Moso bamboo is the most widely distributed bamboo forest type in tropical and subtropical areas. Increasing trends in both expansion and logging of Moso bamboo have occurred over the past four ...decades due to its rapid growth rate and short harvest rotation period. The spatial and temporal distribution of bamboo forest is the basis for the study of the carbon cycle in bamboo forest ecosystems. Previous studies have consistently focused on different influences of scale, data and algorithms for bamboo forest mapping; however, there is still considerable uncertainty in bamboo forest mapping due to the unique on-year and off-year phenomena and their significant differences in spectral characteristics. Northwestern Zhejiang Province has the most widely distributed bamboo forest and was selected as the study area. Based on Sentinel-2 and Landsat 8 time-series data, changes in the spectral characteristics during the growth cycle of bamboo forest were analyzed and the optimum time window for the classification of the bamboo forest was determined. The seasonal index of the bamboo forest based on spectral differences was the proposed approach, and on-year and off-year bamboo forest mapping was conducted seasonally using Landsat 8 and Sentinel-2 data. The results show that (1) the best period to distinguish bamboo forest from other forest types is April–May, followed by December–February, and the best month for distinguishing between on-year and off-year bamboo forests is May; (2) the spectral differences between bamboo forest and other forest types are mainly reflected in the red-edge 3 near-infrared (NIR) and NIR narrow bands (740–865 nm); (3) Sentinel-2 data have obvious advantages over Landsat 8 data in distinguishing the bamboo forest because of better spectral, temporal and spatial resolutions; and (4) the multi-temporal bamboo forest index has a better overall classification accuracy (OA = 91.2) for distinguishing on-year and off-year bamboo forests than the mono-temporal index and can be applied to other regions.
•On-year and off-year phenomena are the critical feature in bamboo forest mapping.•Multi-temporal bamboo indexes can effectively identify on-year and off-year bamboo.•The proposed approach can be expanded to other Moso bamboo forest regions.•The best month to distinguish on-year and off-year bamboo forest is May.
Novel polypyrrole-polyoxometalate/reduced graphene oxide ternary nanohybrids (TNHs) are synthesized via a one-pot redox relay strategy. The TNHs exhibit high areal specific capacitance (2.61 mF ...cm(-2)), and the fabricated solid device also exhibits good rate capability, excellent flexibility and mechanical stability.
Bamboo forests, due to rapid growth and short harvest rotation, play an important role in carbon cycling and local economic development. Accurate estimation of bamboo forest aboveground biomass (AGB) ...has garnered increasing attention during the past two decades. However, remote sensing-based AGB estimation for bamboo forests is challenging due to poor understanding of the mechanisms between bamboo forest growth characteristics and remote sensing data. The objective of this research is to examine the remote sensing characteristics of on-year and off-year bamboo forests at different dates and their AGB estimation performance. This research used multiple Sentinel-2 data to explore AGB estimation of bamboo forests in Zhejiang Province, China, by taking into account the unique characteristics of on-year and off-year bamboo forest growth features. Combining field survey data and Sentinel-2 spectral responses (spectral bands and vegetation indices) and textural images, random forest was used to identify key variables for AGB estimation. The results show that (1) the on-year and off-year bamboo forests have considerably different spectral signatures, especially in the wavelengths between red edge 2 and near-infrared wavelength (NIR2) (740–865 nm), making it possible to separate on-year and off-year bamboo forests; (2) on-year bamboo forests have similar spectral signatures although AGB increases from as small as 40 Mgha−1 to as high as 90 Mgha−1, implying that optical sensor data cannot effectively model on-year bamboo AGB; (3) off-year bamboo AGB has significant relationships with red and shortwave infrared (SWIR) spectral bands in the April image and with red edge 2 in the July image, but the AGB saturation problem yields poor estimation accuracy; (4) stratification considerably improved off-year bamboo AGB estimation but not on-year, non-stratification using the April image is recommended; and (5) Sentinel-2 data cannot solve the bamboo AGB data saturation problem when AGB is greater than 70 Mgha−1, similar to other optical sensor data such as Landsat. More research should be conducted in the future to integrate multiple sources—remotely sensed data (e.g., lidar, optical sensor data) and ancillary data (e.g., soil, topography)—into AGB modeling to improve the estimation. The use of very high spatial resolution images that can effectively extract tree density information may improve bamboo AGB estimation and yield new insights.
The presence of thick clouds in single optical images shows the contamination of interesting objects. Besides, the difficulty of thick cloud removal is mainly the restoration of the weak boundary ...information from cloud-contaminated areas. Recently, many deep-learning-based frameworks were applied for cloud removal by obtain the related semantic information from the weak boundary information. However, the large-size cloud-contaminated areas lead to the artificial textures in the resulting images. Thus, obtaining the optimal semantic information from finite boundary information is the key to solve this problem. In this work, we design a deep-learning framework for cloud removal, especially large-size clouds removal (i.e., more than 30% coverage of the whole image). First, we design a cloud location model (CLM) which adopted the fully convolutional network to locate the cloud. Second, desired by theory of the coarse-to-fine restoration, we build a dense-attention network (termed as DANet) for restoring cloud contaminated areas. In the DANet, we design a dense block into the coarse network for training the features of restoring directions of each pixel from the weak boundary information. Furthermore, a contextual attention module is built into refinement network for restoring contaminated areas relying on the semantic relationship between the background and foreground information. Compared with the state-of-the-art methods, the proposed DANet achieved greater removal performances and reconstruct more natural image textures.
