Remote Sensing Monitoring Technology Research Articles

Space–Ground Remote Sensor Network for Monitoring Suspended Sediments in the Yellow River Basin

The Yellow River, China’s second-largest river, is renowned for its high sediment content. In response to the potential impacts of climate change on Yellow River water resources and water environmental management, an advanced monitoring and forecasting system for water and sediment throughout the entire Yellow River basin—from its source to the sea—is urgently needed. In this paper, based on the current status of water and sediment monitoring technologies, we proposed an integrated remote sensing monitoring network that combines satellite remote sensing, drone remote sensing, and ground-based wireless automatic monitoring networks, aiming to achieve the digital monitoring of water and sediment across the entire Yellow River basin, from its upper reaches to its estuary in the Bohai Sea. By utilizing ground-based in situ hyperspectral stations for sediment source areas in the upper reaches, such as the Qingshui River basin in Ningxia, and satellite remote sensing for midstream processes in the Xiaolangdi reservoir before the flood season in 2023, as well as downstream monitoring at the Yellow River estuary, this paper demonstrates the novelty and efficiency of the space–air–ground integrated remote sensing monitoring technology.

Monitoring of floating structures using segmentation and object classification of remotely-sensed images

Abstract With the rapid development of Marine aquaculture, remote sensing monitoring of high space separation Marine floating raft aquaculture has become a key technology to ensure aquaculture efficiency and environmental sustainability. A novel multi-scale layered cascade capsule network (MSHCN) model is proposed to improve the accuracy and efficiency of remote sensing image analysis for high space separation Marine floating raft culture. Firstly, based on the basic principle of the capsule network, the traditional convolutional neural network is improved, and a multi-scale feature extraction mechanism is introduced to adapt to the diversity and complexity of images in the culture area. Secondly, by building a hierarchical cascade structure, the model can further improve its ability to capture small and large scale features, so as to better understand the spatial distribution characteristics of the farming environment. In addition, in the experimental part, we verified with real remote sensing data sets, and the results showed that the MSHCN model outperformed the current mainstream methods on several key performance indicators, including classification accuracy, detection speed and model generalization ability. Finally, the potential value of this model in practical application and future optimization direction is discussed, which provides a new idea and tool for the development of remote sensing monitoring technology for high space separation floating raft culture.

Comparison of Extraction accuracy of Sugarcane from different resolution satellite images using Deep lab V3+ Mode

Abstract. Sugarcane is an annual or perennial persistent rooted tropical and subtropical herb that grows in tropical and subtropical regions. As China's production ranks among the world's leading, sugarcane industry is an important part of agricultural economy in China. As the largest sugarcane production center in China, Guangxi is one of the most suitable areas for sugarcane cultivation in China and even in the world. Sugarcane industry, as an agricultural advantageous industry in Guangxi, not only has a significant image to the national economy of the region, but also is closely related to the issue of security of national sugar supply. Continuous cropping of sugarcane is very common in Guangxi, which is very helpful for the concentration selection of sugarcane samples. The wide application of satellite remote sensing monitoring technology has become an indispensable means of natural resources monitoring. Using optical satellite remote sensing image to identify and extract sugarcane planting areas is of great significance to quickly and conveniently grasp the information of sugarcane distribution and yield. In this paper, the precision of sugarcane extraction from GF1 and GF2 satellite images is analyzed by using deeplab V3 + model, the effect of optical remote sensing images with different resolution on sugarcane extraction accuracy was studied to provide better data support for dynamic monitoring of sugarcane planting.

Estimating and Mapping Soil Salinity in Multiple Vegetation Cover Periods by Using Unmanned Aerial Vehicle Remote Sensing

