Geodetic Datums: NAD 27, NAD 83 and WGS84

NAD27 Shift to NAD83 (Image credit: NADCON - North American Datum Conversion Utility)

When you need to accurately enter coordinates in a GIS, the first step is that you uniquely define all locations on Earth. This means you need a reference frame for your coordinates becausewhere would you be on Earth without having reference to it?


Because the Earth is curved – and in GIS we deal with flat maps – we need to accommodate both the curved and flat views of the world. Surveyors and geodesists have accurately defined locations on Earth.

We begin modelling the Earth with an ellipse – which is different than a geoid. Over time, the ellipsoid has been estimated to the best of our ability through a massive collection of surface measurements.

When you combine these measurements, we arrive at a geodetic datum. Datums precisely specify each location on Earth’s surface in latitude and longitude or other coordinate systems. NAD 27, NAD 83 and WGS84 are examples of geodetic datums.

A Mammoth Collection of Survey Benchmarks
In order to create a geodetic datum, a mammoth collection of monument locations (survey benchmarks) were collected in the late 1800s. Surveyors installed brass or aluminum disks at each reference location.



Each monument location was connected using mathematical techniques like triangulation. The result of triangulation from the unified network of survey monuments was North American Datum of 1927 (NAD 27) and later the more accurate NAD 83, which is still used today. NAD 27 and NAD 83 provide a frame of reference for latitude and longitude locations on Earth.



Surveyors now rely almost exclusively on the Global Positioning System (GPS) to identify locations on the Earth and incorporate them into existing geodetic datums. Geocaching for survey benchmarks is another popular activity.

NAD27, NAD83 and WGS84 are commonly used geodetic datums in North America.

What is North American Datum 1927 (NAD27)?

Meades Ranch Triangulation Station, fundamental station for the North American Datum of 1927

NAD27 stands for North American Datum of 1927. NAD27 is the adjustment of long-baseline surveys to establish a network of standardized horizontal positions on North America. Most historical USGS topographic maps and projects by the US Army Corps of Engineers used NAD27 as a reference system.

A horizontal datum provides a frame of reference as a basis for placing specific locations at specific points on the spheroid. A horizontal datum is the model that is used to translate a spheroid / ellipsoid into locations on Earth with latitude and longitude lines. Geodetic datums form the basis of coordinates of all horizontal positions on Earth. All coordinates on Earth are referenced to a horizontal datum. The North American Datum of 1927 (NAD27) is one of the main three geodetic datums used in North America.

NAD27 uses all horizontal geodetic surveys collected at this time using a least-square adjustment. This datum uses the Clarke Ellipsoid of 1866 with a fixed latitude and longitude at Meade’s Ranch, Kansas. (39°13’26.686″ north latitude, 98°32’30.506″ west longitude)

Kansas was selected as a common reference point because it was near the center of the contiguous United States. The latitudes and longitudes of every other point in North America were based off its direction, angle and distance away from Meade’s Ranch. Any point with a latitude and longitude away from this reference point could be measured on the Clarke Ellipsoid of 1866.

Approximately 26,000 survey stations were gathered in the United States and Canada. At each station, surveyors collected latitudes and longitude coordinates. NOAA’s National Geodetic Survey used these survey stations and triangulation to form the NAD27 datum.

As time went on, surveyors benchmarked approximately 250,000 stations. This set of horizontal positions formed the basis for the North American Datum of 1983 (NAD83). In 1983, the NAD27 datum was eventually replaced with NAD83.

What is North American Datum 1983 (NAD83)?

Geocentric Datum

The North American datum of 1983 (NAD 83) is the most current datum being used in North America. It provides latitude and longitude and some height information using the reference ellipsoid GRS80. Geodetic datums like the North American Datum 1983 (NAD83) form the basis of coordinates of all horizontal positions for Canada and the United States.

The North American Datum of 1983 (NAD 83) is a unified horizontal or geometric datum and successor to NAD27 providing a spatial reference for Canada and the United States.

NAD83 corrects some of the distortions from NAD27 over distance by using a more sense set of positions from terrestrial and Doppler satellite data. NAD83 is a geocentric datum (referenced to the center of Earth’s mass) offset by about 2 meters.

Even today, horizontal geodetic datums are continuously being improved.

WGS84: Unifying a Global Ellipsoid Model with GPS

GPS Satellite

It wasn’t until the mainstream use of Global Positioning Systems (GPS) until a unified global ellipsoid model was developed. The radio waves transmitted by GPS satellites enable extremely precise Earth measurements across continents and oceans. Global ellipsoid models have been created because of the enhancement of computing capabilities and GPS technology.

