نشریه علوم و فنون نقشه برداری
سال سیزدهم شماره 3 (پیاپی 51، زمستان 1402)

Mangrove forests are known as important sea carbon ecosystems because they play an important role in carbon sequestration among coastal ecosystems. This coastal ecosystem has 10 to 50 times more carbon sequestration capacity compared to terrestrial ecosystems, and among the most productive systems, they can effectively reduce climate change. Therefore, an accurate estimation of the biomass of mangrove forests is a necessity. Meanwhile, the evaluation of the terrestrial carbon storage in mangrove forests relies on the accurate measurement of tree biomass, which is traditionally time-consuming and expensive. In this study, height and crown diameter was estimated by using UAV equipped with an RGB sensor; following sampling and measuring soil carbon in three forest sites of Sirik, Qeshm, and Khamir, the carbon storage in trees and soil was investigated. Orthophoto mosaic and dense point cloud were created based on structure from motion algorithm. Crown diameters were extracted from orthophotos. The canopy height model was extracted by subtracting the digital surface model and digital terrain model which were derived from point cloud. Tree heights were extracted from the canopy height model following imaging in November 2021. Considering that there was no significant difference between the measured variables on the ground and the extracted variables from the UAV images, the data obtained from the UAV images and allometric equations were used to estimate the aboveground carbon storage.  After estimating the biomass according to the two variables of crown diameter and tree height, the carbon storage on land obtained from the information extracted from UAV images in the three sites of Sirik, Khamir, and Qeshm was obtained at 11.63, 7.97, and 9.87 t/ha respectively. The soil carbon was also measured at two depths of 0 to 15 cm and 15 to 30 cm using the Walkley-Black method, and the values were shown as 67.98, 81.9, 85 t/ha, and 187.2, 133.53, and 113.7 for Sirik, Khamir, and Qeshm sites. This research shows that UAV data has a high ability to estimate the variables related to individual trees in forest areas with difficult traffic conditions, and subsequently to estimate the height and crown diameter variables, estimate the forest stock and carbon storage based on the mentioned variables. It can be achieved in relatively homogeneous mangrove forests. Especially because these ecosystems are environments that are often inaccessible or difficult to work in.

The LS-SVR model uses simple linear equations in the training phase. As a result, the complexity of the computational algorithm is reduced; the speed of convergence and the accuracy of the results are increased. Seven parameters of longitude and latitude of GPS station, day of year (DOY), time to universal time (UT), relative humidity (RH), temperature (T) and pressure (P) are considered as inputs of LS-SVR model. And the PWV corresponding to these seven parameters is the output of the model. After the training step, the PWV value was estimated with the trained model and compared with the PWV values obtained from the radiosonde station, the empirical model of Saastamoinen and GPT3, the support vector regression model (SVR) and the radial basis neural network model (RBNN) in the control stations. Statistical indices of relative error, correlation coefficient and root mean square error (RMSE) have been used to evaluate the accuracy of the models. The conducted analyzes show that the average RMSE of RBNN, SVR, LS-SVR, GPT3 and Saastamoinen models in 3 control stations is to 4.92, 4.13, 2.87, 4.22 and 4.29 mm, respectively. Also, the average relative error of the models in 3 control stations is calculated as 38.06, 30.77, 22.37, 34.63 and 32.80% respectively. Analysis of the PPP method shows an improvement of 33 mm in the coordinate components using the LS-SVR model. The results of this thesis show that the LS-SVR model can be considered as an alternative to the empirical troposphere models in the studied area. The LS-SVR model is a local troposphere model with high accuracy.

Category theory is a branch of mathematics that is used to abstractly describe structures in mathematics. Unlike set theory, category theory focuses on relationships between objects, not on objects. Solving complexity in many engineering fields has become possible by structuring these problems on mathematical frameworks. Therefore, many researches have been conducted in the field of building complex problems on various branches of mathematics. In Geospatial information systems, mathematical sciences including set theory, graph theory and topology have been used, and category theory has been proposed as a new branch of mathematics to solve spatial problems. The purpose of this research is to investigate the opportunities and challenges that the category theory has created for the Geospatial information system in order to provide researchers with a more open view to use this framework in structuring spatial problems.

The construction of numerous piezometric wells, their annual maintenance and monitoring have always faced limitations due to financial, time, and technical problems. Determining the relative priority of the existing wells and thus reducing the dimensions of the underground water monitoring network has a direct effect on the financial management of the monitoring network. The main goal of this research is to use the auxiliary information available in the underground water quality monitoring network to reduce the number of piezometric wells in the underground water level monitoring network of Tabriz Plain. In this study, with a new approach, the cokriging method is used to consider auxiliary information in the jackknife method. According to the jackknife sampling theory, if the values of the wells can be estimated with neighboring and auxiliary data, that well has low information importance. To implement the research, first, the correlation coefficient between the groundwater level and the data of the quality monitoring network was calculated, and then the Jackknife and Cokriging theory was implemented to determine the relative value of the wells. Finally, the results were compared with the inverse distance weighting method (IDW) and universal kriging. In this research, groundwater chlorine (CL) values were used in the proposed approach due to the correlation coefficient of -0.61. The results showed that although cokriging with IDW method and universal kriging in jackknife theory have a similarity of more than 65%, changing the interpolation method leads to changing the value of the wells. The proposed approach based on cokriging provided better performance due to the lower RMSE value in the new surface interpolation and increased estimation accuracy in the removed wells. Based on this, 22% of the wells of Tabriz Plain can be removed from the sampling cycle. Therefore, the use of auxiliary information while increasing the accuracy of underground water level interpolation leads to a reduction in the number of samplings and economic savings.

