Geomatics and Environmental Engineering
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ISSN 1898-1135
e-ISSN: 2300-7095
Issue Date
2021
Volume
Vol. 15
Number
No 1
Description
Journal Volume
Geomatics and Environmental Engineering
Vol. 15 (2021)
Projects
Pages
Articles
An accuracy analysis comparison of supervised classification methods for mapping land cover using Sentinel 2 images in the Al‑Hawizeh marsh area, southern Iraq
(Wydawnictwa AGH, 2021) Alwan, Imzahim Abdulkareem; Aziz, Nadia A.
Land cover mapping of marshland areas from satellite images data is not a simple process, due to the similarity of the spectral characteristics of the land cover. This leads to challenges being encountered with some land covers classes, especially in wetlands classes. In this study, satellite images from the Sentinel 2B by ESA (European Space Agency) were used to classify the land cover of Al‑Hawizeh marsh/Iraq‑Iran border. Three classification methods were used aimed at comparing their accuracy, using multispectral satellite images with a spatial resolution of 10 m. The classification process was performed using three different algorithms, namely: Maximum Likelihood Classification (MLC), Artificial Neural Networks (ANN), and Support Vector Machine (SVM). The classification algorithms were carried out using ENVI 5.1 software to detect six land cover classes: deep water marsh, shallow water marsh, marsh vegetation (aquatic vegetation), urban area (built‑up area), agriculture area, and barren soil. The results showed that the MLC method applied to Sentinel 2B images provides a higher overall accuracy and the kappa coefficient compared to the ANN and SVM methods. Overall accuracy values for MLC, ANN, and SVM methods were 85.32%, 70.64%, and 77.01% respectively.
Developing institutional arrangements for sustainable development within mineral rich resource countries - the case of Mongolia
(Wydawnictwa AGH, 2021) Burmaa, Natsag; Baasanjav, Ganbaatar
A primary goal of the article is to explore the theoretical understanding among scholars about how institutional arrangements for sustainable local development partnerships in mineral resource‑rich countries are created. A success factor for implementing sustainable development goals (SDGs) is creating a proper institutional arrangement. Interviews and questionnaires, a mixed research method, were completed with officials and citizens from selected provinces. We reached to the following results: among others there is a weak understanding of partnership‑related local community development among stakeholders. Therefore, long term strategic planning and management which involves all parties in the decision‑making process should be created. In addition, building both horizontal and vertical institutional arrangements that provide for the participation of related stakeholders is an essential element for building successful and sustainable local development partnerships.
Unmanned Aerial Vehicles for three‑dimensional mapping and change detection analysis
(Wydawnictwa AGH, 2021) Gbopa, Adetola Olufunmilayo; Ayodele, Emmanuel Gbenga; Okolie, Chukwuma John; Ajayi, Akinwumi Olaitan; Iheaturu, Chima Jude
Unmanned Aerial Vehicles (UAVs), commonly known as drones are increasingly being used for three‑dimensional (3D) mapping of the environment. This study utilised UAV technology to produce a revised 3D map of the University of Lagos as well as land cover change detection analysis. A DJI Phantom 4 UAV was used to collect digital images at a flying height of 90 m, and 75% fore and 65% side overlaps. Ground control points (GCPs) for orthophoto rectification were coordinated with a Trimble R8 Global Navigation Satellite System. Pix4D Mapper was used to produce a digital terrain model and an orthophoto at a ground sampling distance of 4.36 cm. The change detection analysis, using the 2015 base map as reference, revealed a significant change in the land cover such as an increase of 16,306.7 m2 in buildings between 2015 and 2019. The root mean square error analysis performed using 7 GCPs showed a horizontal and vertical accuracy of 0.183 m and 0.157 m respectively. This suggests a high level of accuracy, which is adequate for 3D mapping and change detection analysis at a sustainable cost.
An evaluation of some machine learning algorithms as tools for predicting soil characteristics based on their spectral response in the Vis‑NIR range
(Wydawnictwa AGH, 2021) Gruszczyński, Stanisław
Using the Land Use and Coverage Frame Survey (LUCAS) database of European soil surface layer properties, statistical and machine learning predictive models for several key soil characteristics (clay content, pH in CaCl2, concentration of organic carbon, calcium carbonates and nitrogen and exchange cations capacity) were compared on the basis of processing their spectral responses in the visible (Vis) and near‑infrared (NIR) parts. Standard methods of relationship modeling were used: stepwise regression, partial least squares regression and linear regression with input data obtained from principal components analysis. Using the inputs extracted by statistical algorithms various machine learning algorithms were used in the modeling. The usefulness of the models was analyzed by comparison with the values of the determination coefficients, the root mean square error and the distribution of residual values. The mean square error of estimation in the cross‑validation procedure for the stack model using the multilayer perceptron and the distributed random forest were as follows: for clay content - ca. 4.5%, for pH - ca. 0.35, for SOC - ca. 7.5 g/kg (0.75% by weight), for CaCO3 content - ca. 19 g/kg, for N content - ca. 0.50 g/kg, and for CEC - ca. 3.5 cmol(+)/kg.
Incorporating inter‑system bias in Single Point Positioning based on GPS, Galileo and BeiDou System
(Wydawnictwa AGH, 2021) Kwaśniak, Dawid Łukasz; Cellmer, Sławomir
The increasing number of satellites provides new opportunities. In the experiment presented in this paper, the Single Point Positioning technique is tested. Data from four different receivers were used in the tests. The GPS, Galileo and BeiDou System observations were collected over a three day long observational session. The computational process was carried out using self‑made software and point positions were obtained as the result. The goal of the test was to verify the impact of the Inter‑System Bias (ISB) on the final results. For this purpose, two cases of processing data were compared: with estimating ISB and without taking into account this parameter. In the paper the formulas of the mathematical models used are presented and, in both of the considered cases, a combination of GPS, BDS and Galileo was used. The results show that in the case where the ISB was taken into account, the accuracy and precision in the positioning was much better than in the approach where the ISB was not considered. Estimating the ISB allows for more precise positioning results to be obtained for car‑navigation or GIS purposes.