Nature is capable of storing solar energy in chemical bonds via photosynthesis through a series of C-C, C-O and C-N bond-forming reactions starting from CO
and light. Direct capture of solar energy ...for organic synthesis is a promising approach. Lead (Pb)-halide perovskite solar cells reach 24.2% power conversion efficiency, rendering perovskite a unique type material for solar energy capture. We argue that photophysical properties of perovskites already proved for photovoltaics, also should be of interest in photoredox organic synthesis. Because the key aspects of these two applications are both relying on charge separation and transfer. Here we demonstrated that perovskites nanocrystals are exceptional candidates as photocatalysts for fundamental organic reactions, for example C-C, C-N and C-O bond-formations. Stability of CsPbBr
in organic solvents and ease-of-tuning their bandedges garner perovskite a wider scope of organic substrate activations. Our low-cost, easy-to-process, highly-efficient, air-tolerant and bandedge-tunable perovskites may bring new breakthrough in organic chemistry.
Clouds and cloud shadows block land surface information in optical satellite images. Accurate detection of clouds and cloud shadows can help exclude these contaminated pixels in further applications. ...Existing cloud screening methods are challenged by cloudy regions where most of satellite images are contaminated by clouds. To solve this problem for landscapes where the typical frequency of cloud-free observations of a pixel is too small to use existing methods to mask clouds and shadows, this study presents a new Automatic Time-Series Analysis (ATSA) method to screen clouds and cloud shadows in multi-temporal optical images. ATSA has five main steps: (1) calculate cloud and shadow indices to highlight cloud and cloud shadow information; (2) obtain initial cloud mask by unsupervised classifiers; (3) refine initial cloud mask by analyzing time series of a cloud index; (4) predict the potential shadow mask using geometric relationships; and (5) refine the potential shadow mask by analyzing time series of a shadow index. Compared with existing methods, ATSA needs fewer predefined parameters, does not require a thermal infrared band, and is more suitable for areas with persistent clouds. The performance of ATSA was tested with Landsat-8 OLI images, Landsat-4 MSS images, and Sentinel-2 images in three sites. The results were compared with a popular method, Function of Mask (Fmask), which has been adopted by USGS to produce Landsat cloud masks. These tests show that ATSA and Fmask can get comparable cloud and shadow masks in some of the tested images. However, ATSA can consistently obtain high accuracy in all images, while Fmask has large omission or commission errors in some images. The quantitative accuracy was assessed using manual cloud masks of 15 images. The average cloud producer's accuracy of these 15 images is as high as 0.959 and the average shadow producer's accuracy reaches 0.901. Given that it can be applied to old satellite sensors and it is capable for cloudy regions, ATSA is a valuable supplement to the existing cloud screening methods.
•ATSA screens thick clouds, thin haze and cloud shadows in optical time series.•ATSA needs fewer parameters and is suitable for areas with persistent clouds.•Cloud and shadow masks from ATSA are more accurate than existing methods.•ATSA requires few clear observations in time series and no thermal band.•ATSA can be applied to historical optical images with limited bands.
The rapid increase in anthropogenic activities, socioeconomic development, and land use land cover (LULC) changes since the opening of economic reforms (1978), have changed the ecosystem service ...value (ESV) in Guangdong, Hong Kong, and Macao (GKHM) region located in South China. This leads to the requirement of a significant tailored analysis of ecosystem services regarding incisive and relevant planning to ensure sustainability at regional level. This study focuses on the use of Landsat satellite imagery to quantify the precise impact of LULC changes on the ecosystem services in GHKM over the past three decades (1986-2017). The most renowned established unit value transfer method has been employed to calculate the ESV. The results show that the total ecosystem service value in GHKM has decreased from 680.23 billion CNY in 1986 to 668.45 billion CNY in 2017, mainly due to the decrease in farmland and fishponds. This overall decrease concealed the more dynamic and complex nature of the individual ESV. The most significant decrease took place in the values of water supply (-22.20 billion CNY, -14.72%), waste treatment (-20.77 billion CNY, -14.63%), and food production (-7.96 billion CNY, -33.18%). On the other hand, the value of fertile soil formation and retention (6.28 billion CNY, +7.26%) and recreation and culture (5.09 billion CNY, +12.91%) increased. Furthermore, total ESV and ESV per capita decreased significantly with the continuous increase in total gross domestic product (GDP) and GDP per capita. A substantial negative correlation exists between farmland ESV and GDP indicating human encroachment into a natural and semi natural ecosystems. The results suggest that in the rapidly urbanizing region, the protection of farmland and to control the intrusion of urban areas has marked an important societal demand and a challenge to the local government. This required a pressing need for smart LULC planning and to improve policies and regulation to guarantee ecosystem service sustainability for acceptable life quality in the study area and other fast expanding urban areas in China.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
An accurate map of forest types is important for proper usage and management of forestry resources. Medium resolution satellite images (e.g., Landsat) have been widely used for forest type mapping ...because they are able to cover large areas more efficiently than the traditional forest inventory. However, the results of a detailed forest type classification based on these images are still not satisfactory. To improve forest mapping accuracy, this study proposed an operational method to get detailed forest types from dense Landsat time-series incorporating with or without topographic information provided by DEM. This method integrated a feature selection and a training-sample-adding procedure into a hierarchical classification framework. The proposed method has been tested in Vinton County of southeastern Ohio. The detailed forest types include pine forest, oak forest, and mixed-mesophytic forest. The proposed method was trained and validated using ground samples from field plots. The three forest types were classified with an overall accuracy of 90.52% using dense Landsat time-series, while topographic information can only slightly improve the accuracy to 92.63%. Moreover, the comparison between results of using Landsat time-series and a single image reveals that time-series data can largely improve the accuracy of forest type mapping, indicating the importance of phenological information contained in multi-seasonal images for discriminating different forest types. Thanks to zero cost of all input remotely sensed datasets and ease of implementation, this approach has the potential to be applied to map forest types at regional or global scales.
