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.

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Evaluation of the accuracy of volume calculation obtained by UAV

Revista Agro mbiente On-line ◽

10.18227/1982-8470ragro.v14i0.6321 ◽

2020 ◽

Vol 14 ◽

Author(s):

Fernanda Helena Oliveira da Silva ◽

Éder Ramon Feitoza Lêdo ◽

Caike Silva Candido Damasceno ◽

José Adriano Da Silva ◽

Iderlan Medeiros De Brito Alves

Keyword(s):

High Reliability ◽

Satellite System ◽

Aerial Images ◽

Volume Data ◽

Volume Calculation ◽

Gnss Receiver ◽

Stabilization Pond ◽

Digital Terrain ◽

Waste Stabilization ◽

Global Navigation Satellite

The emergence of increasingly accurate, fast, and inexpensive tools in the acquisition of data for the management base suitable for each type of environment is fundamental in the development of sustainable engineering. Based on this, the evaluation of the accuracy of the volume calculation performed using Digital Terrain Models (DTM’s), generated by images of Unmanned Aerial Vehicle (UAV) was carried out. The study was conducted in the area of the new landfill in the municipality of Fortaleza, state of Ceará, in a waste stabilization pond. Two DTM’s were generated and evaluated. The first was generated by collecting points from a Global Navigation Satellite System (GNSS) receiver, using 445 points, whereas the second was generated by aerial images obtained through a multirotor UAV, with 17 checkpoints and 10 Ground Control Points (GCP’s). With the two DTM’s of the GNSS Receiver and the UAV, the volume of the stabilization pond was calculated using the TopoEVN and Pix4D software, respectively. The estimated pond volume obtained through the Global Positioning System (GPS) data was 48548,33 m³, while by the UAV DTM, it was 48504,9 m³. The accuracy of the volume data obtained by DTM generated by UAV, even with considerable flight height (120 m), presented a result with variation less than 1% compared to those arising from conventional topography, thus indicating high reliability and data accuracy.

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Study the Effect of Radio Interference of the EHV Transmission Line on GNSS Signals

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.805-806.851 ◽

2013 ◽

Vol 805-806 ◽

pp. 851-854

Author(s):

Zhi Ge Jia ◽

Zhao Sheng Nie ◽

Wei Wang ◽

Xiao Guan ◽

Di Jin Wang

Keyword(s):

Transmission Line ◽

Transmission Lines ◽

Internal Noise ◽

Field Testing ◽

Satellite System ◽

Field Analysis ◽

Radio Interference ◽

Gnss Receiver ◽

Global Navigation Satellite ◽

Gnss Data

This work describes the field testing process of Global Navigation Satellite System (GNSS) receiver under 220KV, 500KV UHV transmission line and standard calibration field. Analysis for GNSS data results shows that the radio interference generated by EHV transmission lines have no effect on GNSS receiver internal noise levels and valid GNSS observation rate. Within 50 meters of the EHV transmission lines, the multi-path effects (mp1 and mp2 value) significantly exceeded the normal range and becomes larger with the increase of the voltage .outside 50 meters of the EHV transmission line, the multi-path effects have almost no effect on the high-precision GNSS observations.

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Improving Biomass and Grain Yield Prediction of Wheat GenoTypes on Sodic Soil Using Integrated High-Resolution MultiSpectral, Hyperspectral, 3D Point Cloud, and Machine Learning Techniques

Remote Sensing ◽

10.3390/rs13173482 ◽

2021 ◽

Vol 13 (17) ◽

pp. 3482

Author(s):

Malini Roy Choudhury ◽

Sumanta Das ◽

Jack Christopher ◽

Armando Apan ◽

Scott Chapman ◽

...

