Spectral reflectance in the near-infrared （NIR） shoulder （750-900 nm） region is affected by internal leaf structure, but it has rarely been investigated. In this study, a dehydration treatment and three paraquat h...Spectral reflectance in the near-infrared （NIR） shoulder （750-900 nm） region is affected by internal leaf structure, but it has rarely been investigated. In this study, a dehydration treatment and three paraquat herbicide applications were conducted to explore how spectral reflectance and shape in the NIR shoulder region responded to various stresses. A new spectral ratio index in the NIR shoulder region （NSRI）, defined by a simple ratio of reflectance at 890 nm to reflectance at 780 nm, was proposed for assessing leaf structure deterioration. Firstly, a wavelength-independent increase in spectral reflectance in the NIR shoulder region was observed from the mature leaves with slight dehydration. An increase in spectral slope in the NIR shoulder would be expected only when water stress developed sufficiently to cause severe leaf dehydration resulting in an alteration in cell structure. Secondly, the alteration of leaf cell structure caused by Paraquat herbicide applications resulted in a wavelength-dependent variation of spectral reflectance in the NIR shoulder region. The NSRI in the NIR shoulder region increased significantly under an herbicide application. Although the dehydration process also occurred with the herbicide injury, NSRI is more sensitive to herbicide injury than the water-related indices （water index and normalized difference water index） and normalized difference vegetation index. Finally, the sensitivity of NSRI to stripe rust in winter wheat was examined, yielding a determination coefficient of 0.61, which is more significant than normalized difference vegetation index （NDVI）, water index （WI） and normalized difference water index （NDWI）, with a determination coefficient of 0.45, 0.36 and 0.13, respectively. In this study, all experimental results demonstrated that NSRI will increase with internal leaf structure deterioration, and it is also a sensitive spectral index for herbicide injury or stripe rust in winter wheat.展开更多
The research was conducted to determine the relationships of protein and starch accumulation dynamics in grains of wheat to post-heading leaf SPAD values and canopy spectral reflectance. The results showed that leaf n...The research was conducted to determine the relationships of protein and starch accumulation dynamics in grains of wheat to post-heading leaf SPAD values and canopy spectral reflectance. The results showed that leaf nitrogen accumulation was exponentially related to leaf SPAD values and linearly related to canopy spectral reflectance, and that there was negative linear relationship between leaf nitrogen accumulation and grain protein accumulation, but positive linear relationship between post-heading leaf nitrogen transloca-tion and grain protein accumulation at maturity. In addition, leaf SPAD values were parabolically related with and ratio indices R(l 500,610)and R(l 220,560)were exponentially related with protein and starch accumulation in grains. These results indicate that leaf SPAD values and canopy spectral reflectance should be good indicators of quality formation dynamics in wheat grains.展开更多
在干热谷地区的研究中,大都根据海拔高度划定干热谷气候区的范围。而实际上,干热谷景观同时受地形特征(包括高程、坡度和坡向)和人类活动的多重影响。单纯利用高程信息划分干热谷景观区,而没有考虑以上多种因素的影响,显然难以正确地识...在干热谷地区的研究中,大都根据海拔高度划定干热谷气候区的范围。而实际上,干热谷景观同时受地形特征(包括高程、坡度和坡向)和人类活动的多重影响。单纯利用高程信息划分干热谷景观区,而没有考虑以上多种因素的影响,显然难以正确地识别不同的自然景观。本文以最为典型的元谋干热谷为例,首先利用多波段混合运算方法提取山体阴影区,从而剔除坡向的影响;然后,以DEM数据为基础,针对陆地卫星影像,分别采用土地利用绘图法、陆面温度阈值法和干热指数法(RTVI,Ratio of Temperature and NDVI)三种方法对干热谷景观进行识别;最后,利用景观生态学方法对以上识别结果进行对比分析。结果发现,干热指数用于识别干热谷景观区域是最为简单可行的方法;从研究区域的典型干热谷景观分布特征来看,该景观虽然已远远超出传统高程划分的1350m上限,但是,超出这一上限的干热谷景观斑块相对破碎,水体及植被斑块与干热谷景观斑块交错连接,说明这一区域生态恢复的能力较强。展开更多
