首先通过原子转移自由基聚合技术(atom transfer radical polymerization,ATRP)在碳纳米管(CNT)表面接枝聚N-异丙基丙烯酰胺(PNIPAM),成功制备出表面润湿性可控的复合载体(CNT-PNIPAM),并以其为载体制备Pd催化剂(Pd/CNT-PNIPAM)。采用...首先通过原子转移自由基聚合技术(atom transfer radical polymerization,ATRP)在碳纳米管(CNT)表面接枝聚N-异丙基丙烯酰胺(PNIPAM),成功制备出表面润湿性可控的复合载体(CNT-PNIPAM),并以其为载体制备Pd催化剂(Pd/CNT-PNIPAM)。采用红外光谱仪(FTIR)、热重仪(TGA)、有机元素分析仪(OEA)、差示量热仪(DSC)、X射线衍射仪(XRD)、透射电镜(TEM)和N2吸附等手段对材料进行表征。制备的催化剂用于1,8-二硝基萘(1,8-DNN)选择性加氢反应,并研究表面化学对催化性能的影响。结果表明,CNT-PNIPAM的最低临界溶液温度(LCST)为37℃左右。利用温敏效应,CNT-PNIPAM在25℃(LCST)下表面润湿性发生转换。PNIPAM接枝引起催化剂表面化学性质的变化,进而使其对底物的吸附性能发生改变。Pd/CNT-PNIPAM上Pd颗粒的高度分散及其对1,8-DNN优良的吸附性能不仅使催化活性得以提高(反应速率常数k=2.1h-1),而且对1,8-DAN的选择性也更高(完全反应时1,8-DAN选择性达98%)。展开更多
Forest disturbance plays a vital role in modulating carbon storage,biodiversity and climate change.Yearly Landsat imagery from 1986 to 2015 of a typical plantation region in the northern Guangdong province of southern...Forest disturbance plays a vital role in modulating carbon storage,biodiversity and climate change.Yearly Landsat imagery from 1986 to 2015 of a typical plantation region in the northern Guangdong province of southern China was used as a case study.A Landsat time series stack(LTSS) was fed to the vegetation change tracker model(VCT) to map long-term changes in plantation forests' disturbance and recovery,followed by an intensive validation and a continuous 27-yr change analysis on disturbance locations,magnitudes and rates of plantations' disturbance and recovery.And the validation results of the disturbance year maps derived from five randomly identified sample plots with 25 km^2 located at the four corners and the center of the scene showed the majority of the spatial agreement measures ranged from 60% to 83%.A confusion matrix summary of the accuracy measures for all four validation sites in Fogang County showed that the disturbance year maps had an overall accuracy estimate of 71.70%.Forest disturbance rates' change trend was characterized by a decline first,followed by an increase,then giving way to a decline again.An undulated and gentle decreasing trend of disturbance rates from the highest value of 3.95% to the lowest value of 0.76% occurred between 1988 and 2001,disturbance rate of 4.51% in 1994 was a notable anomaly,while after 2001 there was a sharp ascending change,forest disturbance rate spiked in 2007(5.84%).After that,there was a significant decreasing trend up to the lowest value of 1.96% in 2011 and a slight ascending trend from 2011 to 2015(2.59%).Two obvious spikes in post-disturbance recovery rates occurred in 1995(0.26%) and 2008(0.41%).Overall,forest recovery rates were lower than forest disturbance rates.Moreover,forest disturbance and recovery detection based on VCT and the Landsat-based detections of trends in disturbance and recovery(LandT rendr) algorithms in Fogang County have been conducted,with LandT rendr finding mostly much more disturbance than VCT.Overall,disturbances and recoveries in northern Guangdong were triggered mostly by timber needs,policies and decisions of the local governments.This study highlights that a better understanding about plantations' changes would provide a critical foundation for local forest management decisions in the southern China.展开更多
探究城市公园绿地空间可达性配置的公平性和有效性对城市规划布局和居民生活宜居性具有重要意义。从公园绿地可达性视角切入,研究集成多种出行模式的可达性度量模型。采用一种结合不同公园绿地类型多级可达时间阈值的改进核密度函数两...探究城市公园绿地空间可达性配置的公平性和有效性对城市规划布局和居民生活宜居性具有重要意义。从公园绿地可达性视角切入,研究集成多种出行模式的可达性度量模型。采用一种结合不同公园绿地类型多级可达时间阈值的改进核密度函数两步移动搜索模型(Kernel Density 2SFCA,KD2SFCA),并与网络分析法、高斯两步移动搜索模型(Gaussian 2SFCA,Ga2SFCA)实验结果对比,量化人口与居民点的分布特征以及不同出行方式、不同类型公园绿地的可达性特征;同时,评价了合肥市绕城高速环路范围内公园绿地空间可达性,探讨其结果分异特征并提出优化建议。结果表明:公园绿地空间呈现南部地区集聚分布模式,且与人口高密度区域高度重合;Ga2SFCA模型法和改进的KD2SFCA模型法的公园绿地可达性在空间上呈现相似现象,但部分地区存在明显差异;从可达面积比和人口比值分析得出,长丰片区(6.54%、9.57%)和庐阳片区(1.24%、1.57%)空间可达性最差;改进的KD2SFCA的可达性计算结果及其空间分布更符合实际情况。研究明确了城市中公园绿地资源短缺的区域,为城市公共空间规划布局提供数据支撑和理论参考。展开更多
Remote sensing images show a very promising perspective for distinguishing tree species,especially those with the very high resolution ranging from 1 to 4 m.However,the traditional methodology for classifying land cov...Remote sensing images show a very promising perspective for distinguishing tree species,especially those with the very high resolution ranging from 1 to 4 m.However,the traditional methodology for classifying land cover types,solely depending on spectral features,while texture and other spatial information are neglected, has the weakness such as inadequately utilization of information,low accuracies of classification,etc. Considering to the texture differences among forest species,it is more important for spatial information description of high-resolution remote sensing image to improve the precision of textural features choosing.In this study,the factors to influence the nine textural features choosing were analyzed and the results showed that the moving window size was the main factor to affect the obtaining processes of textural features based on the gray level co-occurrence matrix(GLCM) method,and the imagery was then classified combining the maximum likelihood classification(MLC) method with the original spectral values and texture features.First,this study utilized a correlation analysis of the images from a