https://journals.sfu.ca/ijg/index.php/journal/issue/feed International Journal of Geoinformatics 2022-04-11T00:00:00-07:00 Prof. Nitin Kumar Tripathi editor@e-geoinfo.com Open Journal Systems <p>ISSN 2673-0014 (Online)<br />ISSN 1686-6576 (Printed)</p> <p><strong>Online version description for website</strong><strong> </strong></p> <p><strong><em>International Journal of Geoinformatics</em></strong> is a peer reviewed journal in the field of Remote Sensing, Geographic Information Systems (GIS), Photogrammetry and Global Positioning Systems (GPS). It publishes papers in the application of RS/GIS/GPS in various fields: environment, health, disaster, agriculture, planning, development, business etc. It has an International Editorial Board and a panel of Peer Reviewers to ensure the quality of research papers. This will enhance citations and H-Index. International Journal of Geoinformatics is indexed by prestigious indexing services such as <strong>SCOPUS, EBSCO, British Library, Google Scholar, Geoscience Australia etc</strong>. We are trying for more indexing services to include IJG.</p> <p><strong>International Journal of Geoinformatics</strong> has been published in two formats, as printed version ISSN 1686-6576 and electronic version ISSN 2673-0014. The first printed edition has been published since 2005 and now years 12 and also electronic version has been published in Vol. 1, No. 1, March, 2005. From 2014, IJG was published both 4 issues (March, June, September and December) <strong>hardcopy and online</strong>. Online version is enhancing the citations and also found easy to access by reader.</p> <p>Therefore, starting 2021, IJG will publish only online version but <strong>numbers of issue are increased to 6 issues as (February, April, June, August, October and December).</strong></p> https://journals.sfu.ca/ijg/index.php/journal/article/view/2151 The Identification of Irrigated Crop Types Using Support Vector Machine, Random Forest and Maximum Likelihood Classification Methods with Sentinel-2 Data in 2018: Tashkent Province, Uzbekistan 2022-03-28T02:04:50-07:00 E. Erdanaev elbek.erdanaev@geo.uni-goettingen.de M. Kappas mkappas@uni-goettingen.de D. Wyss daniel.wyss@uni-goettingen.de <p><em>Accurately mapping land use and land cover including agricultural use and the state of crops at various stages is important to address specific agro-ecological challenges, to implement sustainable agricultural practices, and monitor crops periodically. This study aims to provide a timely and accurate main irrigated crop types mapping at 10m resolution for Tashkent province based on multi-temporal Sentinel-2 data acquired for the growing season in 2018. This paper shows the potential use of multitemporal Sentinel-2 satellite data to derive an up-to-date irrigated crop types classification map of the study area. As single-date satellite imagery does not allow proper cropland classification, multitemporal and high-resolution Sentinel-2 data was used to capture small cropland fields and specific crop types for the vegetation period (April to October 2018). NDVI monthly profiles of crop types as well as additional 10 m resolution bands 2 and 3 were used as input data to perform and assess three classification algorithms: Support Vector Machine (SVM), Random Forest (RF), and Maximum Likelihood Classification (MLC). Accuracy assessment results showed that SVM showed the highest Overall Accuracy (OA) and Kappa Accuracy (KA). KA of classified images for SVM were 0.90 and 0.89 for the RF algorithm. Both performed well with close values. But MLC showed a lower result of KA 0.60. The paper also compares the area of derived irrigated cropland area with data from the State Committee for Statistics of Uzbekistan for selected crop types. Values for the crops "cotton" and "wheat" derived by SVM and RF methods show a high correlation with the provided statistical data. Based on the results, the SVM classification method is recommended for further mapping and monitoring of irrigated crop types in the region when Sentinel-2 data is used. </em></p> 2022-03-28T00:00:00-07:00 Copyright (c) 2022 https://journals.sfu.ca/ijg/index.php/journal/article/view/2155 Soil Moisture Sensor