Monitoring Method Research Articles

Photoelectric sensor-based belt-type high-speed seed guiding device performance monitoring method and system

The belt-type high-speed seed guiding device encounters difficulties in seed guidance due to its distinctive seed transporting belt structure. The monitoring point is situated between the seed belt support plate and the seeds, which pass through it, making it difficult to distinguish the characteristics of the seed pulses. This presents challenges in monitoring the device’s performance. This study proposes a monitoring method for the belt-type high-speed seed guiding device using photoelectric sensors. The monitoring module employs a photoelectric sensor as a signal source and designs hardware and software for the monitoring module. The research introduces the deviation pulse extraction algorithm (DPEA), which utilises the seed guidance performance monitoring mechanism. This achieves precise monitoring of seed transport on the transporting seed belt, thereby eliminating the increased multiple index caused by “pulse sinking”. Simulation experiments demonstrate that the optimal deviation pulse extraction algorithm (ODPEA) is highly effective in identifying and eliminating “pulse sinking”, resolving the issue of increased multiple index. The DPEA is unable to achieve the same effect. The results of comparative tests of seeding monitoring accuracy indicate that both algorithms exhibit average monitoring accuracy above 96% across varying operating speeds. The DPEA, however, demonstrates heightened sensitivity to changes in operating speed. It can be observed that the operating speed has a significant impact on the monitoring accuracy of both algorithms. The monitoring system performance evaluation comparative test reveals that the DPEA-based monitoring system registers the smallest deviations in qualified feed index, miss index, and multiple index at 0.25 percentage points, 0.10 percentage points, and 0.35 percentage points, respectively. In contrast, the ODPEA-based system shows minimum deviations in qualified feed index and miss index at 0.12 percentage points each, and no deviation in multiple index. Both algorithms demonstrate minimal influence of operating speed on missing and multiple seeding rates. The impact of changes in operating speed on these metrics is significantly lower. The efficacy of the ODPEA in eliminating ’pulse sinking’ enhances the algorithm’s ability to assess multiple seeding rates and overall accuracy. The ODPEA-based monitoring system exhibits superior performance, providing theoretical support for the development of monitoring systems in high-speed belt-type seed guiding devices.

Real-Time Structural Health Monitoring of Bridges Using Convolutional Neural Network-Based Loss Factor Analysis for Enhanced Energy Dissipation Detection

This study introduces a novel method for real-time structural health monitoring (SHM) of bridges using a convolutional neural network (CNN) model that leverages loss factor analysis. As bridge structures deteriorate over time, often accelerated by operational loads, maintaining structural integrity and safety becomes critical. The proposed approach utilizes the concept of a loss factor, which represents the process of energy dissipation across different vibration states, as a key indicator of structural health. This factor is computed from vibration energy spectra, which include amplitude and frequency components, processed through the CNN model for high sensitivity to structural changes. The results demonstrate that the energy dissipation of the bridge during operation can be categorized into signals from three distinct sources: structural responses, defects-related indicators, and noise interference. By monitoring variations in the loss factor over time, the model effectively identifies early signs of structural deterioration, which is critical for timely maintenance interventions. The study also highlights the adaptability to different load conditions and environmental factors, ensuring robust performance in various operational scenarios. The findings underscore the potential of the CNN model to transform SHM practices by enhancing early defect detection, supporting preventive maintenance, and ultimately extending the lifespan of bridge infrastructure.

In-situ grown α-MoO3 hollow microspheres achieve high-sensitivity detection of trimethylamine

The direct in-situ growth synthesis method not only ensures a uniform thin layer and an optimized distribution of active sites but also significantly enhances gas adsorption and electron transfer efficiency. In this research, α-MoO3 hollow microspheres were successfully grown in-situ on a ceramic tube using a solvothermal method and applied to the highly sensitive detection of triethylamine (TEA). The α-MoO3 hollow microspheres, due to their unique nanostructure and optimized morphological characteristics, exhibited a high response of 90.965 to 50 ppm TEA at the optimal operating temperature of 217 °C, along with an ultra-low detection limit of 0.1 ppm. The sensitivity mechanism of α-MoO3 to TEA was deeply revealed through characterization techniques such as X-ray photoelectron spectroscopy (XPS), confirming its high selectivity in gas detection. Moreover, the sensor demonstrated excellent reliability in a long-term stability test over 180 days, providing a high-performance detection method for environmental monitoring and industrial safety fields. This research not only contributes a new perspective to the development of TEA detection technology but also provides important experimental evidence and theoretical support for the design and preparation of high-sensitivity gas sensors based on α-MoO3.

