On the one hand, The JavaScript API is performing better when it comes of visualization and geoprocessing and on the other hand Cesium carry out functions like interaction, geometry and programming with sharpened results.

The next chart will get you an idea about the main functionalities of these two APIs and about their performance in each application.

The cartographic viewers need a server in order to deploy. Cesium uses Node.js while JavaScript API behaves well with IIS (Microsoft Internet Information Services).

Both of the applications have developed their own 3D data type formats. One the one hand, the JavaScript API has created the standard ESRI Indexed 3D Scene Layer (i3S), on the other hand, Cesium works with its 3D-Tiles format. Both of them created under OGC standards, i3S being recently approved as an OGC standard.

One of the main difference is the way data is managed when loaded in the web page.

The data used by the JavaScript API must be uploaded in the cloud and then can be accessed by the application, while Cesium usually loads local data from your computer even though it can be loaded from an external cloud. The speed of loading and interaction with the data (in the first case) depends on the internet speed and quality.

There is another fundamental distinction, the zoom levels are different between the two applications.

In Cesium there is no legend widget like in ArcGIS API for JavaScript (which have a dynamic legend widget) so the legend must be created manually.

Regarding Symbolization, Cesium application approach a much more simple one comparing with the one integrated by ArcGIS API for JavaScript which allows a lot of different data visualization options.

The Esri JavaScript API has several strong points, such as hosting data in the cloud, (although this is not recommended for sensitive data), improved visualization design, fast transmission, support and resources. On the other hand, among the positive aspects of Cesium we find that it is open source, supported by a very large and active community and fast transmission of 3D-Tiles.

Data source of information: You can check more about this in the paper Cesium vs. ArcGIS API for JavaScript An assessment of the usability of virtual globe toolkits to create an animated 3D web scene from Institute of Cartography and Geoinformation, ETH Zurich at the link below: http://www.ika.ethz.ch/studium/cartography_lab/2017_staehli_report.pdf

Find out about how to develop cartographic viewers with ESRI products, Carto, Mapbox and Google Maps with our online course Development of Web Based GIS Applications using ESRI products, Carto, Mapbox and Google Maps – Online GIS Training. 

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The ability to analyze and manipulate data according to users needs, differentiate this application from the traditional Google Earth.

Earth Engine stores, more than thirty years of historical imagery and scientific datasets, updated and expanded daily. It organizes them and make them available for public users, as well as, for commercial ones or administrations systems.

Other capabilities include:

• Integration with Python and JavaScript through its API. APIs for JavaScript and Python (hosted on GitHub) for making requests to the Earth Engine servers. These docs focus on JavaScript; (Get started guide for the Javascript API). For Python, see the Python install guide and the Python examples in the Earth Engine GitHub repository.
• Use our web-based code editor for fast, interactive algorithm development with instant access to petabytes of data. Code Editor: An online Integrated Development Environment (IDE) for rapid prototyping and visualization of complex spatial analyses using the Javascript API. Exlore the Code Editor docs.

To work with the application it is necessary to register as a user, for that you have to click on the “Sign up” button.

After your registration will be completed you can access the platform which and start exploring its capabilities. For example load different types of satellite images already processed.

Choose one of the “Scripts” that are already uploaded to the platform, for example, Normalized Difference, it will appear in the “New script” window and if you press the “Run” button the new image will be displayed in the viewer.

Using the GetLink button, the platform will generate a link that can be copied and accessed using another browser tab. This will offer the user a better interaction with the data just created improving the visualization of the data.

One of the best capabilities of this platform is that you can use different types of datasets. Among them you can find satellite, geophysical and weather images as well as demographic data, etc. You can even load and visualize both raster and vector data

If you access the public data catalog, you can browse the data set of Google Earth Engine (Data Catalog) and you can show them in the Workspace:

Here you can search and load different types of data types.

Use Add data option to load data. In this example we uploaded the SRTM data.

Use Add computation option to add and perform analysis on the data you just loaded. For this example we chose to display the Hillshade effect for the DEM data.

Once again, this type of tool demonstrate the utility of web development platforms, the easy integration of both, vector and raster data, as well as the possibilities to develop and run different types of algorithms and analysis. It also allows live interaction with processed data, which can be visualize in a dynamic cartographic viewer.

Check our Web development courses at the following link http://www.giscourse.com/web-development/ and chose what suits you the best.

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We can obtain data from different types of satellites. The European Space Agency carries out many projects to collect images of the Earth continuously.

