Google EarthTM Research Articles

VISUALIZATION OF DATASETS FROM URBAN GEOLOGY STUDIES USING GOOGLE EARTH: THE CASE STUDY OF NAFPLIO, ARGOLIS PREFECTURE.

The systematic use of tools provided in Geographic Information Systems in data mapping makes easy the management of the data acquired from urban geology studies. However, the information derived concern not only the scientific society but also the local authorities and the general public. Thus, a simple way of data dissemination had to be adopted. Online tools, such as those pioneered by Google EarthTM (GE), are changing the way in which scientists and the general public interact with geospatial data in a virtual environment. In the past years since its 2005 introduction, GE has introduced numerous applications, in geosciences as well as in many socio-economic disciplines. I.G.M.E., in the framework of CSF 2000 – 2006 (Operational Program “Competitiveness”), implemented the project called “Collection, codification and documentation of geothematic information for urban and suburban areas in Greece. Pilot studies”. Data management and cartographic representation was performed using G.I.S., where a geographic database was created, including all available information for the studied areas: geology, topography, satellite images, geophysical, geochemical, geotechnical, hydrogeological and geoarchaeological data. Critical information from the geodatabase concerning the study area of the city of Nafplio, Argolis Prefecture, were imported in Google Earth and stored as a Keywhole Markup Language Zipped file, rendering a 2-D layer in GE directly, to facilitate dissemination.

Spatiotemporal-based clusters as a method for dengue surveillance.

ABSTRACTObjectives.To develop and demonstrate the use of a new method for epidemiological surveillance of dengue.Methods.This was a retrospective cohort study using data from the Health Department of São José do Rio Preto (São Paulo, Brazil). The geographical coordinates were obtained using QGIS™ (Creative Commons Corporation, Mountain View, California, United States), based on patient addresses in the dengue notification system of the Government of Brazil. SaTScan™ (Martin Kulldorff, Boston, Massachusetts, United States) was then used to create a space-time scan analysis to find statistically significant clusters of dengue. These results were plotted and visualized using Google Earth™ mapping service (Google Incorporated, Mountain View, California, United States).Results.More clusters were detected when the maximum number of households per cluster was set to 10% (11 statistically significant clusters) rather than 50% (8 statistically significant clusters). The cluster radius varied from 0.18 – 2.04 km and the period of time varied from 6 days – 6 months. The infection rate was more than 0.5 cases/household.Conclusions.When using SaTScan for space-time analysis of dengue cases, the maximum number of households per cluster should be set to 10%. This methodology may be useful to optimizing dengue surveillance systems, especially in countries where resources are scarce and government programs have not had much success controlling the disease.

Review of Spatial and Temporal Distribution of Landslides in Tanzania

This paper intended to inventorize the occurrence of landslides in Tanzania in temporal and spatial terms. Specifically, the inventory focused on identifying areas in Tanzania which are prone to landslides, identifying the frequency of landslide occurrence in the country, identifying the underlying and triggering factors for landslides, the socio-economic impacts of landslides and assessing the people’s perception of landslides in Tanzania. Identification and mapping of landslide was achieved through the use primary and secondary sources of information. Primary data was acquired through exploratory field visit to the 5 landslide scars in the north and south Pare Mountains in the northern highlands of Tanzania. Short discussion with local people in the areas was made to gain insights into their perception of landslides. Secondary data was acquired through the review of achieved sources. This review was made in respect of the landslides that were not covered by the aforementioned exploratory field visit. The EMDAT was also used to provide information on the number of landslide events, fatalities and socio-economic impacts. Google Earth™ search and image interpretation were used to located and describe landslides in other regions of the country that were not covered by the aforementioned exploratory field study. A 90 m resolution Digital Elevation Model (DEM) from USGS for Tanzania was used to map the landslide scars identified through Google Earth search. Google Earth search and image interpretation were supported by secondary information from archived sources. The inventory noted that landslides in Tanzania have received relatively limited attention both in the literature and in government disaster management plans. With exception of studies conducted in Uluguru Mountain in 1970s, no formal landslide research has been made in recent years. Despite extensive web searches, no landslide inventory has been made in Tanzania. This may presumably be due to existing assumption that landslides in Tanzania have low frequency of occurrence and are localized compared to other disasters such as floods, epidemics and drought, whose occurrence affect many more people, drawing more community, government and media attention. This has thus, affected the way landslide events are reported and managed in the country. This assertion however, does not preclude the fact that one/few landslide events, depending on their magnitude, where they occur and nature of elements at risk, can affect significant number of people and vice versa. This inventory has also documented cultural perspectives on landslides. Care must be taken to incorporate appropriate indigenous knowledge and people’s perception pertaining to landslides and other disasters as these are critical in designing and implementing culturallyinformed and low cost disaster management strategies. This may, in turn, obviate the need or reduce cost for early warning systems. Landslide information provided by some reviewed sources is limited in time and space. Most of the visited sources, particularly news sites, do not provide enough details for particular landslide events, leading to underreporting of disaster casualties, impacts and economic damage.

