 2006 National Land Cover Data Set; and c) ground-based temperature measurements monitored during the same time period at 19 residences throughout the Detroit metropolitan region. Associations between these ground-based temperatures and the average LSTs and SI at different radii around the point of the ground-based temperature measurement were evaluated at different time intervals. Spearman correlation coefficients and corresponding p-values were calculated.Results: Satellite-derived LST and SI values were significantly correlated with 24-hr average and August monthly average ground temperatures at all but two of the radii examined (100 m for LST and 0 m for SI). Correlations were also significant for temperatures measured between 0400 and 0500 hours for SI, except at 0 m, but not LST. Statistically significant correlations ranging from 0.49 to 0.91 were observed between LST and SI.Conclusions: Both SI and LST could be used to better understand spatial variation in heat exposures over longer time frames but are less useful for estimating shorter-term, actual temperature exposures, which can be useful for public health preparedness during extreme heat events.

Connected components labeling for giga-cell multi-categorical rasters

Labeling of connected components in an image or a raster of non-imagery data is a fundamental operation in fields of pattern recognition and machine intelligence. The bulk of effort devoted to designing efficient connected components labeling (CCL) algorithms concentrated on the domain of binary images where labeling is required for a computer to recognize objects. In contrast, in the Geographical Information Science (GIS) a CCL algorithm is mostly applied to multi-categorical rasters in order to either convert a raster to a shapefile, or for statistical characterization of individual clumps. Recently, it has become necessary to label connected components in very large, giga-cell size, multi-categorical rasters but performance of existing CCL algorithms lacks sufficient speed to accomplish such task. In this paper we present a modification to the popular two-scan CCL algorithm that enables labeling of giga-cell size, multi-categorical rasters. Our approach is to apply a divide-and-conquer technique coupled with parallel processing to a standard two-scan algorithm. For specificity, we have developed a variant of a standard CCL algorithm implemented as r.clump in GRASS GIS. We have established optimal values of data blocks (stemming from the divide-and-conquer technique) and optimal number of computational threads (stemming from parallel processing) for a new algorithm called r.clump3p. The performance of the new algorithm was tested on a series of rasters up to 160Mcells in size; for largest size test raster a speed up over the original algorithm is 74 times. Finally, we have applied the new algorithm to the National Land Cover Dataset 2006 raster with 1.6×1010 cells. Labeling this raster took 39h using two-processors, 16 cores computer and resulted in 221,718,501 clumps. Estimated speed up over the original algorithm is 450 times. The r.clump3p works within the GRASS environment and is available in the public domain.

A national-level analysis of neighborhood form metrics

Interest in urban neighborhood form is strong among scholars trained in multiple disciplines. The increasing popularity of this field calls for a set of metrics that can be used to describe meaningful patterns of built features in neighborhood environments. This study employs national-level datasets from Add Health, the National Land Cover Dataset (NLCD) 2001, the U.S. Census TIGER, and the U.S. Geological Survey to construct neighborhood form metrics for 20,467 residents, whose residential environments cover a wide array of geographic areas representative of comprehensive neighborhood types across the United States. Buffers of different sizes (1km, 3km, 5km, and 8km, respectively) are drawn around each resident's location as the unit of analysis. For the four sets of 20,467 neighborhood environments, 27 neighborhood form metrics are selected, computed, and further reduced through factor analysis. The results suggest that the derived subsets of univariate metrics can be applied across neighborhood types to characterize diverse neighborhood environments.

The Racial/Ethnic Distribution of Heat Risk–Related Land Cover in Relation to Residential Segregation

