The diffuse attenuation coefficient$K_{d}$continues to play a crucial role in oceanographic research works. Recently, Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) has shown its great ability to estimate$K_{d}$using the water column decay profiles. However, the weak water column backscattered signals are vulnerable to afterpulses and solar background noise, making this way perform not well in the daytime and in nearshore areas. In this study, a method to estimate$K_{d}$is proposed which innovatively uses ICESat-2 bathymetric signal intensities. The main principle is to calculate the attenuation in water column transmission by bathymetric lidar equations. Since the seafloor signal level is much stronger than that of the water column, a significant advantage is the greater noise immunity, i.e., the ability to operate under strong background noise and afterpulses interference. The performance is validated against the moderate-resolution imaging spectroradiometer (MODIS) ocean color measurements with mean relative differences (MRDs) of <32% using both daytime and nighttime ICESat-2 data in six sea and large lake nearshore areas. Based on the new generation of spaceborne lidar data, this study explores a new path to monitor water qualities in nearshore areas. This method is applicable where seafloor photons exist in both daytime and nighttime.

The recent capabilities of the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) photon counting lidar in monitoring water levels have been demonstrated through its precise elevation measurement and small footprint. The accuracy in water level measurements is, however, significantly impacted by the first photon bias, especially when photon-counting detectors are fully saturated due to particular reflections from calm water surfaces. In this study, we propose an analytical model to correct the first photon bias in scenarios where the ICESat-2/Advanced Topographic Altimeter System (ATLAS) is fully saturated. Notably, the model innovatively recovers and estimates the required signal level using after-pulses, which are typically considered as noise. These after-pulses can be used to effectively estimate the signal level when the detector is fully saturated. The experiment analysis, conducted on eight ICESat-2 ground tracks over calm water surfaces near the Great Lakes and the Tibetan Plateau, indicates that the actual received signal photons can surpass 200 and in some cases, reach up to 600 counts for strong beams, introducing a first photon bias exceeding 15 cm. The findings prove that 1) after-pulses can be used to retrieve water surface elevation and reflectance when the primary surface return is distorted by detector saturation and 2) calm waters reflect 5–40 times more than ice and snow surfaces, where first photon bias is a predominant error in water level measurements. The method holds great significance for the accurate monitoring of water levels in small inland water bodies using ICESat-2 and may also inform the design of lidar systems for inland water observations.

The inspection of power transmission line has achieved notable achievements in the past few years, primarily due to the integration of deep learning technology. However, current inspection approaches continue to encounter difficulties in generalization and intelligence, which restricts their further applicability. In this paper, we introduce Power-LLaVA, the first large language and vision assistant designed to offer professional and reliable inspection services for power transmission line by engaging in dialogues with humans. Moreover, we also construct a large-scale and high-quality dataset specialized for the inspection task. By employing a two-stage training strategy on the constructed dataset, Power-LLaVA demonstrates exceptional performance at a comparatively low training cost. Extensive experiments further prove the great capabilities of Power-LLaVA within the realm of power transmission line inspection. Code shall be released.

Recently, spaceborne active lidars relying on backscattered laser signal can observe thin clouds, but the laser beam cannot penetrate clouds with large optical thickness. The new generation photon-counting lidar on Ice, Cloud, and land Elevation Satellite-2 (ICESat-2), whose noise can be treated as observations of a green band camera, provides an excellent opportunity to fuse active and passive information to retrieve cloud optical thickness (COT). Clouds significantly increase the background noise and sharply attenuate the signal returning from oceans, which makes it feasible to observe thin and thick clouds by combining active and passive information of ICESat-2. In this study, we first derive the passive background noise and active signal models over oceans for spaceborne lidars, which considers medium contributions from clouds, aerosols, air molecules, ocean surface, and subsurface. ICESat-2 measured surface signals and noise rates in open oceans of Western Pacific are used to verify the models with auxiliary environment datasets. The results indicate that the theoretical predictions have the mean absolute error (MAE) of less than 0.31 counts for signal and the mean absolute percentage error (MAPE) of less than 35% for noise. Then, based on these theoretical models, we propose a COT estimation method combining ICESat-2 active signal and passive noise data without extra auxiliary datasets, and the MAEs between ICESat-2 retrieved COTs and Himawari-8 (H8) cloud products are less than 1.2 (COTs range from 0 to exceeding 20) over open oceans. In general, the proposed method not only expands the observation range of retrieved COTs compared to methods solely relying on signal or noise data but also has great significance for assessing the availability of lidar surface signal, i.e., producing cloud mask.

