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At least 1,081 records · Page 60Linked to original sources
USGS2018

Grasslands

Key findings: Total grassland carbon stocks in the conterminous United States, estimated to be about 7.4 petagrams of carbon (Pg C) in 2005, are projected to increase to about 8.2 Pg C by 2050. Although U.S. grasslands are expected to remain carbon sinks over this period, the uptake rate is projected to decline by about half. In the U.S. Great Plains, land-use and land-cover changes are expected to cause much of the change in carbon cycling as grasslands are converted to agricultural lands or to woody biomes (medium confidence). Increasing temperatures and rising atmospheric carbon dioxide (CO2) concentrations interact to increase productivity in northern North American grasslands, but this productivity response will be mediated by variable precipitation, soil moisture, and nutrient availability (high confidence, very likely). Soil carbon in grasslands is likely to be moderately responsive to changes in climate over the next several decades. Field experiments in grasslands suggest that altered precipitation can increase soil carbon, while warming and elevated CO2 may have only minimal effects despite altered productivity (medium confidence, likely). Carbon stocks and net carbon uptake in grasslands can be maintained with appropriate land management including moderate levels of grazing. Fire suppression can lead to encroachment of woody vegetation and increasing carbon storage in mesic regions, at the expense of grassland vegetation (high confidence, likely).

Report↗
USGS1986

A coefficient of agreement as a measure of thematic classification accuracy.

The classification error matrix typically contains tabulated results of accuracy evaluation for a thematic classification, such as a land-use and land-cover map. Diagonal elements of the matrix represent counts correct. The usual designation of classification accuracy has been total percent correct. Nondiagonal elements of the matrix have usually been neglected. A coefficient of agreement is determined for the interpreted map as a whole, and individually for each interpreted category. These coefficients utilize all cell values in the matrix.-from Authors

Photogrammetric Engineering and Remote Sensing↗
USGS2011

Disaster risk assessment case study: Recent drought on the Navajo Nation, USA

The Navajo Nation is an ecologically sensitive semi-arid to arid section of the southern Colorado Plateau. In this remote part of the United States, located at the Four Corners (Arizona, New Mexico, Colorado, and Utah), traditional people live a subsistence lifestyle that is inextricably tied to, and dependent upon, landscape conditions and water supplies. Soft bedrock lithologies and sand dunes dominate the region, making it highly sensitive to fluctuations in precipitation intensity, percent vegetation cover, and local land use practices. However, this region has sparse and discontinuous meteorological monitoring records. As a complement to the scant long-term meteorological records and historical documentation, we conducted interviews with 50 Native American elders from the Navajo Nation and compiled their lifetime observations on the changes in water availability, weather, and sand or dust storms. We then used these observations to further refine our understanding of the historical trends and impacts of climate change and drought for the region. In addition to altered landscape conditions due to climatic change, drought, and varying land use practices over the last 130 years, the Navajo people have been affected by federal policies and harsh economic conditions which weaken their cultural fabric. We conclude that a long-term drying trend and decreasing snowpack, superimposed on regional drought cycles, will magnify drought impacts on the Navajo Nation and leave its people increasingly vulnerable.

Report↗
USGS2017

A land cover change detection and classification protocol for updating Alaska NLCD 2001 to 2011

Monitoring and mapping land cover changes are important ways to support evaluation of the status and transition of ecosystems. The Alaska National Land Cover Database (NLCD) 2001 was the first 30-m resolution baseline land cover product of the entire state derived from circa 2001 Landsat imagery and geospatial ancillary data. We developed a comprehensive approach named AKUP11 to update Alaska NLCD from 2001 to 2011 and provide a 10-year cyclical update of the state's land cover and land cover changes. Our method is designed to characterize the main land cover changes associated with different drivers, including the conversion of forests to shrub and grassland primarily as a result of wildland fire and forest harvest, the vegetation successional processes after disturbance, and changes of surface water extent and glacier ice/snow associated with weather and climate changes. For natural vegetated areas, a component named AKUP11-VEG was developed for updating the land cover that involves four major steps: 1) identify the disturbed and successional areas using Landsat images and ancillary datasets; 2) update the land cover status for these areas using a SKILL model (System of Knowledge-based Integrated-trajectory Land cover Labeling); 3) perform decision tree classification; and 4) develop a final land cover and land cover change product through the postprocessing modeling. For water and ice/snow areas, another component named AKUP11-WIS was developed for initial land cover change detection, removal of the terrain shadow effects, and exclusion of ephemeral snow changes using a 3-year MODIS snow extent dataset from 2010 to 2012. The overall approach was tested in three pilot study areas in Alaska, with each area consisting of four Landsat image footprints. The results from the pilot study show that the overall accuracy in detecting change and no-change is 90% and the overall accuracy of the updated land cover label for 2011 is 86%. The method provided a robust, consistent, and efficient means for capturing major disturbance events and updating land cover for Alaska. The method has subsequently been applied to generate the land cover and land cover change products for the entire state of Alaska.

