Search Results (8)

Underground coal mining in coal-grain overlapped areas leads to land subsidence and deformation above the goaf, damaging cultivated land. Understanding the influencing process of coal mining on cultivated land and crops is important for carrying out timely land reclamation and stabilizing crop yield. Research has been carried out by using crop growth parameters to evaluate the damaging degree of cultivated land when the mining subsidence is stable, but few studies focus on the influence of land damage on crop growth when the subsidence is unstable during coal mining. Therefore, this study tracked the three growth stages of winter wheat by using UAV multispectral imagery to explore the dynamic influence of underground mining on winter wheat aboveground biomass (AGB). Firstly, a winter-wheat-AGB estimation model (R² = 0.89, RMSE = 2.18 t/ha) was developed by using vegetation indexes (VIs), textures, and terrain data extracted from UAV imagery. Secondly, based on the winter-wheat-AGB estimation model, the winter wheat AGB was successfully estimated and mapped at different growth stages. The AGB of winter wheat in the coal mining-affected area was approximately 5.59 t/ha at the reviving stage, 8.2 t/ha at the jointing stage, and 15.6 t/ha at the flowering stage. Finally, combined with the progress of coal mining, the dynamic changing process of crops during underground mining can be inferred by analyzing the spatiotemporal variation in winter wheat AGB. Results showed that, in the dip direction, winter wheat AGB at the flowering stage was the highest at the compression zone, followed by the inner stretch zone, outer stretch zone, and neutral zone. The distance from the waterlogged area and the existence of cracks were found to be the important moderating variables affecting the crop growth status in the mining subsidence area. In the strike direction, there were significant differences in the wheat AGB-affected area as the mining proceeded. Even areas where AGB had previously significantly increased gradually transitioned to significant decreases with the end of mining. The research explores the dynamic changes in winter wheat AGB and land damage status during coal mining. It provides a rapid and non-destructive land-damage-monitoring method to protect cultivated land in mining areas. Full article

Urban drainage and waterlogging prevention are critical components of urban water management systems, as they help to mitigate the risks of flooding and water damage in cities. The accurate collection of liquid level and flow rate data at the end of these systems is crucial for their effective monitoring and management. However, existing water equipment for this purpose has several shortcomings, including limited accuracy, inflexibility, and difficulty in operation under specific working conditions. A new type of multi-parameter flexible smart water gauge was developed to address these issues. This technology uses underwater simulation robot technology and is designed to overcome the deficiencies of existing water equipment. The flexibility of the gauge allows it to be adapted to different working conditions, ensuring accurate data collection even in challenging environments. The accuracy of the new water gauge was tested through a series of experiments, and the results showed that it was highly accurate in measuring both liquid level and flow rate. This new technology has the potential to be a key tool in smart water conservancy, enabling the more efficient and accurate monitoring of water levels and flow rates. By providing a new solution to the problem of collecting terminal equipment for urban drainage and waterlogging prevention, this technology can help to improve the resilience and sustainability of urban water management systems. Full article

In high groundwater level mining areas, subsidence resulting from mining can lead to waterlogging in farmland, causing damage to crops and affecting their growth and development, thereby affecting regional food security. Therefore, it is necessary to restore agricultural production in the coal mining subsidence water areas in the densely populated eastern plains. This study focuses on the Yongcheng coal mining subsidence water areas. It utilizes Sentinel-1 and Sentinel-2 data from May to October in the years 2019 to 2022 to monitor the growth and development of crops. The results demonstrated that (1) the accuracy of aquatic crops categorization was improved by adjusting the elevation of the study region with Mining Subsidence Prediction Software (MSPS 1.0). The order of accuracy for classifying aquatic crops using different machine learning techniques is Random Forest (RF) > Classification and Regression Trees (CART) ≥ Support Vector Machine (SVM). Using the RF method, the obtained classification results can be used for subsequent crop growth monitoring. (2) During the early stages of crop growth, when vegetation cover is low, the Radar Vegetation Index (RVI) is sensitive to the volume scattering of crops, making it suitable for tracking the early growth processes of crops. The peak RVI values for crops from May to July are ranked in the following order: rice (2.595), euryale (2.590), corn (2.535), and lotus (2.483). (3) The order of crops showing improved growth conditions during the mid-growth stage is as follows: rice (47.4%), euryale (43.4%), lotus (27.6%), and corn (4.01%). This study demonstrates that in the Yongcheng coal subsidence water areas, the agricultural reclamation results for the grain-focused model with rice as the main crop and the medicinal herb-focused model with euryale as the main crop are significant. This study can serve as a reference for agricultural management and land reclamation efforts in other coal subsidence water areas. Full article