With the increasingly serious ecological problems, energy-efficient scheduling, an effective approach to achieve sustainable development and green manufacturing, has attracted much attention by taking both economic effect and energy conservation into account. This paper addresses an energy-efficient scheduling of the distributed blocking flow-shop problem (EDBFSP) to minimize both makespan and total energy consumption. The mixed-integer linear programming (MILP) model of EDBFSP is designed. A multi-action reinforcement learning algorithm based on problem-specific knowledge called multi-greedy policy optimization (multi-GPO) is proposed to solve the EDBFSP. In addition, after analyzing the characteristics of the problem, an energy-saving strategy and an acceleration strategy are designed to further optimize the solution. Experiments in a large number of benchmark tests have testified that the multi-GPO is superior to the state-of-the-art algorithms in terms of efficiency and importance in solving EDBFSP.

The new generation photon-counting lidar on Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) can obtain the subsurface optical properties of sea waters. Recent studies highlight the applications of deriving the lidar attenuation coefficient$K_{\mathrm {lidar}}$and then substituting$K_{\mathrm {lidar}}$into the bio-optical model to obtain more information of sea waters. As commonly used bio-optical models are built for the diffuse attenuation coefficient$K_{d}$that are traditionally derived by passive ocean color sensors, whether$K_{\mathrm {lidar}}$derived from ICESat-2 can be directly used as$K_{d}$is a fundamental question. Given that$K_{d}$is an apparent optical property (AOP) in the water column rather than an inherent optical property (IOP),$K_{d}$is closely related to the zenith angle of the incident light. The zenith angle of the incident light of the sunlight is normally tens of degrees for ocean color sensors, while the maximum laser off-nadir angle is ~1.5° for the ICESat-2 lidar. To demonstrate this issue, we select hundreds of ground tracks of ICESat-2 in both open ocean and coastal sea waters and compare the derived$K_{\mathrm {lidar}}$with their corresponding Moderate Resolution Imaging Spectroradiometer (MODIS)-derived$K_{d}$. The results indicate that the corrected results of$1.2\times K_{\mathrm {lidar}}$, instead of the direct results of$K_{\mathrm {lidar}}$, are more consistent with MODIS$K_{d}$. This study is of great significance to the better fusion of active lidar data and passive optical data in ocean observations.

The passive multispectral imaging and active bathymetric lidar make a great achievement for bathymetry in optically shallow waters. However, due to the attenuation of the water column precludes deep penetration of the light, accurately obtaining the underwater topography in turbid waters through remote sensing techniques, both passive and active, is still a challenging task. Airborne lidars can obtain water surface topography with high accuracy and resolution, which can further be used to derive the water depth based on wave theory. In this study, an ‘indirect’ method to determine water depth is proposed using airborne lidar measured water surface points. As the wavelength and wave direction can be accurately tracked from the water surface topography, a 20m×20m underwater topography near Ganquan Island, South China Sea, is generated with an RMSE of 0.91 m and a MAPE of 7.1%. The basic theory of deriving water depths is totally different from airborne lidar bathymetry, i.e., this method is independent of water clarity and can be used in turbid waters or even works with near infrared airborne lidar that can only obtain water surface points.

Over the past few decades, spaceborne passive ocean color sensors that measure the solar radiance have provided an unprecedented source of scientific knowledge on marine biology. Recently, spaceborne active lidars that measure the backscattered laser signal from the ocean subsurface provide new insights in deriving vertical profiles of ocean subsurface and obtaining shallow water bathymetry. The solar radiation is the signal source of passive ocean color sensors but acts as the primary noise source of satellite-based lidars, which may limit the extraction and application of the weak subaqueous signal in the daytime. Based on the perspective of the reciprocity, the background noise of the ICESat-2 spaceborne photon-counting lidar in six channels (or pixels) has potential to be regarded as the signal of an “ocean color camera” with a very narrow band. In this study, the remote sensing reflectance, that is the fundamental data of ocean color sensors, is theoretically linked to and accurately transferred from ICESat-2 noise data with an average Mean Absolute Percentage Error (MAPE) of less than 20% compared to thein-situmeasurements. With this method, not only the remote sensing reflectanceRrscan be retrieved from ICESat-2 under strong background noise, which enhances the capability of ICESat-2 to monitor the diurnal variation, but also numerous quantitative applications by passive remote sensing sensors may be achievable by the noise data of spaceborne photon-counting lidars in the future. In addition, a spaceborne lidar can synchronously detect active laser signal and passive solar radiation, which may bring new insights in the data fusion and verification of active and passive techniques.

Airborne LiDAR bathymetry (ALB) is a useful technology for seamless topobathymetric mapping, offering high acquisition rate and point density. However, in very shallow waters (90%). In the simulated dataset, the root mean square error of the laser travel time between the estimated and truth values is 0.22 ns (corresponding to 2.5-cm slant range). The results indicate that the proposed method provides a new solution for filling the bathymetric gap in very shallow water, which is very essential for topobathymetry mapping.

