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Research in vegetation phenology change has been one heated topic of current ecological and climate change study. The Tibetan Plateau, as the highest plateau of the earth, is more vulnerable and sensitive to climate change than many other regions. In this region, shifts in vegetation phenology have been intensively studied during recent decades, primarily based on satellite-retrieved data. In this study, we explored the spatiotemporal changes of vegetation phenology for different land-cover types in the Tibetan Plateau and characterized their relationship with temperature and precipitation by using long-term time-series datasets of normalized difference vegetation index (NDVI) from 1982 to 2014. Diverse phenological changes were observed for different land-cover types, with an advancing start of growing season (SOS), delaying end of growing season (EOS) and increasing length of growing season (LOS) in the eastern Tibetan Plateau where meadow was the dominant vegetation type, but with the opposite changes in the steppe and sparse herbaceous or sparse shrub regions which are mostly located in the northwestern and western edges of the Tibetan Plateau. Correlation analysis indicated that sufficient preseason precipitation may delay the SOS of evergreen forests in the southeastern Plateau and advance the SOS of steppe and sparse herbaceous or sparse shrub in relatively arid areas, while the advance of SOS in meadow areas could be related to higher preseason temperature. For EOS, because it is less sensitive to climate change than SOS, the response of EOS for different landcover types to precipitation and temperature were more complicated across the Tibetan Plateau. [ABSTRACT FROM AUTHOR]

The present work aims to detect bamboo expansion and its impact on carbon storage in a thick forest in the most recent 30 years. The research area is the national nature reserve of Tianmushan, Zhejiang Province, China, and the present paper monitored bamboo expansion from 1984 to 2015. Multi-spectral band and vegetation indices from Landsat images in summer and winter are used combined to improve the accuracy of detection using a support vector machine (SVM) classifier. Expansion of bamboo over this period is evident. Total expansion is 161%, the fastest annual rate being 11.6%. However, over recent decades the growth of bamboo has been inhibited by human activity and the total area has decreased by 21%. Evergreen broadleaf forest is the most vulnerable to invasion by bamboo at a ratio of about 65%, and this expanding trend has been brought under effective control. Carbon storage was estimated using sample plot surveys and modelling based on key ecological forests. According to our estimation using carbon storage models, the total carbon storage of Tianmushan has declined by circa 4.7% due to bamboo expansion in the past three decades. [ABSTRACT FROM AUTHOR]

In this study, we detected the spectrum and vegetation index responses of Masson pine to acid stress using ground-based hyperspectral radiometry and satellite-based multispectral remote sensing. From the hyperspectral detection, we found that the spectral reflectance of stressed Masson pine increased with increase in acidity in the visible region, while an opposite result was found in the near-infrared (NIR) region. The simulated normalized difference vegetation index (NDVI) derived from hyperspectral data of Masson pine presented higher values under weaker acid stress in general. Similar results were observed by satellite-derived NDVI across four acidity gradient transects in subtropical China. Both annual average NDVI and inter-annual NDVI trends of Masson pine forest increased with increase in annual average precipitation acidity (pH), indicating that acid stress would inhibit the growth of Masson pine under certain environmental conditions. However, there are limitations and uncertainties in our present work, and the integration of long-term ground-based and satellite-based observations of vegetation growth and acidity deposition is urgently needed. [ABSTRACT FROM PUBLISHER]

A useful method was developed to establish a diagnostic model using hyperspectral remote sensing to predict and monitor acid stress on plants. We analysed the hyperspectral response of Chinese fir to acid rain by measuring the spectral reflectance of the seedling leaves, sprayed by simulated acid rain (pH, 2.5, 4.0, and 5.6), for three periods. The sensitive bands were located and the rules for predicting classes of simulated acid stress on Chinese fir were established using a classification and regression tree (CART) approach. The acid-sensitive bands of Chinese fir were nearly all located between 380 and 410 nm, 460 and 560 nm, and 640 and 750 nm. CART predictor variables, which were selected from sensitive bands, reduce data dimensionality significantly. The misclassification errors of the CART training process in correctly attributing variables to respective target classes are 7.78%, 6.67%, and 11.67% respectively, at each measurement period, and the cross-validation misclassification errors are 16.6%, 11.1%, and 23.3%, respectively. Our results show that the spectral reference bands, which are related to chlorophyll-aandbaround 670 and 450 nm, as well as the slight peak in the green around 550 nm, significantly affected the classification accuracy on acid stress. These provide useful optical response to acid stress. [ABSTRACT FROM AUTHOR]

