White Matter Hyperintensity is Associated with Malignant Cerebral Edema in Ischemic Stroke Treated with Thrombectomy.

BACKGROUND: White matter hyperintensity (WMH) burden may lead to poor clinical outcomes after endovascular thrombectomy (EVT). But the relationship between WMH burden and cerebral edema (CED) is unclear. PURPOSE: To examine the association between WMH burden and CED and functional outcome in patients treated with EVT. STUDY TYPE: Retrospective. SUBJECT: 344 patients with acute anterior circulation large-vessel occlusion stroke who received EVT at two comprehensive stroke centers. Mean age was 62.6 ± 11.6 years and 100 patients (29.1%) were female. FIELD STRENGTH/SEQUENCE: 3T, including diffusion-weighted imaging and fluid-attenuated inversion recovery (FLAIR) images. ASSESSMENT: The severity of WMH was evaluated using the Fazekas scale on a FLAIR sequence before EVT. The severity of CED was assessed using CED score (three for malignant cerebral edema [MCE]) and net water uptake (NWU)/time on post-EVT cranial CT. The impact of WMH burden on MCE, NWU/time, and 3-month poor outcome (modified Rankin scale >2) after EVT were assessed. STATISTICAL TESTS: Pearson's chi-squared test, Fisher exact test, 2-tailed t test, Mann-Whitney U test, multivariable logistic regression, multivariate regression analysis, Sobel test. A P value <0.05 was considered statistically significant. RESULTS: WMH burden was not significantly associated with MCE and parenchymal hemorrhage (PH) in the whole population (P = 0.072; P = 0.714). WMH burden was significantly associated with an increased risk of MCE (OR, 1.550; 95% CI, 1.128-2.129), higher NWU/time (Coefficient, 0.132; 95% CI, 0.012-0.240), and increased risk of 3-month poor outcome (OR, 1.434; 95% CI, 1.110-1.853) in the subset of patients without PH. Moreover, the connection between WMH burden and poor outcome was partly mediated by CED in patients without PH (regression coefficient changed by 29.8%). DATA CONCLUSION: WMH burden is associated with CED, especially MCE, and poor outcome in acute ischemic stroke patients treated with EVT. The association between WMH burden and poor outcome may partly be attributed to postoperative CED. TECHNICAL EFFICACY: Stage 5.

Further Insight into Extractable (Organo)fluorine Mass Balance Analysis of Tap Water from Shanghai, China.

The ubiquitous occurrence of per- and polyfluoroalkyl substances (PFAS) and the detection of unexplained extractable organofluorine (EOF) in drinking water have raised growing concerns. A recent study reported the detection of inorganic fluorinated anions in German river systems, and therefore, in some samples, EOF may include some inorganic fluorinated anions. Thus, it might be more appropriate to use the term "extractable fluorine (EF) analysis" instead of the term EOF analysis. In this study, tap water samples (n = 39) from Shanghai were collected to assess the levels of EF/EOF, 35 target PFAS, two inorganic fluorinated anions (tetrafluoroborate (BF4-) and hexafluorophosphate (PF6-)), and novel PFAS through suspect screening and potential oxidizable precursors through oxidative conversion. The results showed that ultra-short PFAS were the largest contributors to target PFAS, accounting for up to 97% of ΣPFAS. To the best of our knowledge, this was the first time that bis(trifluoromethanesulfonyl)imide (NTf2) was reported in drinking water from China, and p-perfluorous nonenoxybenzenesulfonate (OBS) was also identified through suspect screening. Small amounts of precursors that can be oxidatively converted to PFCAs were noted after oxidative conversion. EF mass balance analysis revealed that target PFAS could only explain less than 36% of EF. However, the amounts of unexplained extractable fluorine were greatly reduced when BF4- and PF6- were included. These compounds further explained more than 44% of the EF, indicating the role of inorganic fluorinated anions in the mass balance analysis.

An Optimized Convolutional Neural Network for the 3D Point-Cloud Compression.