Hierarchical explicit–implicit combined sensing-based real-time monitoring method for the service performance of complex equipment

PurposeReal-time monitoring of the critical physical fields of core components in complex equipment is of great significance as it can predict potential failures, provide reasonable preventive maintenance strategies and thereby ensure the service performance of the equipment. This research aims to propose a hierarchical explicit–implicit combined sensing-based real-time monitoring method to achieve the sensing of critical physical field information of core components in complex equipment.Design/methodology/approachSensor deployable and non-deployable areas are divided based on the dynamic and static constraints in actual service. An integrated method of measurement point layout and performance evaluation is used to optimize sensor placement, and an association mapping between information in non-deployable and deployable areas is established, achieving hierarchical explicit–implicit combined sensing of key sensor information for core components. Finally, the critical physical fields of core components are reconstructed and visualized.FindingsThe proposed method is applied to the spindle system of CNC machine tools, and the result shows that this method can effectively monitor the spindle system temperature field.Originality/valueThis research provides an effective method for monitoring the service performance of complex equipment, especially considering the dynamic and static constraints during the service process and detecting critical information in non-deployable areas.

A novel online monitoring method of three-phase voltage unbalance factor with Butterworth-based improved SOGI

A novel online monitoring method of three-phase voltage unbalance factor with Butterworth-based improved SOGI

Behavior Tracking and Analyses of Group-Housed Pigs Based on Improved ByteTrack

Daily behavioral analysis of group-housed pigs provides critical insights into early warning systems for pig health issues and animal welfare in smart pig farming. In this study, our main objective was to develop an automated method for monitoring and analyzing the behavior of group-reared pigs to detect health problems and improve animal welfare promptly. We have developed the method named Pig-ByteTrack. Our approach addresses target detection, Multi-Object Tracking (MOT), and behavioral time computation for each pig. The YOLOX-X detection model is employed for pig detection and behavior recognition, followed by Pig-ByteTrack for tracking behavioral information. In 1 min videos, the Pig-ByteTrack algorithm achieved Higher Order Tracking Accuracy (HOTA) of 72.9%, Multi-Object Tracking Accuracy (MOTA) of 91.7%, identification F1 Score (IDF1) of 89.0%, and ID switches (IDs) of 41. Compared with ByteTrack and TransTrack, the Pig-ByteTrack achieved significant improvements in HOTA, IDF1, MOTA, and IDs. In 10 min videos, the Pig-ByteTrack achieved the results with 59.3% of HOTA, 89.6% of MOTA, 53.0% of IDF1, and 198 of IDs, respectively. Experiments on video datasets demonstrate the method’s efficacy in behavior recognition and tracking, offering technical support for health and welfare monitoring of pig herds.

Smart Monitoring Method for Land-Based Sources of Marine Outfalls Based on an Improved YOLOv8 Model

Land-based sources of marine outfalls are a major source of marine pollution. The monitoring of land-based sources of marine outfalls is an important means for marine environmental protection and governance. Traditional on-site manual monitoring methods are inefficient, expensive, and constrained by geographic conditions. Satellite remote sensing spectral analysis methods can only identify pollutant plumes and are affected by discharge timing and cloud/fog interference. Therefore, we propose a smart monitoring method for land-based sources of marine outfalls based on an improved YOLOv8 model, using unmanned aerial vehicles (UAVs). This method can accurately identify and classify marine outfalls, offering high practical application value. Inspired by the sparse sampling method in compressed sensing, we incorporated a multi-scale dilated attention mechanism into the model and integrated dynamic snake convolutions into the C2f module. This approach enhanced the model’s detection capability for occluded and complex-feature targets while constraining the increase in computational load. Additionally, we proposed a new loss calculation method by combining Inner-IoU (Intersection over Union) and MPDIoU (IoU with Minimum Points Distance), which further improved the model’s regression speed and its ability to predict multi-scale targets. The final experimental results show that the improved model achieved an mAP50 (mean Average Precision at 50) of 87.0%, representing a 3.4% increase from the original model, effectively enabling the smart monitoring of land-based marine discharge outlets.

