Multi-seasonal evaluation of hybrid poplar (P. Deltoides) plantations using Worldview-3 imagery and State-Of-The-Art ensemble learning algorithms

Colkesen I., Kavzoglu T., Atesoglu A., Tonbul H., Ozturk M. Y.

Advances in Space Research, 2022 (SCI-Expanded) identifier

  • Publication Type: Article / Article
  • Publication Date: 2022
  • Doi Number: 10.1016/j.asr.2022.10.044
  • Journal Name: Advances in Space Research
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Artic & Antarctic Regions, Communication Abstracts, Compendex, INSPEC, MEDLINE, Metadex, Civil Engineering Abstracts
  • Keywords: Categorical Boosting, Extreme Gradient Boosting, Fast-growing tree, OBIA, Poplar, Random Forest
  • Kocaeli University Affiliated: No


© 2022 COSPARForest resources are the primary components of the ecosystem environment. Poplars (Populus sp.), a member of the fast-growing trees, are one of the most productive forest tree species for industrial production thanks to their desirable traits comprising rapid growth, hybridization ability, and ease of propagation. Determining poplar cultivated areas and mapping their geographical distributions is critical for planners and decision-makers to increase the ecological and economic benefits of poplar trees. Due to the biodiversity of each geographical region and seasonal vegetation variations, classification based on remotely sensed imagery is essential for cropland monitoring. The main goal of this study is to evaluate the potential of high-resolution multi-temporal (growing season and end of the growing season) Worldview-3 imagery in mapping poplar plantations in the Akyazı district of Sakarya, Turkey. For this purpose, pixel- and object-based image analysis with up-to-date ensemble learning algorithms, namely random forest (RF), categorical boosting (CB), and extreme gradient boosting tree (XGB), were employed for mapping poplar fields. Results indicated that the object-based classification approach provided statistically significant improvements in map-level (about 4%) and class-level accuracy (e.g., approximately 7% and %2 for poplar and young poplar classes, respectively) than pixel-based classification. While the CB performed superior classification performance for the object-based approach (92.56%), the highest classification performance was obtained with the XGB algorithm for the pixel-based approach (90.42%) for the end of the growing season data. McNemar's statistical test also confirmed that the performances of CB and XGB algorithms were statistically similar in pixel-based classification. Finally, analysis of multi-season images revealed that sensitivity of the vegetation phenology and seasonal effects considerably affect the separability of poplar tree species.