高光谱遥感森林类型识别及其郁闭度定量估测研究
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摘要
森林类型的识别和森林的某些参数(如郁闭度、叶面积指数等)的估测在林业生产中非常重要，特别是在森林资源二类清查时，必须分树种统计其面积、蓄积量和郁闭度等参数。因此，林业上对森林类型的识别应该是越精细越好，对郁闭度的估测和反演越精确越好。常规的林分参数调查和识别主要是依赖人工外业调查或利用大比例尺航空像片来进行。这两种方法都有不足之处，前者劳动强度太大，后者成本太高。卫星遥感技术的发展，为林分参数的识别和估测提供了新的途径。我国过去近30年林业遥感中曾广泛应用遥感数据(如TM、SPOT)，开展过大量的树种识别、郁闭度估测和生物量的反演研究，但是结果并不理想。主要原因是：一是多光谱遥感数据的光谱分辨率有限，而不同的森林类型常具有极为相似的光谱特性(通常成为“异物同谱”现象)，它们细微的光谱差异用宽波段遥感数据是无法探测的；二是由于光学遥感所依赖的光照条件变化大，从而引起相同的森林类型具有显著不同的光谱特性(即所谓的“同物异谱”现象)。致使目前遥感在林业中的应用程度与林业对遥感的期望还有一定的差距。
与传统遥感手段相比，高光谱分辨率遥感具有窄波段、多通道、图像与光谱合而为一的优点，它以纳米的超高光谱分辨率和几十或几百波段同时对地物成像，从而获得地物的连续光谱信息和更多的精细光谱信息。高光谱的这种特征非常有利于地物的精细识别和分类，能大大改善对植被类型的识别和分类精度，提高植被参数的估测和反演精度。
高光谱遥感技术是本世纪初的遥感前沿技术。它已成功地应用在多种学科中，取得了一些研究结果，并展示了其应用潜力。在林业行业中，国外开展了高光谱遥感的森林叶面积指数、生物量、森林生化信息、森林树种识别等方面的研究工作。但在我国这方面的工作刚刚起步。
本文以吉林省延边朝鲜族自治州汪清林业局为试验区，重点开展了应用高光谱遥感数据进行森林类型识别和森林郁闭度定量估测的研究，并对高光谱遥感的森林类型识别能力和森林郁闭度定量估测能力进行了分析评价。具体的研究内容包括：
1．高光谱遥感在林业中的应用。简述了高光谱遥感的定义、特点以及发展历史和发展趋势。对国内外高光谱遥感数据在林业领域各个方面的应用研究现状进行了总结和阐述，包括森林生物物理参数(如森林树种、森林叶面积指数和森林郁闭度等)和森林生物化学参数(如叶绿素、氮、木质素和淀粉含量等)的信息提取、森林健康状况的高光谱监测等。在此基础上，明确了本论文所从事研究的目的和意义，确定了研究方向和研究内容。
Forest planning and management require information about forest resources. Some of the most important information are the spatial distribution of forest types and the forest crown closure. Both of them are an important parameter in ecological, hydrological and climate models. Their measurement in fields are difficult and time-consuming. This is particularly true over large areas. Therefore, the directly measures of forest parameters are only practical on experimental plots of limited site. Consequently, investigating forest parameter estimation over large area is problematic. Remote Sensing techniques, particularly the use of satellite imagery, may provide a practical means to measure forest parameters at the landscape scale or even global scale. With remote sensing techniques, scientists have made use of methods that correlate remotely sensed data with regional estimates of a number of forest ecosystem variables, including forest crown closure, canopy temperature, etc.
However, most remote sensing systems in the past decades rely on measured reflectance data in a few broad wavelength bands. Current research is focused on the mapping of forest stands and determining their quantitative parameters using reflectance spectra recorded by airborne and spaceborne imaging spectrometery and its analysis methods such as spectral unmixing. The hyperspectral sensors have been developed to provide more than 220 spectral contiguous and very narrow bands across a full spectral range from 0.4 to 2.5 um, such as Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and EO-1 Hyperion, the first spaceborne hyperspectral imager in the world. So that much smaller spectral difference of objects can be detected by hyperspectral image. This is especially important for vegetation recognition because the spectral characteristics of different vegetation are similar and difficult to be classified. In contrast to traditional multispectral sensors, the hyperspectral sensors are expected to improve the ability of observing the earth surface, and it becomes one of the most important leading research fields of remote sensing.
