We developed a three-step classification approach for forest road extraction utilizing LiDAR data. The first step employed the IDW method to interpolate LiDAR point data （first and last pulses） to achieve DSM, DTM a...We developed a three-step classification approach for forest road extraction utilizing LiDAR data. The first step employed the IDW method to interpolate LiDAR point data （first and last pulses） to achieve DSM, DTM and DNTM layers （at 1 m resolution）. For this interpolation RMSE was 0.19 m. In the second step, the Support Vector Machine （SVM） was employed to classify the LiDAR data into two classes, road and non-road. For this classification, SVM indicated the merged distance layer with intensity data and yielded better identification of the road position. Assessments of the obtained results showed 63% correctness, 75% completeness and 52% quality of classification. In the next step, road edges were defined in the LiDAR-extracted layers, enabling accu- rate digitizing of the centerline location. More than 95% of the Li- DAR-derived road was digitized within 1.3 m to the field surveyed nor- rnal. The proposed approach can provide thorough and accurate road inventory data to support forest management.展开更多
基金supported by Tarbiat Modares University(TMU)of Iran
文摘We developed a three-step classification approach for forest road extraction utilizing LiDAR data. The first step employed the IDW method to interpolate LiDAR point data （first and last pulses） to achieve DSM, DTM and DNTM layers （at 1 m resolution）. For this interpolation RMSE was 0.19 m. In the second step, the Support Vector Machine （SVM） was employed to classify the LiDAR data into two classes, road and non-road. For this classification, SVM indicated the merged distance layer with intensity data and yielded better identification of the road position. Assessments of the obtained results showed 63% correctness, 75% completeness and 52% quality of classification. In the next step, road edges were defined in the LiDAR-extracted layers, enabling accu- rate digitizing of the centerline location. More than 95% of the Li- DAR-derived road was digitized within 1.3 m to the field surveyed nor- rnal. The proposed approach can provide thorough and accurate road inventory data to support forest management.