Due to the tremendous volume taken by the 3D point-cloud models, knowing how to achieve the balance between a high compression ratio, a low distortion rate, and computing cost in point-cloud compression is a significant issue in the field of virtual reality (VR). Convolutional neural networks have been used in numerous point-cloud compression research approaches during the past few years in an effort to progress the research state. In this work, we have evaluated the effects of different network parameters, including neural network depth, stride, and activation function on point-cloud compression, resulting in an optimized convolutional neural network for compression. We first have analyzed earlier research on point-cloud compression based on convolutional neural networks before designing our own convolutional neural network. Then, we have modified our model parameters using the experimental data to further enhance the effect of point-cloud compression. Based on the experimental results, we have found that the neural network with the 4 layers and 2 strides parameter configuration using the Sigmoid activation function outperforms the default configuration by 208% in terms of the compression-distortion rate. The experimental results show that our findings are effective and universal and make a great contribution to the research of point-cloud compression using convolutional neural networks.

Geometry Sampling-Based Adaption to DCGAN for 3D Face Generation.

Despite progress in the past decades, 3D shape acquisition techniques are still a threshold for various 3D face-based applications and have therefore attracted extensive research. Moreover, advanced 2D data generation models based on deep networks may not be directly applicable to 3D objects because of the different dimensionality of 2D and 3D data. In this work, we propose two novel sampling methods to represent 3D faces as matrix-like structured data that can better fit deep networks, namely (1) a geometric sampling method for the structured representation of 3D faces based on the intersection of iso-geodesic curves and radial curves, and (2) a depth-like map sampling method using the average depth of grid cells on the front surface. The above sampling methods can bridge the gap between unstructured 3D face models and powerful deep networks for an unsupervised generative 3D face model. In particular, the above approaches can obtain the structured representation of 3D faces, which enables us to adapt the 3D faces to the Deep Convolution Generative Adversarial Network (DCGAN) for 3D face generation to obtain better 3D faces with different expressions. We demonstrated the effectiveness of our generative model by producing a large variety of 3D faces with different expressions using the two novel down-sampling methods mentioned above.

Double-Color-Image Compression-Encryption Algorithm Based on Quaternion Multiple Parameter DFrAT and Feature Fusion with Preferable Restoration Quality.

To achieve multiple color images encryption, a secure double-color-image encryption algorithm is designed based on the quaternion multiple parameter discrete fractional angular transform (QMPDFrAT), a nonlinear operation and a plaintext-related joint permutation-diffusion mechanism. QMPDFrAT is first defined and then applied to encrypt multiple color images. In the designed algorithm, the low-frequency and high-frequency sub-bands of the three color components of each plaintext image are obtained by two-dimensional discrete wavelet transform. Then, the high-frequency sub-bands are further made sparse and the main features of these sub-bands are extracted by a Zigzag scan. Subsequently, all the low-frequency sub-bands and high-frequency fusion images are represented as three quaternion signals, which are modulated by the proposed QMPDFrAT with three quaternion random phase masks, respectively. The spherical transform, as a nonlinear operation, is followed to nonlinearly make the three transform results interact. For better security, a joint permutation-diffusion mechanism based on plaintext-related random pixel insertion is performed on the three intermediate outputs to yield the final encryption image. Compared with many similar color image compression-encryption schemes, the proposed algorithm can encrypt double-color-image with higher quality of image reconstruction. Numerical simulation results demonstrate that the proposed double-color-image encryption algorithm is feasibility and achieves high security.

Specific epigenetic microenvironment and the regulation of tumor-related gene expression by trichloroethylene in human hepatocytes.

Trichloroethylene (TCE), an important volatile organic solvent, causes a series of toxic damage to human. Conventional genetic mechanisms cannot fully explain its toxicity and carcinogenicity, indicative of the possible involvement of epigenetic mechanisms. Our study was intended to investigate the epigenetic toxicity and underlying mechanisms of TCE. Data showed that 0.3 mM TCE treatment for 24 h increased the growth of L-02 cells transiently. In contrast, subacute exposure to TCE inhibited cell growth and induced the genomic DNA hypomethylation and histone hyperacetylation. Further studies have revealed the TCE-induced DNA hypomethylation in the promoter regions of tumor-related genes, N-Ras, c-Jun, c-Myc, c-Fos and IGF-II, promoting their protein levels in a time-dependent manner. These results reveal there is a negative relationship existing between DNA hypomethylation and protein expression in tumor-related gene after TCE exposure under specific epigenetic microenvironment, serving as early biomarkers for TCE-associated diseases.

