Positional Precision Analysis of Orthomosaics Derived from Drone Captured Aerial Imagery

The advancement of drones has revolutionized the production of aerial imagery. Using a drone with its associated flight control and image processing applications, a high resolution orthorectified mosaic from multiple individual aerial images can be produced within just a few hours. However, the positional precision and accuracy of any orthomosaic produced should not be overlooked. In this project, we flew a DJI Phantom drone once a month over a seven-month period over Oak Grove Cemetery in Nacogdoches, Texas, USA resulting in seven orthomosaics of the same location. We identified 30 ground control points (GCPs) based on permanent features in the cemetery and recorded the geographic coordinates of each GCP on each of the seven orthomosaics. Analyzing the cluster of each GCP containing seven coincident positions depicts the positional precision of the orthomosaics. Our analysis is an attempt to answer the fundamental question, “Are we obtaining the same geographic coordinates for the same feature found on every aerial image mosaic captured by a drone over time?” The results showed that the positional precision was higher at the center of the orthomosaic compared to the edge areas. In addition, the positional precision was lower parallel to the direction of the drone flight

Accuracy Assessment on Drone Measured Heights at Different Height Levels

The advancement in unmanned aerial system (UAS) technology has made it possible to attain an aerial unit, commonly known as a drone, at an affordable price with increasing precision and accuracy in positioning and photographing. While aerial photography is the most common use of a drone, many of the models available in the market are also capable of measuring height, the height of the drone above ground, or the altitude above the mean sea level. On board a drone, a barometer is used to control the flight height by detecting the atmospheric pressure change; while a GPS receiver is mainly used to determine the horizontal position of the drone. While both barometer and GPS are capable of measuring height, they are based on different algorithms. Our study goal was to assess the accuracy of height measurement by a drone, with different landing procedures and GPS settings

Slum health: diseases of neglected populations.

BackgroundUrban slums, like refugee communities, comprise a social cluster that engenders a distinct set of health problems. With 1 billion people currently estimated to live in such communities, this neglected population has become a major reservoir for a wide spectrum of health conditions that the formal health sector must deal with.DiscussionUnlike what occurs with refugee populations, the formal health sector becomes aware of the health problems of slum populations relatively late in the course of their illnesses. As such, the formal health sector inevitably deals with the severe and end-stage complications of these diseases at a substantially greater cost than what it costs to manage non-slum community populations. Because of the informal nature of slum settlements, and cultural, social, and behavioral factors unique to the slum populations, little is known about the spectrum, burden, and determinants of illnesses in these communities that give rise to these complications, especially of those diseases that are chronic but preventable. In this article, we discuss observations made in one slum community of 58,000 people in Salvador, the third largest city in Brazil, to highlight the existence of a spectrum and burden of chronic illnesses not likely to be detected by the formal sector health services until they result in complications or death. Lack of health-related data from slums could lead to inappropriate and unrealistic allocation of health care resources by the public and private providers. Similar misassumptions and misallocations are likely to exist in other nations with large urban slum populations.SummaryContinued neglect of ever-expanding urban slum populations in the world could inevitably lead to greater expenditure and diversion of health care resources to the management of end-stage complications of diseases that are preventable. A new approach to health assessment and characterization of social-cluster determinants of health in urban slums is urgently needed

Accuracy of Unmanned Aerial System (Drone) Height Measurements

Vertical height estimates of earth surface features using an Unmanned Aerial System (UAS) are important in natural resource management quantitative assessments. An important research question concerns both the accuracy and precision of vertical height estimates acquired with a UAS and to determine if it is necessary to land a UAS between individual height measurements or if GPS derived height versus barometric pressure derived height while using a DJI Phantom 3 would affect height accuracy and precision. To examine this question, height along a telescopic height pole on the campus of Stephen F. Austin State University (SFASU) were estimated at 2, 5, 10 and 15 meters above ground using a DJI Phantom 3 UAS. The DJI Phantom 3 UAS (i.e., drone) was flown up and down the telescopic height pole to estimate height at the 2, 5, 10 and 15 meter locations using four different user controlled flight modes with a total of 30 observations per flight mode. Flight mode configurations consisted of having GPS estimate height while landing the drone between flights, non-GPS mode to estimate height via barometric pressure while landing the drone between flights, flying continuously up and down the height pole while estimating height with GPS on, and flying continuously up and down the height pole in non-GPS mode to estimate height via barometric pressure. A total of 480 height measurements were recorded (30 measurements per height interval per all four flight mode combinations). Standard deviation results indicated that height measurements taken with the drone were less precise when landing was not reset between measurements. Root mean square error (RMSE) analysis indicated that having the landing reset without GPS on achieved the highest accuracy of all measurements taken. An ANOVA conducted on the absolute errors reconfirmed that having the landing reset before each height measurement using the drone achieved higher accuracy compared to flying the drone continuously. This indicates the practical application of height measurement of the DJI Phantom 3 UAS and the importance of resetting the UAS before each height measurement

