Urban Treetop Detection and Tree-Height Estimation from Unmanned-Aerial-Vehicle Images

Individual tree detection for urban forests in subtropical environments remains a great challenge due to the various types of forest structures, high canopy closures, and the mixture of evergreen and deciduous broadleaved trees. Existing treetop detection methods based on the canopy-height model (CHM) from UAV images cannot resolve commission errors in heterogeneous urban forests with multiple trunks or strong lateral branches. In this study, we improved the traditional local-maximum (LM) algorithm using a dual Gaussian filter, variable window size, and local normalized correlation coefficient (NCC). Specifically, we adapted a crown model of maximum/minimum tree-crown radii and an angle strategy to detect treetops. We then removed and merged the pending tree vertices. Our results showed that our improved LM algorithm had an average user accuracy (UA) of 87.3% (SD± 4.6), an average producer accuracy (PA) of 82.8% (SD± 4.1), and an overall accuracy of 93.3% (SD± 3.9) for sample plots with canopy closures less than 0.5. As for the sample plots with canopy closures from 0.5 to 1, the accuracies were 78.6% (SD± 31.5), 73.8% (SD± 10.3), and 68.1% (SD± 12.7), respectively. The tree-height estimation accuracy reached more than 0.96, with an average RMSE of 0.61 m. Our results show that the UAV-image-derived CHM can be used to accurately detect individual trees in mixed forests in subtropical cities like Shanghai, China, to provide vital tree-structure parameters for precise and sustainable forest management.National Key R&D Program of ChinaNational Natural Science Foundation of ChinaChina Postdoctoral Science FoundationPeer Reviewe

Spatiotemporal Evolution of Urban Agglomeration and Its Impact on Landscape Patterns in the Pearl River Delta, China

An urban agglomeration is the engine of regional and national economic growth, but also causes many ecological and environmental issues that emerge from massive land changes. In this study, the spatiotemporal evolution of an urban agglomeration was quantified and its impacts on the urban and regional landscape patterns were evaluated. It showed that the urbanized land area of the Pearl River Delta Urban Agglomeration (PRDUA) in China nearly quadrupled, having linearly increased from 1819.8 km2 to 7092.2 km2 between 1985 and 2015. The average annual growth rate presented a bimodal wave-like pattern through time, indicating that the PRDUA has witnessed two rounds of the urbanization process. The growth modes (e.g., leapfrog, edge-expansion, infilling) were detected and they exhibited co-existing but alternating dominating patterns during urbanization, demonstrating that the spatiotemporal evolution of the urban development of the PRDUA follows the “spiral diffusion-coalescence” hypothesis. The morphology of the PRDUA presented an alternating dispersal-compact pattern over time. The city-level and regional-level landscape patterns changed synchronously with the spatiotemporal evolution of the PRDUA over time. The urbanization of the PRDUA increased both the complexity and aggregation of the landscape, but also resulted in an increasing fragmentation and decreasing connectivity of the natural landscape in the Pearl River Delta region. These findings are helpful for better understanding how urban agglomerations evolve and in providing insights for regional urban planning and sustainable land management.Natural Science Foundation of ChinaNational Key R&D Program of ChinaChina Postdoctoral Science FoundationJoint-PhD project of Shanghai Jiao Tong University and The University of MelbournePeer Reviewe

Regulating Top-Surface Multilayer/Single-Crystal Graphene Growth by “Gettering” Carbon Diffusion at Backside of the Copper Foil

A unique strategy is reported to constrain the nucleation centers for multilayer graphene (MLG) and, later, single-crystal graphene domains by gettering carbon source on backside of the flat Cu foil, during chemical vapor deposition. Hitherto, for a flat Cu foil, the top-surface-based growth mechanism is emphasized, while overlooking the graphene on the backside. However, the systematic experimental findings indicate a strong correlation between the backside graphene and the nucleation centers on the top-surface, governed by the carbon diffusion through the bulk Cu. This understanding steers to devise a strategy to mitigate the carbon diffusion to the top-surface by using a carbon “getter” substrate, such as nickel, on the backside of the Cu foil. Depth profiling of the nickel substrate, along with the density functional theory calculations, verifies the gettering role of the nickel support. The implementation of the backside carbon gettering approach on single-crystal graphene growth results in lowering the nucleation density by two orders of magnitude. This enables the single-crystal domains to grow by 6 mm laterally on the untreated Cu foil. Finally, the growth of large-area polycrystalline single layer graphene, free of unwanted MLG domains, with significantly improved field-effect mobility of ≈6800 cm^2 V^(−1) s^(−1) is demonstrated