Objective: We examined the distribution of heat risk–related land cover (HRRLC) characteristics across racial/ethnic groups and degrees of residential segregation.Methods: Block group–level tree canopy and impervious surface estimates were derived from the 2001 National Land Cover Dataset for densely populated urban areas of the United States and Puerto Rico, and linked to demographic characteristics from the 2000 Census. Racial/ethnic groups in a given block group were considered to live in HRRLC if at least half their population experienced the absence of tree canopy and at least half of the ground was covered by impervious surface (roofs, driveways, sidewalks, roads). Residential segregation was characterized for metropolitan areas in the United States and Puerto Rico using the multigroup dissimilarity index.Results: After adjustment for ecoregion and precipitation, holding segregation level constant, non-Hispanic blacks were 52% more likely (95% CI: 37%, 69%), non-Hispanic Asians 32% more likely (95% CI: 18%, 47%), and Hispanics 21% more likely (95% CI: 8%, 35%) to live in HRRLC conditions compared with non-Hispanic whites. Within each racial/ethnic group, HRRLC conditions increased with increasing degrees of metropolitan area-level segregation. Further adjustment for home ownership and poverty did not substantially alter these results, but adjustment for population density and metropolitan area population attenuated the segregation effects, suggesting a mediating or confounding role.Conclusions: Land cover was associated with segregation within each racial/ethnic group, which may be explained partly by the concentration of racial/ethnic minorities into densely populated neighborhoods within larger, more segregated cities. In anticipation of greater frequency and duration of extreme heat events, climate change adaptation strategies, such as planting trees in urban areas, should explicitly incorporate an environmental justice framework that addresses racial/ethnic disparities in HRRLC.

Spatial scale effects in environmental risk-factor modelling for diseases

Studies attempting to identify environmental risk factors for diseases can be seen to extract candidate variables from remotely sensed datasets, using a single buffer-zone surrounding locations from where disease status are recorded. A retrospective case-control study using canine leptospirosis data was conducted to verify the effects of changing buffer-zones (spatial extents) on the risk factors derived. The case-control study included 94 case dogs predominantly selected based on positive polymerase chain reaction (PCR) test for leptospires in urine, and 185 control dogs based on negative PCR. Land cover features from National Land Cover Dataset (NLCD) and Kansas Gap Analysis Program (KS GAP) around geocoded addresses of cases/controls were extracted using multiple buffers at every 500 m up to 5,000 m, and multivariable logistic models were used to estimate the risk of different land cover variables to dogs. The types and statistical significance of risk factors identified changed with an increase in spatial extent in both datasets. Leptospirosis status in dogs was significantly associated with developed high-intensity areas in models that used variables extracted from spatial extents of 500-2000 m, developed medium-intensity areas beyond 2,000 m and up to 3,000 m, and evergreen forests beyond 3,500 m and up to 5,000 m in individual models in the NLCD. Significant associations were seen in urban areas in models that used variables extracted from spatial extents of 500-2,500 m and forest/woodland areas beyond 2,500 m and up to 5,000 m in individual models in Kansas gap analysis programme datasets. The use of ad hoc spatial extents can be misleading or wrong, and the determination of an appropriate spatial extent is critical when extracting environmental variables for studies. Potential work-arounds for this problem are discussed.

Environmental, Climatic, and Residential Neighborhood Determinants of Feline Tularemia

Tularemia, caused by a Gram-negative bacterium Francisella tularensis, is an occasional disease of cats in the midwestern United States and a public health concern due to its zoonotic potential. Different environmental, climatic, and pet-owner's housing and socioeconomic conditions were evaluated as potential risk factors for feline tularemia using Geographic Information Systems (GIS) in a retrospective case-control study. The study included 46 cases identified as positive for tularemia based upon positive immunohistochemistry, isolation of F. tularensis using bacterial culture, and 4-fold or greater change in serum antibody titer for F. tularensis. Cats with a history of fever, malaise, icterus, and anorexia but no lesions characteristic of tularemia and/or negative immunohistochemistry, no isolation of bacteria in bacterial culture, and less than 4-fold raise in serum antibody titer for F. tularensis were treated as controls (n=93). Candidate geospatial variables from multiple thematic sources were analyzed for association with case status. Variables from National Land Cover Dataset, Soil Survey Geographic Database, US Census Bureau, and Daymet were extracted surrounding geocoded case-control household locations. Univariable screening of candidate variables followed by stepwise multivariable logistic modeling and odds ratios were used to identify strengths of variable associations and risk factors. Living in a residence located in newly urbanized/suburban areas, residences surrounded by areas dominated by grassland vegetation, and mean vapor pressure conditions recorded during the 8(th) week prior to case arrival at the hospital are significant risk factors for feline tularemia. Prevention strategies such as acaricide applications in residential backyards during spring and early summer periods and any behavior modifications suitable for cats that will prevent them from contracting infection from ticks or dead animals are necessary. Mean vapor pressure conditions recorded during the 8(th) week prior to case arrival at a diagnostic facility is a predictor for feline tularemia.

