Using UAV LiDAR to Extract Vegetation Parameters of Inner Mongolian Grassland

Abstract

The accurate estimation of grassland vegetation parameters at a high spatial resolution is important for the sustainable management of grassland areas. Unmanned aerial vehicle (UAV) light detection and ranging (LiDAR) sensors with a single laser beam emission capability can rapidly detect grassland vegetation parameters, such as canopy height, fractional vegetation coverage (FVC) and aboveground biomass (AGB). However, there have been few reports on the ability to detect grassland vegetation parameters based on RIEGL VUX-1 UAV LiDAR (Riegl VUX-1) systems. In this paper, we investigated the ability of Riegl VUX-1 to model the AGB at a 0.1 m pixel resolution in the Hulun Buir grazing platform under different grazing intensities. The LiDAR-derived minimum, mean, and maximum canopy heights and FVC were used to estimate the AGB across the entire grazing platform. The flight height of the LiDAR-derived vegetation parameters was also analyzed. The following results were determined: (1) The Riegl VUX-1-derived AGB was predicted to range from 29 g/m2 to 563 g/m2 under different grazing conditions. (2) The LiDAR-derived maximum canopy height and FVC were the best predictors of grassland AGB (R2 = 0.54, root-mean-square error (RMSE) = 64.76 g/m2). (3) For different UAV flight altitudes from 40 m to 110 m, different flight heights showed no major effect on the derived canopy height. The LiDAR-derived canopy height decreased from 9.19 cm to 8.17 cm, and the standard deviation of the LiDAR-derived canopy height decreased from 3.31 cm to 2.35 cm with increasing UAV flight altitudes. These conclusions could be useful for estimating grasslands in smaller areas and serving as references for other remote sensing datasets for estimating grasslands in larger areas.

Investigating the Potential of a Newly Developed UAV-Mounted VNIR/SWIR Imaging System for Monitoring Crop Traits—A Case Study for Winter Wheat

“…It occurs because UAV-derived CH data, unlike point-wise manually measured ones, represent a mean plant height value of an entire plot accounting for the plant density resulting from zonal statistics. Another solution to provide CH data for reliably estimating crop traits might be utilizing UAV-LiDAR data [71][72][73] and should be investigated in future research.…”

Assessing the Impact of Soil on Species Diversity Estimation Based on UAV Imaging Spectroscopy in a Natural Alpine Steppe

“…For instance, Guimarães-Steinicke et al [81] regarded mean height and LAI as metrics of vertical structure, community stand gaps, canopy surface variation and emergent flowers as metrics of horizontal structure. Airborne or UAV-based LiDAR data could also be attempted to estimate the canopy structure characteristics of grassland over large areas [82]. However, the detection of grassland structure remains a challenge compared with forests, due to the more complex canopy structure and the smaller size of individual plants.…”

Unmanned Aerial Vehicle (UAV) Remote Sensing in Grassland Ecosystem Monitoring: A Systematic Review

“…The overall payload of the UAV ranges from 2.5 kg to 6.0 kg, with a maximum payload of 50 kg [23,[27][28][29][30][31]. The flight altitude of UAVs varies from 10 to 100 meters, and some scholars have studied the impact of flight altitude on modelling accuracy [32,33].…”

“…For example, Wang et al studied the application ability of UAV LiDAR technology in modelling canopy height and coverage of grassland and used the extracted average canopy height, maximum canopy height and FVC to estimate the AGB [33]. The research results of Zhang et al in Hulunbuir, Inner Mongolia, also showed that UAV LiDAR data could effectively estimate the canopy height, FVC and other parameters of grasslands, and grassland AGB could be retrieved with higher accuracy through multiparameter joint inversion [32].…”