JWSS - Journal of Water and Soil Science

In this study, the effect of some soil parameters on the life forms and total aboveground net primary production (ANPP) in meadow rangelands in Fandoghlou region of Namin county in Ardabil Province were investigated. ANPP in 180 plots of 12 by harvesting and weighting method were measured. Eighteen soil samples were collected along transects. Some physical and chemical attributes of the soil were measured by standard methods. The relationship between these parameters and ANPP was performed using multivariate regression (enter) method. To determine the effects of important soil parameters on ANPP variation, principal component analysis (PCA) was used. The results of regression analysis showed that electrical conductivity (EC), magnesium (Mg), spreadable clay (WDC), volumetric moisture content (VM), organic carbon (OC), soluble potassium (KS), exchangeable potassium (Kexch), sodium (Na) and phosphorus (P) were the effective parameters on the life forms and total ANPP (p<0.01). The accuracy of obtained equations for grasses, forbs and total ANPP were calculated 79, 76 and 70%, respectively. Moreover, results of PCA showed that soil parameters justify 84.52 percent of total ANPP variation and in comparison, with regression results with 28% it provides better results.

The objective of the present study was to investigate the performance of some of the extracted information for mapping land degradation using remote sensing and field data in Fras province. Maps of vegetation cover, net primary production, land use, surface slope, water erosion, and surface runoff indicators were extracted from MOD13A3, MOD17A3, Landsat TM, SRTM, ICONA model, and SCS model, respectively. The rain use efficiency index was obtained from the net primary production and rainfall map, which was calculated from meteorological stations. The final land degradation map was prepared by integrating all the mentioned indicators using the weighted overlay method. According to the ICONA model, 5.1, 9, 47.21, 27.91, and 10.73 percent of the study area were classified as very low, low, moderate, severe, and very severe water erosion; respectively. Overlaying the ICONA map with other indicators showed that very high and high classes, moderate, and low and very low classes of land degradation covered 1.3, 18.7, 70, 0.9, and 9.1 percent of the study area, respectively. According to the results, integrating remote sensing with ICONA and SCS models increases the ability to identify land degradation.