Extraction, processing, production and display of geographic data
Sara Sheshangosht; Hossein Agamohammadi; Nematollah Karimi; Zahra Azizi; Mohammad Hassan Vahidnia
Abstract
Extended Abstract
Introduction
Glaciers and their short-term and long-term elevation changes are among the most critical environmental hazard indices for monitoring climate change and evaluating geomorphology, perpetually posing risks to climbers, environmentalists, and tourists. The Alamkooh ...
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Extended Abstract
Introduction
Glaciers and their short-term and long-term elevation changes are among the most critical environmental hazard indices for monitoring climate change and evaluating geomorphology, perpetually posing risks to climbers, environmentalists, and tourists. The Alamkooh glacier’s snout is known as one of the most dynamic parts of glaciers in Takht-e-Soliman height due to the yearly advance and retreat of glacier movement causing substantial volumes of various glacial deposits to collapse into their downstream areas. Nowadays, the advancements of satellite imagery, aerial photos, and Unmanned Automated vehicles (UAV) pave the path for accurately extracting and evaluating these changes. Therefore, the objectives of this research are: (a) evaluating the use of new and cost-effective technologies (UAVs) in comparison to satellite imagery for monitoring glacier changes, (b) identifying spatiotemporal glacier elevation changes, and (c) evaluation of the elevation change rate of the Alamkooh glacier snout from 2010 to 2020 using high spatial resolution remotely sensing data. In this context, the elevation changes of the snout of Alamkooh Glacier, as the hazardous activist part of this glacier, were assessed using Digital elevation models (DEMs) differences of 2010, 2018, and 2020.
Materials and Methods
Alamkooh Glacier is located on the northern hillside of Alamkooh Summit in the Takht-e-Soliman region. The snout of this glacier is situated in a steep valley known as Lizbonak and its high activity changes the shape and morphology of this area. In this paper, spatial and temporal elevation changes of Alamkooh Snout were identified and evaluated using DEMs subtraction derived from aerial laser scanning (LiDAR) data in 2010, and from images captured by UAV in 2018 and 2020. Before elevation change analysis, the DEMs obtained through UAVs in 2018 and 2020 were carried out using approximately 40 and 20 ground control points, respectively. The resulting outputs displayed a reliable accuracy of around 15 cm for these DEMs. In addition, for assessing elevation changes precisely, the all of extracted DEMs were preprocessed and orthorectified and then subsequently subtracted pairwise. Then after, the accuracy of elevation changes was appraised based on non-glacial area elevation change. The outcomes of elevation change in this region signify a high level of accuracy in the 10-year time span. According to the results, the average and standard division elevation change of non-glacial area was ±0.05 cm and 0.34 cm respectively. Moreover, the average error assessment on the non-glacial area indicates that within eight years from 2010 to 2018 the average error was ±0.16 cm, and within two years it was ±0.11 cm from 2018 to 2020.
Result and discussion
Results of DEMs pairwise differences show significant elevation changes in this part of Alamkooh Glacier from 2010 to 2020. The average and the maximum elevation change rates in this period are -0.8 (m/yr.) and -2.31(m/yr.) respectively. The major elevation changes in the snout of Alamkooh happened in the initial period from 2010 to 2018 where the yearly and the maximum mean elevation change rates were -1.03 (m/yr.) and –2.77 (m/yr.) respectively. On the contrary, the elevation changes from 2018 to 2020 were lower than the first period whereas the yearly mean elevation change was about +0.1 (m/yr.) and the maximum elevation change rate was -1.85 (m/yr.). The positive rate of elevation change from 2018 to 2020 is due to debris and ice cubes flowing from upstream and accumulation downstream. Moreover, the Spatial analysis of elevation changes results show a heterogeneous distribution whereas the most significant elevation change in the snout of Alamkooh glacier has occurred predominantly across and along the largest existing valley rather than being evenly spread out across the entire area. The elevation change domain in this valley is between +1.3±0.05 to -23.05±0.05 and the average elevation change of in ten years from 2010 to 2020 is about -8.01 ± 0.05 meters. These changes mostly were negative with decreasing and eroding rates. In contrast, the elevation changes in other valleys only occurred at the exit area of the glacier and just the entrance of the snout area, and the margins did not show a considerable change. When considering all valleys in the snout of Alamkooh the elevation changes distribution across the snout varies between +0.45 to -13.2 (m) with an average of -7.8 (m) which is less than alongside changes at the main valley.
Conclusion
The results show elevation changes in the Almakooh snout do not have constant rate and largely fluctuate in different years and regions. The maximum elevation changes occurred from 2010 to 2018 and along with the main steepest valley. The main valley plays a vital role in elevation change analysis and flowing debris down. This area is also known as the depletion area of the Alamkooh glacier and its drastic elevation changes are caused due to ice and snow melt. The tremendous historical flood of the SardAbrood River occurred in June 2011 was created and affected by elevation changes in this area. Therefore, the tongue of Alamkooh Glacier is considered one of the most dangerous areas regarding natural hazards, and morphological change studies require precaution regarding approaching or visiting this area. This research also confirms that using time-series of remote sensing data such as UAV and Lidar images is very helpful and cost-effective data for identifying, extracting, and monitoring the spatiotemporal changes of glaciers, debris flow directions, and natural hazards.