Alaska↗
USGS2015

Using Landsat imagery to detect, monitor, and project net landscape change

Detailed landscape information is a necessary component to bird habitat conservation planning. The U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center has been providing information on the Earth’s surface for over 40 years via the continuous series of Landsat satellites. In addition to operating, processing, and disseminating satellite images, EROS is the home to nationwide and global landscape mapping, monitoring, and projection products, including: National Land Cover Database (NLCD) – the definitive land cover dataset for the U.S., with updates occurring at five-year intervals; Global Land Cover Monitoring – producing 30m resolution global land cover; LANDFIRE – Landscape Fire and Resource Management Planning Tools–EROS is a partner in this joint program between U.S. Department of Agriculture and Department of Interior that produces consistent, comprehensive, geospatial data and databases that describe vegetation, wildland fuel, and fire regimes across the U.S.; Land Cover Trends – a landscape monitoring and assessment effort to understand the rates, trends, causes, and consequences of contemporary U.S. land use and land cover change; and Land Use and Land Cover (LULC) Modeling – a project extending contemporary databases of landscape change forward and backward in time through moderate-resolution land cover projections.

All Bird Bulletin↗
USGS2011

Thematic accuracy of the National Land Cover Database (NLCD) 2001 land cover for Alaska

The National Land Cover Database (NLCD) 2001 Alaska land cover classification is the first 30-m resolution land cover product available covering the entire state of Alaska. The accuracy assessment of the NLCD 2001 Alaska land cover classification employed a geographically stratified three-stage sampling design to select the reference sample of pixels. Reference land cover class labels were determined via fixed wing aircraft , as the high resolution imagery used for determining the reference land cover classification in the conterminous U.S. was not available for most of Alaska. Overall thematic accuracy for the Alaska NLCD was 76.2% (s.e. 2.8%) at Level II (12 classes evaluated) and 83.9% (s.e. 2.1%) at Level I (6 classes evaluated) when agreement was defined as a match between the map class and either the primary or alternate reference class label. When agreement was defined as a match between the map class and primary reference label only, overall accuracy was 59.4% at Level II and 69.3% at Level I. The majority of classification errors occurred at Level I of the classification hierarchy (i.e., misclassifications were generally to a different Level I class, not to a Level II class within the same Level I class). Classification accuracy was higher for more abundant land cover classes and for pixels located in the interior of homogeneous land cover patches.

