Search Results (64)

Variable-thrust liquid rocket engines are essential for precision landing in deep-space exploration, reusable launch vehicle recovery, high-accuracy orbital maneuvers, and emergency obstacle evasions of space drones. However, with the increasingly complex space missions, challenges remain with the development of different technical schemes. In view of these issues, this paper systematically reviews the technology’s evolution through mechanical throttling, electromechanical precision regulation, and commercial space-driven deep throttling. Then, the development of key variable thrust technologies for liquid rocket engines is summarized from the perspective of thrust regulation and control strategy. For instance, thrust regulation requires synergistic flow control devices and adjustable pintle injectors to dynamically match flow rates with injection pressure drops, ensuring combustion stability across wide thrust ranges—particularly under extreme conditions during space drones’ high-maneuver orbital adjustments—though pintle injector optimization for such scenarios remains challenging. System control must address strong multivariable coupling, response delays, and high-disturbance environments, as well as bottlenecks in sensor reliability and nonlinear modeling. Furthermore, prospects are made in response to the research progress, and breakthroughs are required in cryogenic wide-range flow regulation for liquid oxygen-methane propellants, combustion stability during deep throttling, and AI-based intelligent control to support space drones’ autonomous orbital transfer, rapid reusability, and on-demand trajectory correction in complex deep-space missions. Full article

Addressing lateral-directional control challenges during unmanned aerial vehicle (UAV) landing in complex wind fields, this study proposes a drift angle control strategy that integrates coordinated heading and trajectory regulation. An adaptive radius optimization method for the Dubins approach path is designed using wind speed estimation. By developing a wind-coupled flight dynamics model, we establish a roll angle control loop combining the $L_1$ nonlinear guidance law with Linear Active Disturbance Rejection Control (LADRC). Simulation tests against conventional sideslip approach and crab approach, along with flight tests, confirm that the proposed autonomous landing system achieves smoother attitude transitions during landing while meeting all touchdown performance requirements. This solution provides a theoretically rigorous and practically viable approach for safe UAV landings in challenging wind conditions. Full article

Carrier-based aircraft recovery is a critical and challenging phase in maritime operations due to the turbulent airwake generated by aircraft carriers, which significantly increases the workload of flight control systems and pilots. This study investigates the airwake effects of an aircraft carrier under varying wind direction conditions. A high-fidelity mathematical model combining delayed detached-eddy simulation (DDES) with the overset grid method was developed to analyze key flow characteristics, including upwash, downwash, and lateral recirculation. The model ensures precise control of aircraft speed and trajectory during landing while maintaining numerical stability through rigorous mesh optimization. The results indicate that the minimum lift occurs in the downwash region aft of the deck, marking it as the most hazardous zone during landing. Aircraft above the deck are primarily influenced by ground effects, causing a sudden increase in lift that complicates arresting wire engagement. Additionally, the side force on the aircraft undergoes an abrupt reversal during the approach phase. The dual overset mesh technique effectively captures the coupled motion of the hull and aircraft, revealing higher turbulence intensity along the glideslope and a wider range of lift fluctuations compared to stationary hull conditions. These findings provide valuable insights for optimizing carrier-based aircraft recovery procedures, offering more realistic data for simulation training and enhancing pilot preparedness for airwake-induced disturbances. Full article

