Object-Based Mapping of Gullies Using Optical Images: A Case Study in the Black Soil Region, Northeast of China

Abstract

Gully erosion is a widespread natural hazard. Gully mapping is critical to erosion monitoring and the control of degraded areas. The analysis of high-resolution remote sensing images (HRI) and terrain data mixed with developed object-based methods and field verification has been certified as a good solution for automatic gully mapping. Considering the availability of data, we used only open-source optical images (Google Earth images) to identify gully erosion through image feature modeling based on OBIA (Object-Based Image Analysis) in this paper. A two-end extrusion method using the optimal machine learning algorithm (Light Gradient Boosting Machine (LightGBM)) and eCognition software was applied for the automatic extraction of gullies at a regional scale in the black soil region of Northeast China. Due to the characteristics of optical images and the design of the method, unmanaged gullies and gullies harnessed in non-forest areas were the objects of extraction. Moderate success was achieved in the absence of terrain data. According to independent validation, the true overestimation ranged from 20% to 30% and was mainly caused by land use types with high erosion risks, such as bare land and farm lanes being falsely classified as gullies. An underestimation of less than 40% was adjacent to the correctly extracted gullied areas. The results of extraction in regions with geographical object categories of a low complexity were usually more satisfactory. The overall performance demonstrates that the present method is feasible for gully mapping at a regional scale, with high automation, low cost, and acceptable accuracy.

Achieving change through gully erosion research

“…However, further research is required to refine computational methods (e.g., Walker et al, 2021) and to develop monitoring routines. Image analysis based on machine learning holds promise for directly mapping gully occurrence across large areas from datasets that are more broadly available such as satellite imagery (Gafurov & Yermolayev, 2020; Wang et al, 2020), and current and historic air photographs (Ding et al, 2020; Phinzi et al, 2020).…”

Introducing GEOBIA to Landscape Imageability Assessment: A Multi-Temporal Case Study of the Nature Reserve “Kózki”, Poland

“…To further improve the process of selecting optimal SP values, an enhancement to ESP software was released as ESP2, an e-Cognition plug-in [36]. The plug-in was successfully used in several studies and is regarded as a credible tool [47][48][49][50] for automating image segmentation at three levels of detail.…”

Quantitative Attribution Analysis of the Spatial Differentiation of Gully Erosion in the Black Soil Region of Northeast China

“…At present, researchers in China and elsewhere are carrying out relevant research on the influencing factors of gully erosion [23][24][25][26]. According to previous studies, the distribution of gullies is related to topography, soil, climate, precipita-tion, vegetation, land use, and human activities, etc.…”