Disaster Monitoring Research Articles

Crop Phenology Modelling Using Proximal and Satellite Sensor Data

Understanding crop phenology is crucial for predicting crop yields and identifying potential risks to food security. The objective was to investigate the effectiveness of satellite sensor data, compared to field observations and proximal sensing, in detecting crop phenological stages. Time series data from 122 winter wheat, 99 silage maize, and 77 late potato fields were analyzed during 2015–2017. The spectral signals derived from Digital Hemispherical Photographs (DHP), Disaster Monitoring Constellation (DMC), and Sentinel-2 (S2) were crop-specific and sensor-independent. Models fitted to sensor-derived fAPAR (fraction of absorbed photosynthetically active radiation) demonstrated a higher goodness of fit as compared to fCover (fraction of vegetation cover), with the best model fits obtained for maize, followed by wheat and potato. S2-derived fAPAR showed decreasing variability as the growing season progressed. The use of a double sigmoid model fit allowed defining inflection points corresponding to stem elongation (upward sigmoid) and senescence (downward sigmoid), while the upward endpoint corresponded to canopy closure and the maximum values to flowering and fruit development. Furthermore, increasing the frequency of sensor revisits is beneficial for detecting short-duration crop phenological stages. The results have implications for data assimilation to improve crop yield forecasting and agri-environmental modeling.

Evaluation of synergy ability and reconstruction of synergy organization for marine disaster monitoring and early warning in coastal cities, China

Evaluation of synergy ability and reconstruction of synergy organization for marine disaster monitoring and early warning in coastal cities, China

Multi-UAV Collaborative Absolute Vision Positioning and Navigation: A Survey and Discussion

The employment of unmanned aerial vehicles (UAVs) has greatly facilitated the lives of humans. Due to the mass manufacturing of consumer unmanned aerial vehicles and the support of related scientific research, it can now be used in lighting shows, jungle search-and-rescues, topographical mapping, disaster monitoring, and sports event broadcasting, among many other disciplines. Some applications have stricter requirements for the autonomous positioning capability of UAV clusters, requiring its positioning precision to be within the cognitive range of a human or machine. Global Navigation Satellite System (GNSS) is currently the only method that can be applied directly and consistently to UAV positioning. Even with dependable GNSS, large-scale clustering of drones might fail, resulting in drone cluster bombardment. As a type of passive sensor, the visual sensor has a compact size, a low cost, a wealth of information, strong positional autonomy and reliability, and high positioning accuracy. This automated navigation technology is ideal for drone swarms. The application of vision sensors in the collaborative task of multiple UAVs can effectively avoid navigation interruption or precision deficiency caused by factors such as field-of-view obstruction or flight height limitation of a single UAV sensor and achieve large-area group positioning and navigation in complex environments. This paper examines collaborative visual positioning among multiple UAVs (UAV autonomous positioning and navigation, distributed collaborative measurement fusion under cluster dynamic topology, and group navigation based on active behavior control and distributed fusion of multi-source dynamic sensing information). Current research constraints are compared and appraised, and the most pressing issues to be addressed in the future are anticipated and researched. Through analysis and discussion, it has been concluded that the integrated employment of the aforementioned methodologies aids in enhancing the cooperative positioning and navigation capabilities of multiple UAVs during GNSS denial.

Enhancing FAIR Data Services in Agricultural Disaster: A Review

The agriculture sector is highly vulnerable to natural disasters and climate change, leading to severe impacts on food security, economic stability, and rural livelihoods. The use of geospatial information and technology has been recognized as a valuable tool to help farmers reduce the adverse impacts of natural disasters on agriculture. Remote sensing and GIS are gaining traction as ways to improve agricultural disaster response due to recent advancements in spatial resolution, accessibility, and affordability. This paper presents a comprehensive overview of the FAIR agricultural disaster services. It holistically introduces the current status, case studies, technologies, and challenges, and it provides a big picture of exploring geospatial applications for agricultural disaster “from farm to space”. The review begins with an overview of the governments and organizations worldwide. We present the major international and national initiatives relevant to the agricultural disaster context. The second part of this review illustrates recent research on remote sensing-based agricultural disaster monitoring, with a special focus on drought and flood events. Traditional, integrative, and machine learning-based methods are highlighted in this section. We then examine the role of spatial data infrastructure and research on agricultural disaster services and systems. The generic lifecycle of agricultural disasters is briefly introduced. Eventually, we discuss the grand challenges and emerging opportunities that range from analysis-ready data to decision-ready services, providing guidance on the foreseeable future.