Alaska↗
USGS2013

Estimation of sediment inflows to Lake Tuscaloosa, Alabama, 2009-11

The U.S. Geological Survey, in cooperation with the City of Tuscaloosa, evaluated the concentrations, loads, and yields of suspended sediment in the tributaries to Lake Tuscaloosa in west-central Alabama, from October 1, 2008, to January 31, 2012. The collection and analysis of these data will facilitate the comparison with historical data, serve as a baseline for future sediment-collection efforts, and help to identify areas of concern. Lake Tuscaloosa, at the reservoir dam, receives runoff from a drainage area of 423 square miles (mi 2 ). Basinwide in 2006, forested land was the primary land cover (68 percent). Comparison of historical imagery with the National Land Cover Database (2001 and 2006) indicated that the greatest temporal land-use change was timber harvest. The land cover in 2006 was indicative of this change, with shrub/scrub land (12 percent) being the secondary land use in the basin. Agricultural land use (10 percent) was represented predominantly by hay and pasture or grasslands. Urban land use was minimal, accounting for 4 percent of the entire basin. The remaining 6 percent of the basin has a land use of open water or wetlands. Storm and monthly suspended-sediment samples were collected from seven tributaries to Lake Tuscaloosa: North River, Turkey Creek, Binion Creek, Pole Bridge Creek, Tierce Creek, Carroll Creek, and Brush Creek. Suspended-sediment concentrations and streamflow measurements were statistically analyzed to estimate annual suspended-sediment loads and yields from each of these contributing watersheds. Estimated annual suspended-sediment yields in 2009 were 360, 540, and 840 tons per square mile (tons/mi 2 ) at the North River, Turkey Creek, and Carroll Creek streamflow-gaging stations, respectively. Estimated annual suspended-sediment yields in 2010 were 120 and 86 tons/mi 2 at the Binion Creek and Pole Bridge Creek streamflow-gaging stations, respectively. Estimated annual suspended-sediment yields in 2011 were 190 and 300 tons/mi 2 at the Tierce Creek and Brush Creek streamflow-gaging stations, respectively. The North River watershed at the streamflow-gaging station contributes 53 percent of the drainage area for Lake Tuscaloosa. A previous study in the 1970s analyzed streamflow and historical suspended-sediment samples to estimate a long-term average suspended-sediment yield of 300 tons per year per square mile in the North River watershed. Analysis of data collected in the North River watershed during the 2009 water year (October 2008 to September 2009) estimated a sediment yield of 360 tons/mi 2 . The North River watershed, a major portion of the Lake Tuscaloosa drainage basin, has not experienced a substantial increase in sedimentation rates. During the 2009 water year, the Turkey Creek watershed (6.16 mi 2 ) and the Carroll Creek watershed (20.9 mi 2 ) produced greater suspended-sediment yields than the North River watershed but contribute a much smaller drainage area to Lake Tuscaloosa. Aerial photography and bathymetric surveys indicate that Carroll Creek has experienced increased sediment deposition in the upstream portions of the channel. Carroll Creek is also the only watershed in the current study that has a substantial percentage (11 percent) of urban

Alabama↗
USGS2022

Detection and characterization of coastal tidal wetland change in the northeastern US using Landsat time series

Coastal tidal wetlands are highly altered ecosystems exposed to substantial risk due to widespread and frequent land-use change coupled with sea-level rise, leading to disrupted hydrologic and ecologic functions and ultimately, significant reduction in climate resiliency. Knowing where and when the changes have occurred, and the nature of those changes, is important for coastal communities and natural resource management . Large-scale mapping of coastal tidal wetland changes is extremely difficult due to their inherent dynamic nature. To bridge this gap, we developed an automated algorithm for DEtection and Characterization of cOastal tiDal wEtlands change (DECODE) using dense Landsat time series. DECODE consists of three elements, including spectral break detection, land cover classification and change characterization. DECODE assembles all available Landsat observations and introduces a water level regressor for each pixel to flag the spectral breaks and estimate harmonic time-series models for the divided temporal segments. Each temporal segment is classified (e.g., vegetated wetlands, open water, and others – including unvegetated areas and uplands) based on the phenological characteristics and the synthetic surface reflectance values calculated from the harmonic model coefficients, as well as a generic rule-based classification system. This harmonic model-based approach has the advantage of not needing the acquisition of satellite images at optimal conditions (i.e., low tide status) to avoid underestimating coastal vegetation caused by the tidal fluctuation. At the same time, DECODE can also characterize different kinds of changes including land cover change and condition change (i.e., land cover modification without conversion). We used DECODE to track status of coastal tidal wetlands in the northeastern United States from 1986 to 2020. The overall accuracy of land cover classification and change detection is approximately 95.8% and 99.8%, respectively. The vegetated wetlands and open water were mapped with user's accuracy of 94.6% and 99.0%, and producer's accuracy of 98.1% and 93.5%, respectively. The cover change and condition change were mapped with user's accuracy of 68.0% and 80.0%, and producer's accuracy of 80.5% and 97.1%, respectively. Approximately 3283 km 2 of the coastal landscape within our study area in the northeastern United States changed at least once (12% of the study area), and condition changes were the dominant change type (84.3%). Vegetated coastal tidal wetland decreased consistently (~2.6 km 2 per year) in the past 35 years, largely due to conversion to open water in the context of sea-level rise.

