Applied Geomatics最新文献

This paper describes the development and implementation of a mobile Geographic Information System (GIS) to improve the traditional method of retaining wall data collection and risk management in Algeria. This mobile GIS application is designed to help assess the condition of retaining walls and identify high-risk retaining walls. A risk assessment method based on retaining wall condition class and inventory data was developed to calculate a composite risk score for each retaining wall. Therefore, this system assists the quick creation of retaining wall condition maps by collecting information about the various defects of the retaining wall (influence zone defects, equipment defects, drainage defects, structure defects) using open-source GIS technologies such as QGIS, IntraMaps Roam, Spatialite, PostgreSQL, PostGIS, etc. In addition, this mobile GIS can be used in online and offline mode during field work in order to work in all network access conditions. To show the contribution of this system in the retaining wall data collection and risk management, an evaluation of the state of a sample of four retaining walls distributed over the Djelfa town (Algeria) was performed by the mobile GIS. A retaining wall risk score mapping was also performed. The evaluation showed that mobile GIS allows reliable data to be collected and recorded more uniformly and efficiently. It allows real-time visualization on the desktop of the updates made in the field. Therefore, mobile GIS can improve the quality and speed of retaining wall data collection and increase the effectiveness of retaining wall risk management.

Cultural heritage faces numerous threats, ranging from armed conflicts to the impacts of climate change. The need for robust documentation methods to safeguard and preserve cultural assets has become increasingly apparent. In this context, 3D documentation has emerged as a pivotal tool, providing a comprehensive means to capture and analyze cultural heritage in intricate detail. Various technologies contribute to 3D documentation, each with unique advantages and limitations. As technology evolves, the accessibility of high-precision methods, such as Light Detection and Ranging (LiDAR), has expanded. The main aim of this paper is to assess the absolute accuracy of the post-processing kinematic (RTK/PPK) approach of the UAV RTK LiDAR system (Matrice M300 RTK + DJI Zenmuse L1 LiDAR) with UAV RTK aerophotogrammetry (Matrice M300 RTK + Zenmuse L1 RGB and DJI Zenmuse P1 camera). Also, the comparison of reconstructed dense clouds is done using C2C and M3C2 within CloudCompare software as well as roughness analysis using the Compute geometric feature tool. The research indicates that aerial photogrammetry using the P1 camera has the highest absolute RTK/PPK approach accuracy. However, L1 RGB is accurate enough for 3D cultural heritage documentation application. Furthermore, the cloud-to-cloud comparison has demonstrated that the L1 RGB camera is more accurate (~ 5 cm) than the L1 LiDAR.

Although underground topography methods have significantly evolved in the past two decades, it is still challenging and often costly to create comprehensive 3D models of caves or artificial cavities. Modern methods used by speleologists, involving the generation of a topographic ‘skeleton’ through laser pointer measurements, enable surveying extensive developments but suffer from a lack of resolution. Conversely, the use of LiDAR technologies, while capable of obtaining sub-millimeter scans, implies the use of expensive equipment and precision electronics often unsuitable for this harsh underground environment. In this study, we propose to test the use of low-cost cameras (action-cam type) for the complete 3D modeling of a cave through photogrammetry aiming to compare the results with a static LiDAR survey based on a series of topographic markers precisely measured with a total station. Our findings indicate that photogrammetry is an approach significantly faster and more adaptable in the field, leading to a substantial reduction in artifacts and shadows compared to static LiDAR usage. Although post-processing involving image correlation is more computationally intensive for photogrammetry, we explore various strategies to reduce the calculation times. Ultimately, we demonstrate that the residual positioning errors on the topographic markers are of similar centimetric magnitude to those of LiDAR. Recent advancements in computing capabilities now make it feasible to consider the use of photogrammetry for extended underground developments, presenting a promising alternative to the more conventionally employed LiDAR in such contexts.

The management of cultural landscapes, which encompasses historic buildings and archaeological heritage, faces complex challenges due to the unique nature of these structures and their vastness. An innovative approach to addressing these challenges involves integrating semantically enriched 3D modeling, such as Historic Building Information Modeling (HBIM), with Historical Geographic Information Systems (HGIS). This integration allows for a comprehensive understanding of historical structures at different scales and levels of knowledge, from individual components to territorial contexts and their history. We conducted an innovative study based on the application of HBIM-HGIS for multi-scale digital documentation of architectural heritage, focusing on archaeological heritage, such as the former 20th-century prisons located on Anchieta Island, São Paulo, Brazil. We proceeded in two stages. In the first stage, we collected, georeferenced, and vectorized historical data, adding descriptive information. In the second stage, we modeled the structures in detailed three-dimensional using terrestrial and aerial scanning techniques, integrated HBIM data into a geographic database, and created a 3D WebGIS for data visualization and management. The results demonstrated significant innovation in HBIM-HGIS integration, providing deeper understanding and precise analysis to support the preservation of historical complexes and cultural landscapes. Our approach contributes to more effective and comprehensive management of cultural heritage at various scales, from landscapes to specific details of historic buildings.

