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Abstract:Telemetry based on Global Positioning Systems (GPS) makes possible to gather large quantities of information in a very fine scale and work with species that were impossible to study in the past. When working with GPS telemetry, the option of storing data on board could be more desirable than the sole satellite transmitted data, due to the increase in the amount of locations available for analysis. Nonetheless, the uncertainty in the retrieving of the collar unit makes satellite-transmitted technologies something to take into account. Therefore, differences between store-on-board (SoB) and satellite-transmitted (IT) data sets need to be considered. Differences between SoB and IT data collected from two lowland tapirs (Tapirus terrestris), were explored by means of the calculation of home range areas by three different methods: the Minimum Convex Polygon (MCP), the Fixed Kernel Density Estimator (KDE) and the Brownian Bridges (BB). Results showed that SoB and IT data sets for the same individual were similar, with fix ranging from 63 % to 85 % respectively, and 16 m to 17 m horizontal errors. Depending on the total number of locations available for each individual, the home ranges estimated showed differences between 2.7 % and 79.3 %, for the 50 % probability contour and between 9.9 % and 61.8 % for the 95 % probability contour. These differences imply variations in the spatial coincidence of the estimated home ranges. We concluded that the use of IT data is not a good option for the estimation of home range areas if the collar settings have not been designed specifically for this use. Nonetheless, geographical representations of the IT based estimators could be of great help to identify areas of use, besides its assistance to locate the collar for its retrieval at the end of the field season and as a proximate backup when collars disappear. Rev. Biol. Trop. 64 (4): 1441-1450. Epub 2016 December 01.

Resumen:La telemetría basada en los sistemas de geopocisionamiento global (GPS) hace posible recopilar gran cantidad de información a escalas muy finas, y trabajar con especies imposibles de estudiar en el pasado. Al trabajar con telemetría de GPS, la opción de guardar información en la memoria interna del instrumento puede ser más deseable que sólo tener acceso a la información enviada vía satélite, debido a la mayor cantidad de localizaciones disponibles para analizar. No obstante, la incertidumbre de recuperar el collar hace que las tecnología de trasmisión vía satélite deba ser tenida en cuenta. Diferencias entre las bases de datos almacenadas en el collar (SoB) y las trasmitidas vía satélite (IT), recolectadas de dos individuos de Tapir de tierras bajas (Tapirus terrestris), son consideradas, en términos de las áreas de los rangos de hogar calculados con cada uno y mediante el uso de tres metodologías diferentes: Mínimo Polígono Convexo (MCP), Estimador de Densidad de Kernel Fijo (KDE) y los Puentes Brownianos (BB). Las bases de datos SoB e IT son similares, con tasas de acierto de localizaciones que oscilan entre 63 % to 85 % y errores horizontales de 16 m y 17 m respectivamente. Dependiendo del número total de localizaciones disponibles para cada individuo, los rangos de hogar estimados muestran diferencias entre 2.7 % y 79.3 %, para el contorno del 50 % de probabilidades, y entre 9.9 % y 61.8 % para el contorno del 95 % de probabilidades. Estas diferencias implican variaciones en la coincidencia espacial de los rangos de hogar estimados. Concluimos que el uso de la información trasmitida vía satélite no es una buena opción para la estimación de rangos de hogar, si la programción de los collares no ha sido diseñada específicamente para tal fin. Sin embargo, las representaciones geográficas de los estimados a partir de las bases de datos IT pueden ser de gran ayuda para la identificación de áreas de uso, además de su utilidad para la localización y recuperación de collares tras su liberación de los individuos monitoreados y como una base de datos de soporte en caso de pérdida del collar.

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Telemetry based on Global Positioning Systems (GPS) makes possible to gather large quantities of information in a very fine scale and work with species that were impossible to study in the past. When working with GPS telemetry, the option of storing data on board could be more desirable than the sole satellite transmitted data, due to the increase in the amount of locations available for analysis. Nonetheless, the uncertainty in the retrieving of the collar unit makes satellite-transmitted technologies something to take into account. Therefore, differences between store-on-board (SoB) and satellite-transmitted (IT) data sets need to be considered. Differences between SoB and IT data collected from two lowland tapirs (Tapirus terrestris), were explored by means of the calculation of home range areas by three different methods: the Minimum Convex Polygon (MCP), the Fixed Kernel Density Estimator (KDE) and the Brownian Bridges (BB). Results showed that SoB and IT data sets for the same individual were similar, with fix ranging from 63 % to 85 % respectively, and 16 m to 17 m horizontal errors. Depending on the total number of locations available for each individual, the home ranges estimated showed differences between 2.7 % and 79.3 %, for the 50 % probability contour and between 9.9 % and 61.8 % for the 95 % probability contour. These differences imply variations in the spatial coincidence of the estimated home ranges. We concluded that the use of IT data is not a good option for the estimation of home range areas if the collar settings have not been designed specifically for this use. Nonetheless, geographical representations of the IT based estimators could be of great help to identify areas of use, besides its assistance to locate the collar for its retrieval at the end of the field season and as a proximate backup when collars disappear.