Health monitoring of automotive clutch system through feature fusion and application of tree-based classifiers

Clutch systems are crucial components of automotive systems, essential for transferring engine power to the wheels through gear shifts. A failed clutch can halt gear shifts and power transmission, rendering a vehicle immobile. Effective fault diagnosis is vital for ensuring reliability, preventing breakdowns, and addressing root causes promptly. Consequently, condition monitoring of the clutch is essential for maintaining system reliability and minimizing unwanted breakdowns. This paper presents an innovative condition-monitoring technique derived from vibration analysis to detect faults in the clutch system. The study involves extracting statistical, histogram, and autoregressive moving average features from acquired vibration signals. The J48 decision tree algorithm is utilized to select the most significant features, which are then classified using tree-based classifiers across three load conditions (no load, 5 kg, and 10 kg) and six clutch conditions. The experimental results demonstrate that the feature fusion strategy significantly enhances classification accuracy. The obtained results state that the classification accuracies for no load, 5 kg load, and 10 kg load were 98.33%, 100.00%, and 99.16%, respectively, while applying feature fusion strategies. This study highlights the effectiveness of feature fusion in improving fault diagnosis accuracy for clutch systems, presenting a robust method for real-time condition monitoring in automotive applications.

Rapid and Continuous Monitoring Method of Radon (222Rn) for Seawater Using a Pulsed Ionization Chamber

Rapid and Continuous Monitoring Method of Radon (²²²Rn) for Seawater Using a Pulsed Ionization Chamber

Design-assisted HPLC-UV method for therapeutic drug monitoring of pholcodine, ephedrine, and guaifenesin in biological fluids.

Drug-drug interactions may amplify or diminish their intended effects, or even produce entirely new effects. Multicomponent mixture HPLC analysis offers a thorough and effective method for comprehending the makeup and behavior of complicated materials, advancing research and development across a range of scientific and industrial domains. A novel experimental design-assisted HPLC methodology for the concurrent investigation of the drug-drug interaction of pholcodine, ephedrine, and guaifenesin in biological fluids has been established. Rather than the routine methodology, the application of the factorial design-HPLC method offers a powerful and efficient tool for the analysis of these compounds. Both mixed and full factorial designs were employed to assess the impact of variable factors on chromatographic results. Utilizing an isocratic elution mode on a C18 column, the chromatographic separation was carried out. 15% Methanol, 5% acetonitrile, and 80% phosphate buffer with 0.1%(v/v) triethylamine set to pH 3 make up the mobile phase flowing at rate 1.0 mL/min. The calibration curves of the drugs show excellent linearity over a concentration ranges: 0.20-13.0µg/mL for PHO, 0.50-20.0µg/mL for EPH and 0.70-20.0µg/mL for GUA with LOQ values of 0.18, 0.38 and 0.50. The fast separation and quantitation in less than 6min is an advantage. Also, the method includes a robust sample preparation protocol for the analysis of complex biological samples, ensuring high selectivity and precision. The ease, speed and cost-effectiveness of the method are ideal for supporting in vitro studies, including drug-drug interaction investigations, especially in bioanalytical labs.

Evaluating the attractiveness of different bait formulations for monitoring Liriomyza sativae (Diptera: Agromyzidae) adults

The growing need for sustainable agricultural practices drives the search for effective alternatives in pest management. Insects like Liriomyza sativae cause significant damage to crops, requiring monitoring and control methods that minimize the use of chemical pesticides. In this context, the use of natural and synthetic attractants for pest capture offers a promising solution while contributing to the preservation of ecological balance. The objective of this work was to study the attractive effect of different formulations for L. sativae adults. The formulations were divided into two groups according to their composition. For group 1, they were based on vinegar and sugar, and for group 2, formulations were based on molasses, invert sugar, hydrolyzed protein, and eugenold. The attractiveness of the formulations was evaluated based on free choice. They were placed in tube-shaped plastic containers and randomly arranged in TNT cages. Evaluations were performed every 48 hours by counting the number of adults in the containers until there was a low capture rate. Data were subjected to analysis of variance testing and means were compared using the Scott-Knott test at 5% significance level. All formulations tested were attractive to adults of L. sativae, with groups 1 and 2 reaching their maximum averages of 55.60 and 68.00 adults, respectively. Both occurred during the second evaluation. Over time, all formulations showed a reduction in capture rate. The use of effective attractants promotes traps that monitor and even control the pest population in a more sustainable way, benefiting the agroecosystem with sustainable agricultural practices. Furthermore, the research expands knowledge about the chemical ecology of insect pests, providing a basis for the chemical communication of these organisms.

A Method for Bioluminescence-Based RNA Monitoring Using Split-Luciferase Reconstitution Techniques.

