Horizontal displacement monitoring in mining areas using UAV data from photogrammetry and from laser scanning

Integrating spatial data from different sources results in visualization, which is the last step in the process of digital basic topographic map creation. Digital elevation model and visualization will used for geomorphological mapping, geospatial database, urban planning and etc. Large scale topographic mapping in the world countries is really a prominent challenge in geospatial industries today. The purpose of this work is to integrate laser scanner data with the ones generated by aerial photogrammetry from UAV, to produce detailed maps that can used by geodetic engineers to optimize their analysis. In addition, terrestrial - based LiDAR scans and UAV photogrammetric data were collected in Sharga hill in the north zone of Mongolia. In this paper, different measurement technology and processing software systems combined for topographic mapping in the data processing scheme. UTM (Universal Transverse Mercator) projected coordinate system calculated in WGS84 reference ellipsoid. Feature compilation involving terrestrial laser scanner data and UAV data will integrated to offer Digital Elevation Models (DEM) as the main interest of the topographic mapping activity. Used UAV generate high-resolution orthomosaics and detailed 3D models of areas where no data, are available. That result issued to create topographic maps with a scale of 1:1000 of geodetic measurements. Preliminary results indicate that discontinuity data collection from UAV closely matches the data collected using laser scanner.

In this study, we evaluated the capability of an unmanned aerial vehicle with a LiDAR sensor (UAV-LiDAR) to classify and map fuel types based on the Prometheus classification in Mediterranean environments. UAV data were collected across 73 forest plots located in NE of Spain. Furthermore, data collected from a handheld mobile laser scanner system (HMLS) in 43 out of the 73 plots were used to assess the extent of improvement in fuel identification resulting from the fusion of UAV and HMLS data. UAV three-dimensional point clouds (average density: 452 points/m2) allowed the generation of LiDAR metrics and indices related to vegetation structure. Additionally, voxels of 5 cm3 derived from HMLS three-dimensional point clouds (average density: 63,148 points/m2) facilitated the calculation of fuel volume at each Prometheus fuel type height stratum (0.60, 2, and 4 m). Two different models based on three machine learning techniques (Random Forest, Linear Support Vector Machine, and Radial Support Vector Machine) were employed to classify the fuel types: one including only UAV variables and the other incorporating HMLS volume data. The most relevant UAV variables introduced into the classification models, according to Dunn’s test, were the 99th and 10th percentile of the vegetation heights, the standard deviation of the heights, the total returns above 4 m, and the LiDAR Height Diversity Index (LHDI). The best classification using only UAV data was achieved with Random Forest (overall accuracy = 81.28%), with confusion mainly found between similar shrub and tree fuel types. The integration of fuel volume from HMLS data yielded a substantial improvement, especially in Random Forest (overall accuracy = 95.05%). The mapping of the UAV model correctly estimated the fuel types in the total area of 55 plots and at least part of the area of 59 plots. These results confirm that UAV-LiDAR systems are valid and operational tools for forest fuel classification and mapping and show how fusion with HMLS data refines the identification of fuel types, contributing to more effective management of forest ecosystems.

Monitoring data is the important signal for safety situation decision of shield method construction.This paper analyzes the abnormal types appearing in safety monitoring data of shield method construction, and points out its caused by the environmental quantity anomaly, structure form change, misreading mistake, instrument error, and human negligence.Based on the test and analysis of the abnormal monitoring data, it considers the monitoring data as abnormal value if the monitoring data beyond the confidence interval.And then uses the theoretical model to eliminate the observation error and systematic error.Furthermore, this paper analyzes the reasons of abnormal of the shield method construction settlement monitoring data, displacement monitoring data, shaft force monitoring data, deep horizontal displacement monitoring data, the water level monitoring data according to theory above.Monitoring data is an important signal of determine the shield method construction safety situation, its accuracy directly affect the evaluation result [1].The abnormal of the subsidence, horizontal displacement monitoring, deep horizontal displacement monitoring, axial force monitoring and water monitoring data are all attribute to narrow and complicated of the shield subway construction site.It should analyzed according to the data types and anomaly genesis as well as its surrounding environment change and construction progress, because the abnormal values are not all gross error or human error, so as to ensure the safety of shield construction method. Safety Monitoring Data Exception TypeAbnormal environment quantity, morphology changes, misreading mistake, instrument error and human negligence are all reasons to cause abnormal monitoring data [2,3].Therefore, it requires a combination of construction environment and other factors on the systematic analysis of outliers.And eliminates the error, identifies human errors and so on, pays close attention to changes caused by the abnormal structure form.Usually, monitoring abnormal value of deep subsidence, horizontal displacement, axial force, horizontal displacement, water level monitoring project are divided into the following several.

