Monitoring the status of natural and ecological resources is necessary for conservation and protection. Soil is one of the most important environmental resources in agricultural lands and natural resources. In this research study, we used Landsat 8 and Artificial Neural Network (ANN) to monitor soil salinity in Qom plain. The geographical location of 72 surface soil samples from 7 land types was determined by the Latin hypercube method, and the samples were taken to determine the electrical conductivity (EC). Thirty percent of the data was considered as a validation set and 70% as a test set. In addition to the Landsat 8 bands, we used spectral indices of salinity, vegetation, topography, and drainage (DEM, TWI, and TCI) because of their impacts on soil formation and development. We used ANN with different algorithms to model soil salinity. We found that the GFF algorithm is the best for soil salinity modeling. Also, the TWI topography index and SI5 salinity index and NDVI vegetation index had the most effect on the outputs of the selected model. It was also found that flood plains and lowlands had the highest levels of salinity accumulation.

Minimizing the impact of timber harvesting on forest stands and soils is one of the main goals of sustainable forest operation (SFO). Thus, it is necessary to make an accurate assessment of forest operations on soil that is based on the SFO perspective. The present study was conducted according to SFO principles to investigate the time required for the natural recovery of soil after disturbance by skidding operations in some Iranian forests. The physical, chemical, and biological properties of soil found in abandoned skid trails from different time periods were compared with undisturbed forest soils. The soil bulk density, the penetration resistance, and the microporosity of a 25-year-old skid trail were 8.4–27.4% and 50.4% greater, and the total porosity, macroporosity, and soil moisture were 1.9–17.1% and 4.6% lower than the undisturbed area. In a 25-year-old skid trail, the values of pH, Electrical conductivity (EC), C, N, available P, K, Ca, and Mg, earthworm density, and biomass were lower than in the undisturbed area, and the C/N ratio value was higher than in the undisturbed area. High traffic intensity and slope classes of 20–30% in a three-year-old skid trail had the greatest impact on soil properties. In order to have sustainable timber production, SFO should be developed and soil recovery time should be reduced through post-harvest management operation.

This study aims to evaluate the enhancement of phytoextraction of heavy metals (Pb, Cd, and Zn) by speciesMarrubium cuneatum,Stipa arabica, andVerbascum speciosum, through EDTA amendment. Assisted phytoextraction pot experiments were performed at different EDTA dosages (0, 1, 3, and 5 mmol kg⁻¹soil). The DTPA-extractable metal content increased in the presence of EDTA, followed by their contents in the tissues of all three studied species. Resulting from oxidative stress, the activity of antioxidant enzymes such as glutathione peroxidase (GPX), superoxide dismutase (SOD), and catalase (CAT) increased when the chelating agent was added. EDTA in higher doses partially decreased chlorophyll concentration, and 5 mmol kg⁻¹of that reduced the biomass of the studied species. The bioconcentration factor (BCF) for Cd was notably high in all studied plants and considerably elevated for Zn and Pb with the addition of EDTA inM. cuneatumandS. arabica(BCF > 1), whilst an accumulation factor greater than one (AF > 1) was found for Cd in all species and for Pb in the case ofS. arabica. In general, the results demonstrated that EDTA can be an effective amendment for phytoextraction of Cd, Zn, and Pb byM. cuneatum,V. speciosumandS. arabicain contaminated soils.