Measurement and Vegetation-Soil-Water Related Indices – A Case Study in Mango Plantation, Nakhorn Ratchasima Province, Thailand 2022-03-28T02:12:00-07:00 A. Pensuk Tibkaew anisara.pensuk@gmail.com J. Miyai nnoomm@hotmail.com W. Buakhao ch_panya@hotmail.com V. Phonekeo vivarad@gmail.com <p><em>This study conducted the soil moisture estimation using remote sensing data and ground soil moisture sensors in the mango plantation. The applied methodology is the spatial and statistical analysis to determine the relationship between the measured soil moisture using ground sensors and the remote sensing indices generated from Sentinel-2A satellite images. The sensors measured the soil moisture ground data from Nov. 2019 to Feb. 2020. However, we used only the data on seven dates. This is because the cloud-free satellite images are available only on these dates to generate the remote sensing indices. The used indices are NDVI, Normalized Water Moisture Index (NDWI) for vegetation water content monitoring, Normalized Soil Moisture Index (NSMI) for data visualization and analysis. In the implementation, we first visualized the soil moisture trend compared with the remote sensing indices value at the image pixels of sensor location on each observation day. Next, we statistically analyzed the spatial data to establish the relationship between the soil moisture from all the ground sensors and the remote sensing indices. However, the output R<sup>2</sup> is very low; then, it brings us to have an idea to apply in-depth analysis based on the ground sensor performance.&nbsp; This method shows an interesting result. We found that only the NDWI for monitoring vegetation water content has a similar trend with the soil moisture. Secondly, we performed the linear regression correlation between soil moisture and remote sensing indices values of each sensor as time-series analysis. The result show that the correlation between soil moisture and NDWI, NSMI and NDVI are classified into 3 groups, which are 0.7 &lt; R<sup>2</sup> &lt; 0.9, 0.6 &lt; R<sup>2</sup> &lt; 0.7, and R<sup>2</sup> &lt; 0.5, where their corresponding p-value ranges are 0.001 &lt; p-value &lt; 0.02, 0.01 &lt; p-value &lt; 0.03, and 0.08 &lt; p-value &lt; 0.9, respectively. Lastly, we investigated the reason that causes the very high correlation between the soil moisture value of the first group of sensors and NDWI and NSMI. The result shows that these sensors are in a sparse vegetation cover area, where NDVI ≤ 0.3. Therefore, according to this, we can conclude that remote sensing indices NDWI and NSMI can be applied for soil moisture estimation in a sparsely vegetated study area, where the NDVI value should be less than or equal to 0.3.</em></p> 2022-03-28T00:00:00-07:00 Copyright (c) 2022 https://journals.sfu.ca/ijg/index.php/journal/article/view/2159 Telemedicine Technology Application for COVID-19 Patient Tracing Using Smartphone GNSS 2022-03-28T02:19:54-07:00 M.N. Cahyadi cahyadi@geodesy.its.ac.id L.O. Susanto levianasusanto.206016@mhs.its.ac.id C.A. Rokhmana caris@ugm.ac.id S.S. Sulistiawan soni.sunarso.s@gmail.com W. Christrijogo Sumartono christrijogo@fk.unair.ac.id S. Agus Budi Raharjo Lagus.budi@its.ac.id Endroyono endroyono@ee.its.ac.id M. Atok moh_atok@statistika.its.ac.id <p><em>In order to cope with a pandemic COVID-19, Indonesia has implemented various measures of public health including contact tracing. This research will integrate three aspects, namely the use of telemedicine for geographic information system, tracking covid-19 patients using smartphones and diagnosed persons. The three aspects are wrapped in interactive and informative application where users can track their journeys, and communicate directly with the doctors. The geographic information system was built based on statistical analysis of the coronavirus disease (COVID-19) pandemic to determine the factors that affect the number of COVID-19 patients in an area using geographically weighted regression. Later on, this application can provide information about the current conditions, increase data transparency, and used as a tool in assessing a particular policy. This telemedicine application utilizes a map-based geographic information system (GIS) feature to display information. This system also has high security so that it can protect user information and can be accessed easily by users.