Aghababaei Masoumeh, Ebrahimi Ataollah, Naghipour Ali Asghar, Asadi Esmaeil, Verrelst Jochem
Department of Range and Watershed Management, Faculty of Natural Resources and Earth Sciences, Shahrekord University, 8818634141 Shahrekord, Iran.
Image Processing Laboratory (IPL), Parc Científic, Universitat de València, 46980 Paterna, València, Spain.
Remote Sens (Basel). 2021 Aug 29;13(17):3433. doi: 10.3390/rs13173433.
Plant Ecological Unit's (PEUs) are the abstraction of vegetation communities that occur on a site which similarly respond to management actions and natural disturbances. Identification and monitoring of PEUs in a heterogeneous landscape is the most difficult task in medium resolution satellite images datasets. The main objective of this study is to compare pixel-based classification versus object-based classification for accurately classifying PEUs with four selected different algorithms across heterogeneous rangelands in Central Zagros, Iran. We used images of Landsat-8 OLI that were pan-sharpened to 15 m to classify four PEU classes based on a random dataset collected in the field (40%). In the first stage, we applied the following classification algorithms to distinguish PEUs: Minimum Distance (MD), Maximum Likelihood Classification (MLC), Neural Network-Multi Layer Perceptron (NN-MLP) and Classification Tree Analysis (CTA) for pixel based method and object based method. Then, by using the most accurate classification approach, in the second stage auxiliary data (Principal Component Analysis (PCA)) was incorporated to improve the accuracy of the PEUs classification process. At the end, test data (60%) were used for accuracy assessment of the resulting maps. Object-based maps clearly outperformed pixel-based maps, especially with CTA, NN-MLP and MD algorithms with overall accuracies of 86%, 72% and 59%, respectively. The MLC algorithm did not reveal any significant difference between the object-based and pixel-based analyses. Finally, complementing PCA auxiliary bands to the CTA algorithms offered the most successful PEUs classification strategy, with the highest overall accuracy (89%). The results clearly underpin the importance of object-based classification with the CTA classifier together with PCA auxiliary data to optimize identification of PEU classes.
植物生态单元（PEUs）是对出现在某一地点的植被群落的抽象，这些植被群落对管理措施和自然干扰有相似的反应。在异质景观中识别和监测PEUs是中等分辨率卫星图像数据集中最困难的任务。本研究的主要目的是比较基于像素的分类和基于对象的分类，以便使用四种选定的不同算法在伊朗扎格罗斯中部的异质牧场中准确分类PEUs。我们使用了经过全色锐化至15米的Landsat-8 OLI图像，根据在实地收集的随机数据集（40%）对四个PEU类别进行分类。在第一阶段，我们应用以下分类算法来区分PEUs：基于像素的方法和基于对象的方法的最小距离（MD）、最大似然分类（MLC）、神经网络-多层感知器（NN-MLP）和分类树分析（CTA）。然后，通过使用最准确的分类方法，在第二阶段纳入辅助数据（主成分分析（PCA））以提高PEUs分类过程的准确性。最后，使用测试数据（60%）对生成的地图进行准确性评估。基于对象的地图明显优于基于像素的地图，特别是使用CTA、NN-MLP和MD算法时，总体准确率分别为86%、72%和59%。MLC算法在基于对象的分析和基于像素的分析之间没有显示出任何显著差异。最后，将PCA辅助波段补充到CTA算法中提供了最成功的PEUs分类策略，总体准确率最高（89%）。结果清楚地表明了使用CTA分类器进行基于对象的分类以及PCA辅助数据对于优化PEU类别的识别的重要性。