Many physiological activities in amphibians show rhythmic changes to adapt to ambient light conditions. However, the effects of photoperiod variation on the immune and antioxidant status and their possible correlation with different tissues have not been reported in Dybowski's frog, Rana dybowskii. In this study, the specific activities of immune and antioxidant enzymes in R. dybowskii under different photoperiods were evaluated. The results demonstrated that the photoperiod significantly impacted antioxidant enzymes and immune enzymes in Dybowski's frog. In terms of antioxidant enzyme activity, catalase (CAT) and superoxide dismutase (SOD) activities in frogs at 6 L:18D and 0 L:24D were significantly higher than those at 18 L:6D and 24 L:0D, respectively. Further, the activities of these enzymes increased during the dark phase and decreased during the light phase. The malondialdehyde (MDA) content decreased significantly with the decrease in photoperiod time. Lysozyme (LZM) activity in serum and the liver decreased with a prolonged photoperiod. LZM activity in the dark cycle was higher, but the peak activity in skin occurred at noon. Thus, long-term light exposure may cause oxidative stress in Dybowski's frog. Accordingly, a 6 L:18D photoperiod may be suitable for optimum physiological functions of this species. [ABSTRACT FROM AUTHOR]

There are few studies on the relationship between photoperiod and the melatonin cycle in amphibians. To elucidate the role of the photoperiodically modulated endogenous melatonin cycle in different tissues of Dybowski's frog (Rana dybowskii), its serum, liver, whole brain, skin, and eyeballs were collected under five photoperiods, and the daily variation in melatonin concentration was determined via high-performance liquid chromatography. Compared with the control group (12 L:12 D), melatonin concentration was increased under photoperiods 0 L:24 D and 6 L:18 D in serum, and the concentration peaked at 00:00 under photoperiod 0 L:24 D. This trend was synchronized with the peak melatonin concentration in the retinal, liver, and whole-brain tissues. After 24 h of light exposure, no significant daily variation was observed under photoperiod 24 L:0 D. Furthermore, we observed a significant peak at 04:00 in the serum under photoperiod 18 L:6 D. However, we observed the opposite trend in the circadian rhythm of the skin tissue. Our results underscore the high sensitivity of melatonin content to different photoperiods. These findings may have real-world implications in terms of furthering our understanding of circadian rhythms disruption induced by exposure to different photoperiods. [ABSTRACT FROM AUTHOR]

Nature reserve establishment can lead to conflict with some stakeholders. Zoning management is useful to mitigate against the conflict between human development and nature reserves, and a nature reserve can be divided into three zones: the core zone, buffer zone, and experimental zone. So far, how to monitor and evaluate the effectiveness of zoning management in nature reserves is a problem faced by remote sensing scientists and ecologists. Net primary productivity (NPP) is a key indicator which can be used to monitor and evaluate the effectiveness of zoning management in nature reserves. However, to date there has been no research on the effectiveness of zoning management on NPP, and the estimation of NPP in the Tianmu Mountain Nature Reserve also has not been studied. Based on remote sensing data and in situ measurements, the Carnegie–Ames–Stanford approach (CASA) model was used to estimate NPP in the Tianmu Mountain Nature Reserve during the period 1984–2014. We used the observed NPP to verify the simulated NPP, and the results show that the simulated NPP was consistent with the observed NPP (R2 ≥ 0.85, p ≤ 0.0002, RMSE = 52.62 g C m−2 year−1, where R2 represents coefficient of determination, p represents statistical significance, and RMSE represents root mean square error). This means that the CASA model is suitable for NPP estimation in the Tianmu Mountain Nature Reserve. The results also indicate that NPP showed an increasing trend during the period 1984–2014, and the increase over the whole period was 6.66%. The total of the annual averaged NPP was 3.07 × 1010 g C year−1, while the annual averaged NPP per unit area was 708 g C m−2 year−1. The largest averaged annual NPP per unit appeared in the core zone (720 g C m−2 year−1), followed by the buffer zone (711 g C m−2 year−1), with the experimental zone having the smallest averaged annual NPP per unit (706 g C m−2 year−1). At the p < 0.1 level, there was no region where NPP had decreased significantly in the core zone and buffer zone, and the area of the regions where NPP had decreased significantly in the experimental zone was 8.04 ha. At the p < 0.05 level, there was no area where NPP had decreased significantly in the three zones of the Tianmu Mountain Nature Reserve. The results show that the zoning management on NPP was effective in the Tianmu Mountain Nature Reserve. [ABSTRACT FROM AUTHOR]