One of the newest projects is the called “Copernicus”. The first satellite taking part of this space project was launched in 2014, and the next one in 2015. It is expected to build a satellite system composed by six of these machines, taking every time images of the Earth, and join this data to the GEOSS (“Global Earth Observation System of Systems”)  system.

We are going to search for the most updated data provided by Sentinel-2A. It renews its images by taking multispectral photos every five days, with a resolution of 10 meters. All the published data is accessible to everybody.

The next high-resolution sample image, which represents the bloom of seaweeds in the Baltic sea,  will show us one of many applications of this public data taken by satellites. This will lead us to know (almost in real time) all the events that are happening in our oceans. This is a very precious tool to monitorise the ocean colour (you can see our post about chlorophyll  data measured by contrasts).

The satellite has a camera which covers 13 spectral bands of resolution, with a 290 km wide stripe.

To download all the data provided by this satellite, ESA has created a web viewer where we can choose our zone of interest:

https://scihub.copernicus.eu/dhus/#/home

In the first place, we have to sign into the website. Go to SIGN UP and then we will fill the fields requested, such as Name, Password, Email, such as the following image:

Once registered, you will receive a confirmation email. Then, we will be able to access the data. The next image represents the interface you will see:

To go back to the Web Map Viewer, click on Scientific Hub, then we will be able to log into the website using our recently created account.

Finally, we proceed to download all the features we need for our study. We could use the “Search” browser or draw a polygon over the map in order to select the grids of image available.

For the next posts, we will learn the projects in which we can use our downloaded data, and how to execute analysis.

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To start with, we are going to search for NASA GSFC Ocean Color. This website hosts a great deal of satellite data, processed from several observations of the oceans in different color detection, made by satellite sensors orbiting polar zones, such as MODIS, SeaWiFS, PTU and CZCS. Their product o is an estimation of type “a” chlorophyll.

This tool is capable of accessing level 3 (L3) of “Standard Mapped Image” (SMI), which has a worlwide extension. It uses the WGS 1984 datum with its geographic coordinate system.

Then, we are going to choose the NASA Ocean Color L3 SMI Product tool. It is located in the ArcToolBox previously installed in ArcMap.

Once the tool is open , it is necessary to fill the following fields:

Once we open the tool , it is necessary to fill the following fields:

1. Sensor: This is the type of sensor to choose among: Aqua, CZCS, MERIS, OCTS, SeaWiFS, Terra. We select the Aqua[1] satellite, which has the MODIS sensor incorporated. This satellite began to colect data from 2002 and it is still collecting nowadays.
2. Temporary Resolution: if data is collected daily, at 8 days, monthly or yearly.
3. Spatial resolution: you can choose between these two values: 4 or 9 km.
4. Product code: The products which are available depend on the sensor. The newer are the sensors – like SeaWiFS and MODIS – the more data they provide. Product code is all characters the name of the product has in NASA files. These characters appear between the temporary resolution and the spatial resolution code. For instance, within the following code: O1997164.L3m_DAY_CHL_chlor_a_9km.bz2.
   Here, the product code is “CHL_chlor_a”.
5. Output workspace: where the data will be downloaded.

Once you have all these fields complete, we execute the tool and wait for the datasets to be stored in the catalog. In this example, we see all of these:

Finally, load the data to the Table of Contents and start to work with this data.

This is an example of Marine Geospatial Ecology Tools (MGET),  a set of tools completely free and ready to use in the ArcMap environment.

[1] http://aqua.nasa.gov/  

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ESRI is the well-known company responsible for developing GIS software, with more than a 30% market share (Câmara, et al., 2012).  ArcGIS as well as its other products are a reference in this sector. Even though it offers lots of possibilities and that they are now offering many free resources that can be modified and improved by developers, you still have to buy its programs.

Open source software refers to a tool that is of free access and can have its code source modified. This phenomenon appeared more than 50 years ago which´s term was introduced already in 1998 (Mitasova and Neteler, 2004). Nowadays, Open source GIS tools are experiencing an important expansion and many developers around the world are collaborating to develop new programs that can compete with commercial software so that these technologies can be available and offer alternatives to a bigger number of users.

The Geographic Information Systems [1] world has been revolutionized by these open source and free systems both for their use and development.  It is expected that during this year the open source tool Quatum GIS (QGIS) [2] , will be the most used program by users due to is friendly interface, high variety of plugins to carry out diverse operations and its permanent evolution thanks to the synergy between many developers.

These types of programs offer the opportunity to bring closer GIS to users, therefore improving employment rates, and at a low cost.

Other popular open source GIS programs can be found in the following table:

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