A modified light use efficiency (LUE) model was tested in the grasslands of central Kazakhstan in terms of its ability to characterize spatial patterns and interannual dynamics of net primary production (NPP) at a regional scale. In this model, the LUE of the grassland biome (ϵ_n) was simulated from ground-based NPP measurements, absorbed photosynthetically active radiation (APAR) and meteorological observations using a new empirical approach. Using coarse-resolution satellite data from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS), monthly NPP was calculated from 1998 to 2008 over a large grassland region in Kazakhstan. The modelling results were verified against scaled up plot-level observations of grassland biomass and another available NPP data set derived from a field study in a similar grassland biome. The results indicated the reliability of productivity estimates produced by the model for regional monitoring of grassland NPP. The method for simulation of ϵ_n suggested in this study can be used in grassland regions where no carbon flux measurements are accessible.

Carbon sequestration was estimated in a semi-arid grassland region in Central Kazakhstan using an approach that integrates remote sensing, field measurements and meteorological data. Carbon fluxes for each pixel of 1 × 1 km were calculated as a product of photosynthetically active radiation (PAR) and its fraction absorbed by vegetation (fPAR), the light use efficiency (LUE) and ecosystem respiration (R_e). The PAR is obtained from a mathematical model incorporating Earth-Sun distance, solar inclination, solar elevation angle, geographical position and cloudiness information of localities. The fPAR was measured in field using hemispherical photography and was extrapolated to each pixel by combination with the Normalized Difference Vegetation Index (NDVI) obtained by the Vegetation instrument on board the Satellite Pour l’Observation de la Terra (SPOT) satellite. Gross Primary Production (GPP) of the aboveground and belowground vegetation of 14 sites along a 230 km west-east transect within the study region were determined at the peak of growing season in different land cover types and linearly related to the amount of PAR absorbed by vegetation (APAR). The product of this relationship is LUE = 0.61 and 0.97 g C/MJ APAR for short grassland and steppe, respectively. The R_e is estimated using complex models driven by climatic data. Growing season carbon sequestration was calculated for the modelling year of 2004. Overall, the short grassland was a net carbon sink, whereas the steppe was carbon neutral. The evaluation of the modelled carbon sequestration against independent reference data sets proved high accuracy of the estimations.

This study combined 30-m spatial resolution Landsat Enhanced Thematic Mapper Plus (ETM+) data and 500-m spatial resolution Moderate Resolution Imaging Spectroradiometer (MODIS) data, together with field data, to up-scale aboveground biomass (AGB) of tropical rainforests in Sulawesi, Indonesia. Contrasting to usual up-scaling approaches, this study uses in the intermediate step an estimation method based on application of geostatistics in the Landsat ETM+ spectral feature space. To connect ground-based measurements with spectral reflectance in Landsat ETM+ bands, this method employs ordinary Kriging in the three-dimesional (3-D) space where spectral features of satellite data are used instead of geographic coordinates. The most appropriate combination of the 3-D space spectral bands (bands 4 and 5) was then assigned with corresponding MODIS bands. In the final step, AGB could be mapped over the large area covered by the MODIS data. The results of the study indicate the effectiveness of the developed up-scaling method with respect to dealing with the problem of resolution mismatch between the ground sampling plots and the MODIS data.

Satellite and ground-based data were combined in a monitoring system to quantify the link between climate conditions and the risk of locust infestations in the southern part of Lake Balkhash’s drainage basin in the Republic of Kazakhstan. In this monitoring system, the Normalized Difference Vegetation Index (NDVI), derived from the SPOT-VGT satellite, was used for mapping potential locust habitats and monitoring their area throughout 1998 to 2007. TOPEX/Poseidon and Jason 1 altimeter data were used to track the interannual dynamics of water level in Balkhash Lake. Climate conditions were represented by weather records for air temperature and precipitation during the same period. The classification procedure, based on an analysis of multitemporal dynamics of SPOT-VGT NDVI values observed by individual vegetation classes, generated annual areas of ten land-cover types, which were then categorized as areas with low, medium, and high risk for locust infestation. Statistical analyses showed significant influences of the climatic parameters and the Balkhash Lake hydrological regime on the spatial extend of annual areas of potential locust habitats. The results also indicate that the linkages between locust infestation risk and environmental factors are characterized by time lags. The expansion of locust risk areas are usually preceded by dry, hot years and lower water levels in Balkhash Lake when larger areas of reed grass are free from seasonal flooding. Years with such conditions are favourable for locust outbreaks due to expansion of the habitat areas suitable for locust oviposition and nymphal development. In contrast, years with higher water levels in Balkhash Lake and lower temperature decrease the potential locust habitat area.