Oil palm mapping using Landsat and PALSAR: a case study in Malaysia

ABSTRACTIrrespective of the positive economic benefit or negative environmental impact of the rapid expansion of oil palm plantations in tropical regions, it is important to be able to create accurate land-cover maps for such areas. Optical remote sensing is vulnerable to the effects of clouds, which can limit data availability for the oil palm plantation areas in the humid tropics. The satellite-flown PALSAR (Phased Array type L-band Synthetic Aperture Radar) instrument, which provides all-day/all-weather Earth observations, offers the opportunity to identify and map oil palm plantations in cloudy regions. This study used a Support Vector Machine (SVM) classifier and a Mahalanobis distance (MD) classifier to undertake supervised classifications of Landsat, PALSAR, and combined Landsat and PALSAR data (Landsat+PALSAR) for two locations in peninsular Malaysia. Results indicate that accuracies from Landsat+PALSAR are better than accuracies from Landsat and PALSAR along for both study areas using both classifiers. The extents of the oil palm areas estimated from these maps were compared with values obtained through human photointerpretation of Google Earth™ images in previous studies. Based on the R2 statistics, it was established that the Landsat+PALSAR combination performed best for both study areas and demonstrated good potential for oil palm plantation mapping.

Frontal recession of Parkachik Glacier between 1971-2015, Zanskar Himalaya using remote sensing and field data

Parkachik Glacier is located in the Suru sub-basin of the Upper Indus River, Zanskar Himalaya. The Glacier has been analysed using Corona KH-4B (1971), Landsat-TM (1999), field survey (2015), Google EarthTM (2015) and ASTER GDEM (2015) for frontal recession and area changes. Overall, from 1971 to 2015, the Glacier has retreated by 127 ± 0.09 m i.e. (0.75 ± 0.07%) at a rate of 2.9 ± 0.004 ma−1 with a simultaneous decrease in area from 49.5 to 48.8 km2 i.e. 740 ± 0.7 m2 (1.5 ± 0.09%) at a rate of 74 ± 0.7 m2a−1. However, during recent decade (1999–2015), the rate of glacier recession of 3.9 ± 0.004 ma−1 with a corresponding area loss of 500 ± 0.74m2 (1 ± 0.1%) was higher than the retreat rate of 2.3 ± 0.001 ma−1 and an area loss of 240 ± 0.02m2 (0.48 ± 0.08%) during 1971–1999. In the field, the evidences of glacier recession are present in the form of separated dead ice blocks from the main Glacier, recessional dumps/moraines, active ice calving activity and a small proglacial pond/lake at the terminus/snout of the Glacier. However, the recession over the studied period has been very slow and is controlled by its topographic configuration, particularly the large altitudinal range (6030–3620 m), almost northerly aspect and steep slope (average ~ 30°).

The value of Google Earth™ for erosion mapping

Google Earth™ (GE) has been used in several geomorphological contexts, especially in the study of river networks, glacial, coastal, landslides and desert landforms. In some cases, quantification is possible and landform-process relations can be inferred. GE has been little used in studies of contemporary soil erosion. This brief case study illustrates the potential for quick, free and accurate surveys that are particularly valuable as an aid to field work. The main drawback of GE is the arbitrary timing of imagery, in this case with only 3years of complete coverage of the study area between 2001 and 2015.

Multi-criteria technique for mapping of debris-covered and clean-ice glaciers in the Shaksgam valley using Landsat TM and ASTER GDEM

Glaciers in the Shaksgam valley provide important fresh water resources to neighbourhood livelihood. Repeated creation of the glacier inventories is important to assess glacier–climate interactions and to predict future runoff from glacierized catchments. For this study, we applied a multi-criteria technique to map the glaciers of the Shaksgam valley of China, using Landsat Thematic Mapper (Landsat TM) (2009) and Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model version two (ASTER GDEM V2) data. The geomorphometric parameters slope, plan, and profile curvature were generated from ASTER GDEM. Then they were organized in similar surface groups using cluster analysis. For accurate mapping of supraglacial debris area, clustering results were combined with a thermal mask generated from the Landsat TM thermal band. The debris-free glaciers were identified using the band ratio (TM band 4/TM band 5) technique. Final vector maps of the glaciers were created using overlay tools in a geographic information system (GIS). Accuracy of the generated glacier outlines was assessed through comparison with glacier outlines based on the Second Chinese Glacier Inventory (SCGI) data and glacier outlines created from high-resolution Google Earth™ images of 2009. Glacier areas derived using the proposed approach were 3% less than in the reference datasets. Furthermore, final glacier maps show satisfactory mapping results, but identification of the debris-cover glacier terminus (covered by thick debris layer) is still problematic. Therefore, manual editing was necessary to improve the final glacier maps.