Remote Sensing of Environment↗
USGS2018

Inventory of lowland-breeding birds on the Alaska Peninsula

We conducted the first systematic inventory of birds in the lowlands (areas ≤100 m above sea level) of the Alaska Peninsula during summers of 2004–2007 to determine their breeding distributions and habitat associations in this remote region. Using a stratified random survey design, we allocated sample plots by elevation and land cover with a preference for wetland cover types used by shorebirds, a group of particular interest to land managers. We surveyed birds during 10-min counts at 792 points across 52, 5 km × 5 km sample plots distributed from south of the Naknek River (58.70°N,157.00°W) to north of Port Moller (56.00°N,160.52°W). We detected 95 bird species including 19 species of shorebirds and 34 species (36% of total) considered at the time to be of conservation concern for the land managers in the region. The most numerous shorebirds on point counts were dunlin Calidris alpina, short-billed dowitcher Limnodromus griseus, and Wilson's snipe Gallinago delicata. We found the breeding-season endemic marbled godwit Limosa fedoa beringiae at 20 plots within a 3,000-km 2 area from north of Ugashik Bay to just north of Port Heiden and east to the headwaters of the Dog Salmon and Ugashik rivers. The most abundant passerines on point counts were American tree sparrow Spizelloides arborea, Lapland longspur Calcarius lapponicus, and savannah sparrow Passerculus sandwichensis. Sandhill crane Antigone canadensis, glaucous-winged gull Larus glaucescens, and greater scaup Aythya marila were also relatively abundant. We categorized habitat associations for 30 common species and found that lowland herbaceous vegetation supported wetland-focused species including sandhill crane, marbled godwit, short-billed dowitcher, and dunlin; whereas, dwarf shrub-ericaceous vegetation supported tundra-associated species such as willow ptarmigan Lagopus lagopus, rock sandpiper Calidris ptilocnemis, and American pipit Anthus rubescens . Tall shrub vegetation was important to several species of warblers and sparrows, as well as one species of shorebird (greater yellowlegs Tringa melanoleuca ). We found that point counts augmented with incidental observations provided an almost complete inventory of lowland-breeding species on the study area. These data form a baseline to monitor any future changes in bird distribution and abundance on the Alaska Peninsula.

Alaska↗
USGS2024

Assessing the utility of uncrewed aerial system photogrammetrically derived point clouds for land cover classification in the Alaska North Slope

Uncrewed aerial systems (UASs) have been used to collect “pseudo field plot” data in the form of large-scale stereo imagery to supplement and bolster direct field observations to monitor areas in Alaska. These data supplement field data that is difficult to collect in such a vast landscape with a relatively short field season. Dense photogrammetrically derived point clouds are created and are facilitated to extract land cover data using a support vector machine (SVM) classifier in this study. We test our approach using point clouds derived from 1-cm stereo imagery of plots in the Alaska North Slope region and compare the results to field observations. The results show that the overall accuracy of six land cover classes (bare soil, shrub, grass, forb/herb, rock, and litter) is 96.8% from classified patches. Shrub had the highest accuracy (>99%) and forb/herb achieved the lowest (<48%). This study reveals that the approach could be used as reference data to check field observations in remote areas.

Alaska↗
USGS2025

RUSH: Rapid remote sensing Updates of land cover for Storm and Hurricane forecast models

Coastal vegetated ecosystems, including tidal marshes, vegetated dunes, and shrub- and forest-dominated wetlands, can mitigate hurricane impacts such as coastal flooding and erosion by increasing surface roughness and reducing wave energy. Land cover maps can be used as input to improve simulations of surface roughness in advanced hydro-morphological models. Consequently, there is a need for efficient tools to develop up-to-date land cover maps that include the accurate distribution of vegetation types prior to an extreme storm. In response, we developed the RUSH tool (Rapid remote sensing Updates of land cover for Storm and Hurricane forecast models). RUSH delivers high-resolution maps of coastal vegetation for near-real-time or historical conditions via a Jupyter Notebook application and a graphical user interface (GUI). The application generates 3 m spatial resolution land cover maps with classes relevant to coastal settings, especially along mainland beaches, headlands, and barrier islands, as follows: (1) open water; (2) emergent wetlands; (3) dune grass; (4) woody wetlands; and (5) bare ground. These maps are developed by applying one of two seasonal random-forest machine learning models to Planet Labs SuperDove multispectral imagery. Cool Season and Warm Season Models were trained on 665 and 594 reference points, respectively, located across study regions in the North Carolina Outer Banks, the Mississippi Delta in Louisiana, and a portion of the Florida Gulf Coast near Apalachicola. Cool Season and Warm Season Models were tested with 666 and 595 independent points, with an overall accuracy of 93% and 94%, respectively. The Jupyter Notebook application provides users with a flexible platform for customization for advanced users, whereas the GUI, designed with user-experience feedback, provides non-experts access to remote sensing capabilities. This application can also be used for long-term coastal geomorphic and ecosystem change assessments.