A bioluminescence-based RNA monitoring method in living cells was developed using a split NanoLuc (NLuc) reconstitution technique. For specific recognition of a target RNA sequence, a mutant PUM-HD (mPUM) was used. The method was applied to β-actin mRNA in various cells, including primary cultures of rat hippocampal neurons. It allowed for continuous observation of intracellular localization distribution of the target mRNA in living cells, providing insights into various biological phenomena involving intracellular RNA localization and dynamics.

High‐Accuracy Geological Deformation Monitoring Through Unmanned Aerial Vehicle Image Registration Without Control Points

ABSTRACTUnmanned aerial vehicles (UAVs) have gained popularity in geological deformation monitoring due to their flexibility, cost‐effectiveness, and ease of image acquisition. However, accurate co‐registration of multi‐phase UAV images, essential for effective monitoring, requires numerous ground control points (GCPs), challenging in deformation‐prone areas. This study introduces a UAV‐based monitoring method that eliminates the need for GCPs. Initially, two‐phase images are integrated for joint aerial triangulation. The first‐phase image and tie points undergo bundle adjustment refinement. Control points are identified as tie points with minimal deviation and used to refine the second‐phase image. Three‐dimensional (3D) models are generated from these refined adjustments and differential analysis between the models reveals geological deformation. Experimental results show that this method automatically identifies stable areas and effectively captures both the location and extent of geological deformation, achieving a relative change accuracy of 0.06 m, which is twice the average ground resolution. Compared to traditional aerial triangulation methods without control points, this approach offers more than a tenfold increase in accuracy. The accuracy is on par with methods that utilize multiple control points, indicating the promising potential of this approach for geological deformation monitoring.

Abstract B015: CSF liquid biopsies reveal disease status and resistance mechanisms in children undergoing targeted therapy for malignant brain tumors

Abstract BACKGROUND: Patients with relapsed or refractory brain tumors face poor outcomes due to the aggressive nature of their disease. Recent studies suggest that administration of cell cycle inhibitors may help slow tumor progression. In the Phase I trial SJDAWN (NCT03434262) conducted at St. Jude Children’s Research Hospital, CDK4/6 inhibitor therapy (ribociclib) was assessed for safety and efficacy in such patients. To understand how tumors evolve in response to this treatment, longitudinal CSF liquid biopsies from SJDAWN were retrospectively analyzed by cell-free DNA (cfDNA) sequencing. METHODS: CSF samples (n=143) were collected by lumbar puncture from 34 of 68 (50%) patients before and during treatment. cfDNA was extracted and analyzed using an innovative DNA methylation-based platform that concurrently detects tumor-associated copy number alterations, classifies tumor subtypes, and distinguishes malignant from non-malignant methylation signatures. cfDNA inputs ranged from 50 to 500 pg. CSF profiles were compared to patient-matched tumor tissue DNA methylation arrays and subsequently correlated with MRI findings at corresponding timepoints. RESULTS: Circulating tumor DNA (ctDNA) was detected in 29 of 34 (85%) patients, yielding accurate tumor classification even in the setting of diploid genomes. CNV profiles from CSF liquid biopsies strongly correlated with patient-matched tumor tissue methylation arrays and often revealed genetic events not previously captured in the tumor tissue. Resistance to CDK4/6 inhibition was characterized by focal amplification of positive cell cycle regulators, including CDK6, CCND2, MYCN, and RRM2 that were detectable in CSF ∼6 months prior to radiographic progression. Remarkably, three high-risk Group 3/4-MB patients were progression-free for >1.5 years. The trend of their longitudinal cfDNA profiles varied: one had persistent ctDNA detected throughout therapy with resistance emerging at ∼1.5 years; another had undetectable ctDNA for ∼2 years before reemergence of tumor; and the third remained MRD-negative, potentially owing to reduced ctDNA release resulting from CDK4/6 inhibition. Methylation changes in response to treatment were also observed, providing unprecedented insights into epigenetic landscapes associated with tumor evolution concurrent with targeted therapy. CONCLUSIONS: Methylation-based analysis of CSF liquid biopsies provides an ultra-sensitive and comprehensive method for serial disease monitoring. Even with sub-nanogram cfDNA inputs, tumor reemergence and resistance mechanisms were detectable months before MRI changes, enabling timely clinical intervention. Resistance to CDK4/6 inhibition is mediated by focal amplification of cell cycle regulators, suggesting alternative approaches will be required to overcome this path of resistance. CDK4/6 inhibitor therapy offers clinical benefit to a specific subset of high-risk Group 3/4-MB patients, motivating expanded evaluation of this population in future trials. Citation Format: Katie Han, Kyle S Smith, Anna Kostecka, Sandeep Dhanda, Hong Lin, Margit Mikkelsen, Giles W Robinson, Paul A Northcott. CSF liquid biopsies reveal disease status and resistance mechanisms in children undergoing targeted therapy for malignant brain tumors [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr B015.