Detailed information on lake bathymetry is essential for both hydrology-related studies and water resource management. Conventionally, lake bathymetry was mapped using high-cost approaches (e.g., ship/boat-based multibeam echosounders or airborne bathymetric lidars). With only satellite remotely sensed data sets, a method for deriving high-resolution bathymetry for dynamic areas was proposed by combining the new Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) lidar data and the Landsat-based Global Surface Water Data Set (GSWD). First, ICESat-2 can provide accurate along-track topographic points after the point cloud processing and bathymetric error correction, and the GSWD can supply water occurrence information within the lake dynamic area between 1984 and 2018. Second, using the derived relationship between the elevation and water occurrence, the bathymetric map of Lake Mead, USA, was produced with the dynamic area exceeding 235 km2, elevation ranging nearly 37 m, and a resolution of 30 m. The local reference data (i.e., the airborne topographic lidar data and ship/boat-based bathymetric data) in six areas around Lake Mead were used for the validations. In general, the produced lake bathymetry achieved an accuracy of approximately 2 m in elevation with$R^{2}$of 0.97. The proposed method is promising to obtain global bathymetry for inland water bodies (e.g., the lake and reservoir) and coastal areas (e.g., the tidal zone) where water level fluctuations are strong and the water clarity is sufficient.

Surface-water-level changes reflect Earth's water resource variations (e.g., trends and fluctuations) and they are helpful to understand potential drivers (e.g., climate change and human activities). Currently, Australia is facing serious water crisis owing to rainfall shortage and climate change, and national-scale data set on surface-water-level changes is required for supporting sustainable water resource management. Here, we used all Landsat Thematic Mapper (TM)/Enhanced Thematic Mapper (ETM)+/Operational Land Imager (OLI) data available on Google Earth Engine to obtain annual surface water during 2003-2019 and produced 1506 boundaries of water bodies with areas greater than 1 km2across Australia. The produced surface water map in Australia is more accurate than the existing global lake databases (e.g., the Global Lakes and Wetlands Database), which can be downloaded for free. Then, 52 water bodies (lakes and reservoirs) with areas larger than 1 km2and available Ice, Cloud, and land Elevation Satellite (ICESat/ICESat-2) data for more than 5 years were combined to estimate trends in surface water levels in Australia. Across Australia, from 2003 to 2019, the area-weighted mean of water level change rates is -0.046 m/year with 17 lakes (32.7%) with increasing water levels and 35 lakes (67.3%) decreasing with water levels. In detail, the largest lakes (>100 km2) dominate the total change trend and most of the largest lakes underwent decreasing trend (-0.046 m/year), whereas the mean water levels of small lakes (2) increased in the past 17 years. In situ water levels of three typical lakes/reservoirs were used to validate our estimation results, which exhibited a very good agreement (R2= 0.98).

As an essential indicator of climatic and environmental change, water levels of lakes are sensitive to various natural factors and anthropogenic activities from local to global scales. Understanding the global lake water level change can help to uncover the earth's water resource change and its response to the potential drivers. To date, most studies on lake water level changes were performed at local and regional scales. However, comprehensive information on the magnitude of lake water level changes at a global scale remains poorly understood. Here, with the help of the lake polygons from the global lakes and wetlands database (GLWD) at a global scale, we analyzed all available Ice, Cloud, and Land Elevation Satellite (ICESat) data to estimate the change in water levels of 14 981 lakes with areas greater than 0.1 km2 and reservoirs with storage capacities greater than 0.5 km3 around the world. We found global lakes exhibiting a significant spatial heterogeneity with 58.68% increase and 41.32% decrease in water levels during 2003 and 2009. The average change rate of global lake levels is 0.013 m/yr. We discovered an obvious and broad lake level increase in North America and Siberia region, Tibetan Plateau, and the Amazon basin. Our results provide detailed satellite-based evidence of the global increase in lake levels. In the future, more works should be conducted to estimate the lake level change at a longer temporal scale using multi-source satellite data and investigate the potential drivers under the climate warming and growing human footprint.

