IEA Training Manual - Module 4

3.1.3 Quantitative data

Quantitative data provide “raw material” for indicator and index development1. They are the primary, raw output of monitoring and observation systems, surveys and other forms of data collection, and normally require analysis to be meaningful to the wider audience.

Characteristics of quantitative data may include:

  • generally have geographic locations (coordinates);
  • are often large in volume (databases, reports, etc.);
  • come from a variety of often heterogeneous sources;
  • have variability of resolution (details) and scales that sometimes hamper their compilation and integration;
  • have a high degree of complexity;
  • are needed at varying temporal frequency (e.g., hourly, daily, monthly, yearly), depending on the phenomena or subject under consideration;
  • are available in varying forms and formats; and
  • more and more available in digital or electronic versions.

Generically, data are categorized as bibliographical materials (including descriptive texts and reports), statistical tables, maps and remotely sensed data (World Bank, 1992) but they can come in many forms such as:

  • maps;
  • remotely sensed data such as satellite imagery, aerial photographs, or other forms of visual data;
  • computer data files;
  • hard copies of reports and documents;
  • bibliographies;
  • videos and films;
  • graphs and charts;
  • tables;
  • computer animated images; and
  • drawings.

All assessment processes ultimately depend on data, but very few have the mandate, resources and capacity to collect primary data, so they rely on monitoring and data collection efforts by others. Therefore, compiling data for assessment usually requires that you obtain data from other sources, usually many different ones, both in terms of statistical (non-spatial) and spatial data.

Non-Spatial Data
Non-spatial data are collected for one particular point and result in a single number. Often, multiple data points for the same parameter are averaged so that a single value is obtained to represent a collection of spatial units. Because non-spatial data are tied to a single point, there is no further resolution for those data—the information cannot be further broken down. This is unlike spatial data, which have resolution that allows you to move from detailed to broad information using the same data. While non-spatial data do not have spatial resolution, they can have temporal resolution if they are collected continuously over a period of time from a specific geographical point.

You can obtain non-spatial data from statistical sources or isolated research. Statistical sources use the same methodology for multiple data, so that they can be statistically compared and averaged. Isolated research, while valuable, often does not provide the breadth you will need for analysis at broader levels.

Spatial Data
Spatial data, also referred to as geospatial data or geographic information, can most simply be defined as information that describes the distribution of phenomena and artifacts upon the surface of the earth. It is information that identifies the location and shape of, and relationships among, geographic features and boundaries, usually stored as coordinates and topology (i.e., the way in which geographical elements are related and linked to each other).
Spatial data are often displayed as layers of data one on top of the other, similar to a giant sandwich, where each layer is a related set of spatial data. Anything that has a geographic location on the Earth can be displayed as spatial data, including country statistics.
Spatial data have become a major resource in environmental analysis and reporting, and present a very immediate and visual message regarding environmental issues and management.


Examples of “layers” you might use are:                                               Layers of spatial data

  • (National Geographic
    Society, 2006)

    aerial photography
  • satellite imagery
  • country boundaries
  • local administrative boundaries
  • streets 
  • cities 
  • utilities 
  • protected natural areas
  • habitat regions
  • lakes and rivers
  • elevation contours
  • climate data
  • soil layer data
  • wildlife populations

You can also link additional non-spatial data, in the form of databases of information, to these spatial data layers by their common coordinates, and analyze and present them alongside spatial data layers. Climate data from different provinces or states in a country for example, could be linked to a provincial or state boundary layer, analyzed and displayed in a spatial form, and produced as maps.

Example

Figure 3: Desertification in the
Caspian Region

Consider the following map (Figure 3), which provides spatial information about the degree of desertification in the Caspian Region in Central Asia in 1998. A simple form of analysis using non-spatial data would be to overlay statistical information about the number of cattle, sheep and camels located within the boundaries of the map. You could then determine if there is a correlation between animal density and desertification.

As shown in this made up example, Community 1 has a lower cattle density, and thus less potential grazing pressure, than Community 2, and it is also located in an area that has less desertification. If a similar pattern emerges when many data points are used, you can begin to associate grazing pressure with desertification. While correlation does not show cause and effect, it does indicate a possible relationship between the two variables.

