Application of object-oriented classification method in the extraction of soil and water conservation measures
ZHAO Bohua1, WANG Xiuru1, YAN Shiyu1, ZHANG Yufei1, ZHANG Ting2
1. School of Soil and Water Conservation, Beijing Forestry University, 100083, Beijing, China; 2. Dalian Institute of Science and Technology, 116052, Dalian, Liaoning, China
Abstract：[Background] The combination of UAV remote sensing images and object-oriented classification methods is more and more widely used in soil and water conservation monitoring. This method can improve the efficiency and accuracy of the calculation after classification of the ground features in the project area. However, different object-oriented classification methods have different extraction accuracy for different ground features. Based on the water and soil conservation monitoring project of the Snowmobile Sled Center in the Yanqing Competition Area of the Beijing 2022 Winter Olympics, this study analyzed the accuracy difference of measure extraction in two areas within the monitoring range of soil and water conservation.[Methods] Based on UAV remote sensing image data and object-oriented classification methods, 5 classification methods, including membership function, the nearest neighbor classification, support vector machine (SVM), classification and regression tree (Cart) and random forest (RF) were adapted to extract the parameters of soil and water conservation measures. Three indicators, namely overall accuracy, Kappa coefficient, and producer accuracy (PA) were adapted to quantitatively evaluate the classification results by the five classification methods. Among them, the overall accuracy and Kappa coefficient were used to compare the overall classification accuracy, and PA was used to evaluate the classification accuracy of the specific land class.[Results] 1) The Kappa coefficients by 5 classification methods were all above 0.69, indicating that the classification effect was good. Among them, the overall classification accuracy of the two sections was better by SVM classification method. 2) The retaining wall was applicable to the nearest neighbor classification method with an accuracy of 71.42%. 3) The classification accuracy of SVM for vegetation and temporary coverage measures (bare soil) in the first area was better, and the PA was 93.25% and 80.0%. The better classification accuracy of weaving bags of surface soil and drainage ditch was also SVM classification method, the classification accuracy was 81.51% and 70.34%. The accuracy of the nearest neighbor classification method for the temporary coverage measures (bare soil) in the second area, temporary coverage measures (plants) and frame models of slope protection was better, with the accuracy of 73.94%, 76.23% and 66.37%, respectively.[Conculsions] The data comparison shows that SVM method has the best classification effect, and the results are higher than those by the other four classification methods. Therefore, the SVM method is more suitable for the extraction of measures in the soil and water conservation monitoring of UAV remote sensing images in this study. In addition, the Kappa coefficients by the 5 classification methods are all above 0.69, and all 5 classification methods have achieved good classification results. On the whole, the combination of UAV remote sensing image and object-oriented classification methods has a broad application prospect in soil and water conservation monitoring.
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ZHAO Bohua, WANG Xiuru, YAN Shiyu, ZHANG Yufei, ZHANG Ting. Application of object-oriented classification method in the extraction of soil and water conservation measures. SSWC, 2022, 20(1): 122-127.
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2022, Vol. 20 
