Lidar Remote Sensing of Tree Heights and Biomass

  1. Choi et al., 2016. Application of the metabolic scaling theory and water–energy balance equation to model large-scale patterns of maximum forest canopy height. Global Ecol. Biogeography, doi:10.1111/geb.12503
  2. Yang et al., 2016. Abiotic Controls on Macroscale Variations of Humid Tropical Forest Height, Remote Sensing, doi:10.3390/rs8060494
  3. Wu et al., 2015. A comparative study of predicting DBH and stem volume of individual trees in a temperate forest using airborne waveform LiDAR, IEEE Geoscience and Remote Sensing, 2015 (doi: 10.1109/LGRS.2015.2466464)
  4. Ni et al., 2015. Mapping forest canopy height over continental China using multi-source remote sensing data. Remote Sensing, 2015 (doi: 10.3390/rs70708436)
  5. Park et al., 2014. Application of physically-based slope correction for maximum forest canopy height estimation using waveform lidar across different footprint sizes and locations: Tests on LVIS and GLAS, Remote Sensing, 6: 6566-6586 (doi:10.3390/rs6076566).
  6. Ni and Park et al., 2014. Allometric Scaling and Resource Limitations Model of Tree Heights: Part 3. Model Optimization and Testing over Continental China, Remote Sens. 2014 (doi: 10.3390/rs6053533)
  7. Choi & Ni et al., 2013. Allometric Scaling and Resource Limitations Model of Tree Heights: Part 2. Site Based Testing of the Model, Remote Sens. 2013, 5, 202-223; doi:10.3390/rs5010202
  8. Shi & Choi et al., 2013. Allometric Scaling and Resource Limitations Model of Tree Heights: Part 1. Model Optimization and Testing over Continental USA, Remote Sens. 2013, 5, 284-306;doi:10.3390/rs5010284