Determination of green aboveground biomass in desert steppe using litter-soil-adjusted vegetation index

Abstract

Accurate estimation of green aboveground biomass in arid and semiarid grassland is essential for a variety of studies, such as sustainable grassland management, fire risk assessment, climate change, and environmental degradation. A great need exists for the establishment of robust method for estimating green aboveground biomass in arid and semiarid grassland due to the influences of soil background and litter. In the study, a new index (litter-soil-adjusted vegetation index, L-SAVI) was proposed to estimate green aboveground biomass in arid and semiarid grassland. The L-SAVI was also evaluated based on biomass and spectra in situ measurements in the desert steppe of Inner Mongolia. Results showed that, the performance of the new index was better than that of NDVI (normalized difference vegetation index), SAVI (soil-adjusted vegetation index), MSAVI (modified soil-adjusted vegetation index), OSAVI (optimised soil-adjusted vegetation index), TSAVI (transformed soil-adjusted vegetation index), ATSAVI (adjusted transformed soil-adjusted vegetation index), PVI (perpendicular vegetation index), GSAVI (green-adjusted vegetation index), and L-ATSAVI (litter-corrected ATSAVI) in our study site. The logic behind the L-SAVI was to enable the SAVI to be less sensitive to litter by incorporating the CAI (cellulose absorption index) in the SAVI. In conclusion, the L-SAVI is a suitable predictor for complementing existing vegetation indices on green aboveground biomass estimation in arid and semiarid grassland.

Estimation of aboveground biomass in conserved areas of Stipa tenacissima L. stands in the high steppes of western Algeria by mean of the Landsat 8 imagery‐based vegetation indices

“…These results are consistent with Brinkmann et al () who modelled the aboveground net primary production of vegetation under arid and semi‐arid climatic conditions, and Prabhakara, Hively, and McCarty () who modelled the biomass of winter cover crops. However, this does not mean that the SAVI and MSAVI perform less well than the NDVI, as they appear to perform better in other environmental conditions (Magiera, Feilhauer, Waldhardt, Wiesmair, & Otte, ; Ren & Zhou, ). Nevertheless, all VI models designed to predict ephemeral plant biomass gave unsatisfactory results in that the r 2 is close to zero.…”

Effects of Different Grazing Systems on Aboveground Biomass and Plant Species Dominance in Typical Chinese and Mongolian Steppes

“…At present, several vegetation indices are used to estimate aboveground biomass, including NDVI, RVI, DVI, EVI, GNDVI, and SAVI [48]. As SAVI is very sensitive to soil background changes [19], this index is suitable for monitoring grasslands with relatively sparse vegetation, such as the Mongolian Plateau [48,49].…”

Above-ground biomass estimation forQuercus rotundifoliausing vegetation indices derived from high spatial resolution satellite images

“…Due to their flexibility, it is also possible to analyse the above-ground biomass variation within different regions or in time, as well as the impact of disturbances, such as deforestation or fire (Potter, 1999). IKONOS, WorldView-2 and QuickBird images, with high spatial resolution, have been used to study biomass in oil palm plantations in Africa (Thenkabail et al, 2004); tree parameters in Amazon forest (Palace, Keller, Asner, Hagen, & Braswell, 2008); structural parameters in Pinus forest in Central Spain (Gómez, Wulder, Montes, & Delgado, 2012); biomass in highdensity biomass wetlands vegetation (Mutanga, Adam, & Cho, 2012); forest attributes and above-ground biomass in boreal forest stands in Canada (Mora, Wulder, White, & Hobart, 2013); above-ground biomass in mangrove forests in Thailand (Hirata et al, 2014) and in desert steppe ecosystems in Mongolia (Ren & Zhou, 2014); and forest biomass in Chile and Germany (Maack et al, 2015). Using GeoEye-1 and Pleiades-1A images, Clerici, Rubiano, Abd-Elrahman, Hoestettler, and Escobedo (2016) developed a methodology to estimate above-ground biomass in a complex forest in the Colombian Andes.…”