This has led to the development of global ellipsoid models such as WGS72, GRS80 and WGS84 (current). The World Geodetic System(WGS84) is the reference coordinate system used by the Global Positioning System.

Never before have we’ve been able to estimate the ellipsoid with such precision because of the global set of measurements provided by GPS. It comprises of a reference ellipsoid, a standard coordinate system, altitude data and a geoid. Similar to NAD 83, it uses the Earth’s center mass as the coordinate origin. The error is believed to be less than 2 centimeters to the center mass.

Question: What is EPSG4326?

Answer: EPSG4326 is just the way to identify WGS84 using EPSG. Here is the spatial reference list.

Geodetic Datums: NAD83 versus NAD27

NAD83 corrects some of the distortions from NAD27 over distance by using a more dense set of positions from terrestrial and Doppler satellite data. Approximately 250,000 stations were used to develop the NAD83 datum. This compares to only 26,000 used in the NAD27 datum.

NAD83 Center of Mass

One of the primary difference is that NAD83 uses an Earth-centered reference, rather than a fixed station in NAD27. All coordinates were referenced to Kansas Meade’s Ranch (39°13’26.686″ north latitude, 98°32’30.506″ west longitude) for NAD27 datum. The National Geodetic Survey relied heavily on the use of Doppler satellite to locate the Earth’s center of mass. However, NAD83 is not geocentric with an offset of about two meters.

North American Datum of 1983 is based off the reference ellipsoid GRS80 which is physically larger than NAD27’s Clarke ellpsoid. The GRS80 reference ellipsoid has a semi-major axis of 6,378,137.0 meters and a semi-minor axis of 6,356,752.3 meters. This compares to the Clarke ellipsoid with a semi-major axis of 6,378,206.4 m and semi-minor axis of 6,356,583.8 meters.

The Varying Historical Accuracy of the Ellipsoid
Is the Earth Round? Earth bulges out more at the equator than at the poles by about 70,000 feet.

And since the beginning of the 19th century, the dimensions of the ellipsoid have been calculated at least 20 different times with considerably different accuracies.

The early attempts at measuring the ellipsoid used small amounts of data and did not represent the true shape of the Earth. In 1880, the Clarke ellipsoid was adopted as a basis for its triangulation computations. The first geodetic datum adopted for the United States was based on the Clarke ellipsoid with its starting point in Kansas known as Meades Ranch

Name	Year	Semi-Major Axis (Equator Radius)	Semi-Minor Axis (Polar Radius)	Users
Clarke	1866	6,378,206.4 m	6,356,583.8 m	North America
International (Hayford) Ellipsoid	1924	6,378,388.0 m	6,356,911.9 m	Most of the World
WGS72	1972	6,378,135.0 m	6,356,750.5 m	NASA
GRS80	1980	6,378,137.0 m	6,356,752.3 m	Worldwide
WGS84	1984	6,378,137.0 m	6,356,752.3 m	Current Worldwide

One Datum with Many Versions and Abbreviations
NAD83 had undergone several updates since 1986. There are several versions of NAD83. For example, the National Geodetic Survey has adjusted the NAD83 datum for times since the original geodetic datum estimation in 1986.

• NAD83 (1986): This version was intended to be geocentric and used the GRS80 ellipsoid.
• NAD83 (1991, HARN, HPGN): High Accuracy Reference Network (HARN) and High Precision Geodetic Network reworked geodetic datums from 1986-1997
• NAD83 (CORS96): Continually Operating Reference Stations (CORS) are comprised of permanently operating Global Positioning System (GPS) receivers
• NAD83 (CSRS, CACS): Canadian Spatial Reference System and Canadian Active ontrol System with GPS processing.
• NAD83 (NSRS 2007, 2011): National Spatial Reference System and current survey standard using multi-year adjusted locations based on GNSS from the CORS.

The Importance of Datum Transformations

Surveyor (NOAA Photo Library)

The coordinates for benchmark datum points are typically different between geodetic datums. For example, the latitude and longitude location in a NAD27 datum differs from that same benchmark in NAD83 or WGS84. This difference is known as adatum shift.

Depending on where you are in North America, NAD27 and NAD83 may differ in tens of meters for horizontal accuracy. The average correction between NAD27 and NAD83 is an average of 0.349″ northward and 1.822″ eastward.

It’s important to note that the physical location has not changed. Most monuments have not moved. Datum shifts happen because survey measurements improve, there are more of them and methods of geodesy change. This results in more accurate geodetic datums over time. The horizontal datums that form the basis of coordinates of all horizontal positions in North America improve.

