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In remote sensing–based forest aboveground biomass (AGB) estimation research, data saturation in Landsat and radar data is well known, but how to reduce this problem for improving AGB estimation has not been fully examined. Different vegetation types have their own species composition and stand structure, thus they have different data saturation values in Landsat or radar data. Optical and radar data also have different characteristics in representing forest stand structures, thus effective use of their features may improve AGB estimation. This research examines the effects of Landsat Thematic Mapper (TM) and ALOS PALSAR L-band data and their integrations in forest AGB estimation of Zhejiang Province, China, and the roles of textural images from both datasets. The linear regression models of AGB were conducted by using (1) Landsat TM alone, (2) ALOS PALSAR data alone, (3) their combination as extra bands, and (4) their data fusion, based on non-stratification and stratification of vegetation types, respectively. The results show that (1) overall, Landsat TM data perform better than PALSAR data, but the latter can produce more accurate estimates for bamboo and shrub, and for forests with AGB values less than 60 Mg/ha; (2) the combination of TM and PALSAR data as extra bands can greatly improve AGB estimation performance, but their fusion using the modified high-pass filter resolution-merging technique cannot; (3) textures are indeed valuable in AGB estimation, especially for forests with complex stand structures such as mixed forests and pine forests with understories of broadleaf species; (4) stratification of vegetation types can improve AGB estimation performance; and (5) the results from the linear regression models are characterized by overestimation and underestimation for the smaller and larger AGB values, respectively, and thus, selecting non-linear models or non-parametric algorithms may be needed in future research. [ABSTRACT FROM AUTHOR]

Isoprene and monoterpene emissions and environmental conditions were measured over a six month period in a Lei bamboo (Phyllostachys violascens) forest in a subtropical region in China. Isoprene and monoterpene emissions were measured using a relaxed eddy accumulation (REA) system on an above-canopy tower. From July to November of 2012, isoprene contributed 99.1% of terpenoid emissions. α-pinene, constituting 0.8% of total observed terpenoid emissions, was the only monoterpene for which a significant flux was detected. Emissions of the sesquiterpenes longifolene and α-cedrene were observed at very low rates. Isoprene and α-pinene emissions exhibited strong diurnal variations, with lower emissions in the morning and late evening, and the highest emissions around noon. BVOC peak emissions typically occurred a few hours after the noon PAR peak and coincided with the daily temperature peak. This behavior can be described reasonably well by the MEGANv2.1 biogenic emission model. During the campaign (i.e., from 7 July, 2012 to 19 Jan., 2013), the mean (and maximum) emission fluxes (mg m −2 h −1 ) were 0.95 (10.32) for isoprene, 0.010 (0.176) for α pinene, 0.001 (0.063) for longifolene, and 2.6 × 10 −4 (0.009) for α-cedrene, respectively. During the winter season, when the ground was covered by organic mulch to increase soil temperature and to increase the yield of bamboo shoot, there was no evident impact on BVOC emissions. The observed seasonal variation followed the general behavior predicted by the MEGANv2.1 model, with lower emissions associated with cooler conditions, but the magnitude of the emission decrease was greater than expected indicating driving variables are missing from the model. Emission factors, representing the emission expected for a Leaf Area Index of 5 at a temperature of 30 °C and PAR of 1500 μmol m −2 s −1 , during the peak growing season for this site were 0.008 mg m −2 h −1 for α-pinene and 3.3 mg m −2 h −1 for isoprene. The isoprene emission factor is similar to the value (3.6 mg m −2 h −1 ) for this location in the MEGANv2.1 global biogenic emission model. A second bamboo plantation, containing Moso bamboo ( Phyllostachys heterocycla ), was investigated and found to have similar isoprene and monoterpene emission rates as Lei bamboo forest. The emission data obtained in this study are the first canopy-scale flux measurements reported for bamboo plantations and demonstrate the potential importance of bamboo isoprene emissions for regional ozone and organic aerosol production. [ABSTRACT FROM AUTHOR]

