Geoexplorer 6000 Series

[Nilsa Housman]

TheTrimble GeoExplorer 6000 series takes GNSS productivity to a whole new level. Combining submeter accuracy GNSS, high quality photo capture, wireless Internet, and connectivity options in a single product.

Use this Power Supply to recharge your GeoExplorer 6000, Pro 6, and Geo 7 series handheld battery pack. The kit contains plug adapters for the U.K., Europe, Australia and the United States. This Power Supply kit is provided as a standard component of the GeoExplorer 6000 series systems. Note: This Power Supply is not compatible with previous GeoExplorer series handhelds.

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Initially, the Windows Mobile Device Center (WMDC), installed on a desktop or laptop computer, may not recognize the Trimble GeoExplorer 6000 series GPS unit after it is connected via USB. Cause Unknown

I don't have Pathfinder on my computer. I need to pull data off of my Trimble unit (Geoexplorer 6000 series) and save it on an external hard drive or USB key, without using Pathfinder. This way, I can send the storage device to a colleague in another location who has Pathdfinder and can import it properly and convert it to a shapefile for me. Is this possible? I've tried to copy and past raw data from the unit to my computer and have been met with an error.

You don't actually need the full Pathfinder Office suite to get your data off the handheld and onto a PC, all you need is the Trimble Data Transfer Utility. It's a free download. The same utility is included in the full Pathfinder Office suite, but Trimble makes the data transfer utility freely available since (as you know) the people using GPS in the field, collecting the data, are often very geographically separated from the people processing and using the data. Ideally, everyone in your organization who uses a Trimble in the field would have the Data Transfer Utility installed.

When you open a new rover file in TerraSync on the GPS make sure you set the save location to the SD Card, NOT Main Memory. If the deed is already done and the data is already on the unit you'll have to connect to the unit and go looking for the files, I haven't had great luck with this approach.

There should be a whole bunch of files in the SD drive, those are the files that TerraSync combines and downloads as an SSF file when you use Pathfinder, so, just grab them and move them onto your computer.

Use this USB data cable to connect the GeoExplorer 6000 series/ Geo7X series handhelds to your office computer, for upload and download of data. This cable is provided as a standard component of the GeoXH, and GeoXT handheld systems. This cable can also be used to connect a Trimble TSC3 data collector to a computer for data import and export as well.

To identify which version is installed on your handheld, tap Start / Settings / System / System Information, then look at the Firmware version number listed under the Geo 7X handheld section of the Device tab.

The process of upgrading to the version 6.5.10 operating system will not affect your files and applications. All application data and files will be retained; however it is considered good practice to back up important data up prior to installing updates.

Connect Power to the Geo 6000 and copy your data off of the Geo 6000 just incase. This OS Upgrade WILL NOT remove your software or data but as a precaution back up your data and note your install code and version of software.

Download this upgrade package to upgrade the GNSS Firmware of your GeoExplorer 6000 series GeoXT or GeoXH handheld to version 3.06.1. All users GeoExplorer 6000 series GeoXT and GeoXH handhelds should have the GNSS firmware upgraded to this latest version.

*Features: Features and issues resolved with this firmware version include:

1. Confirm your handheld requires this update. This upgrade is compatible with the GeoExplorer 6000 series GeoXT and GeoXH handhelds only. To check the current GNSS firmware version, on your handheld tap Start > Settings > System > System Information, then scroll to the GNSS tab. If the firmware version is 3.05.6 or lower, you can upgrade to this version.

A large-scale flood risk analysis that properly evaluates and quantifies the three components of risk (hazard, exposure and vulnerability) is essential in order to support national and global policies, emergency operations and land use management. For example, governments can use risk information for the prioritisation of investments to implement measures for flood damage reduction, for emergency operations and for land-use policies, while reinsurance companies can improve the estimation of the flood risk-based insurance premiums.

Nevertheless, limits in time and data represent significant limitation this kind of applications: i) the significant amount of data and parameters required for the calibration and validation of traditional model; ii) the moderate/coarse resolution of data available at global scale and the sparse availability of high-resolution data that may affect the accuracy of analysis results; iii) the high cost and computational demand of hydraulic models. However, the growing availability of data from new technologies of Earth observation (EO) and environmental monitoring combined with the advances in newly developed algorithms (e.g. machine learning) have extended the range of possibilities for geoscientists, updating and re-inventing the way highly resource- and data-intensive processes, such as risk management and communication, are carried out.