Catastrophic Rock-Slope Failures in NW Scotland: Quantitative Analysis and Implications

ABSTRACT126 postglacial catastrophic rock-slope failures (PRSFs) and 66 debris-free failure scarps (DFFSs) were identified using Google Earth™ imagery on Lewisian Gneiss (LG), Torridon Sandstone (TS), Cambrian Quartzite (CQ) and Moine Schist (MS) terrains in NW Scotland. Normalisation of these data for area and gradient shows that RSF density on slopes ≥25° decreases in the sequence MS > LG ≈ CQ > TS. Most are apparently discordant with structure, suggesting that failure along slope-(sub)parallel joints was more common than failure along bedding or foliation planes. DFFSs represent failure scars from which runout debris has been removed by later glaciation. Eighteen were recorded outside the limits of the Loch Lomond Readvance, implying that they survived the last ice-sheet glaciation, and support the view that many steep upper rock slopes have been dominated by gravity-driven rock detachment rather than glacial erosion. We argue that many RSFs were probably triggered by Lateglacial earthquakes due to fault (re)activation by crustal rebound, as 74% of recorded PRSFs and DFFSs are located at or near slope crests. 44% of recorded PRSFs and DFFSs occur in cirques, suggesting that cirque enlargement during successive glacial-interglacial cycles involved deepening by glacial erosion, headwall retreat by rock-slope failure and removal of RSF debris during subsequent glaciation.

Anthropogenic activities monitoring by a multi-sensor approach: a case study of Rossano (CS) landfill

In this paper the landfill located to Rossano (CS) is analyzed through an integrated approach by the use of different remote sensed data from 1994 to 2010. In particular, the Multispectral Visible and Infrared Imaging Spectrometer (MIVIS) airborne sensor, orthophotos and Google Earthtm satellite image are used. The integrated multi-temporal data and downscaling analysis have identified several evidences such as the increase of land consumption. The Normalized Difference Vegetation Index (NDVI) of MIVIS image allows to observe the distribution of exposed soil and vegetated areas. At the same time, MIVIS thermal data shows some superficial thermal anomalies which highlighted these changes. Finally, this study shows how this approach can be useful to support both monitoring studies of land consumption as well as planning environmental campaigns in such areas.

‘Let's see if you can see me': making connections with Google Earth™ in a preschool classroom

Bringing a social interaction approach to children's geographies to investigate how children accomplish place in everyday lives, we draw on ethnomethodological and conversation analytic approaches that recognize children's competence to manipulate their social and digital worlds. An investigation of preschool-aged children engaged with Google Earth™ shows how they both claimed and displayed technological understandings and practices such as manoeuvring the mouse and screen, and referenced place through relationships with local landmarks and familiar settings such as their school. At times, the children's competing agendas required orientation to each other's ideas, and shared negotiation to come to resolution. A focus on children's use of digital technologies as they make meaning of the world around them makes possible new understandings of place within the geographies of childhood and education.

Visualizing Cross-Sectional Data in a Real-World Context

Combining the capabilities of an open-source drawing tool with Google Earth™ maps allows researchers to visualize real-world cross-sectional data in three dimensions.

The Block Island Bluff Image Project: Using Google Earth™, Spreadsheet Mapper, and Google Drive™ to Build a Database of Spatially Located Field Images of Coastal Bluffs

ABSTRACT Oakley, B.A.; Manzi, M.A.; Sumeersarnauth, B.J., and Boyle, S., 2017. The Block Island Bluff Image Project: Using Google Earth™, Spreadsheet Mapper, and Google Drive™ to build a database of spatially located field images of coastal bluffs. Spatially located field photography provides a mechanism to gather and share images for the purposes of creating virtual fieldtrips, examining change, and conducting outreach. Uploading images to Google Earth™ or other commercial sites is a common way of sharing images; however, these images are not accessible in a central, organized location, and the spatial control is limited. This paper presents a method of providing quick access to spatially located images using standard equipment (i.e. camera and GPS) and then linking these images to Google Earth using Spreadsheet Mapper, a free tool developed by Google. This technique allows image locations to be shared via a single small >10 kB file, and individual images can be viewed and downloaded as needed. A systema...

