Effects of Thinning Intensity on the SBE in Different Types Stand

With 5 main types of stand in Nanjing WuXiangsi National Forest Park being objects of research, the diversity of the scenic beauty values of different thinning intensities were explored, and then the multivariate linear model of scenic beauty values and landscape elements was established. The result indicates: (1) The SBE can be greatly improved by thinning especially high-intensity; (2) Main factors of landscape quality of different stands in the study area are density, Diameter at Breast Height (DBH), canopy density etc.; (3) Better permeability, bigger DBH and tree height do improve SBE, while higher stand density and canopy density will harm scenic beauty, so we should consider the positive and negative influences of tending measures when we construct scenic forest.OtherShinshu University International Symposium 2010 : Sustainable Agriculture and Environment : Asian Networks II 　信州大学国際シンポジウム2010 : 持続的農業と環境 : アジアネットワークII ― アジアネットワークの発展をめざして―. 信州大学農学部, 2010, 59-64conference pape

Short-term effects of forest gap size on soil enzyme activity in a Platycladus orientalis plantation

IntroductionSoil enzymes play a critical role in organic matter decomposition and nutrient cycling in forest ecosystems. However, the effects of forest gaps on soil enzyme activities remain uncertain.MethodsThis study aims to investigate the short-term effects of forest gap size on soil enzyme activities in Platycladus orientalis plantations. We conducted a study in a 50-year Platycladus orientalis plantation in Xuzhou, sampling soils from three levels of forest gap size (4 m radius, S; 8 m radius, M and 12 m radius, L) at different positions (within gap, edge, and outside the gap) and control plots (CK, no gaps) 2a after the creation of gaps. Soil peroxidase, dehydrogenase, urease, and invertase activities were measured.ResultsSpecifically, we found that M and S gaps had significantly (p < 0.05) higher soil peroxidase activity at the outside position in April and October, respectively, than CK. Additionally, L gaps had significantly (p < 0.05) higher soil dehydrogenase activity at the outside position in April than CK. Furthermore, L and S gaps had significantly (p < 0.05) higher soil urease activity at the outside position in October and July, respectively, than CK. Lastly, L and S gaps had significantly (p < 0.05) higher soil urease activity at the outside position in July than CK.ConclusionOur findings highlight the significant impact of canopy gaps on soil enzyme activities, which has important implications for forest management and conservation

Interpretation of Forest Resources at the Individual Tree Level at Purple Mountain, Nanjing City, China, Using WorldView-2 Imagery by Combining GPS, RS and GIS Technologies

This study attempted to measure forest resources at the individual tree level using high-resolution images by combining GPS, RS, and Geographic Information System (GIS) technologies. The images were acquired by the WorldView-2 satellite with a resolution of 0.5 m in the panchromatic band and 2.0 m in the multispectral bands. Field data of 90 plots were used to verify the interpreted accuracy. The tops of trees in three groups, namely 10 cm, 15 cm, and 20 cm DBH (diameter at breast height), were extracted by the individual tree crown (ITC) approach using filters with moving windows of 3 x 3 pixels, 5 x 5 pixels and 7 x 7 pixels, respectively. In the study area, there were 1,203,970 trees of DBH over 10 cm, and the interpreted accuracy was 73.68 +/- 15.14% averaged over the 90 plots. The numbers of the trees that were 15 cm and 20 cm DBH were 727,887 and 548,919, with an average accuracy of 68.74 +/- 17.21% and 71.92 +/- 18.03%, respectively. The pixel-based classification showed that the classified accuracies of the 16 classes obtained using the eight multispectral bands were higher than those obtained using only the four standard bands. The increments ranged from 0.1% for the water class to 17.0% for Metasequoia glyptostroboides, with an average value of 4.8% for the 16 classes. In addition, to overcome the mixed pixels problem, a crown-based supervised classification, which can improve the classified accuracy of both dominant species and smaller classes, was used for generating a thematic map of tree species. The improvements of the crown- to pixel-based classification ranged from -1.6% for the open forest class to 34.3% for Metasequoia glyptostroboides, with an average value of 20.3% for the 10 classes. All tree tops were then annotated with the species attributes from the map, and a tree count of different species indicated that the forest of Purple Mountain is mainly dominated by Quercus acutissima, Liquidambar formosana and Pinus massoniana. The findings from this study lead to the recommendation of using the crown-based instead of the pixel-based classification approach in classifying mixed forests.ArticleREMOTE SENSING. 6(1):87-110 (2014)journal articl

Organic mulching promotes soil organic carbon accumulation to deep soil layer in an urban plantation forest