Optical Simulation and Experimental Assessment with Time-Walk Correction of TOF-PET Detectors with Multi-Ended Readouts.

As a commonly used solution, the multi-ended readout can measure the depth-of-interaction (DOI) for positron emission tomography (PET) detectors. In the present study, the effects of the multi-ended readout design were investigated using the leading-edge discriminator (LED) triggers on the timing performance of time-of-flight (TOF) PET detectors. At the very first, the photon transmission model of the four detectors, namely, single-ended readout, dual-ended readout, side dual-ended readout, and triple-ended readout, was established in Tracepro. The optical simulation revealed that the light output of the multi-ended readout was higher. Meanwhile, the readout circuit could be triggered earlier. Especially, in the triple-ended readout, the light output at 0.5 ns was observed to be nearly twice that of the single-ended readout after the first scintillating photon was generated. Subsequently, a reference detector was applied to test the multi-ended readout detectors that were constructed from a 6 × 6 × 25 mm3 LYSO crystal. Each module is composed of a crystal coupled with multiple SiPMs. Accordingly, its timing performance was improved by approximately 10% after the compensation of fourth-order polynomial fitting. Finally, the compensated full-width-at-half-maximum (FWHM) coincidence timing resolutions (CTR) of the dual-ended readout, side dual-ended readout, and triple-ended readout were 216.9 ps, 231.0 ps, and 203.6 ps, respectively.

Occurrence and risk evaluation of organophosphorus flame retardants in two urban rivers in Yangtze River Delta.

The occurrence and profiles of organophosphate flame retardants (OPFRs) were investigated in the Huangpu and Shiwuli Rivers, two urban rivers in the Yangtze River Delta, China. The total concentrations of OPEs were found at part-per-trillion ranges, with average concentrations that ranged from 424 to 1.84 × 103 ng L-1 for Huangpu River and 221 to 1.84 × 103 ng L-1 for Shiwuli River. Three chlorinated OPFRs including tris(chloroethyl) phosphate (TCEP), tris(2-chloroisopropyl) phosphate (TCIPP), and tris(1,3-dichloroisopropyl) phosphate (TDCIPP) were the most abundant compounds among the investigated OPFRs, accounting for 90.6-99.8% of total concentrations. In Huangpu River, the OPFR concentrations were significantly higher in the dry season than in the wet season which indicates obvious seasonal variation. Chlorinated OPFR concentrations differed significantly between upstream and downstream reaches of the Shiwuli River, as the result of geographic features and wastewater discharge. Estimated risk was calculated to compare predicted no-effect concentrations (PNEC) to observed concentrations of OPFRs. The results indicated no significant acute adverse effects of OPFRs in the two urban rivers for fish, daphnia, or algae.

Critical land change information enhances the understanding of carbon balance in the United States.

Large-scale terrestrial carbon (C) estimating studies using methods such as atmospheric inversion, biogeochemical modeling, and field inventories have produced different results. The goal of this study was to integrate fine-scale processes including land use and land cover change into a large-scale ecosystem framework. We analyzed the terrestrial C budget of the conterminous United States from 1971 to 2015 at 1-km resolution using an enhanced dynamic global vegetation model and comprehensive land cover change data. Effects of atmospheric CO2 fertilization, nitrogen deposition, climate, wildland fire, harvest, and land use/land cover change (LUCC) were considered. We estimate annual C losses from cropland harvest, forest clearcut and thinning, fire, and LUCC were 436.8, 117.9, 10.5, and 10.4 TgC/year, respectively. C stored in ecosystems increased from 119,494 to 127,157 TgC between 1971 and 2015, indicating a mean annual net C sink of 170.3 TgC/year. Although ecosystem net primary production increased by approximately 12.3 TgC/year, most of it was offset by increased C loss from harvest and natural disturbance and increased ecosystem respiration related to forest aging. As a result, the strength of the overall ecosystem C sink did not increase over time. Our modeled results indicate the conterminous US C sink was about 30% smaller than previous modeling studies, but converged more closely with inventory data.

