Most scenario-based climate modeling studies indicate that temperate forest removal will promote cooling. We use temperature and land-cover data to examine forest - surface temperature relationships for the continental United States. Our results are not consistent with most scenario-based climate modeling studies. Surface temperatures declined as the proportion of forest increased for spring, summer, fall, and annually. We also found an inverse relationship between forest and surface temperature for winter up to 35N - 40N. The forest-surface temperature relationship was also scale dependent. Surface temperatures were cooler when forests were spatially extensive. A policy implication of the scenario-based climate studies is temperate forest removal is a potential climate change mitigation strategy. Our results indicate that afforestation is a climate change mitigation strategy that should be implemented to promote spatially extensive forests.
DuPage County, Illinois, is located west of the City of Chicago and is part of the Chicago metropolitan area. During the second half of the 20th century, the county saw explosive population growth, with the total population increasing nearly 600% between 1950 (pop: 154,599) and 2000 (pop: 904,161). The Forest Preserve District of DuPage County sought to account for this growth through land acquisitions, with the Forest Preserve acreage per county resident increasing during that same time period. The Forest Preserve District has also taken an aggressive tack in managing and monitoring their land holdings, including the long-term monitoring of vegetation and land management activities in 35 one-acre plots that began in 1979. This study investigates the corollary between the change in vegetative composition, management, and surrounding land use through time. Historical aerial photography and satellite imagery was used to determine any changes in land cover within the monitored plots and in land use of the surrounding areas. Multivariate statistical approaches were then used to detect changes in the plant communities of the plots through time. Results indicate that both land management strategies and changes in surrounding land use have directly influenced temporal changes in the vegetative communities.
Land change modeling efforts in the United States are fragmented, focused on limited drivers and consequences, lacking a strong theoretical foundation, and constrained by data quality and availability. Moreover, these efforts are challenged by issues of scale, uncertainty, verification, validation, interdisciplinary integration, and meeting the needs of a diverse user community. To address these challenges, the nation needs a National Land-Change Community Modeling (NLCCM) framework. The NLCCM would provide an open-source modeling environment, set standards of practice for modelers and programmers, facilitate access to data and computational resources, and help to establish national- and regional-scale ?benchmark? scenarios. In essence, the NLCCM would provide standards, base software, software tools, and data which may be readily extended to create a variety of land-change models and applications addressing a diverse set of ecosystem management issues. By working within a common framework, modelers could develop interoperable and portable models, ensure transparency and replicability, and increase the efficiency of model production so that resources could be focused on data development, model customization, and the advancement of land-change science. USGS has initiated development of the NLCCM and will highlight its key aspects.
The U.S. Geological Survey?s LandCarbon project produced downscaled, qualitative and quantitative scenarios for ecoregions of the United States, with scenario characteristics and assumptions consistent with IPCC Special Report on Emissions Scenarios (SRES) storylines. The model FOREcasting SCEnarios of land-use change (FORE-SCE) used downscaled scenarios to create spatially explicit LULC projections for ecoregions of the United States. FORE-SCE was designed to take advantage of both historical and contemporary LULC research and data from the USGS. Data from the USGS Land Cover Trends project and the National Land Cover Database (NLCD) were used to parameterize a unique, patch-based modeling approach. Individual patches of LULC ?change? were placed on the landscape in an iterative modeling procedure until the scenario-prescribed proportions of LULC change for a given year were met, with patch characteristics determined by historical regional trends and scenario storylines. We also modeled forest cutting and forest stand age, using regional histories and scenario characteristics to set scenario-based cutting rotations for each region. When complete in late 2012, we will have produced 250m resolution, thematically detailed, annual LULC maps from 2001 to 2100 for each of four IPCC SRES storylines for the entire conterminous United States.
