The global digital elevation model (DEM) is important for various scientific applications. With the recently released TanDEM-X 90-m DEM and AW3D30 version 2.2, the open global or near-global coverage DEM datasets have been further expanded. However, the quality of these DEMs has not yet been fully characterized, especially in the application for regional scale studies. In this study, we assess the quality of five freely available global DEM datasets (SRTM-1 DEM, SRTM-3 DEM, ASTER GDEM2, AW3D30 DEM and TanDEM-X 90-m DEM) and one 30-m resampled TanDEM-X DEM (hereafter called TDX30) over the south-central Chinese province of Hunan. Then, the newly-released high precision ICESat-2 (Ice, Cloud, and land Elevation Satellite-2) altimetry points are introduced to evaluate the accuracy of these DEMs. Results show that the SRTM1 DEM offers the best quality with a Root Mean Square Error (RMSE) of 8.0 m, and ASTER GDEM2 has the worst quality with the RMSE of 10.1 m. We also compared the vertical accuracies of these DEMs with respect to different terrain morphological characteristics (e.g., elevation, slope and aspect) and land cover types. It reveals that the DEM accuracy decreases when the terrain elevation and slope value increase, whereas no relationship was found between DEM error and terrain aspect. Furthermore, the results show that the accuracy increases as the land cover type changes from vegetated to non-vegetated. Overall, the SRTM1 DEM, with high spatial resolution and high vertical accuracy, is currently the most promising dataset among these DEMs and it could, therefore, be utilized for the studies and applications requiring accurate DEMs.

Carbonized and activated wood scraps are appealing scaffolds upon which to host active materials for supercapacitors, realizing the transformation of waste into a valuable device. However, the active material when loaded on the inner walls of the wood tracheids can be easily peeled off, resulting in poor cycling stability of the capacitor and low energy density. Here, we designed a novel composite electrode material for high-performance supercapacitors based on a polyaniline/carbon nanotube composite material with a core–shell structure synthesized in situ in a carbonized wood scaffold. Carbon nanotubes with excellent conductivity were first synthesized in situ on the inner walls of the tracheids via chemical vapor deposition, which were stably embedded in the wood tracheids to increase the specific surface area and active material loading active sites. Then, a layer of polyaniline was deposited on the outer surface of each carbon nanotube via electrochemical deposition to form a core–shell nanostructure. The composite material as a single electrode has high specific capacitances of 240.0 F cm⁻³ and 1019.5 F g⁻¹ at 10 mA cm⁻². Finally, the asymmetric supercapacitor based on the carbon nanotubes/carbonized wood scaffold as the anode and polyaniline/carbon nanotubes/carbonized wood scaffold as the cathode exhibited a high energy density of 40.5 W h kg⁻¹ at 162.5 W kg⁻¹ and a high capacity retention rate of 93.74% after 10 000 charge and discharge cycles at a current density of 20 mA cm⁻².

The Shuttle Radar Topographic Mission (SRTM) digital elevation model (DEM) is a widely utilized product for geological, climatic, oceanic, and ecological applications. However, the accuracy of the SRTM DEM is constrained by topography and vegetation. Using machine learning models to correct SRTM DEM with high-accuracy reference elevation observations has been proven to be useful. However, most of the reference observation-aided approaches rely on either parametric or non-parametric regression (e.g., a single machine learning model), which may lead to overfitting or underfitting and limit improvements in the accuracy of SRTM DEM products. In this study, we presented an algorithm for correcting SRTM DEM using a stacking ensemble machine learning algorithm. The proposed algorithm is capable of learning how to optimally combine the predictions from multiple well-performing machine learning models, resulting in superior performance compared to any individual model within the ensemble. The proposed approach was tested under varying relief and vegetation conditions in Hunan Province, China. The results indicate that the accuracy of the SRTM DEM productions improved by approximately 46% using the presented algorithm with respect to the original SRTM DEM. In comparison to two conventional algorithms, namely linear regression and artificial neural network models, the presented algorithm demonstrated a reduction in root-mean-square errors of SRTM DEM by 28% and 12%, respectively. The approach provides a more robust tool for correcting SRTM DEM or other similar DEM products over a wide area.