Florida, Louisiana, North Carolina↗
USGS2024

Development and evaluation of public-supply community water service area boundaries for the conterminous United States

The water service area dataset, derived from the National Boundary Dataset for public-supply water systems in the United States, offers a detailed resolution surpassing county-level assessments, emphasizing water-centric land use. Crucial for linking populations and infrastructure to system withdrawals, it supports the creation of a national public-supply water-use model, enhancing accuracy in estimating water use and distinguishing between publicly supplied and self-supplied domestic water use. Integrating tabular water system data strengthens the national water-use model by enabling tracking of withdrawal locations, source water, and water quality. Evaluated against U.S. Census-derived population datasets, 16 state-provided water service area datasets, and two national land use datasets, the study covers 22,849 community water systems, excluding most small systems serving fewer than 1000 people. Robust correlations between water service areas (WSAs) and satellite-sourced urban and exurban land use types facilitate tracking changes over time. A comparison of state and national datasets for population and WSAs reveals discrepancies ranging from 5% to 73% in state-level populations and 0% to 167% in state-level WSAs. Significant differences can be attributed to the exclusion of sizable incorporated and unincorporated areas in the state-based datasets. Additional comparisons of major metropolitan areas exhibit differences ranging from 2% to 56%.

conterminous United States↗
USGS2011

Spectral reflectance characteristics of different snow and snow-covered land surface objects and mixed spectrum fitting

The field spectroradiometer was used to measure spectra of different snow and snow-covered land surface objects in Beijing area. The result showed that for a pure snow spectrum, the snow reflectance peaks appeared from visible to 800 nm band locations; there was an obvious absorption valley of snow spectrum near 1030 nm wavelength. Compared with fresh snow, the reflection peaks of the old snow and melting snow showed different degrees of decline in the ranges of 300~1300, 1700~1800 and 2200~2300 nm, the lowest was from the compacted snow and frozen ice. For the vegetation and snow mixed spectral characteristics, it was indicated that the spectral reflectance increased for the snow-covered land types(including pine leaf with snow and pine leaf on snow background), due to the influence of snow background in the range of 350~1300 nm. However, the spectrum reflectance of mixed pixel remained a vegetation spectral characteristic. In the end, based on the spectrum analysis of snow, vegetation, and mixed snow/vegetation pixels, the mixed spectral fitting equations were established, and the results showed that there was good correlation between spectral curves by simulation fitting and observed ones(correlation coefficient R2=0.9509).

Guang Pu Xue Yu Guang Pu Fen Xi/Spectroscopy and S↗
USGS2003

Land Use on the Island of Oahu, Hawaii, 1998

A hierarchical land-use classification system for Hawaii was developed, and land use on the island of Oahu was mapped. The land-use classification system emphasizes agriculture, developed (urban), and barren/mining uses. Areas with other land uses (conservation, forest reserve, natural areas, wetlands, water, and barren [sand, rock, or soil] regions, and unmanaged vegetation [native or exotic]) were defined as 'other.' Multiple sources of digital orthophotographs from 1998 and 1999 were used as source data. The 1998 island of Oahu land-use data are provided in digital format at http://water.usgs.gov/lookup/getspatial?oahu_lu98 for use in a Geographic Information System (GIS), at 1:24,000-scale with minimum mapping units of 2 hectares (4.9 acres) area and 30-meters (98.4 feet) feature width. In 1998, a total of 59,195 acres (15.4 percent) of the island of Oahu were classified as agricultural land use; 98,663 acres (25.7 percent) were classified as developed; 1,522 acres (0.4 percent) were classified as barren/mining; and 224,331 acres (58.5 percent) were classified as other. An accuracy assessment identified 98 percent accuracy for all land-use classes. In windward (moister) areas, dense vegetation and canopy cover along with rapid recolonization by vegetation potentially obscured land use from photo-interpretation. While in leeward (drier) areas, sparse vegetative cover and slower vegetation recolonization may have resulted in more frequent recognition of apparent land-use patterns.

