Patterns and trends of land use and land cover on Atlantic and Gulf Coast barrier islands
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Professional Paper↗
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Considerations in planning a land use and land cover resources inventory update program
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Conference Paper↗
Spatial-temporal carbon sequestration under land use and land cover change
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Conference Paper↗
Guide to land cover and use classification systems employed by western governmental agencies
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FWS/OBS↗
Land-cover change in the central irregular plains, 1973-2000
Spearheaded by the Geographic Analysis and Monitoring Program of the U.S. Geological Survey (USGS) in collaboration with the U.S. Environmental Protection Agency (EPA) and the National Aeronautics and Space Administration (NASA), the Land Cover Trends is a research project focused on understanding the rates, trends, causes, and consequences of contemporary United States land-use and land-cover change. Using the EPA Level III ecoregions as the geographic framework, scientists process geospatial data collected between 1973 and 2000 to characterize ecosystem responses to land-use changes. The 27-year study period was divided into five temporal periods: 1973-1980, 1980-1986, 1986-1992, 1992-2000 and 1973-2000. General land-cover classes for these periods were interpreted from Landsat Multispectral Scanner, Thematic Mapper, and Enhanced Thematic Mapper Plus imagery to categorize land-cover change and evaluate using a modified Anderson Land Use Land Cover Classification System for image interpretation. The rates of land-cover change are estimated using a stratified, random sampling of 10-kilometer (km) by 10-km blocks allocated within each ecoregion. For each sample block, satellite images are used to interpret land-cover change. Additionally, historical aerial photographs from similar timeframes and other ancillary data such as census statistics and published literature are used. The sample block data are then incorporated into statistical analyses to generate an overall change matrix for the ecoregion. These change statistics are applicable for different levels of scale, including total change for the individual sample blocks and change estimates for the entire ecoregion. The results illustrate that there is no single profile of land-cover change but instead point to geographic variability that results from land uses within ecoregions continuously adapting to various factors including environmental, technological, and socioeconomic.
Open-File Report↗
Ecoregional differences in late-20th-century land-use and land-cover change in the U.S. northern great plains
Land-cover and land-use change usually results from a combination of anthropogenic drivers and biophysical conditions found across multiple scales, ranging from parcel to regional levels. A group of four Level 111 ecoregions located in the U.S. northern Great Plains is used to demonstrate the similarities and differences in land change during nearly a 30-year period (1973-2000) using results from the U.S. Geological Survey's Land Cover Trends project. There were changes to major suites of land-cover; the transitions between agriculture and grassland/shrubland and the transitions among wetland, water, agriculture, and grassland/ shrubland were affected by different factors. Anthropogenic drivers affected the land-use tension (or land-use competition) between agriculture and grassland/shrubland land-covers, whereas changes between wetland and water land-covers, and their relationship to agriculture and grassland/shrubland land-covers, were mostly affected by regional weather cycles. More land-use tension between agriculture and grassland/shrubland landcovers occurred in ecoregions with greater amounts of economically marginal cropland. Land-cover change associated with weather variability occurred in ecoregions that had large concentrations of wetlands and water impoundments, such as the Missouri River reservoirs. The Northwestern Glaciated Plains ecoregion had the highest overall estimated percentage of change because it had both land-use tension between agriculture and grassland/shrubland land-covers and wetland-water changes.
