An indicator of forest dynamics using a shifting landscape mosaic
Introduction
The composition of a landscape is a fundamental indicator of patterns within that landscape (O’Neill et al., 1988, Li and Reynolds, 1994), if only because few other pattern indicators are interpretable independent of landscape composition (Gardner et al., 1987, Gustafson, 1998). Pattern indicators for large-area ecological assessments from land-cover maps must include composition indicators that are applicable to all land-cover types and interpretable with respect to many societal and ecological concerns like biodiversity, urban sprawl, and water quality. Establishing a foundation for using those indicators requires testing them with neutral models, examining their statistical properties, and learning how to interpret them (Turner et al., 2001). The objective of this study was to lay part of the foundation for a landscape composition indicator called ‘landscape mosaic’ through analysis and interpretation of land-cover changes from 1996 to 2005 in a study area along the south coast of the United States.
The landscape mosaic indicator comes from the ‘landscape pattern type’ indicator (Wickham and Norton, 1994) which has been adapted for large-area pattern assessments using land-cover maps derived from remote sensing (Riitters and Wickham, 1995, Jones et al., 1997, Riitters et al., 2000). The indicator classifies a land-cover pixel according to the land-cover composition in a fixed-area neighborhood surrounding that pixel, and a map of landscape mosaics may be constructed by classifying every pixel on the land-cover map. A map of landscape mosaics can help to visualize ‘interface zones’ (e.g., the ‘forest–urban interface’) and other spatial gradients of land-cover composition across a region (e.g., Riitters et al., 2000), but more work is needed to develop the indicator as a framework for interpreting land-cover dynamics.
To evaluate the landscape mosaic as a framework for interpreting pixel-level forest dynamics, we defined forest security as the likelihood that a pixel of forest remained as forest over time, which depended on the landscape mosaic that contains a forest pixel. In a dynamic landscape, land-cover change at the pixel level also changes the mosaic at the landscape level, so that the security of a particular pixel of forest can change over time even if that pixel remains as forest. One example is urban sprawl at the forest–urban interface. Forest clearing to build a house is likely within the forest–urban interface because of infrastructure such as roads and water service. Forest removal (a pixel-level change) changes the forest–urban interface (a landscape-level change) by altering its composition and ultimately shifting its location. Additional forest clearing in the same neighborhood may become more or less likely in the future, and the likelihood of forest clearing in new neighborhoods may change as the forest–urban interface itself moves. If forest security depends on forest location relative to a forest–urban interface, then forest security changes when the forest–urban interface moves.
The modeling problem in our study was to link forest change at the pixel level with mosaic change at the landscape level. Gustafson (1998) noted that the conceptual model of a shifting landscape mosaic (Bormann and Likens, 1979) has been useful for describing landscape dynamics, but that the temporal component of that conceptual model needed more development. In contrast, Markov chain models have a long history in temporal analysis of landscape change, but most implementations are not spatial models (see reviews by Baker, 1989, Brown et al., 2004). For Markov models, the most common spatial approach has been to estimate pixel-level transition probabilities based on the spatial attributes or context of each pixel, such that the probabilities of transitions among Markov states can change over time (Brown et al., 2004). An alternate approach to incorporate spatial information was used by Flamm and Turner (1994) in a ‘patch’ transition model. In that study, the transition probabilities were held constant, and the Markov state of a patch was defined by enumerating its spatial attributes such as soil type, land ownership, and vegetation cover type. We used a similar approach at the pixel level in this study, whereby the Markov state of a pixel was defined both by its land-cover and by the landscape mosaic that contained it. We considered two implementations of that basic model, including a temporal model of the shifting landscape mosaic for all land-cover types, and a model that distinguished between forest and nonforest pixels. The first model was used to characterize the long-term or steady-state distribution of all pixels among landscape mosaics, and the second model allowed interpreting forest dynamics and steady-state distributions of forest in the context of a shifting landscape mosaic.
Section snippets
Study area and land-cover data
The 360,000 km2 study area (Fig. 1) was the southern coastal region of the U.S. from the country of Mexico to the State of Georgia. Included were parts of seven ecoregion provinces (Bailey, 1995) from the Southwest Plateau and Plains Dry Steppe and Shrub province in the west to the Outer Coastal Plain Mixed Forest province in the east. The Mississippi River floodplain and delta bisected the region, and the largest cities in the region were Houston and New Orleans. The study area experienced
Results
Observed land-cover changes from 1996 to 2005 provided background for interpreting the results of the Markov models. Overall, the percentage of agriculture land-cover remained constant at 22.8% while the percentage of developed land-cover increased from 4.5 to 4.9% and the percentage of natural land-cover (including forest) decreased from 72.7 to 72.3%. The 0.4% decrease of natural land-cover included a 4.2% net loss of forest (from 37.0 to 32.8% overall). The gross forest loss was 16.9% of the
Discussion
In recent decades, urbanization has been the dominant process affecting land-cover change in the study area. That process was evident not only in the observed transition matrix for Model 1 (Table 1) but also in the steady-state distribution of landscape mosaics (Table 3) which indicated that urbanization will lead eventually to an almost completely developed study region with at least 90% developed land-cover. Of course, it was not really expected that 90% of the study area would be so
Acknowledgments
Funding was provided by the Quantitative Sciences Staff, U.S. Forest Service Research and Development. This manuscript has been subjected to U.S. Environmental Protection Agency peer and administrative review and has been approved for publication.
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