A certain level of horizontal displacement will occur during excavation or subsequent construction of deep foundation pit. If the support is improper and the horizontal displacement of the foundation pit is too large, it will cause collapse and even affect the buildings around the foundation pit, which will endanger people's life and property. Therefore, the horizontal displacement monitoring of deep foundation pit becomes more and more important. At present, the electronic total station is often used to monitor the horizontal displacement of the foundation pit, but this monitoring method is expensive, prone to accidental errors, and can not be used for real-time monitoring. Therefore, a method of monitoring the horizontal displacement of deep foundation pit by using laser projection sensing technique is proposed in this paper. The horizontal displacement of the foundation pit is replaced by the displacement of the laser spot emitted by the laser, and the horizontal displacement of the foundation pit can be obtained by identifying the displacement of the laser spot projected on the screen. A series of experiments show that the accuracy of this monitoring method meets the engineering requirements and greatly reduces the cost, which provides a new technology for the displacement monitoring of deep foundation pit.

The article presents solutions for designing, building and evaluating the accuracy of automatic monitoring system by robotic total station when continuously monitoring horizontal displacement of Hoa Binh Hydropower Plant . The system is designed and built put into operation and used for horizontal displacement monitoring results with high reliability and stability with accuracy of horizontal displacement less than 2.5 mm, for continuous and instantaneous data collection frequency, data can be extracted at any time according the erratic changes of weather, natural disasters or the change of reservoir water level and automatically send an alarm when the horizontal displacement value at the monitoring points reaches the alarm threshold. Continuous horizontal displacement monitoring is the basis to add settlement measurement cycle to check the settlement of the dam when there are abnormal signs of horizontal displacement.

Horizontal displacement of the enclosure structure or soil is one of the key indicators to evaluate the safety of deep foundation pit excavation. However, the monitoring data of horizontal displacement will have short- or long-term abnormal fluctuations due to the interference of various non-structural factors, which would directly affect the assessment and identification of the risk of deep foundation pit excavation. In order to avoid missing and misjudging the true large deformation of the foundation pit and consider the requirements for the accuracy and timeliness of the monitoring data, an abnormal data identification and correction algorithm was proposed in this paper considering the temporal-spatial characteristics of the horizontal displacement monitoring data. The algorithm builds a spatial distribution matrix according to the deployment position relationship of multiple sensors, records the historical measured values of sensors in a period of time, and calculates the estimated values of the measured values of sensors at the next time based on multidimensional Kalman filtering. After obtaining the latest horizontal displacement monitoring value at the current time, calculate the difference between the measured value and the estimated value based on the spatial matrix to correct the data, mark the sensor with the difference greater than the adaptive threshold, and finally update the historical measured value, adaptive threshold, Kalman filter parameters, etc. in the window, waiting for the input of the monitoring data at the next time. The abnormal data identification and correction algorithm described in this paper was successfully applied in a deep foundation pit excavation project in Shanghai. The anomaly identification and correction of single sensor data and the horizontal displacement curve were both given. In general, this method can simultaneously discriminate and correct the data of multiple sensors in real time, effectively eliminate the abnormal fluctuation of monitoring data, and has good engineering application value.