Great Plains Research↗
Land-Cover Change in the East Central Texas Plains, 1973-2000
Project Background: The Geographic Analysis and Monitoring (GAM) Program of the U.S. Geological Survey (USGS) Land Cover Trends project is focused on understanding the rates, trends, causes, and consequences of contemporary U.S. land-use and land-cover change. The objectives of the study are to: (1) develop a comprehensive methodology for using sampling and change analysis techniques and Landsat Multispectral Scanner (MSS) and Thematic Mapper (TM) data for measuring regional land-cover change across the United States, (2) characterize the types, rates and temporal variability of change for a 30-year period, (3) document regional driving forces and consequences of change, and (4) prepare a national synthesis of land-cover change (Loveland and others, 1999). Using the 1999 Environmental Protection Agency (EPA) Level III ecoregions derived from Omernik (1987) as the geographic framework, geospatial data collected between 1973 and 2000 were processed and analyzed to characterize ecosystem responses to land-use changes. The 27-year study period was divided into five temporal periods: 1973-1980, 1980-1986, 1986-1992, 1992-2000, and 1973-2000. General land-cover classes such as water, developed, grassland/shrubland, and agriculture for these periods were interpreted from Landsat MSS, TM, and Enhanced Thematic Mapper Plus imagery to categorize land-cover change and evaluate using a modified Anderson Land-Use Land-Cover Classification System for image interpretation. The interpretation of these land-cover classes complement the program objective of looking at land-use change with cover serving as a surrogate for land use. The land-cover change rates are estimated using a stratified, random sampling of 10-kilometer (km) by 10-km blocks allocated within each ecoregion. For each sample block, satellite images are used to interpret land-cover change for the five time periods previously mentioned. Additionally, historical aerial photographs from similar timeframes and other ancillary data such as census statistics and published literature are used. The sample block data are then incorporated into statistical analyses to generate an overall change matrix for the ecoregion. For example, the scalar statistics can show the spatial extent of change per cover type with time, as well as the land-cover transformations from one land-cover type to another type occurring with time. Field data of the sample blocks include direct measurements of land cover, particularly ground-survey data collected for training and validation of image classifications (Loveland and others, 2002). The field experience allows for additional observations of the character and condition of the landscape, assistance in sample block interpretation, ground truthing of Landsat imagery, and helps determine the driving forces of change identified in an ecoregion. Management and maintenance of field data, beyond initial use for training and validation of image classifications, is important as improved methods for image classification are developed, and as present-day data become part of the historical legacy for which studies of land-cover change in the future will depend (Loveland and others, 2002). The results illustrate that there is no single profile of land-cover change; instead, there is significant geographic variability that results from land uses within ecoregions continuously adapting to the resource potential created by various environmental, technological, and socioeconomic factors.
Open-File Report↗
Causes of variations in water quality and aquatic ecology in rivers of the Upper Mississippi River Basin, Minnesota and Wisconsin
Physical and aquatic biological conditions differ among the Mississippi River and its major tributaries (the St. Croix and Minnesota Rivers) in Minnesota and Wisconsin. The quality of surface water and the ecological condition of rivers affect the ways in which we use them. The St. Croix River is used for recreation; the Mississippi River is used for recreation and is a corridor for commerce; and the Minnesota River primarily drains agricultural lands. Analysis of the environmental framework of the basins and water-quality and ecological information by the National Water-Quality Assessment (NAWQA) Program shows that the conditions of the rivers are a product of a combination of factors including climate, hydrology, geology, soils, land use, land cover, water management, and water use.
Minnesota, Wisconsin↗
Survey methods for assessing land cover map accuracy
The increasing availability of digital photographic materials has fueled efforts by agencies and organizations to generate land cover maps for states, regions, and the United States as a whole. Regardless of the information sources and classification methods used, land cover maps are subject to numerous sources of error. In order to understand the quality of the information contained in these maps, it is desirable to generate statistically valid estimates of accuracy rates describing misclassification errors. We explored a full sample survey framework for creating accuracy assessment study designs that balance statistical and operational considerations in relation to study objectives for a regional assessment of GAP land cover maps. We focused not only on appropriate sample designs and estimation approaches, but on aspects of the data collection process, such as gaining cooperation of land owners and using pixel clusters as an observation unit. The approach was tested in a pilot study to assess the accuracy of Iowa GAP land cover maps. A stratified two-stage cluster sampling design addressed sample size requirements for land covers and the need for geographic spread while minimizing operational effort. Recruitment methods used for private land owners yielded high response rates, minimizing a source of nonresponse error. Collecting data for a 9-pixel cluster centered on the sampled pixel was simple to implement, and provided better information on rarer vegetation classes as well as substantial gains in precision relative to observing data at a single-pixel.