Abstract Background Soil organic carbon (SOC) is important for soil quality and fertility in forest ecosystems. Labile SOC fractions are sensitive to environmental changes, which reflect the impact of short-term internal and external management measures on the soil carbon pool. Organic mulching (OM) alters the soil environment and promotes plant growth. However, little is known about the responses of SOC fractions in rhizosphere or bulk soil to OM in urban forests and its correlation with carbon composition in plants. Methods A one-year field experiment with four treatments (OM at 0, 5, 10, and 20 cm thicknesses) was conducted in a 15-year-old Ligustrum lucidum plantation. Changes in the SOC fractions in the rhizosphere and bulk soil; the carbon content in the plant fine roots, leaves, and organic mulch; and several soil physicochemical properties were measured. The relationships between SOC fractions and the measured variables were analysed. Results The OM treatments had no significant effect on the SOC fractions, except for the dissolved organic carbon (DOC). OM promoted the movement of SOC to deeper soil because of the increased carbon content in fine roots of subsoil. There were significant correlations between DOC and microbial biomass carbon and SOC and easily oxidised organic carbon. The OM had a greater effect on organic carbon fractions in the bulk soil than in the rhizosphere. The thinnest (5 cm) mulching layers showed the most rapid carbon decomposition over time. The time after OM had the greatest effect on the SOC fractions, followed by soil layer. Conclusions The frequent addition of small amounts of organic mulch increased SOC accumulation in the present study. OM is a potential management model to enhance soil organic matter storage for maintaining urban forest productivity

Evaluation of Water Resource Security Based on an MIV-BP Model in a Karst Area

Evaluation of water resource security deserves particular attention in water resource planning and management. A typical karst area in Guizhou Province, China, was used as the research area in this paper. First, based on data from Guizhou Province for the past 10 years, the mean impact value–back propagation (MIV-BP) model was used to analyze the factors influencing water resource security in the karst area. Second, 18 indices involving five aspects, water environment subsystem, social subsystem, economic subsystem, ecological subsystem, and human subsystem, were selected to establish an evaluation index of water resource security. Finally, a BP artificial neural network model was constructed to evaluate the water resource security of Guizhou Province from 2005 to 2014. The results show that water resource security in Guizhou, which was at a moderate warning level from 2005 to 2009 and a critical safety level from 2010 to 2014, has generally improved. Groundwater supply ratio, industrial water utilization rate, water use efficiency, per capita grain production, and water yield modulus were the obstacles to water resource security. Driving factors were comprehensive utilization rate of industrial solid waste, qualifying rate of industrial wastewater, above moderate rocky desertification area ratio, water requirement per unit gross domestic product (GDP), and degree of development and utilization of groundwater. Our results provide useful suggestions on the management of water resource security in Guizhou Province and a valuable reference for water resource research

Retraction: Liu, L., et al. Evaluation of Water Resource Security Based on an MIV-BP Model in a Karst Area. Water 2018, 10, 786

The Water Editorial Office has been made aware that the published paper [1] has a significant overlap with a previously published manuscript from the same authors, submitted to a Chinese journal.[...

Estimating Forest Aboveground Biomass by Combining ALOS PALSAR and WorldView-2 Data: A Case Study at Purple Mountain National Park, Nanjing, China

Enhanced methods are required for mapping the forest aboveground biomass (AGB) over a large area in Chinese forests. This study attempted to develop an improved approach to retrieving biomass by combining PALSAR (Phased Array type L-band Synthetic Aperture Radar) and WorldView-2 data. A total of 33 variables with potential correlations with forest biomass were extracted from the above data. However, these parameters had poor fits to the observed biomass. Accordingly, the synergies of several variables were explored to identify improved relationships with the AGB. Using principal component analysis and multivariate linear regression (MLR), the accuracies of the biomass estimates obtained using PALSAR and WorldView-2 data were improved to approximately 65% to 71%. In addition, using the additional dataset developed from the fusion of FBD (fine beam dual-polarization) and WorldView-2 data improved the performance to 79% with an RMSE (root mean square error) of 35.13 Mg/ha when using the MLR method. Moreover, a further improvement (R2 = 0.89, relative RMSE = 17.08%) was obtained by combining all the variables mentioned above. For the purpose of comparison with MLR, a neural network approach was also used to estimate the biomass. However, this approach did not produce significant improvements in the AGB estimates. Consequently, the final MLR model was recommended to map the AGB of the study area. Finally, analyses of estimated error in distinguishing forest types and vertical structures suggested that the RMSE decreases gradually from broad-leaved to coniferous to mixed forest. In terms of different vertical structures (VS), VS3 has a high error because the forest lacks undergrowth trees, while VS4 forest, which has approximately the same amounts of stems in each of the three DBH (diameter at breast height) classes (DBH > 20, 10 ≤ DBH ≤ 20, and DBH < 10 cm), has the lowest RMSE. This study demonstrates that the combination of PALSAR and WorldView-2 data is a promising approach to improve biomass estimation