Remote sensing based grassland carrying capacity assessments are not commonly applied in rangeland man-agement. Possible reasons for this include non-equilibrium thinking in rangeland management, and the costli-ness of existing remotely sensed biomass estimation that carrying capacity assessments require. Here, we presenta less demanding approach for grassland biomass estimation using the MODIS Net Primary Production (NPP)product and demonstrate its use in carrying capacity assessment over the mountain grasslands of Azerbaijan.Based on publicly available estimates of the fraction of total NPP partitioned to aboveground NPP (fANPP) wecalculate the aboveground biomass produced from 2005 to 2014. Validation of the predicted abovegroundbiomass with independentfield biomass data collected in 2007 and 2008 confirmed the accuracy of theaboveground biomass product and hence we considered it appropriate for further use in the carrying capacityassessment. Afirst assessment approach, which allowed for consumption of 65% of aboveground biomass, re-sulted in an average carrying capacity of 12.6 sheep per ha. A second more realistic approach, which furtherrestricted grazing on slopes steeper than 10%, resulted in a stocking density of 6.20 sheep per ha and a carryingcapacity of 3.93 million sheep. Our analysis reveals overgrazing of the mountain grasslands because the currentlivestock population which consists of at least 8 million sheep, 0.5 million goats and an unknown number ofcattle exceeds the predicted carrying capacity of 3.93 million sheep. We consider that the geographically explicitadvice on sustainable stocking densities is particularly attractive to regulate grazing intensity in geographicallyvaried terrain such as the mountain grasslands of Azerbaijan. We further conclude that the approach, given itsgeneric nature and the free availability of most input data, could be replicated elsewhere. Hence, we adviseconsidering its use where traditional carrying capacity assessments are difficult to implement.
|Number of pages||11|
|Journal||International Journal of Applied Earth Observation and Geoinformation (JAG)|
|Publication status||Published - 1 Jun 2019|