Above-ground biomass estimation based on NPP time-series − A novel approach for biomass estimation in semi-arid Kazakhstan. (January 2017) - Eisfelder, Christina; Klein, Igor; Bekkuliyeva, Aruzhan; Kuenzer, Claudia; Buchroithner, Manfred F.; Dech, Stefan
Highlights: We present a novel approach for biomass estimation in natural semi-arid environments. The approach is based on Net Primary Productivity data and Relative Growth Rates. We apply the approach to derive grass and shrub biomass in Kazakhstan. The developed approach is validated with field data from three study areas. Fractional cover data provide essential input regarding grass and shrub distribution. Abstract: Biomass is a sensitive indicator of environmental change and ecological functioning. Quantification of biomass is essential to identify and monitor those areas threatened by degradation and desertification. This is especially important in arid and semi-arid environments. However, robust techniques to monitor carbon stocks over large areas and through time are still missing. The major objective of the presented study is to develop a novel approach for biomass estimation in semi-arid environments using remote-sensing based Net Primary Productivity (NPP) data. The developed methodical concept aims at derivation of above-ground grass and shrub biomass for natural environments. It is based on NPP time-series and plants' relative growth rates. Fractional cover data provide information about grass and shrub coverage. The developed approach has been applied to three study areas in Kazakhstan, in which field data were collected for validation. Biomass maps were derived that show the spatial distribution of grass and shrub biomass. Validation revealed a moderate correlation (R = 0.68) with field data for grass biomass. For shrub biomass, a high correlation (R = 0.83) is retrieved when fractional cover information from field observations is used. The presented novel approach for biomass estimation is based on remote sensing derived NPP time-series and is thus potentially transferable in space and time. This is a great advantage compared to commonly applied empirical relationships. The presented concept can be adapted to be applied to other vegetation communities. Providing the necessary data about fractional vegetation cover is available, the method will allow for repeated and large-area biomass estimation for natural semi-arid environments as needed for observing changes in biomass and support sustainable land management.