Satellite time series with high spatial resolution is critical for monitoring land surface dynamics in heterogeneous landscapes. Although remote sensing technologies have experienced rapid ...development in recent years, data acquired from a single satellite sensor are often unable to satisfy our demand. As a result, integrated use of data from different sensors has become increasingly popular in the past decade. Many spatiotemporal data fusion methods have been developed to produce synthesized images with both high spatial and temporal resolutions from two types of satellite images, frequent coarse-resolution images, and sparse fine-resolution images. These methods were designed based on different principles and strategies, and therefore show different strengths and limitations. This diversity brings difficulties for users to choose an appropriate method for their specific applications and data sets. To this end, this review paper investigates literature on current spatiotemporal data fusion methods, categorizes existing methods, discusses the principal laws underlying these methods, summarizes their potential applications, and proposes possible directions for future studies in this field.
Plant phenology, the annually recurring sequence of plant developmental stages, is important for plant functioning and ecosystem services and their biophysical and biogeochemical feedbacks to the ...climate system. Plant phenology depends on temperature, and the current rapid climate change has revived interest in understanding and modeling the responses of plant phenology to the warming trend and the consequences thereof for ecosystems. Here, we review recent progresses in plant phenology and its interactions with climate change. Focusing on the start (leaf unfolding) and end (leaf coloring) of plant growing seasons, we show that the recent rapid expansion in ground‐ and remote sensing‐ based phenology data acquisition has been highly beneficial and has supported major advances in plant phenology research. Studies using multiple data sources and methods generally agree on the trends of advanced leaf unfolding and delayed leaf coloring due to climate change, yet these trends appear to have decelerated or even reversed in recent years. Our understanding of the mechanisms underlying the plant phenology responses to climate warming is still limited. The interactions between multiple drivers complicate the modeling and prediction of plant phenology changes. Furthermore, changes in plant phenology have important implications for ecosystem carbon cycles and ecosystem feedbacks to climate, yet the quantification of such impacts remains challenging. We suggest that future studies should primarily focus on using new observation tools to improve the understanding of tropical plant phenology, on improving process‐based phenology modeling, and on the scaling of phenology from species to landscape‐level.
This review examines the recent progresses in plant phenology and its interactions with climate change. Over its long history, phenology has grown from an empirical subject of observing and recording the timing of annual natural events for specific species to a comprehensive field that involves expanded observations, experiments, modeling, as well as the ecological consequences and climatic feedbacks of phenological changes. This review also emphasizes the need for future studies on the understanding of tropical plant phenology with new tools, on improving process‐based modeling, and on the scaling of phenology from species level to landscape level.
•Realization of coastal zero-energy communities by hybrid wind-tidal energy systems.•Comparison between wind-based and tidal-based renewable energy generations.•Impact of renewable mixing of offshore ...wind and tidal on the system performances.•Solutions of energy matching enhancement for large-scale hybrid wind-tidal systems.•Techno-economic feasibility analysis of the hybrid offshore wind-tidal system.
In the current academic fields of zero-energy community, there is still limited knowledge on the integration of a coastal community with hybrid ocean-related energy systems. This study investigates the feasibility of a coastal community to reach zero-energy with the support of a hybrid offshore wind and tidal stream energy generation system, as well as an ocean and solar thermal energy supported district cooling and heating system. TRNSYS simulation was performed to demonstrate a proposed community that comprises 8 high-rise residential buildings and 2 mid-rise office buildings with a 9.86 MW community peak power demand. This study considered 21 hybrid renewable energy cases and investigated their performance in 2 scenarios – scenario 1 without battery and scenario 2 with battery. The system performance is assessed from the technical, economic, and emission perspectives by analysing the system load matching, net present value, discounted payback period, and equivalent CO2 emission. In scenario 1, the hybrid renewable energy case 5 with 6 offshore wind turbines (12 MW) and 117 tidal stream converters (29.25 MW) has the best annual load matching (56.68% “onsite energy matching” and 57.84% “onsite energy fraction”) mainly due to their complementary generation pattern during specific periods. In scenario 2, the community-scale electricity storage significantly increases the system technical performance by raising the “onsite energy matching” and “onsite energy fraction” of case 5 to 75.25% and 74.75%, respectively. In addition, the techno-economic analysis reveals the market competitiveness of the 21 RE cases and demonstrates the significant economic impact of the FiT policy. The comparison between scenario 1 and scenario 2 indicates that the community-scale battery diminishes the operation-cycle profits but reduces the equivalent CO2 emission. Furthermore, with the current price settings, tidal stream energy generation is considered less profitable than offshore wind energy generation. This study could provide important insights into the development of coastal zero-energy communities with hybrid offshore wind and tidal stream energy generation at other locations worldwide, especially densely populated coastal cities.