Waterlogging harms winter wheat growth. To enable accurate monitoring of agricultural waterlogging, this paper conducts a winter wheat waterlogging monitoring study using multi-source data in Guzhen County, Anhui Province, China. The hydrological model HYDRUS-1D is coupled with the crop growth model WOFOST, and the Ensemble Kalman Filter is used to assimilate Sentinel-1 inversion soil moisture data. According to the precision and continuity of soil moisture, the damage of winter wheat waterlogging were obtained. The experimental results show that the accuracy of the soil moisture is improved after data assimilation compared with that before data assimilation, and the Nash–Sutcliffe efficiency (NSE) of the simulated soil moisture values at three monitoring sites increased from 0.528, 0.541 and 0.575 to 0.752, 0.692 and 0.731, respectively. A new waterlogging identification criterion has been proposed based on the growth periods and probability distribution of soil moisture. The proportion, calculated from this identification criterion, of the waterlogging wheat farmland in total farmland shows a high correlation with the yield reduction rate. The correlation coefficient of the waterlogging farmland proportion and the yield reduction rate in 11 towns of Guzhen County reaches 0.78. Through the synchronization of geography, agriculture and meteorology, the framework shows great potential in waterlogging monitoring. Full article

On 20 July 2021, an extraordinary rainfall event occurred in Henan Province, China, resulting in heavy waterlogging, flooding, and hundreds of fatalities and causing considerable property damage. Because the damaged region was a major grain-producing region of China, assessing crop food production losses following this event is very important. Because the crop rotation production system is utilized in the region to accommodate two crops per year, it is very valuable to accurately identify the types of crops affected by the event and to assess the crop production losses separately; however, the results obtained using these methods are still inadequate. In this study, we used China’s first commercial synthetic aperture radar (SAR) data source, named Hisea-1, together with other open-source and widely used remote sensing data (Sentinel-1 and Sentinel 2), to monitor this catastrophic flood. Both the modified normalized difference water index (MNDWI) and Sentinel-1 dual-polarized water index (SDWI) were calculated, and an unsupervised classification (k-means) method was adopted for rapid water body extraction. Based on time-series datasets synthesized from multiple sources, we obtained four flooding characteristics, including the flooded area, flood duration, and start and end times of flooding. Then, according to these characteristics, we conducted a more precise analysis of the damages to flooded farmlands. We used the Google Earth Engine (GEE) platform to obtain normalized difference vegetation index (NDVI) time-series data for the disaster year and normal years and overlaid the flooded areas to extract the effects of flooding on crop species. According to the statistics from previous years, we calculated the areas and types of damaged crops and the yield reduction amounts. Our results showed that (1) the study area endured two floods in July and September of 2021; (2) the maximum areas affected by these two flooding events were 380.2 km² and 215.6 km², respectively; (3) the floods significantly affected winter wheat and summer grain (maize or soybean), affecting areas of 106.4 km² and 263.3 km², respectively; and (4) the crop production reductions in the affected area were 18,708 t for winter wheat and 160,000 t for maize or soybean. These findings indicate that the temporal-dimension information, as opposed to the traditional use of the affected area and the yield per unit area when estimating food losses, is very important for accurately estimating damaged crop types and yield reductions. Time-series remote sensing data, especially SAR remote sensing data, which have the advantage of penetrating clouds and rain, play an important role in remotely sensed disaster monitoring. Hisea-1 data, with a high spatial resolution and first flood-monitoring capabilities, show their value in this study and have the potential for increased usage in further studies, such as urban flooding research. As such, the approach proposed herein is worth expanding to other applications, such as studies of water resource management and lake/wetland hydrological changes. Full article

