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Mapping gully affected areas based on Sentinel 2 imagery and digital elevation model

Authors: HUANG XiaoHui; XIONG LiYang;

Mapping gully affected areas based on Sentinel 2 imagery and digital elevation model

Abstract

Gully poses a great threat to agricultural production and ecological environment. Mapping accurate gully affected areas plays an important role in regional environmental monitoring and management. In this paper, an object-based image analysis method based on Google Earth Engine (OBIA-GEE) for mapping gully affected areas was proposed by using Sentinel 2 imagery and AW3D 30 DEM data. The method utilized the simple non-iterative clustering (SNIC) segmentation and the random forest (RF) algorithm to segment imagery and map gully affected areas. And terrain skeleton lines were further used to optimize the mapping results. The proposed method was applied to five study areas with different landform types on the Chinese Loess Plateau, and the results showed that the method achieved good performance with the overall accuracy of 86.44%, user’s accuracy of 84.97%, and producer’s accuracy of 83.90%. The OBIA-GEE method provides the possibility of large-scale gullies mapping, which is beneficial to monitor gullies and manage soil erosion.

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Keywords

Object-based image analysis, Gully affected area mapping, Google Earth Engine

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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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