The recognition and classification of forest parameters is the basis of forestry remote sensing. In this dissertation, taking Wangqing Forestry Bureau, Jilin Province of China as a study area, focuses on the research on extraction of forest types information and estimation of forest crown closure using hyperspectral remote sensing data (EO-1 Hyperion data). The research contents and major conclusions of this paper can be summarized as following:
1. Review of hyperspectral remote sensing application in forestry
与传统遥感手段相比，高光谱分辨率遥感具有窄波段、多通道、图像与光谱合而为一的优点，它以纳米的超高光谱分辨率和几十或几百波段同时对地物成像，从而获得地物的连续光谱信息和更多的精细光谱信息。高光谱的这种特征非常有利于地物的精细识别和分类，能大大改善对植被类型的识别和分类精度，提高植被参数的估测和反演精度。
高光谱遥感技术是本世纪初的遥感前沿技术。它已成功地应用在多种学科中，取得了一些研究结果，并展示了其应用潜力。在林业行业中，国外开展了高光谱遥感的森林叶面积指数、生物量、森林生化信息、森林树种识别等方面的研究工作。但在我国这方面的工作刚刚起步。
本文以吉林省延边朝鲜族自治州汪清林业局为试验区，重点开展了应用高光谱遥感数据进行森林类型识别和森林郁闭度定量估测的研究，并对高光谱遥感的森林类型识别能力和森林郁闭度定量估测能力进行了分析评价。具体的研究内容包括：
1．高光谱遥感在林业中的应用。简述了高光谱遥感的定义、特点以及发展历史和发展趋势。对国内外高光谱遥感数据在林业领域各个方面的应用研究现状进行了总结和阐述，包括森林生物物理参数(如森林树种、森林叶面积指数和森林郁闭度等)和森林生物化学参数(如叶绿素、氮、木质素和淀粉含量等)的信息提取、森林健康状况的高光谱监测等。在此基础上，明确了本论文所从事研究的目的和意义，确定了研究方向和研究内容。
Forest planning and management require information about forest resources. Some of the most important information are the spatial distribution of forest types and the forest crown closure. Both of them are an important parameter in ecological, hydrological and climate models. Their measurement in fields are difficult and time-consuming. This is particularly true over large areas. Therefore, the directly measures of forest parameters are only practical on experimental plots of limited site. Consequently, investigating forest parameter estimation over large area is problematic. Remote Sensing techniques, particularly the use of satellite imagery, may provide a practical means to measure forest parameters at the landscape scale or even global scale. With remote sensing techniques, scientists have made use of methods that correlate remotely sensed data with regional estimates of a number of forest ecosystem variables, including forest crown closure, canopy temperature, etc.
However, most remote sensing systems in the past decades rely on measured reflectance data in a few broad wavelength bands. Current research is focused on the mapping of forest stands and determining their quantitative parameters using reflectance spectra recorded by airborne and spaceborne imaging spectrometery and its analysis methods such as spectral unmixing. The hyperspectral sensors have been developed to provide more than 220 spectral contiguous and very narrow bands across a full spectral range from 0.4 to 2.5 um, such as Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and EO-1 Hyperion, the first spaceborne hyperspectral imager in the world. So that much smaller spectral difference of objects can be detected by hyperspectral image. This is especially important for vegetation recognition because the spectral characteristics of different vegetation are similar and difficult to be classified. In contrast to traditional multispectral sensors, the hyperspectral sensors are expected to improve the ability of observing the earth surface, and it becomes one of the most important leading research fields of remote sensing.
The recognition and classification of forest parameters is the basis of forestry remote sensing. In this dissertation, taking Wangqing Forestry Bureau, Jilin Province of China as a study area, focuses on the research on extraction of forest types information and estimation of forest crown closure using hyperspectral remote sensing data (EO-1 Hyperion data). The research contents and major conclusions of this paper can be summarized as following:
1. Review of hyperspectral remote sensing application in forestry
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