Systematic healthcare facilities and accessibility system in developing countries are mostly located in urban areas instead of rural areas, causing inconvenience to low-and-middle-income residents to reach healthcare centres. Therefore, an innovative mapping and geomobile application or app is developed for the modernization of healthcare systems in Pulau Indah, Klang Malaysia. This study aimed to provide relevant geoinformation of the healthcare centres and the optimum route to the nearby healthcare facilities. Data collection comprised of road access, lists of healthcare facilities, the type of healthcare facilities, travelling time, cost, as well as base map from Google Earth. The framework of geospatial functions and System Development Life Cycle (SDLC) were implemented to create the proposed system. Survey on 45 respondents revealed that 80% of them are displeased with the existing system performances. Thus, the proposed healthcare accessibility system has created eminent geospatial modules and cartographic elements to intensify the existing system. This personal app system also offers several spatial network analyses to assist the local community in finding the optimum accessibility to reach ideal healthcare centres. Inclusively, seven selected local communities agreed with the functions proposed in this system but there are several parts can be further improved.
Nowadays, UAV is preferred by experts since it is more affordable with reliable accuracy. However, debates on its accuracy draw worldwide attention in order to maintain the output’s quality. Flight altitude is one of the most debated issues of UAV employment due to various ground conditions. Thus, this study intends to investigate the effects of flight altitude towards the final output accuracy. In this study, three different flight altitudes (60m, 80m and 100m) were used to test the outputs accuracy within selected sites in UPNM campus by employing DJI Phantom 4 Pro V2.0 drone. Orthophotos and Digital Surface Model (DSM) of the selected sites were then generated using Pix4D Mapper Software. On-screen measurements of selected features within the selected sites were taken and compared with the actual measurements obtained on grounds. Later, these outputs were used to generate contours using ArcGIS software. The generated contours were compared with available as-built plan. The results were examined qualitatively and quantitatively. From this study, it is found that the mean variance values on flat surface using different flight elevation were 0.86m, 0.99m and 1.16m for 60m, 80m and 100m respectively. Whereas, the mean variance values on hilly surface were 6.95m, 4.35m and 4.3m for 60m, 80m and 100m. On flat surfaces, 60m flight altitude was the best height to be used for UAV mapping. However, for hilly surfaces, 100m flight altitude was the best height to be used. This contrast may due to the lower overlapping images in 60m flight altitude image capture. From the study also, it is found that the accuracy of UAV mapping on hilly surfaces tends to be lower than flat surfaces. This called for further studies to identify the best measures to reduce the errors resulted by extreme ground characteristics.
Unmanned Aerial Vehicles (UAVs) are increasingly used in forestry as they are economical and flexible. This study aims to present the advantages of the drone photogrammetry method in collecting individual tree crowns, as individual tree crown detection could deliver essential ecological and economic information. The referred accuracy for individual tree crown extraction is 79.2%. Only crowns that were clearly visible were selected and manually delineated on the image because the distribution of the true crown size is significantly different from the segmented crowns. The aim of this study is to investigate UAVs orthomosaics in individual tree crown detection. The objectives of this study are to produce the orthomosaic of tree crown extraction mapping using the Pix4D software and analyze the tree crowns using tree crown delineation and the OBIA algorithm. Data processing involves the processing of aerial images using Pix4Dmapper. Automatic tree crown detection involves a tree crown delineation algorithm and OBIA operations to process the tree crown extraction. The crown delineation algorithm and OBIA algorithm operation will be compared to the actual tree crown measurement in terms of diameter and area. The tree crown delineation method obtained a 0.347m mean diameter difference from the actual tree crown diameter, while the OBIA approach obtained 4.98m. The tree crown delineation method obtained 97.26% of the actual tree crown area, while OBIA obtained 91.74%.
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