نشریه علوم و فنون نقشه برداری
سال دوازدهم شماره 3 (پیاپی 47، زمستان 1401)

One of the serious challenges in remote sensing is extracting suitable features. With the presence of a new generation of deep neural networks, automatic and accurate feature extraction and classification of crops have become possible. On the other hand, appropriate features can partially reduce the effects of spectral similarity in the detection of different crops while improving classification accuracy. Also, the use of time-series data during the crop growth period provides useful information about crops to researchers. In this regard, this research aimed to investigate and evaluate three methods, three-dimensional convolutional neural network (3D-CNN), long short-term memory (LSTM), and gated recurrent unit (GRU), to extract appropriate features from time-series optical images. In the architecture of 3D-CNN, an attempt was made to design a structure so that the optimal spatial-temporal features could be extracted from time series images, and then the results were evaluated and compared with two other methods (i.e. LSTM, GRU). Finally, according to the results, 3D-CNN, with an overall accuracy (OA) of 90.70% and a kappa coefficient (KC) of 89.37%, which were about 3.50% and 4.00% higher than the OA and KC of LSTM, respectively, demonstrated a greater capability to identify crops. Moreover, the OA of the results of the classification by GRU was close to the OA of 3D-CNN, and only the OA of this method was 1.48% better than GRU. Therefore, the results confirmed the efficiency and suitability of 3D-CNN for crop classification.

One of the most contagious diseases of recent years is Covid 19 disease (Corona), which has spread from Wuhan, China to the rest of the world since late 2019, causing many crises and a profound impact on the world and our daily lives. In most people infected with the disease, it causes respiratory symptoms, the severity of which depends on the person's immune system. The main objectives of this study are to discover the clusters and predict the high risk areas of Covid 19 disease, compare the efficiency of the two proposed methods and determine the effective parameters by city. In this study, Moran index and hot spot analysis index were used to investigate the distribution pattern of disease incidence rates and clusters, respectively, and Pearson correlation coefficient was used to determine effective disease parameters. In this study, statistical data of Covid 19 disease of East Azerbaijan province in the city along with environmental and topographic, health, economic and urban facilities data in the period from February 22, 2020 to November 20, 2020, were collected weekly. According to the results, the incidence of Covid 19 disease during this period has passed two peaks and according to the maps obtained from the two models, in some weeks the GWR model and in some weeks the MLP model was the superior model; also, for the GWR model, the goodness of fit index value is 0.8985 and the normalized root mean square error is 0.0822 and for the MLP model is 0.8226 and 0.1340, respectively, which shows that the GWR method is more appropriate. Sensitivity analysis of different parameters showed that the parameters of the Covid 19 incidence rate of the previous week and wind speed are more important than other modeled parameters in this issue. In this study, effective parameters were extracted separately for each city and a local model was presented that compared to the general state of the model, the local model had better accuracy than the general model of the MLP method.

Given the special situation of Iran in terms of tectonics, it can be claimed that all faults in the country are seismic. Every year, financial and human losses and losses occur, especially in rural areas due to earthquakes. Today, interferometry synthetic aperture radar technology with various capabilities and products in the field of phase and amplitude has become a powerful tool for monitoring movements. Satellite imagery for two-pass (ascending and descending) capture can provide different information about faults. In this paper, seismic fault parameters are estimated by solving the inverse problem with surface displacement field boundary values ​​obtained from InSAR observations. The study of active fault parameters to identify earthquakes and improve their predictability is a topic of interest for geologists. For this purpose, a pair of Sentinel-1 radar images before and after the earthquake have been studied as images of the study area. As the results show, the maximum amount of ground displacement was 19 cm up and 8 cm down. To obtain the fault geometry and slip distribution on the fault plane, these components have been inverted using the genetic algorithm optimization method and the Acada ​​elastic half-space analysis model. The inverse modeling method showed that InSAR is a useful method for estimating the amount of ground displacement due to an earthquake and also determining the parameters of the fault causing the earthquake. Also, there is 1 and 3 cm uncertainty for two observed and modeled data, respectively. This indicates a better accuracy of descending geometry.