Transition metal oxides (TMOs) can provide high theoretical capacitance due to the change of multiple valence states of transition metals. However, their intrinsic drawbacks, including poor electrical conductivity, lower energy density, and huge volume expansion, will result in the pulverization of electrode materials and restricted electrochemical kinetics, thus leading to poor rate capability and rapid capacity fading. Composite electrodes based on transition metal oxides and carbon-based materials are considered to be promising candidates for overcoming these limitations. Herein, we reported a preparation method of hybrid ZIFs derived Zn-doped Co₃O₄/carbon (Zn-Co₃O₄/C-230) particles semi-embedded in wood-derived carbon skeleton for integrated electrodes. A large specific surface area, excellent conductivity, and electrochemical stability provide a larger electrochemical activity and potential window for the electrode. Prepared Zn-Co₃O₄@CW-230 electrode (0.6 mm thick) displays ultrahigh area specific capacitances of 7.83 and 6.46 F cm⁻² at the current densities of 5 and 30 mA cm⁻², respectively. Moreover, a symmetric supercapacitor assembled by two identical Zn-Co₃O₄@CW-230 electrodes delivers a superior area-specific capacitance of 2.61 F cm⁻² at the current densities of 5 mA cm⁻² and great energy densities of 0.36 mWh cm⁻² (6.0 mWh cm⁻³) at 2.5 mW cm⁻², while maintaining 97.3% of initial capacitance over 10,000 cycles. It notably outperforms those of most carbon-based metal oxides, endowing the Zn-Co₃O₄@CW-230 with extensive prospects for practical application.

This paper presents a terrain-related method to simultaneously correct the global error trends and local linear/nonlinear terrain-related errors of the Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM), which have not been focused in most of the previous methods. To meet this goal, an adaptive strategy for modelling the SRTM DEM errors is first proposed, especially over mountainous areas, based on the Bayesian information criterion. Then, the M-estimator, instead of the ordinary least squares’ solver, is utilized to estimate the model parameters of the constructed model to improve the estimation robustness. The proposed method was tested over the Zhangjiajie areas of China, where the ground surface terrain varies from plains to steep mountains. The results show that the errors of the SRTM DEM over the region of interest decrease from 10.1 m to approximately 8.1 m after correction with the proposed method, indicating an improvement of approximately 20%. In addition, compared with two existing and common methods that can correct the global error trends and local linear terrain-related errors of the SRTM DEM, the accuracy of the corrected SRTM DEM improves by approximately 29% and 27.9% over mountainous areas with slopes larger than 20° when the proposed method is used. Moreover, the proposed method will be beneficial to the correction of other airborne or spaceborne DEM products, especially over mountainous areas.

Forest fires are among the biggest threats to forest ecosystems and forest resources, and can lead to ecological disasters and social crises. Therefore, it is imperative to detect and extinguish forest fires in time to reduce their negative impacts. Satellite remote sensing, especially meteorological satellites, has been a useful tool for forest-fire detection and monitoring because of its high temporal resolution over large areas. Researchers monitor forest fires directly at pixel level, which usually presents a mixture of forest and fire, but the low spatial resolution of such mixed pixels cannot accurately locate the exact position of the fire, and the optimal time window for fire suppression can thus be missed. In order to improve the positioning accuracy of the origin of forest fire (OriFF), we proposed a mixed-pixel unmixing integrated with pixel-swapping algorithm (MPU-PSA) model to monitor the OriFFs in time. We then applied the model to the Japanese Himawari-8 Geostationary Meteorological Satellite data to obtain forest-fire products at subpixel level. In this study, the ground truth data were provided by the Department of Emergency Management of Hunan Province, China. To validate the positioning accuracy of MPU-PSA for OriFFs, we applied the model to the Himawari-8 satellite data and then compared the derived fire results with fifteen reference forest-fire events that occurred in Hunan Province, China. The results show that the extracted forest-fire locations using the proposed method, referred to as forest fire locations at subpixel (FFLS) level, were far closer to the actual OriFFs than those from the modified Himawari-8 Wild Fire Product (M-HWFP). This improvement will help to reduce false fire claims in the Himawari-8 Wild Fire Product (HWFP). We conducted a comparative study of M-HWFP and FFLS products using three accuracy-evaluation indexes, i.e., Euclidean distance, RMSE, and MAE. The mean distances between M-HWFP fire locations and OriFFs and between FFLS fire locations and OriFFs were 3362.21 m and 1294.00 m, respectively. The mean RMSEs of the M-HWFP and FFLS products are 1225.52 m and 474.93 m, respectively. The mean MAEs of the M-HWFP and FFLS products are 992.12 m and 387.13 m, respectively. We concluded that the newly proposed MPU-PSA method can extract forest-fire locations at subpixel level, providing higher positioning accuracy of forest fires for their suppression.