Remotely sensed cropland abandonment monitoring is crucial for providing spatially explicit references for maintaining sustainable agricultural practices and ensuring food security. However, abandoned cropland is commonly detected based on multi-date classification or the dynamics of a single vegetation index, with the interactions between vegetation and soil time series often being neglected, leading to a failure to understand its full-life-cycle succession processes. To fill this gap, we propose a new full-life-cycle modeling framework based on the interactive trajectories of vegetation–soil-related endmembers to identify abandoned and reclaimed cropland in Jinan from 2000 to 2022. In this framework, highly accurate annual fractional vegetation- and soil-related endmember time series are generated for Jinan City for the 2000–2022 period using spectral mixture models. These are then used to integrally reconstruct temporal trajectories for complex scenarios (e.g., abandonment, weed invasion, reclamation, and fallow) using logistic and double-logistic models. The parameters of the optimization model (fitting type, change magnitude, start timing, and change duration) are subsequently integrated to develop a rule-based hierarchical identification scheme for cropland abandonment based on these complex scenarios. After applying this scheme, we observed a significant decline in green vegetation (a slope of −0.40% per year) and an increase in the soil fraction (a rate of 0.53% per year). These pathways are mostly linked to a duration between 8 and 15 years, with the beginning of the change trend around 2010. Finally, the results show that our framework can effectively separate abandoned cropland from reclamation dynamics and other classes with satisfactory precision, as indicated by an overall accuracy of 86.02%. Compared to the traditional yearly land cover-based approach (with an overall accuracy of 77.39%), this algorithm can overcome the propagation of classification errors (with product accuracy from 74.47% to 85.11%), especially in terms of improving the ability to capture changes at finer spatial scales. Furthermore, it also provides a better understanding of the whole abandonment process under the influence of multi-factor interactions in the context of specific climatic backgrounds and human disturbances, thus helping to inform adaptive abandonment management and sustainable agricultural policies. Full article

Wildfire is a critical driver of ecological processes in western U.S. forests, but recent shifts in climate, land use, and fire suppression have altered forest structure and disturbance regimes. Understanding post-fire recovery is essential for land management, particularly across complex montane landscapes like the southern Rocky Mountains. We assessed forest recovery in montane conifer forests, ranging from ponderosa pine to spruce-fir, following a large mixed-severity fire using field-based forest stand data and remotely sensed Leaf Area Index (LAI) measurements. Our objectives were to determine whether LAI is a meaningful proxy for post-fire vegetative recovery and how recovery patterns vary by forest type, burn severity, and abiotic factors. Stand characteristics predicted crown burn severity inconsistently and did not predict soil burn severity. LAI correlated strongly with live overstory tree density and shrub cover (R² = 0.70). Recovery trajectories varied by forest type, with lower-severity burns generally recovering four years post-fire, while high-severity burns showed delayed recovery. Regeneration patterns were strongly influenced by climate, with higher seedling densities occurring at wetter sites. Our findings highlight the utility of LAI as a proxy for vegetative recovery and underscore the importance of forest type, fire severity, and climatic factors when assessing post-fire resilience. Full article

In light of the soaring growth of pumped hydro energy storage (PHES) plants in China in recent years, there is an urgent need for a comprehensive understanding of their developmental trajectory and the identification of their multidimensional impacts. This paper reviews the development of PHES in China and highlights its various impacts. Despite the relatively late start of PHES development in China, the country has recently ranked first worldwide with an aggregated installed capacity of 50.94 GW in operation in 2023. These plants are primarily distributed in North China, East China, and South China, contributing to the safe and stable operation of regional power grids. Furthermore, over 300 plants are under construction or in the planning stage across the whole country, aiming to support large-scale renewable energy development and facilitate a sustainable energy transition. However, it is important to recognize that such extensive PHES development requires significant land resources, which can lead to disturbances in local ecosystems and affect nearby residents. Additionally, environmental emissions may arise from a life-cycle perspective. Finally, several countermeasures are proposed to enhance sustainable PHES development in China. They include strengthening the rational planning of new plants to optimize their spatial distribution, refining the engineering design of new plants, and exploring avenues for sharing the benefits of PHES development with a broad spectrum of local residents. Full article

Aiming at the problem of lateral deviation of large-scale long-endurance solar-powered UAVs relative to the runway during takeoff or landing, a UAV ground motion control structure based on the combination of engine differential and rudder was proposed. According to the structural characteristics of large-scale long-endurance solar-powered UAVs, a ground motion model of a three-point layout UAV including landing gear was established, and the ground rolling dynamics and modal characteristics were analyzed. In order to accurately correct the trajectory error, the outer loop designs a trajectory correction control law and gives the inner loop desired control instructions. In order to solve the problem of environmental disturbance and small heading damping, the inner loop adopts the adaptive back-stepping control method. The disturbance signal is estimated through the adaptive law and compensated into the control system to achieve balanced control of speed and rolling correction. Finally, medium-speed and high-speed sliding tests were designed to verify the rationality of the proposed control scheme and control structure, as well as the efficiency of the control law design method adopted. Full article

