Abstract
Effective and accurate assessment of grassland above-ground biomass (AGB) especially via remote sensing (RS), is crucial for forage-livestock balance and ecological environment protection of alpine grasslands. Because of complexity and extensive spatial distribution of natural grassland resources, the RS estimation models based on moderate resolution imaging spectroradiometer (MODIS) data exhibited low accuracy and poor stability. In this study, various methods for estimating the AGB of alpine grassland vegetation using MODIS vegetation indices were evaluated by combining with meteorology, soil, topography geography and in situ measured AGB data (during grassland growing season from 2011 to 2016) in Gannan region. Results show that 1) five out of ten factors (elevation, slope, aspect, topographic position, temperature, precipitation and the concentration of clay and sand in the soil) exert significant effects on grassland AGB, with R 2 0.04–0.39, and RMSE 859.68–1075.09 kg/ha, respectively; 2) the accuracy and stability of AGB estimation model can be improved by constructing multivariate models, especially using multivariate nonparameter models; 3) the optimum estimation model is constructed on the basis of random forest algorithm (RF). Compared with univariate/multivariate parameter models, RMSE of RF model decreased 26.45%–44.27%. Meanwhile, RF models can explain 89.41% variation in AGB during grass growing season. This study presented a more suitable RS inversion model integrated MODIS vegetation indices and other effect factors. Besides, the accuracy based on MODIS data was greatly improved. Thus, our study provides a scientific basis for effective and accurate estimating alpine grassland AGB.
Highlights
G RASSLAND ecosystem, as the largest terrestrial ecosystem on earth’s surface [1], accounts for about 40% of land area [2], its net primary productivity accounts for 20% of the total terrestrial ecosystem capacity [3]
Where Biomassi represented the ith observed grassland biomass, fBiomass(i) represented the ith grassland biomass estimated by model, n represented the plots of the test set, xi was repeated RMSE and R2 of the test set, xwas the average of xi, and N was the number of modeling and validation repetition
This study aimed to explore the best method for grassland Above ground biomass (AGB) estimation, the MODIS vegetation indices and ten factors (DEM, S, A, topographic position index (TPI), Clay1, Sand1, Clay2, Sand2, T, and P) are considered
Summary
G RASSLAND ecosystem, as the largest terrestrial ecosystem on earth’s surface [1], accounts for about 40% of land area [2], its net primary productivity accounts for 20% of the total terrestrial ecosystem capacity [3]. Ground biomass (AGB), usually expressed as dry grass weight of aboveground portion within one unit area [4], is an important indicator of regional carbon cycle [5], [6]. Its temporal and spatial patterns reflect carbon sink potential of grassland vegetation [7], [8]. Grassland AGB and its change directly reflect degree of grassland degradation, soil erosion [9]–[11], and desertification [12]. Changes in grassland AGB can be used to monitor pasture overgrazing and land use change [13]. Accurate estimation of grassland AGB is of great significance for grassland management, grass and livestock balance, grassland growth assessment, and ecological environmental protection [14]–[16]
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