Keyword(s):

Machine Learning ◽

Grain Yield ◽

Point Cloud ◽

Vegetation Index ◽

3D Point Cloud ◽

Sodic Soil ◽

Proximal Sensing ◽

Crop Height ◽

Wheat Genotypes ◽

Grain Yields

Sodic soils adversely affect crop production over extensive areas of rain-fed cropping worldwide, with particularly large areas in Australia. Crop phenotyping may assist in identifying cultivars tolerant to soil sodicity. However, studies to identify the most appropriate traits and reliable tools to assist crop phenotyping on sodic soil are limited. Hence, this study evaluated the ability of multispectral, hyperspectral, 3D point cloud, and machine learning techniques to improve estimation of biomass and grain yield of wheat genotypes grown on a moderately sodic (MS) and highly sodic (HS) soil sites in northeastern Australia. While a number of studies have reported using different remote sensing approaches and crop traits to quantify crop growth, stress, and yield variation, studies are limited using the combination of these techniques including machine learning to improve estimation of genotypic biomass and yield, especially in constrained sodic soil environments. At close to flowering, unmanned aerial vehicle (UAV) and ground-based proximal sensing was used to obtain remote and/or proximal sensing data, while biomass yield and crop heights were also manually measured in the field. Grain yield was machine-harvested at maturity. UAV remote and/or proximal sensing-derived spectral vegetation indices (VIs), such as normalized difference vegetation index, optimized soil adjusted vegetation index, and enhanced vegetation index and crop height were closely corresponded to wheat genotypic biomass and grain yields. UAV multispectral VIs more closely associated with biomass and grain yields compared to proximal sensing data. The red-green-blue (RGB) 3D point cloud technique was effective in determining crop height, which was slightly better correlated with genotypic biomass and grain yield than ground-measured crop height data. These remote sensing-derived crop traits (VIs and crop height) and wheat biomass and grain yields were further simulated using machine learning algorithms (multitarget linear regression, support vector machine regression, Gaussian process regression, and artificial neural network) with different kernels to improve estimation of biomass and grain yield. The artificial neural network predicted biomass yield (R2 = 0.89; RMSE = 34.8 g/m2 for the MS and R2 = 0.82; RMSE = 26.4 g/m2 for the HS site) and grain yield (R2 = 0.88; RMSE = 11.8 g/m2 for the MS and R2 = 0.74; RMSE = 16.1 g/m2 for the HS site) with slightly less error than the others. Wheat genotypes Mitch, Corack, Mace, Trojan, Lancer, and Bremer were identified as more tolerant to sodic soil constraints than Emu Rock, Janz, Flanker, and Gladius. The study improves our ability to select appropriate traits and techniques in accurate estimation of wheat genotypic biomass and grain yields on sodic soils. This will also assist farmers in identifying cultivars tolerant to sodic soil constraints.

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Results of the GNSS receiver experiment OCAM-G on Ariane-5 flight VA 219

Proceedings of the Institution of Mechanical Engineers Part G Journal of Aerospace Engineering ◽

10.1177/0954410016648351 ◽

2016 ◽

Vol 231 (6) ◽

pp. 1100-1114 ◽

Cited By ~ 5

Author(s):

André Hauschild ◽

Markus Markgraf ◽

Oliver Montenbruck ◽

Horst Pfeuffer ◽

Elie Dawidowicz ◽

...

Keyword(s):

French Guiana ◽

Satellite System ◽

Navigation Satellite ◽

Navigation Solution ◽

Gnss Receiver ◽

Upper Stage ◽

Lift Off ◽

Global Navigation Satellite ◽

Navigation Accuracy ◽

First Time

The fifth Automated Transfer Vehicle was launched on 29 July 2014 with Ariane-5 flight VA 219 into orbit from Kourou, French Guiana. For the first time, the ascent of an Ariane rocket was independently tracked with a Global Navigation Satellite System (GNSS) receiver on this flight. The GNSS receiver experiment OCAM-G was mounted on the upper stage of the rocket. Its receivers tracked the trajectory of the Ariane-5 from lift-off until after the separation of the Automated Transfer Vehicle. This article introduces the design of the experiment and presents an analysis of the data gathered during the flight with respect to the GNSS tracking status, availability of navigation solution, and navigation accuracy.