A mileage correction method of pipeline inertial measurement for dam internal deformation monitoring

Internal deformation monitoring is of great significance to the safe operation and maintenance of dams. Some scholars have carried out dam internal deformation monitoring by pipeline inertial measurement with a pipeline measurement robot, which has improved the monitoring accuracy and efficiency considerably, compared with the traditional monitoring methods. However, there are still shortcomings in this method. Because the odometer on the pipeline measurement robot will slip or slide, resulting in errors in the mileage of the multiple measurement routes, which hinders the development of the accuracy of pipeline inertial measurement. This paper proposes a mileage correction method based on junction detection to improve the accuracy of pipeline inertial measurement for better dam internal deformation monitoring. The mileage correction method establishes a reference model of pipeline junction location, detects the locations of pipeline junction welds on the measurement routes using two k-mean clustering, and corrects the odometer data based on the difference between the detected and reference locations. We evaluate the enhancement of this mileage correction method on the internal accuracy and deviation values compared with other method for pipeline inertial measurement. The proposed method is validated using measured data from the internal pipeline of Tianchi Dam. The results show that compared with the results directly fused without detection–correction, the method proposed in this paper reduces the root-mean-square error of the results by an average of 30.30% and improves the deviation compared with the reference deformation by an average of 2.3 mm. Therefore, the method has good results and practical applications.

Distribution grid monitoring based on feature propagation using smart plugs

AbstractSmart home power hardware makes it possible to collect a large number of measurements from the distribution grid with low latency. However, the measurements are imprecise, and not every node is instrumented. Therefore, the measured data must be corrected and augmented with pseudo-measurements to obtain an accurate and complete picture of the distribution grid. Hence, we present and evaluate a novel method for distribution grid monitoring. This method uses smart plugs as measuring devices and a feature propagation algorithm to generate pseudo-measurements for each grid node. The feature propagation algorithm exploits the homophily of buses in the distribution grid and diffuses known voltage values throughout the grid. This novel approach to deriving pseudo-measurement values is evaluated using a simulation of SimBench benchmark grids and the IEEE 37 bus system. In comparison to the established GINN algorithm, the presented approach generates more accurate voltage pseudo-measurements with less computational effort. This enables frequent updates of the distribution grid monitoring with low latency whenever a measurement occurs.

Intelligent water quality prediction system with a hybrid CNN–LSTM model

ABSTRACT Water pollution remains a longstanding challenge globally, prompting substantial investment in water quality protection. The integration of advanced machine learning models offers promising avenues for accurate water quality prediction, enabling proactive measures to safeguard water sources. Presently, water quality assessment relies predominantly on physical and chemical metrics. This study developed MultiLayer Perceptron (MLP), eXtreme Gradient Boosting (XGBoost), long short-term memory (LSTM), and a hybrid CNN–LSTM model to forecast pH and dissolved oxygen (DO) levels. Results demonstrated the hybrid model's superior performance, with mean squared errors (MSEs) of 0.0015 and 0.0361 for pH and DO prediction, respectively. For Water Quality Classification (WQC), Random Forest (RF), k-Nearest Neighbors (kNNs), Support Vector Machine (SVM), and Light gradient Boosting Machine (Light GBM) were employed, with SVM achieving the highest accuracy at 88.75%. The research underscores the effectiveness of the CNN–LSTM model in predicting pH and DO levels. Leveraging these predictions as inputs to the SVM model offers valuable insights, particularly in regions where conventional monitoring methods face limitations. This streamlined approach, requiring only two parameters, signifies a significant advancement in accurate water quality prediction.

