Developing a Volume Model Using South NTS-372R Total Station without Tree Felling in a Populus canadensis Moench Plantation in Beijing, China

Volume table preparation using the traditional method and a collection model requires the harvest of approximately 200–300 trees of individual species. Although high precision could be achieved using that method, it causes huge damage to the forest. To minimize these losses, in this study, a South NTS-372R total station with a precise angle and distance measurement mode was used to measure 507 trees of Populus canadensis Moench without single tree felling. Moreover, the C# programming language was used in this study and the collected volume data were inserted in the total station. Using this method, a real-time precise measurement of volume could be achieved. After data collection, the optimal binary volume model of Populus canadensis Moench could be obtained through a comparative analysis. It turns out that the Yamamoto model is the optimal binary volume model (also known as two predictor variable model), with 0.9641 as the coefficient of determination (R2) and 0.19 m3 as the standard deviation of estimated value (SEE), which presents a good imitative effect. Moreover, it showed relative stability with the general relative error (TRE) of –0.12% and the mean system error (MSE) of –1.24%. The mean predicted error (MPE) of 1.18% and the mean predicted standard error (MPSE) of 9.25% showed high estimated precision of the average and individual tree volumes. The model has only three parameters, so it is suitable for volume table preparation. Finally, this study will present some new technical methods and means for volume modeling for further application in forestry

Combination of Artificial Neural Network with Multispectral Remote Sensing Data as Applied in Site Quality Evaluation in Inner Mongolia

While abundant ground surface and site information is included in multispectral remote sensing data, traditional site quality evaluation system mainly uses artificial ground surface survey data. To construct an effective site quality evaluation system, this paper, with Wangyedian Forest Farm in Inner Mongolia as the object of study, has adopted an improved back propagation artificial neural network (BPANN) model based on a combination of multispectral remote sensing and surface survey data of the zone. With dahurian larch as an example, a neural network model based on a combination of remote sensing spectrum factor, site index and site factors has been constructed, which, applied in the site quality evaluation of sub compartments of the studied zone, has led to an optimized geographical position prediction model with an accuracy of 95.36%, and an increase of 9.83% as compared with neural network model based on traditional sub compartment survey data. The result indicates that multispectral remote sensing data is very suitable for forest site quality evaluation. Besides, the improved BP neural system features ideal accuracy of prediction, which testifies to the effectiveness and advantage of the methodology described in this paper

Combination of Artificial Neural Network with Multispectral Remote Sensing Data as Applied in Site Quality Evaluation in Inner Mongolia

While abundant ground surface and site information is included in multispectral remote sensing data, traditional site quality evaluation system mainly uses artificial ground surface survey data. To construct an effective site quality evaluation system, this paper, with Wangyedian Forest Farm in Inner Mongolia as the object of study, has adopted an improved back propagation artificial neural network (BPANN) model based on a combination of multispectral remote sensing and surface survey data of the zone. With dahurian larch as an example, a neural network model based on a combination of remote sensing spectrum factor, site index and site factors has been constructed, which, applied in the site quality evaluation of sub compartments of the studied zone, has led to an optimized geographical position prediction model with an accuracy of 95.36%, and an increase of 9.83% as compared with neural network model based on traditional sub compartment survey data. The result indicates that multispectral remote sensing data is very suitable for forest site quality evaluation. Besides, the improved BP neural system features ideal accuracy of prediction, which testifies to the effectiveness and advantage of the methodology described in this paper

Heavy-Metal Pollution Characteristics and Influencing Factors in Agricultural Soils: Evidence from Shuozhou City, Shanxi Province, China

Although soil quality can be highly altered by mining activities, there are few reports on soil pollution in mining cities. We systematically characterized the heavy metals (HMs) pollution, risks, sources, and influencing factors in the surrounding soils of Shuozhou. Specifically, 146 samples were collected, and the potential ecological risk index (RI) and the single-factor index were jointly used to understand the environmental risk of HMs. Meanwhile, correlation analysis was applied to find the influencing factors of HMs. The results of the soil pollution risk assessment in the entire area of Shuozhou were compared with those in the open-pit mine area. (1) The mean concentrations of Cr, As, Cd, Pb, and Hg in our study were found to be higher than the background value. The RI results indicated that most soil samples (82.88%) in Shuozhou had a low potential ecological risk. Compared with the Pingshuo open-pit mine (average RI value: 200.07), the potential ecological RI was lower. (2) The HM correlation indicated that Cr and As were associated with the parent rock, whereas Cd, together with Hg and Pb, were associated with anthropic activities. (3) There was no significant correlation between HM concentrations and farmland slope. Located in the Datong Basin, the terrain of Shuozhou is relatively flat and open and has little impact on the distribution of HMs. (4) Only Hg and Pb have a negative correlation with pH. This suggests that soil with a lower pH value may be beneficial to the accumulation of Hg and Pb in soil. (5) Among the eight industry types examined, the pollution capacity level of the leather, fur, feather, and footwear industries is the strongest, indicating that HMs around LI industry sites represent the maximum level among the eight types