Scenario-based analyses have emerged as useful tools for evaluating uncertain futures in ecological systems across a broad range of geographic scales. The U.S. Geological Survey has developed a land use and land cover (LULC) model to downscale coarse-scale global environmental change scenarios, using an ecoregion framework, for the conterminous United States. The IPCC Special Report on Emission Scenarios (SRES) provide narrative storylines commonly used to support the understanding of possible future developments in complex systems with high levels of scientific uncertainty. We use a global integrated assessment model, ecoregion-based land-use histories, and expert knowledge to develop 1) national-scale projections of LULC change across major LULC types and 2) hierarchically-nested ecoregion projections of LULC conversions. Results of the downscaling process are incorporated into a LULC model to create spatially explicit annual maps of LULC at a 250-m resolution for the Conterminous U.S. from 2001-2100.
The Energy Independence and Security Act of 2007 (EISA) mandated the U.S. Department of Interior to assess carbon storage, carbon sequestration, and fluxes of other greenhouse gases (GHG) for ecosystems of the United States. The U.S. Geological Survey developed a methodology to quantify baseline (current) carbon sequestration and GHG fluxes, and to evaluate carbon sequestration potential and GHG fluxes for multiple future scenarios. The baseline component relied on existing inventory and land-use data to analyze spatial distributions of carbon stocks and GHG fluxes. Potential future conditions were analyzed using IPCC scenarios. Qualitative storylines and quantitative proportions of land use and land cover (LULC) were downscaled to individual ecoregions for each scenario. The FORE-SCE model was then used to produce spatially explicit LULC projections based on each IPCC SRES scenario. A separate disturbance model was used to model fire occurrence and distribution for each scenario. Integrated LULC and disturbance projections were used by the General Ensemble Modeling System (GEMS) to analyze future carbon stocks and GHG fluxes for each scenario. Our scenario-based LULC and disturbance projections have the potential to support analyses of other ecosystem processes, including impacts of LULC change on hydrology, biodiversity, and socially or economically important ecosystem services.
The weak economy, tight budgets, and newly imposed regulations on nutrient and sediment pollution to the Chesapeake Bay pose challenges to local governments. These challenges are made even more difficult by ever-increasing amounts of impervious surfaces and fertilized turf grass. Land-use policies, regulations and cooperative land conservation strategies present an economical means to minimize future increases in water pollution. Convincing taxpayers of the need for new policies and regulations, however, is also challenging. To provide planners with information on how land-use policies and land conservation efforts can facilitate compliance with water quality regulations, the U.S. Geological Survey developed a Chesapeake Bay Land Change Model (CBLCM) to simulate the historical and future extent of impervious surfaces and turf grass in the Chesapeake Bay watershed. These data were used to calibrate the US Environmental Protection Agency's Chesapeake Watershed Model (CWM) and to forecast changes in nutrient and sediment loads to the Bay resulting from alternative future urbanization scenarios developed in consultation with land-use planners. Results illustrate that land-use policies can serve the role of Best Management Practices for reducing nutrient and sediment loads by minimizing future increases in impervious surfaces and turf grass while accommodating population growth and protecting natural areas.
The U.S. Geological Survey (USGS) is the premiere source for consistent, national-scale land-use and land-cover data for the United States. The National Land Cover Database (NLCD) was produced using Landsat satellite data, and includes land-cover data for 1992, 2001, and 2006, including both thematic land-cover classifications, and continuous variable mapping for impervious surface and forested canopy cover percentage (2001 and 2006 only). The NLCD project is currently working on an additional 2011 database, with plans for periodic future updates The USGS Land Cover Trends project has produced land-use and land-cover change information for the 1972 to 2000 time period using the historical Landsat archive. These data were produced using a sampling approach and manual interpretation of Landsat satellite data for five dates. The LANDFIRE project produces several thematic and continuous variable land-cover data sets from remote sensing imagery, including data on vegetation type, vegetation condition, vegetation structure, and disturbance. In combination, USGS land-use and land-cover databases provide a comprehensive summary of current and historical landscape characteristics, information that has been used to examine the impacts of land surface change on carbon and biogeochemical cycles, hydrologic change, regional weather and climate variability, biodiversity, and a host of other ecosystem processes.