Geographic Information System (GIS)
Omid Faraji; Alireza Gharaghozlou; Hosein Aghamohammadi Zanjirabad; Zahra Azizi; Alireza Vafaeinejad
Abstract
Extended Abstract:Introduction:Undoubtedly, the main motivation of all planning is to achieve sustainable development, regional balance, proper distribution of activities and maximum use of environmental capabilities in the process of regional development. Land is a limited and vulnerable resource, but ...
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Extended Abstract:Introduction:Undoubtedly, the main motivation of all planning is to achieve sustainable development, regional balance, proper distribution of activities and maximum use of environmental capabilities in the process of regional development. Land is a limited and vulnerable resource, but many of its benefits, if not to be abused, are eternal and renewable. In the planning system, the dimension of space is very important and the principles of spatial planning include the principles of sustainability, integrity and comprehensiveness.Developed countries and developing countries both need skills and guidelines in the field of spatial information, methods, frameworks, tools and templates that can use spatial information in timely and accessible decision-making and move forward and to be supportive to sustainable development goals.The purpose of this study is to investigate the applications and political and economic effects as well as the feasibility of using modern technologies such as: Internet of Things - Cloud Computing and Edge Computing - Artificial Intelligence and Machine Learning - Data Mining and Spatial Behavior Mining with the help of Virtual Reality and monitoring Spatial-Temporal changes, all of which are essential to Digital Twin, focusing on land management and sustainable development.Materials & Methods:This article is based on studying the findings and trends implemented in the three leading areas of land administration: The United States, Australia and the European Union and trying to localize those trends in our country.In this research, the comparative study method has been used, which shows a gap of at least ten years between the current situation of spatial information management in our country and what is happening in the leading countries in this field. The study areas include: architecture and structure-political and governance approach-emerging technologies-software- rules and restrictions.The enabler tools used in this research are the complete familiarity with architecture, software, technologies and current instructions in the field of geospatial sciences in the country, and on the other hand, the study of more than eighty articles and more than ten books in the field of the latest global achievements and the review of all reliable portals of the geospatial information and finally a comparative comparison of these two concepts and drawing a road map.Preparation of 2D and 3D cadastral maps of all geographic entities and assignment of legal and ownership information to these entities and then 3D visualization of these combined spatial data in a suitable portal for the preparation of functional spatial analysis are discussed in detail in the implementation method of this research.Results & Discussion:Utilizing the knowledge and technology in creating a Digital Twin platform and adding its powerful tools to the country's national geospatial information infrastructure will lead to maximum productivity and the growth of the economy and social equality, and its highest feedback is the realization of sustainable development in the country.As the Gartner Institute's technology evaluation indexes as well as the CAGR index and the economic impact evaluation reports of the leading countries show, the acquisition of the Digital Twin technology will greatly contribute to the prosperity of the country's economy.In this research, an attempt has been made to mention all the technical and management tools necessary to achieve this infrastructure, and also a prioritization has been done according to the time of achieving the goals.Conclusion:The results of this research show that filling the gap between the current state of the country's spatial information management and the desired state requires investment, culture and serious efforts of those involved in this field, and on the other hand, a positive point can be mentioned that with following the paths taken by developed countries, it is possible to achieve an optimal model and a clear and error-free path to achieve these goals.The localization of global instructions and the development of a conceptual model of action to resolve the existing technology gap will pave the way for the establishment of new technologies in the country, and with the maturity of the technical and operational branches of these models, we will come closer to the realization of sustainable development in the country.On the other hand, without cooperation and coordination between all the governmental bodies involved in the country's geomatic sciences, in addition to the private sector and the society, it seems unlikely to achieve this great achievement, because the creation of such a powerful infrastructure requires a harmonious and coherent national movement.It is hoped that this article will make a small achievement to the formation of strategic thinking in the management of spatial information sciences in the country and provide a complete picture of the dark corners of the development path as well as helping the decision makers in our beloved country.
Mohammad Fallah Zazuli; Alireza Vafaei Nezhad; Ali Asghar Alesheikh; Mahdi Modiri; Hossein Aghamohammadi
Abstract
Extended Abstract Introduction Landslide is one of the most important types of natural disasters,which endangers lives and financial security of many people and destroys environment and natural resources.With the present population growth and expansion of urban areas towardsteep areas and hillsides, ...