Survey of spatial distribution of soil elements and the pattern  of  their distribution  in order to identify the characteristics and  capabilities of the land for efficiency and sustainable cultivation  is essential. Geographical information system as an efficient tool make this possibile to produce of reliable data. The purpose of  this study is to investigate the  special distribution  of soil elements in the ajabshir plain. In this regard after receiving  the results of  soil  analysis  in 136 soil samples in  the form of 10 parameters factor and  elements of plants growth by using IDW  algorithm prepared maps of soil elements distribution  in study  area. The results of  PH  as an influential  factor  show  that  the area has  alkaline  soil. On the other hand  potassium (K)  as an essential elements, especially against salinity  stress , which is increasing  due to the decrease in the level of urmia lake in the  region  has a suitable  ration in  agricultural  farms. However , the  electrical  conductivity  is relatively  uniform  in other  parts  of  the lake , except  for  the  northwestern of the area which  shows  the  highest value. This is while the electrical  conductivity  except  for  northwestern  parts  of   the range  on the lake  shore , which shows  the  highest  value . However  the electrical conductivity  is  relatively uniform in other parts of distribution , except for the  northwestern  part of the lake shore, which shows the highest value. Although  the  results  of  the 2000 to  2022 shawl  vegetation  map  extracted using the  NDVI  index  show  a decrease  of  1495.07  hectares  of  cultivated area over  to two  decades, but the amount and  distribution of  elements indicate  the quality  and  fertility of  soil  in  this  area  which  is  exposed  to  environmental  hazards  , especially  the  spread of  saline lands  and  dust  resulting from it.

Zagros forests are one of the important and strategic areas of the country. Forest inventories in areas like Zagros is expensive and time-consuming due to the difficult access and low density of trees. Laser scanning methods to prepare point clouds and produce maps provide valuable and significant information in forest management. With the release of the LiDAR short-range light detection sensor in 2020 by Apple, it became possible to use alternative scanning methods. In the present study, in order to check the accuracy of iPhone LiDAR in preparing point clouds of tree trunks, 37 Iranian oak trees were selected and specific longitudinal and transverse distances were marked on their trunks. The high accuracy of the point clouds for these distances resulted in a high correlation between the generated point clouds and the real distances. The RMSE% was obtained for longitudinal distances of 1.33 cm and for transverse distances of 2.2 cm respectively, which proves the high accuracy of the mentioned technology and its high capability in extracting quantitative components of tree trunks.

UAV-based images are now widely used to generate large-scale orthophoto mosaics. The generation of an orthophoto mosaic is divided into two stages: image registration and image stitching. Using the DSM associated with each image, orthophotos are generated throughout the image registration. The single orthophotos generated are then joined to each other step by step in the second stage, utilizing various methods of image stitching. Image stitching methods depend on the complex and challenging processes of image matching and seamline determination. In addition, the registration and stitching of images in UAV-based mapping projects with a significant number of images is a time-consuming process. In this study, a straightforward method is provided for creating large-scale true orthophoto mosaics from UAV-based images without the requirement to generate single orthophotos, image registration and seamline network determination. Instead of registrating the images and then stitching them step-by-step, this method processes the DSM of the entire area and all of the images simultaneously. First, for each DSM point, the optimal image is determined from among all the visible images based on an optimization procedure. Optimization is based on two criteria: distance from nadir point and distance from the projection center. Using the determined optimal images, the differential rectification procedure is then run, and the orthophoto mosaic cells are filled. The results of this investigation demonstrated that the proposed method yielded a mosaic with minimal changes along the seamline. In addition, the proposed method is compared with the conventional orthophoto mosaic production method, which is based on image matching and determination of seamlines. Evaluations indicate that the proposed method is able to increase the production rate of orthophoto mosaic by 39% and 45% in dataset 1 and 2 respectively. Additionally, the geometric accuracy calculated using the checkpoints in the orthophoto mosaic generated by the suggested method has decreased by an average of 2 cm, indicating more precise results.

The use of accurate and up-to-date photogrammetric products as a basis for urban planning is very important because cities are complex and dynamic physical and social systems that are constantly changing. Urban planning experts and scientists need a wide range of spatial maps and technologies to make decisions to advance its benefits. This requires providing fast and accurate mapping methods. One of the low-cost and accurate mapping method is UAV-based photogrammetry but the limitations of this method require special solutions for developing the system in urban areas. Currently, most UAV photogrammetry projects are carried out in urban areas, without considering changes in the elevation of the ground and the heights of various features, as well as occlusion points. All three of these cases have a great impact on the accuracy and quality of output products in areas with altitude features. The developed three-dimensional flight planning in this article, in addition to paying attention to the height of various features such as buildings and terrain, makes the scale as uniform as possible and prevents the UAV from hitting obstacles. The flight design also includes a new technique such as oblique or semi-oblique imaging to reduce occlusion points. The proposed method was implemented in an urban area. The results of the algorithm were evaluated based on the accuracy, and quality of point clouds and output products, the accuracy of the checkpoints and comparison with the expected standard errors, scale stability in images, density of point clouds, and reduction of occlusion points. The results show the high quality of the products and the planimetric accuracy of 3.6 cm and the altitude accuracy of 7 and 4 cm at the requested scale of 1.750. Also, the results show that oblique images have a significant effect in generating information in the connection between walls and the ground and the density of building facades in the area. The results show that the design and implementation of the UAV photogrammetric flight planning algorithm based on the topographic model and features is successful.