Study on characteristic maps of multi-spectral remote sensing images of geological hazards

Geological disasters, as the main problems facing urban construction and development, directly threaten the life and property of social residents and living environment, with sudden, wide distribution, destruction and other characteristics. Nowadays, the traditional ground monitoring technology has been unable to meet the needs of geological disaster monitoring research in the new era. Therefore, on the basis of mastering the traditional monitoring experience, scholars from various countries put forward the use of multi-spectral remote sensing image data to obtain the data information of geological disasters in various regions, study the main causes and distribution rules of geological disasters, and put forward effective prediction models and solutions as soon as possible. On the basis of understanding the current situation of geological disaster monitoring and prevention work in China, this paper uses multi-source remote sensing image fusion technology to deeply study the multi-spectral remote sensing image feature map of geological disaster and clarify the relationship between different types of remote sensing image features, so as to provide effective basis for earthquake disaster monitoring and prevention work.

Heat-Resistant and Color-Changing Luminescent Polysulfone for Information Encryption and Fire Alarming.

An intrinsic difficulty with thermally responsive photoluminescent materials is that high temperatures usually destroy luminance due to the notorious thermal quenching effect. Limited by the vulnerable chemical structure and soft skeleton, most of the existing photoluminescent responsive materials fail to indicate or work at a surging temperature over 100 °C, thus limiting application in display and alarming in harsh conditions. Herein, enlightened by chameleon's adaptive nature to external stimulus, we introduce a topologically optimized electron donor-acceptor (DA) structure and supramolecular interactions of lanthanide ions into the polymer backbone. The emission color determined by the DA structure is stable at high temperatures, and metal-ligand interaction phosphorescence is temperature-adjustable. Owing to the excellent reproducibility and heat resistance of composite films, the sensors can be bent into different three-dimensional structures and adhered to metal surfaces as flexible thermometers with superior display resolution. The polymer composite film could be directly applied as a photoluminescent quick response (QR) code, with patterns simultaneously variable to a temperature from 30 to 150 °C free of manual operation. More importantly, the polymeric composite could be in-situ-oxidized to a "sulfone" structure with an enhanced glass transition temperature of 297-304 °C. The heat- and flame-resistant characteristics of the oxidized films give rise to the application of fire alarming devices since it can locate the fire source and respond exactly depending on the distance from the fire. The unique display, encryption, and alarming functions of the polymeric composite studied in this work bring forward a new concept of developing a great information security and disaster monitoring system with the application of temperature-responsive materials.

Deep Machine Learning Based Possible Atmospheric and Ionospheric Precursors of the 2021 Mw 7.1 Japan Earthquake

Global Navigation Satellite System (GNSS)- and Remote Sensing (RS)-based Earth observations have a significant approach on the monitoring of natural disasters. Since the evolution and appearance of earthquake precursors exhibit complex behavior, the need for different methods on multiple satellite data for earthquake precursors is vital for prior and after the impending main shock. This study provided a new approach of deep machine learning (ML)-based detection of ionosphere and atmosphere precursors. In this study, we investigate multi-parameter precursors of different physical nature defining the states of ionosphere and atmosphere associated with the event in Japan on 13 February 2021 (Mw 7.1). We analyzed possible precursors from surface to ionosphere, including Sea Surface Temperature (SST), Air Temperature (AT), Relative Humidity (RH), Outgoing Longwave Radiation (OLR), and Total Electron Content (TEC). Furthermore, the aim is to find a possible pre-and post-seismic anomaly by implementing standard deviation (STDEV), wavelet transformation, the Nonlinear Autoregressive Network with Exogenous Inputs (NARX) model, and the Long Short-Term Memory Inputs (LSTM) network. Interestingly, every method shows anomalous variations in both atmospheric and ionospheric precursors before and after the earthquake. Moreover, the geomagnetic irregularities are also observed seven days after the main shock during active storm days (Kp > 3.7; Dst < −30 nT). This study demonstrates the significance of ML techniques for detecting earthquake anomalies to support the Lithosphere-Atmosphere-Ionosphere Coupling (LAIC) mechanism for future studies.