The UAV landing process has higher requirements for automatic flight control systems due to factors such as wind disturbances and strong constraints. Considering the proven effective adaptation of the out-of-loop L1 adaptive control (OLAC) system proposed in previous studies, this paper applies it to landing control to enhance robustness and control accuracy in the presence of complex uncertainties. Based on modern control theory, an LQR-based OLAC algorithm for multi-input–multi-output (MIMO) systems is proposed, which is conducive to the coupling control of the flight attitude mode. To evaluate the robustness of the designed system, an equivalence stability margin analysis method for nonlinear systems is proposed based on parameter linearization. Along with a detailed autonomous landing strategy, including trajectory planning, control, and guidance, the effectiveness of the proposed methods is verified on a high-fidelity simulation platform. The Monte–Carlo simulation is implemented in the time domain, and the results demonstrate that OLAC exhibits strong robustness and ensures the state variables strictly meet the flight safety constraints. Full article

Forest disturbance mapping plays an important role in furthering our understanding of forest dynamics. The Landsat-based detection of Trends in Disturbance and Recovery (LandTrendr) algorithm is widely used in forest disturbance mapping. However, it neglects the quality of the temporal trajectory and its change trends for forest disturbance mapping. Therefore, the aim of this paper is to improve LandTrendr (iLandTrendr) for forest disturbance mapping by optimizing its temporal trajectories and the post-processing of detection results. Specifically, the temporal trajectory of complex forest disturbance types was optimized using the Savitzky–Golay (SG) filter with constraints. That is, the smooth value generated from the SG filter for the disturbance year was replaced by the satellite observations when the nonlinear abrupt signals were included in the multi-temporal data. The forest disturbance detected by LandTrendr was further modified using the consistency of spectral variation trends. A case study using iLandTrendr to detect forest disturbance in Yunnan Province was conducted. Compared to the LandTrendr method, which has an overall accuracy (OA) of 35.88%, iLandTrendr generated forest disturbance mapping with an OA of 89.32%, which was significantly higher. The total mapped area of disturbance was 1,985,820.9 km², accounting for 49.69% of the total area. The disturbances were predominately caused by natural factors, such as wildfires, pests and diseases, and forest degradation, accounting for 85.31% of the total disturbed area. iLandTrendr can quickly and accurately detect the occurrence year of complex forest disturbance types and can be extended for the forest disturbance mapping of a large area. Full article

This paper investigates a carrier landing controller for unmanned aerial vehicles (UAVs), and a nonlinear model predictive control (NMPC) approach is proposed considering a precise motion control required under dynamic landing platform and environment disturbances. The NMPC controller adopts constraint aware particle filtering (CAPF) to predict deck positions for disturbance compensation and to solve the nonlinear optimization problem, based on a model establishment of carrier motion and wind field. CAPF leverages Monte Carlo sampling to optimally estimate control variables for improved optimization, while utilizing constraint barrier functions to keep particles within a feasible domain. The controller considers constraints such as fuel optimization, control saturation, and flight safety to achieve trajectory control. The advanced control method enhances the solution, estimating optimal control sequences of UAV and forecasting deck positions within a moving visual field, with effective trajectory tracing and higher control accuracy than traditional methods, while significantly reducing single-step computation time. The simulation is carried out using UAV “Silver Fox”, considering several scenarios of different wind scales compared with traditional CAPF–NMPC and the nlmpc method. The results show that the proposed NMPC approach can effectively reduce control chattering, with a landing error in rough marine environments of around 0.08 m, and demonstrate improvements in trajectory tracking capability, constraint performance and computational efficiency. Full article

