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4 results for “helicopter survey”
Рис. 1. Αиния маршрута; цифры — места, гΑе быΛи отмечены особи бурого меΑвеΑя во время учетов с вертоΛета 22.05.2018. РезуΛьтаты учетов бурого меΑвеΑя на о. ЗавьяΛова с вертоΛета «Еврокоптер 120». 11:55 выΛет с нефтепирса г. МагаΑана, 12:14 поΑΛет к острову, 12:20 (1) отмечен первый моΛоΑой меΑвеΑь на террасе, 12:52 (2) отмечен оΑин взросΛый меΑвеΑь, 13:06 (3, 4) отмечены Αва взросΛых меΑвеΑя, 13:08 (5, 6, 7) отмечены три взросΛых меΑвеΑя, 13:18 (8) отмечен оΑин взросΛый меΑвеΑь. 13:56 переΛет в гороΑ МагаΑан Fig. 1. Route line; the figures indicate areas where brown bears were seen during the helicopter surveys on 22 May 2018. The results of the brown bear surveys on Zavyalov island from the Eurocopter 120 helicopter. 11:55 departure from the oil pier of Magadan, 12:14 hovering near the island, 12:20 (1) the first young bear identified on the terrace, 12:52 (2) one adult bear identified, 13:06 (3, 4) two adult bears identified, 13:08 (5, 6, 7) three adult bears identified, 13:18 (8) one adult bear identified, 13:56 Flight to Magadan in Brown bear (Ursus arctos) of Zavyalov Island (Sea of Okhotsk): Abundance and possible migration routes
Рис. 1. Αиния маршрута; цифры — места, гΑе быΛи отмечены особи бурого меΑвеΑя во время учетов с вертоΛета 22.05.2018. РезуΛьтаты учетов бурого меΑвеΑя на о. ЗавьяΛова с вертоΛета «Еврокоптер 120». 11:55 выΛет с нефтепирса г. МагаΑана, 12:14 поΑΛет к острову, 12:20 (1) отмечен первый моΛоΑой меΑвеΑь на террасе, 12:52 (2) отмечен оΑин взросΛый меΑвеΑь, 13:06 (3, 4) отмечены Αва взросΛых меΑвеΑя, 13:08 (5, 6, 7) отмечены три взросΛых меΑвеΑя, 13:18 (8) отмечен оΑин взросΛый меΑвеΑь. 13:56 переΛет в гороΑ МагаΑан Fig. 1. Route line; the figures indicate areas where brown bears were seen during the helicopter surveys on 22 May 2018. The results of the brown bear surveys on Zavyalov island from the Eurocopter 120 helicopter. 11:55 departure from the oil pier of Magadan, 12:14 hovering near the island, 12:20 (1) the first young bear identified on the terrace, 12:52 (2) one adult bear identified, 13:06 (3, 4) two adult bears identified, 13:08 (5, 6, 7) three adult bears identified, 13:18 (8) one adult bear identified, 13:56 Flight to Magadan
Helicopter-based GPR survey of Rutor glacier and nearby glaciers, Aosta Valley, Italy, in May 2012
<p>This dataset contains Ground Penetrating Radar (GPR) data collected on May 2012, from helicopter, with a 70 Mhz antenna, on the Rutor Glacier (files numbered from 6 to 10), and nearby glaciers situated in Aosta Valley (European Alps). They were collected for ARPA Val d'Aosta, the local environmental protection agency.</p> <p>The Rutor glacier data and their interpretation, at August 2024, are also being submitted to The Cryosphere scientific journal, in a possible future publication entitled Ground penetrating radar on Rutor temperate glacier supported by ice-thickness modeling algorithms for bedrock detection, by Andrea Vergnano, Diego Franco and Alberto Godio.</p> <p><br>The .DZT files are the data files. They can be opened with specialistic geophysical software. In particular, a free and open access package is available, called RGPR and installable in the R environment. (https://cran.r-project.org/) (https://emanuelhuber.github.io/RGPR/). After loading the RGPR library with the R command</p> <p>library(RGPR)</p> <p>a file can be read and stored in the "data" variable by the command</p> <p>data=readGPR("FILE____001.DZT")</p> <p> </p> <p>The .PLT files are simple text files that contain the geolocation information in standard NMEA format (https://aprs.gids.nl/nmea/#gga).<br>Each row is a measurement, and there is a measurement every second. To assign the correct geolocation to each GPR trace contained in the .DZT files there are two options:</p> <p>1) follow your specialistic software manual</p> <p>2) do it manually by considering that the GPR sample rate of traces is constant in time. The first .PLT line refers to the first GPR trace, and the last .PLT line refers to the last GPR trace.</p> <p> </p> <p> </p> <p> </p>
Data from: Effects of distance on detectability of Arctic waterfowl using double-observer sampling during helicopter surveys
Aerial survey is an important, widely employed approach for estimating free‐ranging wildlife over large or inaccessible study areas. We studied how a distance covariate influenced probability of double‐observer detections for birds counted during a helicopter survey in Canada's central Arctic. Two observers, one behind the other but visually obscured from each other, counted birds in an incompletely shared field of view to a distance of 200 m. Each observer assigned detections to one of five 40‐m distance bins, guided by semi‐transparent marks on aircraft windows. Detections were recorded with distance bin, taxonomic group, wing‐flapping behavior, and group size. We compared two general model‐based estimation approaches pertinent to sampling wildlife under such situations. One was based on double‐observer methods without distance information, that provide sampling analogous to that required for mark–recapture (MR) estimation of detection probability, urn:x-wiley:20457758:media:ece34824:ece34824-math-0001, and group abundance, urn:x-wiley:20457758:media:ece34824:ece34824-math-0002, along a fixed‐width strip transect. The other method incorporated double‐observer MR with a categorical distance covariate (MRD). A priori, we were concerned that estimators from MR models were compromised by heterogeneity in urn:x-wiley:20457758:media:ece34824:ece34824-math-0003 due to un‐modeled distance information; that is, more distant birds are less likely to be detected by both observers, with the predicted effect that urn:x-wiley:20457758:media:ece34824:ece34824-math-0004 would be biased high, and urn:x-wiley:20457758:media:ece34824:ece34824-math-0005 biased low. We found that, despite increased complexity, MRD models (ΔAICc range: 0–16) fit data far better than MR models (ΔAICc range: 204–258). However, contrary to expectation, the more naïve MR estimators of urn:x-wiley:20457758:media:ece34824:ece34824-math-0006 were biased low in all cases, but only by 2%–5% in most cases. We suspect that this apparently anomalous finding was the result of specific limitations to, and trade‐offs in, visibility by observers on the survey platform used. While MR models provided acceptable point estimates of group abundance, their far higher stranded errors (0%–40%) compared to MRD estimates would compromise ability to detect temporal or spatial differences in abundance. Given improved precision of MRD models relative to MR models, and the possibility of bias when using MR methods from other survey platforms, we recommend avian ecologists use MRD protocols and estimation procedures when surveying Arctic bird populations.
Data from: Effects of distance on detectability of Arctic waterfowl using double-observer sampling during helicopter surveys
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