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With the advantages of high accuracy, low cost, and flexibility, Unmanned Aerial Vehicle (UAV) images are now widely used in the fields of land survey, crop monitoring, and soil property prediction. Since the distribution of soil and landscape are closely related, this study makes use of the advantages of UAV images to classify the landscape to bui...
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... UAV is also equipped with Real-Time Kinematic (RTK) and TimeSync time synchronization systems, which can achieve centimeterlevel positioning. Among them, the camera resolution is about 2.12 million pixels, the focal length is 5.74 mm, and the spectral parameters are shown in Table 1. ...Context 2
... UAV is also equipped with Real-Time Kinematic (RTK) and TimeSync time synchronization systems, which can achieve centimeter-level positioning. Among them, the camera resolution is about 2.12 million pixels, the focal length is 5.74 mm, and the spectral parameters are shown in Table 1. Near InfraRed 840 52 ...Citations
... The geomorphology of the study area is mainly hilly, with an overall distribution of north-south-trending bar-shaped hills, large changes in elevation, and distinct topographic relief [21]. In the northeastern part of the study area, there is the Lunshan Reservoir and Gaoli Mountain [22], and the total elevation of the study area ranges from 28 m to 403 m. elevation, and distinct topographic relief [21]. In the northeastern part of the study area, there is the Lunshan Reservoir and Gaoli Mountain [22], and the total elevation of the study area ranges from 28 m to 403 m. ...
... In the northeastern part of the study area, there is the Lunshan Reservoir and Gaoli Mountain [22], and the total elevation of the study area ranges from 28 m to 403 m. elevation, and distinct topographic relief [21]. In the northeastern part of the study area, there is the Lunshan Reservoir and Gaoli Mountain [22], and the total elevation of the study area ranges from 28 m to 403 m. The land used in the area is primarily forest lands, followed by cultivated lands and gardens. ...
In digital soil mapping, machine learning models have been widely applied. However, the accuracy of machine learning models can be limited by the use of a single model and a small number of soil samples. This study introduces a novel method, semi-supervised classification combined with stacking learning (SSC-SL), to enhance soil classification mapping in hilly and low-mountain areas of Northern Jurong City, Jiangsu Province, China. This study incorporated Gaofen-2 (GF-2) remote sensing imagery along with its associated remote sensing indices, the ALOS Digital Elevation Model (DEM) and their derived topographic factors, and soil parent material data in its modelling process. We first used three base learners, Ranger, Rpart, and XGBoost, to construct the SL model. In addition, we employed the fuzzy c-means clustering algorithm (FCM) to construct a clustering map. To fully leverage the information from a multitude of environmental variables, understand the distribution of data, and enhance the effectiveness of the classification, we selected unlabelled samples near the boundaries of the patches on the clustering map. The SSC-SL model demonstrated superior stability and performance, with optimal accuracy at a 0.9 confidence level, achieving an overall accuracy of 0.77 and a kappa coefficient of 0.73. These metrics exceeded those of the highest performing base learner (Ranger model) by 10.4% and 12.3%, respectively, and they outperformed the least effective base learner (Rpart model) by 27.3% and 32.9%. It notably improves the spatial distribution accuracy of soil types. Key environmental variables influencing soil type distribution include soil parent material (SPM), land use (LU), the multi-resolution valley bottom flatness index (MRVBF), and Elevation (Ele). In conclusion, the SSC-SL model offers a novel and effective approach for enhancing the predictive accuracy of soil classification mapping.
