Rice Height Monitoring between Different Estimation Models Using UAV Photogrammetry and Multispectral Technology

Unmanned aerial vehicle (UAV) photogrammetry was used to monitor crop height in a flooded paddy field. Three multi-rotor UAVs were utilized to conduct flight missions in order to capture RGB (RedGreenBlue) and multispectral images, and these images were analyzed using several different models to provide the best results. Two image sets taken by two UAVs, mounted with RGB cameras of the same resolution and Global Navigation Satellite System (GNSS) receivers of different accuracies, were applied to perform photogrammetry. Two methods were then proposed for creating crop height models (CHMs), one of which was denoted as the M1 method and was based on the Digital Surface Point Cloud (DSPC) and the Digital Terrain Point Cloud (DSPT). The other was denoted as the M2 method and was based on the DSPC and a bathymetric sensor. An image set taken by another UAV mounted with a multispectral camera was used for multispectral-based photogrammetry. A Normal Differential Vegetation Index (NDVI) and a Vegetation Fraction (VF) were then extracted. A new method based on multiple linear regression (MLR) combining the NDVI, the VF, and a Soil Plant Analysis Development (SPAD) value for estimating the measured height (MH) of rice was then proposed and denoted as the M3 method. The results show that the M1 method, the UAV with a GNSS receiver with a higher accuracy, obtained more reliable estimations, while the M2 method, the UAV with a GNSS receiver of moderate accuracy, was actually slightly better. The effect on the performance of CHMs created by the M1 and M2 methods is more negligible in different plots with different treatments; however, remarkably, the more uniform the distribution of vegetation over the water surface, the better the performance. The M3 method, which was created using only a SPAD value and a canopy NDVI value, showed the highest coefficient of determination (R2) for overall MH estimation, 0.838, compared with other combinations.

Research and Development of Real-time High-precision GNSS Receivers: A Feasible Application for Surveying and Mapping in Vietnam

In recent years, the Global Navigation Satellite System (GNSS) has been widely applied insurveying and mapping. Currently, in Vietnam, dual-frequency GNSS receivers are quite extensivelyapplied with the real-time kinematic (RTK) measurement technique using a continuously operatingreference station network. However, high-accuracy GNSS receivers are often expensive, sometimes notmeeting the needs of users for specific applications. This research develops two types of low-cost highprecisionGNSS receivers for RTK positioning for different purposes. First, the millimeter precisionGNSS receiver used in real-time displacement monitoring is based on Trimble's BD970 mainboardtechnology and some other modules. These components are interconnected according to a standarddesign scheme and assembled in an enclosure to form a GNSS receiver. In addition, a GNSS datatransmission in the National Marine Electronics Association standard format by Networked Transport ofRadio Technical Commission for Maritime Services via Internet Protocol (NTRIP) has beendesigned and developed. The GNSS receiver after development is loaded with program code written inthe C# programming language, using the Arduino programming tool. Second, the GNSS receivers havethe centimeter accuracy for RTK positioning used in surveying and mapping based on U-blox'smainboard technology and some other modules. These modules are also connected together according toa standard design scheme and assembled in an enclosure to form a complete GNSS receiver. Theevaluation results show that the designed and developed GNSS receivers completely meet therequirements of surveying and mapping in coal mines in Vietnam, such as real-time monitoring oflandslides, surveying and topographical mapping and other surveying works to serve the mining process.

Five-State Extended Kalman Filter for Estimation of Speed over Ground (SOG), Course over Ground (COG) and Course Rate of Unmanned Surface Vehicles (USVs): Experimental Results

Small USVs are usually equipped with a low-cost navigation sensor suite consisting of a global navigation satellite system (GNSS) receiver and a magnetic compass. Unfortunately, the magnetic compass is highly susceptible to electromagnetic disturbances. Hence, it should not be used in safety-critical autopilot systems. A gyrocompass, however, is highly reliable, but it is too expensive for most USV systems. It is tempting to compute the heading angle by using two GNSS antennas on the same receiver. Unfortunately, for small USV systems, the distance between the antennas is very small, requiring that an RTK GNSS receiver is used. The drawback of the RTK solution is that it suffers from dropouts due to ionospheric disturbances, multipath, interference, etc. For safety-critical applications, a more robust approach is to estimate the course angle to avoid using the heading angle during path following. The main result of this article is a five-state extended Kalman filter (EKF) aided by GNSS latitude-longitude measurements for estimation of the course over ground (COG), speed over ground (SOG), and course rate. These are the primary signals needed to implement a course autopilot system onboard a USV. The proposed algorithm is computationally efficient and easy to implement since only four EKF covariance parameters must be specified. The parameters need to be calibrated for different GNSS receivers and vehicle types, but they are not sensitive to the working conditions. Another advantage of the EKF is that the autopilot does not need to use the COG and SOG measurements from the GNSS receiver, which have varying quality and reliability. It is also straightforward to add complementary sensors such as a Doppler Velocity Log (DVL) to the EKF to improve the performance further. Finally, the performance of the five-state EKF is demonstrated by experimental testing of two commercial USV systems.