为探究Sentinel-2遥感影像林分类型分类的优选特征组合,实现对阔叶林、马尾松林、杉木林和竹林的分类及其效果评价,选取福建省长汀县为研究区,利用Sentinel-2影像提取10个原始波段(O),计算9个光谱指数(S)、7个红边光谱指数(R)和8个纹理...为探究Sentinel-2遥感影像林分类型分类的优选特征组合,实现对阔叶林、马尾松林、杉木林和竹林的分类及其效果评价,选取福建省长汀县为研究区,利用Sentinel-2影像提取10个原始波段(O),计算9个光谱指数(S)、7个红边光谱指数(R)和8个纹理特征(Te),以及基于数字高程数据计算2个地形特征指数(To),共计36个特征;利用随机森林算法分析不同特征在林分类型分类中的重要性,并利用袋外样本(Out of Band,OOB)数据与平均不纯度减少方法优选特征组合(Optimum Individuality Combination,OIC);对6种不同试验方案(O、O+To、O+To+S、O+To+S+R、O+To+S+R+Te和OIC)进行林分类型分类,并利用混淆矩阵评价分类结果。结果表明,参与林分类型分类的36个特征的重要性为2.11%~5.43%,其中,海拔因子的重要性最高,红边波段、红边光谱指数、纹理特征中均值与相关性也具有较高的重要性;单独使用原始波段对林分类型进行分类,分类精度不高,总体精度为73.26%,Kappa系数为0.64;以原始波段为基础引入其他特征,除原始波段外,其他特征均可以提高分类精度;优选特征组合(OIC)为重要性前27个特征,包含海拔、8个原始波段、7个红边光谱指数和3个纹理特征,分类精度最高,总体精度为83.13%,Kappa系数为0.77,比其余5种试验方案的总体分类精度提高了0.82%~9.87%。以Sentinel-2影像为数据源,随机森林算法优选的特征组合综合多类型特征中对林分类型分类有重要贡献的特征,从而提高了分类精度。研究结果可为GEE平台Sentinel-2影像在森林资源调查中林分类型信息的提取提供参考。展开更多
Coal has been used as an energy resource around the world, primarily for the generation of electricity. The cleaning of coal by removing its unwanted sulfur and mineral matter components is utmost essential before the...Coal has been used as an energy resource around the world, primarily for the generation of electricity. The cleaning of coal by removing its unwanted sulfur and mineral matter components is utmost essential before their gainful utilizations. The ionic liquids （ILs） are considered as non-toxic solvents for using in different industrial processes. The effect of two ILs namely, 1-n-butyl, 3-methylimidazolium tetrafluoro borate （ILl） and 1-n-butyl, 3-methylimidazolium chloride （IL2） in oxidative de-sulfurization and de-ashing of two industrially important high sulfur coal samples from Meghalaya （India） is discussed in this paper. The maximum removal of total sulfur, pyritic sulfur, sulfate sulfur and organic sulfur are observed to be 37.36 %, 62.50 %, 83.33 % and 31.63 % respectively during this oxidative process. The quantitative diffuse reflectance Fourier transform-infrared spectroscopy analysis supports the formation of sulfoxides （S--O） and sulfones （-SO2） and their subsequent removal during the oxidation of the coals in presence of ILs. The X-ray fluorescence combined with near edge X-ray absorption fine structure and scanning electron microscopic studies reveal the removal of mineral matters （ash yields） from the coal samples. The thermogravimetric analysis of the raw and clean coals indicates their high combustion efficiencies and suitability for using in thermal plants. The method is partially green and the ILs could be recovered and reused in the process.展开更多
Hyperspectral remote sensing makes it possible to non-destructively monitor leaf chlorophyll content （LCC）. This study characterized the geometric patterns of the first derivative reflectance spectra in the red edge...Hyperspectral remote sensing makes it possible to non-destructively monitor leaf chlorophyll content （LCC）. This study characterized the geometric patterns of the first derivative reflectance spectra in the red edge region of rapeseed （Brassica napus L.） and wheat （Triticum aestivum L.） crops. The ratio of the red edge area less than 718 nm to the entire red edge area was negatively correlated with LCC. This finding allowed the construction of a new red edge param- eter, defined as red edge symmetry （RES）. Compared to the commonly used red edge parameters （red edge position, red edge amplitude, and red edge area）, RES was a better predictor of LCC. Furthermore, RES was easily calculated using the reflectance of red edge boundary wavebands at 675 and 755 nm （R675 and R755） and reflectance of red edge center wavelength at 718 nm （R718）, with the equation RES = （R71s - R675）/（R755 - R675）. In addition, RES was simulated effectively with wide wavebands from the airborne hyperspectral sensor AVIRIS and satellite hyperspectral sensor Hyperion. The close relationships between the simulated RES and LCC indicated a high feasibility of estimating LCC with simulated RES from AVIRIS and Hyperion data. This made RES readily applicable to common airborne and satellite hyperspectral data derived from AVIRIS and Hyperion sources, as well as ground-based spectral reflectance data.展开更多