Water-Resources Investigations Report↗
USGS2026

Linking distribution and return-on-investment models to optimize woody management for prairie grouse in Nebraska

Grasslands in Nebraska, USA, face threats from agricultural conversion, urban development, and woody encroachment, all of which negatively affect prairie grouse ( Tympanuchus spp.) populations. To optimize conservation planning, Nebraska wildlife agencies developed probabilistic area-based surveys for greater prairie-chicken ( T. cupido ) and sharp-tailed grouse ( T. phasianellus ) to sample landscapes across a range of environmental conditions. This design improves historical surveys and enables the development of distribution models that quantitatively define habitat associations and support scenario-based conservation planning. Using survey data collected during 2020–2022, we modeled prairie grouse occurrence and abundance as functions of land cover, topography, and climate using Bayesian logistic and zero-inflated negative binomial models with regularized horseshoe priors. We then conducted a maximum potential return-on-investment analysis of woody cover treatments, assuming sustained treatment success, relative to projected impacts of woody encroachment on prairie grouse populations by 2050. Among modeled associations were a positive association with grasslands having low woody cover and a negative association with grasslands having high woody cover. Across the 3-year period, median estimated annual populations were 142,380 for greater prairie-chicken (range of 95% CIs across years = 68,821–277,615) and 64,154 for sharp-tailed grouse (range of 95% CIs across years = 27,550–146,559). Under projected woody encroachment, mean predicted population declines were 10% for greater prairie-chicken (range of 95% CIs = 7–14%) and 6% for sharp-tailed grouse (range of 95% CIs = 5–7%). Areas with high prairie grouse density and low treatment costs in 2021, and high projected woody encroachment and population loss by 2050, offered the greatest return on investment for woody management. Return on investment was greatest in the northwestern Shortgrass Prairie ecoregion (northwestern Nebraska) for sharp-tailed grouse and the eastern Sandhills ecoregion (central Nebraska) for both species. These models underscore the value of evidence-based, quantitative approaches for prioritizing conservation actions on working lands. Scenario-based modeling could be extended to guide other treatments, such as optimizing restoration (e.g., Conservation Reserve Program) or incentivizing grassland persistence in areas with predicted climate resilience.

Nebraska↗
USGS2005

A comparative analysis of the Global Land Cover 2000 and MODIS land cover data sets

Accurate and up-to-date global land cover data sets are necessary for various global change research studies including climate change, biodiversity conservation, ecosystem assessment, and environmental modeling. In recent years, substantial advancement has been achieved in generating such data products. Yet, we are far from producing geospatially consistent high-quality data at an operational level. We compared the recently available Global Land Cover 2000 (GLC-2000) and MODerate resolution Imaging Spectrometer (MODIS) global land cover data to evaluate the similarities and differences in methodologies and results, and to identify areas of spatial agreement and disagreement. These two global land cover data sets were prepared using different data sources, classification systems, and methodologies, but using the same spatial resolution (i.e., 1 km) satellite data. Our analysis shows a general agreement at the class aggregate level except for savannas/shrublands, and wetlands. The disagreement, however, increases when comparing detailed land cover classes. Similarly, percent agreement between the two data sets was found to be highly variable among biomes. The identified areas of spatial agreement and disagreement will be useful for both data producers and users. Data producers may use the areas of spatial agreement for training area selection and pay special attention to areas of disagreement for further improvement in future land cover characterization and mapping. Users can conveniently use the findings in the areas of agreement, whereas users might need to verify the informaiton in the areas of disagreement with the help of secondary information. Learning from past experience and building on the existing infrastructure (e.g., regional networks), further research is necessary to (1) reduce ambiguity in land cover definitions, (2) increase availability of improved spatial, spectral, radiometric, and geometric resolution satellite data, and (3) develop advanced classification algorithms.

Remote Sensing of Environment↗
USGS2015

Land-cover change in the Gulf Coastal Plains and Ozarks Landscape Conservation Cooperative, 1973 to 2000

This report summarizes baseline land-cover change information for four time intervals from between 1973 and 2000 for the Gulf Coastal Plains and Ozarks Landscape Conservation Cooperative (LCC). The study used sample data from the USGS Land Cover Trends dataset to develop estimates of change for 10 land-cover classes in the LCC. The results show that an estimated 17.7 percent of the LCC land cover had a change during the 27-year period. Cyclic forest dynamics&mdash;of timber harvest and regrowth&mdash;are the most extensive types of land conversion. Agricultural land had an estimated net decline of 3.5 percent as cropland and pasture were urbanized and developed and converted to forest use. Urban and other developed land covers expanded from 2.0 percent of the LCC in 1973 to 3.1 percent in 2000. The report also highlights causes and challenges of land-cover change.

Gulf Coastal Plains, Ozarks↗