</em></p> 2022-03-28T00:00:00-07:00 Copyright (c) 2022 https://journals.sfu.ca/ijg/index.php/journal/article/view/2161 Remote Sensing Image Analysis for Identification of Peat Thickness Using Spectral Transformation Approach: Case Study of Bengkalis Island, Riau, Indonesia 2022-03-28T02:29:06-07:00 N. Ambhika nafian.ambhika@mail.ugm.ac.id W. Widyatmanti wwidyatmanti@ugm.ac.id W.K. Mahendra william.krista.mahendra@outlook.com D. Awanda disyaawanda@hotmail.com D.A. Umarhadi wwidyatmanti@ugm.ac.id <p><em>Peatland plays an important role in the global climate. Balancing economic, social and conservation needs on peatland utilization become an obligation in developing sustainable peatland regulation. To identify the appropriate land function in the peatland environment, the depth of peat is the main property to manage those balance needs. On the other hand, vast areas of peatland changing hinder rapid peat depth mapping method to have high accuracy. Multi-temporal remote sensing data were used to identify peatland-related land-use changes. The vegetation and wetness indices spectral transformations had been analyzed. The method used for the accuracy test in this study was correlation and regression analysis for modeling and the Standard Error of Estimate (SEE). The results of this study showed the vegetation indices and NDSI was not able to obtain peat thickness model due to the unstable vegetation and land cover changes. However, the NDWI was satisfied with the statistical assessment and was able to obtain the peat thickness with 41.96% accuracy. The determination of a sample design, the number and distribution of samples in preserved land covers, and missed variables and external factors in this study need to be considered in further research. The vegetation indices and wetness indices potentially can be the alternative variables to construct the peat depth map.</em></p> 2022-03-28T00:00:00-07:00 Copyright (c) 2022 https://journals.sfu.ca/ijg/index.php/journal/article/view/2165 Surveillance Model of Parasitic Zoonosis in Cyprinoid Fishes in Northern Zone and Northeastern Zone of Thailand and Myanmar Using GIS 2022-03-28T02:35:36-07:00 E.E. Phyo Myint nithikethkul2016@gmail.com A. Sereemaspun nithikethkul2016@gmail.com H. Sjödin nithikethkul2016@gmail.com J. Rocklöv nithikethkul2016@gmail.com Y.S. Lai nithikethkul2016@gmail.com A. Ribas nithikethkul2016@gmail.com N. Pakdeenarong nithikethkul2016@gmail.com C. Nithikathkul nithikethkul2016@gmail.com <p><em>The parasitic zoonosis, opisthorchis viverrini has been an important public health problem in many parts of the globe. In Thailand, fish-borne parasitic zoonosis is highly spread in the northern and northeastern regions, where a large impact of cholangiocarcinoma occurs, a crucial source of the liver cancer. The rare occasions reports date published about the Opisthorchiasis in the middle zone of Myanmar. In our study, a total of&nbsp; a few species of fish borne trematode metacercariae i.e.; three kind of small intestinal flukes, the family of Heterophyidae; Haplorchoides sp., Haplorchis pumilio, Haplorchis taichi and one species of carcinogenic liver fluke, the family of Opisthorchiidae; <strong>Opisthorchis viverrini </strong>have been detected from seven study areas from Thailand and Myanmar. The geographic information relevant with the rate of infection with vulnerable species of freshwater fishes was also posted from Thailand and Myanmar, and built a parasitic diseases combine with georeference for Geographical Information System (GIS) implementation. Furthermore, secondary descriptive analysis of the prevalence of fishborne trematodes metacercariae from countries of golden triangles (Southeast Asia) ie; Thailand, Myanmar, and Laos PDR have been created a GIS database for infection status of parasite infections. The outcome from this study may be helpful in strategies for protocol of the prevention of parasitic zoonosis in freshwater fishes reportin Thailand and Myanmar.</em></p> 2022-03-28T00:00:00-07:00 Copyright (c) 2022