Classifying types of gully changes with unoccupied aircraft vehicles 3D multitemporal point clouds for training of satellite data analysis in Northwest Namibia

• Verfasst von: Vallejo Orti, Miguel [VerfasserIn]
• Verfasst von: Castillo, Carlos [VerfasserIn]
• Verfasst von: Zahs, Vivien [VerfasserIn]
• Verfasst von: Bubenzer, Olaf [VerfasserIn]
• Verfasst von: Höfle, Bernhard [VerfasserIn]
• Titel: Classifying types of gully changes with unoccupied aircraft vehicles 3D multitemporal point clouds for training of satellite data analysis in Northwest Namibia
• Verf.angabe: Miguel Vallejo, Carlos Castillo, Vivien Zahs, Olaf Bubenzer, Bernhard Höfle
• E-Jahr: 2024
• Jahr: 15 March 2024
• Umfang: 21 S.
• Illustrationen: Illustrationen
• Fussnoten: Zuerst veröffentlicht: 11. Januar 2024 ; Gesehen am 28.02.2024
• Titel Quelle: Enthalten in: Earth surface processes and landforms
• Ort Quelle: New York, NY [u.a.] : Wiley, 1976
• Jahr Quelle: 2024
• Band/Heft Quelle: 49(2024), 3 vom: März, Seite 1135-1155
• ISSN Quelle: 1096-9837
• DOI: doi:10.1002/esp.5759
• Datenträger: Online-Ressource
• Sprache: eng
• Bibliogr. Hinweis: Forschungsdaten: Vallejo Orti, Miguel, 1983 - : Classification of types of changes in gully environments using time series forest algorithm [data]
• Sach-SW: change detection
• Sach-SW: gully monitoring
• Sach-SW: machine learning
• Sach-SW: Namibia
• Sach-SW: radar time series
• Sach-SW: spatial aggregation
• Sach-SW: spatial classification
• K10plus-PPN: 1882047575

Abstract

The development of standardised data acquisition strategies and analytical workflows is crucial to quantify gully changes. In this study, we explore synergies between unoccupied aircraft vehicles (UAV) and satellite remote sensing in order to classify gully morphodynamics. Using Time Series Forest (TSF) and the Sentinel-1 radar backscatter coefficient (σo), gully scenarios can be classified into four categories: gully topographical change, no change outside gully, no change inside gully, and non-topographical change. In addition, a Random Forest (RF) classification is performed employing individual features obtained from elevation models and temporally aggregated datasets. Training data are generated from multitemporal UAV-borne photogrammetric point clouds through a manual segmentation of different gullies in Namibia. This information is transferred from point clouds (sub-m) to satellite imagery (10 m) generating training data at Sentinel-1 pixel level. Results indicate that the TSF (on the σo Vertical-Vertical polarisation) and RF (on temporally aggregated features) perform best when training and testing areas are located within the same geographical extent. Both approaches yield similar Total Accuracy (TA ≈ 79%-80%) and Cohen's Kappa value (Kappa ≈ 0.7), but TSF achieves superior Producer Accuracy (PA = 78.5%) and User Accuracy (UA = 84.6%) for the gully topographical change class. Additionally, the utilisation of TSF in Vertical-Vertical polarisation is the most effective method if the testing and training areas are in different geographical locations, allowing gully identification with TA > 80% and Kappa = 0.49. However, this method presents limitations to precisely delineate the change types, as dynamics are rain-driven and therefore are geographically related. In summary, by combining the complementary benefits of UAV-based and satellite-based solutions, this study opens a line of research for the study and classification of surface land dynamics and geomorphological feature extraction in regional extents.