The traditional measurement method for the horizontal displacement of deep soil usually uses an inclinometer for piecewise measurement and then generates an artificial reading, which takes a long time and often contains errors; in addition, the anti-jamming and long-term stability of the inclinometer is poor. In this paper, a technique for monitoring horizontal displacement based on distributed optical fibres is introduced. The relationship between the strain and the deflection was described by a theoretical model, and the strain distribution of the inclinometer tube was measured by the cables laid on its surface so that the deflection of the inclinometer tube could be calculated by the difference algorithm and regarded as the horizontal displacement of deep soil. The horizontal displacement monitoring technology of deep soil based on distributed optical fibre sensors developed in this paper not only overcame the shortcomings of traditional inclinometer technology to realize automatic real-time monitoring but also allowed for distributed measurement. The experiment was similar to the expected engineering situations, and the deflection calculated from the strain was compared with an inclinometer. The results demonstrated that the relative error between the distributed optical fibre sensors and the inclinometer was less than 8.0%, and the results also verified both the feasibility of using distributed optical fibre to monitor the horizontal displacement of soil as well as the rationality of the theoretical model and difference algorithm. The application of distributed optical fibre in monitoring the horizontal displacement of deep soil in the engineering of foundation pits and slopes can more accurately evaluate the safety of engineering during construction.

Coal production will cause serious damage to regional vegetation, especially in ecologically fragile grasslands. It is the consensus of all major countries to conduct vegetation restoration and management monitoring in areas damaged by coal production. This paper compares the adaptability of different data sources and different vegetation indices to grassland mining areas and proposes a normalized environmental vegetation index (NEVI) suitable for vegetation monitoring in grassland mining areas. Based on the Landsat and Sentinel data from 2005 to 2019, this paper uses NEVI to monitor the vegetation destruction and restoration of the Shengli mining area. The main result is that the vegetation restoration work in the Shengli mining area started in 2007 and was gradually carried out in subsequent years. The restoration effect of vegetation is significantly better in the east than in the west. The NEVI of the vegetation in the east can reach, or exceed, the level of natural vegetation in the same period. The restoration of vegetation degradation in some areas requires strengthening of management and maintenance measures.

<p>High-mountainous Lake Sevan (Republic of Armenia) laying at an altitude of about 1900 m above sea level is a unique object of remote environmental monitoring due to the multidirectional dynamics of water level over the past 100 years. The artificial decrease in the lake water level began in 1930s, with the most intensive fall (over 10 m) from 1949 to 1962. In the 1990s, there was a slight increase in the level, then water level continued to fall until 2001. According to the current program of Armenian government, the lake level is planned to rise by at least 6 m in the coming years.</p><p>The current increase in water level of Lake Sevan leads to activation of both abrasive and accumulative coastal processes, intensification of eutrophication and mass flowering of lake waters. Planned increase in water level also threats residential and recreational facilities which are abundant along shoreline of Lake Sevan. At the same time, the spatial and temporal differentiation between the current intensity of coastal processes and the state of coastal ecosystems is quite significant. In order to reveal the regularities of this differentiation, we preliminary carried out a retrospective large-scale mapping of the shoreline dynamics of Lake Sevan using archival and modern cartographic small-scaled materials and high-detailed remote sensing data for the period of over 100 years. Based on the results of interpretation of the mosaic of large-scale aerial imagery of 1960s different types of coasts were identified; the speed of receding of the lake shoreline during the period of its maximum decline was reconstructed.</p><p>For several chosen key coastal areas, characterized by the most significant changes in shoreline and different types of current coastal processes, since 2018 we have been conducting operational remote monitoring of the coastal zone from light-weight UAVs DJI Phantom 4 Pro. UAV surveys are conducted at the low altitude range (100–200 m) with the use of both optical and thermal cameras. Resulted multitemporal UAV data are dense photogrammetric point clouds (with the density more than 300 points per sq. m), three-dimensional digital surface and terrain models with spatial and vertical resolution up to 10 cm, ultrahigh-detailed orthoimagery with the spatial extent about 1 sq. km. Thematic interpretation of acquired UAV data resulted to detailed land cover mapping of key coastal areas, reliable detection of local sources of water pollution, identification of buildings and facilities more threatened to inundation under the different scenarios of water level rising. The integral synthetic assessment is made for the current environmental state of coastal ecosystems under risk. Among more vulnerable ecosystems are coastal lagoons with associated wetland complexes and planted coastal forests which being degraded and damaged as a result of increase in lake level and inadequate management can substantially contribute to the deterioration of integral water quality in Lake Sevan.</p><p>The study is supported by the RFBR project no. 18-55-05015-Arm_a.</p><p> </p>