This contribution identifies the uncertainty sources influencing the component uncertainties for an airborne LiDAR bathymetry (ALB) measurement and presents the models for various component uncertainties (arising from the device, environment, and target for ALBs). Since various instrumental and environmental factors introduce vertical and horizontal uncertainties in ALB data, these uncertainties should be first analyzed and then precisely modeled to ensure the accuracy of the measurements. For this purpose, ten different effects that influence the accuracy of ALB data are systematically analyzed and modeled for four aspects in this article: the device aspect (laser pointing deflection, trajectory uncertainty, and boresight/lever arm offset), environmental aspect (atmospheric limitation, refraction on the sea surface, refraction in water, scattering in water, and water level fluctuation), target aspect (irregular bottom), and other aspect (accuracy of coordinate transformation model). In addition, the effect of the laser spot size is also discussed. To verify the presented uncertainty models, an ALB survey was operated around Yuanzhi Island in the South China Sea. For a water depth of 10 m, the theoretical overall root-sum-squares (RSS) for ten different effects of an ALB system (approximately 34 cm calculated using the total vertical uncertainty (TVU) models) is generally in accordance with the actual performance of the ALB data (approximately 39 cm) performance. The difference is mainly attributed to the limited accuracy of the ground truth data, and the difference between the water depth and laser ranging is reasonable. In this process, the topography data in the same region captured by a shipborne multibeam echo sounder (MBES) were used as the ground truth. The results indicated that for the typical ALB system, the laser pointing uncertainty and refraction uncertainty on the sea surface are primary uncertainty sources and should be corrected in a higher priority to meet the seafloor topographic accuracy demand of the International Hydrographic Organization (IHO) Standards for Hydrographic Surveys (S-44). The proposed uncertainty models can be used not only to guide the actual measurement of an ALB system but also to provide the uncertainty correction reference for ALB data postprocessing.

Parturient paresis(milk fever) is a common disease associated with the onset of parturition in dairy cows. The disease is considered due to a large increased demand for calcium. Several work has mathematically and biologically modelled this process. Based on the existing models on calcium dynamics in diary cows, an optimal impulse treatment is proposed in this paper. The treatment is executed at a fixed time interval and lasts a relatively very small time duration, which is termed as a "fixed time impulse" control. For the optimization, with a selected objective function, a series of equations for optimality are to be satisfied, including control equations, costate equations and state equations. Those impulsive differential equations form a two point boundary value problem and are difficult to solve. A numerical scheme, SNAC(Single Network Adaptive Critic), is then proposed. The algorithm key is to use one neural network to capture the optimal relation between the pre-impulse state and the after-impluse costate. After the neural network is trained and the relation is captured, the optimal impulse dosage of medicine can be provided when a parturient paresis is detected, and the cow's calcium level can be brought back to the normal status. Simulations are presented for illustrative purposes.

We investigate the optimization of linear impulse systems with the reinforcement learning based adaptive dynamic programming (ADP) method. For linear impulse systems, the optimal objective function is shown to be a quadric form of the pre-impulse states. The ADP method provides solutions that iteratively converge to the optimal objective function. If an initial guess of the pre-impulse objective function is selected as a quadratic form of the pre-impulse states, the objective function iteratively converges to the optimal one through ADP. Though direct use of the quadratic objective function of the states within the ADP method is theoretically possible, the numerical singularity problem may occur due to the matrix inversion therein when the system dimensionality increases. A neural network based ADP method can circumvent this problem. A neural network with polynomial activation functions is selected to approximate the pre-impulse objective function and trained iteratively using the ADP method to achieve optimal control. After a successful training, optimal impulse control can be derived. Simulations are presented for illustrative purposes.

This paper investigates the optimal control problem for linear impulsive systems with impulsive moments fixed. Based on adaptive dynamic programming(ADP), a numerical method is proposed to iteratively solve for this optimal impulsive control. The temporal difference of the value functions is used to determine whether the optimality has been achieved. A gradient based optimization is carried on to update the controller; The optimality principle is used to update the value function. When the optimality has been achieved, the controller output converges to the optimal impulsive control satisfying the optimality conditions. The convergence proof of this impulsive ADP algorithm is presented. Results of a scalar and a multi-variable linear impulsive systems are given in the simulation section for illustrative purpose.

Method of normalized spectrum was presented for problem-saving of spectral complexity and separating capacity, which were used to differ prtrous mountain from exposed soil and desert. Using the method, normalized spectral index (NSI) was established; then, the preous mountain index (RMI) was created; finally, we established desert-exposed soil difference model (DS-Def). The above results indicated that the precision is higher than traditional classification. But the method is too complex to extract the information quickly, so we selected above sensitive factors as new bands to classify mass ingredient in non-vegetation area using supervise classification. The result indicted that the method is relatively simple and effective.

This paper presents a systematic approach to solve for the optimal control of a variable-time impulsive system. First, optimality condition for a variable-time impulsive system is derived using the calculus of variations method. Next, a single network adaptive critic technique is proposed to numerically solve for the optimal control and the detailed algorithm is presented. Finally, two examples-one linear and one nonlinear-are solved applying the conditions derived and the algorithm proposed. Numerical results demonstrate the power of the neural network based adaptive critic method in solving this class of problems.