Extended Abstract Introduction Monitoring and assessment of the biosphere are two essential tasks at any scale. Based on this, forests play an important role in controlling the climate and the global carbon cycle. For this reason, biomass and consequently forest height are known as the key information for forest monitoring. In the recent decade, several studies have shown that the Synthetic Aperture RADAR (SAR) imaging systems in Compact Polarimetry (CP) mode can overcome the disadvantages of Full Polarimetric (FP) SAR imaging systems and provide a good performance in various remote sensing applications such as monitoring and managing the important natural resources like forests. In this regard, a novel technique named Polarimetric Interferometry SAR (PolInSAR) has been further considered as a powerful tool for forest height estimation. Materials & Methods In this research, the performance of the Compact PolInSAR (C-PolInSAR) data in Dual Circular Polarization (DCP) mode has been investigated in order to retrieve the forest height. For this reason, the common methods which are used for forest height estimation including Digital Elevation Model (DEM) differential method, coherence amplitude inversion, and phase & coherence inversion methods were applied and implemented on these data. In all of the aforementioned methods, LL+RR and LR polarizations were considered as the selected channels for estimating the volumetric and ground coherences, respectively. Then, the estimated coherences were considered as the input parameters for all of the mentioned methods. Results & Discussion To evaluate the performance and the efficiency of C-PolInSAR data in DCP mode, the results obtained from these data were compared with those obtained from Full PolInSAR (F-PolInSAR) data. The results obtained in this study in two datasets simulated from PolSARProSim software in both L and P bands showed that the C-PolInSAR data in DCP mode yielded a similar result compared to the F-PolInSAR data for forest height estimation (when the HH􀀍 VV polarization is adopted as the ground backscattering), because, in this case the LL+RR and the LR polarizations are equal to the HV and the HH􀀍 VV polarizations, respectively, particularly, the C-PolInSAR data in DCP mode yielded 􀀗 􀀖 ، پاییز 􀀏 􀀏 􀀏 ، شماره 􀀐 􀀖 دوره 􀀆 􀀇 لنامه علمی-پژوهشی اطلاعات جغرافیایی 􀂐 ف 12 / Scientific-Research Quarterly of Geographical Data (SEPEHR) Vo. 28, No. 111, Autumn 2019 0. 78 m and 0. 55 m improvements for forest height estimation in L and P bands, respectively. In addition, all of the employed methods provided better and closer results compared to the real forest height (i. e. 18 m) in L band compared to P band, because the electromagnetic (EM) waves have a more penetration into the canopy in L band compared to P band. Thus, the attenuation of these waves is low and consequently the height estimation is more accurate. Without considering the used bands, the DEM method provided the lowest precision compared to other methods, because the HV (or LL􀀍 RR) phase center can lie anywhere between half the tree height and top of the canopy. The exact location of this phase depends on two vegetation parameters which are the wave mean attenuation and the vertical canopy structure variations. In this case, the trees have very thin canopies, and consequently, the attenuation is small, but the phase center is high due to the structure. In other words, when the canopy extends over the entire forest height, then the phase center can be at half the true height for low density (low attenuation), through to the top of the canopy for dense vegetation (high attenuation). This ambiguity is inherent in single baseline methods, and in order to overcome this, model-based correction methods need to be employed. It was also observed that the coherence amplitude method is among the weak algorithms due to ignoring the phase and its sensitivity to the attenuation and structural variations but it can be used as a backup solution when other approaches fail. Finally, the phase and the coherence inversion method had better results than two aforementioned methods for the forest height estimation. In this method, selecting the factor 􀅎 􀀀 􀅏 is very important and it should be selected in a way to be strong towards the attenuation changes. In this study, 􀀀 􀀟 0. 4 was adopted to maintain the height error variations. Conclusion As the final result, the C-PolInSAR data can be an efficient strategy due to its performance, when the full polarimetric imaging systems are either limited or not available. Moreover, utilizing these data in long wavelengths (e. g. P band) is more appropriate due to the effect of the Faraday rotation on the transmitted polarization.

This paper addresses an algorithm for forest height estimation using single frequency and single baseline dual polarization radar interferometry data. The proposed method is based on a physical two layer volume over ground model and is represented by using polarimetric synthetic aperture radar interferometry (PolInSAR) technique. The presented algorithm provides the opportunity to take advantages of the dual polarimetric data, i. e, better spatial resolution and wider swath width, in comparison with the full polarimetric data, in forest height estimation application. In this research, a polarimetric optimization method is utilized to choose the optimum volume polarization basis in order to improve the results of the three-stage inversion algorithm. For the performance analysis of the proposed approach, the L-band ESAR data of the European Space Agency from BioSAR 2007 campaign (ESA) which is acquired over the Remningstorp test site in southern Sweden, is employed. The experimental result shows the dual PolInSAR HH/HV data capability in the forest height estimation without decreasing the accuracy of the result compared with the full polarimetric data. The suggested method leads to the average root mean square error (RMSE) of 4. 39 m and the determination of coefficient of 0. 66 in the forest height estimation in 15 predetermined stands in comparison with the LiDAR reference heights.