Because maps were created in different geodetic datums throughout history, datum transformations are often necessary when using historical data. For example, USGS topographic maps generally were published using a NAD27 datum. A datum transformation would be required when worrying with other NAD83 data.

NAD27 Shift to NAD83 (Image credit: NADCON – North American Datum Conversion Utility)
When are Datum Transformations Needed?

Datum Transformation

A coordinate transformation is the conversion from a non-projected coordinate system to a coordinate system. A coordinate transformation is done through a series of mathematical equations.

The geodetic datum is an integral part of projections. All coordinates are referenced to a datum. A datum describes the shape of the Earth in mathematical terms. A datum defines the radius, inverse flattening, semi-major axis and semi-minor axis for an ellipsoid. The North American datum of 1983, NAD 83, is United States horizontal or geometric datum. It provides latitude and longitude and some height information.

Unfortunately NAD 83 is not the only datum you’ll encounter. Before the current datum was defined, many maps were created using different starting points. And even today, people continue to change geodetic datums in an effort to make them more accurate. A common problem is when different coordinate locations are stored in different reference systems. When combining data from different users or eras, it is important to transform all information to common geodetic datums.

Projected coordinate systems are based on geographic coordinates, which are in turn referenced to a datum. For example, State Plane coordinate systems can be referenced to either NAD83 and NAD27 geodetic datums.

The NAD27 datum was based on the Clarke Ellipsoid of 1866:
Semi-major axis: 6,378,206.4 m
Semi-minor axis: 6,356,583.8 m
Inverse flattening: 294.98


The NAD83 datum was based on theGeodetic Reference System (GRS80) Ellipsoid:
Semi-major axis: 6,378,137.0 m
Semi-minor axis: 6,356,752.3 m
Inverse flattening: 298.26


When you transform NAD83 and NAD27 geographic coordinates to projected State Plane coordinates, it is the same projection method. However, because the geodetic datums were different, the resulting projected coordinates will also be different. In this case, a datum transformation is necessary.


For any type of work where it’s important for coordinates to be consistent with each other, it is critical that the same geodetic datum is used. If you are marking property or land boundaries or building roads or planning for coastal inundation scenarios, you must know about and use the correct geodetic datums.

Source: GIS Geography

Putting the Trees Back on the Map: How GIS is Helping Reforest South America

BY DEVON REESER
GIS tools have revolutionized tracking in the sometimes hazy field of reforestation in developing countries. Deforestation contributes to more greenhouse gas emissions than the entire global transportation sector. Clean Development Mechanism (CDM) programs borne from the Kyoto Protocol have burst onto the international funding scene to pay forest-rich, poorer nations for forest ecosystem services, whether that means reforesting or not deforesting. Hundreds of other private businesses offset their carbon emissions by funding forest “carbon sinks” as well, as part of their corporate social responsibility charters, usually in partnership with NGOs.

Many, if not most, of the countries where reforestation is most viable have fuzzy systems of accountability, however, as well as gaps in technology know how and systems to implement tracking. Illegal logging often negates efforts. South America is home to more than a third of the world’s remaining rain forests and has possibly the most potential to reforest with a relatively low population and easily convertible land. But nearly all countries in South America score under 40 on a scale of 100 on Transparency International’s Corruption Index – and hence it has been difficult to ensure trees will stay put.

New, simplified Web-based GIS systems and UN trainings as part of the Reducing Emissions from Deforestation and Degradation (REDD) program of the UN Convention on Climate Change are revolutionizing forest tracking in South America. Here are three innovative examples from South America of how GIS is being used to strengthen and control forest monitoring to put – and to keep – the trees back on the map.

Monitoring Reforestation in Paraguay
Paraguay has suffered the most from deforestation for monoculture agriculture of any of its Latin neighbors. Only 10% of primary forest cover is left, gone in only 60 years, mostly for soy and cattle production by foreign entities.[i]The NGO, A Todo Pulmon, Paraguay Respira (With All Its Lungs, Paraguay Is Breathing) is tackling reforestation head on and using GIS to monitor and measure. The goal of the project, begun in 2009 by a national radio personality, was to plant 14 million trees – 85% to restore the Bosque Atlántico del Alto Paraná (BAAPA, the Atlantic Rainforest) and 15% for urban areas and schools as an educational initiative to spread the importance of reforesting the Atlantic Rainforest. It has planted 40 million.[ii]

The challenge now is to monitor to make sure they stay there. Paraguay is the most corrupt country in the Americas – it is the 27th most corrupt in the world on the Corruption Index, and has been in the top 10 in the new millennium. Though a “Zero Deforestation” law has been in existence for nearly a decade, officials often look the other way when illegal logging occurs. To date, the country has lacked the capacity to monitor and mitigate that corruption and crime. World Wildlife Federation is training every municipality in the forest districts to use GIS monitoring with satellite imagery from Brazil’s Space Research Institute.[iii] GIS satellite images are matched with the GPS coordinates of trees planted to track if reforested trees have been cut, and, if so, landowners pay heavy fines per national law and ramped up police monitoring spurred by enhanced media coverage and social consciousness.

