No abstract

Appropriate rangeland management requires rangeland function analysis at broad scales. This study aimed to examine the potential of remotely sensed function indices extracted from Landsat data to evaluate the function of semi-arid rangelands in central Iran at the sub-basin scale. Three replicate 30-m transects were randomly placed in the dominant slope direction of 14 selected sub-basins. Various structural properties of vegetation (e.g. number and size of vegetation patches and interpatch lengths) and soil surface were scored based on the landscape function analysis (LFA) procedure. The obtained structural and function indices of the LFA, as well as field percent vegetation cover, were compared with the perpendicular distance vegetation index and remotely sensed function indices including proximity, lacunarity, leakiness index, and weighted mean patch size (WMPS). Remotely sensed function indices were found to be capable of discriminating rangeland landscapes with different conditions. Results showed that the structural properties of vegetation considered in the LFA could also be obtained through WMPS and proximity indices (R >0.76; P < 0.01). All indices, except for lacunarity, had significant correlations with percent vegetation cover and the strongest correlation was observed between WMPS and proximity. Our findings highlight the usefulness and efficiency of function indices derived from satellite data in the estimation of structural and functional properties of rangeland landscapes at the sub-basin scale.

No abstract

Mapping and updating grazing capacity are necessary due to spatio-temporal variations of production in rangelands as a result of climatic and management changes. This study utilised short- and long-term grazing capacity mapping and monitoring by using satellite images in the rangelands of southern Zagros, Iran. In 2018, production of 16 rangeland types was estimated at spatial scales of 250 and 10 m from MODIS and Sentinel-2 images, and validated with field production data measured at 185 sampling sites through the R2 coefficient of determination. The production maps, along with the parameters of allowable utilisation of rangeland plant species, animal grazing area, animal daily requirement and length of grazing period, were used to calculate grazing capacity. In addition, the effect of climatic fluctuations on grazing capacity was investigated using the Standard Index of Annual Precipitation (SIAP) index from 2009 to 2018. The production obtained from satellite images varied between 2.4 and 393.2 kg ha−1 in 2018. The high correlation (80%) between image production maps and field measurements, as well as the significance of these relationships in all rangeland types (P < 0.05), allowed grazing capacity estimation by using satellite-based production. The minimum and maximum grazing capacities in a 100-day period were 1809 and 297 146 animal units (AU) respectively, in 258 387 ha. Grazing capacity monitoring from 2009 to 2018 showed that during a drought period, AUs were about 0.7 ha−1 below those in years of above-average rainfall. Use of satellite remote-sensing with different spatio-temporal scales therefore appears capable of mapping and monitoring grazing capacity, and can be used as a management tool by rangeland owners and related organisations.