Journal
REMOTE SENSING LETTERS
Volume 3, Issue 5, Pages 443-451Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/01431161.2011.618814
Keywords
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Funding
- UMBS
- National Science Foundation (NSF) [DGE-0504552, DEB-0911461]
- BART
- US Department of Energy's Office of Science through the Midwestern Regional Center of the National Institute for Global Environmental Change (NIGEC) [DE-FC03-90ER610100]
- Midwestern Regional Center of the National Institute for Climatic Change Research (NICCR) at Michigan Technological University [DE-FC02-06ER64158]
- US Department of Agriculture-National Institute for Food & Agriculture (NIFA) - Air Quality [CSREES-OHOR-2009-04566]
- USDA [10-JV-11242302-013]
- NSF-NCALM
- Forest Service Northern Research Station, East Lansing, MI [10-JV-11242302-013]
- Directorate For Geosciences [0851421] Funding Source: National Science Foundation
- Div Atmospheric & Geospace Sciences [0851421] Funding Source: National Science Foundation
- Division Of Earth Sciences
- Directorate For Geosciences [1043051] Funding Source: National Science Foundation
- Division Of Earth Sciences
- Directorate For Geosciences [1339015] Funding Source: National Science Foundation
- Division Of Environmental Biology
- Direct For Biological Sciences [0911461] Funding Source: National Science Foundation
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Object-oriented classification methods are increasingly used to derive plant-level structural information from high-resolution remotely sensed data from plant canopies. However, many automated, object-based classification approaches perform poorly in deciduous forests compared with coniferous forests. Here, we test the performance of the automated spatial wavelet analysis (SWA) algorithm for estimating plot-level canopy structure characteristics from a light detection and ranging (LiDAR) data set obtained from a northern mixed deciduous forest. Plot-level SWA-derived and co-located ground-based measurements of tree diameter at breast height (DBH) were linearly correlated when canopy cover was low (correlation coefficient (r) = 0.80) or moderate (r = 0.68), but were statistically unrelated when canopy cover was high. SWA-estimated crown diameters were not significantly correlated with allometrically based estimates of crown diameter. Our results show that, when combined with allometric equations, SWA can be useful for estimating deciduous forest structure information from LiDAR in forests with low to moderate (<175% projected canopy area/ground area) levels of canopy cover.
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