Sea surface height measurements using a low-cost GNSS buoy with multiple GNSS receivers

Accurate Sea surface height (SSH) measurements are important for global climate change and sea level rise monitoring. Satellite altimeters can provide global coverage observations but with coarse temporal and spatial resolution, while tide gauges offer high-precision measurements but are limited to coastal regions. The advent of Global Navigation Satellite System (GNSS) buoys bridge the gap between the tide station and the satellite altimeter, which realize continuous and reliable absolute SSH measurements in the coastal zone. However, most of buoys are equipped with GNSS geodetic receivers and antennas, which are expensive and with high power consumption. The emergence of low-cost receivers has been applied in fields such as autonomous driving and disaster monitoring, yet low-cost receivers suffer precision limitations due to signal quality issue in the harsh marine environment. To solve the reliability issue of low-cost receivers in SSH measurements, firstly, a low-cost buoy platform with four dual-frequency receivers is designed in this contribution. GNSS antennas are installed on the four corners of a square with a side length of 1.180 m, and the total power consumption is less than 4 W. Additionally, a robust outlier detection method, the Geometric Constraint algorithm, is proposed to improve the reliability and accuracy of GNSS SSH results. The performance of our buoy was validated in Jingye Lake at Tianjin University on April 30, 2022, and another experiment was conducted in Bohai Bay, Tianjin, on August 20, 2022, to assess the performance of a self-developed MG-Buoy for SSH measurement under real oceanic conditions. The experimental results indicate that the precision of single-frequency SSH measurement is 0.91 cm using the GNSS Post Processed Kinematic (PPK) technique with a single antenna, while the dual-frequency can achieve 0.50 cm accuracy. After adopting the Geometric Constraint method, the measurement accuracy of single-frequency and dual-frequency SSH values are 0.49 cm and 0.40 cm, respectively, representing a 46.2% and 20.0% improvements compared to the root mean square (RMS) value for the single GNSS antenna.

A Biologically Inspired Self-Organizing Underwater Sensor Network

Wireless underwater sensor networks have various applications—such as ocean exploration and deep-sea disaster monitoring—making them a hot topic in the research field. To cover a larger area and gather more-precise information, building large-scale underwater sensor networks has become a trend. In such networks, acoustic signals are used to transmit messages in an underwater environment. Their features of low speed and narrow bandwidth make media access control (MAC) protocols unsuitable for radio communications. Furthermore, a network consists of a large number of randomly deployed nodes, making it impossible to pre-define an optimized routing table or assign a central controller to coordinate the message propagation process. Thus, optimized routing should emerge via interaction among individual nodes in the network. To address these challenges, in this paper we propose a communication coordinator under the time division multiple access (TDMA) framework. Each node in the network is equipped with such a coordinator so that messages in the network can be sent following the shortest path in a self-organized way. The coordinator consists of a slot distributor and a forwarding guide. With the slot distributor, nodes in the sensor network occupy proper communication slots and the network finally converges to the state without communication collision. This is achieved with a set of ecological niche- and pheromone-inspired laws, which encourage nodes to occupy slots that can decrease the waiting time for a node to send a message packet while weakening the enthusiasm for a node to occupy the slots that it fails to occupy several times. With the forwarding guide, a node can send the message packet to the best successor node so that the message packet can be sent to the base station along the shortest path. It has been proven that the laws in the forwarding guide are equivalent to the Dijkstra Algorithm. Simulation experiment results indicate that with our coordinator, the network can converge to the state without collision using fewer coordination messages. In addition, the time needed to send a message to the destination is shorter than that of the classical Aloha protocol.