Soil salinization is a severe soil degradation issue in arid and semiarid regions. The distribution of soil salinization can prove useful in mitigating soil degradation. Remote sensing monitoring technology is available for obtaining the distribution of soil salinization rapidly and nondestructively. In this study, experimental data were collected from seven study areas of the Hetao Irrigation District from July to August in 2021 and 2022. The soil salt content (SSC) was considered at various soil depths, and the crop type and time series were considered as environmental factors. We analyzed the effects of various environmental factors on the sensitivity response of unmanned aerial vehicle (UAV)-derived spectral index variables to the SSC and assessed the accuracy of SSC estimations. The five indices with the highest correlation with the SSC under various environmental factors were the input parameters used in modeling based on three machine learning algorithms. The best model was subsequently used to derive prediction distribution maps of the SSC. The results revealed that the crop type and time series did not affect the relationship strength between the SSC and spectral indices, and that the classification of the crop type and time series can considerably enhance the accuracy of SSC estimation. The mask treatment of the soil pixels can improve the correlation between some spectral indices and the SSC. The accuracies of the ANN and RFR models were higher than SVR accuracy (optimal R2 = 0.52–0.79), and the generalization ability of ANN was superior to that of RFR. In this study, considering environmental factors, a UAV remote sensing estimation and mapping method was proposed. The results of this study provide a reference for the high-precision prediction of soil salinization during the vegetation cover period.

Monitoring and Analyzing the Effectiveness of the Effective Refuge Area of Emergency Shelters by Using Remote Sensing: A Case Study of Beijing’s Fifth Ring Road

The effective refuge area is a key indicator in the study of emergency shelters. Accurately extracting the effective refuge area and analyzing the effectiveness of emergency shelters are of great significance for site selection, spatial distribution, and the evaluation of suitability. Beijing is one of only three capitals in the world located in a high-seismic-intensity zone of magnitude 8. The fast and accurate monitoring of effective refuge areas and an analysis of the effectiveness of emergency shelters are conducive to evacuation planning and disaster prevention and mitigation, and they promote the construction of a resilient city. However, the extraction of effective refuge areas in existing studies is not only a time-consuming and labor-intensive task but also has accuracy and efficiency problems, resulting in less precise validity analyses. In this paper, a remote sensing monitoring technology system for the effective refuge areas of emergency shelters is proposed based on multi-source data. Different methods were used to extract various land features, such as buildings and collapsed areas, water, dense areas of understory vegetation, and steep slope areas that cannot be evacuated, to obtain the effective refuge area at a detailed scale, in combination with the service radius of emergency shelters, the population distribution, and the actual road network, the criteria for effectiveness analysis were established for the effective open space ratio, capacity, per capita accessible effective refuge area, and population allocation gap. Taking the area within the Fifth Ring Road of Beijing as an example, the effectiveness of emergency shelters was analyzed at both the whole scale and a local scale. The results show that the effective refuge areas of different emergency shelters in Beijing vary significantly, with the smallest effective refuge area being located in Rings 2–3 and the largest one being located in Rings 4–5; between different regions, there are differences in the effectiveness. This study provides a feasible method for the fast, accurate, and detailed extraction of the effective refuge areas of emergency shelters and also provides a reference for emergency planning for disaster prevention and mitigation.

An Improved YOLOv5 Method to Detect Tailings Ponds from High-Resolution Remote Sensing Images

Tailings ponds’ failure and environmental pollution make tailings monitoring very important. Remote sensing technology can quickly and widely obtain ground information and has become one of the important means of tailings monitoring. However, the efficiency and accuracy of traditional remote sensing monitoring technology have difficulty meeting the management needs. At the same time, affected by factors such as the geographical environment and imaging conditions, tailings have various manifestations in remote sensing images, which all bring challenges to the accurate acquisition of tailings information in large areas. By improving You Only Look Once (YOLO) v5s, this study designs a deep learning-based framework for the large-scale extraction of tailings ponds information from the entire high-resolution remote sensing images. For the improved YOLOv5s, the Swin Transformer is integrated to build the Swin-T backbone, the Fusion Block of efficient Reparameterized Generalized Feature Pyramid Network (RepGFPN) in DAMO-YOLO is introduced to form the RepGFPN Neck, and the head is replaced with Decoupled Head. In addition, sample boosting strategy (SBS) and global non-maximum suppression (GNMS) are designed to improve the sample quality and suppress repeated detection frames in the entire image, respectively. The model test results based on entire Gaofen-6 (GF-6) high-resolution remote sensing images show that the F1 score of tailings ponds is significantly improved by 12.22% compared with YOLOv5, reaching 81.90%. On the basis of both employing SBS, the improved YOLOv5s boots the mAP@0.5 of YOLOv5s by 5.95%, reaching 92.15%. This study provides a solution for tailings ponds’ monitoring and ecological environment management.

