Monitoring 2019 Forest Fires in Southeastern Australia with GNSS Technique

Guo, Jinyun; Hou, Rui; Zhou, Maosheng; Jin, Xin; Li, Chengming; Liu, Xin; Gao, Hao

doi:10.3390/rs13030386

Cited by 11 publications

(7 citation statements)

References 48 publications

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“…Therefore, the positioning accuracy of GNSS directly influences the service quality and level of smart forestry. For example, in forest resource inventory, the positioning accuracy of GNSS determines the accuracy of plot location and relocation, which affects the estimation and evaluation of forest resources [2]; in forest fire emergency, the positioning accuracy of GNSS affects the location and analysis of fire scene, which affects the efficiency and outcome of firefighting [3]; in wildlife protection, the positioning accuracy of GNSS affects the tracking and monitoring of animals, which affects the protection strategies and measures [4]. A study has analyzed the positioning accuracy of using GNSS technology for kinematic positioning under different canopy closure levels, and the results show that due to the low sensitivity of single-frequency GNSS receivers, their accuracy is not affected much by forest conditions, while the accuracy of dual-frequency GNSS receivers is highly dependent on satellite visibility [5].…”

Section: Introductionmentioning

confidence: 99%

Assessment Accuracy of Standard Point Positioning Enhanced by Observation and Position Domain Filtering Utilizing a Multi-Epoch Least-Squares Integration Method

Li,

Psimoulis,

et al. 2024

Remote Sensing

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To enhance the positioning accuracy of standalone GNSS receivers in environments unable to provide precise ephemeris and clock offset, such as undeveloped forest areas that lack network communication and power supply, this study employed the Time Difference Carrier Phase (TDCP) technology to improve the positioning accuracy of Standard Point Positioning (SPP), where the Least-Squares (LS) and the extended Multi-Epoch Least Squares (MELS) method were applied in the position domain filtering for a single GNSS receiver and compare its performance with the existing observation domain filtering method. Firstly, the simulated data sets with various positioning accuracies were used to verify the effectiveness and convergence of the LS filtering methods. The results indicate that the LS filtering method produces a lower root mean square (RMS) error than the original strategy. Secondly, this study uses two kinematic GNSS data sets to evaluate the performance of the observation and position domain filtering, with an emphasis on the MELS method. The numerical experiment results show that the position domain LS filtering method outperforms the other two methods. The open environment experiments result shows that the positioning domain filtering method achieved positioning accuracies of 0.202 m, 0.843 m, and 2.036 m in the E, N, and U directions, respectively, with improvements of 68.0%, 21.6%, and 24.0%, compared to the original algorithm which achieved positioning accuracies of 0.631 m, 1.076 m, and 2.680 m. It also achieved improvements of 24.0%, 4.0%, and 18.3%, respectively, compared to the observation domain filtering method with positioning accuracies of 0.353 m, 0.886 m, and 2.526 m. The forest scenes experiments result shows that the positioning domain filtering method achieved positioning accuracies of 1.308 m, 1.375 m, and 2.133 m in the E, N, and U directions, respectively, with improvements of 42.4%, 36.2%, and 27.6%, compared to original algorithm which achieved positioning accuracies of 1.863 m, 1.873 m, and 2.722 m, and also achieved improvements of 27.0%, 19.4% and 10.6%, respectively, comparing to observation domain filtering method with positioning accuracies of 1.661 m, 1.642 m and 2.359 m. Moreover, the examination of the LS method results based on different epochs reveals that the filtering accuracy increases as more epochs are incorporated into the position domain integration and the enhancement value reaches a few millimeters.

