Sheng Nie

TL;DR: In this paper, the authors explored the potential of estimating vegetation structural parameters such as vegetation height and leaf area index (LAI) for short wetland vegetation using airborne discrete-return LiDAR data.

TL;DR: In this article, the potential of fused LiDAR and hyperspectral data for biomass estimation was tested in the middle Heihe River Basin, northwest China, and the best estimation model was using a fusion of LIDAR and HSI metrics (R 2 ǫ = 0.785, 0.893 and 0.882 for BGB, AGB and TB, respectively).

TL;DR: The objective of this paper is to develop and validate an automated approach for better estimating vegetation canopy height and results indicated that the estimated vegetation canopy heights have a relatively strong correlation with the reference vegetation heights derived from airborne discrete-return LiDAR data.

TL;DR: The objective of this paper was to explore the potential of hyperspectral and light detection and ranging (LiDAR) data for better estimation of the biomass of maize by investigating the relationship between field-observed biomass with each metric, including vegetation indices derived from hyperspectrals and LiDAR-derived metrics.

TL;DR: Wang et al. as mentioned in this paper proposed a pixel-based phenological feature composite method (Ppf-CM) based on Google Earth Engine, which can mitigate the phonological variation and augment the spectral separability between S. alterniflora and the background species regardless of the significant cloud coverage in the study area.