Abstract 4138494: Remote Blood Pressure Monitoring and Intervention as a better and cost-effective method of Hypertension control compared to Usual care: A Systematic Review

Introduction/Background: Hypertension is a leading cause of morbidity and mortality. Despite treatment recommendations and encouragement of lifestyle changes, the actual rate of blood pressure control is still not at par. Remote intervention methods have been shown to achieve better rates of blood pressure control utilising lesser resources and lower healthcare costs. We conducted a systematic review by qualitatively evaluating the potential impact of remote monitoring and interventions compared to usual care in controlling Hypertension. Research Question: Are remote monitoring techniques and interventions better than usual in-person blood pressure checks for hypertension control? Methods: We did a comprehensive literature search of Pubmed/MEDLINE, Google scholar, Cochrane central Library, PLOS ONE, ScienceDirect and Clinicaltrials.gov to identify eligible randomised control trials and clinical trials published between July 2013 and May 2025. A Risk of Bias assessment was conducted using Cochrane Risk of Bias tools such as ROB-2 for randomised control trials and ROBINS-1 for cohort studies. This study follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Results: Of the 5,185 records analysed, 10 randomised control trials, 1 cohort study and 1 implementation study satisfied our inclusion criteria. Compared to usual care, remote monitoring methods showed significant difference in systolic blood pressure control at 6-12 months in 9 out of 12 studies, while 3 studies did not show any significant difference. Quality of life, physical health, mental health and drug compliance have also shown a significant positive difference in patients. Remote self monitoring interventions were found to be more cost-effective than usual care. They also reduced the load of patients seeking appointments for hypertension control thereby improving the quality of patient care. Conclusion: The management of clinical hypertension is a challenging entity. Conventional methods of regular in-office blood pressure checks are both tedious and resource consuming. Using remote monitoring methods have shown to significantly improve blood pressure control compared to routine methods. It also eliminates white-coat hypertension during follow-up blood pressure checks which leads to more appropriate interventions. Remote monitoring and intervention for hypertension control is time-effective, cost-effective and improves quality of life.

Changing snow conditions are challenging moose (Alces alces) surveys in Alaska

AbstractSnow conditions are changing rapidly across our planet, which has important implications for wildlife managers. In Alaska, USA, the later arrival of snow is challenging wildlife managers' ability to conduct aerial fall (autumn) moose (Alces alces) surveys. Complete snow cover is required to reliably detect and count moose using visual observation from an aircraft. With inadequate snow to help generate high‐quality moose survey data, it is difficult for managers to determine if they are effectively meeting population goals and optimizing hunting opportunities. We quantified past relationships and projected future trends between snow conditions and moose survey success across 7 different moose management areas in Alaska using 32 years (1987–2019) of moose survey data and modeled snow data. We found that modeled mean snow depth was 15 cm (SD = 11) when moose surveys were initiated, and snow depths were greater in years when surveys were completed compared to years when surveys were canceled. Further, we found that mean snow depth toward the beginning of the survey season (1 November) was the best predictor of whether a survey was completed in any given year. Based on modeled conditions, the trend in mean snow depth on 1 November declined from 1980 to 2020 in 5 out of 7 survey areas. These findings, coupled with future projections, indicated that by 2055, the delayed onset of adequate snow accumulation in the fall will prevent the completion of moose surveys over roughly 60% of Alaska's managed moose areas at this time of the year. Our findings can be used by wildlife managers to guide decisions related to the future reliability of aerial fall moose surveys and help to identify timelines for development of alternate measurement and monitoring methods.

The Effect of Vegetation Ecological Restoration by Integrating Multispectral Remote Sensing and Laser Point Cloud Monitoring Technology

This research aims to evaluate and monitor the effectiveness of vegetation ecological restoration by integrating Multispectral Remote Sensing (MRS) and laser point cloud (LPC) monitoring technologies. Traditional vegetation restoration monitoring methods often face challenges of inaccurate data and insufficient coverage, and the use of MRS or LPC techniques alone has its limitations. Therefore, to more accurately monitor the vegetation restoration status, this study proposes a new monitoring method that combines the advantages of the large-scale coverage of MRS technology and the high-precision three-dimensional structural data analysis capability of LPC technology. This new method was applied in the Daqing oilfield area of China, aiming to provide effective ecological restoration assessment methods through the precise monitoring and analysis of regional vegetation growth and coverage. The results showed that there was a negative correlation between the vegetation humidity index and vegetation growth in the Daqing oilfield in 2023. The estimated monitoring effect of the research method could reach over 90%, and the coverage area of hydrangea restoration in the monitoring year increased by 7509 km2. The research technology was closer to the actual coverage situation. The simulation image showed that the vegetation coverage in the area has significantly improved after returning farmland to forests. Therefore, the technical methods used can effectively monitor the ecological restoration of vegetation, which has great research significance for both vegetation restoration and monitoring.