Remote Sensing based Net Primary Productivity Modelling and Derivation of Above-Ground Biomass Estimates for Kazakhstan
Eisfelder, Christina1; Kuenzer, Claudia1; Buchroithner, Manfred F.2
1German Aerospace Center (DLR), GERMANY; 2Dresden University of Technology, Institute for Cartography, GERMANY
Previous studies on biomass estimation revealed several challenges for remote-sensing based biomass estimation in semi-arid regions. Most important for repeatable application and coverage of large areas is the transferability of biomass estimation approaches. Modelling approaches are among the methods that obtained most promising results. Nevertheless, modelling approaches have not been extensively analysed in the context of biomass estimation for semi-arid regions yet (Eisfelder et al. 2012). Net Primary Productivity (NPP) models are commonly applied for large areas. They are suitable for obtaining NPP time-series for several years. Remote-sensing based NPP models allow for regional NPP calculation. This information may be of value for the estimation of standing biomass, because NPP is closely related to above-ground biomass (AGB; e.g. Fensholt et al. 2006). The most relevant time for biomass estimation is at peak biomass or at maximum productivity. For Kazakhstan, the period of maximum vegetation productivity is in June. The aim of this study was, thus, to develop a method for estimation of standing biomass for the period of maximum vegetation growth. The biomass estimation approach developed in this study is based on NPP data, plantsí Relative Growth Rates (RGRs), and fractional cover information. The application of the methodological concept is demonstrated for three study areas in semi-arid Kazakhstan. NPP is calculated with the model BETHY/DLR (Biosphere Energy Transfer Hydrology Model; Knorr and Heimann 2001, Eisfelder et al. 2013), based on meteorological data from the European Centre for Medium-Range Weather Forecasts (ECMWF), Leaf Area Index (LAI) data from MODIS, and a regional Central Asia land cover and land use map (Klein et al. 2012). For the validation of the AGB estimates, field data were collected from the three study areas in Kazakhstan in June 2011. The validation with field data showed that the NPP-based above-ground grass biomass estimates are lower than the field-observed grass biomass for most test sites. This indicates that the NPP input is too low and/or that the applied RGRs for grass are too high. To reduce the error associated with the RGR values, more experimental studies with plants typical for the study areas in Kazakhstan are needed. The moderate overall correlation between modelled grass biomass and field-observed grass biomass (R=0.64) might be caused by a high variation of herbaceous species present within the test sites. For NPP calculation with BETHY/DLR based on the Central Asia land cover and land use map, only two grass types are distinguished. A map that provides information on the spatial distribution of different vegetation types and the separate modelling of additional vegetation types with BETHY/DLR would be needed to improve the grass biomass results. The validation of the shrub biomass estimates showed a large difference between the results obtained with fractional cover information based on the land cover map (R=0.33) and the estimates obtained with fractional cover from field observations (R=0.83). These results reveal the high importance of accurate fractional cover information for shrub biomass estimation. The accuracy for individual sites is obviously not sufficient for reliable shrub biomass estimation. Unfortunately, a suitable fractional cover dataset currently not exists for Kazakhstan. Such information can, however, be derived from remote sensing. Provided that accurate shrub fractional cover is available, the developed approach seems to yield good estimates of above-ground shrub biomass. Future research could comprise to use ESA Envisat MERIS and Sentinel-3-OLCI data to derive LAI time series as input for the model.
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