Berhane Tedros M, Lane Charles R, Wu Qiusheng, Anenkhonov Oleg A, Chepinoga Victor V, Autrey Bradley C, Liu Hongxing
Pegasus Technical Services, Inc., c/o U.S. Environmental Protection Agency, Cincinnati, OH 45219, USA.
U.S. Environmental Protection Agency, Office of Research and Development, Cincinnati, OH 45268, USA.
Remote Sens (Basel). 2018;10(1):46. doi: 10.3390/rs10010046.
Wetland ecosystems straddle both terrestrial and aquatic habitats, performing many ecological functions directly and indirectly benefitting humans. However, global wetland losses are substantial. Satellite remote sensing and classification informs wise wetland management and monitoring. Both pixel- and object-based classification approaches using parametric and non-parametric algorithms may be effectively used in describing wetland structure and habitat, but which approach should one select? We conducted both pixel- and object-based image analyses (OBIA) using parametric (Iterative Self-Organizing Data Analysis Technique, ISODATA, and maximum likelihood, ML) and non-parametric (random forest, RF) approaches in the Barguzin Valley, a large wetland (~500 km) in the Lake Baikal, Russia, drainage basin. Four Quickbird multispectral bands plus various spatial and spectral metrics (e.g., texture, Non-Differentiated Vegetation Index, slope, aspect, etc.) were analyzed using field-based regions of interest sampled to characterize an initial 18 ISODATA-based classes. Parsimoniously using a three-layer stack (Quickbird band 3, water ratio index (WRI), and mean texture) in the analyses resulted in the highest accuracy, 87.9% with pixel-based RF, followed by OBIA RF (segmentation scale 5, 84.6% overall accuracy), followed by pixel-based ML (83.9% overall accuracy). Increasing the predictors from three to five by adding Quickbird bands 2 and 4 decreased the pixel-based overall accuracy while increasing the OBIA RF accuracy to 90.4%. However, McNemar's chi-square test confirmed no statistically significant difference in overall accuracy among the classifiers (pixel-based ML, RF, or object-based RF) for either the three- or five-layer analyses. Although potentially useful in some circumstances, the OBIA approach requires substantial resources and user input (such as segmentation scale selection-which was found to substantially affect overall accuracy). Hence, we conclude that pixel-based RF approaches are likely satisfactory for classifying wetland-dominated landscapes.
湿地生态系统横跨陆地和水生生境，执行许多直接和间接造福人类的生态功能。然而，全球湿地流失情况严重。卫星遥感和分类有助于明智地进行湿地管理和监测。使用参数化和非参数化算法的基于像素和基于对象的分类方法都可有效地用于描述湿地结构和栖息地，但应选择哪种方法呢？我们在俄罗斯贝加尔湖流域的一个大型湿地（约500平方公里）巴尔古津山谷，使用参数化方法（迭代自组织数据分析技术，ISODATA，和最大似然法，ML）和非参数化方法（随机森林，RF）进行了基于像素和基于对象的图像分析（OBIA）。利用实地采样的感兴趣区域分析了四个快鸟多光谱波段以及各种空间和光谱指标（如纹理、非分化植被指数、坡度、坡向等），以表征最初基于ISODATA的18个类别。分析中简约地使用三层叠加（快鸟波段3、水比指数（WRI）和平均纹理）可得到最高精度，基于像素的RF为87.9%，其次是基于对象的RF（分割尺度5，总体精度84.6%），然后是基于像素的ML（总体精度83.9%）。通过添加快鸟波段2和4将预测变量从三个增加到五个，降低了基于像素的总体精度，同时将基于对象的RF精度提高到90.4%。然而，麦克尼马尔卡方检验证实，对于三层或五层分析，分类器（基于像素的ML、RF或基于对象的RF）之间的总体精度在统计上没有显著差异。尽管在某些情况下可能有用，但基于对象的图像分析方法需要大量资源和用户输入（如分割尺度选择，发现其对总体精度有重大影响）。因此，我们得出结论，基于像素的RF方法可能足以用于对以湿地为主的景观进行分类。