Tracking Deforestation in Ecuador
With help from REDD, Ecuador is serving as a model country to input data into a simplified reforestation and deforestation Web-based GIS portal.[iv] A challenge for Ecuador in preserving the Amazon is a desire and need to develop petroleum resources within its most dense forest area. Not only does the portal easily show deforestation in protected areas and biodiversity corridors with petroleum and natural resource development, but it also shows overlap of reforested areas. The information helps policy makers and other investors immediately visualize effects of development and identify priority conservation areas. The web-based system does not require expensive software purchases and can hence be used across the nation by forest land managers and other specialists to keep one full, up to date resource publically available. The information was shared at a conference in Buenos Aires to replicate in Argentina.

ECUADOR REFORESTATION PLOTS FROM SISTEMA ÚNICO DE INFORMACIÓN AMBIENTAL.
Exposing Illegal Loggin Using GPS in Brazil

The ability of NGOs and the media to expose illegal activity is a new method of creating social accountability and transparency that supersedes corrupt policing and state-based systems. Greenpeace activists used covert GPS surveillance to track illegal logging in the Amazon. Activists placed GPS trackers on logging trucks to find their destinations, and then shockingly show with GIS satellite imagery illegal logging deep within protected forest. The Brazilian government is now being pressured to overturn a 2013 law weakening control.[v] The full report, The Amazon’s Silent Crisis: Night Terrors was published in October 2014 and is being read all over the world.

The prevalence and easy technology transfer of GPS and GIS has now made it possible to monitor and make sure that, even in the most unpoliced, corrupt areas of the world, trees meant to be on the map are put there and stay for good.

References
[i] Mongabay. 2014. Paraguay.

[ii] WWF Global. 2014. With All its Lungs, Paraguay is Breathing.

[iii] WWF. March 2011. Making a Pact to Tackle Deforestation in Paraguay (PDF).

[iv] UN REDD Programme. 2014. National Programmes in South America strengthen capacities on forest monitoring and web-based geographic information systems. REDD Programme Newsletter. September 2014.

[v] Carrington, Damian. 15 Oct. 2014. Activists Use GPS to Track Illegal Loggers in Brazil’s Amazon Rainforest. The Guardian.


Source: GIS Loung - Maps and GIS

Mapping the entire UK rail network

NM Group is pleased to announce it is nearing completion on an aerial mapping project of unparalleled scale and accuracy, covering more than 16,000 km of UK rail network.
In 2014 NM Group was engaged to map the rail assets with a mix of high-tech laser measurement and imaging equipment. This was part of a ground breaking project to improve asset maintenance, operational effectiveness, efficiency and safety. Using a mix of specially commissioned Light Detection and Ranging (LiDAR) and high resolution multi-angle cameras mounted on helicopters, carrying out aerial operations and ground control activities over a four month period, completing before the winter set in. The survey information was rapidly transported to NM Group’s Technology Centre in Knaresborough, North Yorkshire, where a large team of specialists have been hard at work converting nearly a petabyte of raw data into a wide range of terrain, asset and imaging outputs.

NM Group’s CEO Kevin Jacobs said of the project ‘I am incredibly proud of the way our team has responded to this large and challenging project, completing the data capture within an unprecedented timescale and producing a high quality output that will serve the rail network for years to come’.

NM Group’s contribution to the programme provides the geospatial fabric on which other layers are overlaid, the basis for asset location mapping and the start point for the design of upgrades and modifications. Traditionally, this information would have been created by a visit to the site by a team of surveyors, the new method will significantly reduce the need for future field work and trackside access. It will also facilitate more efficient maintenance, allowing crews to identify and access assets more safely and efficiently than in the past, via apps on a range of mobile devices.

NM Group is a specialist service provider of asset management, surveying and mapping solutions to sectors including energy transmission and distribution and road and rail transport. Applying a range of remote sensing and geospatial technologies, the company offers a full range of services from data acquisition through to analytics and web applications for wider access to information.

