Unmanned Aerial Vehicle (UAV) photogrammetry is increasingly used for local-scale mapping because it enables rapid generation of high-resolution orthophotos and elevation products. However, the positional and vertical quality of these products depends strongly on the georeferencing strategy, particularly the use of Ground Control Points (GCPs). This study evaluates the effect of GCPs on the accuracy of UAV-derived orthophoto, Digital Surface Model (DSM), and Digital Terrain Model (DTM) at the premises of the Land Management Training Center (LMTC), Dhulikhel, Nepal. The same UAV image block of 334 images was processed under two workflows: one using only onboard image geolocation and another using five surveyed GCPs. Eight independent checkpoints were used for accuracy assessment. Orthophoto planimetric accuracy was evaluated using checkpoint coordinates, while vertical agreement of DSM and DTM was assessed using surveyed ground elevations. In addition, pairwise checkpoint-distance analysis and supplementary object-based comparison were used to examine relative geometric differences in the orthophotos. The with-GCP orthophoto achieved a horizontal RMSEH of 0.063 m, whereas the without-GCP orthophoto showed an RMSEH of 3.507 m. Similarly, the with-GCP DSM and DTM achieved RMSEZ values of 0.131 m and 0.143 m, respectively, compared with 15.385 m and 15.347 m in the without-GCP workflow. The without-GCP orthophoto also exhibited systematic westward and northward displacement and minor scale-related geometric differences. The results demonstrate that GCP-based processing remains essential for reliable campus-scale UAV photogrammetry when the outputs are intended for measurement, terrain representation, planning, and other applications requiring dependable absolute accuracy.
B. Bisht, Nabraj Subedi, Ram Kumar Sapkota et al.· Journal of Land Management a...· 0 citations
Landslides are recurrent geomorphic hazards in the Nepal Himalaya, where fragile geology, steep terrain, monsoonal rainfall and expanding road construction increase slope instability. This study assesses landslide susceptibility and relative risk in Tamakoshi Rural Municipality, Dolakha, Nepal, by comparing three GIS-based bivariate models: frequency ratio (FR), Shannon entropy (SE) and statistical information index (SII). A landslide inventory of 121 events was prepared from Google Earth imagery, satellite-image interpretation and field verification, and divided into 70% training and 30% validation subsets. Ten conditioning factors were analysed at 30 m spatial resolution: slope, aspect, curvature, elevation, topographic wetness index, lithology, soil type, land use/land cover, distance from roads and distance from rivers. Model discrimination was evaluated using receiver operating characteristic-area under the curve analysis. Landslides were concentrated on steep, mid-elevation slopes, particularly within 250 m of rivers and 100 m of roads, indicating the influence of fluvial undercutting, road excavation and drainage disturbance. SII produced the highest success and prediction AUC values (0.654 and 0.628), followed by FR (0.645 and 0.623) and SE (0.634 and 0.613), although all three models showed only modest discrimination. An AHP-based exposure–vulnerability index was prepared from settlement, population, road, school, hospital and temple indicators. The resulting relative risk zonation identified approximately 15% of the municipality as high or very high risk, mainly in wards 1, 3 and 5. The findings provide a spatial basis for field prioritization, risk-sensitive land-use planning and local disaster risk reduction.
Nabaraj Bajagain, B. Bisht· American Journal of Applied...· 0 citations