Catalytic dehydrogenation of light alkanes can effectively produce olefins and hydrogen. Even though Pt and CrOx‐based catalysts are widely applied in industry, research to improve the activity and ...stability of these catalysts continued. This review summarizes important achievements obtained in recent years, focusing on the development of supports, promoters and preparation methods of Pt and CrOx‐based catalysts, which mainly aimed to improve the dispersion of the active species and to enhance coke resistance. Furthermore, the high cost of Pt‐based catalysts and environmental problems encountered with CrOx‐based catalysts have spurred the development of alternative catalysts. The dehydrogenation performances and characteristics of promising alternative VOx‐, modified Ni‐ and Sn‐based catalysts are also reviewed. Comparison with the catalytic reforming process of naphtha further probes the necessity of catalyst acidity in these two different processes. The choice of the dehydrogenation reactor is discussed, and future perspectives and research directions are indicated.
Even though Pt and CrOx dehydrogenation catalysts are widely applied in industry, research to improve the activity and stability of these catalysts continued. This review summarizes important achievements obtained in recent years aimed to improve the dispersion of active species and to enhance coke resistance. Furthermore, the high cost of Pt catalyst and environmental problems encountered with CrOx catalyst have spurred the development of alternative catalysts. Future perspectives and research directions of dehydrogenation are also indicated.
► Spring phenology responded divergently to climate warming in Tibetan grasslands. ► Thermal spring onset date was determined using linear programming. ► Increasing temperature tended to advance ...green-up onset of wetter grasslands. ► Temperature and precipitation explained 5%∼55% of variations in green-up onset. ► Results suggest complex responses of spring phenology to climate change.
Spatial variations in phenological responses to temperature have not been reported for grasslands of the Qinghai-Tibetan Plateau. Using satellite-derived normalized difference vegetation index and meteorological records from 1982 to 2006, we characterized the spatial patterns of grassland green-up onset in relation to air temperature and precipitation before the growing season (“preseason” henceforth) in the central and eastern plateau by combining linear programming with correlation analysis. Green-up onset near half of the meteorological stations was significantly correlated (p<0.10) with precipitation and thermal spring onset (TSO) date based on the cumulative temperature less than 6 weeks before the onset. The green-up onset paralleled the advance in TSO in the southwestern, southeastern, eastern, and northeastern parts of the plateau. The TSO and preseason precipitation (PPT) explained part of the inter-annual phenological variations, with r2 varying between 0.05 and 0.55 and averaging 0.28, and did not explain delay of green-up onset in some areas. Increasing preseason temperature tended to advance green-up onset in relatively moist areas. PPT exerted a stronger influence on green-up onset in drier areas. These results indicate spatial differences in the key environmental influences on spring phenology. To improve the ability to predict onset, ground-based community-level phenological studies and spatial scaling-up of the phenology–climate relationship will be necessary.
Due to technical and budget limitations, remote sensing instruments trade spatial resolution and swath width. As a result not one sensor provides both high spatial resolution and high temporal ...resolution. However, the ability to monitor seasonal landscape changes at fine resolution is urgently needed for global change science. One approach is to “blend” the radiometry from daily, global data (e.g. MODIS, MERIS, SPOT-Vegetation) with data from high-resolution sensors with less frequent coverage (e.g. Landsat, CBERS, ResourceSat). Unfortunately, existing algorithms for blending multi-source data have some shortcomings, particularly in accurately predicting the surface reflectance of heterogeneous landscapes. This study has developed an enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) based on the existing STARFM algorithm, and has tested it with both simulated and actual satellite data. Results show that ESTARFM improves the accuracy of predicted fine-resolution reflectance, especially for heterogeneous landscapes, and preserves spatial details. Taking the NIR band as an example, for homogeneous regions the prediction of the ESTARFM is slightly better than the STARFM (average absolute difference
AAD 0.0106 vs. 0.0129 reflectance units). But for a complex, heterogeneous landscape, the prediction accuracy of ESTARFM is improved even more compared with STARFM (
AAD 0.0135 vs. 0.0194). This improved fusion algorithm will support new investigations into how global landscapes are changing across both seasonal and interannual timescales.
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