Morphology-based landslide monitoring with an unmanned aerial vehicle

PhD ThesisLandslides represent major natural phenomena with often disastrous consequences. Monitoring landslides with time-series surface observations can help mitigate such hazards. Unmanned aerial vehicles (UAVs) employing compact digital cameras, and in conjunction with Structure-from-Motion (SfM) and modern Multi-View Stereo (MVS) image matching approaches, have become commonplace in the geoscience research community. These methods offer a relatively low-cost and flexible solution for many geomorphological applications. The SfM-MVS pipeline has expedited the generation of digital elevation models at high spatio-temporal resolution. Conventionally ground control points (GCPs) are required for co-registration. This task is often expensive and impracticable considering hazardous terrain. This research has developed a strategy for processing UAV visible wavelength imagery that can provide multi-temporal surface morphological information for landslide monitoring, in an attempt to overcome the reliance on GCPs. This morphological-based strategy applies the attribute of curvature in combination with the scale-invariant feature transform algorithm, to generate pseudo GCPs. Openness is applied to extract relatively stable regions whereby pseudo GCPs are selected. Image cross-correlation functions integrated with openness and slope are employed to track landslide motion with subsequent elevation differences and planimetric surface displacements produced. Accuracy assessment evaluates unresolved biases with the aid of benchmark datasets. This approach was tested in the UK, in two sites, first in Sandford with artificial surface change and then in an active landslide at Hollin Hill. In Sandford, the strategy detected a ±0.120 m 3D surface change from three-epoch SfM-MVS products derived from a consumer-grade UAV. For the Hollin Hill landslide six-epoch datasets spanning an eighteen-month duration period were used, providing a ± 0.221 m minimum change. Annual displacement rates of dm-level were estimated with optimal results over winter periods. Levels of accuracy and spatial resolution comparable to previous studies demonstrated the potential of the morphology-based strategy for a time-efficient and cost-effective monitoring at inaccessible areas

Precision analysis of 3D camera

Three dimensional mapping is becoming an increasingly attractive product nowadays. Many devices like laser scanner or stereo systems provide 3D scene reconstruction. A new type of active sensor, the Time of Flight (ToF) camera obtains direct depth observations (3rd dimensional coordinate) in a high video rate, useful for interactive robotic and navigation applications. The high frame rate combined with the low weight and the compact design of the ToF cameras constitute an alternative solution of the 3D measuring technology. However a deep understanding of the error involved in the ToF camera observations is essential in order to upgrade their accuracy and enhance the ToF camera performance. This thesis work addresses the depth error characteristics of the SR4000 ToF camera and indicates potential error models for compensating the impact. In the beginning of the work the thesis investigates the error sources, their characteristics and how they influence the depth measurements. In the practical part, the work covers the above analysis via experiments. Last, the work proposes simple methods in order to reduce the depth error so that the ToF camera can be used for high accuracy applications.   An overall result of the work indicates that the depth acquired by the Time of Flight (ToF) camera deviates several centimeters, specifically the SR4000 camera provides 35 cm error size for the working range of 1-8 m. After the error compensation the depth offset fluctuates 15cm within the same working range. The error is smaller when the camera is set up close to the test field than when it is further away

A Framework for Water Security Data Gathering Strategies

At the international level, the term “water security” (WS) has gained increasing attention in recent decades. At the operational level, WS is assessed using tools that define the concept using a variety of dimensions and sub-dimensions, with qualitative and quantitative indicators and parameters. The breadth of tools and concepts is an obstacle to the operationalisation of the concept of water security (WS). Clearly, we need a range of diverse data to evaluate water security (WS). However, there are several barriers to designing an optimal Data Gathering Strategy (DGS). Such a strategy must strike a balance between a wide range of competing and overlapping data requirements and characteristics including: resources, information, and impact. The proposed framework aims at filling the existing gaps, not by providing a strict procedure, but instead acting as a “compass”: five interfaces between data and context are identified to orient practitioners towards an optimal DGS. The conceptual aim of the framework can be summarised as shifting the focus of the DGS from a “data-to-information approach” to a “data-to-action approach,” therefore stressing the importance of reaching key stakeholders with information. The specific aims of this paper are to: identify the key issues that should be addressed in designing a Data Gathering Strategy for Water Security (DGSxWS); communicate the key issues with a clear conceptual framework; and suggest approaches and activities that could help water practitioners in dealing with the issues identified