Environmental and Ecological Statistics↗
Optimizing selection of training and auxiliary data for operational land cover classification for the LCMAP initiative
The U.S. Geological Survey’s Land Change Monitoring, Assessment, and Projection (LCMAP) initiative is a new end-to-end capability to continuously track and characterize changes in land cover, use, and condition to better support research and applications relevant to resource management and environmental change. Among the LCMAP product suite are annual land cover maps that will be available to the public. This paper describes an approach to optimize the selection of training and auxiliary data for deriving the thematic land cover maps based on all available clear observations from Landsats 4–8. Training data were selected from map products of the U.S. Geological Survey’s Land Cover Trends project. The Random Forest classifier was applied for different classification scenarios based on the Continuous Change Detection and Classification (CCDC) algorithm. We found that extracting training data proportionally to the occurrence of land cover classes was superior to an equal distribution of training data per class, and suggest using a total of 20,000 training pixels to classify an area about the size of a Landsat scene. The problem of unbalanced training data was alleviated by extracting a minimum of 600 training pixels and a maximum of 8000 training pixels per class. We additionally explored removing outliers contained within the training data based on their spectral and spatial criteria, but observed no significant improvement in classification results. We also tested the importance of different types of auxiliary data that were available for the conterminous United States, including: (a) five variables used by the National Land Cover Database, (b) three variables from the cloud screening ‘‘Function of mask” (Fmask) statistics, and (c) two variables from the change detection results of CCDC. We found that auxiliary variables such as a Digital Elevation Model and its derivatives (aspect, position index, and slope), potential wetland index, water probability, snow probability, and cloud probability improved the accuracy of land cover classification. Compared to the original strategy of the CCDC algorithm (500 pixels per class), the use of the optimal strategy improved the classification accuracies substantially (15-percentage point increase in overall accuracy and 4-percentage point increase in minimum accuracy).
ISPRS Journal of Photogrammetry and Remote Sensing↗
Completion of the National Land Cover Database (NLCD) 1992–2001 Land Cover Change Retrofit product
The Multi-Resolution Land Characteristics Consortium has supported the development of two national digital land cover products: the National Land Cover Dataset (NLCD) 1992 and National Land Cover Database (NLCD) 2001. Substantial differences in imagery, legends, and methods between these two land cover products must be overcome in order to support direct comparison. The NLCD 1992-2001 Land Cover Change Retrofit product was developed to provide more accurate and useful land cover change data than would be possible by direct comparison of NLCD 1992 and NLCD 2001. For the change analysis method to be both national in scale and timely, implementation required production across many Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) path/rows simultaneously. To meet these requirements, a hybrid change analysis process was developed to incorporate both post-classification comparison and specialized ratio differencing change analysis techniques. At a resolution of 30 meters, the completed NLCD 1992-2001 Land Cover Change Retrofit product contains unchanged pixels from the NLCD 2001 land cover dataset that have been cross-walked to a modified Anderson Level I class code, and changed pixels labeled with a 'from-to' class code. Analysis of the results for the conterminous United States indicated that about 3 percent of the land cover dataset changed between 1992 and 2001.
Open-File Report↗
Challenges for monitoring the extent and land use/cover changes in monarch butterflies’ migratory habitat across the United States and Mexico
This paper presents a synopsis of the challenges and limitations presented by existing and emerging land use/ land cover (LULC) digital data sets when used to analyze the extent, habitat quality, and LULC changes of the monarch (Danaus plexippus) migratory habitat across the United States of America (US) and Mexico. First, the characteristics, state of the knowledge, and issues related to this habitat are presented. Then, the characteristics of the existing and emerging LULC digital data sets with global or cross-border coverage are listed, followed by the data sets that cover only the US or Mexico. Later, we discuss the challenges for determining the extent, habitat quality, and LULC changes in the monarchs’ migratory habitat when using these LULC data sets in conjunction with the current state of the knowledge of the monarchs’ ecology, behavior, and foraging/roosting plants used during their migration. We point to approaches to address some of these challenges, which can be categorized into: (a) LULC data set characteristics and availability; (b) availability of ancillary land management information; (c) ability to construct accurate forage suitability indices for their migration habitat; and (d) level of knowledge of the ecological and behavioral patterns of the monarchs during their journey.