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Algorithm for Generating 3D Geometric Representation Based on Indoor Point Cloud Data

Applied Sciences ◽

10.3390/app10228073 ◽

2020 ◽

Vol 10 (22) ◽

pp. 8073

Author(s):

Min Woo Ryu ◽

Sang Min Oh ◽

Min Ju Kim ◽

Hun Hee Cho ◽

Chang Baek Son ◽

...

Keyword(s):

Point Cloud ◽

Indoor Environment ◽

Three Dimensional ◽

Satellite System ◽

Normal Vector ◽

Geometric Representation ◽

Point Cloud Data ◽

Cloud Data ◽

Data Process ◽

Global Navigation Satellite

This study proposes a new method to generate a three-dimensional (3D) geometric representation of an indoor environment by refining and processing an indoor point cloud data (PCD) captured through backpack laser scanners. The proposed algorithm comprises two parts to generate the 3D geometric representation: data refinement and data processing. In the refinement section, the inputted indoor PCD are roughly segmented by applying random sample consensus (RANSAC) to raw data based on an estimated normal vector. Next, the 3D geometric representation is generated by calculating and separating tangent points on segmented PCD. This study proposes a robust algorithm that utilizes the topological feature of the indoor PCD created by a hierarchical data process. The algorithm minimizes the size and the uncertainty of raw PCD caused by the absence of a global navigation satellite system and equipment errors. The result of this study shows that the indoor environment can be converted into 3D geometric representation by applying the proposed algorithm to the indoor PCD.

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Research on Absolute Calibration of GNSS Receiver Delay through Clock-Steering Characterization

Sensors ◽

10.3390/s20216063 ◽

2020 ◽

Vol 20 (21) ◽

pp. 6063

Author(s):

Feng Zhu ◽

Huijun Zhang ◽

Luxi Huang ◽

Xiaohui Li ◽

Ping Feng

Keyword(s):

Measurement Errors ◽

Periodic Variation ◽

Satellite System ◽

Absolute Calibration ◽

Universal Method ◽

Gnss Receiver ◽

Combined Uncertainty ◽

Zero Baseline ◽

Global Navigation Satellite ◽

Operation Status

The receiver delay has a significant impact on global navigation satellite system (GNSS) time measurement. This article comprehensively analyzes the difficulty, composition, principle, and calculation of GNSS receiver delay. A universal method, based on clock-steering characterization, is proposed to absolutely calibrate all types of receivers. We use a hardware simulator to design several experiments to test the performance of GNSS receiver delay for different receiver types, radio frequency (RF) signals, operation status and time-to-phase (TtP). At first, through the receivers of Novatel and Septentrio, the channel delay of Septentrio is 2 ns far lower than 65 ns for Novatel, and for the inter-frequency bias of GLONASS L1, Septentrio tends to increase within 10 ns compared with decreasing of Novatel within 5 ns. Secondly, a representative receiver of UniNav-BDS (BeiDou) is chosen to test the influence of Ttp which may be ignored by users. Under continuous operation, the receiver delay shows a monotone reduction of 10 ns as TtP increased by 10 ns. However, under on-off operation, the receiver delay represents periodic variation. Through a zero-baseline comparison, we verifies the relation between receiver delay and TtP. At last, the article analyzes instrument errors and measurement errors in the experiment, and the combined uncertainty of absolute calibration is calculated with 1.36 ns.

