Horizontal and vertical accuracy of Google Earth®: comment on ‘Positional accuracy of the Google Earth terrain model derived from stratigraphic unconformities in the Big Bend region, Texas, USA’ by S.C. Benker, R.P. Langford and T.L. Pavlis

Salinas-Castillo, Wilver Enrique; Paredes-Hernández, Cutberto Uriel

doi:10.1080/10106049.2013.821176

Cited by 3 publications

(2 citation statements)

References 4 publications

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“…Horizontal accuracies have been estimated from 113 m of misalignment [114] to 40 m around cities [115] or even down to 1m in Rome [116]. In rural areas of the world misalignments range from 10.5 m [81,117] to 4.1 m [118]. Since the horizontal positional accuracy of GE is very much dependent on imagery resolution [118], a fairly good estimate of the positional accuracy is derived from the resolution of the imagery used in our study, which was very high for the Galapagos coastline.…”

Section: Discussionmentioning

confidence: 99%

Mangroves in the Galapagos islands: Distribution and dynamics

2019

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Mangrove forests provide valuable coastal protection from erosion, habitat for terrestrial and marine species, nursery grounds for commercial fisheries and are economically important for tourism. Galapagos’ mangroves usually grow directly on solid lava and fragmented rocky shores, thereby stabilizing the sediment and facilitating colonisation by other plants and many animals. However, until very recently, only inaccurate data described mangrove coverage and its distribution. We mapped mangroves using freely available Google Earth Very High Resolution images based on on-screen classification and compared this method to three semi-automatic classification algorithms. We also analysed mangrove change for the period 2004–2014. We obtained an area of 3657.1 ha of fringing mangrove that covers 35% of the coastline. Eighty percent of mangrove cover is found in Isabela island, and 90% in the western and central south-eastern bioregions. The overall accuracy of mangrove classification was 99.1% with a Kappa coefficient of 0.97 when validated with field data. On-screen digitization was significantly more accurate than other tested methods. From the semi-automated methods, Maximum Likelihood Classification with prior land-sea segmentation yielded the best results. During the 2004–2014 period, mangrove coverage increased 24% mainly by expansion of existing mangroves patches as opposed to generation of new patches. We estimate that mangrove cover and growth are inversely proportional to the geological age of the islands. However, many other factors like nutrients, currents or wave exposure protection might explain this pattern. The precise localization of mangrove cover across the Galapagos islands now enables documenting whether it is changing over time.

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Section: Discussionmentioning

confidence: 99%

Mangroves in the Galapagos islands: Distribution and dynamics

2019

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“…Using GCPs can improve the accuracy of generating DEM and Orthomosaics from aerial mapping surveys. The ground control points can be measured by some methods, i.e., traditional surveying methods, LiDAR, an existing map, and even Google Earth [48][49][50]. Many researchers think that adding more ground control points takes more time but creates more accurate drone maps, but does it?…”

Section: Accuracy Of Dems and Orthomosaicsmentioning

confidence: 99%

Summer Mass Balance and Surface Velocity Derived by Unmanned Aerial Vehicle on Debris-Covered Region of Baishui River Glacier No. 1, Yulong Snow Mountain

Che

Wang

et al. 2020

Remote Sensing

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Glacier retreat is a common phenomenon in the Qinghai-Tibetan Plateau (QTP) with global warming during the past several decades, except for several mountains, such as the glaciers in the Karakoram and the western Kunlun Mountains. The dynamic nature of glaciers significantly influences the hydrologic, geologic, and ecological systems in the mountain regions. The sensitivity and dynamic response to climate change make glaciers excellent indicators of regional and global climate change, such as glacier melting and retreat with the rise of local air temperature. Long-term monitoring of glacier change is important to understand and assess past, current, and possible future climate environments. Some glacier surfaces are safe and accessible by foot, and are monitored using mass balance stakes and snow pits. Meanwhile, some glaciers with inaccessible termini may be surveyed using satellite remote images and Unmanned Aerial Vehicles (UAVs). Those inaccessible glaciers are generally covered by debris in the southeast QTP, which is hardly accessible due to the wide distribution of crevasses and cliffs. In this paper, we used the UAV to monitor the dynamic features of mass balance and velocity of the debris-covered region of Baishui River Glacier No. 1 (BRG1) on the Yulong Snow Mountain (YSM), Southeast QTP. We obtained the Orthomosaic and DEM with a high resolution of 0.10 m on 20 May and 22 September 2018, respectively. The comparison showed that the elevation of the debris-covered region of the BRG1 decreased by 6.58 m ± 3.70 m on average, and the mean mass balance was −5.92 m w.e. ± 3.33 m w.e. during the summer, correspondingly. The mean displacement of debris-covered glacier surface was 18.30 m ± 6.27 m, that is, the mean daily velocity was 0.14 m/d ± 0.05 m/d during the summer. In addition, the UAV images not only revealed the different patterns of glacier melting and displacement but also captured the phenomena of mass loss due to ice avalanches at the glacier front and the development of large crevasses. This study provides a feasible method for understanding the dynamic features of global debris-covered glaciers which are inaccessible and unobservable by other means.

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