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.

Leaf area index (LAI) is one of the key biophysical indicators for characterizing the growth and status of vegetation and is also used in modeling earth system processes. Machine learning algorithms (MLAs) such as random forest regression (RFR), artificial neural network regression (ANNR) and support vector regression (SVR) based on satellite data have been widely used for the estimation of LAI. However, the selection of input variables has a great impact on the estimation performance of MLAs. In this study, we aimed to improve the LAI inversion model of Inner Mongolia grassland based on MLAs incorporating empirical knowledge. Firstly, we used the ANNR, SVR and RFR approaches, respectively, to rank the input variables including vegetation indices, climate factors, soil factors and topography factors and found that Normalized Difference Phenology Index (NDPI) contributed the most to LAI estimation. Secondly, we selected four sets of input variables, namely, all variables—A, model selected variables—B, overlapping variables—C and self-defined variables—D, respectively. Subsequently, we built twelve LAI estimation models (RFR-A, RFR-B, RFR-C, etc.) based on three MLAs and four sets of input variables. The evaluation of them showed the RFR produced higher prediction accuracy, followed by ANNR and SVR. Furthermore, the RFR-D presented the highest accuracy in predicting LAI (R² = 0.55, RMSE = 0.37 m²/m², MAE = 0.29 m²/m²). Finally, we compared our results with MODIS LAI and GEOV2 LAI products and found that all of them showed a similar spatial distribution of grassland LAI in the four sub-regions covering all grassland types, but our model exhibited larger LAI values in the desert steppe and smaller LAI values in the others. These findings demonstrated that MLAs incorporating empirical knowledge could improve the accuracy of modelling LAI and further study is necessary to reduce the uncertainty in LAI mapping in grassland.

Accurately estimating grassland carbon stocks is important in assessing grassland productivity and the global carbon balance. This study used the regression kriging (RK) method to estimate grassland carbon stocks in Northeast China based on Landsat8 operational land imager (OLI) images and five remote sensing variables. The normalized difference vegetation index (NDVI), the wide dynamic range vegetation index (WDRVI), the chlorophyll index (CI), Band6 and Band7 were used to build the RK models separately and to explore their capabilities for modeling spatial distributions of grassland carbon stocks. To explore the different model performances for typical grassland and meadow grassland, the models were validated separately using the typical steppe, meadow steppe or all-steppe ground measurements based on leave-one-out crossvalidation (LOOCV). When the results were validated against typical steppe samples, the Band6 model showed the best performance (coefficient of determination (R²) = 0.46, mean average error (MAE) = 8.47%, and root mean square error (RMSE) = 10.34 gC/m²) via the linear regression (LR) method, while for the RK method, the NDVI model showed the best performance (R² = 0.63, MAE = 7.04 gC/m², and RMSE = 8.51 gC/m²), which were much higher than the values of the best LR model. When the results were validated against the meadow steppe samples, the CI model achieved the best estimation accuracy, and the accuracy of the RK method (R² = 0.72, MAE = 8.09 gC/m², and RMSE = 9.89 gC/m²) was higher than that of the LR method (R² = 0.70, MAE = 8.99 gC/m², and RMSE = 10.69 gC/m²). Upon combining the results of the most accurate models of the typical steppe and meadow steppe, the RK method reaches the highest model accuracy of R² = 0.69, MAE = 7.40 gC/m², and RMSE = 9.01 gC/m², while the LR method reaches the highest model accuracy of R² = 0.53, MAE = 9.20 gC/m², and RMSE = 11.10 gC/m². The results showed an improved performance of the RK method compared to the LR method, and the improvement in the accuracy of the model is mainly attributed to the enhancement of the estimation accuracy of the typical steppe. In the study region, the carbon stocks showed an increasing trend from west to east, the total amount of grassland carbon stock was 79.77 × 10⁴ Mg C, and the mean carbon stock density was 47.44 gC/m². The density decreased in the order of temperate meadow steppe, lowland meadow steppe, temperate typical steppe, and sandy steppe. The methodology proposed in this study is particularly beneficial for carbon stock estimates at the regional scale, especially for countries such as China with many grassland types.