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A study was established to investigate soil and site variables associated with early growth of eastern white pine (Pinus strobus L.) on reclaimed strip mines in southern West Virginia and Virginia. A total of 78 plantations were studied on 14 different mines, 5 to 9 yr following planting. Two‐year terminal height growth at ages 4 and 5 was selected as the growth variable in a multiple linear regression equation developed to show the relationship between tree growth and soil and site variables. Many soil/site variables were tested, but rooting depth, electrical conductivity, surface soil P and Mn, and slope were the dominant independent variables, accounting for 48% of the variability in growth. Soil depth had a standardized regression coefficient of 0.35, and was the most significant variable. Through proper reclamation, it is possible to establish a site that will not only provide for adequate early height growth to facilitate bond release, but also provide for future long‐term productivity of surface‐mined lands.

The relationship between leaf area index (LAI) of loblolly pine plantations and the broadband simple ratio (SR) vegetation index calculated from Landsat 7 Enhanced Thematic Mapper Plus (ETM+) data was examined. An equation was derived to estimate LAI from readily available Landsat 7 ETM+ data. The equation developed to predict LAI with Landsat 7 ETM+ data was tested with ground LAI measurements taken in 12 plots. The root mean square error of prediction was 0.29, an error of approximately 14% in prediction. The ability of Landsat 7 ETM+ data to consistently estimate SR over time was tested using two scenes acquired on different dates during the winter (December to early March). Comparison between the two images on a pixel-by-pixel basis showed that approximately 96% of the pixels had a difference of <0.5 units of SR (approximately 0.3 units of LAI). When the comparison was made on a stand-by-stand basis (average stand SR), a maximum difference of 0.2 units of SR (approximately 0.12 units of LAI) was found. These results suggest that stand LAI of loblolly pine plantations can be accurately estimated from readily available remote sensing data and provide an opportunity to apply the findings from ecophysiological studies in field plots to forest management decisions at an operational scale.