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BACKGROUND: Estimating the size of forcibly displaced populations is key to documenting their plight and allocating sufficient resources to their assistance, but is often not done, particularly during the acute phase of displacement, due to methodological challenges and inaccessibility. In this study, we explored the potential use of very high resolution satellite imagery to remotely estimate forcibly displaced populations. METHODS: Our method consisted of multiplying (i) manual counts of assumed residential structures on a satellite image and (ii) estimates of the mean number of people per structure (structure occupancy) obtained from publicly available reports. We computed population estimates for 11 sites in Bangladesh, Chad, Democratic Republic of Congo, Ethiopia, Haiti, Kenya and Mozambique (six refugee camps, three internally displaced persons' camps and two urban neighbourhoods with a mixture of residents and displaced) ranging in population from 1,969 to 90,547, and compared these to "gold standard" reference population figures from census or other robust methods. RESULTS: Structure counts by independent analysts were reasonably consistent. Between one and 11 occupancy reports were available per site and most of these reported people per household rather than per structure. The imagery-based method had a precision relative to reference population figures of <10% in four sites and 10-30% in three sites, but severely over-estimated the population in an Ethiopian camp with implausible occupancy data and two post-earthquake Haiti sites featuring dense and complex residential layout. For each site, estimates were produced in 2-5 working person-days. CONCLUSIONS: In settings with clearly distinguishable individual structures, the remote, imagery-based method had reasonable accuracy for the purposes of rapid estimation, was simple and quick to implement, and would likely perform better in more current application. However, it may have insurmountable limitations in settings featuring connected buildings or shelters, a complex pattern of roofs and multi-level buildings. Based on these results, we discuss possible ways forward for the method's development.

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Despite early speculation to the contrary, all tropical forests studied to date display seasonal variations in the presence of new leaves, flowers, and fruits. Past studies were focused on the timing of phenological events and their cues but not on the accompanying changes in leaf area that regulate vegetation-atmosphere exchanges of energy, momentum, and mass. Here we report, from analysis of 5 years of recent satellite data, seasonal swings in green leaf area of approximately 25% in a majority of the Amazon rainforests. This seasonal cycle is timed to the seasonality of solar radiation in a manner that is suggestive of anticipatory and opportunistic patterns of net leaf flushing during the early to mid part of the light-rich dry season and net leaf abscission during the cloudy wet season. These seasonal swings in leaf area may be critical to initiation of the transition from dry to wet season, seasonal carbon balance between photosynthetic gains and respiratory losses, and litterfall nutrient cycling in moist tropical forests.

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Traditional ecological knowledge (TEK) can play an important role in the understanding of ecological systems. Although TEK has complemented scientific and managerial programs in a variety of contexts, its formal incorporation into remote sensing exercises has to date been limited. Here, we show that the vegetation classifications of the Ache, an indigenous hunter-gatherer tribe of the Mbaracayu Forest Reserve in Paraguay, are reflected in a supervised classification of satellite imagery of the reserve. Accuracy of classification was toward the low end of the range of published values, but was reasonable given the difficult nature of separating forest classes from satellite images. Comparison of the resultant map with a more traditionally elaborated vegetation map highlights differences between the two approaches and the gain in information obtained by considering TEK classifications. We suggest that integration of TEK and remote sensing may provide alternative insights into the ecology of vegetation communities and land cover, particularly in remote and densely forested areas where ecological field research is often limited by roads and/or trail systems.

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This study utilized telemetric systems to sample microbes and pathogens in forest, burned forest, rangeland, and urban watersheds to assess surface water quality in northern New Mexico. Four sites included remote mountainous watersheds, prairie rangelands, and a small urban area. The telemetric system was linked to dataloggers with automated event monitoring equipment to monitor discharge, turbidity, electrical conductivity, water temperature, and rainfall during base flow and storm events. Site data stored in dataloggers was uploaded to one of three types of telemetry: 1) radio in rangeland and urban settings; 2) a conventional phone/modem system with a modem positioned at the urban/forest interface; and 3) a satellite system used in a remote mountainous burned forest watershed. The major variables affecting selection of each system were site access, distance, technology, and cost. The systems were compared based on operation and cost. Utilization of telecommunications systems in this varied geographic area facilitated the gathering of hydrologic and water quality data on a timely basis.

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Remote collaboration for anesthesia requires considerable sharing of physiologic data, audio, and images on a consistent data platform. A low-bandwidth connection between Ecuador and the United States supported effective joint management of operative plan, airway, intraoperative decisions, and recovery. Transmission with a 64-Kbps InMarSat satellite telephone (Thrane & Thrane, Denmark) connection from hospitals in Macas and Sucúa, Ecuador, to Richmond, Virginia, included preoperative patient evaluations, video of endotracheal intubations, electrocardiogram waveforms, pulse oximetry measurements, arterial blood pressure readings, capnography readings, and auscultation of breath sounds.

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Although several types of satellite data provide temporal information of the land use at no cost, digital satellite data applications for agricultural studies are limited compared to applications for forest management. This study assessed the suitability of vegetation indices derived from the TERRA-Moderate Resolution Imaging Spectroradiometer (MODIS) sensor and SPOT-VEGETATION (VGT) sensor for identifying corn growth in western Mexico. Overall, the Normalized Difference Vegetation Index (NDVI) composites from the VGT sensor based on bi-directional compositing method produced vegetation information most closely resembling actual crop conditions. The NDVI composites from the MODIS sensor exhibited saturated signals starting 30 days after planting, but corresponded to green leaf senescence in April. The temporal NDVI composites from the VGT sensor based on the maximum value method had a maximum plateau for 80 days, which masked the important crop transformation from vegetative stage to reproductive stage. The Enhanced Vegetation Index (EVI) composites from the MODIS sensor reached a maximum plateau 40 days earlier than the occurrence of maximum leaf area index (LAI) and maximum intercepted fraction of photosynthetic active radiation (fPAR) derived from in-situ measurements. The results of this study showed that the 250-m resolution MODIS data did not provide more accurate vegetation information for corn growth description than the 500-m and 1000-m resolution MODIS data.

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