Graphene oxide nanosheets were used to induce the in situ gelation of doxorubicin hydrochloride as an antitumor drug. When a very small amount of the graphene oxide was introduced into an aqueous ...solution of doxorubicin hydrochloride at room temperature, a strong and thixotropic gel was rapidly formed without any polymers or chemical additives. The gelation mechanism was investigated by fluorescence spectroscopy, X-ray diffraction and scanning electron microscopy. The encapsulated doxorubicin hydrochloride was found to show sustained release and antitumor efficacy.
The levels of 13 organochlorine pesticides (OCPs) in surface water and sediments from Qiantang River in East China were investigated to evaluate their potential pollution and risks. A total of 180 ...surface water samples at 45 sampling sites and 48 sediment samples at 19 sampling stations were collected along the river in four seasons of 2005. Soil samples and wet deposition samples were also collected to provide evidence on the source of OCPs pollution. The total OCPs concentrations in surface water and sediments were 7.68–269.4ng/L and 23.11–316.5ng/g-dry weight (dw), respectively. The concentrations of OCPs in sediments were in the range of 8.22–152.1ng/g-dw for HCHs (α-, β-, γ-, δ-HCH), 1.14–100.2ng/g-dw for DDTs (p,p′-DDD, p,p′-DDE, p,p′-DDT o,p′-DDD), 9.41–69.66ng/g-dw for other OCPs (aldrin, diedrin, endrin, heptachlor, heptachlor epoxide). The total OCPs concentrations in soils and wet deposition were 5.04–214.9ng/g-dw and 16.18–242.4ng/L, respectively. Among the OCPs, HCHs, DDTs and heptachlor were the most dominant compounds in the sediments. The dominant OCPs in water were γ-HCH among HCHs, heptachlor among other OCPs and p,p′-DDE among DDTs. Also, different contamination patterns among sampling seasons were found. The concentrations of OCPs in sediment collected in spring were higher than those in summer and autumn. In contrast, the concentrations of OCPs in surface water in summer and autumn were higher among four sampling seasons. Distribution of HCHs, DDTs and other OCPs were different indicating their different contamination sources. The notable contamination was found in Fuchun reservoir. Composition analyses in sediments indicated a recent usage or discharge of lindane into the river.
Microwave radiometer (MRM) is one of the important payloads on the Chang’e-2 (CE-2) Lunar satellite. In the Chang’e satellite’s observation of the microwave radiation brightness temperature (TB) on ...the lunar surface, there are some “cold spots” of microwave thermal radiation at night containing the Jackson crater. In order to compare the diurnal radiation TB differences of “cold spots” on the lunar surface, two typical craters at similar latitudes on the northern hemisphere on the lunar farside were selected: Jackson, which represents the new craters with a large number of discrete rocks on their surfaces; and Morse, which no longer has a large number of rocks after long-term meteorite impact and lunar evolution. In this paper, the diurnal variation of CE-2 MRM data in the two craters is presented, and a comparative analysis is made with the (FeO + TiO2) abundance (FTA) obtained by Clementine UV-VIS data and the rock abundance (RA) data of LRO Diviner. We find that the variation of the "cold spots" of lunar surface thermal radiation is closely related to the RA distribution in the newly formed craters on the lunar surface, and also has a certain correlation with the FTA.
The distribution and ecological risks of 11 phenolic compounds were studied in Weihe River, Northwest China. The concentrations of phenolic compounds were determined by ultra-high performance liquid ...chromatography (UPLC). The total concentration of 11 phenolic compounds (∑PC
) ranged from 0.06 to 14.12 μg/L with an average of 5.22 μg/L in water, from 0.92 to 34,885 μg/g with an average of 4,446 μg/g in suspended particulate matter (SPM), and from 3.54 to 34.09 μg/g with an average of 11.09 μg/g in sediment. For individual phenolic compound, the mean concentration of pentachlorophenol was the highest in water (2.65 μg/L) and in SPM (3,865 μg/g), while in sediment the mean concentration of 2,4,6-trichlorophenol was the highest (3.05 μg/g). The total concentration of 5 chlorophenols (∑CP
) was significantly higher than that of 6 non-chlorophenols (∑NCP
) in all three studied compartments. The phenolic compounds in Weihe River were at moderate levels in water and at high levels in sediment. The ecological risk assessment results indicated that phenolic compounds exhibited a high ecological risk in Weihe River water. In most sites, the distribution coefficient (Kd) (SPM) was much higher than Kd (sediment), which probably suggested fresh phenolic compounds input in Weihe River.
As a new information provider of autonomous navigation, the on-orbit landmark observation offers a new means to improve the accuracy of autonomous positioning and attitude determination. A novel ...autonomous navigation method based on the landmark observation and the inertial system is designed to achieve the high-accuracy estimation of the missile platform state. In the proposed method, the navigation scheme is constructed first. The implicit observation equation about the deviation of the inertial system output is derived and the Kalman filter is applied to estimate the missile platform state. Moreover, the physical observability of the landmark and the mathematical observability of the navigation system are analyzed. Finally, advantages of the proposed autonomous navigation method are demonstrated through simulations compared with the traditional celestial-inertial navigation system and the deeply integrated celestial-inertial navigation system.
Aiming at the problem of fault diagnosis in continuous time systems, a kind of fault diagnosis algorithm based on adaptive nonlinear proportional integral (PI) observer, which can realize the ...effective fault identification, is studied in this paper. Firstly, the stability and stability conditions of fault diagnosis method based on the PI observer are analyzed, and the upper bound of the fault estimation error is given. Secondly, the fault diagnosis algorithm based on adjustable nonlinear PI observer is designed and constructed, it is analyzed and we proved that the upper bound of fault estimation under this algorithm is better than that of the traditional method. Finally, the L-1011 unmanned aerial vehicle (UAV) is taken as the experimental object for numerical simulation, and the fault diagnosis method based on adaptive observer factor achieves faster response speed and more accurate fault identification results.
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