The evolution of glaciers’ internal structure, primarily driven by their thermal state, reflects ongoing climate change. Understanding the thermal structure is essential for studying glacier dynamics, mass balance, and hydrological system. Radio-echo sounding enabled the indirect identification of water-saturated temperate ice (W-STI) and water-free ice (W-FI). A novel automatic image processing method has been applied to eliminate the subjectivity and the time consumption of manual determining layer boundaries. The technique based on local binarization was used to assess the internal state and changes of the Arctic glacier Hansbreen from 2007 to 2021, revealing its evolution from a two-layer to a nearly homogeneous structure containing only temperate ice. The average difference between manual and automatic structure recognition results is 1.8 percentage points. The primary mechanisms of the disappearance of the W-FI layer are surface ablation, reducing the W-FI thickness, and the expansion of drainage zones, diminishing the range of W-FI.

Analysis of land use/land cover changes is vital to hydrologists, planners and watershed management decision makers to design appropriate response strategies to tackle the undesirable effects of the changes. This study was aimed at analyzing the land use/cover changes of Gilgel Gibe Catchment in 1991 – 2021. LULC data of 1991–2021 were derived from multispectral Landsat images. Data were also gathered using field observations and key informant interview. Data of LULC classes (1991–2021) were generated using supervised classification with maximum likelihood algorithm of ENVI 5.1 and ArcGIS 10.5. Change detection analysis and accuracy assessment were done where accuracy levels all the study periods were > 85%, and the overall Kappa statistics of the periods were > 0.89. Cultivated land and built-up area of the catchment are increasing with increasing magnitude of change; whereas, while forest cover and water body of the catchment are shrinking with declining magnitudes of change, grazing land and shrubland covers are declining with increasing magnitude of change in the catchment. The net increase in degraded land is a reflection of the increasing degradation of natural resources in the catchment. Rapid population growth and the subsequent raising demand for farmland and forest and shrub (e.g. fuel-wood and construction) products, decline yield, unemployment and lack of alternative income source, and open access and limited conservation of resources are the main causes for the dramatic shrinkages of forest, grazing, water body and shrubland resources. Thus, concerned bodies should take rehabilitation measures to restore degraded lands, improve production and yield of farmland by increasing improved farm-inputs and irrigation, and create employment and alternative income sources for the youth, women and the poor so as to ensure sustainable rural livelihoods and to curb the impacts on forest, shrubland and other resources.

This study assesses the slope stability of various road cut slopes along National Highway (NH-44), which runs from Jawahar Tunnel Banihal to Ramban in the Kashmir Himalaya, India. Frequent landslides along NH-44 disrupt infrastructure, transportation, and the region’s development and economy. Ongoing road widening, tunneling activities, and heavy rainfall during the monsoon season lead to regular slope failures and blockages of NH-44. A comprehensive field survey was conducted to collect lithological and structural data for rock mass characterization, kinematic analysis, and slope stability evaluation. A total of 27 road cut slopes were analyzed for Rock Mass Rating (RMR) and Slope Mass Rating (SMR), while 48 sites were assessed for kinematic analysis. The area was divided into four domains (D-1, D-2, D-3, and D-4) based on major structural discontinuities for detailed study and analysis. The RMR values ranged from 8 to 82 (Very Poor to Very Good), while SMR values ranged from 7 to 82 (Very Bad to Very Good). Kinematic analysis identified that most failures are due to planar and wedge failures, with some toppling failures. The slopes, ranging from moderately stable to unstable, are prone to instability and collapse, especially due to changes in slope morphometry and frequent rainfall. The region lies within a tectonically active zone, making the slopes inherently unstable. This study’s integrated approach provides valuable insights into hazard mitigation, highlighting critical failure-prone areas and supporting better planning for infrastructure and sustainable development in the Himalayan region.