For more information, contact Tom Hall on 01276 857 800 or visit www.nmgroup.com.

Source

Crowd-sourced StreetView start-up – Mapillary turns images into 3D points clouds

By Aleks Buczkowski


Mapillary is a service for crowdsourcing map photos. Using smartphones or action cams users collect images that are combined into a collective street level photo view. This is why Mapillary is often referred to as OpenStreetMap-like StreetView.

Until today Mapillary users have taken over 40 million photos. “Each of these are stitched together with computer vision – a type of artificial intelligence that extracts information from images. For every single photo uploaded, we can automatically match features to the ones in photos from the same geographic vicinity so that we can compute how the images relate to each other and how to navigate from one to the other. This is how users navigate between photos in the Mapillary app and web browser” we can read on Mapillary’s blog.

Now the company has developed a technology which takes their image data one step further. They started to use image recognition to improve positioning quality which was an issue especially in cities with tall buildings. The technique used for that purpose computes the relative camera positions and a 3D reconstruction of the environment based on images of the same area. A side effect this process is really amazing. It allows Mapillary to generate 3D point cloud models based on images.

“We couldn’t keep this to ourselves so now users can explore our underlying 3D data by simply clicking the ‘show point clouds’ option in the sidebar in your web browser. We’ve also added full support for panoramas, which enables users to move seamlessly between regular photos and panoramas in 3D, giving them a smooth and other-worldly viewing experience.”

This feature is very promising as laser point clouds are a part of 3D maps for autonomous cars. It could complement places were for some reasons Lidar data could not be captured by mapping companies like Google, TomTom or HERE.

source: Mapillary and Geoawesomeness

Line Of Sight: What are the satellites in your line of sight?

By Muthukumar Kumar



It's amazing to see how a seemingly “boringly scientific” question turns can be turned into fascinating and informative one with the right visualization techniques and tools.

Line Of Sight by Patricio Gonzalez Vivo

Line Of Sight
“What are the satellites in your line of sight?” seems like a question that only a space science enthusiast might be interested in but then Line Of Sight, an open-data visualization by Patricio Gonzalez makes it really interesting and informative! It’s really fascinating to see how many satellites are whizzing past us every minute – here’s the link to geoawesome visualization. Satellite gazing was never this cool.

More info:

If you are interested in finding out more about the code behind the project – here’s the linkand here’s the link to the data behind the project. Happy satellite gazing!

Source: Geoawesomeness

Using Near-Infrared Aerial Imagery to Inventory Oak Trees

BY CAITLIN DEMPSEY MORAIS


The first ordinance passed by the California city of Santa Clarita after it was incorporated was the Oak Tree Ordinance. Thousands of oak trees cover the Santa Clarita Valley and the presence of the trees is an important part of the city’s landscape (the city logo prominently displays an oak tree). The ordinance covers oak trees of the Quercus species native to the area which includes Valley Oak, California Live Oak, Canyon Oak, Interior Live Oak, and Scrub Oak. As well as regulating the pruning, encroachment and removal of oak trees, the city sought to protect “heritage oaks” under the ordinance. Heritage oaks are the largest and oldest oaks in the city and are defined as oaks measuring at least 108 inches in circumference for a single trunk, or 72 inches in diameter for multiple trunks, at 4 1/2 feet above the ground.

Having been incorporated in 1987, the city of Santa Clarita is still experiencing significant growth and development. Development plans submitted to the city have to be carefully scrutinized to make sure they are in compliance with the Oak Tree Ordinance. Because of this, it had long been a priority of the city’s GIS group to develop a geographic layer identifying the location of oak trees within the city. When the GIS group began participating in Los Angeles County’s inaugural aerial imagery acquisition program (called LAR-IAC), the opportunity arose to experiment with remotely sensing tree locations.

After reviewing different academic and commercial options, the project manager, Edgardo David, along with the rest of the GIS team (Kristina Jacob, Anthony Calderon, and Kelly Minniti) opted to contract with the Los Angeles firm Engineering Systems. The goal of the project was to utilize 4-inch resolution near-infrared aerial imagery to extract the locations of specific oak tree species with the city. The challenge was in using aerial imagery to develop a spectral signature for the oak trees that could be automated in order to extract all tree locations.

FOR THE PILOT STUDY, OAK TREE LOCATIONS WERE IDENTIFIED BY THE CITY'S ARBORIST.