Migration Path Tracking Algorithm of Egret Birds Based on Intelligent Remote Sensing Monitoring

There are billions of migratory birds migrating between breeding grounds and wintering grounds in the world every year. Egret is no exception. During the migration period, they need to travel for a long time and long distances. At present, the protection strategies of egrets and the dynamic mechanism of migration routes are concerned by experts in related fields. Researchers generally explore the above problems by tracking the migration paths of egrets. Most of the existing path tracking algorithms use some classical tracking algorithms, but the tracking accuracy of the classical algorithms is low. Therefore, this paper has applied the intelligent remote sensing monitoring technology to the improvement of the path tracking algorithm, used artificial intelligence technology to remove the redundant information of the image, and combined the Kalman filter with the single-target long-term algorithm to improve the tracking algorithm. 100 samples were simulated, and the experimental results showed that the tracking accuracy of the egret bird migration path tracking algorithm based on intelligent remote sensing monitoring was improved by 8.37% compared with the algorithm before improvement, which has better utilization value.

Dynamics of blowouts indicating the process of grassland desertification

AbstractSand desertification is a serious ecological and environmental problem in semiarid grassland areas; thus an accurate understanding of its causes and process mechanisms is of significance for grassland desertification control. With the popularization of remote sensing monitoring technology, most research has focused on analyzing the trends of grassland sand patches and their impact on regional desertification, whereas fewer studies have explored the mechanisms of grassland desertification development. This study analyzed the dynamic changes in grassland landscape in the Hulun Buir grassland of China via multiperiod high‐resolution remote sensing images from 1959 to 2020, and examined the morphological changes, erosion patterns, grain size characteristics and sediment compaction of two trough blowouts. Our study noted that under climate change and human activities, grassland desertification is becoming increasingly serious, and its main manifestation is the occurrence and expansion of blowouts. Changes in blowout morphologies indicate that the area of the deflation basin shows a state of continuous growth, whereas the area of the depositional lobe shows fluctuating changes, which could mean that the deflation basin may be a useful indicator of desertification. Based on a detailed analysis of both climate and grassland changes, we believe that human activities determine the occurrence of blowouts, and climatic conditions determine the expansion of blowouts and the desertification degree. The morphological changes of blowouts are related to the removal mode and erosion resistance of the grassland soil, and the rate of removal of the soil limits the rate of blowout expansion.

What is the relationship between land use and surface water quality? A review and prospects from remote sensing perspective.

Good surface water quality is critical to human health and ecology. Land use determines the surface water heat and material balance, which cause climate change and affect water quality. There are many factors affecting water quality degradation, and the process of influence is complex. As rivers, lakes, and other water bodies are used as environmental receiving carriers, evaluating and quantifying how impacts occur between land use types and surface water quality is extremely important. Based on the summary of published studies, we can see that (1) land use for agricultural and construction has a negative impact on surface water quality, while woodland use has a certain degree of improvement on surface water quality; (2) statistical methods used in relevant research mainly include correlation analysis, regression analysis, redundancy analysis, etc. Different methods have their own advantages and limitations; (3) in recent years, remote sensing monitoring technology has developed rapidly, and has developed into an effective tool for comprehensive water quality assessment and management. However, the increase in spatial resolution of remote sensing data has been accompanied by a surge in data volume, which has caused difficulties in information interpretation and other aspects.

Market Development and Prospects of Farmland Irrigation Remote Sensing Monitoring Technology

Market Development and Prospects of Farmland Irrigation Remote Sensing Monitoring Technology

Intelligent Management of Land Resources Based on Internet of Things and GIS Technology

In order to improve the effect of land resource management, this paper combines the Internet of Things technology and GIS technology to build an intelligent management system for gradient resources to improve the efficiency of land resource management. Aiming at the hybrid intelligent model of wetland resource remote sensing monitoring technology, this paper analyzes and studies the remote sensing image processing theory. Moreover, this paper studies in detail remote sensing image restoration, TM image reflectivity simulation imaging, image enhancement technology, optimal band selection based on the characteristics of wetland resources, expert decision analysis, deep mining of image data, knowledge reasoning, and decision tree analysis to form a theoretical support system for a hybrid intelligent classification model for wetland resources. The research shows that the intelligent management system of land resources based on the Internet of Things and GIS technology has a good effect in the collection and processing of land resource information and can effectively improve the management efficiency of land resources.

