Four new compounds (
-
), including two brevianamides and two mycochromenic acid derivatives along with six known compounds were isolated from the deep-sea-derived fungus
DFFSCS025. Their structures ...were elucidated by spectroscopic analysis. Moreover, the absolute configurations of
and
were determined by quantum chemical calculations of the electronic circular dichroism (ECD) spectra. Compound
showed moderate cytotoxicity against human colon cancer HCT116 cell line with IC
value of 15.6 μM. In addition,
and
had significant antifouling activity against
larval settlement with EC
values of 13.7 and 22.6 μM, respectively. The NMR data of
,
, and
were assigned for the first time.
Poyang Lake is not only a globally important stopover for migratory birds and a habitat for fish, but also one of the main sand mining areas in China. However, sand mining activities significantly ...affect the suspended sediment concentration in Poyang Lake, thereby impacting water quality, altering lake bed topography, and potentially disturbing the living environment of plants and animals. Therefore, understanding the suspended sediment concentration and the spatiotemporal patterns of sand mining activities in Poyang Lake holds vital significance for effective lake management and biodiversity conservation. Utilizing surface reflectance (SR) data derived from Landsat satellite imagery sourced via Google Earth Engine spanning from 1989 to 2018, coupled with SSC data recorded by daily observation at Hukou hydrological stations, we conducted a comparative analysis of five distinct methods: Linear Regression (LR), Support Vector Machine (SVM), Random Forest (RF), Classification and Regression Trees (CART), and Back Propagation Neural Network (BPNN). Moreover, the applicability of machine learning methods was analyzed and compared across four scenarios (#01 Landsat 5, #02 Landsat 7, #03 Landsat 8, and #04, Landsat 5, 7, and 8). Using the optimal SSC estimation model, we investigated the spatial and temporal trends of sand mining activities in Poyang Lake from 1989 to 2021, along with their potential impact on suspended sediment levels in the water body. The results showed that the prediction accuracy of machine learning is better than that of linear regression models, and the RF model has the best performance. The RF model generated R<inline-formula> <tex-math notation="LaTeX">^{2} \gt 0.9 </tex-math></inline-formula> for four scenarios (#01 Landsat 5, #02 Landsat 7, #03 Landsat 8, and #04 Landsat 5, 7, and 8) and showed little to no overfitting. The generated SSC map can clearly show the distribution of SSC in Poyang Lake, unveiling the impact of sand mining activities on suspended sediment, especially around sand dredgers where SSC can exceed 0.15 g/L. Sand mining activities in Poyang Lake emerged after 2000 and gradually shifted southward and expanded, reaching a peak in 2016. Fortunately, under government regulation, illegal sand mining has been effectively controlled and is currently concentrated near Songmenshan Island. Despite the fact that sand mining has been brought under control, there remains a necessity for heightened oversight to prevent any impact on national nature reserves. The Random Forest (RF) model demonstrates significant potential in utilizing Landsat satellite data to predict SSC in Poyang Lake, as well as to monitor sand mining activities in the area.
Forest canopy height is defined as the distance between the highest point of the tree canopy and the ground, which is considered to be a key factor in calculating above-ground biomass, leaf area ...index, and carbon stock. Large-scale forest canopy height monitoring can provide scientific information on deforestation and forest degradation to policymakers. The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) was launched in 2018, with the Advanced Topographic Laser Altimeter System (ATLAS) instrument taking on the task of mapping and transmitting data as a photon-counting LiDAR, which offers an opportunity to obtain global forest canopy height. To generate a high-resolution forest canopy height map of Jiangxi Province, we integrated ICESat-2 and multi-source remote sensing imagery, including Sentinel-1, Sentinel-2, the Shuttle Radar Topography Mission, and forest age data of Jiangxi Province. Meanwhile, we develop four canopy height extrapolation models by random forest (RF), Support Vector Machine (SVM), K-nearest neighbor (KNN), Gradient Boosting Decision Tree (GBDT) to link canopy height in ICESat-2, and spatial feature information in multi-source remote sensing imagery. The results show that: (1) Forest canopy height is moderately correlated with forest age, making it a potential predictor for forest canopy height mapping. (2) Compared with GBDT, SVM, and KNN, RF showed the best predictive performance with a coefficient of determination (R2) of 0.61 and a root mean square error (RMSE) of 5.29 m. (3) Elevation, slope, and the red-edge band (band 5) derived from Sentinel-2 were significantly dependent variables in the canopy height extrapolation model. Apart from that, Forest age was one of the variables that the RF moderately relied on. In contrast, backscatter coefficients and texture features derived from Sentinel-1 were not sensitive to canopy height. (4) There is a significant correlation between forest canopy height predicted by RF and forest canopy height measured by field measurements (R2 = 0.69, RMSE = 4.02 m). In a nutshell, the results indicate that the method utilized in this work can reliably map the spatial distribution of forest canopy height at high resolution.