Water on the Earth’s surface is an essential part of the hydrological cycle. Water resources include surface waters, groundwater, lakes, inland waters, rivers, coastal waters, and aquifers. Monitoring lake dynamics i...Water on the Earth’s surface is an essential part of the hydrological cycle. Water resources include surface waters, groundwater, lakes, inland waters, rivers, coastal waters, and aquifers. Monitoring lake dynamics is critical to favor sustainable management of water resources on Earth. In cryosphere, lake ice cover is a robust indicator of local climate variability and change. Therefore, it is necessary to review recent methods, technologies, and satellite sensors employed for the extraction of lakes from satellite imagery. The present review focuses on the comprehensive evaluation of existing methods for extraction of lake or water body features from remotely sensed optical data. We summarize pixel-based, object-based, hybrid, spectral index based, target and spectral matching methods employed in extracting lake features in urban and cryospheric environments. To our knowledge, almost all of the published research studies on the extraction of surface lakes in cryospheric environments have essentially used satellite remote sensing data and geospatial methods. Satellite sensors of varying spatial, temporal and spectral resolutions have been used to extract and analyze the information regarding surface water. Multispectral remote sensing has been widely utilized in cryospheric studies and has employed a variety of electro-optical satellite sensor systems for characterization and extraction of various cryospheric features, such as glaciers, sea ice, lakes and rivers, the extent of snow and ice, and icebergs. It is apparent that the most common methods for extracting water bodies use single band-based threshold methods, spectral index ratio (SIR)-based multiband methods, image segmentation methods, spectral-matching methods, and target detection methods (unsupervised, supervised and hybrid). A Synergetic fusion of various remote sensing methods is also proposed to improve water information extraction accuracies. The methods developed so far are not generic rather they are specific to either the location or satellite imagery or to the type of the feature to be extracted. Lots of factors are responsible for leading to inaccurate results of lake-feature extraction in cryospheric regions, e.g. the mountain shadow which also appears as a dark pixel is often misclassified as an open lake. The methods which are working well in the cryospheric environment for feature extraction or landcover classification does not really guarantee that they will be working in the same manner for the urban environment. Thus, in coming years, it is expected that much of the work will be done on object-based approach or hybrid approach involving both pixel as well as object-based technology. A more accurate, versatile and robust method is necessary to be developed that would work independent of geographical location (for both urban and cryosphere) and type of optical sensor.展开更多
基金the National High-Tech R&D Program of China(2012AA12A30701)the National Natural Science Foundation of China(91125003,41222008)
文摘Spectral reflectance in the near-infrared （NIR） shoulder （750-900 nm） region is affected by internal leaf structure, but it has rarely been investigated. In this study, a dehydration treatment and three paraquat herbicide applications were conducted to explore how spectral reflectance and shape in the NIR shoulder region responded to various stresses. A new spectral ratio index in the NIR shoulder region （NSRI）, defined by a simple ratio of reflectance at 890 nm to reflectance at 780 nm, was proposed for assessing leaf structure deterioration. Firstly, a wavelength-independent increase in spectral reflectance in the NIR shoulder region was observed from the mature leaves with slight dehydration. An increase in spectral slope in the NIR shoulder would be expected only when water stress developed sufficiently to cause severe leaf dehydration resulting in an alteration in cell structure. Secondly, the alteration of leaf cell structure caused by Paraquat herbicide applications resulted in a wavelength-dependent variation of spectral reflectance in the NIR shoulder region. The NSRI in the NIR shoulder region increased significantly under an herbicide application. Although the dehydration process also occurred with the herbicide injury, NSRI is more sensitive to herbicide injury than the water-related indices （water index and normalized difference water index） and normalized difference vegetation index. Finally, the sensitivity of NSRI to stripe rust in winter wheat was examined, yielding a determination coefficient of 0.61, which is more significant than normalized difference vegetation index （NDVI）, water index （WI） and normalized difference water index （NDWI）, with a determination coefficient of 0.45, 0.36 and 0.13, respectively. In this study, all experimental results demonstrated that NSRI will increase with internal leaf structure deterioration, and it is also a sensitive spectral index for herbicide injury or stripe rust in winter wheat.