principal component analysis.Second,through multiple information sources,including textual features derived from the data.For the high-resolution remote sensing image, the most proper moving window size was determined from 3×3 to 31×31.Classification of the major tree species throughout the study area (the SunYat-Sen Mausoleum in Nanjing) was undertaken using the MLC.Third,to aid forest research,classification accuracy was improved using the GLCM.According to correlations among textures and richness of the data,GLCM provided the best window size and textural parameters. Results indicated that the texture characteristics were add in the spectral characteristics to improve the precision of the results of the classification, 19×19 window for best window.The total precision can reach 66.322 6%,Kappa coefficient is 0.584 0.Each tree species has greatly improved accuracies of the classification.By the calculation of R^2 values,the textural features of mean, homogeneity and correlation were chosen to be best combination for the size of 19×19 and the combination of skewness,homogeneity and mean was considered the most properly for the moving window size 19×19.Precision assessment of different textural combinations showed that VA,HO, CR combination with optimal moving window size (from 3×3 to 31×31) could evidently improve the classification precision for high-resolution remote sensing image.And the combination of mean,homogeneity,skewness,and contrast texture factors correlation can effectively reduce data redundancy,which obtained the similar results.In the texture features,the mean is the most important factor and impacts the classification of the tree species.This method could solve problems of forestry type classification,tree species classification,etc.It is much better than traditional method of based on pixel values.This procedure effectively reduced data redundancy and could assist in tree species classification.展开更多
文摘首先通过原子转移自由基聚合技术(atom transfer radical polymerization,ATRP)在碳纳米管(CNT)表面接枝聚N-异丙基丙烯酰胺(PNIPAM),成功制备出表面润湿性可控的复合载体(CNT-PNIPAM),并以其为载体制备Pd催化剂(Pd/CNT-PNIPAM)。采用红外光谱仪(FTIR)、热重仪(TGA)、有机元素分析仪(OEA)、差示量热仪(DSC)、X射线衍射仪(XRD)、透射电镜(TEM)和N2吸附等手段对材料进行表征。制备的催化剂用于1,8-二硝基萘(1,8-DNN)选择性加氢反应,并研究表面化学对催化性能的影响。结果表明,CNT-PNIPAM的最低临界溶液温度(LCST)为37℃左右。利用温敏效应,CNT-PNIPAM在25℃(LCST)下表面润湿性发生转换。PNIPAM接枝引起催化剂表面化学性质的变化,进而使其对底物的吸附性能发生改变。Pd/CNT-PNIPAM上Pd颗粒的高度分散及其对1,8-DNN优良的吸附性能不仅使催化活性得以提高(反应速率常数k=2.1h-1),而且对1,8-DAN的选择性也更高(完全反应时1,8-DAN选择性达98%)。
基金Under the auspices of the‘948’Project sponsored by the State Forestry Administration(SFA)of China(No.2014-4-25)National Natural Science Foundation of China(No.31670552,31270587)Doctorate Fellowship Foundation of Nanjing Forestry University,the PAPD(Priority Academic Program Development)of Jiangsu Provincial Universities,Graduate Research and Innovation Projects in Jiangsu Province(No.KYLX15_0908)
文摘Forest disturbance plays a vital role in modulating carbon storage,biodiversity and climate change.Yearly Landsat imagery from 1986 to 2015 of a typical plantation region in the northern Guangdong province of southern China was used as a case study.A Landsat time series stack(LTSS) was fed to the vegetation change tracker model(VCT) to map long-term changes in plantation forests' disturbance and recovery,followed by an intensive validation and a continuous 27-yr change analysis on disturbance locations,magnitudes and rates of plantations' disturbance and recovery.And the validation results of the disturbance year maps derived from five randomly identified sample plots with 25 km^2 located at the four corners and the center of the scene showed the majority of the spatial agreement measures ranged from 60% to 83%.A confusion matrix summary of the accuracy measures for all four validation sites in Fogang County showed that the disturbance year maps had an overall accuracy estimate of 71.70%.Forest disturbance rates' change trend was characterized by a decline first,followed by an increase,then giving way to a decline again.An undulated and gentle decreasing trend of disturbance rates from the highest value of 3.95% to the lowest value of 0.76% occurred between 1988 and 2001,disturbance rate of 4.51% in 1994 was a notable anomaly,while after 2001 there was a sharp ascending change,forest disturbance rate spiked in 2007(5.84%).After that,there was a significant decreasing trend up to the lowest value of 1.96% in 2011 and a slight ascending trend from 2011 to 2015(2.59%).Two obvious spikes in post-disturbance recovery rates occurred in 1995(0.26%) and 2008(0.41%).Overall,forest recovery rates were lower than forest disturbance rates.Moreover,forest disturbance and recovery detection based on VCT and the Landsat-based detections of trends in disturbance and recovery(LandT rendr) algorithms in Fogang County have been conducted,with LandT rendr finding mostly much more disturbance than VCT.Overall,disturbances and recoveries in northern Guangdong were triggered mostly by timber needs,policies and decisions of the local governments.This study highlights that a better understanding about plantations' changes would provide a critical foundation for local forest management decisions in the southern China.