Evaluating the use of publicly available remotely sensed land cover data for areal interpolation

Areal interpolation is used to transfer attribute data between geographically incongruous zonal systems. Remotely sensed land cover data are widely used in intelligent areal interpolation methods to solve this problem. This article examines the usefulness of different publicly available remotely sensed land cover data sets as ancillary data used in conjunction with different areal interpolation methods. Two land cover data sets were compiled at the national scale; one by the Multi-Resolution Land Characteristics Consortium (the National Land Cover Dataset or NLCD) and one by the Coastal Change Analysis Program. A third land cover data set was compiled at a regional scale for the state of Connecticut by the Center for Land Use Education and Research. Results show that for areal interpolation, greater detail in the classification of developed areas was important whether the data were developed for use at a national or a regional scale. Even more important is the further enhancement of remotely sensed land use categories by incorporating local road or parcel data layers. The worst performing interpolation method using enhanced remote sensing-derived land cover data produced more accurate results than the best performing method using only the original land cover data. The results also show that parcels produce better enhancements than road buffers because they remove the areas of the roads themselves from population consideration.

Landscape factors influencing lake phosphorus concentrations across Minnesota

Total phosphorus (TP) concentrations are known to be a significant factor influencing fish populations in Minnesota lakes. Consequently, a primary focus of the Minnesota Department of Natural Resources to address fish habitat in lakes across the state has been to determine relationships between TP concentrations and watershed conditions in Minnesota lakes. Because phosphorus concentrations in Minnesota lakes vary widely corresponding to differences in geomorphology, nutrient criteria were established by the Minnesota Pollution Control Agency for specific ecoregions. To refine these relationships in Minnesota lakes managed for fishing, we gathered mean summer epilimnetic TP concentrations on 1330 natural lakes to identify where agricultural and urban development have elevated phosphorus levels. Random forest, regression tree, and generalized additive models were used to model spatial variation in lake phosphorus concentrations across Minnesota. Key landscape variables known to influence TP concentrations in lakes, including lake depth and watershed size, were used as explanatory variables in these models, along with agricultural and urban development quantified for lake watersheds from the National Land Cover Dataset. These models explained up to 60% of the variation in TP in lakes across the state and showed a critical benchmark of anthropogenic land use disturbance at 40%, that once exceeded could significantly alter TP levels and consequently fish populations. This information should be useful for fish managers to prioritize conservation efforts and to set appropriate fish population goals.

A comprehensive change detection method for updating the National Land Cover Database to circa 2011

The importance of characterizing, quantifying, and monitoring land cover, land use, and their changes has been widely recognized by global and environmental change studies. Since the early 1990s, three U.S. National Land Cover Database (NLCD) products (circa 1992, 2001, and 2006) have been released as free downloads for users. The NLCD 2006 also provides land cover change products between 2001 and 2006. To continue providing updated national land cover and change datasets, a new initiative in developing NLCD 2011 is currently underway. We present a new Comprehensive Change Detection Method (CCDM) designed as a key component for the development of NLCD 2011 and the research results from two exemplar studies. The CCDM integrates spectral-based change detection algorithms including a Multi-Index Integrated Change Analysis (MIICA) model and a novel change model called Zone, which extracts change information from two Landsat image pairs. The MIICA model is the core module of the change detection strategy and uses four spectral indices (CV, RCVMAX, dNBR, and dNDVI) to obtain the changes that occurred between two image dates. The CCDM also includes a knowledge-based system, which uses critical information on historical and current land cover conditions and trends and the likelihood of land cover change, to combine the changes from MIICA and Zone. For NLCD 2011, the improved and enhanced change products obtained from the CCDM provide critical information on location, magnitude, and direction of potential change areas and serve as a basis for further characterizing land cover changes for the nation. An accuracy assessment from the two study areas show 100% agreement between CCDM mapped no-change class with reference dataset, and 18% and 82% disagreement for the change class for WRS path/row p22r39 and p33r33, respectively. The strength of the CCDM is that the method is simple, easy to operate, widely applicable, and capable of capturing a variety of natural and anthropogenic disturbances potentially associated with land cover changes on different landscapes.