Climate data are particularly important in Species Distribution Models ( SDMs) that are used for predicting global warming consequences on plant and animal distributions. A number of the plants and animals, especially the endangered species such as the giant panda ( Ailuropoda melanolecua), are limited in their scope of distribution due to climate changes. Thus, predicting the habitat quality distribution under climate change is important for protecting these species. In this paper the existing and potential habitats of the giant pandas are used as the study area, the calculated Habitat Suitability Index ( HSI) maps in 1989 and 2002, and climate change data in 1989, 2002, 2050 and 2099 from the IPCC are used as the data sources. A multivariable linear regression model for mapping HSI is created with the regressive results in 2002 and 1989. The HSIs of the study area in 2050 and 2099 were then mapped with the model. These maps indicate that, from 2002 to 2050, about 2.64% of the unsuitable habitat in the study area will become suitable, while about 1.5% of the suitable habitat will turn into unsuitable habitat. This leads to an increase of the suitable habitat area on the whole from 2050 to 2099: about 3.43% of the unsuitable habitat will become suitable, while about 6.59% of the suitable habitat will turn into unsuitable habitat, which leads to a decrease of the suitable habitat area on the whole. From the suitable habitat distribution, it can be seen that the suitable habitat of giant pandas gradually moves north under projected global climate change. Copyright © 2013 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

Along with fast economic development, the Yangtze Delta region is experiencing rapid environmental changes. A high concentration of aerosols with diverse properties is emitted in this region, providing a unique opportunity for understanding the impact of environmental change on climate systems, especially in urban regions. Aerosol optical properties including aerosol optical depth (AOD), the Ångström exponent (α), and water vapour and their relationship with aerosol particle concentrations were examined using ground-based measurements at three sites in the Hangzhou district: Hangzhou (120.01° E, 30.26° N), Lin'an (119.73° E, 30.26° N) and Qiandaohu (119.05° E, 29.55° N). The results from these observations show that there exists strong temporal and spatial variation in aerosol optical properties and particle matter (PM) concentrations. Monthly averaged AODs at the Hangzhou and Lin'an sites show a similar trend. AODs in Qiandaohu have the highest values in winter and spring and the lowest values in summer and autumn. In 2008, the minimum monthly mean α for every site was lowest in December due to the weather conditions. From spring to summer, these three locations experienced a drop in AOD and an increase in α due to the humidity-swelling effect. There is a positive correlation between AOD and aerosol particle concentrations; the correlation coefficients were obtained as 0.40, 0.55 and 0.69 in Hangzhou, Lin'an and Qiandaohu, respectively. The association between daily PM10 concentration and AOD is marginally statistically significant at the three sites, which suggests that AOD is a useful tool for mapping PM distribution over large spatial domains. [ABSTRACT FROM PUBLISHER]

Water use efficiency (WUE) is an important variable used in climate change and hydrological studies in relation to how it links ecosystem carbon cycles and hydrological cycles together. However, obtaining reliable WUE results based on site-level flux data remains a great challenge when scaling up to larger regional zones. Biophysical, process-based ecosystem models are powerful tools to study WUE at large spatial and temporal scales. The Integrated BIosphere Simulator (IBIS) was used to evaluate the effects of climate change and elevated CO2 concentrations on ecosystem-level WUE (defined as the ratio of gross primary production (GPP) to evapotranspiration (ET)) in relation to terrestrial ecosystems in China for 2009–2099. Climate scenario data (IPCC SRES A2 and SRES B1) generated from the Third Generation Coupled Global Climate Model (CGCM3) was used in the simulations. Seven simulations were implemented according to the assemblage of different elevated CO2 concentrations scenarios and different climate change scenarios. Analysis suggests that (1) further elevated CO2 concentrations will significantly enhance the WUE over China by the end of the twenty-first century, especially in forest areas; (2) effects of climate change on WUE will vary for different geographical regions in China with negative effects occurring primarily in southern regions and positive effects occurring primarily in high latitude and altitude regions (Tibetan Plateau); (3) WUE will maintain the current levels for 2009–2099 under the constant climate scenario (i.e. using mean climate condition of 1951–2006 and CO2 concentrations of the 2008 level); and (4) WUE will decrease with the increase of water resource restriction (expressed as evaporation ratio) among different ecosystems. [Copyright &y& Elsevier]

