Digital soil mapping using remote sensing indices, terrain attributes, and vegetation features in the rangelands of northeastern Iran.

Digital soil mapping has been introduced as a viable alternative to the traditional mapping methods due to being fast and cost-effective. The objective of the present study was to investigate the capability of the vegetation features and spectral indices as auxiliary variables in digital soil mapping models to predict soil properties. A region with an area of 1225 ha located in Bajgiran rangelands, Khorasan Razavi province, northeastern Iran, was chosen. A total of 137 sampling sites, each containing 3-5 plots with 10-m interval distance along a transect established based on randomized-systematic method, were investigated. In each plot, plant species names and numbers as well as vegetation cover percentage (VCP) were recorded, and finally one composite soil sample was taken from each transect at each site (137 soil samples in total). Terrain attributes were derived from a digital elevation model, different bands and spectral indices were obtained from the Landsat7 ETM+ images, and vegetation features were calculated in the plots, all of which were used as auxiliary variables to predict soil properties using artificial neural network, gene expression programming, and multivariate linear regression models. According to R 2 RMSE and MBE values, artificial neutral network was obtained as the most accurate soil properties prediction function used in scorpan model. Vegetation features and indices were more effective than remotely sensed data and terrain attributes in predicting soil properties including calcium carbonate equivalent, clay, bulk density, total nitrogen, carbon, sand, silt, and saturated moisture capacity. It was also shown that vegetation indices including NDVI, SAVI, MSAVI, SARVI, RDVI, and DVI were more effective in estimating the majority of soil properties compared to separate bands and even some soil spectral indices.

A multi-criteria inference approach for anti-desertification management.

We propose an approach for classifying land zones into categories indicating their resilience against desertification. Environmental management support is provided by a multi-criteria inference method that derives a set of value functions compatible with the given classification examples, and applies them to define, for the rest of the zones, their possible classes. In addition, a representative value function is inferred to explain the relative importance of the criteria to the stakeholders. We use the approach for classifying 28 administrative regions of the Khorasan Razavi province in Iran into three equilibrium classes: collapsed, transition, and sustainable zones. The model is parameterized with enhanced vegetation index measurements from 2005 to 2012, and 7 other natural and anthropogenic indicators for the status of the region in 2012. Results indicate that grazing density and land use changes are the main anthropogenic factors affecting desertification in Khorasan Razavi. The inference procedure suggests that the classification model is underdetermined in terms of attributes, but the approach itself is promising for supporting the management of anti-desertification efforts.

Bioaugmentation and bioaugmentation-assisted phytoremediation of heavy metal contaminated soil by a synergistic effect of cyanobacteria inoculation, biochar, and purslane (Portulaca oleracea L.).

In recent decades, soil contamination with heavy metals has become an environmental crisis due to their long-term stability and adverse biological effects. Therefore, bioremediation is an eco-friendly technology to remediate contaminated soil, which the efficiency requires further research. This study was designed to comparatively investigate two strategies: bioaugmentation by using a cyanobacterial species (Oscillatoria sp.) and bioaugmentation-assisted phytoremediation by using Oscillatoria sp. and purslane (Portulaca oleracea L.) for the bioremediation of soil contaminated by heavy metals (Cr (III), Cr (VI), Fe, Al, and Zn). Various quantities of biochar (0.5, 2, and 5% (w/w)) were used as an amendment in the experiments to facilitate the remediation process. The results of the bioaugmentation test showed that applying biochar and cyanobacteria into contaminated soil significantly increased the chlorophyll a, nitrogen, and organic carbon contents. In contrast, the extractable fractions of Cr (III), Cr (VI), Zn, Al, and Fe declined compared with those of the control treatment. The highest reduction content (up to 87 %) in the extractable portion was obtained for Cr (VI). The development of longer root and hypocotyl lengths and vigour index from lettuces and radish seeds grown in the remediated soil confirmed the success of remediation treatments. Moreover, the findings of the bioaugmentation-assisted phytoremediation test displayed a reduction in the bioavailable fraction of Cr (III), Cr (VI), Zn, Al, and Fe. Cr (III) presented the highest reduction (up to 90 %) in metal bioavailability. With cyanobacteria inoculation and biochar addition, the shoot and root lengths of purslane grew 4.6 and 3-fold while the heavy metal accumulation decreased significantly. Besides, these treatments enhanced the tolerance index (TI) quantities of purslane whereas diminished its bioaccumulation coefficient (BAC) and bioconcentration factor (BCF) values. For all heavy metals (except Zn), translocation factor (TF) and BAC values were found to be less than 1.0 at all treatments, indicating the successful phytoextraction by the purslane. These results suggest that the purslane can be considered an excellent phytoextracting agent for soils contaminated with heavy metals.