In urban areas, deformation of transport and road infrastructure may lead to serious safety incidents. Therefore, management and monitoring are vital to ensure the quality of constructions and prevent transportation accidents, especially in areas with land subsidence such as Qom province. Low annual rainfall statistics, successive droughts and the type of soil in the region have caused this province, especially Qom city, to be among areas prone to land subsidence. The absence of a permanent geodynamic station in Qom's urban area, as well as the costly and time-consuming leveling operation, made radar interferometric technology to be chosen as one of the best methods for monitoring land deformation. In this study, the permanent scatterer interferometric synthetic aperture radar (PS-InSAR) technique has been used for infrastructure monitoring and inspection because it allows obtaining reliable results in the detection and prevention of infrastructure instabilities during time provides. For estimating land subsidence rate in the city of Qom, 29 descending radar images of the Sentinel-1 sensor were used during the period of January 2019 to November 2020. GMTSAR2StaMPS (G2S) software was used to process radar images and time series analysis. The results showed that southeastern area of Qom city has a subsidence rate of -54.5 mm/yr along the line of sight (LOS) of the satellite. The worrisome issue is the extension of land subsidence to the central area of the city and damage to important urban infrastructures, which should be taken into account in order to prevent this problem. The investigation of regional piezometers and Qom-Kahak hydrograph shows a drop of 1.8 meters in the water level between October 2017 and October 2021, which has caused subsidence of about -7.5 cm per year in the vertical direction in the southeast of Qom. Also, due to the good agreement of the results of radar interferometry and the drop of the underground level in the region, the excessive exploitation of underground water resources for agricultural purposes can be considered as the main reason for the subsidence in this area.

In this paper, the performance of 8 change indices including Euclidean Distance (ED), Spectral Angle Mapper (SAM), Spectral Correlation Mapper (SCM), image regression, ERGAS, spectral-spatial correlation, mutual information (MI), and Jeffries-Matusita Distance (JMD) has been compared on two different datasets from the accuracy and computational time points of view. The first dataset includes a pair of bi-temporal images taken by Landsat TM5 and ETM+ sensors over the southern shores of Lake Urmia, and the second dataset is taken by Landsat TM4 and TM5 sensors overs the Maragheh city and sorrounding area. Implementing the mentioned indices on the first dataset indicates the SAM's significant superiority compared to the other indices. False alarm (FA), missed error (ME), and total error (TE) of the change map resulting from SAM are 3.40%, 13.91%, and 8.86%, respectively. The change map resulting from SCM is in the second order, its FA, ME, and TE values are almost twice the corresponding values derived from SAM. JMD, regression, MI, ED, and ERGAS indices were in the next ranks respectively with 20.17%, 20.61%, 20.84%, 21.22%, and 21.47% TE on their change maps. In the first dataset, the worst change detection result was obtained from the correlation index (TE=27.80%). In the second dataset, the best results have been obtained first from the ERGAS and then from the magnitude of change, which were at the top compared to others. The FA, ME, and TE values of the change map resulting from the ERGAS were 0.63%, 26.54%, and 7.5%, respectively, and the FA, ME, and TE values of the change map resulting from the ED were 0.63%, 32.23%, and 9.01%, respectively. In the lower ranks, SCM, SAM, regression, spectral-spatial correlation, and JMD have led to 11.41%, 12.62%, 14.45%, 17.34%, and 18.03% total errors in change detection, respectively. The worst result with 26.56% TE was achieved from the MI. It is noted should be that the significance of the differences in accuracy between the mentioned indices was tested and verified by McNemar’s test. In terms of computation time, the ED was the most efficient, while the MI was time-consuming on the analysed datasets.

In this study, a regional model for the Earth's gravity field across Antarctica is presented. So, observations of gravity difference along the line of sight of the satellite (LGD), obtained from the GRACE-FO mission, are used, and the local gravity model is calculated based on the Adjusted Spherical Cap Harmonics (ASCH) basis functions. in this method, by introducing a scale factor and applying a mapping to the domain and boundary of the problem, we can use legendre functions of the integer degree and order (similar to global harmonics). According to the characteristics of basic functions, first, a new method for converting LGD data to the ASCH domain and calculating harmonic coefficients is provided. In order to reduce the edge error effect, the gravity grid data beyond the boundary of the studied area is generated using a geopotential model. To verify the validity of the study, a set of control points are selected from the LGD data and in the path of the profiles (orbital paths of the GRACE satellite over the studied area) to verify the accuracy of the local model. Therefore, when the results of the local model are compared with the control points, the root mean square error is equal to 0.9 (nm/s2). which is comparable to the accuracy of LGD data of 0.15 nm/s2. On the other hand, the root mean square error of global geopotential models against LGD data is equal to 6 nm/s2. Because of this, the local harmonic function (ASCH) can get more information from the gravity field and make a more accurate model of the local geopotential.