文摘为探究Sentinel-2遥感影像林分类型分类的优选特征组合,实现对阔叶林、马尾松林、杉木林和竹林的分类及其效果评价,选取福建省长汀县为研究区,利用Sentinel-2影像提取10个原始波段(O),计算9个光谱指数(S)、7个红边光谱指数(R)和8个纹理特征(Te),以及基于数字高程数据计算2个地形特征指数(To),共计36个特征;利用随机森林算法分析不同特征在林分类型分类中的重要性,并利用袋外样本(Out of Band,OOB)数据与平均不纯度减少方法优选特征组合(Optimum Individuality Combination,OIC);对6种不同试验方案(O、O+To、O+To+S、O+To+S+R、O+To+S+R+Te和OIC)进行林分类型分类,并利用混淆矩阵评价分类结果。结果表明,参与林分类型分类的36个特征的重要性为2.11%~5.43%,其中,海拔因子的重要性最高,红边波段、红边光谱指数、纹理特征中均值与相关性也具有较高的重要性;单独使用原始波段对林分类型进行分类,分类精度不高,总体精度为73.26%,Kappa系数为0.64;以原始波段为基础引入其他特征,除原始波段外,其他特征均可以提高分类精度;优选特征组合(OIC)为重要性前27个特征,包含海拔、8个原始波段、7个红边光谱指数和3个纹理特征,分类精度最高,总体精度为83.13%,Kappa系数为0.77,比其余5种试验方案的总体分类精度提高了0.82%~9.87%。以Sentinel-2影像为数据源,随机森林算法优选的特征组合综合多类型特征中对林分类型分类有重要贡献的特征,从而提高了分类精度。研究结果可为GEE平台Sentinel-2影像在森林资源调查中林分类型信息的提取提供参考。
文摘Coal has been used as an energy resource around the world, primarily for the generation of electricity. The cleaning of coal by removing its unwanted sulfur and mineral matter components is utmost essential before their gainful utilizations. The ionic liquids （ILs） are considered as non-toxic solvents for using in different industrial processes. The effect of two ILs namely, 1-n-butyl, 3-methylimidazolium tetrafluoro borate （ILl） and 1-n-butyl, 3-methylimidazolium chloride （IL2） in oxidative de-sulfurization and de-ashing of two industrially important high sulfur coal samples from Meghalaya （India） is discussed in this paper. The maximum removal of total sulfur, pyritic sulfur, sulfate sulfur and organic sulfur are observed to be 37.36 %, 62.50 %, 83.33 % and 31.63 % respectively during this oxidative process. The quantitative diffuse reflectance Fourier transform-infrared spectroscopy analysis supports the formation of sulfoxides （S--O） and sulfones （-SO2） and their subsequent removal during the oxidation of the coals in presence of ILs. The X-ray fluorescence combined with near edge X-ray absorption fine structure and scanning electron microscopic studies reveal the removal of mineral matters （ash yields） from the coal samples. The thermogravimetric analysis of the raw and clean coals indicates their high combustion efficiencies and suitability for using in thermal plants. The method is partially green and the ILs could be recovered and reused in the process.
基金Supported by the Program for New Century Excellent Talents in University of China(No.NCET-08-0797)the National Natural Science Foundation of China(No.30871448)+1 种基金the Natural Science Foundation of Jiangsu Province,China(No.BK2008330)the Program for the Creative Scholars of Jiangsu Province,China(No.BK20081479)
文摘Hyperspectral remote sensing makes it possible to non-destructively monitor leaf chlorophyll content （LCC）. This study characterized the geometric patterns of the first derivative reflectance spectra in the red edge region of rapeseed （Brassica napus L.） and wheat （Triticum aestivum L.） crops. The ratio of the red edge area less than 718 nm to the entire red edge area was negatively correlated with LCC. This finding allowed the construction of a new red edge param- eter, defined as red edge symmetry （RES）. Compared to the commonly used red edge parameters （red edge position, red edge amplitude, and red edge area）, RES was a better predictor of LCC. Furthermore, RES was easily calculated using the reflectance of red edge boundary wavebands at 675 and 755 nm （R675 and R755） and reflectance of red edge center wavelength at 718 nm （R718）, with the equation RES = （R71s - R675）/（R755 - R675）. In addition, RES was simulated effectively with wide wavebands from the airborne hyperspectral sensor AVIRIS and satellite hyperspectral sensor Hyperion. The close relationships between the simulated RES and LCC indicated a high feasibility of estimating LCC with simulated RES from AVIRIS and Hyperion data. This made RES readily applicable to common airborne and satellite hyperspectral data derived from AVIRIS and Hyperion sources, as well as ground-based spectral reflectance data.