Example-Based Retrieval of Alike Land-Cover Scenes From NLCD2006 Database

Query-by-image-content (QBIC) tools are in demand in geospatial community because they enable exploration and mining of the rapidly increasing database of remotely sensed images. Accompanying the growth of the imagery database is the increase in the number of image-derived products, such as high-resolution large-spatial-extent maps of land cover/land use (LCLU). QBIC-like tools for exploration and mining of such products would significantly enhance their value. In this letter, we present a method for retrieval of alike scenes from a category-valued geospatial database of which an LCLU map is a particular example. Alikeness between the two scenes is tantamount to similarity between their spatial patterns of class labels. Our method works on the principle of query by example, its input is a reference scene, and its output is a similarity map indicating a degree of alikeness between a location on the map and the reference. The two core components of the method are as follows: scene signature—an encapsulation of the scene pattern by means of probability distribution of class labels and the sizes of the patches that they form, and scene similarity–a mutual-information-based function that assigns a level of similarity between any two scenes based on their signatures. The method is described in detail and applied to the National Land Cover Dataset 2006. Two examples of queries on this data set are presented and discussed. The applicability of the method to other data sets is discussed.

Estimation of Evapotranspiration Across the Conterminous United States Using a Regression With Climate and Land‐Cover Data1

Sanford, Ward E. and David L. Selnick, 2012. Estimation of Evapotranspiration Across the Conterminous United States Using a Regression with Climate and Land‐Cover Data. Journal of the American Water Resources Association (JAWRA) 1‐14. DOI: 10.1111/jawr.12010Abstract: Evapotranspiration (ET) is an important quantity for water resource managers to know because it often represents the largest sink for precipitation (P) arriving at the land surface. In order to estimate actual ET across the conterminous United States (U.S.) in this study, a water‐balance method was combined with a climate and land‐cover regression equation. Precipitation and streamflow records were compiled for 838 watersheds for 1971‐2000 across the U.S. to obtain long‐term estimates of actual ET. A regression equation was developed that related the ratio ET/P to climate and land‐cover variables within those watersheds. Precipitation and temperatures were used from the PRISM climate dataset, and land‐cover data were used from the USGS National Land Cover Dataset. Results indicate that ET can be predicted relatively well at a watershed or county scale with readily available climate variables alone, and that land‐cover data can also improve those predictions. Using the climate and land‐cover data at an 800‐m scale and then averaging to the county scale, maps were produced showing estimates of ET and ET/P for the entire conterminous U.S. Using the regression equation, such maps could also be made for more detailed state coverages, or for other areas of the world where climate and land‐cover data are plentiful.

A 2010 map estimate of annually tilled cropland within the conterminous United States

A ca. 2010, 30m resolution map depicting annually tilled areas across the conterminous United States was developed. Input sources included fouryears, spanning 2008–2011, of annual national-level coverage Cropland Data Layer (CDL) land cover classifications as produced by the National Agricultural Statistics Service. Derived total land area under tillage from the aggregate CDL product equaled 112.8million hectares (278.7million acres). By comparison, the 2007 Census of Agriculture (CoA) produced an estimate of 122.9million hectares, suggesting the map is under representing tilled area by 10.1million hectares or 8.2%. Regression analysis using state-level summaries showed a strong, albeit biased, correlation (r-squared=0.99) between the CDL derived tilled area and the CoA information. Notable outliers were North Dakota and Montana. Comparisons of the CDL tilled map were also made against the 2006 National Land Cover Dataset (NLCD) land cover product’s Cultivated Crops category. Strong state-level regression agreement (r-squared=0.98) was also found between the NLCD and the CDL acreages, but the NLCD estimated 8.5% more area than the CDL and thus closely matched that of the CoA. However, significant pixel level differences were found between the CDL and the NLCD. Nationally 5.6% of the maps were in disagreement as to whether cultivated or not, a large proportion considering around a seventh of the country’s land area is tilled. States of Arkansas, Montana and Wisconsin had the largest absolute discrepancies between the NLCD and CDL. Accepting the CDL as reference showed a national level NLCD cropland commission error of 23.0% and omission error of 14.5%. Much of what is believed to be problematic in the NLCD could be explained by definitional issues having included alfalfa hay into their cultivated category for many areas. Ultimately, while it is likely that the CDL annually tilled area model is an underestimate of the true total, taken contextually in map form and adjusted for undercount bias it likely is the best available.

