Evaluation of Remote Sensing Inversion Error for the Above-Ground Biomass of Alpine Meadow Grassland Based on Multi-Source Satellite Data

Abstract

It is not yet clear whether there is any difference in using remote sensing data of different spatial resolutions and filtering methods to improve the above-ground biomass (AGB) estimation accuracy of alpine meadow grassland. In this study, field measurements of AGB and spectral data at Sangke Town, Gansu Province, China, in three years (2013)(2014)(2015) are combined to construct AGB estimation models of alpine meadow grassland based on these different remotely-sensed NDVI data: MODIS, HJ-1B CCD of China and Landsat 8 OLI (denoted as NDVI MOD , NDVI CCD and NDVI OLI , respectively). This study aims to investigate the estimation errors of AGB from the three satellite sensors, to examine the influence of different filtering methods on MODIS NDVI for the estimation accuracy of AGB and to evaluate the feasibility of large-scale models applied to a small area. The results showed that: (1) filtering the MODIS NDVI using the Savitzky-Golay (SG), logistic and Gaussian approaches can reduce the AGB estimation error; in particular, the SG method performs the best, with the smallest errors at both the sample plot scale (250 m × 250 m) and the entire study area (33.9% and 34.9%, respectively); (2) the optimum estimation model of grassland AGB in the study area is the exponential model based on NDVI OLI , with estimation errors of 29.1% and 30.7% at the sample plot and the study area scales, respectively; and (3) the estimation errors of grassland AGB models previously constructed at different spatial scales (the Tibetan Plateau, Gannan Prefecture and Xiahe County) are higher than those directly constructed based on the small area of this study by 11.9%-36.4% and 5.3%-29.6% at the sample plot and study area scales, respectively. This study presents an improved monitoring algorithm of alpine natural grassland AGB estimation and provides a clear direction for future improvement of the grassland AGB estimation and grassland productivity from remote sensing technology.

High Spatial Resolution Visual Band Imagery Outperforms Medium Resolution Spectral Imagery for Ecosystem Assessment in the Semi-Arid Brazilian Sertão

“…The distinction between trees and shrubs is critical for ecosystem assessment in dryland regions because while tree cover in semi-arid regions is an important driver of overall landscape processes and carbon sequestration, seasonal variation in shrub and grass cover is a principal driver of the interannual variability in the carbon cycle [5,46]. Our results thus complement recent studies showing that using high spatial resolution imagery can increase the accuracy of above-ground biomass estimation [47,48].…”

Temporal Variability of Precipitation and Biomass of Alpine Grasslands on the Northern Tibetan Plateau

“…Finally, we found that the RMSE values were small, i.e., 13.35 for the data from Sun [47], 9.40 for Wu et al [48], and 12.15 for their data mixed together ( Figure 2), which indicated that the best-fitted linear model can be applied to AGB peak estimation across the entire study area even though some uncertainties remain. Similar studies that used an empirical model between remote sensing vegetation index and field measured biomass at the local scale to broader spatial scales can also be found on the Tibetan Plateau and other grassland regions of the world [52][53][54][55][56]. Validation of the best-fitted linear AGB peak -GEVI model by comparing the observed AGB peak from (a) Sun [47], (b) Wu, et al [48], and (c) both of them (see Figure 1 for site locations) with the simulated AGB peak values.…”

A Preliminary Investigation of the Potential of Sentinel-1 Radar to Estimate Pasture Biomass in a Grazed Pasture Landscape

“…The model was inspected to confirm normality of residuals and homoscedasticity using the Breusch and Pagan test [58]. Since the data for model calibration was limited in size, the GAM model was validated using a leave-one-out cross-validation (LOOCV) method as it has been assessed by earlier work to be appropriate under such constraints [59,60]. The performance of the GAM model was evaluated using adjusted R 2 , deviance and RMSE.…”