Prior Semantic Information Guided Change Detection Method for Bi-temporal High-Resolution Remote Sensing Images

High-resolution remote sensing image change detection technology compares and analyzes bi-temporal or multitemporal high-resolution remote sensing images to determine the change areas. It plays an important role in land cover/use monitoring, natural disaster monitoring, illegal building investigation, military target strike effect analysis, and land and resource investigation. The change detection of high-resolution remote sensing images has developed rapidly from data accumulation to algorithm models because of the rapid development of technologies such as deep learning and earth observation in recent years. However, the current deep learning-based change detection methods are strongly dependent on large sample data, and the training model has insufficient cross-domain generalization ability. As a result, a prior semantic information-guided change detection framework (PSI-CD), which alleviates the change detection model’s dependence on datasets by making full use of prior semantic information, is proposed in this paper. The proposed method mainly includes two parts: one is a prior semantic information generation network that uses the semantic segmentation dataset to extract robust and reliable prior semantic information; the other is the prior semantic information guided change detection network that makes full use of prior semantic information to reduce the sample size of the change detection. To verify the effectiveness of the proposed method, we produced pixel-level semantic labels for the bi-temporal images of the public change detection dataset (LEVIR-CD). Then, we performed extensive experiments on the WHU and LEVIR-CD datasets, including comparisons with existing methods, experiments with different amounts of data, and ablation study, to show the effectiveness of the proposed method. Compared with other existing methods, our method has the highest IoU for all training samples and different amounts of training samples on WHU and LEVIR-CD, reaching a maximum of 83.25% and 83.80%, respectively.

Optimization of Multi-Mission CubeSat Constellations with a Multi-Objective Genetic Algorithm

The increasing demand for low-cost space-borne Earth observation missions has led to small satellite constellation systems development. CubeSat platforms can provide a cost-effective multiple-mission space system using state-of-the-art technology. This paper presents a new approach to CubeSat constellation design for multiple missions using a multi-objective genetic algorithm (MOGA). The CubeSat constellation system is proposed to perform multi-missions that should satisfy global Earth observation and regional disaster monitoring missions. A computational approach using a class of MOGA named non-dominated sorting genetic algorithm II is implemented to optimize the proposed system. Pareto optimal solutions are found that can minimize the number of satellites and the average revisit time (ART) for both regional and global coverage while maximizing the percentage coverage. As a result, the study validates the feasibility of implementing the CubeSat constellation design with an acceptable level of performance in terms of ART and percentage coverage. Moreover, the study demonstrates CubeSat’s ability to perform a multi-missions.

Multi-Attention-Based Semantic Segmentation Network for Land Cover Remote Sensing Images

Semantic segmentation is a key technology for remote sensing image analysis widely used in land cover classification, natural disaster monitoring, and other fields. Unlike traditional image segmentation, there are various targets in remote sensing images, with a large feature difference between the targets. As a result, segmentation is more difficult, and the existing models retain low accuracy and inaccurate edge segmentation when used in remote sensing images. This paper proposes a multi-attention-based semantic segmentation network for remote sensing images in order to address these problems. Specifically, we choose UNet as the baseline model, using a coordinate attention-based residual network in the encoder to improve the extraction capability of the backbone network for fine-grained features. We use a content-aware reorganization module in the decoder to replace the traditional upsampling operator to improve the network information extraction capability, and, in addition, we propose a fused attention module for feature map fusion after upsampling, aiming to solve the multi-scale problem. We evaluate our proposed model on the WHDLD dataset and our self-labeled Lu County dataset. The model achieved an mIOU of 63.27% and 72.83%, and an mPA of 74.86% and 84.72%, respectively. Through comparison and confusion matrix analysis, our model outperformed commonly used benchmark models on both datasets.

