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  Subjects -> GEOGRAPHY (Total: 493 journals)
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American Journal of Geographic Information System
Number of Followers: 14  

  This is an Open Access Journal Open Access journal
ISSN (Print) 2163-1131 - ISSN (Online) 2163-114X
Published by SAP Homepage  [105 journals]
  • Assessing the Accuracy of Tree Height Quantification Models Derived from
           Unmanned Aerial System Imagery

    • Abstract: Publication year: 2020Source: American Journal of Geographic Information System, Volume 9, Number 2Mohammadreza Safabakhsh Pachehkenari, Hadi FadaeiIn recent years, scholars have witnessed the increasing progress of using unmanned aerial systems (UASs) in topographic mapping due to its lower cost compared with alternative systems These UASs enables tree height estimation by capturing overlapped images and generating 3D point cloud through the structure from motion (SfM) algorithm. To ensure that the normalized digital surface model (nDSM) in the mountain areas is created accurately, careful attention to flight patterns and uniform distribution of ground control points (GCPs) are necessary. To this end, a quadcopter equipped with an RGB camera is used for imaging an area of 131 hectares in two steps: firstly, through a single flight strip with an optimized distribution of GCP and secondly through an improvement of the flight configuration. Afterward, two nDSMs were created by the automatic processing of raw images of both approaches. The prominent results demonstrate that the smart integration of key parameters in flight design can bring the root mean square errors (RMSE) down to 52.43 cm without the need to include GCPs. However, using GCPs with an appropriate distribution culminates in RMSE of 33.59 cm, which means 35.93% better performance. This study highlights the impacts of optimal distribution in GCP on nDSM accuracy, as well as the strategy of using images extracted from the combination of two flight strips with different altitudes and high overlap when local GCP is inaccessible, was found to be beneficial for increasing the overall nDSM accuracy.
       
  • Spatial Patterns of African Indigenous Vegetables Value Chain Actors: The
           Case of Narok and Kajiado County, Kenya

    • Abstract: Publication year: 2020Source: American Journal of Geographic Information System, Volume 9, Number 2Juma R. Magogo, David E. Lawver, Mathew T. Baker, Boren-Alpizar Amy, Cynthia McKenney, Agnes O. NkurumwaThe ever-increasing demand of vegetables has emphasized the importance of vegetable commodities in the horticulture industry. Vegetables grown and consumed in Kenya are categorized as either exotic or indigenous. Indigenous vegetables have comparative advantages over exotic vegetables such as high resilience to harsh climates. These vegetables among other crops have in the past significantly contributed to the nutritional and economic wellbeing of agricultural communities. Thus, this study of African indigenous vegetable (AIVs) value chain actors was conducted for the purpose of analyzing spatial patterns of AIVs value chain actors in Narok and Kajiado County to encourage more farmers, particularly women and youth consider AIVs production and marketing as an occupation. The study locations were selected through a systematic sampling technique and households were selected through a simple random sampling technique. Data were collected from 217 (n = 217) respondents and analysis was conducted using nearest neighbor ratio (NNR). The study found clustering of value chain actors and concluded that value chain actors were clustering based on proximity and commodity. The findings imply that value chain actors can form clusters to achieve higher volumes and clusters can be used by Extension service providers as reference points in disseminating agricultural information. Further research is recommended into cluster farming on its suitability as a tool for Extension and organization of farmers’ associations for improving livelihoods. Lastly, information generated by this study would positively contribute towards the development of AIVs value chain strategies in Kenya.
       
  • Land Cover Mapping Using Remote Sensing Data

    • Abstract: Publication year: 2020Source: American Journal of Geographic Information System, Volume 9, Number 1Jwan Al-doski, Shattri B. Mansor, H'ng Paik San, Zailani KhuzaimahLand cover is a complex parameter because it represents the relationship between socio-economic activities and regional environmental changes, which is why it is important to review and update it periodically. This paper seeks to navigate via a range of subtopics on Land Cover Mapping (LCM) using Remote Sensing (RS) technology for providing enough information that play a significant and prime role in planning, management and monitoring programmes at local, regional and national levels. The literature review structure is described as; give a review of information type and sources with highlights on the strengths and weaknesses of distinct RS information as well as distinct variables extracting from RS information that have been used for LCM. Similarly, the highpoint was done on the LCM techniques which comprise conventional and remote sensed techniques for accurate LCM. For detailed knowledge of the methods, phases, and algorithms of Image classification (IC) for LCM, a brief overview is provided and some issues that influence the efficiency and accuracy of the IC methods were also discussed. From this investigated literature, the most common RS data used for LCM are multispectral, hyperspectral, light detection and ranging (LiDAR), and radio detection and ranging (radar). The choice of appropriate RS data for LCM, however, relies on data accessibility and the particular goal to be obtained and type of classification algorithms. Non-parametric classification algorithms tend to be superior to parametric classification algorithms in LCM using RS data. Nevertheless, the issue which non-parametric algorithms are better than other LCM algorithms was not normally answered. As conclusion, LCM efficiency is influenced by numerous variables like landscape, sampling schedule, training selection techniques and training size, type of non-parametric algorithms, raw data, etc. Thus, these influenced variables need to be addressed before LCM using RS data.
       