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Section: Introductionmentioning

confidence: 99%

Assessment Accuracy of Standard Point Positioning Enhanced by Observation and Position Domain Filtering Utilizing a Multi-Epoch Least-Squares Integration Method

Li,

Psimoulis,

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

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“…As a representative positioning technology, GNSS have the advantages of high accuracy, low cost, high temporal resolution, and all-weather capabilities [5,6]. The use of GNSS to invert atmospheric water vapor is of great significance for quantitatively describing water vapor changes [7], analyzing climate causes [8], conducting disaster monitoring [9], and carrying out short-term meteorological forecasting [10].…”

Section: Introductionmentioning

confidence: 99%

A New Empirical Model of Weighted Mean Temperature Combining ERA5 Reanalysis Data, Radiosonde Data, and TanDEM-X 90m Products over China

Zhang,

Yang,

Wang

et al. 2024

Remote Sensing

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Weighted mean temperature (Tm) is an important parameter in the water vapor inversion of global navigation satellite systems (GNSS). High-precision Tm values can effectively improve the accuracy of GNSS precipitable water vapor. In this study, a new regional grid Tm empirical model called the RGTm model over China and the surrounding areas was proposed by combining ERA5 reanalysis data, radiosonde data, and TanDEM-X 90m products. In the process of model establishment, we considered the setting of the reference height in the height correction formula and the bias correction for the Tm lapse rate. Tm values derived from ERA5 and radiosonde data in 2019 were used as references to validate the performance of the RGTm model. At the same time, the GPT3, GGNTm, and uncorrected seasonal model were used for comparison. Results show that compared with the other three models, the accuracy of the RGTm model’s Tm was improved by approximately 12.21% (15.32%), 1.17% (3.09%), and 2.31% (5.05%), respectively, when ERA5 (radiosonde) Tm data were used as references. In addition, the introduction of radiosonde data prevented the accuracy of the Tm empirical model from being entirely dependent on the accuracy of the reanalysis data.

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“…It is of great significance to Remote Sens. 2021, 13, 3546 2 of 18 monitor temperature, pressure, water vapor pressure, T m , and other tropospheric parameters for high-precision GNSS positioning and navigation, as well as GNSS meteorologic applications [2,3].…”

Section: Introductionmentioning

confidence: 99%

A New Approach for the Development of Grid Models Calculating Tropospheric Key Parameters over China

et al. 2021

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Pressure, water vapor pressure, temperature, and weighted mean temperature (Tm) are tropospheric parameters that play an important role in high-precision global navigation satellite system navigation (GNSS). As accurate tropospheric parameters are obligatory in GNSS navigation and GNSS water vapor detection, high-precision modeling of tropospheric parameters has gained widespread attention in recent years. A new approach is introduced to develop an empirical tropospheric delay model named the China Tropospheric (CTrop) model, providing meteorological parameters based on the sliding window algorithm. The radiosonde data in 2017 are treated as reference values to validate the performance of the CTrop model, which is compared to the canonical Global Pressure and Temperature 3 (GPT3) model. The accuracy of the CTrop model in regards to pressure, water vapor pressure, temperature, and weighted mean temperature are 5.51 hPa, 2.60 hPa, 3.09 K, and 3.35 K, respectively, achieving an improvement of 6%, 9%, 10%, and 13%, respectively, when compared to the GPT3 model. Moreover, three different resolutions of the CTrop model based on the sliding window algorithm are also developed to reduce the amount of gridded data provided to the users, as well as to speed up the troposphere delay computation process, for which users can access model parameters of different resolutions for their requirements. With better accuracy of estimating the tropospheric parameters than that of the GPT3 model, the CTrop model is recommended to improve the performance of GNSS positioning and navigation.

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Monitoring 2019 Forest Fires in Southeastern Australia with GNSS Technique

Cited by 11 publications

References 48 publications

Assessment Accuracy of Standard Point Positioning Enhanced by Observation and Position Domain Filtering Utilizing a Multi-Epoch Least-Squares Integration Method

Assessment Accuracy of Standard Point Positioning Enhanced by Observation and Position Domain Filtering Utilizing a Multi-Epoch Least-Squares Integration Method

A New Empirical Model of Weighted Mean Temperature Combining ERA5 Reanalysis Data, Radiosonde Data, and TanDEM-X 90m Products over China

A New Approach for the Development of Grid Models Calculating Tropospheric Key Parameters over China

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