Remotely sensed data

What is remote sensing?
Essentially, we can describe remote sensing as a technique used to acquire images of the Earth’s land and water surface, and to provide data on features on the face of the Earth without the observer being in direct contact with the object of observation. These images are taken with devices sensitive to electromagnetic energy such as:

  • light – cameras and scanners;
  • heat – thermal scanners; and
  • radio waves – radar.

Remotely sensed data are useful when data are difficult to acquire, such as when the area is difficult to access, or the areas of interest cross country boundaries. In other cases, it is useful when the cost of acquiring ground-based data for extensive areas, for which SoE reports are often required, is beyond the means of many governments and organizations. For these cases, remote sensing provides a partial solution for data acquisition for SoE reporting. But even for areas where conventional methods have been used to acquire data, remote sensing still provides many added advantages.

How is remote sensing useful for IEA?
Remote sensing is particularly useful for environmental monitoring and reporting because it provides a unique overhead or “bird’s-eye” perspective from which to observe large areas or regions. Because of this, it can be used for management and planning in large local areas, and for monitoring the progress of ongoing projects. In many cases, these data collection can offer proof of progress towards success of projects that are a result of policy decisions designed to improve the state of the environment. Such data may be essential for further investments.

Another benefit of remotely sensed data is that they are often available on a repetitive basis. This type of time series data is extensively used to monitor changes in the environment over long periods (examples in Box 3). This is particularly important for SoE reporting in very rapidly changing environments.

Box 3: Remotely sensed data

  • Provide a unique perspective from which to observe large regions.
  • Sensors can measure energy at wavelengths which are beyond the range of human vision (ultraviolet, infrared, microwave).
  • Monitoring is possible from nearly anywhere on earth.
  • Remotely sensed images provide good “pictures” for convincing the public and decision makers to participate in discussions on issues of importance that may not be part of their daily life.
  • Used to monitor long-term changes.
  • Readily integrated into GIS.

Types of remotely sensed data

Satellite imagery
Satellite imagery is digital information obtained from sensors carried in satellites, and includes data both in the visible and non-visible portions of the electromagnetic spectrum (i.e., optical, thermal, radar). Satellite imagery is available from several sources from around the world (i.e., Landsat, SPOT, Quickbird, Envisat, ERS, IRS, Radarsat, NOAA, ASTER), and from numerous companies that process and distribute satellite data products.

Landsat, one of the longest running sources of commercial satellite imagery (Landsat 4, 5 and 7 in particular), refers to a series of US-owned satellites put into orbit around the Earth to acquire images and collect environmental data about the Earth’s surface. These satellites have been collecting images of the Earth’s surface for more than 30 years and have acquired millions of images. These images provide a unique resource for people who work in agriculture, geology, forestry, regional planning, education, mapping and global change research.

Figure 4 and Figure 5: Lake Tonga,
Africa, 1995 and Lake Tonga,
Africa, 2000
Source: UNEP Grid – Sioux Falls

One of the benefits of satellite imagery is the ability to capture multispectral images (i.e., images in two or more spectral bands, such as visible and infrared). This allows complex image processing and analysis in many different ways. Satellite imagery is also provided in a standardized spatial format, so integrating these data with socio-economic data for integrated environmental assessment becomes much easier. Nevertheless, problems of organizations and governments using different formats still exist over large areas, and across national boundaries.

The following images are an example of satellite imagery. The images show the status of the lake and surrounding areas of Lake Tonga in Africa, 1995 and 2000.

Aerial photography
Aerial photography consists of images taken of the Earth’s surface from a camera on an airplane flying at a relatively low altitude. Depending on their purpose, aerial photographs are taken in black and white, colour, and/or infrared. For example, simple planning or navigation may only require black and white photography, while vegetation studies require infrared in order to distinguish among landforms based on infrared heat signals. Similar to remote sensing, aerial photography provides a unique overhead view of an area, and can be used to acquire data on local areas without the observer being in direct contact.