Retrieval Algorithm of Water Pollutant Concentration Based on UAV Remote Sensing Technology

With the development of the society and economy, traditional water pollution monitoring methods can no longer meet the normal needs of work. Unmanned aerial vehicle remote sensing technology has gradually emerged, and it has shown a development trend of multimodel and multifunction. However, the application of UAV remote sensing technology in water pollution monitoring is in its infancy and has not formed a unified method and standard. This paper introduces the disadvantages of UAV Remote Sensing Technology in water pollution monitoring and provides a way to improve the application level of UAV Remote Sensing Technology in water pollution monitoring. In order to make full use of the advantages of remote sensing monitoring technology in water ecological environment monitoring, including wide coverage, fast analysis speed, accurate and reliable monitoring results, and large amount of information, according to the spectral effect of polluted water, a remote sensing inversion model of river and lake parameters is established to determine the water quality of rivers and lakes. Concentrations of pollutants in the body are inverted and analyzed. The analysis results show that the fitting effect of the inversion model is accurate and the accuracy errors reflected by the degree of dispersion and deviation are also acceptable. The inversion results of the concentration of river and lake characteristic parameters based on the Gaofen-1 remote sensing image data are in line with the actual situation. The research results can greatly strengthen the monitoring capacity of the sewage outlet and provide reference for related research on water pollution caused by the sewage outlet.

Study on Meteorological Disaster Monitoring of Field Fruit Industry by Remote Sensing Data

Meteorological disasters have brought a great negative impact on people’s lives. With the rapid development of modern science and technology, the detection technology of meteorological disasters has been continuously improved. At present, satellite remote sensing detection technology has made gratifying achievements, and it has a good application in meteorological disaster prediction. In this paper, the application of satellite remote sensing technology in the process of meteorological disaster monitoring is discussed in depth. In traditional work, the accuracy and timeliness of meteorological disaster monitoring is the key and difficult point of meteorological disaster prevention. Using satellite telemetry to monitor meteorological disasters can effectively improve the accuracy and timeliness of meteorological disaster monitoring, provide reasonable solutions and decision-making basis for meteorological disaster prevention, and achieve the purpose of disaster prevention and mitigation. This paper introduces the basic principle, technical system, and important role of satellite remote sensing technology, expounds on the application of satellite remote sensing technology in the monitoring of agricultural meteorological disasters such as water, drought, freezing, and hail, and provides a scientific reference for farmers, agricultural sustainability, and agricultural decision-making. The continuous development of our country’s modern social economy has put forward higher requirements for agricultural production. Traditional monitoring technology can no longer meet the needs of agricultural meteorological disaster monitoring. The scientific application of remote sensing monitoring technology has important value, which can effectively improve the detection level and make it have higher accuracy and real-time performance, thereby promoting modern agricultural production in China. Based on the analysis of the application value of remote sensing monitoring technology, this paper comprehensively discusses the specific conditions of different disasters monitored by remote sensing monitoring technology in agricultural production.

Progress of environmental remote sensing monitoring technology in China and some related frontier issues

Progress of environmental remote sensing monitoring technology in China and some related frontier issues

Machine learning method for quick identification of water quality index (WQI) based on Sentinel-2 MSI data: Ebinur Lake case study

Abstract Surface water quality is an important factor affecting the ecological environment and human living environment. The monitoring of surface water quality by remote sensing monitoring technology can provide important research significance for water resources protection and water quality evaluation. Finding the optimal spectral index sensitive to water quality for remote sensing monitoring of water quality is extremely important for surface water quality analysis and treatment in the Ebinur Lake Basin in arid areas. This study used Sentinel-2MSI data at 10 m resolution to quickly monitor the water quality of the watershed. Through laboratory experiments and measurement data from the Ebinur Lake Basin, 22 water quality parameters (WQPs) were obtained. Through Z-score and redundancy analysis, 9 WQPs with significant contributions were extracted. Based on the remote sensing spectral band, four water indexes (NDWI, NWI, EWI, AWEI-nsh) and 2D modeling spectral index (DI, RI, NDI), the correlation analysis between WQPs and two kinds of spectral band indexes is carried out, and it is concluded that the overall correlation between WQP and 2D spectral modeling is more relevant. This paper calculates the evaluation and models the 2D spectrum of the Water Quality Index (WQI). The WQI is predicted and modeled through four machine learning algorithms (RF, SVM, PLSR, PLSR-SVM).The results show that the inversion effect of the two-dimensional spectral modeling index on water quality parameters (WQPs) is superior to that of the water index, and the correlation coefficient of the DI (R12-R1) SWIR-2 and BLUE band interpolation index reaches 0.787. On this basis, three kinds of two-dimensional spectral modeling indexes are used to inversely synthesize the WQI, and the correlation coefficient of the ratio index of the RI (R11/R8) SWIR-1 and near-infrared (NIR) bands is preferably 0.69. In the WQI prediction, the partial least squares regression support vector machine (PLSR-SVM) model in machine learning algorithms has good modeling and prediction effects (R2c = 0.873, R2v = 0.87), which can provide a good basis. The research results provide references for remote monitoring of surface water in arid areas, and provide a basis for water quality prediction and safety evaluation.

