A COMPARISON of TREE-BASED ALGORITHMS for COMPLEX WETLAND CLASSIFICATION USING the GOOGLE EARTH ENGINE
Küçük Resim Yok
Tarih
2021
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
International Society for Photogrammetry and Remote Sensing
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Wetlands are endangered ecosystems that are required to be systematically monitored. Wetlands have significant contributions to the well-being of human-being, fauna, and fungi. They provide vital services, including water storage, carbon sequestration, food security, and protecting the shorelines from floods. Remote sensing is preferred over the other conventional earth observation methods such as field surveying. It provides the necessary tools for the systematic and standardized method of large-scale wetland mapping. On the other hand, new cloud computing technologies for the storage and processing of large-scale remote sensing big data such as the Google Earth Engine (GEE) have emerged. As such, for the complex wetland classification in the pilot site of the Avalon, Newfoundland, Canada, we compare the results of three tree-based classifiers of the Decision Tree (DT), Random Forest (RF), and Extreme Gradient Boosting (XGB) available in the GEE code editor using Sentinel-2 images. Based on the results, the XGB classifier with an overall accuracy of 82.58% outperformed the RF (82.52%) and DT (77.62%) classifiers. © Author(s) 2021. CC BY 4.0 License.
Açıklama
6th International Conference on Smart City Applications -- 27 October 2021 through 29 October 2021 -- Safranbolu -- 175815
Anahtar Kelimeler
Big data, Decision Tree, Extreme Gradient Boosting, Google Earth Engine, Random Forest, Sentinel Imagery, Wetland Mapping
Kaynak
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
WoS Q Değeri
Scopus Q Değeri
N/A
Cilt
46
Sayı
4/W5-2021