Chlorinated Paraffins in Human Milk from Urban Sites in China, Sweden, and Norway.

Short-, medium-, and long-chain chlorinated paraffins (SCCPs, MCCPs, and LCCPs) were analyzed in human milk from the Yangtze River Delta (YRD) and Scandinavia. Individual samples were collected from Shanghai, Jiaxing, and Shaoxing (China), Stockholm (Sweden), and Bodø (Norway) between 2010 and 2016. Mean concentrations (range) of SCCPs, MCCPs, and LCCPs in samples from the YRD were 124 [

Locating multiple information sources in social networks based on the naming game.

Identifying the source of information in a network plays a key role in controlling the impact of information. Herein, we study the problem of multiple source localization in the context of information propagation in social networks. We use the theory of the naming game to conduct observations. Moreover, we divide the observations into different sets based on the information provided by them and then estimate the source of each set. Finally, we combine the source of each observation set to obtain all the estimated information sources. The proposed method can locate sources without knowing the number of information sources. Simulations on four real data sets are provided to verify the performance of our method.

An Image Encryption Algorithm Based on Random Hamiltonian Path.

In graph theory, Hamiltonian path refers to the path that visits each vertex exactly once. In this paper, we designed a method to generate random Hamiltonian path within digital images, which is equivalent to permutation in image encryption. By these means, building a Hamiltonian path across bit planes can shuffle the distribution of the pixel's bits. Furthermore, a similar thought can be applied for the substitution of pixel's grey levels. To ensure the randomness of the generated Hamiltonian path, an adjusted Bernoulli map is proposed. By adopting these novel techniques, a bit-level image encryption scheme was devised. Evaluation of simulation results proves that the proposed scheme reached fair performance. In addition, a common flaw in calculating correlation coefficients of adjacent pixels was pinpointed by us. After enhancement, correlation coefficient becomes a stricter criterion for image encryption algorithms.

DNA methyltransferase expression and DNA hypomethylation status in human hepatocytes following trichloroacetic acid exposure.

Trichloroacetic acid (TCA) is one of the major metabolites of trichloroethylene (TCE) as the significant factor of environmental and occupational pollution. TCA has been shown to induce a series of epigenetic mutation in mouse liver. However, the epigenetic cytotoxicity of TCA is still in infancy. In this study, we explored the cellular biological characteristics, the genome DNA methylation status and the expression profile of DNA methyltransferases in human hepatic L-02 cells treated with TCA with certain time and dose effects. The cell cycle measured by flow cytometry revealed an increasing S + G2 (M) phase of TCA (0.9 mM 24 h, 48 h and 72 h) treated cells after a recovery day, and sub-G1 phase was not appeared. The levels of 5 -mC were decreased in TCA (0.9 mM 24 h and 72 h) treated cells by 5-mC immunolocalization process and HPCE (decreased from 27.2% to 50.1% respectively). Meanwhile, the mCpG% in normal L-02 cells and TCA (0.9 mM 48 h) treated cells was 79.6% ± 6.5% and 50.8% ± 3.8%, respectively (P < 0.05). It also revealed that treatment of L-02 cells with TCA induced decreased in DNMT1 and DNMT3a mRNA and protein levels with a time-dependent manner and a dose-response relationship, while DNMT3b had no obvious change. These results establish a link between DNA methyltransferases and Genome DNA hypomethylation, which is associated with TCA exposure.

Carbon Dioxide Emissions and Methane Flux from Forested Wetland Soils of the Great Dismal Swamp, USA.