文摘探究城市公园绿地空间可达性配置的公平性和有效性对城市规划布局和居民生活宜居性具有重要意义。从公园绿地可达性视角切入,研究集成多种出行模式的可达性度量模型。采用一种结合不同公园绿地类型多级可达时间阈值的改进核密度函数两步移动搜索模型(Kernel Density 2SFCA,KD2SFCA),并与网络分析法、高斯两步移动搜索模型(Gaussian 2SFCA,Ga2SFCA)实验结果对比,量化人口与居民点的分布特征以及不同出行方式、不同类型公园绿地的可达性特征;同时,评价了合肥市绕城高速环路范围内公园绿地空间可达性,探讨其结果分异特征并提出优化建议。结果表明:公园绿地空间呈现南部地区集聚分布模式,且与人口高密度区域高度重合;Ga2SFCA模型法和改进的KD2SFCA模型法的公园绿地可达性在空间上呈现相似现象,但部分地区存在明显差异;从可达面积比和人口比值分析得出,长丰片区(6.54%、9.57%)和庐阳片区(1.24%、1.57%)空间可达性最差;改进的KD2SFCA的可达性计算结果及其空间分布更符合实际情况。研究明确了城市中公园绿地资源短缺的区域,为城市公共空间规划布局提供数据支撑和理论参考。
文摘Remote sensing images show a very promising perspective for distinguishing tree species,especially those with the very high resolution ranging from 1 to 4 m.However,the traditional methodology for classifying land cover types,solely depending on spectral features,while texture and other spatial information are neglected, has the weakness such as inadequately utilization of information,low accuracies of classification,etc. Considering to the texture differences among forest species,it is more important for spatial information description of high-resolution remote sensing image to improve the precision of textural features choosing.In this study,the factors to influence the nine textural features choosing were analyzed and the results showed that the moving window size was the main factor to affect the obtaining processes of textural features based on the gray level co-occurrence matrix(GLCM) method,and the imagery was then classified combining the maximum likelihood classification(MLC) method with the original spectral values and texture features.First,this study utilized a correlation analysis of the images from a principal component analysis.Second,through multiple information sources,including textual features derived from the data.For the high-resolution remote sensing image, the most proper moving window size was determined from 3×3 to 31×31.Classification of the major tree species throughout the study area (the SunYat-Sen Mausoleum in Nanjing) was undertaken using the MLC.Third,to aid forest research,classification accuracy was improved using the GLCM.According to correlations among textures and richness of the data,GLCM provided the best window size and textural parameters. Results indicated that the texture characteristics were add in the spectral characteristics to improve the precision of the results of the classification, 19×19 window for best window.The total precision can reach 66.322 6%,Kappa coefficient is 0.584 0.Each tree species has greatly improved accuracies of the classification.By the calculation of R^2 values,the textural features of mean, homogeneity and correlation were chosen to be best combination for the size of 19×19 and the combination of skewness,homogeneity and mean was considered the most properly for the moving window size 19×19.Precision assessment of different textural combinations showed that VA,HO, CR combination with optimal moving window size (from 3×3 to 31×31) could evidently improve the classification precision for high-resolution remote sensing image.And the combination of mean,homogeneity,skewness,and contrast texture factors correlation can effectively reduce data redundancy,which obtained the similar results.In the texture features,the mean is the most important factor and impacts the classification of the tree species.This method could solve problems of forestry type classification,tree species classification,etc.It is much better than traditional method of based on pixel values.This procedure effectively reduced data redundancy and could assist in tree species classification.