In recent years, heavy rain and waterlogging accidents in subway stations have occurred many times around the world. With the comprehensive development trend of underground space, the accidents caused by flood flow intruding complex subway stations and other underground complexes in extreme precipitation disasters will be lead to more serious casualties and property damage. Therefore, it is necessary to conduct numerical simulation of flood intrusion process under malfunction of flood retaining facilities in complex subway stations. In order to prevent floods from intruding subway stations and explore coping strategies, in this study, the simulation method was used to study the entire process of flood intrusion into complex subway stations when the flood retaining facilities fail in extreme rain and flood disasters that occur once-in-a-century. The three-dimensional numerical simulation model was constructed by taking a subway interchange station with a property development floor in Nanning as a prototype. Based on the Volume of Fluid (VOF) model method, the inundated area in the subway station during the process of flood intrusion from the beginning to the basic stability was simulated, and it was found that the property development floor has serious large-scale water accumulation under extreme rainfall conditions. Through the dynamic monitoring of the flood water level depth at important positions such as the entrances of the evacuation passages, and the analysis of the influence of the design structure and location distribution of different passages on the personnel evacuation plan, it was found that the deep water accumulation at the entrances of the narrow, long, and multi-run emergency safety passages are not conducive to the evacuation of personnel. Finally, the flow of flood water into the subway tunnel through the subway station was calculated. The research results provide certain reference and guidance for the safety design of subway stations under extreme rainfall climatic conditions. Full article

Waterlogging crop disasters are caused by continuous and excessive soil water in the upper layer of soil. In order to enable waterlogging monitoring, it is important to collect continuous and accurate soil moisture data. The distributed hydrology soil vegetation model (DHSVM) is selected as the basic hydrological model for soil moisture estimation and winter-wheat waterlogging monitoring. To handle the error accumulation of the DHSVM and the poor continuity of remote sensing (RS) inversion data, an agro-hydrological model that assimilates RS inversion data into the DHSVM is used for winter-wheat waterlogging monitoring. The soil moisture content maps retrieved from satellite images are assimilated into the DHSVM by the successive correction method. Moreover, in order to reduce the modeling error accumulation, monthly and real-time RS inversion maps that truly reflect local soil moisture distributions are regularly assimilated into the agro-hydrological modeling process each month. The results show that the root mean square errors (RMSEs) of the simulated soil moisture value at two in situ experiment points were 0.02077 and 0.02383, respectively, which were 9.96% and 12.02% of the measured value. From the accurate and continuous soil moisture results based on the agro-hydrological assimilation model, the waterlogging-damaged ratio and grade distribution information for winter-wheat waterlogging were extracted. The results indicate that there were almost no high-damaged-ratio and severe waterlogging damage areas in Lixin County, which was consistent with the local field investigation. Full article

With the acceleration of the urbanization process, the problems caused by extreme weather such as heavy rainstorm events have become more and more serious. During such events, the road and its auxiliary facilities may be damaged in the process of the rainstorm and waterlogging, resulting in the decline of its traffic capacity. Rainfall is a continuous process in a space–time dimension, and as rainfall data are obtained through discrete monitoring stations, the acquired rainfall data have discrete characteristics of time interval and space. In order to facilitate users in understanding the impact of urban waterlogging on traffic, the visualization of waterlogging information needs to be displayed under different spatial and temporal granularity. Therefore, the appropriateness of the visualization granularity directly affects the user’s cognition of the road waterlogging map. To solve this problem, this paper established a spatial granularity and temporal granularity computing quantitative model for spatio-temporal visualization of road waterlogging and the evaluation method of the model was based on the cognition experiment. The minimum visualization unit of the road section is 50 m and we proposed a 5-level depth grading method and two color schemes for road waterlogging visualization based on the user’s cognition. To verify the feasibility of the method, we developed a prototype system and implemented a dynamic spatio-temporal visualization of the waterlogging process in the main urban area of Nanjing, China. The user cognition experiment showed that most participants thought that the segmentation of road was helpful to the local visual expression of waterlogging, and the color schemes of waterlogging depth were also helpful to display the road waterlogging information more effectively. Full article