基金supported by the National High Tech R&D Program,China(863 Program,2002AA243011)the National Natural Science Foundation of China(30030090)the Natural Science Foundation of Jiangsu Province,China(BK2003079).
文摘The research was conducted to determine the relationships of protein and starch accumulation dynamics in grains of wheat to post-heading leaf SPAD values and canopy spectral reflectance. The results showed that leaf nitrogen accumulation was exponentially related to leaf SPAD values and linearly related to canopy spectral reflectance, and that there was negative linear relationship between leaf nitrogen accumulation and grain protein accumulation, but positive linear relationship between post-heading leaf nitrogen transloca-tion and grain protein accumulation at maturity. In addition, leaf SPAD values were parabolically related with and ratio indices R(l 500,610)and R(l 220,560)were exponentially related with protein and starch accumulation in grains. These results indicate that leaf SPAD values and canopy spectral reflectance should be good indicators of quality formation dynamics in wheat grains.
文摘在干热谷地区的研究中,大都根据海拔高度划定干热谷气候区的范围。而实际上,干热谷景观同时受地形特征(包括高程、坡度和坡向)和人类活动的多重影响。单纯利用高程信息划分干热谷景观区,而没有考虑以上多种因素的影响,显然难以正确地识别不同的自然景观。本文以最为典型的元谋干热谷为例,首先利用多波段混合运算方法提取山体阴影区,从而剔除坡向的影响;然后,以DEM数据为基础,针对陆地卫星影像,分别采用土地利用绘图法、陆面温度阈值法和干热指数法(RTVI,Ratio of Temperature and NDVI)三种方法对干热谷景观进行识别;最后,利用景观生态学方法对以上识别结果进行对比分析。结果发现,干热指数用于识别干热谷景观区域是最为简单可行的方法;从研究区域的典型干热谷景观分布特征来看,该景观虽然已远远超出传统高程划分的1350m上限,但是,超出这一上限的干热谷景观斑块相对破碎,水体及植被斑块与干热谷景观斑块交错连接,说明这一区域生态恢复的能力较强。
文摘Water on the Earth’s surface is an essential part of the hydrological cycle. Water resources include surface waters, groundwater, lakes, inland waters, rivers, coastal waters, and aquifers. Monitoring lake dynamics is critical to favor sustainable management of water resources on Earth. In cryosphere, lake ice cover is a robust indicator of local climate variability and change. Therefore, it is necessary to review recent methods, technologies, and satellite sensors employed for the extraction of lakes from satellite imagery. The present review focuses on the comprehensive evaluation of existing methods for extraction of lake or water body features from remotely sensed optical data. We summarize pixel-based, object-based, hybrid, spectral index based, target and spectral matching methods employed in extracting lake features in urban and cryospheric environments. To our knowledge, almost all of the published research studies on the extraction of surface lakes in cryospheric environments have essentially used satellite remote sensing data and geospatial methods. Satellite sensors of varying spatial, temporal and spectral resolutions have been used to extract and analyze the information regarding surface water. Multispectral remote sensing has been widely utilized in cryospheric studies and has employed a variety of electro-optical satellite sensor systems for characterization and extraction of various cryospheric features, such as glaciers, sea ice, lakes and rivers, the extent of snow and ice, and icebergs. It is apparent that the most common methods for extracting water bodies use single band-based threshold methods, spectral index ratio (SIR)-based multiband methods, image segmentation methods, spectral-matching methods, and target detection methods (unsupervised, supervised and hybrid). A Synergetic fusion of various remote sensing methods is also proposed to improve water information extraction accuracies. The methods developed so far are not generic rather they are specific to either the location or satellite imagery or to the type of the feature to be extracted. Lots of factors are responsible for leading to inaccurate results of lake-feature extraction in cryospheric regions, e.g. the mountain shadow which also appears as a dark pixel is often misclassified as an open lake. The methods which are working well in the cryospheric environment for feature extraction or landcover classification does not really guarantee that they will be working in the same manner for the urban environment. Thus, in coming years, it is expected that much of the work will be done on object-based approach or hybrid approach involving both pixel as well as object-based technology. A more accurate, versatile and robust method is necessary to be developed that would work independent of geographical location (for both urban and cryosphere) and type of optical sensor.
