研究目的
To introduce and test an innovative, field-applicable methodology to detect heavy metal accumulation using drone-based photogrammetry and micro-rill network modelling.
研究成果
The study demonstrated that the TI and CTI indices can be used to predict points of heavy metal accumulation for small field catchments. The innovative methodology reduces the cost of soil sampling by focusing on points of maximum CTI (or TI) to determine sedimentation points of heavy metals.
研究不足
The study's methodology is limited by the need for highly accurate land-form mapping to apply indices at smaller scales. Additionally, the approach may not be as effective in soils with highly heterogeneous permeability.
1：Experimental Design and Method Selection
The study utilized drone-based photogrammetry and micro-rill network modelling to detect heavy metal accumulation. The methodology involved the use of a hexacopter equipped with fifth-generation software for photogrammetry to generate a high-resolution digital elevation model (DEM).
2：Sample Selection and Data Sources
The study area was the municipality of Trentola Ducenta, Caserta Province in the Campania region in southern Italy, covering an area of 4,500 m2 where a patchy occurrence of pollution of heavy metals and organic contaminants was expected.
3：List of Experimental Equipment and Materials
Tarot FY690s hexacopter frame, Canon PowerShot S100 camera, Trimble Total Station geodimeter, Agisoft Photoscan professional software, ArcGIS Software.
4：Experimental Procedures and Operational Workflow
The flight was programmed to ensure longitudinal and transversal overlap of the frames. The processing of aerial photos allowed the generation of a DEM. The DEM was then used to model the transport process and calculate the TI and CTI indices to predict heavy metal sedimentation points.
5：Data Analysis Methods
The analysis involved the calculation of the topographic index (TI) and the clima-topographic index (CTI) to predict heavy metal accumulation points. The effectiveness of these indices was evaluated based on their ability to identify sedimentation zones similar to wetlands.
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