Estimation of Grassland Canopy Height and Aboveground Biomass at the Quadrat Scale Using Unmanned Aerial Vehicle

Abstract

Aboveground biomass is a key indicator of a grassland ecosystem. Accurate estimation from remote sensing is important for understanding the response of grasslands to climate change and disturbance at a large scale. However, the precision of remote sensing inversion is limited by a lack in the ground truth and scale mismatch with satellite data. In this study, we first tried to establish a grassland aboveground biomass estimation model at 1 m 2 quadrat scale by conducting synchronous experiments of unmanned aerial vehicle (UAV) and field measurement in three different grassland ecosystems. Two flight modes (the new QUADRAT mode and the commonly used MOSAIC mode) were used to generate point clouds for further processing. Canopy height metrics of each quadrat were then calculated using the canopy height model (CHM). Correlation analysis showed that the mean of the canopy height model (CHM_mean) had a significant linear relationship with field height (R 2 = 0.90, root mean square error (RMSE) = 19.79 cm, rRMSE = 16.5%, p < 0.001) and a logarithmic relationship with field aboveground biomass (R 2 = 0.89, RMSE = 91.48 g/m 2 , rRMSE = 16.11%, p < 0.001). We concluded our study by conducting a preliminary application of estimation of the aboveground biomass at a plot scale by jointly using UAV and the constructed 1 m 2 quadrat scale estimation model. Our results confirmed that UAV could be used to collect large quantities of ground truths and bridge the scales between ground truth and remote sensing pixels, which were helpful in improving the accuracy of remote sensing inversion of grassland aboveground biomass.

A Systematic Review of the Factors Influencing the Estimation of Vegetation Aboveground Biomass Using Unmanned Aerial Systems

“…The choice of parameter(s) derived from UAS imagery is likely the most important factor influencing the accuracy and predictive ability of AGB estimation. Some studies used spectral information [2,11,18,24,26,43,58,[60][61][62] and some structural information [1,8,22,23,28,34,48,50,55,[63][64][65]. Others used both [3][4][5][6]9,12,16,20,21,25,30,33,49,57,[66][67][68], while a few studies used spectral and structural metrics plus another data type [13,27,69] (Table A1).…”

“…Mean height [3,9,12,13,15,16,[19][20][21]23,25,28,30,34,[48][49][50]57,58,63,65,[67][68][69][74][75][76] Maximum height [1,3,4,13,28,30,34,48,57,63,65,69] Minimum height [3,28,34,48,57,63,65,69] Median height [12,21,27,48,63,65,…”

“…Grüner et al [19] found that, when grouped, grass species showed a good relationship between mean canopy height and ground-level AGB measurements (R 2 = 0.73) but that individual species models had variable accuracy (R 2 = 0.62-0.81). Zhang et al [65] found that a logarithmic regression using mean height had an excellent relationship with grasslands biomass (R 2 = 0.80).…”

Estimating Above-Ground Biomass of Maize Using Features Derived from UAV-Based RGB Imagery

“…As one of the most important indicators to describe the growing status of the crop, plant height (PH) has been widely used to estimate AGB [26,27]. Zhang et al [28] estimated AGB of grassland by using PH at three different study sites selected from the Gansu, Inner Mongolia, and Jiangsu provinces of China, and indicated a high correlation between the PH and AGB with the coefficient of determination (R 2 ) values greater than 0.66. In addition to PH, vegetation indices (VIs) which could provide reliable information about crop growing status [26,29,30], such as green canopy cover and PH, have also been investigated as a reliable source to estimate AGB [31][32][33].…”

UAV-Based Biomass Estimation for Rice-Combining Spectral, TIN-Based Structural and Meteorological Features

“…SfM is a computer vision technology that generates 3D geometry by repetitive bundle adjustment of the multiple unordered overlapped images and image matching techniques, like the scale-invariant feature transform (SIFT) [14,15]. Crop surface models (CSMs) derived from 3D point clouds contain crop canopy vertical distribution information, which can be used for crop monitoring, e.g., plant height measurement [16], biomass estimation [17][18][19][20], and yield prediction [21]. In addition to CSMs, RGB images, and multi-and hyper-spectral images acquired for UAV were combined with CSMs to estimate biomass [22][23][24][25][26][27].Although CSMs derived from 3D point clouds were used in the research mentioned above, they focused mostly only on the plant heights derived from gridded CSMs.…”