Substantial development has been achieved in snow cover delineations through binary mapping techniques. Continuous efforts for development and institution of methodologies in fractional snow cover mapping are steadily...Substantial development has been achieved in snow cover delineations through binary mapping techniques. Continuous efforts for development and institution of methodologies in fractional snow cover mapping are steadily conducted by the research communities. In this work, the attempts are driven towards the attainment of the same. MODIS (Moderate Resolution Imaging Spectroradiometer) images are worked upon Landsat 8 images under multivariate polynomial regression schemes utilizing corresponding count of pixels in a test region of Himachal Pradesh. 11.00 μm centered waveband is employed to develop a scheme for snow mapping followed by a qualitative and quantitative comparison with NDSI and S3 snow index where the values of correlation coefficient between fractional snow cover and index values have been obtained as 77.04%, 78.82% and 85.15% for NDSI, S3 and our scheme respectively. Exponential empirical relationships have been tried to be employed to attain improvements in prediction of snow cover followed by a test of correlation between true and theoretical fractional snow cover values. An improvement in degree of correlation is obtained over the conventional methodologies which serves for the verification of scheme employed and empirical relationship defined, collectively. The results provide a scope for improvements and investigations in the subject of fractional snow cover mapping.展开更多
Utilizing multispectral satellite data and digital elevation models (DEMs) has emerged as the primary approach for cartographically representing landforms. By using high-resolution satellite photos that capture spatia...Utilizing multispectral satellite data and digital elevation models (DEMs) has emerged as the primary approach for cartographically representing landforms. By using high-resolution satellite photos that capture spatial, temporal, spectral, and radiometric data, one may get a fresh comprehension of the geomorphology of a particular area by recognizing its landforms. In addition, a synergistic method is used by using data produced from digital elevation models (DEMs) such as Slope, Aspect, Hillshade, Curvature, Contour Patterns, and 3-D Flythrough Visuals. The increasing use of UAV (drone) technology for obtaining high-resolution digital images and elevation models has become an essential element in developing complete topographic models in landslide scars that are very unstable and prone to erosion. Comparison (differences in values) of seven (7) different DEMs between two algorithms used, i.e., QGIS and White Box Tool (WBT), were successfully attempted in the present research. The TLS, UAV and Satellite data of the study area—Kshetrapal Landslide, Chamoli (District), Uttarakhand (State), India was subjected to two different algorithms (QGIS and WBT) to evaluate and differentiate seven different DEMs (CARTOSAT, ASTER, SRTM, Alos 3D, TanDEM, MERIT, and FabDEM/FATHOM) taking into consideration various parameters viz. Aspect, Hillshade, Slope, Mean Curvature, Plan Curvature, Profile Curvature and Total Curvature. The different values of aforesaid parameters of various DEMs evaluated (using algorithms QIGS and WBT) reveal that only three parameters, i.e., Aspect, Hillshade, and Slope, show results. In contrast, the remaining ones do not show any meaningful results, and therefore, the comparison was possible only with regard to these three parameters. The comparison is drawn by comparing minimum, maximum, and elevation values (by subtracting WBT values from QGIS values) regarding Aspect, Hillshade, and Slope, arranging the differences in values as per their importance. (Increasing or decreasing order), assigning merit scores individually, and then cumulatively, and ascertaining the order of application suitability of various Dems, which stand in the order of (CARTOSAT, ASTER, SRTM, Alos 3D, TanDEM, and MERIT, and FabDEM/FATHOM).展开更多
This study presents an integrated TLS-UAV photogrammetry approach for monitoring the Kshetrapal landslide in Chamoli,Uttarakhand,India,where steep slopes(45˚-65˚)and dense vegetation challenge conventional methods.By ...This study presents an integrated TLS-UAV photogrammetry approach for monitoring the Kshetrapal landslide in Chamoli,Uttarakhand,India,where steep slopes(45˚-65˚)and dense vegetation challenge conventional methods.By combining terrestrial laser scanning(TLS)and unmanned aerial vehicle(UAV)photogrammetry,the method enhances ground control point(GCP)density in visible areas while employing assumed control points(ACPs)de-rived from TLS data to reconstruct UAV models in obscured zones.The New-ton integration model quantifies discrepancies between coordinate displace-ment(CD)and real displacement(RD),achieving sub-pixel accuracy(errors:0.0235-0.8021 pixels)validated through field data.Key results include a 40%improvement in spatial coverage through TLS-UAV fusion,reduced RMSE to 0.2 m in fused point clouds,and detection of monsoon-induced displacements up to 1.7 m during the 2023 reactivation.The approach demonstrates 26%higher accuracy than traditional GCP-dependent methods in Himalayan con-ditions,offering a replicable framework for landslide risk management in ge-ologically sensitive regions.Validated through multi-temporal datasets and real-world scenarios,this methodology addresses critical gaps in accessibility and precision,providing actionable insights for disaster management agencies like the Uttarakhand State Disaster Management Authority.展开更多
文摘Substantial development has been achieved in snow cover delineations through binary mapping techniques. Continuous efforts for development and institution of methodologies in fractional snow cover mapping are steadily conducted by the research communities. In this work, the attempts are driven towards the attainment of the same. MODIS (Moderate Resolution Imaging Spectroradiometer) images are worked upon Landsat 8 images under multivariate polynomial regression schemes utilizing corresponding count of pixels in a test region of Himachal Pradesh. 11.00 μm centered waveband is employed to develop a scheme for snow mapping followed by a qualitative and quantitative comparison with NDSI and S3 snow index where the values of correlation coefficient between fractional snow cover and index values have been obtained as 77.04%, 78.82% and 85.15% for NDSI, S3 and our scheme respectively. Exponential empirical relationships have been tried to be employed to attain improvements in prediction of snow cover followed by a test of correlation between true and theoretical fractional snow cover values. An improvement in degree of correlation is obtained over the conventional methodologies which serves for the verification of scheme employed and empirical relationship defined, collectively. The results provide a scope for improvements and investigations in the subject of fractional snow cover mapping.