Vegetation phenology is sensitive to climate change and, as such, is often regarded as an indicator of climate change. It is a common practice to extract vegetation phenological indicators based on satellite remote sensing data. In this study, we used the Advanced Very High Resolution Radiometer (AVHRR) Global Inventory Modeling and Mapping Study (GIMMS) Third-Generation normalized difference vegetation index (NDVI3G) to investigate temporal and spatial changes in phenology in Northeast Asia. Based on the maximum rate of change in the NDVI and dynamic threshold, we used the Asymmetric Gaussian model, Double Logistic method, and Savitzky-Golay filter to extract the start of the growing season (SOS), the end of the growing season (EOS), and the length of the growing season (LOS), respectively, along the North–South Transect of Northeast Asia (NSTNEA) from 1982 to 2014. We then compared the differences in SOS, EOS, and LOS and considered their spatio-temporal dynamics and relationship with temperature. The results show that the Asymmetric Gaussian model has the highest stability among the three methods. Dynamic thresholds corresponding to the maximum change rate of NDVI were mainly between 0.5 and 0.6. From 1982 to 2014, the SOS in the NSTNEA region occurred approximately 0.19 days earlier each year; the trends in EOS and LOS were not significant. In general, temperature and latitude have a strong linear relationship, both of which significantly impact vegetation phenology in the NSTNEA region. In addition, elevation also significantly impacts on vegetation phenology in the NSTNEA region. [ABSTRACT FROM AUTHOR]

Observations of carbon monoxide (CO) retrieved from Scanning Imaging Absorption SpectroMeter for Atmospheric Chartography (SCIAMACHY), Measurement of Pollution in the Troposphere (MOPITT), and Atmospheric Infrared Sounder (AIRS) are compared in this article. To better validate the retrieved data from SCIAMCHY, AIRS, and MOPITT, six surface stations at different locations and with various elevations were chosen. The results show these three instruments can all reflect CO spatial distribution well and show same temporal variations of CO concentration as well as six surface station measurements. MOPITT and AIRS have similar retrieval results with correlation coefficients being mostly over 0.70, except for a sixth field station on Crozet Island. The three satellites all have the ability to monitor CO concentration change on land, but SCIAMCHY results show a relatively larger bias than MOPITT and AIRS in low CO concentration areas because of systematic error. [ABSTRACT FROM PUBLISHER]

Owing to human activity, global nitrogen (N) cycles have been altered. In the past 100 years, global N deposition has increased. Currently, the monitoring and estimating of N deposition and the evaluation of its effects on global carbon budgets are the focus of many researchers. NO2columns retrieved by space-borne sensors provide us with a new way of exploring global N cycles and these have the ability to estimate N deposition. However, the time range limitation of NO2columns makes the estimation of long timescale N deposition difficult. In this study we used ground-based NOxemission data to expand the density of NO2columns, and 40 years of N deposition (1970–2009) was inverted using the multivariate linear model with expanded NO2columns. The dynamic of N deposition was examined in both global and biome scales. The results show that the average N deposition was 0.34 g N m–2year–1in the 2000s, which was an increase of 38.4% compared with the 1970s’. The total N deposition in different biomes is unbalanced. N deposition is only 38.0% of the global total in forest biomes; this is made up of 25.9%, 11.3, and 0.7% in tropical, temperate, and boreal forests, respectively. As N-limited biomes, there was little increase of N deposition in boreal forests. However, N deposition has increased by a total of 59.6% in tropical forests and croplands, which are N-rich biomes. Such characteristics may influence the effects on global carbon budgets. [ABSTRACT FROM PUBLISHER]