A pilot project was developed that involved the identification of oak trees by the City’s arborist for a single aerial tile. Staff at Engineering Systems then used those marked locations to create a polygon layer in AutoCAD of all oak trees present. Engineering Systems then developed an application using Microsoft .NET that scanned the TIFF image (a 000 x 8000 4-inch pixel grid comprising 64 million pixels). Those pixel within the polygons were extract and were analyzed to prepare histograms to represent the frequency of individual Red, Green and Blue (RGB) values to determine the peak values representing the spectral signature of the oak trees.

THREE HISTOGRAMS REPRESENTING FREQUENCIES OF RED, GREEN, AND BLUE VALUES.

The spectral signature was then used as input parameters for a second application that scanned the TIFF image tile and extracted pixels that matched the signature. The process went through several iterations matched against field surveys to verify that the correct species of trees were being selected. The overall analysis found that the spectral signature was accurate in identifying more mature oak trees but younger trees with smaller canopies were not being identified. Engineering Systems is working on refining the process to be able to identify those younger trees. Over 166,000 trees were located using this automated process.

The resulting geographic layer also identifies the diameter of the oak tree canopy. Since the oak tree locations are now georeferenced, the oak trees were spatially identified with the parcel number. This now allows the staff within the various city departments to know when a property has an oak tree and is subject to the constraints of the Oak Tree Ordinance when plans are submitted by developers and property owners.

IDENTIFIED OAK TREE LOCATIONS.

Source: GIS Lounge - Maps and GIS

Using Remote Sensing to Count Trees

Operational administration of green assets such as forests and urban green cover ordinarily necessitates reliable, timely and well-run information about its developments and current status. Tree count management is important for sustaining conservational stability and ecological biodiversity. A systematic tree inventory of the forested areas and in the urban areas can help us involvedly view the causes of decline of forests in the area, decline in green cover in urban areas etc. and assist in decision making. Customary methods for counting trees are labor-intensive catalogue in the field or on anelucidation of large scale aerial photographs. Nevertheless these methods are pricey, time consuming and not pertinent to large, sequestered areas. Remote sensing technology know-how is the operational method for management and monitoring of green resources.

Methods of Extracting Remotely Sensed Data
There are different methods of getting the remotely sensed data, like the ones listed below.

• LiDAR
• Satellite Images
• UAV/Drone Images
• Terrestrial Photogrammetry

LiDAR
LiDAR methods of data collection is progressively used in forestry applications but also employed in urban environments for green cover calculations, tree canopy mapping and tree counting. Vast point clouds are usually converted software specific readable formats and are used to do the mapping for the tree counting and urban forestry mapping.

Satellite Images
One of the most important resources in the earth that needs constant monitoring and needs to be accurately measured for effective management is forest resources. Remotely sensed high-resolution or very high resolution satellite image data are crucial in this management, since it provides detailed information to administrators and planners for better decision making

UAV/Drone Images
Hyperspectral remote sensing, which uses the modern satellite sensors ability to capture the data in multiple-bands, in amalgamation with a properly updated land information system is understood to be a worthy technique to assist in making fast decisions. The practice of using Unmanned Aerial Vehicle (UAV) platform for many remote sensing applications is done to combine the advantages of traditional remote sensing techniques and the inexpensiveness of operating such techniques. UAV drones can fly at varying altitudes subject to the objective of the mission and end-result type. This tractability allows for optimization of the procedures according the meteorological conditions over a given area and the user requirements.
Terrestrial Photogrammetry

Tree counting is crucial for cultivated area and environmental management, biodiversity monitoring and many other applications. Regardless of the factor that satellite and aerial images have been widely used to distinguish, demarcate and count individual tree in urban areas and forested lands, till such techniques becomes widely accessible and knowledge of processing such data is increased, the traditional methods still hold the sway and might be detrimental for the green cover we all wish to have.

About the Author
Anil Narendran Pillai – (Vice President – Geomatics @ SBL) Mr. Pillai heads the GSS (Geospatial Services) domain at SBL. He has worked in the digital mapping, remote sensing, and GIS industries for over 23 years. He has 23+ years experience managing and coordinating GIS projects and 12 years senior management experience. He has extensive experience in all aspects of aerial and satellite imaging technology and applications. He has utilized remotely sensed satellite and airborne imagery for a variety of environmental applications including site location analysis, forestry, telecommunications and utility corridor mapping. He has a strong background in management of GIS and Photogrammetry imaging projects to support Government and private industry needs.His Passion lies in Need Analysis and Documentation, Topographical Mapping (ArcGIS), Spatial Data Management, Integrity and Security, GIS Data transformations and projections from multiple sources, Image Processing Software user testing and documentation, Project Coordination and Tech. Support, Inter-agency communication and support, 3D Data Generation and Management,Project Management, Digital Photogrammetry, Satellite Image Processing, Pre-Sales Presentations.