Non-technical summary There is abundant research about the impacts that large-scale mining produces on territories to the detriment of their social and environmental sustainability. However, during our research in Chile and Peru, we also identified local transformative initiatives that pursue sustainable development by proposing alternatives to how the socio-ecological impacts of natural resource extraction are produced and distributed throughout society. Specifically, we ask the question: How do local communities in Chile and Peru that are affected by mining activities engage in community-based environmental monitoring (CBEM)? Technical summary By examining how local communities in Chile and Peru engage in community environmental monitoring, this paper argues that local political organisation and institutional innovations are crucial for the emergence of transformations towards sustainability. Local political organisation and mobilisation can create a window of opportunity for discussion about extractive activities and their impacts, as well as possible proposals for alternatives. Institutional innovations triggered by local political work can lead to the implementation of such initiatives. Our findings are based on qualitative case studies of CBEM in Chile and Peru, in areas with high levels of environmental degradation due to mining. In Chile we analysed a case of community air monitoring in a copper processing area, and in Peru a case of community water monitoring in a mining area. Drawing on debates on social transformation and political ecology theory, this study aims to show CBEM promotes changes towards a more democratic and preventive environmental governance, and encourages the recognition of environmental injustices. Social media summary This paper analyses how local communities in Chile and Peru engage in community environmental monitoring in areas affected by the presence of extractive industries. We identified local transformative initiatives that pursue sustainable development by proposing alternatives to how the socio-environmental impacts of natural resource extraction are produced and distributed in society. Our findings are based on qualitative case studies of community-based air and water monitoring in extractive areas in Chile and Peru.

A method for extracting mining-induced surface subsidence based on multi-source data fusion is proposed by integrating the advantages of InSAR, PIM, and UAV technologies. This approach first employs InSAR to obtain time-series cumulative subsidence basins and constructs constraint conditions to distinguish between high-gradient deformation zones, enabling the differentiation of subsidence edges and centers. In the subsidence edge areas, InSAR monitoring results are retained, while in the central zones with large-gradient subsidence, a weighted fusion of PIM and UAV data is conducted to establish a subsidence basin model for the mining working face. Subsequently, spatial interpolation is applied to derive continuous surface subsidence information in a geographic coordinate system, ultimately generating a complete surface subsidence map of the mining area. The method is validated using Sentinel-1A satellite imagery, UAV data, and leveling measurements from the 120101 working face of the Yangchangwan Coal Mine in the Ningdong mining area. The results demonstrate that this method effectively compensates for the limitations of using InSAR, PIM, or UAV data individually, enhances monitoring capability in high-gradient subsidence areas, and enables the acquisition of more accurate and spatially continuous surface subsidence information.

Horizontal displacement monitoring was carried out for the complicated planar foundation pit. After the excavation, monitoring results indicated the horizontal displacement of the top retaining structure exceeded the advance warning and dangerous situation continued to aggravate. A comprehensive site investigation was carried out aiming at finding out the reasons for the warning alarm. The measures were taken to treat the dangerous situation pertinently and obtained good effect.

This paper aims to introduce the free stationing and its accuracy analysis in horizontal displacement monitoring of deep foundation fit, as well as discuss the feasibility and efficiency of using free stationing to maintain a horizontal displacement automatic monitoring, which is based on the automatic deformation monitoring of Georobot. It also shows that such kind of system can achieve an automatic, precise and efficient horizontal displacement monitoring, and can provide timely and reliable monitoring data for the safety construction through the application of specific project cases.

A low-cost multi-sensoral mobile mapping system and its feasibility for tree measurements

Transmission tower fault monitoring has always been a challenge for power system engineers. Vertical and horizontal displacement monitoring is important for evaluating health status of transmission tower's infrastructures. Quick fault detection can help protect the equipment by allowing the disconnection of transmission tower before any significant damage of the equipment. The purpose of this study is to propose an IoT monitoring system for transmission towers. The monitoring system adheres to Three-Layer IoT architecture. In the perception layer, sensors MPU6050 and Anemometer are used to measure a specific parameter. The data, which includes the Roll, Pitch, Yaw, and wind velocity, is gathered and delivered to the ESP32 through network layer using WiFi, which then stored the information in the cloud using InfluxDB. For visualization purposes in the application layer, Grafana is utilized which is hosted in the web dashboard and the mobile application. Flutter framework is used to develop the mobile application. An alert will be displayed in the mobile application when the threshold of the wind speed exceeds 10m/s.