Mapping tropical forests and rubber plantations in complex landscapes by integrating PALSAR and MODIS imagery

Knowledge of the spatial distribution of forest types in tropical regions is important for implementation of Reducing Emissions from Deforestation and Forest Degradation (REDD), better understanding of the global carbon cycle, and optimal forest management. Frequent cloud cover in moist tropical regions poses challenges for using optical images to map and monitor forests. Recently, Japan Aerospace Exploration Agency (JAXA) released a 50m orthorectified mosaic product from the Phased Array Type L-band Synthetic Aperture Radar (PALSAR) onboard the Advanced Land Observing Satellite (ALOS). PALSAR data provides information about the land surface without cloud interference. In this study we use the fine beam dual (FBD) polarization PALSAR 50m mosaic imagery and a Neural Network (NN) method to produce a land cover map in Hainan Island, China. Subsequently, forest areas are classified into evergreen and deciduous forests and rubber plantations are mapped using vegetation and land surface water indices derived from 250 to 500m resolution MODIS products. The PALSAR 50m forest cover map, MODIS-based forest types and rubber plantation maps are fused to generate fractional maps of evergreen forest, deciduous forest and rubber plantation within 500m or 250m pixels. PALSAR data perform well for land cover classification (overall accuracy=89% and Kappa Coefficient=0.79) and forest identification (both the Producer’s Accuracy and User’s Accuracy are higher than 92%). The resulting land cover maps of forest, cropland, water and urban lands are consistent with the National Land Cover Dataset of China in 2005 (NLCD-2005). Validation from ground truth samples indicates that the resultant rubber plantation map is highly accurate (the overall accuracy=85%). Overall, this study provides insight on the potential of integrating cloud-free 50m PALSAR and temporal MODIS data on mapping forest types and rubber plantations in moist tropical regions.

Development of an accurate fine-resolution land cover timeline: Little Rock, Arkansas, USA (1857–2006)

The need for accurate and compatible historical land cover datasets is increasing due to the growing interest in the drivers and impacts of landscape changes. In this article, we present a framework that uses readily available data to develop historical land cover timelines over large areas with fine resolution. We applied this framework to a 10,000 km2 area around Little Rock, Arkansas (USA) to document land cover changes at 60 m resolution from 1857 to 2006. First, we improved the earliest available maps of the region (1857) and digitized the first available aerial photographs (1943). We then modified the first national land cover dataset (GIRAS, 1975) to make it compatible with the contemporary national land cover database (1994–2006). These modifications increased the overall mapping accuracy of GIRAS from 66.5% to 77.8%, mainly by improving its resolution so that small, heterogeneous land cover patches were represented. Finally, we used our newly created fine-resolution land cover timeline to characterize major transitions in central Arkansas, which over a 149 y period was converted from a forest- and wetland-dominated landscape to an urban- and agriculture-dominated landscape. By applying our framework to other regions, researchers can begin to understand the drivers and impacts of land cover changes.

Land‐Cover Change and Avian Diversity in the Conterminous United States

Changes in land use and land cover have affected and will continue to affect biological diversity worldwide. Yet, understanding the spatially extensive effects of land-cover change has been challenging because data that are consistent over space and time are lacking. We used the U.S. National Land Cover Dataset Land Cover Change Retrofit Product and North American Breeding Bird Survey data to examine land-cover change and its associations with diversity of birds with principally terrestrial life cycles (landbirds) in the conterminous United States. We used mixed-effects models and model selection to rank associations by ecoregion. Land cover in 3.22% of the area considered in our analyses changed from 1992 to 2001, and changes in species richness and abundance of birds were strongly associated with land-cover changes. Changes in species richness and abundance were primarily associated with changes in nondominant types of land cover, yet in many ecoregions different types of land cover were associated with species richness than were associated with abundance. Conversion of natural land cover to anthropogenic land cover was more strongly associated with changes in bird species richness and abundance than persistence of natural land cover in nearly all ecoregions and different covariates were most strongly associated with species richness than with abundance in 11 of 17 ecoregions. Loss of grassland and shrubland affected bird species richness and abundance in forested ecoregions. Loss of wetland was associated with bird abundance in forested ecoregions. Our findings highlight the value of understanding changes in nondominant land cover types and their association with bird diversity in the United States.