See more about SBL Geospatial services http://www.sblcorp.com/geospatial-services

Source: GIS Lounge - Maps and GIS

University of Edinburgh teams up with Ecometrica to establish Earth Observation Lab

The University of Edinburgh has teamed up with sustainability software and data firm Ecometrica to establish a new Earth Observation Lab aimed at deriving maximum benefit from the vast amounts of spatial data produced by satellites. It will allow researchers around the world to share data and create customised applications to monitor environmental changes in forests, agriculture and coastal ecosystems.

The Earth Observation Lab built on Ecometrica's advanced cloud-based satellite data and mapping platform will be hosted at the university's School of GeoSciences, one of Europe's leading centres for earth systems and environmental sciences. It will be linked to a number of other 'EO Labs' being established by Ecometrica around the world, facilitated by the UK Space Agency's International Partnership Space Programme (IPSP).

Professor Mathew Williams, head of Global Change Research Institute, School of GeoSciences, University of Edinburgh, said: “Space is a big frontier for economic growth and job creation, and Ecometrica's platform allows scientists querying large spatial data sets to share their research findings with organisations around the world and make it available to a wide variety of users in developing countries."

He added: “One of our first applications will be to generate reports from our CARDAMOM ecosystem model that estimates the changes in biomass for all forests across the world, every month.

“At the same time as helping those working to improve the environment in far-flung corners of the world, the facility will help keep the University of Edinburgh at the cutting edge of global research.”

Dr Richard Tipper, executive chairman of Ecometrica, said: “It’s excellent news that the University of Edinburgh is establishing an EO Lab. The larger the network, the more efficient and powerful it becomes. This allows scientists and environmental specialists around the world to share their research and collaborate on the development of innovative products and uses. We expect more universities to join the global network and set up EO Labs, which support sustainable forestry, agriculture and natural resource management.”

For more information on how to establish an Earth Observations Lab, please visit www.ecometrica.com

Distributed by The Communications Business on behalf of Ecometrica and University of Edinburgh School of GeoSciences.

For further information, please contact:

Denise Hannestad
The Communications Business
Tel +44 131 208 1500
Email: Email Contact

About Ecometrica
In October 2015 Ecometrica was appointed by the UK Space Agency to co-ordinate a major new international project to set up a network of virtual regional Earth Observation (EO) Labs to develop suitable products for the forest sector.

Ecometrica will spearhead the use of satellite data for forest monitoring across the globe. It will initially work with Brazilian space research institute INPE and Mexican research group ECOSUR, as well as several of the UK's leading forest EO researchers and experts.

Forest satellite data & mapping project led by Ecometrica and funded by the UK Space Agency's International Partnership Space Programme (IPSP) secured funding of over $1 million from the Global Environment Facility (GEF) to support conservation of the El Ocote Biosphere Reserve, an internationally recognised biodiversity hotspot in the state of Chiapas.

Ecometrica’s team of recognised experts in sustainability accounting and reporting has been named as one of the world’s top Sustainability and EH&S brands by industry analyst Verdantix. Ecometrica has unrivalled experience in environmental assessments and natural capital accounting, and the Ecometrica Platform brings clarity to environmental and natural resource challenges by combining earth observation data from satellites with local information and business intelligence.

Ecometrica supports all aspects of sustainability planning, operations and reporting by businesses and public organisations. Its sustainability data and software services are available worldwide through offices in Boston, Edinburgh, London and Montreal.

About University of Edinburgh CARDAMOM Data Model
The Carbon Data Model Framework (CARDAMOM) is a software system that combines satellites observations of vegetation, plus climate and soil maps, with a computer model of plant and soil processes, to produce consistent analyses of the terrestrial carbon cycle. The model ensures that carbon movements through terrestrial ecosystems can be tracked and counted effectively. The data from satellites ensures that the model is tested and updated against independent observations including estimates of forest biomass, and of the greenness of forest canopies. Further, satellite observations of fires and of forest clearance are used to adjust the model so that it represents the local dynamic situation as closely as possible, across the globe. The end product is an analysis that generates monthly maps of photosynthesis, plant growth, decomposition, linked to local weather and disturbance - CARDAMOM in effect produces a health check on ecosystems.

About the University of Edinburgh School of GeoSciences
University of Edinburgh School of GeoSciences explores the factors and forces that shape our world. We aim to develop a better understanding of the coupled Earth System, that is, the interactions between the Earth’s geosphere, atmosphere, oceans, biosphere and cryosphere, the drivers of variability and change, and the roles and responses of humans in this complex interplay. With over 370 academics, researchers and research students, we are the largest grouping of geoscientists in the UK. Research activity is currently coordinated within three main Research Institutes – Global Change, Earth and Planetary Science, and Geography and the Lived Environment – and within many smaller research groupings that may reach beyond the School.