In vivo reactive astrocyte imaging using 18FSMBT-1 in tauopathy and familial Alzheimer’s disease mouse models: A multi-tracer study

Background: Reactive astrocytes play an important role in the development of Alzheimer's disease and primary tauopathies. Here, we aimed to investigate the relationships between reactive astrocytes. Microgliosis and glucose metabolism with Tau and amyloid beta pathology by using multi-tracer imaging in widely used tauopathy and familial Alzheimer's disease mouse models. Results: Positron emission tomography imaging using 18FPM-PBB3 (tau), 18Fflorbetapir (amyloid-beta), 18FSMBT-1 (monoamine oxidase-B), 18FDPA-714 (translocator protein) and 18Ffluorodeoxyglucose was carried out in 3- and 7-month-old rTg4510 tau mice, 5 × FAD familial Alzheimer's disease mice and wild-type mice. Immunofluorescence staining was performed to validate the pathological distribution in the mouse brain after in vivo imaging. We found increased regional levels of 18FPM-PBB3, 18FSMBT-1, and 18FDPA-714 and hypoglucose metabolism in the brains of 7-month-old rTg4510 mice compared to age-matched wild-type mice. Increased 18FSMBT-1 uptake was observed in the brains of 3, 7-month-old 5 × FAD mice, with elevated regional 18Fflorbetapir and 18FDPA-714 uptakes in the brains of 7-month-old 5 × FAD mice, compared to age-matched wild-type mice. Positive correlations were shown between 18FSMBT-1 and 18FPM-PBB3, 18FDPA-714 and 18FPM-PBB3 in rTg4510 mice, and between 18Fflorbetapir and 18FDPA-714 SUVRs in 5 × FAD mice. Conclusion: In summary, these findings provide in vivo evidence that reactive astrocytes, microglial activation, and cerebral hypoglucose metabolism are associated with tau and amyloid pathology development in animal models of tauopathy and familial Alzheimer's disease

Interlayer gap widened α-phase molybdenum trioxide as high-rate anodes for dual-ion-intercalation energy storage devices

Employing high-rate ion-intercalation electrodes represents a feasible way to mitigate the inherent trade-off between energy density and power density for electrochemical energy storage devices, but efficient approaches to boost the charge-storage kinetics of electrodes are still needed. Here, we demonstrate a water-incorporation strategy to expand the interlayer gap of α-MoO3, in which water molecules take the place of lattice oxygen of α-MoO3. Accordingly, the modified α-MoO3 electrode exhibits theoretical-value-close specific capacity (963 C g−1 at 0.1 mV s−1), greatly improved rate capability (from 4.4% to 40.2% at 100 mV s−1) and boosted cycling stability (from 21 to 71% over 600 cycles). A fast-kinetics dual-ion-intercalation energy storage device is further assembled by combining the modified α-MoO3 anode with an anion-intercalation graphite cathode, operating well over a wide discharge rate range. Our study sheds light on a promising design strategy of layered materials for high-kinetics charge storage.</p

Sustained methane emissions from China after 2012 despite declining coal production and rice-cultivated area