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Processing Chain for Estimation of Tree Diameter from GNSS-IMU-Based Mobile Laser Scanning Data

Remote Sensing ◽

10.3390/rs11060615 ◽

2019 ◽

Vol 11 (6) ◽

pp. 615 ◽

Cited By ~ 8

Author(s):

Juraj Čerňava ◽

Martin Mokroš ◽

Ján Tuček ◽

Michal Antal ◽

Zuzana Slatkovská

Keyword(s):

Point Cloud ◽

Laser Scanning ◽

Forest Canopy ◽

Point Clouds ◽

Satellite System ◽

Forest Road ◽

Forest Mapping ◽

Fitting Method ◽

Progressive Technology ◽

Global Navigation Satellite

Mobile laser scanning (MLS) is a progressive technology that has already demonstrated its ability to provide highly accurate measurements of road networks. Mobile innovation of the laser scanning has also found its use in forest mapping over the last decade. In most cases, existing methods for forest data acquisition using MLS result in misaligned scenes of the forest, scanned from different views appearing in one point cloud. These difficulties are caused mainly by forest canopy blocking the global navigation satellite system (GNSS) signal and limited access to the forest. In this study, we propose an approach to the processing of MLS data of forest scanned from different views with two mobile laser scanners under heavy canopy. Data from two scanners, as part of the mobile mapping system (MMS) Riegl VMX-250, were acquired by scanning from five parallel skid trails that are connected to the forest road. Misaligned scenes of the forest acquired from different views were successfully extracted from the raw MLS point cloud using GNSS time based clustering. At first, point clouds with correctly aligned sets of ground points were generated using this method. The loss of points after the clustering amounted to 33.48%. Extracted point clouds were then reduced to 1.15 m thick horizontal slices, and tree stems were detected. Point clusters from individual stems were grouped based on the diameter and mean GNSS time of the cluster acquisition. Horizontal overlap was calculated for the clusters from individual stems, and sufficiently overlapping clusters were aligned using the OPALS ICP module. An average misalignment of 7.2 mm was observed for the aligned point clusters. A 5-cm thick horizontal slice of the aligned point cloud was used for estimation of the stem diameter at breast height (DBH). DBH was estimated using a simple circle-fitting method with a root-mean-square error of 3.06 cm. The methods presented in this study have the potential to process MLS data acquired under heavy forest canopy with any commercial MMS.

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IWV retrieval from ship-borne GNSS receiver in the framework of the MAP-IO project

10.5194/ems2021-172 ◽

2021 ◽

Author(s):

Pierre Bosser ◽

Joël Van Ballen ◽

Olivier Bousquet

Keyword(s):

Marine Biology ◽

Weather Prediction ◽

Vertical Component ◽

Satellite System ◽

Sea Surface Height ◽

Sea Surface ◽

Gnss Receiver ◽

Height Determination ◽

Global Navigation Satellite ◽

Height Model

In the framework of the research project &#8220;Marion Dufresne Atmospheric Program &#8211; Indian Ocean&#8221; (MAP-IO), which is aiming at collecting long-term atmospheric and marine biology observations in the under-instrumented Indian and Austral Oceans, a Global Navigation Satellite System (GNSS) receiver was installed on the research vessel (RV) Marion Dufresne in October 2020 to describe, and monitor, global moisture changes in these areas. GNSS raw data are recorded continuously and used to retrieve integrated water vapor contents (IWV) along the RV route.After a data quality check that confirmed that a wise choice of location of the GNSS antenna on the RV is crucial to avoid mask, signal reflection and interference from other instruments that may degrade IWV retrieval, a first assessment of the GNSS analysis performances was carried out by comparing the vertical component of the estimated positions to sea surface height model. The differences are on the order of 20 to 30 cm; they are consistent with both the error budget for sea surface height determination using GNSS and the sea surface height model formal errors.An evaluation of GNSS-derived IWV was conducted using IWV estimates from the ECMWF fifth ReAnalysis (ERA5) and ground-based GNSS reference stations located nearby the tracks of RV Marion Dufresne. Preliminary analyses show encouraging results with a mean root mean square error of ~2-3&#160;kg&#160;m-2 between ERA5 and GNSS-derived IWV. The use of ultra-rapid GNSS orbit and clock product was also investigated to assess the performance of near real-time GNSS-derived IWV estimation for numerical weather prediction purposes.