  • Spatial Trends and Distribution Patterns of Organic Crop Production in
           Central Kenya

    • Abstract: Publication year: 2020Source: American Journal of Geographic Information System, Volume 9, Number 1Raphael Mwiti Gikunda, David E. Lawver, Matt Baker, Amy E. Boren-AlpizarThis research adds to knowledge on trends and distribution patterns of organic agriculture (OA) systems. The study was descriptive in nature involving 329 organic farmers selected through stratified random sampling from four counties in Central Kenya. The counties were Nyeri, Murang’a, Kirinyaga, and Kiambu. A peer and expert reviewed semi-structured questionnaire and GPS devices were used for data collection. The mean acreage under OA in the four counties rose significantly from M = 264.88, (tau) τ = .0.44, p < .05 in 2012 to M = 508.95,τ = .95, p < .05 in 2017. The annual income from organic crops increased by 84% from KShs 29,926 ($299.26) in 2012 to KShs 181,635 ($1816.35) in 2017. Most of the organic farms were clustered as reported by the average neighbor index (index = .05, p
       
  • Global Geopotential Models Assessment Using Accurate DGPS/Precise
           Levelling Observations Along the Mediterranean Coastal Line, Egypt: Case
           Study

    • Abstract: Publication year: 2020Source: American Journal of Geographic Information System, Volume 9, Number 1A. M. Hamdy, B. A. ShaheenThe performance of Global Geopotential Models (GGMs) to calculate Geoid undulation, along the Mediterranean Western Coastal Line from El- Salloum to El- Alameen, Egypt, has been evaluated. The selected region has the both tourism and geodetic of interests. The quality of geoid undulation (N) will obviously affect the resulting orthometric height (H) determined from Differential Global Positioning System (DGPS). The EGM96 and EGM08 (Bi-Linear Interpolation, Bi-Quadratic Interpolation, Triangulation, Nearest Neighbour) have been tested in this study. 𝑁GGMs was computed from “AllTrans v.3.002” EGM08 geoid calculator and free website of “ICGEM” while Nobs was computed from the relationship N= h-H. Over 52 DGPS/Precise Levelling Stations, the computed standard deviation (σ) of differences in (Nobs – 𝑁GGMs) is used as an accuracy indicator. The standard deviation “RMSE” of the undulation differences has been estimated to be ±24cm for EGM08-Bi-Linear Interpolation to ±45cm for EGM08-Nearest Neighbour and ±1.393m for EGM96. There is a marked improvement in the overall RMSE from (EGM08-Nearest Neighbour) to (EGM08-Bi-Linear Interpolation) by 54%. This study showed that EGM08-Bi-Linear Interpolation model has made significant improvement over other models for such like this Northern-coastal line objects. Such a practice presents a suitable alternative, from an economical point of view, to substitute the expensive traditional levelling technique particularly for linear topographic projects with intermediate accurate survey.
       
  • Modeling the Impact of Traveling Time on the Utilization of Maternity
           Services Using Routine Health Facility Data in Siaya County, Western Kenya
           

    • Abstract: Publication year: 2020Source: American Journal of Geographic Information System, Volume 9, Number 1Oluoch Felix, Ayodo George, Owino Fredrick, Okuto ErickIn Kenya, no studies have attempted to use routine health facility data disaggregated by level of care, to find out if there is a significant statistical relationship between physical accessibility and utilization of maternity services at the ward administrative level. A cross-sectional study design used publicly available geospatial data in combination with routine data from the web-based district health information software (DHIS2) platform. AccessMod (version 5.2.6) was used for travelling time analysis. ArcGIS (version 10.5) and R (version 3.5.3) sufficed for the preparation of geospatial input and the manipulation of AccessMod results respectively. The associations between the independent and dependable outcome was computed using a Zero-inflated Poisson regression model at 95% confidence level. The findings in Siaya County revealed a higher likelihood of a skilled delivery 35% (0.353; CI: 0.349–0.357) and 16% (0.164; CI: 0.162–0.167) respectively, for every unit increase in the proportion of pregnant women who could reach a hospital and health centre within an hour of walking, as compared to being within an hour of a dispensary 4.6% (0.046; CI: 0.045–0.048) using motorcycle transport. Simply advising women to opt for motorized transportation schemes to improve their access to low quality facilities, may in fact, result in diminishing returns. The immediate implication is that policy makers need to upgrade lower tier maternity health services in Siaya County, as pregnant women may value quality of services regardless of the distance. Future research should consider looking at the relationship between skilled delivery and the capacity of existing maternity health services.
       