Extracting the Glacial - Geomorphological Landforms in West Liddar (NW) Himalayas Kashmir India

The paper presents a glacial geomorphological map of landforms produced by different glaciers in west liddar during the past. The map has been created as the necessary precursor for an improved understanding of the glacial history of the region, and to strengthen a programme of dating glacial limits in the region. The map was produced using Landsat TM, ASTER DEM, Google Earth TM imagery and field validation. The different landforms which were mapped include: lateral moraines, terminal moraines, proglacial lakes, palaeo-cirques, erratic boulders, outwash plains, whale backs, glacial grooves, rochee mountanee, drumlins, glacial striations, hanging valleys debris/talus cones, subglacial material. Glacio-Geomorphological features are very significant for palaeo-climatic reconstruction, showing variations, temporally and spatially. At the same time these landforms, which are also altered by processes prevailing during interglacial period, helps in geo-environment studies.

Mapping the Glacial-Geomorphological Landforms in East Liddar Valley, NW Himalaya Kashmir India

The paper presents a glacial geomorphological map of landforms produced by different glaciers in East liddar during the past. The map has been created as the necessary precursor for an improved understanding of the glacial history of the region, and to strengthen a program of dating glacial limits in the region. The map was produced using Landsat TM, ASTER DEM, Google Earth™ imagery and field validation. The different landforms which were mapped include: lateral moraines, terminal moraines, proglacial lakes, palaeo-cirques, erratic boulders, outwash plains, whale backs, glacial grooves, rochee mountanee, drumlins, glacial striations, hanging valleys debris/talus cones, subglacial material. Glacial-Geomorphological features are very significant for palaeo-climatic reconstruction, showing variations, temporally and spatially. At the same time these landforms, which are also altered by processes prevailing during interglacial period, helps in geo-environment studies.

Remote Sensing: A New Revolution in Geotechnical Engineering

Remote sensing in the form of satellite imagery has become part of our everyday life—from displaying boring locations at a job site to the various mapping apps on our smart phones that help us get there. In the past, geotechnical engineers might have searched out air photos to help evaluate current and past conditions at a site (e.g., drainage features, faults, failure scarps), but now we can quickly open Google EarthTM to view a site and use the time slider to see how conditions have evolved over the last, 10, 20, or even 50 years. The ubiquitous availability of high-resolution imagery has made us all appreciate the value of the “bird’s eye view” to our work.

Utilization of Google Earth for Distribution Mapping of Cholangiocarcinoma: a Case Study in Satuek District, Buriram, Thailand.

Cholangiocarcinoma (CCA) is a serious public health problem in the Northeast of Thailand. CCA is considered to be an incurable and rapidly lethal disease. Knowledge of the distribution of CCA patients is necessary for management strategies. This study aimed to utilize the Geographic Information System and Google EarthTM for distribution mapping of cholangiocarcinoma in Satuek District, Buriram, Thailand, during a 5-year period (2008-2012). In this retrospective study data were collected and reviewed from the OPD cards, definitive cases of CCA were patients who were treated in Satuek hospital and were diagnosed with CCA or ICD-10 code C22.1. CCA cases were used to analyze and calculate with ArcGIS 9.2, all of data were imported into Google Earth using the online web page www.earthpoint.us. Data were displayed at village points. A total of 53 cases were diagnosed and identified as CCA. The incidence was 53.57 per 100,000 population (65.5 for males and 30.8 for females) and the majority of CCA cases were in stages IV and IIA. The average age was 67 years old. The highest attack rate was observed in Thung Wang sub-district (161.4 per 100,000 population). The map display at village points for CCA patients based on Google Earth gave a clear visual deistribution. CCA is still a major problem in Satuek district, Buriram province of Thailand. The Google Earth production process is very simple and easy to learn. It is suitable for the use in further development of CCA management strategies.

A multi-model Python wrapper for operational oil spill transport forecasts

The Hydrodynamic and oil spill modeling system for Python (HyosPy) is presented as an example of a multi-model wrapper that ties together existing models, web access to forecast data and visualization techniques as part of an adaptable operational forecast system. The system is designed to automatically run a continual sequence of hindcast/forecast hydrodynamic models so that multiple predictions of the time-and-space-varying velocity fields are already available when a spill is reported. Once the user provides the estimated spill parameters, the system runs multiple oil spill prediction models using the output from the hydrodynamic models. As new wind and tide data become available, they are downloaded from the web, used as forcing conditions for a new instance of the hydrodynamic model and then applied to a new instance of the oil spill model. The predicted spill trajectories from multiple oil spill models are visualized through Python methods invoking Google MapTM and Google EarthTM functions. HyosPy is designed in modules that allow easy future adaptation to new models, new data sources or new visualization tools.