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CORS ARCHITECTURE AND EVALUATION OF POSITIONING BY LOW-COST GNSS RECEIVER

Geodesy and Cartography ◽

10.3846/gac.2018.1255 ◽

2018 ◽

Vol 44 (2) ◽

pp. 36-44 ◽

Cited By ~ 5

Author(s):

Massimiliano Pepe

Keyword(s):

Low Cost ◽

Satellite System ◽

Ease Of Use ◽

Quality Data ◽

Gnss Receiver ◽

Stop And Go ◽

Gnss Receivers ◽

Spatial Positioning ◽

Global Navigation Satellite

In recent years, the use of low cost GNSS receivers is becoming widespread due to their increasing performance in the spatial positioning, flexibility, ease of use and really interesting price. In addition, a recent technique of Global Navigation Satellite System (GNSS) survey, called Network Real Time Kinematic (NRTK), allows to obtain to rapid and accurate positioning measurements. The main feature of this approach is to use the raw measurements obtained and stored from a network of Continuously Operating Reference Stations (CORS) in order to generate more reliable error models that can mitigate the distance-dependent errors within the area covered by the CORS. Also, considering the huge potential of this GNSS positioning system, the purpose of this paper is to analyze and investigate the performance of the NTRK approach using a low cost GNSS receiver, in stop-and-go kinematic technique. By several case studies it was shown that, using a low cost RTK board for Arduino environment, a smartphone with open source application for Android and the availability of data correction from CORS service, a quick and accurate positioning can be obtained. Because the measures obtained in this way are quite noisy and, more in general, increasing with the baseline, by a simple and suitable statistic treatment, it was possible to increase the quality of the measure. In this way, this low cost architecture could be applied in many geomatics fields. In addition to presenting the main aspects of the NTRK infrastructure and a review of several types of correction, a general workflow in order to obtain quality data in NRTK mode, regardless of the type of GNSS receiver (multi constellations, single or many frequencies, etc.) is discussed.

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UNMANNED AERIAL VEHICLE LASER SCANNING FOR EROSION MONITORING IN ALPINE GRASSLAND

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-iv-2-w5-405-2019 ◽

2019 ◽

Vol IV-2/W5 ◽

pp. 405-412 ◽

Cited By ~ 2

Author(s):

A. Mayr ◽

M. Bremer ◽

M. Rutzinger ◽

C. Geitner

Keyword(s):

Unmanned Aerial Vehicle ◽

Point Cloud ◽

Laser Scanning ◽

Test Site ◽

Satellite System ◽

Alpine Grassland ◽

Proof Of Concept ◽

Aerial Vehicle ◽

Geomorphological Changes ◽

Global Navigation Satellite

Abstract. With this contribution we assess the potential of unmanned aerial vehicle (UAV) based laser scanning for monitoring shallow erosion in Alpine grassland. A 3D point cloud has been acquired by unmanned aerial vehicle laser scanning (ULS) at a test site in the subalpine/alpine elevation zone of the Dolomites (South Tyrol, Italy). To assess its accuracy, this point cloud is compared with (i) differential global navigation satellite system (GNSS) reference measurements and (ii) a terrestrial laser scanning (TLS) point cloud. The ULS point cloud and an airborne laser scanning (ALS) point cloud are rasterized into digital surface models (DSMs) and, as a proof-of-concept for erosion quantification, we calculate the elevation difference between the ULS DSM from 2018 and the ALS DSM from 2010. For contiguous spatial objects of elevation change, the volumetric difference is calculated and a land cover class (bare earth, grassland, trees), derived from the ULS reflectance and RGB colour, is assigned to each change object. In this test, the accuracy and density of the ALS point cloud is mainly limiting the detection of geomorphological changes. Nevertheless, the plausibility of the results is confirmed by geomorphological interpretation and documentation in the field. A total eroded volume of 672&thinsp;m3 is estimated for the test site (48&thinsp;ha). Such volumetric estimates of erosion over multiple years are a key information for improving sustainable soil management. Based on this proof-of-concept and the accuracy analysis, we conclude that repeated ULS campaigns are a well-suited tool for erosion monitoring in Alpine grassland.

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