Journal
INTERNATIONAL JOURNAL OF REMOTE SENSING
Volume 31, Issue 5, Pages 1261-1280Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/01431160903380656
Keywords
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Funding
- Ministry of Industry, Tourism and Trade of Spanish Government [FIT 330221-2006-10]
- Technical University of Madrid [CCG07-UPM/AMB-2056]
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The mean and standard deviation (SD) of light detection and ranging (LiDAR)-derived canopy height are related to forest structure. However, LiDAR data typically cover a limited area and have a high economic cost compared with satellite optical imagery. Optical images may be required to extrapolate LiDAR height measurements across a broad landscape. Different spectral indices were obtained from three Landsat scenes. The mean, median, SD and coefficient of variation (CV) of LiDAR canopy height measurements were calculated in 30-m square blocks corresponding with Landsat Enhanced Thematic Mapper Plus (ETM+) pixels. Correlation and forward stepwise regression analysis was applied to these data sets. Mean and median LiDAR height versus normalized difference vegetation index (NDVI), normalized difference moisture index (NDMI), normalized burn ratio (NBR) and wetness Tasseled Cap showed the best correlation coefficients (R(2) ranging between -0.62 and -0.76). Nineteen regression models were obtained (R(2) = 0.65-0.70). These results show that LiDAR-derived canopy height may be associated with Landsat spectral indices. This approach is of interest in sustainable forest management, although further research is required to improve accuracy.
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