Land subsidence analysis using synthetic aperture radar data

Land subsidence is considered a threat to developing cities and is triggered by several natural (geological and seismic) and human (mining, groundwater withdrawal, oil and gas extraction, constructions) factors. This research has gathered datasets consisting of 80 Sentinel-1A ascending and descending SLC images from July 2017 to July 2019. This dataset, concerning InSAR and PS-InSAR, is processed with SARPROZ software to determine the land subsidence in Gwadar City, Balochistan, Pakistan. Later, the maps were created with ArcGIS 10.8. Due to InSAR’s limitations in measuring millimeter-scale surface deformation, Multi-Temporal InSAR techniques, like PS-InSAR, are introduced to provide better accuracy, consistency, and fewer errors of deformation analysis. This remote-based SAR technique is helpful in the Gwadar area; for researchers, city mobility is constrained and has become more restricted post-Covid-19. This technique requires multiple images acquired of the same place at different times for estimating surface deformation per year, along with surface uplifting and subsidence. The InSAR results showed maximum deformation in the Koh-i-Mehdi Mountain from 2017 to 2019. The PS-InSAR results showed subsidence up to −92 mm/year in ascending track and −66 mm/year in descending track in the area of Koh-i-Mehdi Mountain, and up to −48 mm/year in ascending track and −32 mm/year in descending track in the area of the deep seaport. From our experimental results, a high subsidence rate has been found in the newly evolving Gwadar City. This city is very beneficial to the country’s economic development because of its deep-sea port, developed by the China-Pakistan Economic Corridor (CPEC). The research is associated with a detailed analysis of Gwadar City, identifying the areas with significant subsidence, and enlisting the possible causes that are needed to be resolved before further developments. Our findings are helpful to urban development and disaster monitoring as the city is being promoted as the next significant deep seaport with the start of CPEC.

A Task Planning Algorithm for High-Mobility Satellites Based on Trajectory Splicing

Natural disasters are global issues that have led to huge casualties and property losses. Satellite observation is one of the predominant methods of disaster monitoring, and high-mobility satellites can normally provide faster responses. A study of the high-mobility satellite task planning problem for global decentralized multi-target observation is conducted in this paper. First, a “trajectory-target” database composed of candidate trajectories and the corresponding visibilities of targets is established. Second, a splicing algorithm is devised to connect the individual trajectories as a whole so that the task planning problem can be converted to a combinational optimization problem of candidate trajectories. To solve the optimization problem, a heuristic algorithm is designed based on NSGA-II with a double-layer encoding structure and hybrid-heuristic mutation operators. The results presented at the end of the paper verify the effectiveness of the proposed algorithms.

Comprehensive framework for optimization of overlapping pixels in optically butted focal planes

Very high-resolution space borne missions demand finer spatial sampling and larger swath coverage for several commercial and strategic applications including cartography, disaster monitoring, urban planning, and surveillance. To meet these contrasting requirements, optical sensors often employ optical butting techniques in their focal planes as this enables the usage of small format detectors instead of large format single detectors, thereby reducing fabrication costs. In the optical butting technique, small reflecting mirrors placed before the focal plane split the optical field into smaller segments, which are alternately imaged by individual small format detectors. A single continuous image is formed using the small image segments through image processing technique. This requires optimal overlaps among the image segments with adequate image quality in the common imaged region. Factors, such as the geometric alignment of detectors with the butting mirror edge, edge-vignetting effects causing modulation transfer function, and signal-to-noise ratio degradations in the overlapping regions, largely determine the achievable overlap and image quality in the optically butted focal planes. Our study presents a comprehensive framework for the optimization of overlapping pixels in the optically butted focal planes based on a quantitative multicriteria analytical approach. The proposed framework has been tested for various sensor configurations to establish its efficacy. Our study will enable the efficient design and development of high-performance focal plane assemblies for high-resolution optical sensors.

High Spatiotemporal Flood Monitoring Associated with Rapid Lake Shrinkage Using Planet Smallsat and Sentinel-1 Data

High spatiotemporal flood monitoring is critical for flood control, mitigation, and management purposes in areas where tectonic and geological events significantly exacerbate flood disasters. For example, the rapid lake shrinkage resulting from the transformations of enclosed seas into lakes by the rapid land movement in the collision zone dramatically increases the flood risks in Indonesia, which requires frequent and detailed monitoring and assessment. This study primarily quantified the detailed flood disasters associated with the rapid lake shrinkage in Gorontalo Regency in Gorontalo Province, Indonesia using high spatiotemporal monitoring with a combination of PlanetScope smallsat constellations, Sentinel-1, and surface water datasets. Based on the findings that indicated its volume, distribution, pace, and pattern, the flood event that occurred in Gorontalo in November 2022 was demonstrated within a short interval of 2–12 days. The results also indicate both direct and indirect floodwater overflow from different water resources. Combining these results with the surface water occurrences from 1984 to 2021, our findings reveal the historical major flood-prone areas associated with the rapid lake shrinkage. These findings are expected to aid in the timely high spatiotemporal monitoring of rapid environmental change-induced flood disasters, even in tropical regions with high cloud coverage. Furthermore, these are also expected to be integrated into the flood hazard mitigation and management strategies associated with local-specific tectonic and geological systems.