One of the major barriers to hindering the sustainable development of the terrestrial environment is the desertification process, and revegetation is one of the most significant duties in ...anti-desertification. Desertification deteriorates land ecosystems through species decline, and remote sensing is becoming the most effective way to monitor desertification. Mu Us Sandy Land is the fifth largest desert and the representative area under manmade vegetation restorations in China. Therefore, it is essential to understand the spatiotemporal characteristics of artificial desert transformation for seeking the optimal revegetation location for future restoration planning. However, there are no previous studies focusing on exploring regular patterns between the spatial distribution of vegetation restoration and human-related geographical features. In this study, we use Landsat satellite data from 1986 to 2020 to achieve annual monitoring of vegetation change by a threshold segmentation method, and then use spatiotemporal analysis with Open Street Map (OSM) data to explore the spatiotemporal distribution pattern between vegetation occurrence and human-related features. We construct an artificial vegetation restoration suitability index (AVRSI) by considering human-related features and topographical factors, and we assess artificial suitability for vegetation restoration by mapping methods based on that index and the vegetation distribution pattern. The AVRSI can be commonly used for evaluating restoration suitability in Sandy areas and it is tested acceptable in Mu Us Sandy Land. Our results show during this period, the segmentation threshold and vegetation area of Mu Us Sandy Land increased at rates of 0.005/year and 264.11 km2/year, respectively. Typically, we found the artificial restoration vegetation suitability in Mu Us area spatially declines from southeast to northwest, but eventually increases in the most northwest region. This study reveals the revegetation process in Mu Us Sandy Land by figuring out its spatiotemporal vegetation change with human-related features and maps the artificial revegetation suitability.
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► NanoG was prepared and chemically coated by nano-Ni. ► PPy/NanoG and PPy/Ni/NanoG were prepared via in situ polymerization. ► The samples were characterized by SEM, XRD, EDS and ...FTIR. ► PPy/NanoG and PPy/Ni/NanoG exhibit good electromagnetic properties.
Nanocomposites PPy/NanoG and PPy/Ni/NanoG were prepared via in situ polymerization of pyrrole in the presence of NanoG and nickel-coated graphite nanosheet (Ni/NanoG), respectively. The morphologies and nanostructures of NanoG, Ni/NanoG, PPy, PPy/NanoG and PPy/Ni/NanoG were characterized by scanning electron microscope (SEM), energy dispersive spectroscopy (EDS), Fourier transmission infrared (FTIR) and X-ray diffraction analysis (XRD). Results show that most of PPy chains disperse on NanoG and Ni/NanoG's surfaces for the high aspect ratio (300–500) of NanoG and Ni/NanoG. From the thermogravimetric analysis (TG) it can be seen that the introduction of Ni and NanoG leads the composites PPy/NanoG and PPy/Ni/NanoG to exhibit a better thermal stability than pure PPy. According to the four-point-probe test, the conductivities of the final PPy/NanoG and PPy/Ni/NanoG composites are dramatically increased compared to pure PPy. Measurement of electromagnetic parameters shows that the reflection loss (R) of PPy/Ni/NanoG is below −19dB at the X band (8.2–12.4GHz) and the minimum loss value is −23.46dB at 9.88GHz. The reflection loss of PPy/NanoG is below −10dB at 8.2–12.4GHz and the minimum loss value is −13.44dB at 10.28GHz. The microwave absorbing properties of PPy/NanoG and PPy/Ni/NanoG are superior to those of PPy.
Quantifying secondary forest age (SFA) is essential to evaluate the carbon processes of forest ecosystems at regional and global scales. However, the successional stages of secondary forests remain ...poorly understood due to low-frequency thematic maps. This study aimed to estimate SFA with higher frequency and more accuracy by using dense Landsat archives. The performances of four time-series change detection algorithms—moving average change detection (MACD), Continuous Change Detection and Classification (CCDC), LandTrendr (LT), and Vegetation Change Tracker (VCT)—for detecting forest regrowth were first evaluated. An ensemble model was then developed to determine more accurate timings for forest regrowth based on the evaluation results. Finally, after converting the forest regrowth year to the SFA, the spatiotemporal and topographical distributions of the SFA were analyzed. The proposed ensemble model was validated in Jiangxi province, China, which is located in a subtropical region and has experienced drastic forest disturbances, artificial afforestation, and natural regeneration. The results showed that: (1) the developed ensemble model effectively determined forest regrowth time with significantly decreased omission and commission rates compared to the direct use of the four single algorithms; (2) the optimal ensemble model combining the independent algorithms obtained the final SFA for Jiangxi province with the lowest omission and commission rates in the spatial domain (14.06% and 24.71%) and the highest accuracy in the temporal domain (R2 = 0.87 and root mean square error (RMSE) = 3.17 years); (3) the spatiotemporal and topographic distribution from 1 to 34 years in the 2021 SFA map was analyzed. This study demonstrated the feasibility of using change detection algorithms for estimating SFA at regional to national scales and provides a data foundation for forest ecosystem research.