We are addressing many current ‘grand challenges’ effectively though: building on strong core disciplines that include ecology, environmental sciences, geography, geology, geophysics, meteorology and oceanography; utilising approaches that range from whole-system-scale modelling to process studies and critical analysis; and actively fostering collaboration with academic and non-academic stakeholders within and beyond the University.

The School of GeoSciences holds a Bronze Athena SWAN award in recognition of our commitment to advance the representation of women in science, mathematics, engineering and technology.

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How to make a killer map using Excel in under 5 minutes with PowerMap plugin

By Aleks Buczkowski

Many times it happens that you need to quickly visualize your data on a map but you don’t have a time to play for hours with ArcGIS or QGIS to make it look good. In this tutorial I’ll show you how make an awesome looking map in Excel in under 5 minutes with a free plugin called PowerMap (formerly GeoFlow).

1. Download and install the plugin.

Of course the first step would be to download and install the plugin from Microsoft website. You should download it and install without any issues.

Initially the plugin was restricted only for MS Office 365 subscribers and owners of an expensive MS Office Professional Plus suite but recently it has been released for all MS Excel 2013 users. Both 32bit and 64bit Windows version are supported but it will work only with Excel 2013 (no previous Excel version is supported). It won’t work on any other operating system though. If you successfully install the plugin, you should be able to see it in your Excel under the “INSERT” tab.

If you don’t see it you might need to activate it in Add-Ins menu. To do that go to File -> Option -> Add-Ins. You should the following window:

In the dropdown list on the bottom of the window select COM Add-ins and click GO. Now select Microsoft Power Map and click on OK button to activate it.

Now everything should work fine. The most difficult part is behind us:). Let’s start mapping.

2. Prepare your data.
Now its time to prepare your data. Power Map gives a couple of options to visualise it. The most obvious is to use coordinates. In this method you need to have two separates columns for latitudes and longitudes. There are a couple of cool things you can do with that and we will cover it in the future #GeoawesomeHoTo. Today lets focus on another great option of the plugin – geocoding.

Geocoding is a process which allows you to pinpoint a geographic location based on a description e.g. address, city name, postal code, country name etc. For the purpose of this tutorial I took a list of the largest agglomerations by population from Wikipedia. Selected the table in a web browser and copied it directly to Excel. Than I pressed “Text to Columns” button in the DATA tab and I came up with the following table.

Geocoding will allow me to show a phenomena on a map based on a city or a country name. It will automatically connect the record to a centroid of a city or a country without a need to give a geographic coordinates.

This is it. Now we can start mapping.

3. Select you data.
Now you just need to select the data you want to map, in my case it would be the whole table, go to INSERT tab, and click on a “Map” icon, which we’ve added to the menu at the beginning of the tutorial. Click on “Launch Power Map” and here we go.

4. Create a map.
The Power Map window will pop-up on your screen. Below you can see that plugin automatically detected two parameters from our table “City” and “Country”. Based on these two columns Power Map will join the name to a centroid of a Country or a City on our list. In that case we are interested in visualizing the data for cities, so we should select only this option.

After choosing “the geography” click on the NEXT button in the bottom-right side of the page. You will be moved to the settings menu. Now you need to select which data you want to visualize. In our case we’ve got only the Population column, so we should select it. Below that there is a menu to select how you want to visualize the data. There are 5 options:

• Stacked Columns
• Clustered Columns
• Bubble
• Heat Map
• Region

As you can see on the example below I’ve selected the Bubbles.

Now it’s time to play with the visualization and Power Map gives plenty of cool features to do that. You can Flat the map, you can select one of the predefined map themes, add text description (via Text Box) and so one. You can also play with the data visualization. Under settings menu on the right side you can change the opacity, size and thickness of your data points. You can also select a color. The size of the bubbles will change with the map scale so you can select “Lock current scale” if you want to keep make them look the same when you zoom in or zoom out.

5. Save the map
Our map is ready! We can present it directly in Power Map to maintain the interactive features or we can save it as a picture. To do that we just need to press the button “Capture Screen” in the main menu and paste (Ctrl + V) the picture to any graphical editor like Paint or a Photoshop.

Here we are. Awesome looking map of the population of 75 largest cities in the world in under 5 minutes! Have fun!

Souce: Geoawesomeness