Abstract:
Abstract: By taking Agricultural High-tech Industrial Park of Chinese Academy of Agricultural Sciences (Wan Zhuang) and its peripheral regions with a total area of 4.2 × 3.1 km as the study area, this paper carried out an aerial photogrammetry experiment by using the RICOH GXR A12 camera carried on an unmanned aerial vehicle (UAV), and the experiment mainly tested the precisions of planar positioning under a POS (positioning and orientation system) supported bundle block adjustment method and of area measurement, as well as the precision of the crop area identification of an UAV orthophoto map obtained from an aerial triangulation correction. We use an unmanned aerial vehicle (UAV) to obtain 690 images which covered the whole study area. After a series of processes such as image screen, POS-supported aerial triangulation correction, digital elevation model making, image fusion, and digital differential rectification, we have obtained the ortho-photo map of the whole study area. Since the deployment of high precision ground control point wastes time and energy, POS-supported aerial triangulation employs a non-control point model. Therefore, its absolute positioning precision may be affected by the error of the GPS carried on an UAV. In order to eliminate this error, the project team used a high precision wordview image to rectify the ortho-photo map. In this way, we could improve the image positioning precision, and meanwhile unify the study sample areas with the overall larger scope image coordinate system, so as to provide high precision samples for large-scale agriculture remote sensing statistics and monitoring. The result shows that, under the condition of no control point and after direct POS data bundle block adjustment, the mean square error of plane positioning precision of the X axis direction is 2.29 m, Y direction is 2.78 m, and overall plane error is 3.61 m. If a three order general polynomial model is adopted to conduct a geometric precision correction, then the mean square error of the X axis direction is 1.59 m, the Y direction is 1.8965 m, and the mean square error of the overall plane is 2.32 m. The above figures conform to the 1:10 000 ground plane precision requirements specified in the 'Standard for Aerotriangulation of Digital Aerophotogrammetry' and can meet the positioning precision requirements of a crop area survey in remote sensing monitoring. After obtaining the ortho-photo map, the four ground objects in the area evaluation areas of spring corn, summer corn, alfalfa, and bare soil were classified by employing two methods of supervised classification and object-oriented classification. By taking the differential GPS survey results as the evaluation criteria, the overall precisions of the four crops reached 88.2% (supervised classification) and 92.0% (object-oriented classification) respectively. The separate classification precisions of the two classification methods of the four ground objects were 88.9%, 86.7%, 93.0%, 86.6%, and 90.35%, as well as 90.35%, 92.61%, 94.93%, and 93.30% respectively. The result showed that remote sensing images of unmanned aerial vehicle (UAV), by acquiring small scale and quadrat sampled crop images, have a prospect of wide application. After promotion, it can meet the demands of nationwide crop ground sampling on high spatial resolution images, and can partially replace the operation model of GPS measurement.