The Great Dismal Swamp, a freshwater forested peatland, has accumulated massive amounts of soil carbon since the postglacial period. Logging and draining have severely altered the hydrology and forest composition, leading to drier soils, accelerated oxidation, and vulnerability to disturbance. The once dominant Atlantic white cedar, cypress, and pocosin forest types are now fragmented, resulting in maple-gum forest communities replacing over half the remaining area. In order to determine the effect of environmental variabes on carbon emissions, this study observes 2 years of CO2 and CH4 soil flux, which will also help inform future management decisions. Soil emissions were measured using opaque, non-permanent chambers set into the soil. As soil moisture increased by 1 unit of soil moisture content, CH4 flux increased by 457 µg CH4-C/m2/h. As soil temperature increased by 1 °C, CO2 emissions increased by 5109 µg CO2-C/m2/h. The area of Atlantic white cedar in the study boundary has an average yearly flux of 8.6 metric tons (t) of carbon from CH4 and 3270 t of carbon from CO2; maple-gum has an average yearly flux of 923 t of carbon from CH4 and 59,843 t of carbon from CO2; pocosin has an average yearly flux of 431 t of carbon from CH4 and 15,899 t of carbon from CO2. Total Cha-1year-1 ranged from 1845 kg of Cha-1year-1 in maple-gum to 2024 kg Cha-1year-1 for Atlantic white cedar. These results show that soil carbon gas flux depends on soil moisture, temperature and forest type, which are affected by anthropogenic activities.

A Symmetric Image Encryption Algorithm Based on a Coupled Logistic-Bernoulli Map and Cellular Automata Diffusion Strategy.

In this paper, the properties of the classical confusion-substitution structure and some recently proposed pseudorandom number generators using one-dimensional chaotic maps are investigated. To solve the low security problem of the original structure, a new bit-level cellular automata strategy is used to improve the sensitivity to the cryptosystem. We find that the new evolution effects among different generations of cells in cellular automata can significantly improve the diffusion effect. After this, a new one-dimensional chaotic map is proposed, which is constructed by coupling the logistic map and the Bernoulli map (LBM). The new map exhibits a much better random behavior and is more efficient than comparable ones. Due to the favorable properties of the new map and cellular automata algorithm, we propose a new image-encryption algorithm in which three-dimensional bit-level permutation with LBM is employed in the confusion phase. Simulations are carried out, and the results demonstrate the superior security and high efficiency of the proposed scheme.

The role of driving factors in historical and projected carbon dynamics of upland ecosystems in Alaska.

It is important to understand how upland ecosystems of Alaska, which are estimated to occupy 84% of the state (i.e., 1,237,774 km2 ), are influencing and will influence state-wide carbon (C) dynamics in the face of ongoing climate change. We coupled fire disturbance and biogeochemical models to assess the relative effects of changing atmospheric carbon dioxide (CO2 ), climate, logging and fire regimes on the historical and future C balance of upland ecosystems for the four main Landscape Conservation Cooperatives (LCCs) of Alaska. At the end of the historical period (1950-2009) of our analysis, we estimate that upland ecosystems of Alaska store ~50 Pg C (with ~90% of the C in soils), and gained 3.26 Tg C/yr. Three of the LCCs had gains in total ecosystem C storage, while the Northwest Boreal LCC lost C (-6.01 Tg C/yr) because of increases in fire activity. Carbon exports from logging affected only the North Pacific LCC and represented less than 1% of the state's net primary production (NPP). The analysis for the future time period (2010-2099) consisted of six simulations driven by climate outputs from two climate models for three emission scenarios. Across the climate scenarios, total ecosystem C storage increased between 19.5 and 66.3 Tg C/yr, which represents 3.4% to 11.7% increase in Alaska upland's storage. We conducted additional simulations to attribute these responses to environmental changes. This analysis showed that atmospheric CO2 fertilization was the main driver of ecosystem C balance. By comparing future simulations with constant and with increasing atmospheric CO2 , we estimated that the sensitivity of NPP was 4.8% per 100 ppmv, but NPP becomes less sensitive to CO2 increase throughout the 21st century. Overall, our analyses suggest that the decreasing CO2 sensitivity of NPP and the increasing sensitivity of heterotrophic respiration to air temperature, in addition to the increase in C loss from wildfires weakens the C sink from upland ecosystems of Alaska and will ultimately lead to a source of CO2 to the atmosphere beyond 2100. Therefore, we conclude that the increasing regional C sink we estimate for the 21st century will most likely be transitional.