Discussion on Remote Sensing Monitoring of Agrometeorological Disasters

<p>The continuous development of modern society and economy puts forward higher requirements for agricultural production in China. Traditional monitoring technology can no longer meet the needs of agrometeorological disaster monitoring. Scientific application of remote sensing monitoring technology has extremely important value, which can effectively improve the detection effect, make it more accurate and real-time, and then promote China’s modern agricultural production. This article analyzes the application value of remote sensing monitoring technology. Based on this, it comprehensively explores the specific situation of monitoring different disasters by remote sensing monitoring technology in the agricultural production process.</p>

Monitoring of Interfacial Debonding of Concrete Filled Pultrusion-GFRP Tubular Column Based on Piezoelectric Smart Aggregate and Wavelet Analysis.

The concrete filled pultrusion-GFRP (Glass Fiber Reinforced Polymer) tubular column (CFGC) is popular in hydraulic structures or regions with poor environmental conditions due to its excellent corrosion resistance. Considering the influence of concrete hydration heat, shrinkage, and creep, debonding may occur in the interface between the GFRP tube and the concrete, which will greatly reduce the cooperation of the GFRP tube and concrete, and will weaken the mechanical property of CFGC. This paper introduces an active monitoring method based on the piezoelectric transducer. In the active sensing approach, the smart aggregate (SA) embedded in the concrete acted as a driver to transmit a modulated stress wave, and the PZT (Lead Zirconate Titanate) patches attached on the outer surface of CFGC serve as sensors to receive signals and transfer them to the computer for saving. Two groups of experiments were designed with the different debonding areas and thicknesses. The artificial damage of CFGC was identified and located by comparing the value of the delay under pulse excitation and the difference of wavelet-based energy under sweep excitation, and the damage indexes were defined based on the wavelet packet energy to quantify the level of the interface damage. The results showed that the debonding damage area of CFGC can be identified effectively through the active monitoring method, and the damage index can accurately reflect the damage level of the interface of GFRP tube and concrete. Therefore, this method can be used to identify and evaluate the interface debonding of CFGC in real time. In addition, if the method can be combined with remote sensing technology, it can be used as a real-time remote sensing monitoring technology to provide a solution for interface health monitoring of CFGC.

Sea Ice Remote Sensing Monitoring Technology and Application over Hebei Coastal Zone

Sea Ice Remote Sensing Monitoring Technology and Application over Hebei Coastal Zone

Study and analysis of application of ZY-1 02C satellite data to remote sensing monitoring technology for exploration of mineral resources

With the HR / MUX data of ZY- 1 02 C obtained from Chengde and Xunwu experimental areas as examples,the authors applied the remote sensing monitoring technology to the exploration of mineral resources at the scale of 1 ∶ 50 000. The problems in the application process were studied and analyzed,the solutions and recommendations,which play a demonstration role in the large- scale application of mineral exploration monitoring,were proposed. Some conclusions have been reached: In the data processing,the 02 C HR1and HR2 data with 2. 36 m resolution must be orthorectified first before splicing,and the MUX data with 10 m resolution should be composed with simulated true color by using the false color composition method. In the aspect of mine object recognition,the 02 C image can better reflect mining position,transit field,mine construction and other types of mining features in comparison with SPOT5 image,but its relative smooth texture information and relative fuzzy boundary of the mine ground objects increase the difficulty of indoor interpretation. In economic efficiency,the price advantage of 02 C data is obvious. Although the 02 C data processing and interpreting have larger workload,it can meet the remote sensing monitoring of mineral exploration at 1∶ 50 000 scale,and the overall economic benefit is significant.