Geodesy and Cartography

Evaluation of the performance of Multi-GNSS advanced orbit and clock augmentation – precise point positioning (MADOCA-PPP) in Japan region

Atınç Pırtı — Fri, 12 Apr 2024 00:00:00 +0300

For users of Precise Point Positioning (PPP), Multi-GNSS Advanced Orbit and Clock Augmentation PPP signals provide corrective data. When using the PPP approach and/or PPP-Ambiguity Resolution (AR) method, the QZSS signal provides globally applicable error corrections on satellite orbit, clock offset, and code/phase biases. In addition, from FY2024, as a part of the MADOCA-PPP technology demonstration, wide-area ionospheric correction data will be provided for the Asia-Oceania region. A software estimator of precise satellite information developed by JAXA, Multi-GNSS Advanced Demonstration Tool for Orbit and Clock Analysis (MADOCA), allows u-blox CO99-ZED-F9P and MSJ 3008-GM4-QZS utilizing MADOCA-PPP to be used in GNSS applications that need sub-decimetre precision but don’t have to be expensive. Errors caused by positioning satellites are computed by using observation data from domestic and overseas GNSS monitoring station networks such as IGS and MIRAI, and obtained correction data is transmitted from QZSS signal to provide highly precise positioning augmentation services that can be used in the Asia-Oceania Region. Users may utilize the PPP technique for high-precision locating by employing a GNSS receiver that supports the QZSS signals. This paper describes an experiment carried out with the static method to combine GPS, GLONASS, and QZSS signals in the project site (ISHI, USUD and MIZU stations in Japan). This paper examines the GPS/GLONASS/QZSS obtainable accuracy. These obtained results indicate that integrating GPS system with GLONASS and QZSS is favoured for surveying applications. It appears that integrating GPS/GLONASS/QZSS (MADOCA precise ephemeris file) static measurements in the study area between 0–4 millimetres accuracy can be guaranteed on all occasions.

Accuracy assessment of the effect of different feature descriptors on the automatic co-registration of overlapping images

Oluibukun Gbenga Ajayi, Ifeanyi Jonathan Nwadialor — Fri, 12 Apr 2024 10:28:24 +0300

This research seeks to assess the effect of different selected feature descriptors on the accuracy of an automatic image registration scheme. Three different feature descriptors were selected based on their peculiar characteristics, and implemented in the process of developing the image registration scheme. These feature descriptors (Modified Harris and Stephens corner detector (MHCD), the Scale Invariant Feature Transform (SIFT) and the Speeded Up Robust Feature (SURF)) were used to automatically extract the conjugate points common to the overlapping image pairs used for the registration. Random Sampling Consensus (RANSAC) algorithm was used to exclude outliers and to fit the matched correspondences, Sum of Absolute Differences (SAD) which is a correlation-based feature matching metric was used for the feature match, while projective transformation function was used for the computation of the transformation matrix (T). The obtained overall result proved that the SURF algorithm outperforms the other two feature descriptors with an accuracy measure of -0.0009 pixels, while SIFT with a cumulative signed distance of 0.0328 pixels also proved to be more accurate than MHCD with a cumulative signed distance of 0.0457 pixels. The findings affirmed the importance of choosing the right feature descriptor in the overall accuracy of an automatic image registration scheme.

Simulation of flood-prone areas using machine learning and GIS techniques in Samangan Province, Afghanistan

Fri, 12 Apr 2024 10:35:49 +0300

Flood events are the most sophisticated and damaging natural hazard compared to other natural catastrophes. Every year, this hazard causes human-financial losses and damage to croplands in different locations worldwide. This research employs a combination of artificial neural networks and geographic information systems (GIS) to simulate flood-vulnerable locations in the Samangan Province of Afghanistan. First, flood-influencing factors, such as soil, slope layer, elevation, flow direction, and land use/cover, were evaluated as influential factors in simulating flood-prone areas. These factors were imported into GIS software. The Fishnet command was used to partition the information layers. Furthermore, each layer was converted into points, and this data was fed into the perceptron neural network along with the educational data obtained from Google Earth. In the perceptron neural network, the input layers have five neurons and 16 nodes, and the outputs showed that elevation had the lowest possible weight (R2 = 0.713) and flow direction had the highest weight (R2 = 0.913). This study demonstrated that combining GIS and artificial neural networks results in acceptable performance for simulating and modeling flood susceptible areas in different geographical locations and significantly helps prevent or reduce flood hazards.

The reduction of geomagnetic data for the territory of Latvia to the epoch 2021.5

Lubova Sulakova, Janis Kaminskis — Fri, 12 Apr 2024 10:57:42 +0300

The article describes the sources of geomagnetic data, the reduction of geomagnetic data for the territory of Latvia to the epoch 2021.5, the history of previous magnetic observations in Latvia, the information available in the State Geodetic Network database and the information available in the World Geomagnetism Data Centre. The sequence of absolute measurements is described in detail. To visualise the changes in the magnetic declination value in the territory of Latvia, a 2021.5 year declination fluctuation has been created using ArcGIS Pro. The declination values in Latvia range from 6.68° to 10°, the inclination values range from 71.089° to 72.245° and the total magnetic field values from 51100 nT to 52594 nT. The values obtained for the magnetic field components refer to a magnetically clean environment, and there can be, and are, differences in the natural conditions in the Latvian territory, in natural anomalous locations and in locations with artificially high magnetic field noise (e.g. in cities, near railways, near high voltage lines, etc.). In the Latvian network, points have been selected in locations where the magnetic noise is minimal, as this is the technological process for building such stations. Magnetic observatories are even stricter, so the data coming from the observatories reflect the natural magnetic field without the influence of magnetic anomalies. The reduced magnetic field values and their representation on a map can be used for aeronautical navigation, military applications, identification of local magnetic anomaly sites or search for magnetically clean environments.