The demonstrative application shows how the description of flood risk may particularly benefit from the integrated use of geomorphic methods, machine learning algorithms and EO freely available monitoring data. The ability of the proposed cost-efficient model to carry out high-resolution and large-scale analyses in data-scarce environments allows performing future risk assessments keeping abreast of temporal and spatial changes in terms of hazard, exposure and vulnerability.

On 3rd July 2019 a paroxysm without long-term precursors has occurred, followed by lava flows from a vent localized in the SW crater area and sporadically from the NE one. Afterwards, on 28th August 2019, a new paroxysmal explosion has occurred followed by strong volcanic activity, culminating with a lava flow from the SW-Central crater area.

This study is focusing on environmental aftermath of the 2019 Stromboli eruptions. The analysis of Land Cover (LC) and Land Use (LU) changes is used to describe the impact on the environment of the island. The detection of impacted areas is mainly based on the integration of very high-spatial-resolution PLEIADES-1, moderate-spatial-resolution SENTINEL-2 satellite imagery, and field surveys. Normalized Burn Ratio (NBR), Normalized Difference Vegetation Index (NDVI), and Relativized Burn Ratio (RBR) were used to map the areas covered by fires. NBR easily allows to easily identify the areas impacted by wildfire and the degree of severity of the damage. This index is calculated on two SENTINEL-2 images acquired on different dates before and after the fire (after a not excessively high number of days, especially if the area affected by the fire consists mainly of pasture or low bush). RBR is obtained as the difference between the NBR index of the images acquired before and after the event. LC and LU classifications has involved the detection of new classes whose details have been calibrated on different reduction scales from 1:2.000 to 1:10.000, following the environmental units that made up the Strombolian landscape. New LC and LU classifications are the result of the intersection between classes of CORINE Land Cover project (CLC) and local landscape patterns. Field survey has been useful to conduce semi-structured interviews to the local population; the purpose of the social investigation was to collect detailed and direct information about damages.

The most impacted areas by tephra fallout are located in the south-western and southern part of the island, nearby the village of Ginostra. The results of multi-temporal comparison show that fire-damaged areas amount to 39% of the total area of the island. Artificial areas have not been particularly impacted (max 14% of decrease), whereas agricultural and semi-natural vegetated areas show a much more consistent decrease of 34% and 81%, respectively.

Raw radar images are processed with the ESA SNAP remote sensing software. A radiometric threshold is estimated to distinguish water surfaces from surfaces without water. Moreover, coherence data derived from InSAR processing (2) provide additional data to detect flooded buildings in city centers. For multispectral images, the MNDWI index (3) was selected as it allows to delineate more precisely water surfaces. Finally, a Random Forest classification has proved effective in defining the spatial distribution of the flooded areas on river basins from the learning areas integrated to the algorithm. In the confusion matrix, implemented for validation, the Kappa index (4) reaches 96.2 % with an overall accuracy of 97.7 %.

With expeditions into glaciated regions on the planet becoming more commonplace there is a need to be able to make route assessments to identify potential hazards for safe operational planning. We use an example from the recently completed "the Longest Journey", a polar expedition that has broken the record for the longest solo unsupported polar journey in human history. The expedition route is in excess of 5,600 kilometres, commencing at the Russian Novolaskaya Station (Novo), to the Pole of Inaccessibility, to Dome Argus (Dome A), and returning to Novo. The estimation and provision of several derived quantities were provided along the route that included inferred crevassing potential of the, supplemented by reporting of additional terrain conditions and hazards. Here we present the route analysis and evaluation with what was actually found under field conditions with footage obtained during the traverse. We show significant success with apriori route planning can be obtained by careful analysis and expert interpretation of available data, that include satellite data based on visible and radar imagery. This approach to minimising hazard exposure can be usefully applied to other operations, including travel over remote and glaciated field locations for science and expedition purposes.

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