Invasive Saltcedar (Tamarisk spp.) Distribution Mapping Using Multiresolution Remote Sensing Imagery
Invasive Saltcedar (Tamarisk spp.) Distribution Mapping Using Multiresolution Remote Sensing Imagery
复制标题
使用多分辨率遥感图像绘制入侵性盐杉（柽柳属）分布图
DOI：
10.1080/00330124.2012.679440
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发表时间：
2013
期刊：
影响因子：
--
通讯作者：
Amy E. Frazier
中科院分区：
文献类型：
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作者：
Le Wang;J. L. Silván;Jun Yang;Amy E. Frazier
盐柏通常被认为是美国最具威胁性的入侵物种之一，并有可能在未来十年造成巨大的环境危害。及时准确绘制盐柏分布和丰富度的地图在协助有效控制方面发挥着核心作用。目前的研究主要集中在粗分辨率遥感数据的大面积检测。在这项研究中，一个全面的测试的设计和进行了检查的能力，将多时间和多分辨率的图像区分saltcedar从其他河岸植被类型在得克萨斯州的格兰德河盆地，包括非常高的空间分辨率（QuickBird），高光谱分辨率图像（AISA），和中等分辨率的卫星图像（Landsat TM）。完成了两种类型的分析。首先，采用五种基于像素的分类方法分别评估QuickBird和AISA识别盐柏的有效性;即最大似然分类器（MLC），神经网络分类器（NNC），支持向量机（SVM），光谱角映射器（SAM）和最大匹配特征（MMF）。第二，Landsat TM图像合成AISA和测试映射盐杉丰富的四个线性光谱分解方法和三个反向传播神经网络方法。结果表明，AISA优于QuickBird图像区分盐雪松从其他河岸植被物种。在这五种分类器中，SVM的分类精度最高。线性光谱解混方法表现出类似的映射精度神经网络方法在30 × 30 m2的空间分辨率，但具有显着更好的计算效率，以估计丰富的saltcedar。
Saltcedar is commonly recognized as one of the most threatening invasive species in the United States and has the potential to cause great environmental harm over the coming decade. Accurate mapping of saltcedar distribution and abundance in a timely manner plays a central role in assisting with effective control. Current studies have mostly concentrated on large-area detection with coarse-resolution remote sensing data. In this study, a comprehensive test was designed and carried out to examine the ability to integrate multitemporal and multiresolution imagery for differentiating saltcedar from other riparian vegetation types in the Rio Grande basin of Texas, including very high spatial resolution (QuickBird), hyperspectral resolution imagery (AISA), and moderate resolution satellite imagery (Landsat TM). Two types of analyses were fulfilled. First, five pixel-based classification methods were adopted for assessing the effectiveness of QuickBird and AISA for discerning saltcedar, respectively; that is, the maximum likelihood classifier (MLC), neural network classifier (NNC), support vector machine (SVM), spectral angle mapper (SAM), and maximum matching feature (MMF). Second, Landsat TM imagery was synthesized from AISA and tested for mapping the abundance of saltcedar with four linear spectral unmixing methods and three back-propagation neural network methods. Results indicate that AISA outperformed QuickBird imagery in differentiating saltcedar from other riparian vegetation species. SVM achieved the highest classification accuracy among the five classifiers. Linear spectral unmixing methods exhibited similar mapping accuracy to neural network methods in estimating the abundance of saltcedar at a spatial resolution of 30 by 30 m2 but with significantly better computing efficiency.