Biomass estimation plays an important role in the investigation of climate changes and global warming on terrestrial ecosystems. In recent years, related researches show PolInSAR techniques can significantly improve biomass estimation. Tree height can be estimated using PolInSAR techniques which by using that, the tree’s biomass can also be estimated. It is known that coherence optimization has an effective role on improvement of tree height estimation using PolInSAR. In this paper, various tree height estimation methods, such as coherence amplitude inversion algorithms, DEM differentiating, and combined methods are validated and compared using simulated data. Coherence optimization methods which are applied in these algorithms are numerical radius and phase diversity coherence optimization algorithms were estimated respectively. According to the fact that phase diversity algorithm was a phase based method, it didn’t have significant effect on improvement of tree height using coherence amplitude algorithm. However, improvement of tree height estimation by DEM differentiating method is obvious. In comparison to the previous method, although numerical radius method is time consuming and has a complicated process but it improves tree height estimation in great deals.

Background and Objectives Psychological flexibility is the main goal of acceptance and commitment treatment. However, an efficient tool is needed to measure it. Thus, the aim of this study was to investigate the psychometric properties of the Persian version of the Comprehensive assessment of Acceptance and Commitment Therapy processes (Compact) Scale. Methods The statistical population of the present study included all people aged 18 to 60 years in the period from August to October 1400. The statistical sample of the study included 256 people who were selected by available sampling method through online call. In order to evaluate the convergent and divergent validity, the second version of the Acceptance and Practice Questionnaire (AAQ-II), the difficulty in emotion regulation scale (DERS-16) and the Depression, Anxiety and Stress Scale were used. The factor structure of the questionnaire was examined through confirmatory factor analysis. Reliability was performed using Cronbach’, s alpha method and retesting and analyzing the data with version 25 of SPSS software and version 8. 8 of LISREL. Results The results of confirmatory factor analysis, as in the original form, confirmed three factors: Openness to Experience, Behavioral Awareness and Valued Action. Also, Cronbach’, s alpha coefficient for the whole scale is 0. 89 and for the subscales of Openness to Experience, Behavioral Awareness and Valued Action were equal to 0. 91, 0. 88, 0. 85, respectively. In addition, the four-week retest coefficient for the whole questionnaire was 0. 80 and for the subscales of Openness to Experience, Behavioral Awareness and Valued Action were equal to 0. 89, 0. 78, 0. 79, respectively. Also, correlation coefficients indicated favorable convergent and discriminant validity of the questionnaire (P<0/001). Conclusion According to the obtained results, it can be said that the Comprehensive assessment of Acceptance and Commitment Therapy processes (Compact) Scale is a reliable and valid scale for measuring psychological flexibility.

Forest biomass is one of the most important parameters in the ecosystem changes assessment and global carbon cycle modelling. In the other hand, the forest height is an effective parameter in the allometric equations which are used for biomass estimation. In this research, the effect of two physical factors forest height and forest density, will be evaluated in the applicability of the four common inversion algorithms for forest height estimation based on the polarimetric Interferometry SAR (PolInSAR) technique. The applicability of the digital elevation model (DEM) differencing, volume coherence amplitude, hybrid and three-stage methods are studied for different forest height and forest density by using simulated polarimetric interferometric SAR data in L-band. The experimental results of the forest height estimation in simulated data with a density of 100 to 900 trees per hectare and a height of 10 to 18 meters show that the results of the hybrid method show high sensivity to changes in both height and density. The root mean square of error was 5. 8, 5. 6, 3. 2 and 4 m for data with variable height and 11. 6, 6. 7, 5. 8 and 5. 3 m for data with different densities, respectively.

The aim of this paper is to introduce and give preliminary investigation of ∂-partial-locally Compact spaces. Locally Compactness and ∂-partial-locally Compactness are independent of each other. Every locally Compact Hausdorff space is ∂-partial-locally Compact. But the converse is not true even though it be Hausdorff. ∂-partial-locally Compactness is a topological property. ∂-partial-locally Compactness is not preserved by the product topology.

This paper is devoted to assessing the accuracy of the Compact and the super-Compact finite difference methods for spatial differencing of the linearized shallow water equations. The second-order centered, the fourth-order centered, the fourth-order Compact, the sixth-order Compact and the sixth-order super-Compact schemes are used to carry out the spatial differencing of the linearized shallow water equations on Arakawa's A and C grids. For the frequency and group velocity of linear inertia gravity waves on different numerical methods, the sixth-order super-Compact method shows a substantial improvement on traditional centered and Compact finite difference schemes.