Ground control point (GCP) is an important calibration factor when correcting position information during unmanned aerial vehicle (UAV) remote sensing. Studies of the optimal number and distribution shape of GCPs have been conducted worldwide in recent years. However, when conducting surveys at houses, construction sites, farming lands, forests, and some other locations, it is both difficult and destructive to install GCP inside the subject area. In many cases, it is only possible to install GCP at the outer edge around the area. Therefore, this study aims to suggest the optimal GCP distribution pattern, which can provide the highest accuracy, when only the outer edge of a particular area is available. In this research, 88 GCP patterns have been validated and compared at an 18 ha farm. Results show that the patterns with GCPs distributed evenly around the field provided the best calibration (RMSE = 0.15 m). If this kind of pattern is not achievable because of obstructions, patterns with GCPs distributed evenly around half of the field or forming an evenly distributed triangle can provide moderate accuracy (RMSE = 0.18 m and 0.43 m, respectively). Patterns with GCPs forming a straight line yielded the worst accuracy (RMSE = 2.10 m). This shows that GCP distributions of a two-dimensional shape, even if the surrounding area is small, are better calibrated than a long, straight line. These results strongly suggest that appropriate GCP distribution patterns in the study areas will provide satisfactory accuracy for constructing integrated monitoring systems of diverse resources.

Identification and monitoring of diverse resources or wastes on the ground is important for integrated resource management. The unmanned aerial vehicle (UAV), with its high resolution and facility, is the optimal tool for monitoring ground objects accurately and efficiently. However, previous studies have focused on applying classification methodology on land use and agronomy, and few studies have compared different classification methods using UAV imagery. It is necessary to fully utilize the high resolution of UAV by applying the classification methodology to ground object identification. This study compared three classification methods: A. NDVI threshold, B. RGB image-based machine learning, and C. object-based image analysis (OBIA). Method A was the least time-consuming and could identify vegetation and soil with high accuracy (user’s accuracy > 0.80), but had poor performance at classifying dead vegetation, plastic, and metal (user’s accuracy < 0.50). Both Methods B and C were time- and labor-consuming, but had very high accuracy in separating vegetation, soil, plastic, and metal (user’s accuracy ≥ 0.70 for all classes). Method B showed a good performance in identifying objects with bright colors, whereas Method C showed a high ability in separating objects with similar visual appearances. Scientifically, this study has verified the possibility of using the existing classification methods on identifying small ground objects with a size of less than 1 m, and has discussed the reasons for the different accuracy of the three methods. Practically, these results help users from different fields to choose an appropriate method that suits their target, so that different wastes or multiple resources can be monitored at the same time by combining different methods, which contributes to an improved integrated resource management system.

Image-based 3D reconstruction technology has been widely used in automatic driving, medical image reconstruction, positioning and navigation. At present, the digital design is still dominated by drawing 3D models manually, which has some shortcomings such as time-consuming and limited accuracy. To solve the above problems and improve the efficiency of digital design, a learning-based multi-view stereo 3D reconstruction technique is introduced for dense reconstruction, and then a grid model is generated for parametric design. We experimented our method and the results show that it can greatly improve the efficiency of 3D digital design, obtain high precision, and retain the texture information of the object.

Hydrological non-stationarity is increasingly disrupting rainfall–runoff relationships in tropical agricultural watersheds, elevating flood risk and complicating water management in sub-Saharan Africa, where hydrometric data remain limited. Flood assessment in such basins often remains rainfall-centric, despite growing evidence that internal basin structure regulates streamflow response. This study applies continuous semi-distributed HEC-HMS modelling to diagnose how sub-basin configuration governs flood generation, flow partitioning, and spatial flood exposure in the Diamphwe watershed, central Malawi. Runoff–baseflow partitioning metrics, flow-duration characteristics, and peak-flow diagnostics were integrated into a composite Flood Contribution Index (FCI). These hydrological diagnostics were further linked with riparian geomorphological analysis. A targeted sensitivity experiment evaluated the influence of storage and flow-timing parameters in runoff-dominated sub-basins. Results indicate that runoff-dominated sub-basins disproportionately amplify downstream flood peaks, whereas storage-supported units sustain baseflow and moderate hydrological extremes. Increasing internal storage redistributes seasonal flow and attenuates wet-season peaks but does not increase total water yield. Integration with geomorphological analysis identifies river reaches where upstream hydrological dominance coincides with channel openness, concentrating flood exposure. These findings demonstrate that flood response is governed by internal basin configuration beyond rainfall forcing alone. The study provides a transferable, process-based framework for identifying dominant flood-generating areas and enabling spatially targeted flood management in data-limited tropical agricultural watersheds.