Lidar Remote Sensing of Tree Heights and Biomass
- Zhang et al., 2019. Mapping Maximum Tree Height of the Great Khingan Mountain, Inner Mongolia Using the Allometric Scaling and Resource Limitations Model. Forests, doi:10.3390/f10050380
- Yang et al., 2018. Post-drought decline of the Amazon carbon sink. Nature Communications, doi:10.1038/s41467-018-05668-6
- 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
- Yang et al., 2016. Abiotic Controls on Macroscale Variations of Humid Tropical Forest Height, Remote Sensing, doi:10.3390/rs8060494
- 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)
- Ni et al., 2015. Mapping forest canopy height over continental China using multi-source remote sensing data. Remote Sensing, 2015 (doi: 10.3390/rs70708436)
- 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).
- 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)
- 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
- 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