Land↗
Species distribution modeling in regions of high need and limited data: waterfowl of China
Background A number of conservation and societal issues require understanding how species are distributed on the landscape, yet ecologists are often faced with a lack of data to develop models at the resolution and extent desired, resulting in inefficient use of conservation resources. Such a situation presented itself in our attempt to develop waterfowl distribution models as part of a multi-disciplinary team targeting the control of the highly pathogenic H5N1 avian influenza virus in China. Methods Faced with limited data, we built species distribution models using a habitat suitability approach for China’s breeding and non-breeding (hereafter, wintering) waterfowl. An extensive review of the literature was used to determine model parameters for habitat modeling. Habitat relationships were implemented in GIS using land cover covariates. Wintering models were validated using waterfowl census data, while breeding models, though developed for many species, were only validated for the one species with sufficient telemetry data available. Results We developed suitability models for 42 waterfowl species (30 breeding and 39 wintering) at 1 km resolution for the extent of China, along with cumulative and genus level species richness maps. Breeding season models showed highest waterfowl suitability in wetlands of the high-elevation west-central plateau and northeastern China. Wintering waterfowl suitability was highest in the lowland regions of southeastern China. Validation measures indicated strong performance in predicting species presence. Comparing our model outputs to China’s protected areas indicated that breeding habitat was generally better covered than wintering habitat, and identified locations for which additional research and protection should be prioritized. Conclusions These suitability models are the first available for many of China’s waterfowl species, and have direct utility to conservation and habitat planning and prioritizing management of critically important areas, providing an example of how this approach may aid others faced with the challenge of addressing conservation issues with little data to inform decision making.
Avian Research↗
Towards monitoring land-cover and land-use changes at a global scale: the global land survey 2005
Land cover is a critical component of the Earth system, infl uencing land-atmosphere interactions, greenhouse gas fl uxes, ecosystem health, and availability of food, fi ber, and energy for human populations. The recent Integrated Global Observations of Land (IGOL) report calls for the generation of maps documenting global land cover at resolutions between 10m and 30m at least every fi ve years (Townshend et al., in press). Moreover, despite 35 years of Landsat observations, there has not been a unifi ed global analysis of land-cover trends nor has there been a global assessment of land-cover change at Landsat-like resolution. Since the 1990s, the National Aeronautics and Space Administration (NASA) and the U.S. Geological Survey (USGS) have supported development of data sets based on global Landsat observations (Tucker et al., 2004). These land survey data sets, usually referred to as GeoCover ™, provide global, orthorectifi ed, typically cloud-free Landsat imagery centered on the years 1975, 1990, and 2000, with a preference for leaf-on conditions. Collectively, these data sets provided a consistent set of observations to assess land-cover changes at a decadal scale. These data are freely available via the Internet from the USGS Center for Earth Resources Observation and Science (EROS) (see http://earthexplorer.usgs.gov or http://glovis.usgs.gov). This has resulted in unprecedented downloads of data, which are widely used in scientifi c studies of land-cover change (e.g., Boone et al., 2007; Harris et al., 2005; Hilbert, 2006; Huang et al. 2007; Jantz et al., 2005, Kim et al., 2007; Leimgruber, 2005; Masek et al., 2006). NASA and USGS are continuing to support land-cover change research through the development of GLS2005 - an additional global Landsat assessment circa 20051 . Going beyond the earlier initiatives, this data set will establish a baseline for monitoring changes on a 5-year interval and will pave the way toward continuous global land-cover monitoring at Landsat-like resolution in the next decade.
