| Learning objectives and
|Questions||This exercise contains 8 questions.|
|Climatic conditions of Burkina Faso|| Image interpretation
For interpretation of the images one has to
realise that Burkina Faso is situated between the 9th and 16th northern
parallels. Generally the growing season starts in May and ends in September.
The image in this example below is a mid-August image, which is the peak of the
|Can you detect bio-climatic zones ?||
Remote Data Access
| Displaying images is easy with the IDV. The IDV is in some aspects similar to "Google Earth", providing real-time access to data collections relevant to scientists with the same ease. With remote data access technology, holistic approaches to natural resources management finaly become within reach. This technology consist of interconnected servers and clients, who communicate (request for) data through the Internet.
The IRI/LDEO Climate Data Library (http://iridl.ldeo.columbia.edu/ ) of the International Research Institute for Climate and Society is an example of such a server. This library offers the same data collections as other data webportals do, but also supporting a much broader variety of file formats, and more importantly, data access is provided through so-called Internet protocols. This allows selected clients such as the IDV to access their data collections remotely without the need for binary downloading them to the local desktop first. The IRI/LDEO Climate Data Library contains over 300 datasets from a variety of earth science disciplines and climate-related topics, including drought monitoring and food security related hazards. As an example, the Desert Locust Information Service (DLIS), as part of the UN Food and Agriculture Organization (FAO), collaborates with IRI in providing products to estimate favorable ecological conditions in the Desert Locust recession area. Various clients (e.g. FERRET, GrADS, matlab, IDL/ENVI, IDV, etc.) can remotely access these and other data collections by their Internet protocol support through so-called Application Protocols Interfaces (API). These API's follow open-source software standards developed by Unidata ( http://www.unidata.ucar.edu/ ) and the Open Geospatial Consortium (OGC). OPeNDAP ( http://www.opendap.org/ ) is an example of such an API which downloads data directly to software, allowing remote sub-setting of data collections based on their spatial, temporal, and parameter characteristics. Bandwidth requirements are thus drastically minimized, often resulting in download sizes by a factor 10 smaller than the original.
The remote data server of the IRI/LDEO Climate Data Library holds two data sets relevant to our exercise with dekadal (10 daily) NDVI and RFE images covering the whole of Africa:
In the next section we will lean more about accessing remote OPeNDAP GRIDded NDVI data.
Here is the metadata of one of the NDVI remote data sources, originally produced by FEWS/Africa Data Dissemination Service (ADDS) in Harare, Zimbabwe.
For Long-term (1982-1999) mean dekadal NDVI, the remote data access url takes the following form:
Alternatively, we could also load USGS ADDS NDVI dekadal maximum as listed below.
NDVI Dekadal Maximum 1981-2004:
dods://iridl.ldeo.columbia.edu/SOURCES/.USGS/.ADDS/.NDVI/.NDVIg/.dekadal/.maximum/dodsNDVI Dekadal Maximum 2004-now:
Now, let us load the NDVI time series.
There are 2 ways to access the ITC-TDS data:
Now we are ready to load and display the data.
Visualizing country outlines
When the NDVI images appear, only the world coastlines are displayed when you first start the IDV. You can easily change this default setting to use other system maps, or add in your own. We will visualise country outlines, so that you can locate Zimbabwe:
Loading another color table
From the Download page, download the fews1.xml and fews2.xml files from the Zimbabwe case study to your local harddisk and remember where you stored them. To load another color table:
The following information maybe helpful:
Red Green Blue NDVI 127 127 150 Clouds/Water 225 0 0 0.00-0.05 175 0 0 0.05-0.10 150 50 0 0.10-0.15 200 100 0 0.15-0.20 230 230 0 0.20-0.25 200 200 0 0.25-0.30 150 150 0 0.30-0.35 0 150 50 0.35-0.40 0 200 100 0.40-0.45 0 150 150 0.45-0.50 0 100 200 0.50-0.55 0 0 128 0.55-0.60 100 0 128 0.60-0.67
Use the navigation buttons (or listbox) of the time animation to display the image for the 36th decade. This image reflects the peak of the growing season in Zimbabwe.
The following historical average image of the 36th dekad of Zimbabwe should result.
Additional exercise: You will create a formula, and you will calculate the long term average NDVI per province.
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