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Extended Abstract Introduction Landslide is one of the most important types of natural disasters,which endangers lives and financial security of many people and destroys environment and natural resources.With the present population growth and expansion of urban areas towardsteep areas and hillsides, landslide-related losses can be catastrophic. For an instance, landslides in Badakhshan Province in Afghanistan killed around 2,700 people in 2014, and a landslide in China (Shan’xiprovince)resulted in the disappearance of 64 people in 2015.Therefore, assessingthe possibility of landslides occurrence seems to becrucial. Providing zoning maps is one of the measures which makes identification of areas prone to future landslides possible. Inferences drawn from these maps can be used for land use planning, prevention of unauthorized construction activities, infrastructure development, refurbishment and restoration. Materials & Methods The present research selectsEast Rudbar-e Alamut (a district of Qazvin province), which is affected by landslides and instability of hillsides, as the study area. It takes advantage of Shannon entropy and information value models to develop landslide susceptibility map of the study areain GIS environment.Shannon entropy theory has been used in extensive researcheswith the aim of prioritizinginfluential factors in the probable occurrence of natural disasters such as landslide. Information value (IV) model is one of the statistical models drawn from information theory with a widespread application in the modeling of geological hazards and disaster risk assessment. Information value model aims to find a combination of significant factors anddeterminetheir impacton theoccurrence oflandslide in an area.To implement this model, relevant data and its related criteria maps were prepared. In this regard, the location of previous landslide events in the study area was determined based on the information received from Forests, Range and Watershed Management Organization. 49 landslides were identified in this way. Then, data was randomly divided into 2 categories: training data and validation data. Thus, 70% of data (35 landslides) were used to produce the models and the remaining 30% (14 landslides) were used for validation purposes. In addition to previous landslides, a collection of topographic, environmental and climatic characteristics of the study area including seven criteria of lithology, slope, distance from faults, land use, precipitation, slope-direction and elevation were selected as the most effective independent variablesto produce criteria maps with 30-meter spatial resolution. Basic information used to map these seven influential factors was obtained from Forests, Range and Watershed Management Organization, as well as the SRTM Digital Elevation Model (DEM), and used after some modifications. Considering the capability of ArcGIS in spatial data analysis, thissoftwarewas used to produce information layers and implement the models. Results & Discussion Prioritizing influential factors using Shannon entropy model introducesthree factors (i.e. land use, elevation and precipitation)as the most significant factorsin the occurrence of landslides in the study area. Factors of slope angle, distance from faults (almost equal to slope angle), lithology and slope-direction were in the next influential factors.Also, results of information value model indicate that looking from lithology perspective, the category of marl, calcareous sandstone, sandy limestone and minor conglomerate has an information value of 1 and thus, the highest probability of landslide occurrence. Category of basaltic volcanic rocks, along with category of well bedded green tuff and tuffaceous shale have the lowest probability of landslide occurrence with information values of -2.03 and -1.70, respectively.Only two categories of theslope angle criterionhave a positive-index. The highest information value (0. 93) in this category occurs in the class of 5-12 degrees, followed by the class of 12-20 degrees. The lowest information value occurs in slopes of more than 30 degrees. Based on this observation, it can be clearly concluded that the slope angles of 5 to 20 degrees are most prone to landslides. Distance to faults criterion indicate that the category of500 to 1000-meter distance to faultshave the highest information value (1.67). Regarding land use criterion, three land uses of garden, agriculture and garden-agriculture have the highest information values of 2.16 and 1.59 and 1.11, respectively. Regarding precipitation, average annual rainfall of less than 400 millimeters have the highest information value (1.50). Regardingslope-direction criterion, most landslides occur in southwest, south and eastdirections.Northeast, west, and northwest directions have the lowest probability of landslide occurrence, respectively. In terms of elevation, the information value is reduced as the height increases, and the maximum information value is related to the elevations of less than 1200 meters.After assigning a weight to each criterion and related classes, the landslide risk zone map was generated based on Shannon entropy and information valuemodels. The resulting zoning map produced based on natural breaks methods dividesthe area into five classeswith very high, high, moderate, low and very low risk. Resultsof Shannon entropy modelindicate that out of 14 landslides considered as the validation data, 3, 7, 2, 1, 1 landslideshave occurred in very high, high, moderate, low and very low risk zones, respectively. Resultsof the information value modelindicatethat 8, 4, 0, 1, 1 landslideshave occurred in very high, high, moderate, low and very low risk zones, respectively. Conclusion Evaluation of results using experimental probability index indicates that with 86% experimental probability,both models of Shannon entropy and information value are effective inidentification of landslide hazard in the East Rudbar-e Alamut region. Also, considering the number of landslides in very high and high risk zones, Shannon entropy and information value modelshave an experimental probability index of 72% and 86%, respectively, which prove higher efficiency of information value model. In Shannon entropy model, total area of very high, high and moderate risk zones covers 34% and 56% of the study area,respectively. In information value model,total area of very high and high risk zones covers 20% and 29% of the study area, respectively. Based on the landslide risk zone map, high and very high risk zones are mainly located in the west of the study area.