Identifying the feature as a specific target, whether static or moving, is one of the main challenges in image processing and can be used in fields such as monitoring, determining the position of strategic features, co-referencing images, producing 3D models and etc. Due to the fact that satellite images can cover a large area with less time and cost, therefore, target detection on the satellite images is more important and useful. In the research, the aim is using the ability of radar images to identify geometric structures and the ability of multispectral images to identify the nature of features, to reduce the problems in this field. Some of the problems include: the presence of futures with a weak geometric pattern, the geometric or spectral similarity of features with each other, the presence of multiple candidates and the occurrence of problems to achieve a single target and the fading of the target due to high noise. In the algorithm designed and implemented in the research, step by step by analyzing the information extracted from the images, the target identification process is improved and the error is reduced until finally, by integrating the results of radar and spectral image processing, the number of candidates reaches to an acceptable level. The implementation of the proposed algorithm showed that in the integration process, the selection of target and specification of study area have a significant impact on the results. Also, there are many challenges in the target detection process. Among these challenges is the selection of the best, most useful image data and features to identify the target and also determine the optimal thresholds. Also, in the process of target detection with radar images, it was found that both the geometric features and the height information extracted from the images have valuable contents that should be used together to achieve the desired result.

The acquisition of knowledge and the continued development of cosmic ray detection technology has led to new connections between academic disciplines such as physics, geology, agriculture, mining and archaeology. Because of the interaction of these beams with the Earth's atmosphere, a massive flux of secondary particles is produced and collided with the Earth. As one of these energetic charged particles, the muons are capable of penetrating large surface and deep earth structures, which provides a new tool in surface/subsuface mapping. Due to the density dependent interaction of these charged particles with matter, it is possible to image various surface structures such as volcanoes, antiquities and civil structures or reservoirs, cavities, radioactive wastes and subsurface valuable mineral veins. The aim of this paper is to review the recent years of research on this new technique called muography and it was tried here in a new structure to use this technique in all three fields of mapping, surveying and remote sensing to extract information about large surface/subsurface volumes.

The ability to interpret the image is one of the key and important features in the ability of imaging systems, which is usually used in the concept of the ability to extract information from the image. Quantifying this feature is one of the main challenges in the fields of information content and image integration in remote sensing, which is needed and important in various applications such as target detection. By evaluating the interpretability of remote sensing images, it is possible to estimate the appropriate image characteristics for various target detection applications.
In general, the interpretability of remote sensing images depends on the conditions of imaging, environment and target. Polarimetric radar images are a very useful source of data in target detection applications with more resolution than single-channel radar images. So far, various methods and models have been presented to evaluate the performance of detection in polarimetric radar images; most of them are based on the theory of signal detection or based on the probability of target detection and false alarm. National Image Interpretability Rating Scale (NIIRS) models, the automatic target detection (ATD) and simultaneous tracking and tracking and recognition (STAR) models are among the common and conventional methods of evaluating the interpretability of radar images in target detection applications. The NIIRS radar is based on the general image quality equation (GIQE) and takes into account the parameters of resolution, landing angle, squint and background type. The ATD model is designed based on false alarm analysis and estimates the measure of performance (MoP) according to available ground information. The STAR model is also based on the modelling of parameters related to the environment, target and sensor and calculates the probability of success in detecting, detecting and identifying the target in the radar image for a target with specific dimensions and physical conditions.
Proposed

In this study, a unified method has been presented to evaluate the interpretability of polarimetric radar images in the application of target detection. In this method, the performance of the radar detector is first calculated based on the parameters of the detector in the STAR model. After that, for each of the images related to the channels and polarimetric parameters, the target detection performance is calculated by calculating the performance measure (MoP), which relates the detection probability to the false alarm rate is determined. Finally, by combining the information obtained from the MoP criterion and the STAR model, the interpretability of polarimetric radar images is calculated.
Experimental

In this study, the L-band polarimetric radar dataset related to the Flevoland region of the Netherlands, which was obtained by the AIRSAR sensor in 2009, is used to evaluate the effectiveness of the proposed method. Based on the obtained results, the proposed method increases the probability of detecting, detecting and identifying the target by 30% compared to the STAR model.
For the development of this research, it is suggested to improve the performance of the radar sensor by using the techniques of combining radar data with optical data and auxiliary data. This case, especially in tracking targets in complex environments, can significantly improve continuous target tracking.

The purpose of this study is to obtain  gravity anomalies from the data of altimeter satellites and to examine and compare them annually in the regions of the ocean where tsunamis have occurred.For this purpose, by analyzing the data of Jason 1,2, Envisat, Cryosat satellites from 2003  to 2014, the mean sea level was calculated, Then, from the difference of this level of mean dynamic topography MDT calculated from altimeter satellite data, geoid height was obtained.To obtain the gravitational anomaly, the remove-restore technique was used, in which the monthly geopotential models of Grace satellites, the size of the geoid height and the gravitational anomaly were extracted, and then the geoid residue was derived from the geoid difference between altimetry and geopotential model.In the next step, using the inverse solution of the Stokes integral by the fast Fourier technique, we obtain the residue of gravity anomalies.By comparing the annual changes of gravity anomalies in the tsunami area, it was observed that the changes of gravity anomalies in the desired area in the year of the tsunami have a jump compared to previous years.