Study on Characteristics of Acoustic Emission b Value of Coal Rock with Outburst-Proneness under Coupled Static and Dynamic Loads

In this paper, a rock dynamic and static triaxial mechanical test system is adopted to conduct triaxial compression failure acoustic emission test on coal rock with outburst-proneness. The purpose is to study the evolution law of the acoustic emission (AE) b value of coal rock under static and dynamic load of varying frequencies. The test results show that the dynamic load disturbance will accelerate the instability failure process of coal rock, that the average stress level/ultimate strain of coal rock decline/rises correspondingly with the increasing dynamic load frequency. Moreover, the overall AE b value after dynamic load disturbance is relatively low, and the higher the dynamic load frequency, the larger the decrease in the b value. In addition, as the dynamic load frequency increases, the AE ringing count and AE energy show a trend of first increasing and then decreasing, and they are negatively correlated with the evolution characteristics of the acoustic emission b value. Furthermore, the change rule of AE b value is closely related to the instability and failure process of coal rock. A second-stage of the overall b value of acoustic emission shows a “cliff jumping type” decrease dip angle or “fault cliff type” decrease dip angle indicates the imminent failure of coal rock, which can be used as an evaluation index for real-time monitoring of dynamic disasters of coal rock with outburst-proneness.

FESSD: Feature Enhancement Single Shot MultiBox Detector Algorithm for Remote Sensing Image Target Detection

Automatic target detection of remote sensing images (RSI) plays an important role in military surveillance and disaster monitoring. The core task of RSI target detection is to judge the target categories and precise location. However, the existing target detection algorithms have limited accuracy and weak generalization capability for RSI with complex backgrounds. This study presents a novel feature enhancement single shot multibox detector (FESSD) algorithm for remote sensing target detection to achieve accurate detection of different categories targets. The FESSD introduces feature enhancement module and attention mechanism into the convolution neural networks (CNN) model, which can effectively enhance the feature extraction ability and nonlinear relationship between different convolution features. Specifically, the feature enhancement module is used to extract the multi-scale feature information and enhance the model nonlinear learning ability; the self-learning attention mechanism (SAM) is used to expand the convolution kernel local receptive field, which makes the model extract more valuable features. In addition, the nonlinear relationship between different convolution features is enhanced using the feature pyramid attention mechanism (PAM). The experimental results show that the mAP value of the proposed method reaches 81.9% and 81.2% on SD-RSI and DIOR datasets, which is superior to other compared state-of-the-art methods.

X-Band Microstrip Array Antenna for UAV Onboard Full Circularly Polarized Synthetic Aperture Radar

Recently, unmanned aerial vehicles onboard synthetic aperture radar (UAV-SAR) have been developed by many institutions to provide high-resolution, real-time data acquisition and local emergency observation. The SAR system remains the issue to realize the system with the high resolution, lightweight, minimalized payload, power efficiency, high altitude operation, extreme environment, long-duration flight, and full polarimetric mode. For this purpose, our research group plans to develop the X-band circularly polarized UAV-SAR for disaster and environmental monitoring using the 25 kg payload of the UAV. The SAR system requires a broadband antenna and full circular polarization for precise observations. The antenna for the SAR systems requires a small size, lightweight, high gain, broadband for chirp pulse, and good antenna isolation and axial ratio (AR). For this purpose, we proposed the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4\times4$ </tex-math></inline-formula> ax-shaped radiator array antenna. The proposed antenna was simulated, fabricated, and measured in an anechoic chamber. Furthermore, the indoor polarimetric scattering SAR experiment was conducted using canonical targets to investigate the polarimetric SAR capability of the system with the proposed antenna. The result proves the performance of circularly polarized SAR.

Editorial for the Special Issue “Disaster Monitoring Using Remote Sensing”

Human civilization is rapidly developing thanks to the spread of geospatial technology and artificial intelligence [...]