Water is an essential source for our daily needs and occurs in groundwater or surface water. In the present climatic manifestation, their occurrence, usage, recharge, and future sustainability are indispensable for mankind, agriculture, industries and ecological environmental equilibrium. Integrated studies of RS (Remote Sensing) satellite data and GIS (Geographic Information System) applications effectively evaluate GPZ (Groundwater Potential Zones) and provide vitality to futuristic scenario development. The present study is aimed to delineate the GPZ of Uttar Pradesh using MCDM (Multi-Criteria Decision Making) and AHP (Analytical Hierarchy Process) taking seven parameters such as rainfall, geomorphology, LULC (Land Use-Land Cover), drainage density, slope, soils and lineament density along with field survey. Using the free web-based AHP, all parameters were assigned a weightage, and weightage overlay analysis (WOA) using ArcGIS was performed. The results identified a total of 8% area in the piedmont region under the excellent potential zone followed by 46% of area of the Ganga River left bank. At the same time, the southern part of Uttar Pradesh indicated good potential zones. Similarly, the intermediate zone (45%) along the Ganga River right bank and the southern zone of Uttar Pradesh demarcate fair potential zone. The poor potential zone (1%) is predicted in the southwestern part. These results were tested with the pre and post monsoon well water levels of the last 11 years (2010- 2021) which satisfies our findings of the AHP method. Hence, AHP is a new method for identifying GPZ maps that can be used to carry out extensive ground-based hydrological studies that aid in the identification of potential bore well/dug well locations.

This study aims to decipher groundwater potential zones using an integrated approach of remote sensing, geographical information system, and analytical hierarchy process in the Koyna-Warna region of western India, an area affected by the reservoir-induced seismicity (RIS). This region serves as a key example of the RIS, primarily due to the construction of the Koyna dam in 1964. The filling of the reservoir water behind the dam has been associated with a significant increase in seismic activity, particularly in the surrounding area. This seismicity is thought to be triggered by the weight of the water, which induces stress on the Earth’s crust, leading to the faults slipping. Moreover, the groundwater potential zones in this region are crucial for understanding the dynamics of seismic events. Thus, multiple important factors affecting groundwater such as geology, geomorphology, soils, land use and land cover, slope, lineaments density, drainage density, rainfall, normalized vegetation index, and topography wetness index were considered for deciphering the groundwater potential zones. Spatially distributed thematic layers of all these factors were generated using remotely sensed data and ground-based data in GIS platform. The assigned weights of all these layers and their attributes were then normalized by using analytical hierarchy process technique. The deciphered groundwater potential zones of this RIS area were categorized as very good (15.68%), good (27.34%), moderate (29.25%), poor (19.54%), and very poor (8.19%). These assessed groundwater potentialities were positively correlated with the well specific yields with a correlation coefficient of R = 0.90, and was found reasonable. It was also observed that the very good to good potential zones were in the upstreams. Most of the very good groundwater potential zones (~ 16.79%) were found in the northern part, namely Koyna region (which was more the seismically active) than the Warna region (~ 14.57%) located in the southern part. It indirectly indicated that the groundwater potentially also induced the seismicity of earthquakes along with both Koyna and Warna reservoir waters. The deciphered groundwater potential zones in this RIS area will aid in better study of the earthquake seismicity in future.

The Rarh region of West Bengal, India, faces a critical challenge to its agricultural sustainability and environmental well-being due to soil erosion. This study aims to develop a scalable model for estimating soil erosion rates and identifying vulnerable areas within the region. By employing the Revised Universal Soil Loss Equation in conjunction with GIS and remote sensing techniques, the research integrates factors such as rainfall erosivity, soil erodibility, topography, land cover, and conservation practices to create a comprehensive soil erosion risk map. Findings reveal that annual soil loss in the study area ranges from 0.53 to 955.39 t ha⁻¹ yr⁻¹, with an average of 13.014 t ha⁻¹ yr⁻¹, exceeding the tolerable soil loss limit of 11.2 t ha⁻¹ yr⁻¹. The erosion map classifies the region into zones of varying soil loss severity from very low to very severe. Approximately 2.67% of the area falls under severe to very severe categories, 10.63% is classified as high to very high risk, while 48.92% and 17.60% are in moderate and low-risk zones, respectively. The western and central parts of the Rarh region, characterized by steep slopes, intensive agriculture, and high rainfall intensity, are identified as areas with high soil loss potential. The model's performance was validated using an Area under the Curve (AUC) analysis, achieving a score of 0.832, indicating good predictive capability. This study's results offer valuable insights for policymakers and land managers, enabling the implementation of targeted soil conservation measures and sustainable land use practices in the Rarh region of West Bengal.