Regional scale terrace mapping in fragmented mountainous areas using multi-source remote sensing data and sample purification strategy

As a stepped cross section of farmland built along the contour lines, terrace is widely distributed on hill -slopes. It changes the original surface slope and runoff coefficient, reduces soil nutrient loss, and has become the most important soil erosion control measure in China. Accurate terrace mapping at regional scale is crucial for soil conservation, agriculture sustainability and ecological planning. Due to the influence of cloudy and rainy weather, poor data availability makes it difficult to identify terrace distribution only using optical remote sensing images in mountainous areas. In this study, we incorporated multi -spectral optical and SAR data, features of terrain, texture and time sequence information, and proposed a pixel -based supervised classification method based on sample purification strategy to obtain a 10 m resolution terraced map in a plateau mountainous region. With 610 terrace/non-terrace validation sample data, 10 -fold cross -validation was used to test the classification results. For identified terrace, the values of Overall Accuracy (OA), Producer ' s Accuracy (PA) and User ' s Accuracy (UA) stay stable above 90 %, the F1 score and Kappa coefficient show the smallest fluctuation and is stable in the range of 0.90 - 0.93 and 0.81 - 0.87, respectively. The accuracy evaluation of grid units show that the uncertainty of the terrace distribution is mainly concentrated in the north and south of the study area. Slope cultivated land, low -slope terrace and non-agricultural vegetation are easily mixed due to the heterogeneity of terrace features and the spectrum similarity among these land types. It should be noted that the features of time series and texture play a key role in the terrace recognition process, rather than terrain factors, which is different