Aerial photography has several benefits over satellite imagery; one is that it provides a much higher resolution of an area, allowing you to get a very close-up and detailed picture of a fairly small feature on the Earth’s surface. With the necessary corrections for distortion and processing, aerial photographs are powerful tools for studying the earth’s environment.

Typical applications of aerial photographs include land-use surveys and habitat analysis. For example, they are often used by cartographers and planners to take detailed measurements for the preparation of maps, and by trained interpreters to determine land-use and environmental conditions and changes.

Some of the down sides to aerial photography over satellite imagery, however, are that they only take a picture of a relatively small area, regular images taken of the same area are uncommon and laborious, and acquiring aerial photography for an area is much more expensive than obtaining satellite imagery.

Discussion Questions: Spatial data in environmental reporting

Option 1. Discussion

Working in small groups, discuss how you have personally used spatial data, as well as data combinations including spatial data, in your profession, or how you have seen it used.

For Example: You may have at some point used a satellite image of your country as a base layer with an overlay showing regional boundaries. You may have then linked data, such as a climate database, to the map to show average precipitation for each region across the country.

Provide examples of any environmental monitoring or reporting you may have done, and whether or not spatial data were used for this reporting.

Choose someone in your group to record aspects of the stories collected, including what worked and what could be different. Photo Source: US Geological Service 
      
Option 2. Questions to discuss:

What are the benefits of spatial data?

Identify an environmental problem or concern. What kind of spatial data could you use to help understand and communicate the issues involved?

What are some of the challenges you might encounter when using spatial data?
       
Spatial data and the Internet

The Internet has become a major source of data used for assessment and reporting. There is an unprecedented amount of free environmental and socio-economic data on the Internet, and more and more websites also allow the exploration of the data through online mapping and/or statistical analysis (see Box 4 for some current examples of available sources). In addition, there are many online data and map services available that are fairly simple to use with most Internet browser programs, and this has become a very effective way to communicate images, maps and other types of datasets to potential users without the need to acquire and run specialized computer software. The GEO Data Portal (http://geodata.grid.unep.ch), described later in this module in detail, has been specifically developed to provide the most important global, regional and national data from authoritative international sources to the assessment community, while offering at the same time various possibilities to look at the data online by means of maps, graphs and tables.

Footnotes:
1. In general, for data is understood a representation of facts, concepts, or instructions in a formalized manner suitable for communication, interpretation and processing by human or automated means (Rosenberg, 1987).

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Patrick,Checked it out. The issue is the time scale. Your regional study was done over years, and even then iecfrenens on cattle displacement are, at most, indirect and certainly difficult to link to singular conflict events or their perpetrators. The latter is supposed to be the rationale of the Sentinel initiative. I understand that the timescale of that action is more in the order of months, or maybe a year (depending on the outbreak of conflict). Paparazzi tend to move on quickly There are too many variables (weather mostly) that would influence short term vegetation patterns. On the other hand, the method (using MODIS or similar) is relatively straightforward, so should not be completely ignored. MODIS would anyhow be a good idea to use for anomalous fire detection.I originally understood the cattle issue to be linked to the detection of sudden displacement of herds, e.g. as an effect of people fleeing from a conflict area. As stated, that would be somewhat difficult/impractical.Another thought came up after I posted the previous reply. It would probably be a good idea to involve (retired?) military experts in the image analysis. They should be good at detecting military maneuvers, encampments, defence works, etc., esp. since the Sudanese Army seems to use a lot of Russian material and probably tactics as well. For the sake of neutrality, you would need to do the same for the SPLA deployment (not sure what tactics they follow). In that way, you could probably use the remote sensing data more consistently as a pre-alerting and more likely as a deterrent. Again, there is a need for some good field knowledge on where to look for the most significant built-up. But whether you want to widely share such knowledge is another question. I guess the Sentinel folks have thought that one out already. A last point. The European Space Agency also has a Sentinel programme, linked to the European Global Monitoring of Environment and Securitiy initiative (a contribution to GEOSS). It has nothing to do with the Google et al initiative, but may lead to some future name confusion. Curious.