Since the 1950s, with national policy changes and socio-economic development, the habitat of the giant pandas has altered accordingly. This can also be inferred from the population changes of the giant pandas as reported in three national surveys. Thus, monitoring the changes in giant panda habitat and then taking appropriate action would be a valuable contribution to giant panda protection. In this paper, using existing habitats and potential habitats of the giant pandas as the study area, multitemporal remotely sensed data from the three national surveys are used as the data source. The land cover of the study area is mapped by the maximum likelihood classification (MLC) method. The overall accuracy and kappa statistics for the resulting classification are 0.8 and 90%, respectively. The results reveal that the current status of the giant panda habitat is very good. Between 1974 and 1989, because of deforestation in the area, the giant panda habitat deteriorated considerably; the total area of broadleaved forests, coniferous forests, mixed coniferous and deciduous broadleaved forests and shrubs decreased from 62.03% to 57.40% in the study area. However, from 1989 to 2002, due to the conservation policies put into action, the giant panda habitat recovered to some extent; the total area of broadleaved forests, coniferous forests, mixed coniferous and deciduous broadleaved forests and shrubs increased from 57.40% to 60.68% in the study area. However, conditions are different among the mountain systems. Taking into account only the total of broadleaved forests, coniferous forests, mixed coniferous and deciduous broadleaved forests and shrubs, in the Minshan mountain system, the forest cover changed from 57.70% in 1974 to 56.74% in 1989 and to 56.30% in 2002, which can be regarded as stable. In the Qinling mountain system, forested areas changed from 70.05% in 1974 to 66.93% in 1989 and to 67.17% in 2002, which reveals little change in this area. In the Qionglai mountain system, forested areas changed from 72.84% in 1974 to 71.53% in 1989 and to 73.22% in 2002; therefore, an increase can be noted in this area. In the Xiangling mountain system, forested areas also increased from 50.81% in 1974 to 50.20% in 1989 and to 53.15% in 2002. In the Liangshan mountain system, forested areas changed from 68.43% in 1974 to 55.81% in 1989 and to 60.07% in 2002. These results are in accordance with the giant panda population changes in different mountain systems. Thus, the best way to improve the threatened status of the panda population is to protect the current habitat and the potential habitat. [ABSTRACT FROM AUTHOR]

One risk of planting transgenic crops is the escape of transgenes to conspecifics and sexually compatible wild relatives. Detecting transgene escape is thus a crucial biosafety issue world-wide, but most current detection methods are expensive and laborious, as well as being unfeasible for large-scale use in commercial cultivation. We undertook field spectral reflectance studies of non-transgenic oilseed rape (B. napus cv. Westar), a transgenic oilseed rape, Wild Indian mustard (B. juncea var. gracilis) and a hybrid Wild Indian mustard. Simulated reflectances for the spectral bands of several different satellite-flown hyperspectral and multi-spectral scanners were generated. The differences obtained between the simulated reflectances of the different plants leads to the possibility that these differences could be used to detect transgene escape and genomic effects among related taxa from the Moderate Resolution Imaging Spectroradiometer (MODIS) hyperspectral scanner and from the Landsat Thematic Mapper (TM), Satellite Pour l'Observation de la Terre (SPOT) High Resolution Visible (HRV) and IKONOS multi-spectral scanners. [ABSTRACT FROM AUTHOR]

