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Influence of Fusing Lidar and Multispectral Imagery on Remotely Sensed Estimates of Stand Density and Mean Tree Height in a Managed Loblolly Pine Plantation

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Stereo aerial photography has long been used to measure tree density and height photogrammetrically. Recent attempts have been made to locate and measure trees automatically in high-resolution digital imagery. This study used small-footprint lidar (1.057 ┬Ám, 1 mrad divergence, 0.67 m footprint) and high-resolution (0.61 m) multispectral (550, 675, 700, and 800 nm) data sets to estimate stem counts and tree heights in 15-yr-old loblolly pine stands. A data fusion process was used to combine the datasets. Tree identification accuracy and mean height estimation derived from the separate and fused data sets were compared against field data.

Tree identification was more accurate using spectral data (78.6% and 92.4%) than lidar data (64.7% and 87.3%) within the two planting densities, respectively. The fused dataset improved accuracy of tree identification over the single-dataset approaches (83.5% and 94.8%). Plot-level mean height of lidar-located trees provided the best estimates of mean field height (average difference = 0.15 m). Missed trees for all methods were shorter than mean field height by up to 3.03 m (fused data). These results indicate fusion of spectral and lidar data will likely improve estimates of mean tree height and stem density. Increased lidar posting density is identified as a key factor to improve tree recognition and measurement. FOR. SCI.49(3):457–466.
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Keywords: ALTMS; Inventory; environmental management; forest; forest management; forest resources; forestry; forestry research; forestry science; infrared; natural resource management; natural resources; sensor fusion

Document Type: Miscellaneous

Affiliations: 1: Research Assistant Department of Forestry, Forest and Wildlife Research Center, Mississippi State University, Box 9681 Mississippi State, MS, 39762, Phone: (662) 325-3540; Fax: (662) 325-8726 [email protected] 2: Associate Professor Department of Forestry, Forest and Wildlife Research Center, Mississippi State University, Box 9681 Mississippi State, MS, 39762, [email protected] 3: Associate Professor Department of Forestry, Forest and Wildlife Research Center, Mississippi State University, Box 9681 Mississippi State, MS, 39762, [email protected]

Publication date: 2003-06-01

More about this publication?
  • Forest Science is a peer-reviewed journal publishing fundamental and applied research that explores all aspects of natural and social sciences as they apply to the function and management of the forested ecosystems of the world. Topics include silviculture, forest management, biometrics, economics, entomology & pathology, fire & fuels management, forest ecology, genetics & tree improvement, geospatial technologies, harvesting & utilization, landscape ecology, operations research, forest policy, physiology, recreation, social sciences, soils & hydrology, and wildlife management.
    Forest Science is published bimonthly in February, April, June, August, October, and December.

    2016 Impact Factor: 1.782 (Rank 17/64 in forestry)

    Average time from submission to first decision: 62.5 days*
    June 1, 2016 to Feb. 28, 2017

    Also published by SAF:
    Journal of Forestry
    Other SAF Publications
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