Background: Nowadays, aquatic organisms are used as bio-indicators to assess ecological water quality in western regions, but have hardly been used in an Iranian context. We, therefore, evaluated the suitability of several indices to assess the water quality for an Iranian case study.Methods: Measured data on biotic (fish and macroinvertebrates) and abiotic elements (28 physicochemical and habitat parameters), were used to calculate six indices for assessment of water quality and the impact of human activities in the Tajan river, Iran. GIS, uni- and multivariate statistics were used to assess the correlations between biological and environmental endpoints.Results: The results showed that ecological condition and water quality were reduced from up- to downstream. The reduced water quality was revealed by the biotic indices better than the abiotic ones which were linked to a variety of ecological water quality scales.Conclusion: The fish index showed a strong relationship with long-term database of physicochemical parameters (12 years (94%)), whereas macroinvertebrates index is more correlated with short-term data (76%). Meanwhile, the biotic and abiotic elements in this study were also classified well by PCA. Pulp and wood plants and sand mining are indicated to have the most negative effects on the river ecosystem.

Remote sensing of low biomass forests has challenges related to the contribution of soil and understory reflectance recorded by sensors, hampering accurate forest aboveground carbon (AGC) quantification. To improve Landsat-based AGC estimates in forests with low biomass, this study explored the use of multi-temporal Landsat 8 Operational Land Imager (OLI) derived spectral information in Zagros forests by testing four machine learning algorithms: support vector machine (SVM), boosted regression trees (BRT), random forest (RF) and multivariate adaptive regression splines (MARS). We selected two forest areas with different levels of human activity for AGC reference plots: un-degraded forest (UD) and highly-degraded forest (HD). The results of the study showed that the Landsat image acquired in the peak of the growing season (10 August) provided the best AGC estimates for the UD site, but that for the HD site, AGC estimates were not affected by the timing of the imagery. The comparison of different modelling methods demonstrated lower accuracies from BRT, considerably biased estimates from SVM, and generally robust results from the RF algorithm. Overall, the study demonstrated the utility of applying the free Landsat 8 OLI dataset to AGC estimation, in particular non-commercial forests in developing countries where little budget is allocated for management.

The objectives of this study were to assess the biological water of the Iranian Tajan River using different metrics, i.e., a Multimetric Macroinvertebrate Index (MMI) and a traits-based method. Twenty-eight physico-chemical parameters, 10 habitat factors, and abundance of macroinvertebrates were obtained for 17 sites. The Shahid-Rajaie dam divides the Tajan River into an up- and downstream part, with different land uses. Eighteen metrics were used to represent four components of ecosystem quality, including tolerance (Hilsenhoff, SIGNAL), diversity (Margalef, Shannon–Wiener, Simpson, and Evenness), abundance (total number of taxa, individuals, Ephemeroptera, Plecoptera, Trichoptera, EPT, and Insects), and composition of assemblages (% Ephemeroptera, % Plecoptera, % Trichoptera, and % EPT Taxa). The integrated MMI was calculated by averaging the obtained scores of all indices. In the next step, we gathered information on 22 biological traits of macroinvertebrates to evaluate whether (group of) traits could be identified that are indicative for specific or general stress. Result showed a decrease in MMI from upstream (very good water quality) to downstream (bad) due to human activities. Industrial activities like pulping and papermaking operations or sand mining in the downstream part had more effects than agriculture and fish ponds in the upstream part. A redundancy analysis biplot showed the variation between the modalities of trait of macroinvertebrates and their correlation with physico-chemical parameters in Tajan River. The findings show that traits can be indicative for different kind of stress but that more effort has to be put in gathering data sets to disentangle the effect of habitat quality, pollution, and the physico-chemical properties of high- versus lowland rivers.

In view of the important role played by roots against shallow landslides, root tensile force was evaluated for two widespread temperate tree species within the Caspian Hyrcanian Ecoregion, i.e., Fagus orientalis L. and Carpinus betulus L. Fine roots (0.02 to 7.99 mm) were collected from five trees of each species at three different elevations (400, 950, and 1350 m a.s.l.), across three diameter at breast height (DBH) classes (small = 7.5–32.5 cm, medium = 32.6–57.5 cm, and large =57.6–82.5 cm), and at two slope positions relative to the tree stem (up- and down-slope). In the laboratory, maximum tensile force (N) required to break the root was determined for 2016 roots (56 roots per each of two species x three sites x three DBH classes x two slope positions). ANCOVA was used to test the effects of slope position, DBH, and study site on root tensile force. To obtain the power-law regression coefficients, a nonlinear least square method was used. We found that: 1) root tensile force strongly depends on root size, 2) F. orientalis roots are stronger than C. betulus ones in the large DBH class, although they are weaker in the medium and small DBH classes, 3) root mechanical resistance is higher upslope than downslope, 4) roots of the trees with larger DBH were the most resistant roots in tension in compare with roots of the medium or small DBH classes, and 5) the root tensile force for both species is notably different from one site to another site. Overall, our findings provide a fundamental contribution to the quantification of the protective effects of forests in the temperate region.

The use of freely accessible Sentinel-1 synthetic aperture radar (S-1 SAR) and Sentinel-2 multispectral instrument (S-2 MSI) data are currently a feasible way of mapping forest aboveground biomass (AGB) over large areas. Despite the extensive mapping of forest AGB by remote sensing, how to effectively combine different sensors data, selecting the proper statistical modelling method, and variable screening are still poorly understood. This paper presents a framework for Sentinel-based AGB estimation through the use of four variable screening techniques, namely, genetic algorithm (GA), least absolute shrinkage and selection operator (LASSO), boruta, and removal-based; and three statistical modelling methods including multiple linear regression (MLR), artificial neural network (ANN), and random forest (RF). These methods were examined across two forested sites with different levels of human activity (highly-degraded: HD and minorly-degraded: MD) in Zagros coppice oak forests. We used backscatter coefficients and texture metrics of S-1 SAR, in addition to raw bands, simple band ratios, vegetation indices and texture metrics of S-2 MSI to extract 53 explanatory variables. Our findings showed that GA and removal-based variable screening techniques outperformed the others and RF and MLR statistical modelling methods obtained the best results. The use of different variable screening techniques and modelling methods demonstrated higher accuracies of AGB estimates for the MD site compared to the HD site. S-2 MSI (Coefficient of determination (R ²) = 0.76 and Root Mean Square Error (RMSE) = 35.08% of the mean) provided a higher accuracy than S-1 SAR (R ² = 0.42 and RMSE = 52.94% of the mean). Overall, the study demonstrates encouraging results in the retrieval and predictive mapping of the AGB of Zagros forests using the freely accessible Sentinel imagery.