Accurate and timely information describing wetland resources and their changes over time, especially in coastal urban areas, is becoming more important. In this study, we mapped and monitored land-cover change in an urban wetland using high spatial resolution IKONOS images acquired in June 2003 and January 2006. An optimal iterative unsupervised classification (OIUC) method was used to overcome the limitations of unsupervised classification. The images were categorized into six classes, and an accuracy assessment was conducted using error matrices and the Kappa coefficient. The overall accuracies were 83.2% and 86.3% for the 2003 and 2006 images, respectively. A post-classification comparison method was used to detect the wetland change by calculating a detailed land-cover type transformation matrix. The results indicated a decrease in the area of water bodies and an increase in the area of vegetation in the wetland. This paper shows that high spatial resolution remote sensing data is advanced in studying an urban wetland at a local scale. An OIUC method, combined with visual interpretation, could yield high classification accuracy. A post-classification comparison method is also efficient in wetland change detection. [ABSTRACT FROM AUTHOR]

Abstract: This study has analyzed TRIPLEX1.0 by applying the model to the subtropical forest regions in Zhejiang Province, southeastern China. The main objective was to test the process-based hybrid model TRIPLEX1.0 in simulating density, tree height (H), diameter at breast height (DBH), litter pool and biomass using forest growth and yield data collected from three forest types: subtropical evergreen broad-leaved, coniferous broad-leaved mixed and warm temperate pine (Pinus massoniana Lamb.) forests. The results show that simulated density, H, DBH, litter pool, aboveground and total biomass are consistent with observed data collected through Zhejiang Province, suggesting that the TRIPLEX1.0 model is capable in simulating forest growth and biomass dynamics of subtropical forest ecosystems. The coefficient of determination (r 2) between simulated values and yield measurements show a 0.91 variability for density, 0.86 for DBH, 0.83 for H, 0.89 for aboveground biomass and 0.91 for total biomass (except for litter pool that showed a 0.54 variability). The independent validations obtained by utilizing TRIPLEX1.0 demonstrate that the model offers competency while providing confidence when applying its ability to extrapolate outcomes at regional scales and its ability to withstand rigorous testing for simulating carbon storage in subtropical forest ecosystems. [Copyright &y& Elsevier]

• Graphene-MnFe 2 O 4 hybrid was used for an efficient catalyst of ozonation decomposition. • GO doping and calcination significantly increased the concentration of surface-OH. • The generation of OH significantly improved DBP removal in 5-rGO-MFO-200/O 3 system. • The synergistic effect between rGO and MnFe 2 O 4 accelerated electron transfer. Magnetic, stable graphene-MnFe 2 O 4 hybrids were synthesized by co-precipitation method for efficient degradation of di-n-butyl phthalate (DBP) using catalytic ozonation in water. The various synthesized catalysts were systematically characterized, and the catalytic activities in catalytic ozonation system were investigated. Moreover, a detailed mechanism of catalytic ozonation by synergistic function of graphene oxide (GO) and magnetic MnFe 2 O 4 was proposed. Results showed that catalytic ozonation by 5-rGO-MFO-200 (MnFe 2 O 4 calcined at 200 °C after 5 wt% GO doped) could significant boost the DBP degradation, which illustrated by the apparent rate constant (k = 0.0058 min−1) being nearly 4.2 times than that in ozone alone (k = 0.0241 min−1). The increased activities of 5-rGO-MFO-200 for the catalytic ozonation was attributed to its rich concentration of surface hydroxyl sites (SHSC), better charge electron transfer ability, favorable lattice structure and nanoparticles diffusion in contrast to bare MnFe 2 O 4. All catalysts presented good recycling and stability in the repeated batch experiment, and the removal efficiency of DBP only decreased 3% after 5 times. The radical quenching tests verified that hydroxyl radicals (OH) was the dominant reactive oxygen species responsible for DBP degradation. Electrons cycling between Mn2+ and Mn3+ led to the decomposition of ozone to produce OH, which attacked DBP molecules to degrade into small molecular species and then further mineralized into CO 2 and H 2 O. The synergistic function between magnetic MnFe 2 O 4 and GO induced efficient ozone decomposition and more OH production. [ABSTRACT FROM AUTHOR]