China’s anthropogenic methane emissions are the largest of any country in the world. A recent study using atmospheric observations suggested that recent policies aimed at reducing emissions of methane due to coal production in China after 2010 had been largely ineffective. Here, based on a longer observational record and an updated modelling approach, we find a statistically significant positive linear trend (0.36 ± 0.04 ( $\pm1\sigma$ ) Tg CH _4 yr ^−2 ) in China’s methane emissions for 2010–2017. This trend was slowing down at a statistically significant rate of -0.1 ± 0.04 Tg CH _4 yr ^−3 . We find that this decrease in growth rate can in part be attributed to a decline in China’s coal production. However, coal mine methane emissions have not declined as rapidly as production, implying that there may be substantial fugitive emissions from abandoned coal mines that have previously been overlooked. We also find that emissions over rice-growing and aquaculture-farming regions show a positive trend (0.13 ± 0.05 Tg CH _4 yr ^−2 for 2010–2017) despite reports of shrinking rice paddy areas, implying potentially significant emissions from new aquaculture activities, which are thought to be primarily located on converted rice paddies

Spatiotemporal Evolution of Urban Agglomeration and Its Impact on Landscape Patterns in the Pearl River Delta, China

An urban agglomeration is the engine of regional and national economic growth, but also causes many ecological and environmental issues that emerge from massive land changes. In this study, the spatiotemporal evolution of an urban agglomeration was quantified and its impacts on the urban and regional landscape patterns were evaluated. It showed that the urbanized land area of the Pearl River Delta Urban Agglomeration (PRDUA) in China nearly quadrupled, having linearly increased from 1819.8 km2 to 7092.2 km2 between 1985 and 2015. The average annual growth rate presented a bimodal wave-like pattern through time, indicating that the PRDUA has witnessed two rounds of the urbanization process. The growth modes (e.g., leapfrog, edge-expansion, infilling) were detected and they exhibited co-existing but alternating dominating patterns during urbanization, demonstrating that the spatiotemporal evolution of the urban development of the PRDUA follows the “spiral diffusion-coalescence” hypothesis. The morphology of the PRDUA presented an alternating dispersal-compact pattern over time. The city-level and regional-level landscape patterns changed synchronously with the spatiotemporal evolution of the PRDUA over time. The urbanization of the PRDUA increased both the complexity and aggregation of the landscape, but also resulted in an increasing fragmentation and decreasing connectivity of the natural landscape in the Pearl River Delta region. These findings are helpful for better understanding how urban agglomerations evolve and in providing insights for regional urban planning and sustainable land management

Potential of Core-Collapse Supernova Neutrino Detection at JUNO

JUNO is an underground neutrino observatory under construction in Jiangmen, China. It uses 20kton liquid scintillator as target, which enables it to detect supernova burst neutrinos of a large statistics for the next galactic core-collapse supernova (CCSN) and also pre-supernova neutrinos from the nearby CCSN progenitors. All flavors of supernova burst neutrinos can be detected by JUNO via several interaction channels, including inverse beta decay, elastic scattering on electron and proton, interactions on C12 nuclei, etc. This retains the possibility for JUNO to reconstruct the energy spectra of supernova burst neutrinos of all flavors. The real time monitoring systems based on FPGA and DAQ are under development in JUNO, which allow prompt alert and trigger-less data acquisition of CCSN events. The alert performances of both monitoring systems have been thoroughly studied using simulations. Moreover, once a CCSN is tagged, the system can give fast characterizations, such as directionality and light curve

Detection of the Diffuse Supernova Neutrino Background with JUNO

As an underground multi-purpose neutrino detector with 20 kton liquid scintillator, Jiangmen Underground Neutrino Observatory (JUNO) is competitive with and complementary to the water-Cherenkov detectors on the search for the diffuse supernova neutrino background (DSNB). Typical supernova models predict 2-4 events per year within the optimal observation window in the JUNO detector. The dominant background is from the neutral-current (NC) interaction of atmospheric neutrinos with 12C nuclei, which surpasses the DSNB by more than one order of magnitude. We evaluated the systematic uncertainty of NC background from the spread of a variety of data-driven models and further developed a method to determine NC background within 15\% with {\it{in}} {\it{situ}} measurements after ten years of running. Besides, the NC-like backgrounds can be effectively suppressed by the intrinsic pulse-shape discrimination (PSD) capabilities of liquid scintillators. In this talk, I will present in detail the improvements on NC background uncertainty evaluation, PSD discriminator development, and finally, the potential of DSNB sensitivity in JUNO