2.2 UAV Very High Resolution imagery acquisition and processing
Wetlands are difficult features to map from the ground because of the complexity of the landscape, especially during months when they are flooded. In order to map a wetland with accuracy it is necessary to use an aerial perspective (orthogonal projection, angle of 90º), preferably three dimensional and combined with field experiments to fully understanding the character and spatial extent of this phenomenon (Madden et al., 2015). Considering the hydrodynamic characterization of the wetland surface, we used remote sensing and geoprocessing tools in the study area, including photogrammetry by Unmanned Aerial Vehicle (UAV) very high resolution (VHR) images, providing satisfactory results for the calculation of the area and the volume of water that the wetland could, theoretically, store.
The images were obtained with fixed-wing UAV: model eBee Plus RTK/PPK; wingspan 110 cm; weight 1.1 kg; powered by electricity; radio link range ~ 3 km; cruising speed 40–110 km/h (24.8–68.3 mph); wind resistance up to 45 km/h (72.45 mph); maximum flight time ~ 50 minutes, with no need for ground control points (GCP) in real-time kinematic (RTK) or post-processed kinematic (PPK) mode; and absolute and relative accuracy of X, Y, Z coordinates (RTK-/-PPK) of up to 3 cm, with a flight altitude of 120 meters. For imaging, we used the optical sensor RGB SenseFly S.O.D.A. (Sensor Optimised for Drone Applications), with 20 megapixels.
The flight plan and the correction of the images in PPK mode were done in Emotion Software 3.0, by SenseFly (Figure 2 A). The predicted cross-flight lateral and longitudinal overlap was 70%, ensuring high data accuracy by obtaining a large number of images and covering the target with overlaps in two different directions (d’Oleire-Oltmanns, Marzolff, Peter & Ries, 2012), resulting in better digital surface models (DSM) for the relief analysis. During processing, we were able to overlap an average of nine images of the area where photogrammetry was performed (Figure 2 B).
Next, we performed planialtimetric correction using GNSS data installed in the field during flyovers, which allowed us to obtain post-processing through the Precise Point Positioning service of the Brazilian Institute of Geography and Statistics (IBGE-PPP, www.ibge.gov.br). This operation performs a correction to PPP mode using GPS RINEX (Receiver Independent Exchange Format) data (Matsuoka, de Azambuja, Souza & Veronez, 2009). Our fieldwork equipment was the GNSS Topcon Hiper PRO RTK (GPS/GLONASS dual receiver), used only for the base unit in operation for 04 hours of point recording. The rover unit was onboard eBee Plus RTK/PPK (Figure 3).
The coordinates corrected by GNSS have significantly greater accuracy than the conventional GPS onboard the UAV. The PPK processing greatly increased the model’s reliability, resulting in a precision of 0.038 m, against 6.220 m with the common GPS (Table 1).
The last step was to process all aerial photographs and GNSS data in the Agisoft Metashape software and perform the digital photogrammetry. This computational operation aligned all the images, creating an orthomosaic. Processing proceeded with the tie point step (generated 5.716 points), followed by the generation of dense cloud (15.699.250 points) and the creation of the 3D model (1.046.616 faces). The digital surface model (6677x5328) and the orthomosaic (19623x16048) were then processed and readied for classification. Finally, after the classification of image data, the contours of the wetland’s compartments were created (shapefile) and their area and volume calculated. In order to identify each compartment of the wetland, the digital surface model and the orthomosaic data were processed in a GIS platform. The processing consisted in tests of percentage declivity of the surface and by visual interpretation of the relief. Thus, the Minimum bounding geometry, Erase, Multipart to singlepart and dissolve tools were applied to define the exact declivity of the murundus, which enabled the differentiation of three declivity-density distinct compartments.
To calculate the volume, Agisoft Metashape performs interpolation and value integration using the Best Fit Plane (BFP) tool, by the generation of a plane between each of the compartments and the calculation of the volume below that plane. The plane is generated from automatic analysis of the topographic perimeter profiles of the compartments (Figure 4). The generated plane minimizes the root mean square (RMS) of the orthogonal distance between each vertex of its polygon.
Considering that soil saturation and the elevation of the water level above the land surface occur first in the center of the topographic depression and the cumulative saturation increases towards the border, we used the Best Fit Plane tool to establish an average water height limit for each compartment based in field observations, in order to produce a more reliable volume estimate.
The vegetation does not influence the estimated volume considerably, since wetlands consist largely of grassy area. Therefore, it was not necessary to generate a digital terrain model; a digital surface model was adequate for all calculations. The volume of each compartment is directly influenced by the elevations of the perimeter, by the number of mounds, and by the terrain’s unique morphology. In order to subtract the space occupied by the mounds it was necessary to quantify them. The processing is performed through the slope classes that better represented the murundus contour of the study area. Percentage slope tests were performed with indices of 10%, 15%, 20% and 25%. The slope index that came closest to the correct mapping was 20%.