文摘Utilizing multispectral satellite data and digital elevation models (DEMs) has emerged as the primary approach for cartographically representing landforms. By using high-resolution satellite photos that capture spatial, temporal, spectral, and radiometric data, one may get a fresh comprehension of the geomorphology of a particular area by recognizing its landforms. In addition, a synergistic method is used by using data produced from digital elevation models (DEMs) such as Slope, Aspect, Hillshade, Curvature, Contour Patterns, and 3-D Flythrough Visuals. The increasing use of UAV (drone) technology for obtaining high-resolution digital images and elevation models has become an essential element in developing complete topographic models in landslide scars that are very unstable and prone to erosion. Comparison (differences in values) of seven (7) different DEMs between two algorithms used, i.e., QGIS and White Box Tool (WBT), were successfully attempted in the present research. The TLS, UAV and Satellite data of the study area—Kshetrapal Landslide, Chamoli (District), Uttarakhand (State), India was subjected to two different algorithms (QGIS and WBT) to evaluate and differentiate seven different DEMs (CARTOSAT, ASTER, SRTM, Alos 3D, TanDEM, MERIT, and FabDEM/FATHOM) taking into consideration various parameters viz. Aspect, Hillshade, Slope, Mean Curvature, Plan Curvature, Profile Curvature and Total Curvature. The different values of aforesaid parameters of various DEMs evaluated (using algorithms QIGS and WBT) reveal that only three parameters, i.e., Aspect, Hillshade, and Slope, show results. In contrast, the remaining ones do not show any meaningful results, and therefore, the comparison was possible only with regard to these three parameters. The comparison is drawn by comparing minimum, maximum, and elevation values (by subtracting WBT values from QGIS values) regarding Aspect, Hillshade, and Slope, arranging the differences in values as per their importance. (Increasing or decreasing order), assigning merit scores individually, and then cumulatively, and ascertaining the order of application suitability of various Dems, which stand in the order of (CARTOSAT, ASTER, SRTM, Alos 3D, TanDEM, and MERIT, and FabDEM/FATHOM).
文摘This study presents an integrated TLS-UAV photogrammetry approach for monitoring the Kshetrapal landslide in Chamoli,Uttarakhand,India,where steep slopes(45˚-65˚)and dense vegetation challenge conventional methods.By combining terrestrial laser scanning(TLS)and unmanned aerial vehicle(UAV)photogrammetry,the method enhances ground control point(GCP)density in visible areas while employing assumed control points(ACPs)de-rived from TLS data to reconstruct UAV models in obscured zones.The New-ton integration model quantifies discrepancies between coordinate displace-ment(CD)and real displacement(RD),achieving sub-pixel accuracy(errors:0.0235-0.8021 pixels)validated through field data.Key results include a 40%improvement in spatial coverage through TLS-UAV fusion,reduced RMSE to 0.2 m in fused point clouds,and detection of monsoon-induced displacements up to 1.7 m during the 2023 reactivation.The approach demonstrates 26%higher accuracy than traditional GCP-dependent methods in Himalayan con-ditions,offering a replicable framework for landslide risk management in ge-ologically sensitive regions.Validated through multi-temporal datasets and real-world scenarios,this methodology addresses critical gaps in accessibility and precision,providing actionable insights for disaster management agencies like the Uttarakhand State Disaster Management Authority.