Particulate matter (PM) with an aerodynamic diameter of <2.5 μm (PM2.5) has become the primary air pollutant in most major cities in China. Some studies have indicated that there is a positive correlation between the aerosol optical thickness (AOT) and surface-level PM2.5concentration. In order to estimate PM2.5concentration over large areas, a model relating the concentration of PM2.5and AOT has been established. The scale height of aerosol and relative humidity as well as the effect of surface temperature and wind velocity were introduced to enhance the model. 2013 full year Moderate Resolution Imaging Spectroradiometer (MODIS) AOT data and ground measurements of the PM2.5concentration in the Beijing–Tianjin–Hebei region were used to fit a seasonal multivariate linear equation relating PM2.5concentration and AOT, and the accuracy of the model has been determined. When comparing MODIS-estimated PM2.5with the measurements from ground monitoring stations during spring, summer, autumn and winter, we found theR2values were 0.45, 0.45, 0.37, and 0.31, respectively. Based on this model, the spatial distribution of PM2.5concentration during four typical haze events sampled by seasons was derived, and displayed with the backward air trajectories calculated using the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model. We undertook a preliminary analysis about the source of surface-level PMs and the process of its accumulation and dispersion during the haze episodes by analysing the effect of terrain and topography in the specific location of the Beijing–Tianjin–Hebei region. The spatial distribution of the PM2.5concentration showed that the high value region was generally in the southeast of the study area, which approximately overlapped an area of lower vegetation coverage, and the temporal variation of PM2.5concentration indicated that the air pollution was more severe during winter and spring than summer and autumn. The results of the analysis of backward air trajectories suggested that the hazy weather in the Beijing–Tianjin–Hebei region was mainly caused by unfavourable terrain and weather conditions. [ABSTRACT FROM PUBLISHER]

Satellite observations of carbon dioxide (CO2) are important because of their potential for improving the scientific understanding of global carbon cycle processes and budgets. We present an analysis of the column-averaged dry air mole fractions of CO2(denoted XCO2) of the Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY) retrievals, which were derived from a satellite instrument with relatively long-term records (2003–2009) and with measurements sensitive to the near surface. The spatial-temporal distributions of remotely sensed XCO2have significant spatial heterogeneity with about 6–8% variations (367–397 ppm) during 2003–2009, challenging the traditional view that the spatial heterogeneity of atmospheric CO2is not significant enough (<4%) to have any large effect on terrestrial ecosystem carbon cycles. By comparison with surface measurements from the NOAA Earth System Research Laboratory (ESRL) GLOBALVIEW network, significant statistical relationships between XCO2and surface CO2were found for major ecosystems, with the exception of tropical forest. In addition, when compared with a simulated terrestrial carbon uptake from the Integrated Biosphere Simulator (IBIS) and the Emissions Database for Global Atmospheric Research (EDGAR) carbon emission inventory, the latitudinal gradient of XCO2seasonal amplitude was influenced by the combined effect of terrestrial carbon uptake, carbon emission, and atmospheric transport, suggesting no direct implications for terrestrial carbon sinks. From the investigation of the growth rate of XCO2we found that the increase of CO2concentration was dominated by temperature in the northern hemisphere (20–90°N) and by precipitation in the southern hemisphere (20–90°S), with the major contribution to global average occurring in the northern hemisphere. These findings indicated that the satellite measurements of atmospheric CO2improve not only the estimations of atmospheric inversion, but also the understanding of the terrestrial ecosystem carbon dynamics and its feedback to atmospheric CO2. [ABSTRACT FROM PUBLISHER]

Global nitrogen deposition has increased over the past 100 years. Monitoring and simulation studies of  nitrogen deposition have evaluated nitrogen deposition at both the global and regional scale. With the development of remote-sensing instruments, tropospheric NO2column density retrieved from Global Ozone Monitoring Experiment (GOME) and Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) sensors now provides us with a new opportunity to understand changes in reactive nitrogen in the atmosphere. The concentration of NO2in the atmosphere has a significant effect on atmospheric nitrogen deposition. According to the general nitrogen deposition calculation method, we use the principal component regression method to evaluate global nitrogen deposition based on global NO2column density and meteorological data. From the accuracy of the simulation, about 70% of the land area of the Earth passed a significance test of regression. In addition, NO2column density has a significant influence on regression results over 44% of global land. The simulated results show that global average nitrogen deposition was 0.34 g m−2yr−1from 1996 to 2009 and is increasing at about 1% per year. Our simulated results show that China, Europe, and the USA are the three hotspots of nitrogen deposition according to previous research findings. In this study, Southern Asia was found to be another hotspot of nitrogen deposition (about 1.58 g m−2yr−1and maintaining a high growth rate). As nitrogen deposition increases, the number of regions threatened by high nitrogen deposits is also increasing. With N emissions continuing to increase in the future, areas whose ecosystem is affected by high level nitrogen deposition will increase. [ABSTRACT FROM PUBLISHER]