Unmanned aerial vehicles (UAVs) are a new, adaptable technology that has found its way into both military and civilian applications. Preserving the integrity of the UAV and its security during flight and, in particular, during the landing stage is essential for the performance of the assigned mission of the aircraft. This research examines a developed aircraft scheme. It was tested for static and dynamic stability in an XFLR5 virtual aerodynamic environment. The obtained results were transferred to MATLAB-Simulink, where the flight control algorithm was synthesized, the landing mode was set using an engineering flight plan, and an autonomous landing was simulated in the presence of wind disturbances with turbulence and noisy operation of the information measurement complex of the UAV. The algorithm for controlling the landing during the execution of the set flight trajectory, which contains a Kalman estimator and an optimal LQR controller combined in a general LQG control algorithm, is studied. Full article

Climate change and human activities exert significant impacts on runoff generation and convergence mechanisms. Understanding the evolution mechanisms and driving forces of runoff is crucial for the sustainable development of water resources. This study is based on the runoff data of the Huayuankou (HYK), Jiahetan (JHT), and Gaocun (GC) stations in the lower reaches of the Yellow River from 1951 to 2019. The objectives are to identify and quantify the hydrological pattern and its driving mechanism of the three stations by the Mann–Kendall test, cumulative deviation method, wavelet analysis, the IHA-RVA method, SCRCO method, and the Budyko method. Our study revealed that the runoff disturbance points at all three stations occurred in 1985. During the two periods before and after the disturbance, the trends and periodicities within the year exhibited high consistency, showing an overall downward trend, with runoff increasing in October and decreasing in June and the primary cycles being 5 and 7 years. All three stations experienced high-degree changes in their hydrological situations, with the high-degree change occupying the largest proportion. At the HYK, JHT, and GC stations, human activities accounted for 66.05%, 71.94%, and 74.38% of runoff changes, respectively. Furthermore, we verified the attribution conclusion of runoff at HYK using the Budyko model, confirming that human activities are the primary factor influencing runoff. Finally, we explored the interactive relationships along the spatial trajectory of runoff at the three stations, analyzed 32 hydrological indicators, and detailed the land use changes in the Yellow River Basin. Our research findings complement the understanding of hydrological change mechanisms in the lower reaches of the Yellow River Basin and provide a scientific basis for future water resource management and flood prevention measures. Full article

The rapid expansion of built-up land, a hallmark of accelerated urbanization, has emerged as a pivotal factor contributing to regional climate change and the degradation of ecosystem functions. The decline in ecosystem service value (ESV) has consequently garnered significant attention in global sustainable development research. The Shandong Peninsula urban agglomeration is crucial for promoting the construction of the Yellow River Economic Belt in China, with its ecological status increasingly gaining prominence. This study investigated the ESV response to land use/cover change (LUCC) through the elasticity coefficient in order to analyze the degree of disturbance caused by land use activities on ecosystem functions in the Shandong Peninsula urban agglomeration. This analysis was based on the examination of LUCC characteristics and ESV from 1990 to 2020. The findings reveal that (1) the Shandong Peninsula urban agglomeration experienced a continuous increase in the proportion of built-up land from 1990 to 2020, alongside a highly complex transfer between different land use types, characterized by diverse transfer trajectories. The most prominent features were noted to be the rapid expansion of built-up land and the simultaneous decline in agricultural land. (2) The analysis of four landscape pattern indices, encompassing Shannon’s diversity index, indicates that the continuous development of urbanization has led to increased fragmentation in land use and decreased connectivity. However, obvious spatial distribution differences exist among different districts and counties. (3) The ESV was revised using the normalized difference vegetation index, revealing a slight decrease in the total ESV of the Shandong Peninsula urban agglomeration. However, significant differences were observed among districts and counties. The number of counties and districts exhibiting low and high ESVs continuously increased, whereas those with intermediate levels generally remained unchanged. (4) The analysis of the elasticity coefficient reveals that LUCC exerts a substantial disturbance and influence on ecosystem services, with the strongest disturbance ability occurring from 2000 to 2010. The elasticity coefficient exhibits obvious spatial heterogeneity across both the entire urban agglomeration and within individual cities. Notably, Qingdao and Jinan, the dual cores of the Shandong Peninsula urban agglomeration, exhibit markedly distinct characteristics. These disparities are closely related to their development foundations in 1990 and their evolution over the past 30 years. The ESV response to LUCC displays significant variation across different time periods and spatial locations. Consequently, it is imperative to formulate dynamic management policies on the basis of regional characteristics. Such policies aim to balance social and economic development while ensuring ecological protection, thereby promoting the social and economic advancement and ecological environment preservation of the Shandong Peninsula urban agglomeration. Full article