The role of environmental driving factors in historical and projected carbon dynamics of wetland ecosystems in Alaska.

Wetlands are critical terrestrial ecosystems in Alaska, covering ~177,000 km2 , an area greater than all the wetlands in the remainder of the United States. To assess the relative influence of changing climate, atmospheric carbon dioxide (CO2 ) concentration, and fire regime on carbon balance in wetland ecosystems of Alaska, a modeling framework that incorporates a fire disturbance model and two biogeochemical models was used. Spatially explicit simulations were conducted at 1-km resolution for the historical period (1950-2009) and future projection period (2010-2099). Simulations estimated that wetland ecosystems of Alaska lost 175 Tg carbon (C) in the historical period. Ecosystem C storage in 2009 was 5,556 Tg, with 89% of the C stored in soils. The estimated loss of C as CO2 and biogenic methane (CH4 ) emissions resulted in wetlands of Alaska increasing the greenhouse gas forcing of climate warming. Simulations for the projection period were conducted for six climate change scenarios constructed from two climate models forced under three CO2 emission scenarios. Ecosystem C storage averaged among climate scenarios increased 3.94 Tg C/yr by 2099, with variability among the simulations ranging from 2.02 to 4.42 Tg C/yr. These increases were driven primarily by increases in net primary production (NPP) that were greater than losses from increased decomposition and fire. The NPP increase was driven by CO2 fertilization (~5% per 100 parts per million by volume increase) and by increases in air temperature (~1% per °C increase). Increases in air temperature were estimated to be the primary cause for a projected 47.7% mean increase in biogenic CH4 emissions among the simulations (~15% per °C increase). Ecosystem CO2 sequestration offset the increase in CH4 emissions during the 21st century to decrease the greenhouse gas forcing of climate warming. However, beyond 2100, we expect that this forcing will ultimately increase as wetland ecosystems transition from being a sink to a source of atmospheric CO2 because of (1) decreasing sensitivity of NPP to increasing atmospheric CO2 , (2) increasing availability of soil C for decomposition as permafrost thaws, and (3) continued positive sensitivity of biogenic CH4 emissions to increases in soil temperature.

Assessing historical and projected carbon balance of Alaska: A synthesis of results and policy/management implications.

We summarize the results of a recent interagency assessment of land carbon dynamics in Alaska, in which carbon dynamics were estimated for all major terrestrial and aquatic ecosystems for the historical period (1950-2009) and a projection period (2010-2099). Between 1950 and 2009, upland and wetland (i.e., terrestrial) ecosystems of the state gained 0.4 Tg C/yr (0.1% of net primary production, NPP), resulting in a cumulative greenhouse gas radiative forcing of 1.68 × 10-3  W/m2 . The change in carbon storage is spatially variable with the region of the Northwest Boreal Landscape Conservation Cooperative (LCC) losing carbon because of fire disturbance. The combined carbon transport via various pathways through inland aquatic ecosystems of Alaska was estimated to be 41.3 Tg C/yr (17% of terrestrial NPP). During the projection period (2010-2099), carbon storage of terrestrial ecosystems of Alaska was projected to increase (22.5-70.0 Tg C/yr), primarily because of NPP increases of 10-30% associated with responses to rising atmospheric CO2 , increased nitrogen cycling, and longer growing seasons. Although carbon emissions to the atmosphere from wildfire and wetland CH4 were projected to increase for all of the climate projections, the increases in NPP more than compensated for those losses at the statewide level. Carbon dynamics of terrestrial ecosystems continue to warm the climate for four of the six future projections and cool the climate for only one of the projections. The attribution analyses we conducted indicated that the response of NPP in terrestrial ecosystems to rising atmospheric CO2 (~5% per 100 ppmv CO2 ) saturates as CO2 increases (between approximately +150 and +450 ppmv among projections). This response, along with the expectation that permafrost thaw would be much greater and release large quantities of permafrost carbon after 2100, suggests that projected carbon gains in terrestrial ecosystems of Alaska may not be sustained. From a national perspective, inclusion of all of Alaska in greenhouse gas inventory reports would ensure better accounting of the overall greenhouse gas balance of the nation and provide a foundation for considering mitigation activities in areas that are accessible enough to support substantive deployment.

