The identification of rock mass hazard sources is fundamental for preventing rockfall and landslide disasters in mountainous regions,with rock mass structural characteristics playing a vital role in hazard assessment....The identification of rock mass hazard sources is fundamental for preventing rockfall and landslide disasters in mountainous regions,with rock mass structural characteristics playing a vital role in hazard assessment.In this study,terrestrial laser scanning(TLS)and unmanned aerial vehicle(UAV)technologies were integrated to enhance the evaluation methodology for rock mass hazard sources,focusing on the Sichuan Yanjiang Expressway project in China.The findings demonstrate that TLS-UAV technology enhanced both spatial coverage and data density in slope modeling.Through integrated algorithmic analysis,rock discontinuities within heterogeneous datasets were systematically identified,enabling quantitative extraction and statistical analysis of key geometric parameters,including orientation,trace length,spacing,and roughness.Furthermore,quantitative models were developed for cohesion,friction angle and the morphology parameter M of in situ discontinuities,respectively,facilitating efficient mechanical parameter acquisition.A novel rock mass hazard index(RHI)was developed incorporating discontinuity geometric rating(DGR),discontinuity mechanical rating(DMR),and slope mass rating(SMR).Field validation confirmed the methodology's effectiveness in evaluating risk levels and spatial heterogeneity of rock mass hazard sources,revealing the contribution of different discontinuity sets to the rock mass hazard and identifying the primary discontinuity sets controlling instability mechanisms.This study is of great significance for evaluating discontinuity-controlled rock mass hazard sources and preventing rockfall disasters.展开更多
Unmanned aerial vehicle laser scanning(ULS)and terrestrial laser scanning(TLS)systems are effective ways to capture forest structures from top and side views,respectively.The registration of TLS and ULS data is a prer...Unmanned aerial vehicle laser scanning(ULS)and terrestrial laser scanning(TLS)systems are effective ways to capture forest structures from top and side views,respectively.The registration of TLS and ULS data is a prerequisite for a comprehensive forest structure representation.Conventional registration methods based on geometric features(e.g.,points,lines,and planes)are likely to fail due to the irregular natural point distributions of forest point clouds.Currently,automatic registration methods for forest point clouds typically rely on tree attributes(such as tree position and stem diameter).However,these methods are often unsuitable for forests with diverse compositions,complex terrains,irregular tree layouts,and insufficient common trees.In this study,an automated method is proposed to register ULS and TLS forest point clouds using ground points as registration primitives,which operates independently of tree attribute extraction and is estimated to reduce processing time by over 50%.A new evaluation method for registration accuracy evaluation is proposed,where transformation parameters from each TLS scan to the ULS obtained by the proposed registration algorithm are used to derive transformation parameters between TLS scans,which are then compared to reference parameters obtained using artificial spherical targets.Conventional ULS-TLS registration evaluation methods mostly rely on the manual corresponding points selection that is subject to inherent subjective errors,or control points in both TLS and ULS data that are difficult to collect.The proposed method presents an objective and accurate solution for ULS-TLS registration accuracy evaluation that effectively eliminates these limitations.The proposed method was tested on 12 plots with diverse stem densities,tree species,and altitudes located in a mountain forest.A total of 124 TLS scans were successfully registered to ULS data.The registration accuracy was assessed using both the conventional evaluation method and the proposed new evaluation method,with average rotation errors of 2.03 and 2.06 mrad,and average translation errors of 7.63 and 6.51 cm,respectively.The registration accuracies demonstrate that the proposed algorithm effectively and accurately registers TLS to ULS point clouds.展开更多
基金support from the National Natural Science Foundation of China(Grant Nos.42177142 and 52378477)the Key Research and Development Program of Shaanxi(Grant No.2023-YBSF-486).
文摘The identification of rock mass hazard sources is fundamental for preventing rockfall and landslide disasters in mountainous regions,with rock mass structural characteristics playing a vital role in hazard assessment.In this study,terrestrial laser scanning(TLS)and unmanned aerial vehicle(UAV)technologies were integrated to enhance the evaluation methodology for rock mass hazard sources,focusing on the Sichuan Yanjiang Expressway project in China.The findings demonstrate that TLS-UAV technology enhanced both spatial coverage and data density in slope modeling.Through integrated algorithmic analysis,rock discontinuities within heterogeneous datasets were systematically identified,enabling quantitative extraction and statistical analysis of key geometric parameters,including orientation,trace length,spacing,and roughness.Furthermore,quantitative models were developed for cohesion,friction angle and the morphology parameter M of in situ discontinuities,respectively,facilitating efficient mechanical parameter acquisition.A novel rock mass hazard index(RHI)was developed incorporating discontinuity geometric rating(DGR),discontinuity mechanical rating(DMR),and slope mass rating(SMR).Field validation confirmed the methodology's effectiveness in evaluating risk levels and spatial heterogeneity of rock mass hazard sources,revealing the contribution of different discontinuity sets to the rock mass hazard and identifying the primary discontinuity sets controlling instability mechanisms.This study is of great significance for evaluating discontinuity-controlled rock mass hazard sources and preventing rockfall disasters.
基金supported partially by the National Key Research and Development Program of China(No.2023YFF1303901)the National Natural Science Foundation of China(Nos.32171789,12411530088,and 32371654)the Joint Open Funded Project of State Key Laboratory of Geo-Information Engineering and Key Laboratory of the Ministry of Natural Resources for Surveying and Mapping Science and Geo-spatial Information Technology(No.2022-02-02).
文摘Unmanned aerial vehicle laser scanning(ULS)and terrestrial laser scanning(TLS)systems are effective ways to capture forest structures from top and side views,respectively.The registration of TLS and ULS data is a prerequisite for a comprehensive forest structure representation.Conventional registration methods based on geometric features(e.g.,points,lines,and planes)are likely to fail due to the irregular natural point distributions of forest point clouds.Currently,automatic registration methods for forest point clouds typically rely on tree attributes(such as tree position and stem diameter).However,these methods are often unsuitable for forests with diverse compositions,complex terrains,irregular tree layouts,and insufficient common trees.In this study,an automated method is proposed to register ULS and TLS forest point clouds using ground points as registration primitives,which operates independently of tree attribute extraction and is estimated to reduce processing time by over 50%.A new evaluation method for registration accuracy evaluation is proposed,where transformation parameters from each TLS scan to the ULS obtained by the proposed registration algorithm are used to derive transformation parameters between TLS scans,which are then compared to reference parameters obtained using artificial spherical targets.Conventional ULS-TLS registration evaluation methods mostly rely on the manual corresponding points selection that is subject to inherent subjective errors,or control points in both TLS and ULS data that are difficult to collect.The proposed method presents an objective and accurate solution for ULS-TLS registration accuracy evaluation that effectively eliminates these limitations.The proposed method was tested on 12 plots with diverse stem densities,tree species,and altitudes located in a mountain forest.A total of 124 TLS scans were successfully registered to ULS data.The registration accuracy was assessed using both the conventional evaluation method and the proposed new evaluation method,with average rotation errors of 2.03 and 2.06 mrad,and average translation errors of 7.63 and 6.51 cm,respectively.The registration accuracies demonstrate that the proposed algorithm effectively and accurately registers TLS to ULS point clouds.
