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A classification-based assessment of the optimal spatial and spectral resolution of coastal wetland imagery.

机译:基于分类的沿海湿地图像最佳空间和光谱分辨率评估。

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Great Lakes wetlands are increasingly being recognized as vital ecosystem components that provide valuable functions such as sediment retention, wildlife habitat, and nutrient removal. Aerial photography has traditionally provided a cost effective means to inventory and monitor coastal wetlands, but is limited by its broad spectral sensitivity and non-digital format. Airborne sensor advancements have now made the acquisition of digital imagery with high spatial and spectral resolution a reality.; In this investigation, we selected two Lake Huron coastal wetlands, each from a distinct eco-region, over which, digital, airborne imagery (AISA or CASI-II) was acquired. The 1-meter images contain approximately twenty, 10-nanometer-wide spectral bands strategically located throughout the visible and near-infrared. The 4-meter hyperspectral imagery contains 48 contiguous bands across the visible and short-wavelength near-infrared. Extensive, in-situ, reflectance spectra (SE-590) and sub-meter GPS locations were acquired for the dominant botanical and substrate classes field-delineated at each location.; Normalized in-situ spectral signatures were subjected to Principal Components and 2nd Derivative analyses in order to identify the most botanically explanative image bands. Three image-based investigations were implemented in order to evaluate the ability of three classification algorithms (ISODATA, Spectral Angle Mapper and Maximum-Likelihood) to differentiate botanical regions-of-interest. Two additional investigations were completed in order to assess classification changes associated with the independent manipulation of both spatial and spectral resolution.; Of the three algorithms tested, the Maximum-Likelihood classifier best differentiated (89%) the regions-of-interest in both study sites. Covariance-based PCA rotation consistently enhanced the performance of the Maximum-Likelihood classifier. Seven non-overlapping bands (425.4, 514.9, 560.1, 685.5, 731.5, 812.3 and 916.7 nanometers) were identified that represented the best performing bands with respect to classification performance. A spatial resolution of 2 meters or less was determined to be the as being most appropriate in Great Lakes coastal wetland environments. This research represents the first step in evaluating the effectiveness of applying high-resolution, narrow-band imagery to the detailed mapping of coastal wetlands in the Great Lakes region.
机译:大湖湿地越来越被认为是重要的生态系统组成部分,可提供有价值的功能,例如沉积物保留,野生生物栖息地和营养去除。传统上,航空摄影提供了一种经济有效的方法来清点和监测沿海湿地,但受到其广泛的光谱敏感性和非数字格式的限制。机载传感器的发展现已使获取具有高空间和光谱分辨率的数字图像成为现实。在这项调查中,我们选择了两个休伦湖沿岸湿地,每个湿地都来自不同的生态区域,在该湿地上获取了数字化航空影像(AISA或CASI-II)。 1米长的图像包含大约20个10纳米宽的光谱带,这些光谱带战略性地位于整个可见光和近红外区域。 4米高光谱图像包含可见光和短波近红外的48个连续带。 ;获取了在每个位置现场描绘的主要植物和基质类别的广泛的原位反射光谱(SE-590)和亚米GPS位置。对归一化的原位光谱特征进行主成分分析和2 nd 导数分析,以识别最植物性的解释性图像带。为了评估三种分类算法(ISODATA,光谱角度映射器和最大似然性)区分植物感兴趣区域的能力,实施了三个基于图像的调查。为了评估与空间和光谱分辨率的独立操纵相关的分类变化,完成了另外两项研究。在所测试的三种算法中,最大似然分类器在两个研究地点都对目标区域进行了最佳区分(89%)。基于协方差的PCA旋转一致地增强了最大似然分类器的性能。确定了七个非重叠的波段(425.4、514.9、560.1、685.5、731.5、812.3和916.7纳米),它们代表了分类性能最佳的波段。确定在大湖沿岸湿地环境中最合适的空间分辨率为2米或更小。这项研究代表了评估将高分辨率,窄带图像应用于大湖区沿海湿地的详细地图绘制的有效性的第一步。

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