Assessing the Effects of Fire Disturbances and Timber Management on Carbon Storage in the Greater Yellowstone Ecosystem.

Accurate characterization of Carbon (C) consequences of forest disturbances and management is critical for informed climate mitigation and adaptation strategies. While research into generalized properties of the forest C cycle informs policy and provides abstract guidance to managers, most management occurs at local scales and relies upon monitoring systems that can consistently provide C cycle assessments that explicitly apply to a defined time and place. We used an inventory-based forest monitoring and simulation tool to quantify C storage effects of actual fires, timber harvests, and forest regeneration conditions in the Greater Yellowstone Ecosystem (GYE). Results show that (1) the 1988 fires had a larger impact on GYE's C storage than harvesting during 1985-2011; (2) continuation of relatively high harvest rates of the region's National Forest land, which declined after 1990, would have shifted the disturbance agent primary importance on those lands from fire to harvest; and (3) accounting for local heterogeneity of post-disturbance regeneration patterns translates into large regional effects on total C storage. Large fires in 1988 released about 8.3 ± 0.3 Mg/ha of C across Yellowstone National Park (YNP, including both disturbed and undisturbed area), compared with total C storage reductions due to harvest of about 2.3 ± 0.3 Mg/ha and 2.6 ± 0.2 Mg/ha in adjacent Caribou-Targhee and Gallatin National Forests, respectively, from 1985-2011. If the high harvest rates observed in 1985-1989 had been maintained through 2011 in GYE National Forests, the C storage effect of harvesting would have quintupled to 10.5 ± 1.0 Mg/ha, exceeding the immediate losses associated with YNP's historic fire but not the longer-term net loss of carbon (16.9 ± 0.8 Mg/ha). Following stand-replacing disturbance such as the 1988 fires, the actual regeneration rate was slower than the default regional average rate assumed by empirically calibrated forest growth models. If regeneration following the 1988 fire had reached regionally average rates, either through different natural circumstances or through more active management, YNP would have had approximately 4.1 Mg/ha more forest carbon by year 2020. This study highlights the relative effects of fire disturbances and management activities on regional C storage, and demonstrates a forest carbon monitoring system that can be both applied consistently across the US and tailored to questions of specific local management interest.

Modeling Aboveground Biomass in Hulunber Grassland Ecosystem by Using Unmanned Aerial Vehicle Discrete Lidar.

Accurate canopy structure datasets, including canopy height and fractional cover, are required to monitor aboveground biomass as well as to provide validation data for satellite remote sensing products. In this study, the ability of an unmanned aerial vehicle (UAV) discrete light detection and ranging (lidar) was investigated for modeling both the canopy height and fractional cover in Hulunber grassland ecosystem. The extracted mean canopy height, maximum canopy height, and fractional cover were used to estimate the aboveground biomass. The influences of flight height on lidar estimates were also analyzed. The main findings are: (1) the lidar-derived mean canopy height is the most reasonable predictor of aboveground biomass (R² = 0.340, root-mean-square error (RMSE) = 81.89 g·m-2, and relative error of 14.1%). The improvement of multiple regressions to the R² and RMSE values is unobvious when adding fractional cover in the regression since the correlation between mean canopy height and fractional cover is high; (2) Flight height has a pronounced effect on the derived fractional cover and details of the lidar data, but the effect is insignificant on the derived canopy height when the flight height is within the range (<100 m). These findings are helpful for modeling stable regressions to estimate grassland biomass using lidar returns.