Land Use and Land Cover Changes and Prediction Based on Multi-Scenario Simulation: A Case Study of Qishan County, China

Research on land use change is helpful to better understand the processes and mechanisms of land use changes and provide a decision base for reasonable land development. However, studies on LUCC were mainly conducted for megalopolises and urban agglomerations in China, but there is a gap in the scholarly community when it comes to shrinking small cities where the population decreased sharply under the influence of the urban expansion of megacities. Hence, it is necessary to investigate the evolution rule of land use in these regions. This study takes Qishan County in Shanxi Province as the research subject and analyzes the land use change over the last 20 years with remote sensing technology. Comparing the two LUCC models of the CA-Markov Model and the LCM Model, an optimal model is used to predict and simulate land use change under three potential scenarios in 2030. The conclusions are stated as follows: (1) From 2000 to 2020, the cultivated land area increased originally and subsequently decreased, and forest land continued to decrease at a progressively slower speed. In contrast, the urban land area expanded significantly. (2) The comprehensive dynamic change in water land is the most significant, indicating that this is an unstable land resource in the region and more attention should be given to this matter. (3) The scenario of water area protection indicates that the inhibition of the transition of water areas can protect their vulnerable ecological environment without negatively impacting economic development. Furthermore, the ongoing focus on economic development in the region is related to the rapid disappearance of cultivated land, which is not an optimistic perspective for the area’s ecosystem. The results of this study implied land transition features and mechanisms in Qishan County, providing novel insights for decision support for county-level land use planning

Molecular Communication in Nanonetworks

With the development of nanotechnology, bioengineering and biology, it is envisioned that biological nanomachines may flourish in assorted valuable applications considering their unique characteristics including energy efficiency, bio-compatibility and extremely small scale. However, current biological nanomachines are only able to perform simple tasks at nano-level. Therefore, nanonetworks which interconnect bio-nanomachines into a network have been proposed to overcome the limitations of individual biological nanomachine. Among the possible communication schemes for nanonetworks, modern electromagnetic communication techniques are not good solutions due to the limitation of antenna size. Inspired by nature, one promising candidate is molecular communication proposed from the perspective of communication and computer engineering. Integrated with the knowledge from communication and computer engineering, molecular communication enables biological nanomachines to interface with other biological nanomachines and existing biological systems. Their interconnections form a bio-nanonetwork which is capable to provide functions that individual nanomachines cannot accomplish. In this paper, we introduce the state-of-the-art progress in the emerging field of molecular communication. The framework, design and engineering of components and theoretical modeling of molecular communication are discussed. The research challenges and opportunities are also talked about to inspire future researches of more feasible molecular communication systems

Identification and characterization of magnetotactic Gammaproteobacteria from a salt evaporation pool, Bohai Bay, China

Magnetotactic bacteria (MTB) are phylogenetically diverse prokaryotes that can produce intracellular chain-assembled nanocrystals of magnetite (Fe O ) or greigite (Fe S ). Compared with their wide distribution in the Alpha-, Eta- and Delta-proteobacteria classes, few MTB strains have been identified in the Gammaproteobacteria class, resulting in limited knowledge of bacterial diversity and magnetosome biomineralization within this phylogenetic branch. Here, we identify two magnetotactic Gammaproteobacteria strains (tentatively named FZSR-1 and FZSR-2 respectively) from a salt evaporation pool in Bohai Bay, at the Fuzhou saltern, Dalian City, eastern China. Phylogenetic analysis indicates that strain FZSR-2 is the same species as strains SHHR-1 and SS-5, which were discovered previously from brackish and hypersaline environments respectively. Strain FZSR-1 represents a novel species. Compared with strains FZSR-2, SHHR-1 and SS-5 in which magnetite particles are assembled into a single chain, FZSR-1 cells form relatively narrower magnetite nanoparticles that are often organized into double chains. We find a good relationship between magnetite morphology within strains FZSR-2, SHHR-1 and SS-5 and the salinity of the environment in which they live. This study expands the bacterial diversity of magnetotactic Gammaproteobacteria and provides new insights into magnetosome biomineralization within magnetotactic Gammaproteobacteria.This study was supported financially by the National Natural Science Foundation of China (Grants No. 41920104009,41890843 and 41621004), The Senior User Project(RVKEXUE2019GZ06) (Center for Ocean Mega-Science,Chinese Academy of Sciences) and the Australian Research Council (Grants DP160100805 and DP200100765