Renewed Estimates of Grassland Aboveground Biomass Showing Drought Impacts

Abstract

Variations in ecosystem composition and structure within a cover type of the same biome can be responsible for the large amounts of uncertainty in remote sensing modeling of aboveground biomass (AGB). Based on 820 ground measurements of AGB and the pure vegetation index models, we proposed a spatially variable remote sensing scalar (i.e., M scalar) in estimating AGB by addressing within‐type variations of vegetation. We tested this new modeling approach in Inner Mongolia grasslands and achieved a higher R2 (>0.88) and lower RMSE value (<28.17 g/m2) than those in the conventional remote sensing models. The annual average (STD) of the grassland AGB for 2001–2016 was ~87 (±9) Tg, with an AGB density of 104 ± 74 g/m2. The interannual variation of AGB appeared insignificant over the study period, except in the western region where it increased significantly. The dynamics of AGB were highly correlated with drought severity during the growing season (R2 = 0.691, P < 0.001). AGB under normal climate (i.e., without drought) was ~90.88 Tg, but it increased by ~19% (17.11 Tg) in a wet year (2012) and decreased by 20% (18.16 Tg) in a dry year (2001). More importantly, the high grazing intensity played a less significant role in directing grassland AGB dynamics (R2 = 0.024, P = 0.599). Future efforts are needed to explore how within‐type variation of vegetation may change temporally and spatially in accurate estimate of AGB across heterogeneous landscapes, as well as the potential underlying mechanisms from biological, physical, and human perspectives.

Remote Sensing of Grassland Production and Management—A Review

“…When comparing census data, which is related to human activity (e.g., stocking rate) and grassland production, in most cases an existing relationship was revealed [97]. Compared to the influence of climate, human activity was found to have a larger [119] or a smaller [115,122,123] influence on grassland production.…”

“…The influence of precipitation and temperature on grassland production is relatively complex as it can change through the growing season, vary among different grasslands and determine and depend on each other [117,120,121]. When compared to the influence of human activity, both larger [119] and smaller [115,122,123] influence on grassland production were found. The information on human activity in analysis on larger scales is usually census-based, such as stocking rate, and is therefore not a spatial information.…”

High-Throughput Switchgrass Phenotyping and Biomass Modeling by UAV

“…Meanwhile, the model coefficients (i.e., α and β vary with choice of spectral index, and its associated phenological stage, as well as taxa. These variations in model forms and coefficients prevent us from crossanalysis among traits, including phenotyping heterogeneity, biomass composition and density, as well as evaluating NUE (Hardin et al, 2013;Li et al, 2018). Taking this a step further, it will impede our understanding of whether biomass production is largely explained by genotype and canalized phenotypes (Casler, 2012).…”

“…One problem inherent to optical imagery techniques is the potential for natural light saturation for detecting the high-density biomass plants (Mutanga and Skidmore, 2004;Li et al, 2014). Integration of LiDAR and spectral index technologies have been used to address these underlying factors determining plant biomass varying with plant type and phenotyping parameters (e.g., plant height and fractional canopy cover) (Tucker et al, 1985;Popescu et al, 2003;Li et al, 2018).…”

Vegetation traits of pre-Alpine grasslands in southern Germany

“…Remotely sensed data from unmanned aircraft systems (UAS) and satellites are one option to overcome this gap in the mapping and assessment of vegetation traits on a larger spatial scale 13 . In the last years, several studies made use of the potential of remotely sensed data to map grassland vegetation traits like above-ground biomass [14][15][16][17][18][19][20][21] and quality parameters [22][23][24][25][26][27] . Geo-referenced in-situ data are of utmost importance for the development of remote sensing-based models for the estimation of grassland vegetation traits, for both calibration and validation purposes.…”