Land Cover Trends is a research project designed to document the types and rates, causes, and consequences of land cover change from 1973-2000 within each of 84 ecoregions spanning the conterminous United States. Our research objectives are as follows:

• Develop a comprehensive methodology using sampling and change analysis techniques and Landsat multispectral scanner (MSS) and thematic mapper (TM) data for estimating regional land cover change.
• Characterize the spatial and temporal characteristics of conterminous United States land cover change for five periods from 1973-2000 (1973, 1980, 1986, 1992, and 2000).
• Document the regional driving forces and consequences of change.
• Prepare a national synthesis of land cover change.

Land use and land cover changes occur at all scales, and changes can have dramatic, cumulative impacts. Due to the impacts on land management practices, economic health and sustainability, and social processes, land use and land cover changes are of concern globally as well as locally and regionally. The challenge facing policy-makers and scientists is that there is generally a lack of comprehensive data on the types and rates of land use and land cover changes, and even less systematic evidence on the causes and consequences of the changes. Our information will contribute to the geographic understanding of regional land change in the U.S. and may lead to improved policies for regional management of environmental resources.

Background

Calls for Land-Cover Change Research

Perhaps the clearest call for research on land-use and land-cover dynamics resulted from the National Research Council (NRC) response to a National Science Foundation (NSF) request to identify the "Grand Challenges in Environmental Sciences" (NRC, 2001). An interdisciplinary committee was asked to determine the most important research challenges over the next 20 to 30 years within the context of environmental problems. One of eight grand challenges is Land-Use Dynamics, which calls for the development of a comprehensive understanding of changes in land use and land cover that are critical to biogeochemical cycling, ecosystem functioning and services, and human welfare. The report concluded that “…improved information on and understanding of land use and land cover dynamics are essential for society to respond effectively to environmental changes and to manage human impacts on environmental systems" (NRC, 2001).

Two additional NRC reports emphasized the importance of land-use and land-cover change research. A 1999 report on Measures of Environmental Performance and Ecosystem Condition called for investigations of the complex relationships between humans and the environment and emphasized data collection and monitoring of both ecosystem processes and land-use and land-cover change (NRC, 1999). An NRC report titled Ecological Indicators for the Nation declared that the largest ecological changes caused by humans result from land use (NRC, 2000). Because these changes affect the ability of ecosystems to provide the goods and services that society depends on, an assessment of land-cover change is needed to understand the status of the Nation's biological resources.

Current Approaches for Estimating Change

While a great deal has been written regarding change-detection techniques using remotely sensed data, very little guidance exists for addressing large-area change detection (Dobson and Bright, 1994). Large-area change detection has generally relied on low-resolution sensors, such as the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR), to provide information on general changes in vegetation indices or similar measures (Tucker et a1., 1986; Helldenand Eklundh, 1988; Lambin and Strahler, 1994). The spatial resolution of such sensors, however, makes it difficult to identify and quantify the types of fine-scale landcover changes that are often associated with anthropogenic change. The use of high-resolution imagery, such as Landsat TM data, makes this task much more feasible. However, wall-to-wall change detection using moderate- to high-resolution imagery for large areas presents stiff challenges with respect to accuracy, time, processing loads, and budgets. Despite these obstacles, some studies of note have used this approach. Perhaps the most ambitious effort was the Humid Tropical Forest project, where Landsat imagery from the 1970s to the present were manually interpreted to identify patterns of deforestation across the humid tropics (Skole and Tucker,1993). The NOAA Coastal Change Analysis Project (C-CAP) uses Landsat TM data and computer-assisted techniques to map land-cover change in the coastal zones of the United States (Dobson et a1., 1995).

Characterizing Land-Cover Change Using Remote Sensing

Spectral data recorded by remote sensing instruments can provide information on land-cover conversions and on changes in condition, but it is generally not a consistent indicator of landcover change. Land-cover transitions often have very small changes in spectral response and may not be readily identifiable. Interpretation of tone, texture, shape, size, and pattern can help to identify land-cover change, but these elements are disregarded in many change analysis studies. The most straightforward technique for detecting change is the comparison of land-cover classifications from two dates. The use of independently produced classifications has the advantage of compensating for varied atmospheric and phenological conditions between dates, or even the use of different sensors between dates, because each classification is independently produced and mapped to a common thematic reference. The method has been criticized. however, because it tends to compound any errors that may have occurred in the two initial classifications (Gordon, 1980; Stow et al.,1980; Singh, 1989). The procedure has been successfully used in various landcover change investigations, including assessing deforestation (Massart et al., 1995), urbanization (Dimyati et al., 1996), sand dune changes (Kumar et al., 1993), and the conversion of semi natural vegetation to agricultural grassland (Wilcock and Cooper, 1993).

Simultaneous analysis techniques, including image differencing, ratioing, principal components analysis (PCA), and change vector analysis, are common change analysis approaches. Image differencing. i.e., subtraction between georegistered images (raw or transformed) from two dates, is probably the most widely used approach (Weismiller et al.. 1977, Vogelmann.1988). Image ratioing (Howarth and Wickware, 1981) and PCA (Bryne et al., 1980; Ribed and Lopez, 1995) have also been widely used. Sohl (1999) successfully used change vector analysis to document land-cover change in the United Arab Emirates. Although often effective at identifying areas of spectral change, these techniques typically result in the creation of a simple, binary change mask.