The timing, location, intensity, and drivers of forest disturbance and recovery are crucial for developing effective management strategies and policies for forest conservation and ecosystem resilience. Although many algorithms and improvement methods have been developed, it is still difficult to guarantee the detection accuracy for forest disturbance and recovery patterns in southern China due to the complex climate and topography, faster forest recovery after disturbance, and the low availability of noise-free Landsat images. Here, we improved the LandTrendr parameters for different provinces to detect forest disturbances and recovery trajectories based on the LandTrendr change detection algorithm and time-series Landsat images on the GEE platform, and then applied the secondary random forest classifier to classify the forest disturbance and recovery patterns in southern China during 1990–2020. The accuracy evaluation indicated that our approach and improved parameters of the LandTrendr algorithm can increase the detection accuracy for both the spatiotemporal patterns and multiple events of forest disturbance and recovery, with an overall accuracy greater than 86% and a Kappa coefficient greater than 0.91 for different provinces. The total forest loss area was 1.54 × 10⁵ km² during 1990–2020 (4931 km²/year); however, most of these disturbed forests were recovered and only 6.39 × 10⁴ km² was a net loss area (converted to other land cover types). The area with two or more times of disturbance events accounted for 11.50% of the total forest loss area. The total forest gain area (including gain after loss and the afforestation area) was 5.44 × 10⁵ km², among which, the forest gain area after loss was 8.94 × 10⁴ km², and the net gain area from afforestation was 4.55 × 10⁵ km². The timing of the implementation of forestry policies significantly affected the interannual variations in forest disturbance and recovery, with large variations among different provinces. The detected forest loss and gain area was further compared against with inventory and other geospatial products, and proved the effectiveness of our method. Our study suggests that parameter optimization in the LandTrendr algorithm could greatly increase the accuracy for detecting the multiple and lower rate disturbance/recovery events in the fast-regrowing forested areas. Our findings also offer a long-term, moderate spatial resolution, and precise forest dynamic data for achieving sustainable forest management and the carbon neutrality goal in southern China. Full article

Recently, as global climate change and local disturbances such as wildfires continue, long- and short-term changes in the high-latitude vegetation systems have been observed in various studies. Although remote sensing technology using optical satellites has been widely used in understanding vegetation dynamics in high-latitude areas, there has been limited understanding of various landscape changes at different spatiotemporal scales, their mutual relationships, and overall long-term landscape changes. The objective of this study is to devise a change monitoring strategy that can effectively observe landscape changes at different spatiotemporal scales in the boreal ecosystems from temporally sparse time series remote sensing data. We presented a new post-classification-based change analysis scheme and applied it to time series Landsat data for the central Yakutian study area. Spectral variability between time series data has been a major problem in the analysis of changes that make it difficult to distinguish long- and short-term land cover changes from seasonal growth activities. To address this issue effectively, two ideas in the time series classification, such as the stepwise classification and the lateral stacking strategies were implemented in the classification process. The proposed classification results showed consistently higher overall accuracies of more than 90% obtained in all classes throughout the study period. The temporal classification results revealed the distinct spatial and temporal patterns of the land cover changes in central Yakutia. The spatiotemporal distribution of the short-term class illustrated that the ecosystem disturbance caused by fire could be affected by local thermal and hydrological conditions of the active layer as well as climatic conditions. On the other hand, the long-term class changes revealed land cover trajectories that could not be explained by monotonic increase or decrease. To characterize the long-term land cover change patterns, we applied a piecewise linear model with two line segments to areal class changes. During the former half of the study period, which corresponds to the 2000s, the areal expansion of lakes on the eastern Lena River terrace was the dominant feature of the land cover change. On the other hand, the land cover changes in the latter half of the study period, which corresponds to the 2010s, exhibited that lake area decreased, particularly in the thermokarst lowlands close to the Lena and Aldan rivers. In this area, significant forest decline can also be identified during the 2010s. Full article