Classification of Plant Ecological Units in Heterogeneous Semi-Steppe Rangelands: Performance Assessment of Four Classification Algorithms

• Published in: Remote sensing (Basel, Switzerland) Switzerland), 2021-08, Vol.13 (17), p.3433-3433
• Main Authors: Aghababaei, Masoumeh, Ebrahimi, Ataollah, Naghipour, Ali Asghar, Asadi, Esmaeil, Verrelst, Jochem
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial intelligence; Classification; Datasets; Flowers & plants; Image classification; Landsat; Landsat satellites; Machine learning; machine learning algorithms; Methods; Natural disturbance; Neural networks; object-based classification; Performance assessment; Pixels; plant ecological units mapping; principal component analysis; Principal components analysis; Rangelands; Remote sensing; Satellite imagery; Steppes; Vegetation mapping
• ISSN: 2072-4292; 2072-4292
• 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.