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Estimating dry grass biomass residues using AVIRIS image analysis

机译:使用AVIRIS图像分析估算干草生物量残留

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The amount of dry grass residue remaining in grasslands in the autumn is indicative of land management practices, especially grazing, and for predicting future fire and erosion potentials. This study has examined factors necessary to provide a remote assessment of grassland condition (biomass, patch distribution), based on a linear spectral mixing analysis of Advanced Visible Infrared Imaging Spectrometer (AVIRIS) images calibrated to surface reflectance for an 80 km/sup 2/ area east of Lake Berryessa, California. The fine-scale spectral features that distinguish soils and dry plant material in the shortwave infrared (SWIR) region are thought to be primarily due to the presence of cellulose and lignin which are absent from soils. The authors have used the endmember fractions of dry grass (mixed annual grasses), green vegetation (Heteromeles arbitufolia, Toyon), and soils (Sehorn Clay series), contained in a spectral library of common plant and soil materials of the area for the analysis. Estimated abundance of dry grass biomass were also estimated by lignin and cellulose absorption features around 1.72 /spl mu/m and compared to endmember fractions and against ground measured biomass samples. The endmember fractions were combined with topographic data, slope, aspect, and cumulative drainage in a geographic information system (GIS) and used to produce a maximum likelihood classification of vegetation of the region. The region contains grazed and ungrazed grasslands, oak woodlands, chaparral, riparian woodlands, agricultural cultivation, and other vegetation types. The prediction accuracy of the vegetation map was estimated to be 0.58 overall, based on comparison against a vegetation map of the University of California's Stebbins Cold Canyon Reserve (SCCR) which was derived through field-based and photogrammatic interpretation a small area in the AVIRIS image.
机译:秋季在草原上残留的干草残留量指示着土地管理的实践,尤其是放牧,并预示了未来的火灾和潜在侵蚀。这项研究基于高级可见红外成像光谱仪(AVIRIS)图像的线性光谱混合分析,对以80 km / sup 2 / s校准的表面反射率进行线性光谱混合分析,研究了提供草地状况远程评估所需的因素。加利福尼亚州贝里耶萨湖以东地区。在短波红外(SWIR)区域中区分土壤和干燥植物材料的精细光谱特征被认为主要是由于土壤中不存在纤维素和木质素。作者使用了该地区常见植物和土壤材料的光谱库中包含的干草(混合一年生草),绿色植被(Heteromeles arbitufolia,Toyon)和土壤(Sehorn Clay系列)的末级分数进行分析。 。木质素和纤维素的吸收特征还估计了大约1.72 / splμm/ m的干草生物量的估计丰度,并将其与端部馏分和地面测量的生物量样品进行了比较。最终成员分数与地形数据,坡度,坡向和地理信息系统(GIS)中的累积排水量相结合,用于产生该地区植被的最大似然分类。该地区包括放牧和未草场的草原,橡树林地,丛林,河岸林地,农业耕作和其他植被类型。根据与加州大学斯特宾斯冷峡谷自然保护区(SCCR)的植被图进行比较的结果,该植被图的预测准确性总体估计为0.58,该图是通过基于野外和摄影语法解释在AVIRIS图像中的一小部分得出的。

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