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Iran is located in a dry climate zone, and climate change has substantially reduced its precipitation and water resources. Reusing wastewaters from urban communities can meet some requirements for irrigation and fertilization of tree plantations in arid environments, leading to sustainable wastewater recycling, enhanced biomass production, and reduced land degradation. The objective of this study was to test the growth, biomass, nutrition, and heavy-metal accumulation of poplars [Populus nigra L. "62/154," P. alba L. "20/45," P. euramericana (Dode) Guinier "92/40"], and willow (Salix excelsa S.G. Gmel) in a pot experiment at four and eight months after planting when grown in soils irrigated with tap water (SITW) and wastewater (SIWW). After four months, SIWW treatment had no significant effect on growth, biomass, nor absorption of macronutrients. After eight months, SIWW treatment of poplars and willow significantly (p = 0.000) increased: (1) height, (2) leaf area, (3) root, stem, leaf, and total biomass, and (5) phytoextraction and phytoaccumulation of macro-/micro-nutrients and heavy metals in tree tissues, over trees receiving the SITW treatment. There were significant differences in growth, biomass, and accumulation of micronutrients and heavy metals in poplar versus willow tissues, with the highest biomass production and tissue-specific content of heavy metals in P. nigra trees, and the greatest total concentrations of heavy metals in P. alba and S. excelsa trees. In contrast, uptake of Fe, Cu, Ni, Cr and Pb were similar between poplar and willow, and phytoaccumulation of these elements was primarily in the roots. Leaf concentrations were highest for Zn and Mn. While P. nigra outperformed all other species overall, tolerance index (TI; defined as the tolerance to the heavy metals as calculated by the ratio of the biomass of SIWW trees relative to SITW trees) values exceeding 100% for all one-year-old poplar and willow trees demonstrated that they can be considered for planting in soil affected by urban wastewaters with similar contaminant profiles as in the current study. Since the species would differ in their growth, biomass, and phytoremediation responses to the nutrient and heavy metal concentrations of the wastewater over time, this research is important for the development of silvicultural prescriptions of these fast-growing trees that support effective wastewater reuse strategies throughout heterogeneous landscapes and across variable human community resources and needs. [ABSTRACT FROM AUTHOR]

Background: Traditional field-based methods of measurement of biomass and carbon storage face difficulty in collecting time-consuming and expensive, suggests the use of remote sensing-based techniques. It estimates the economic value of the aboveground biomass (AGB) using satellite remote sensing across the Hyrcanian forests of Iran. Methods: The Landsat-8 OLI sensor data were combined with field-based allometric information of 186 circular sample plots. The AGB was calculated at the plot level using the collected data and specific volumetric mass for species in the studied area. It was followed by calculating the carbon storage using a 50% carbon coefficient and the photosynthesis equation at the forest parcel level. Model results using the random forest and support vector machines. The carbon sequestration value was calculated with USD 25.3 as a shadow value of carbon in 2014 and using the replacement cost approach. Results: The highest performances achieved by RF for biomass, carbon storage and the carbon storage value (Iranian Rials of 0.67% and 16%, respectively). The value was derived once at the plot level of 12.22 million IRR (370.43 USD) per ha. In addition, at the parcel level, which resulted in an estimated value of 12.87 million IRR (390.24 USD) per ha. [ABSTRACT FROM AUTHOR]

Satellite remote sensing has greatly facilitated the assessment of aboveground biomass in rangelands. Soil-adjusted vegetation indices have been developed to provide better predictions of aboveground biomass, especially for dryland regions. Semi-arid rangelands often complicate a remote sensing based assessment of aboveground biomass due to bright reflecting soils combined with sparse vegetation cover. We aim at evaluating whether soil-adjusted vegetation indices perform better than standard, i.e. unadjusted, vegetation indices in predicting dry aboveground biomass of a saline and semi-arid rangeland in NE-Iran. 672 biomass plots of 2 × 2 m were gathered and aggregated into 13 sites. Generalized Linear Regression Models (GLM) were compared for six different vegetation indices, three standard and three soil-adjusted vegetation indices. Vegetation indices were calculated from the MODIS MCD43A4 product. Model comparison was done using Akaike Information Criterion (AICc), Akaike weights and pseudo R2. Model fits for dry biomass showed that transformed NDVI and NDVI fitted best with R2 = 0.47 and R2 = 0.33, respectively. The optimized soil-adjusted vegetation index (OSAVI) behaved similar to NDVI but less precise. The soil-adjusted vegetation index (SAVI), the modified soil-adjusted vegetation index (MSAVI2) and the enhanced vegetation index (EVI) performed worse than a null model. Hence, soil-adjusted indices based on the soil-line concept performed worse than a simple square root transformation of the NDVI. However, more studies that compare MODIS based vegetation indices for rangeland biomass estimation are required to support our findings. We suggest applying a similar model comparison approach as performed in this study instead of relying on single vegetation indices in order to find optimal relationships with aboveground biomass estimation in rangelands. [ABSTRACT FROM AUTHOR]