Mapping gully affected areas by using Sentinel 2 imagery and digital elevation model based on the Google Earth Engine - Huang, Xiaohui; Xiong, Liyang; Jiang, Yinghui; Li, Sijin; Liu, Kai; Ding, Hu; Tang, Guoan (Catena (Giessen), 2023)
•We proposed a novel OBIA-GEE method for mapping gully affected areas.•50 features from Sentinel 2 imagery and AW3D 30 DEM are used in OBIA-GEE.•OBIA-GEE considers the complex dynamic spectral characteristics of gully.•OBIA-GEE achieved good performance and robustness in mapping gully affected areas.•This research can be expanded globally since freely available dataset and platform. Gullies pose a great threat to the sustainable development of agricultural production and the ecological environment. Accurate mapping of gully affected areas plays an important role in regional environmental monitoring and management. In this paper, an object-based image analysis method based on the Google Earth Engine (OBIA-GEE) is proposed for mapping gully affected areas by using Sentinel 2 imagery and AW3D30 DEM data. The method utilizes the simple non-iterative clustering (SNIC) algorithm to segment the top-five principal component images derived from Sentinel-2 surface reflectance images. Then, the random forest (RF) algorithm is used for feature importance analysis and gully affected area mapping. Finally, the gully affected area mapping results are further optimized by establishing terrain skeleton lines. The proposed method is applied to five study areas with different landform types on the Chinese Loess Plateau, and the results show that the method achieves good performance in mapping gully affected areas, with an overall accuracy (OA) of 86.44%, an F score (F1) of 0.84, a user’s accuracy (UA) of 84.97%, and a producer’s accuracy (PA) of 83.90%. Compared with the OA of the results of a previous study (OA was 78.8%), the accuracy of the gully affected area mapping results herein improved by 7.64%. The RF feature importance results and findings of different feature combination cases demonstrate that textural information, time series spectral information, and topographic factors are important for mapping gully affected areas. Object-based and pixel-based classification results are compared, and the performance of the OBIA-GEE method is better than that of the pixel-based RF method. In addition, the OBIA-GEE method is highly efficient, which provides the possibility of applying the proposed method in large-scale studies. This research is beneficial to studies related to monitoring gullies and managing soil erosion.