  • Modelling of Lake Water Quality Parameters by Deep Learning Using Remote
           Sensing Data

    • Abstract: Publication year: 2019Source: American Journal of Geographic Information System, Volume 8, Number 6Randrianiaina Jerry J. C. F., Rakotonirina Rija I., Ratiarimanana Jean R., Lahatra Razafindramisa FilsThe modelling of lake water quality is very important to make some prediction and to monitor it. Modelling Lake water quality, utilizing the data obtained from the in-situ measurements, collecting samples from the in situ and analyzing them in a laboratory are very expensive and time consuming. Several algorithms can be used to model lake water quality using the in-situ measurements data. In this work, Deep Learning was used to perform pH, dissolved oxygen, conductivity and turbidity modelling of the Itasy Lake. Deep Learning is another branch of machine learning or automatic training that works well to solve many problems. The obtained results demonstrated that the developed model of a deep neural network (deep learning) provides an excellent relationship between the observed and simulated water quality parameters. Moreover, the coefficient of correlation (R2) was 0.89 for pH, 0.97 for the dissolved oxygen, 0.96 for the conductivity and 0.99 for the turbidity. The root mean square error (RMSE) values for pH, dissolved oxygen, conductivity and turbidity were below 0.22, 0.21 mg.l-1, 1.37 µS.cm-1, and 0.53 NTU respectively, during the deep learning training and validation phases. After getting the model, the parameters of the Itasy Lake water were estimated on 1st January 2019. The obtained results are as follows: pH was ranged from 7.1 to 7.6, 7.0 mg.l-1 to 7.2 mg.l-1 for the dissolved oxygen, 55.7 µS.cm-1 to 57.1 µS.cm-1 for the conductivity and 12.0 to 12.2 for the turbidity. It was shown that the water quality of Itasy respects the Malagasy norm with regard to pH, dissolved oxygen, conductivity and turbidity. And the mean values for these parameters were respectively 7.6 for pH, 7.01 mg.l-1 for the dissolved oxygen, 57.05 µS.cm-1 for the conductivity and 12.03 NTU for the turbidity.
       
  • Integrating Modern Classifiers for Improved Building Extraction from
           Aerial Imagery and LiDAR Data

    • Abstract: Publication year: 2019Source: American Journal of Geographic Information System, Volume 8, Number 5Haidy Elsayed, Mohamed Zahran, Ayman ElShehaby, Mahmoud SalahThis research proposed an approach for automatic extraction of buildings from digital aerial imagery and LiDAR data. The building patches are detected from the original image bands, normalized Digital Surface Model (nDSM) and some ancillary data. Support Vector Machines (SVMs) and artificial neural network (ANNs) classifiers have been applied individualey as member classifiers. In order to improve the obtained results, SVMs and ANNs have been combined in serial, parallel and hybrid forms. The results showed that hybrid system has performed the best with an overall accuracy of about 87.211% followed by parallel combination, serial combination, ANNs and SVMs with 84.709, 82.102, 77.605 and 74.288% respectively.
       
  • Flood Frequency Analysis and Urban Flood Modelling of Sidi Ifni Basin,
           Southern Morocco

    • Abstract: Publication year: 2019Source: American Journal of Geographic Information System, Volume 8, Number 5Aicha Saad, Adam Milewski, Lahcen Benaabidate, Zin El Abidine El Morjani, Lhoucine BouchaouFlood impact is one of the most significant disasters in the world. Floods are due to natural factors such as heavy rainfall, high floods and high tides, etc., and human factors such as blocking of channels or aggravation of drainage channels, improper land use, deforestation in headwater regions, among others. With the increasing use of GIS, HEC-RAS and digital databases in the floodplain mapping and management processes, the present paper develops a simplified one-dimensional model applied to the Sidi Ifni basin. This approach allows visualizing and quantifying the results in a spatial format. A Digital elevation Model (DEM) was used to create hydraulic model geometry data. The computational procedures were based on a 1D energy equation for steady flow water surface profile calculations. Flows were assessed from a frequency analysis of the only gauging station data. The results have shown that the return period of the observed floods 1985, 2002 and 2015 is respectively about 20-year, 10-year and 50-year, a lateral overflow of the decadal and centennial floods where the maximum water depth is 6.78m for Q (10) and 6.96 m for Q (100). The results obtained can be useful to guide the development plan of the Sidi Ifni city, which is growing.
       
  • Study the Effect of Surrounding Surface Material Types on the Multipath of
           GPS Signal and Its Impact on the Accuracy of Positioning Determination

    • Abstract: Publication year: 2019Source: American Journal of Geographic Information System, Volume 8, Number 5Ahmed S. Mohamed, Mohamed I. Doma, Mostafa M. RabahGlobal Positioning System (GPS) results are suffering from one of the major errors in high precision GPS positioning, the ones caused by reflections, known as Multipath error. Here, we study some of the multipath factors affecting on the accuracy observables obtained from GPS measurements. This will be achieved through monitoring and record the multipath effect according to different types of surface which reflected the signals specific set of generating and monitoring systems for multipath signal is established and a series of controlled experiments are carried out. Experimental results show that Aluminium caused the highest amount of multipath. This is followed by Glass and Wood.
       
 
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