The GAMDAM glacier inventory: a quality-controlled inventory of Asian glaciers

Abstract. We present a new glacier inventory for high-mountain Asia named "Glacier Area Mapping for Discharge from the Asian Mountains" (GAMDAM). Glacier outlines were delineated manually using 356 Landsat ETM+ scenes in 226 path-row sets from the period 1999–2003, in conjunction with a digital elevation model (DEM) and high-resolution Google EarthTM imagery. Geolocations are largely consistent between the Landsat imagery and DEM due to systematic radiometric and geometric corrections made by the United States Geological Survey. We performed repeated delineation tests and peer review of glacier outlines in order to maintain the consistency and quality of the inventory. Our GAMDAM glacier inventory (GGI) includes 87 084 glaciers covering a total area of 91 263 ± 13 689 km2 throughout high-mountain Asia. In the Hindu Kush–Himalaya range, the total glacier area in our inventory is 93% that of the ICIMOD (International Centre for Integrated Mountain Development) inventory. Discrepancies between the two regional data sets are due mainly to the effects of glacier shading. In contrast, our inventory represents significantly less surface area (−24%) than the recent global Randolph Glacier Inventory, version 4.0 (RGI), which includes 119 863 ± 9201 km2 for the entirety of high Asian mountains. Likely causes of this disparity include headwall definition, effects of exclusion of shaded glacier areas, glacier recession since the 1970s, and inclusion of seasonal snow cover in the source data of the RGI, although it is difficult to evaluate such effects quantitatively. Further rigorous peer review of GGI will both improve the quality of glacier inventory in high-mountain Asia and provide new opportunities to study Asian glaciers.

Mapping Above-Ground Biomass in a Tropical Forest in Cambodia Using Canopy Textures Derived from Google Earth

This study develops a modelling framework for utilizing very high-resolution (VHR) aerial imagery for monitoring stocks of above-ground biomass (AGB) in a tropical forest in Southeast Asia. Three different texture-based methods (grey level co-occurrence metric (GLCM), Gabor wavelets and Fourier-based textural ordination (FOTO)) were used in conjunction with two different machine learning (ML)-based regression techniques (support vector regression (SVR) and random forest (RF) regression). These methods were implemented on both 50-cm resolution Digital Globe data extracted from Google Earth™ (GE) and 8-cm commercially obtained VHR imagery. This study further examines the role of forest biophysical parameters, such as ground-measured canopy cover and vertical canopy height, in explaining AGB distribution. Three models were developed using: (i) horizontal canopy variables (i.e., canopy cover and texture variables) plus vertical canopy height; (ii) horizontal variables only; and (iii) texture variables only. AGB was variable across the site, ranging from 51.02 Mg/ha to 356.34 Mg/ha. GE-based AGB estimates were comparable to those derived from commercial aerial imagery. The findings demonstrate that novel use of this array of texture-based techniques with GE imagery can help promote the wider use of freely available imagery for low-cost, fine-resolution monitoring of forests parameters at the landscape scale.

A new band ratio technique for mapping debris-covered glaciers using Landsat imagery and a digital elevation model

Mapping of debris-covered glaciers using optical remote-sensing data is a difficult task due to the fact that the debris cover on the glacier surface has a similar reflectance to surrounding rocky or sandy areas in the visible to near-infrared wavelength region. Therefore, a new method was developed for mapping debris-covered glaciers that considers thermal and slope information in addition to visible and near-infrared information. The method was tested for delineating the Koxkar glacier and Yengisogat glacier using Landsat Enhanced Thematic Mapper Plus and Landsat Thematic Mapper imagery and Shuttle Radar Topography Mapping Mission (SRTM) data. Specifically, the method combines a new band ratio image with slope information derived from the SRTM digital elevation model to better distinguish periglacial debris and supraglacial debris. The new band ratio image considers the near infrared (NIR), shortwave infrared (SWIR), and thermal infrared (TIR) bands, and is given by TIR/(NIR/SWIR). The debris-covered glacier boundaries are generated by thresholding the new band ratio image and overlaying it onto a binary slope map. Accuracy assessment was carried out through comparisons of the classified maps with reference maps from the Randolph Glacier Inventory, Glacier Inventory GIS data of China, and a manual delineation done using Google Earth™ images. The accuracy assessment shows that the result from the proposed method has a good agreement with the different reference data sets.