首页> 外文期刊>Frontiers in Marine Science >Remote Sensing of Seagrass Leaf Area Index and Species: The Capability of a Model Inversion Method Assessed by Sensitivity Analysis and Hyperspectral Data of Florida Bay
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Remote Sensing of Seagrass Leaf Area Index and Species: The Capability of a Model Inversion Method Assessed by Sensitivity Analysis and Hyperspectral Data of Florida Bay

机译:海草叶面积指数和物种的遥感:通过佛罗里达湾的敏感性分析和高光谱数据评估的模型反演方法的能力

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The capability for mapping two species of seagrass, Thalassia testudinium and Syringodium filiforme, by remote sensing using a physics based model inversion method was investigated. The model was based on a three-dimensional canopy model combined with a model for the overlying water column. The model included uncertainty propagation based on variation in leaf reflectances, canopy structure, water column properties and the air-water interface. The uncertainty propagation enabled both a-priori predictive sensitivity analysis of potential capability and the generation of per-pixel error bars when applied to imagery. A primary aim of the work was to compare the sensitivity analysis to results achieved in a practical application using airborne hyperspectral data, to gain insight on the validity of sensitivity analyses in general. Results showed that while the sensitivity analysis predicted a weak but positive discrimination capability for species, in a practical application the relevant spectral differences were extremely small compared to discrepancies in the radiometric alignment of the model with the imagery – even though this alignment was very good. Complex interactions between spectral matching and uncertainty propagation also introduced biases. Ability to discriminate LAI was good, and comparable to previously published methods using different approaches. The main limitation in this respect was spatial alignment with the imagery with in situ data, which was heterogeneous on scales of a few meters. The results provide insight on the limitations of physics based inversion methods and seagrass mapping in general. Complex models can degrade unpredictably when radiometric alignment of the model and imagery is not perfect and incorporating uncertainties can have non-intuitive impacts on method performance. Sensitivity analyses are upper bounds to practical capability, incorporating a term for potential systematic errors in radiometric alignment may be advisable. While Thalassia testudinium and Syringodium filiforme were too spectrally similar to be discriminated purely on spectral grounds, mapping of these and other species may be achievable by exploiting co-incident factors based on ecological zonation.
机译:研究了使用基于物理的模型反演方法通过遥感绘制海藻睾丸和丝状丁香两种海草的能力。该模型基于三维顶篷模型以及上覆水柱模型。该模型包括基于叶片反射率,冠层结构,水柱特性和空气-水界面变化的不确定性传播。当应用于图像时,不确定性传播既可以对潜在能力进行先验的预测灵敏度分析,也可以生成每像素误差线。这项工作的主要目的是将灵敏度分析与使用机载高光谱数据的实际应用中获得的结果进行比较,以了解总体上灵敏度分析的有效性。结果表明,尽管灵敏度分析预测的是物种的辨别能力较弱,但正向分辨能力强,但在实际应用中,与模型与图像的辐射线对齐方式相比,相关光谱差异非常小,即使这种对齐方式非常好。频谱匹配和不确定性传播之间的复杂相互作用也引入了偏差。区分LAI的能力很好,并且可以与以前使用不同方法发表的方法相媲美。在这方面的主要限制是与具有原位数据的图像的空间对齐,这在几米的尺度上是异类的。结果提供了对基于物理学的反演方法和海草制图的局限性的一般见解。当模型和图像的辐射线对齐不完美时,复杂的模型可能无法预测地退化,并且合并不确定性可能会对方法性能产生非直观的影响。灵敏度分析是实践能力的上限,建议在辐射测量对准中引入潜在系统误差的术语。尽管塔拉虫睾丸和丝状丁香在光谱上过于相似,以至于不能仅基于光谱基础进行区分,但通过利用基于生态区划的共同事件因素,可以对这些物种和其他物种进行制图。

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