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[1] Little is known about the tropical forests that undergo clearing as urban/built-up and other developed lands spread. This study uses remote sensing-based maps of Puerto Rico, multinomial logit models and forest inventory data to explain patterns of forest age and the age of forests cleared for land development and assess their implications for forest carbon storage and tree species richness. Accessibility, arability and spatial contagion emerge strongly as overriding spatial controls on tropical forest age, determining (1) the pattern of agricultural abandonment that permits forest regrowth, and (2) where humans leave old-growth forest remnants. Covariation between the factors patterning forest age and land development explains why most forest cleared for land development is younger. Forests are increasingly younger in more accessible and fertile areas where agriculture has lasted longer and land development is most common. All else equal, more species-rich older forest on less arable lands are somewhat less likely to undergo development, but they are still vulnerable to clearing for land development if close to urban centers and unprotected. Accounting for forest age leads to a 19% lower estimate of forest biomass cleared for land development than if forest age is not accounted for.Citation: Helmer, E. H., T. J. Brandeis, A. E. Lugo, and T. Kennaway (2008), Factors influencing spatial pattern in tropical forest clearance and stand age: Implications for carbon storage and species diversity,

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Current information on land cover, forest type and forest structure for the Virgin Islands is critical to land managers and researchers for accurate forest inventory and ecological monitoring. In this study, we use cloud free image mosaics of panchromatic sharpened Landsat ETM+ images and decision tree classification software to map land cover and forest type for the Virgin Islands, illustrating a low cost, repeatable mapping approach. Also, we test if coarse-resolution discrete lidar data that are often collected in conjunction with digital orthophotos are useful for mapping forest structural attributes. This approach addresses the factors that affect vegetation distribution and structure by testing if environmental variables can improve regression models of forest height and biomass derived from lidar data. The overall accuracy of the 29 forest and non-forest classes is 72%, while most the forest types are classified with greater than 70% accuracy. Due to the large point spacing of this lidar dataset, it is most appropriate for height measurements of dominant and co-dominant trees (R 2 = 72%) due to its inability to accurately represent forest understory. Above ground biomass per hectare is estimated by its direct relationship with plot canopy height (R 2 = 0.72%).

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