Performance assessment of spatial interpolations for traffic noise mapping on undulating and level terrain

Nevil Wickramathilaka, Uznir Ujang, Suhaibah Azri, Tan Liat Choon — Mon, 15 Apr 2024 00:00:00 +0300

Traffic noise mapping frequently employs Kriging, Inverse Distance Weighted (IDW), and Triangular Irregular Networks (TIN) spatial interpolations. This study uses the Henk de Kluijver noise model to evaluate the performance of spatial interpolations. Effective traffic noise mapping requires that noise observation points (Nops) be designed as 2 m grids. The upper and lower slopes function as noise barriers to reduce sound levels. Therefore, assessment of accuracy is essential for visualising noise levels in undulating and level terrain. In addition, this work gives an accurate comparison of traffic noise interpolation in undulating areas. The elements of spatial interpolations, such as the weighting factor, variogram, radius, and number of points influence the interpolation accuracy. The Kriging with a Gaussian variogram, where the radius is 5 m and the number of points is 12 demonstrates the highest level of precision. However, there is no direct relationship between accuracy validation and cross-validation. In cross-validation, however, the accuracy of the Gaussian variogram with a 7 m radius and 18 points is more accurate. In addition, this study demonstrates that Kriging is superior for extrapolating noise levels in undulating regions. Accurate visualising traffic noise levels requires a prior understanding of spatial interpolations.

Land use land cover change mapping from Sentinel 1B & 2A imagery using random forest algorithm in Côte d’Ivoire

Mon, 15 Apr 2024 00:00:00 +0300

Monitoring crop condition, soil properties, and mapping tillage activities can be used to assess land use, forecast crops, monitor seasonal changes, and contribute to the implementation of sustainable development policy. Agricultural maps can provide independent and objective estimates of the extent of crops in a given area or growing season, which can be used to support efforts to ensure food security in vulnerable areas. Satellite data can help detect and classify different types of soil. The evolution of satellite remote sensing technologies has transformed techniques for monitoring the Earth’s surface over the last several decades. The European Space Agency (ESA) and the European Union (EU) created the Copernicus program, which resulted in the European satellites Sentinel-1B (S1B) and Sentinel-2A (S2A), which allow the collection of multi-temporal, spatial, and highly repeatable data, providing an excellent opportunity for the study of land use, land cover, and change. The goal of this study is to map the land cover of Côte d’Ivoire’s West Central Soubre area (5°47′1′′ North, 6°35′38′′ West) between 2014 and 2020. The method is based on a combination of S1B and S2A imagery data, as well as three types of predictors: the biophysical indices Normalized Difference Vegetation Index “(NDVI)”, Modified Normalized Difference Water Index “(MNDWI)”, Normalized Difference Urbanization Index “(NDBI)”, and Normalized Difference Water Index “(NDWI)”, as well as spectral bands (B1, B11, B2, B3, B4, B6, B7, B8) and polarization coefficients VV. For the period 2014–2020, six land classifications have been established: Thick_Forest, Clear_Drill, Urban, Water, Palm_Oil, Bareland, and Cacao_Land. The Random Forest (RF) algorithm with 60 numberOfTrees was the primary categorization approach used in the Google Earth Engine (GEE) platform. The results show that the RF classification performed well, with outOfBagErrorEstimates of 0.0314 and 0.0498 for 2014 and 2020, respectively. The classification accuracy values for the kappa coefficients were above 95%: 96.42% in 2014 and 95.28% in 2020, with an overall accuracy of 96.97% in 2014 and 96 % in 2020. Furthermore, the User Accuracy (UA) and Producer Accuracy (PA) values for the classes were frequently above 80%, with the exception of the Bareland class in 2020, which achieved 79.20%. The backscatter coefficients of the S1B polarization variables had higher GINI significance in 2014: VH (70.80) compared to VH (50.37) in 2020; and VV (57.11) in 2014 compared to VV (46.17) in 2020. Polarization coefficients had higher values than the other spectral and biophysical variables of the three predictor variables. During the study period, the Thick_Forest (35.90% ± 1.17), Palm_Oil (57.59% ± 1.48), and Water (5.90% ± 0.47) classes experienced a regression in area, while the Clear_Drill (16.96% ± 0.80), Urban (2.32% ± 0.29), Bareland (83.54% ± 1.79), and Cacao_Land (35.14% ± 1.16) classes experienced an increase. The approach used is regarded as excellent based on the results obtained.

Variability of Equivalent Water Height (EWH) in Indonesia during 14 years of grace gravity satellite mission

Safri Yanti Rahayu, Ira Mutiara Anjasmara — Tue, 23 Apr 2024 00:00:00 +0300

GRACE Satellite (Gravity Recovery and Climate Experiment) is a gravity monitoring satellite, that is very sensitive to mass changes, especially the signals that produce by redistribution of water masses. Data from this satellite can be used to observe water distribution in the form of spherical harmonic coefficients. Water mass variations are presented as Equivalent Water Height (EWH). The results of GRACE processing showed that the largest EWH was 27.298 cm in January 2015 and the smallest EWH was 29.816 cm in June 2004 in Sumatera Island. The positive trend occurred in Sumatra island and the negative trend occurred in eastern Indonesia. Generally, the trend of Indonesian rainfall throughout 2002 to 2016 was constant. However, there was a change in seasonal patterns in 2014. This research also use Mascon data from GRACE satellite to determine the radius of the gaussian filter and TRMM satellite’s data to observe rainfall data to be compared with the EWH variability from GRACE.