Photogrammetric Engineering and Remote Sensing↗
Incorporating location uncertainty improves inference with stop-level North American Breeding Bird Survey data
Ecological models should account for uncertainty to be most effective and useful. Yet, uncertainty from model covariates—unlike that from other sources, such as sampling error or process variability—is seldom explicitly incorporated. This can cause underestimates of uncertainty to cascade through model parameter estimates, predictions, and downstream uses. Burner et al. proposed a method for quantifying uncertainty in covariates and incorporating it into models using informative Bayesian priors. This method was applied to stop-level Breeding Bird Survey (BBS) analyses, where land cover uncertainty at each stop arises from substantial stop location uncertainty. A limited validation of model-estimated land cover, using stops with known locations, indicated the method’s potential effectiveness, but it was not rigorously evaluated. We conduct a robust simulation-based test, generating stop locations, extracting land cover, and simulating bird communities across 210 BBS routes in the upper Midwest. We compare 3 models: a “known” model with true land cover, a “naive” model assuming consistent 800-m stop spacing, and a “full” model using informative priors to estimate land cover. Species parameter estimates and predicted prevalence patterns across gradients in land cover from the full model approached those of the known model and were substantially closer to the true values used in simulations relative to those from the naive model. Naive model parameters were more biased relative to the other models, and credible intervals of predicted species prevalence rarely included the true simulated values. The full model also produced land cover covariate estimates closer to true simulation values relative to the mean informative priors. Our results show that, for the BBS, informative priors enable more accurate stop-level analyses despite location uncertainty. In contrast, naive models that ignore this uncertainty yield poor inferences. More broadly, we demonstrate empirically the utility of informative priors to account for covariate uncertainty in ecological models.
Michigan, Minnesota, Wisconson↗
How well do route survey areas represent landscapes at larger spatial extents? An analysis of land cover composition along Breeding Bird Survey routes
The occurrence of birds in a survey unit is partly determined by the habitat present. Moreover, some bird species preferentially avoid some land cover types and are attracted to others. As such, land cover composition within the 400 m survey areas along a Breeding Bird Survey (BBS) route clearly influences the species available to be detected. Ideally, to extend survey results to the larger landscape, land cover composition within the survey area should be similar to that at larger spatial extents defining the landscape. Such representativeness helps minimize possible roadside effects (bias), here defined as differences in bird species composition and abundance along a roadside as compared to a larger surrounding landscape. We used land cover data from the 2011 National Land Cover Database to examine representativeness of land cover composition along routes. Using ArcGIS, the percentages of each of 15 land cover types within 400 m buffers along 2,696 U.S. BBS routes were calculated and compared to percentages in 2 km, 5 km, and 10 km buffers surrounding each route. This assessment revealed that aquatic cover types and highly urbanized land tend to be slightly underrepresented in the survey areas. Two anthropogenic cover types (pasture/hay and cropland) may be slightly overrepresented in the survey areas. Over all cover types, 92% of the 2,696 routes exhibited “good” representativeness, with <5 percentage points per cover type difference in proportional cover between the 400 m and 10 km buffers. This assessment further supports previous research indicating that any land-cover-based roadside bias in the bird data of the BBS is likely minimal.
Condor↗
Combining satellite data with ancillary data to produce a refined land-use/land-cover map
As part of the U.S. Geological Survey's National Water-Quality Assessment Program in the Western Lake Michigan Drainages Study Unit, a current map of land use and land cover is needed to gain a better understanding of how land use and land cover may influence water quality. Satellite data from the Landsat Thematic Mapper provides a means to map and measure the type and amount of various land-cover types across the Study Unit and can be easily updated as changes occur in the landscape or in water quality. Translating these land cover categories to land use, however, requires the use of other thematic maps or ancillary data layers, such as wetland inventories, population data, or road networks. This report describes a process of (1) using satellite imagery to produce a land-cover map for the Fox/Wolf River basin, a portion of the Western Lake Michigan Drainages NAWQA Study Unit and (2) improving the satellite-derived land-cover map by using other thematic maps. The multiple data layers are processed in a geographic information system (GIS), and the combination provides more information than individual sources alone.
Michigan, Wisconsin↗