A national distribution of secondary forest age (SFA) is essential for understanding the forest ecosystem and carbon stock in China. While past studies have mainly used various change detection ...algorithms to detect forest disturbance, which cannot adequately characterize the entire forest landscape. This study developed a data-driven approach for improving performances of the Vegetation Change Tracker (VCT) and Continuous Change Detection and Classification (CCDC) algorithms for detecting the establishment of forest stands. An ensemble method for mapping national-scale SFA by determining the establishment time of secondary forest stands using change detection algorithms and dense Landsat time series was proposed. A dataset of national secondary forest age for China (SFAC) for 1 to 34 and with a 30-m spatial resolution was produced from the optimal ensemble model. This dataset provides national, continuous spatial SFA information and can improve understanding of secondary forests and the estimation of forest carbon storage in China.
The hydrological situations of wetlands are critical to the habitat qualities of wintering migratory birds. It is of great value to evaluate the habitat vulnerabilities within more precise intervals ...of water levels and quantitatively assess the influences of water level changes. The findings are advantageous for managing wetland ecosystems and for migratory bird habitat protection. This study identified the ideal habitats for wintering Siberian cranes in Poyang Lake wetland within 1-meter water level intervals (from 5 to 16 m) based on the Landsat thematic mapper (TM), enhanced thematic mapper plus (ETM+), and operational land imager (OLI) remote sensing images taken on multiple dates in the past 30 years. Three indicators—sustainability, stability, and variety—were used to evaluate the vulnerabilities of crane habitats within various water level intervals; the spatial variations and distribution patterns of the habitat vulnerabilities were further explored. The explanatory powers of water level intervals (and others) and their paired interactive effects on the habitat vulnerabilities were quantified using the geographical detector method. The results showed that crane habitat vulnerabilities were significantly sensitive to the water level changes of Poyang Lake; the habitat vulnerabilities and their spatial distribution patterns both exhibited specific tendencies with water level increases. A water level of 12 m was identified as the potential upper threshold for the maintenance of sustainable crane habitats and a water level interval of 9–10 m was expected to be the optimal interval for facilitating the aggregation features of crane habitats. The water level interval was identified as the most dominant factor in habitat vulnerability. It explained 14.46%, 42.89%, and 21.78% of the sustainability, stability, and variety of crane habitats; the numbers were expected to increase to 22%, 49.25%, and 25.84%, respectively, with water level intervals interacting with other factors. This article provides a novel perspective in evaluating the habitat vulnerabilities of wintering migratory birds and quantifying the responses to water level changes in wetlands; the proposed approaches are applicable and practicable for habitat vulnerability assessments of other wintering birds in other typical wetlands.
Microwave absorbing materials have been widely studied and applied nowadays, but their loss mechanism, especially the correlation between attenuation characteristics and impedance matching, is still ...not clear. In this system comparative study, a novel metal − organic framework with the Co ions (Co-MOF), CoS
2
@MoS
2
and Co
x
S
y
/C@MoS
2
nanofibers were prepared by hydrothermal reaction and heat treatment. All the samples were characterized by scanning electron microscope (SEM), X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS) and vector network analyzer (VSA). The results indicated that the minimum value of reflection loss (RL) for CoS
2
@MoS
2
-2 was − 31.12 dB and the corresponding bandwidth with effective attenuation (RL ≤ − 10 dB) was up to 2.36 GHz (from 7.0 to 10.6 GHz) at 2.7 mm. Compared with CoS
2
@MoS
2
composites, the microwave absorbing performance (MAP) of Co
x
S
y
/C@MoS
2
nanofibers was enhanced obviously: the bandwidth with effective attenuation of Co
x
S
y
/C@MoS
2
was up to 3.67 GHz (7.24 GHz − 10.91 GHz) with coating thickness 3.7 mm, and the minimum RL value was − 41.32 dB. The enhanced MAP originated from the synergistic effect between polarization loss and conductive loss, which results from Co
x
S
y
, MoS
2
and introducing C, respectively.
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