Accuracy of Unmanned Aerial System (Drone) Height Measurements

Vertical height estimates of earth surface features using an Unmanned Aerial System (UAS) are important in natural resource management quantitative assessments. An important research question concerns both the accuracy and precision of vertical height estimates acquired with a UAS and to determine if it is necessary to land a UAS between individual height measurements or if GPS derived height versus barometric pressure derived height while using a DJI Phantom 3 would affect height accuracy and precision. To examine this question, height along a telescopic height pole on the campus of Stephen F. Austin State University (SFASU) were estimated at 2, 5, 10 and 15 meters above ground using a DJI Phantom 3 UAS. The DJI Phantom 3 UAS (i.e., drone) was flown up and down the telescopic height pole to estimate height at the 2, 5, 10 and 15 meter locations using four different user controlled flight modes with a total of 30 observations per flight mode. Flight mode configurations consisted of having GPS estimate height while landing the drone between flights, non-GPS mode to estimate height via barometric pressure while landing the drone between flights, flying continuously up and down the height pole while estimating height with GPS on, and flying continuously up and down the height pole in non-GPS mode to estimate height via barometric pressure. A total of 480 height measurements were recorded (30 measurements per height interval per all four flight mode combinations). Standard deviation results indicated that height measurements taken with the drone were less precise when landing was not reset between measurements. Root mean square error (RMSE) analysis indicated that having the landing reset without GPS on achieved the highest accuracy of all measurements taken. An ANOVA conducted on the absolute errors reconfirmed that having the landing reset before each height measurement using the drone achieved higher accuracy compared to flying the drone continuously. This indicates the practical application of height measurement of the DJI Phantom 3 UAS and the importance of resetting the UAS before each height measurement

Investigation of Miocene Methane Hydrate Generation Potential in the Transylvanian Basin, Romania

In geology we often revise theoretical models; upon finding new evidence,such as the discovery of methane hydrates, the initial model will be challenged immediately. Hereby the authors put forward two postulates:1) There is a third, previously unexplored source of methane in the Transylvanian Basin, based on a new theoretical approach on methane hydrate formation; 2) The dissociation of methane hydrates creates a strong chlorinity anomaly. Based on a recent analogy with the Black Sea basin model, we apply our statements to the Transylvanian Basin. Using direct and indirect indicators and the published system tract analysis, we claim that there are substantial grounds to believe that this model of methane hydrate formation applies to the Miocene Transylvanian Basin. Due to the increase of the geothermal gradient as a result of the volcanic activity from the Eastern Carpathians, the clathrates dissociated into methane and freshwater. This process of dilution resulted in a chlorinity anomaly that can be spotted in the formation waters of several gas fields from the Transylvanian Basin

The Law of Standard Form Contracts: Misguided Intuitions and Suggestions for Reconstruction

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Spatial Analysis of Historic Cemeteries: Using High Spatial Resolution Imagery as a Visual Aid

Oak Grove Cemetery, located within the City of Nacogdoches in Nacogdoches County Texas, is one of the earliest cemeteries in the county dating to the early 1800’s. Several historic Texans are interred within this cemetery including Thomas J. Rusk and Charles S. Taylor who was the great-great-grandfather of current United States Senator Kay Bailey Hutchison. Due to a fire circa 1910 many of the records for the original section of the cemetery were lost. In the summer of 2006, the GPS coordinates of each grave marker within the cemetery were plotted on a backdrop of 6 inch spatial resolution multispectral digital imagery using a Trimble GeoExplorer 3 GPS unit. The genealogical data from each marker were collected for spatial and genealogical analysis. By incorporating high spatial resolution digital imagery with GPS collected grave marker data we were able to use spatial analysis to display familial relationships and perform statistical analysis of this historic cemetery as well as recover much of the lost historical data while simultaneously creating an enhanced genealogical database for current and future ancestral queries

Accuracy Assessment of Land Cover Maps of Forests within an Urban and Rural Environment

Land cover maps of forests within an urban and rural environment derived from high spatial resolution multispectral data (QuickBird) and medium spatial resolution multispectral data (Landsat ETM+ and SPOJ 4) were compared to ascertain whether increased spatial resolution increases map accuracy of forests and whether map accuracy varies across land cover classification schemes. It is commonly assumed that increased spatial resolution would probably increase land cover map accuracy regardless of land cover classification methodology. This study assessed whether that assumption is correct within a rural and an urban environment. Map accuracy for modified National Land Cover Data (NLCD) 2001 Level II, Level I, and Unique (a modified NLCD 2001 Level II and Level I combination) shows that 30-m Landsat ETM-H data had the highest overall map accuracy for rural, urban, and combined rural/urban land cover maps. Analysis of user\u27s and producer\u27s accuracies shows that Landsat FTM-f data had higher levels of producer\u27s accuracy of \u3e90.0°/o for the coniferous cover type for modified NLCD 2001 Level II and Unique, excluding one instance for which SPOT 4 had a user\u27s accuracy of 98.5% for the rural coniferous cover type. Modified NLCD 2001 Level I Landsat ETM+ data had user\u27s and producer\u27s accuracies for a homogeneous forest cover type of 98.4 and 90.6%, respectively. Landsat ETM+ data also outperformed SPOT 4 and Quick8ird within an urban environment, creating the only map products with forest cover type user\u27s and producer\u27s accuracies of \u3e90.0%