Previous: Indexed approaches to monitoring of Crops, Rangeland and Food Security at National Level Next: Analysis of vegetation index and rainfall estimate images Table of contents Index Glossary Images Frames U09-NRM-127: The role of Distributed Data Access Technologies in NRM - for ITC-IDV version 2.7 > Thematic Expert Models > Food security > Remotely Sensed Indices > NDVI > Exercises > Indexed approaches to monitoring of Crops, Rangeland and Food Security at National Level

7.0.0.0.0.0.0 Visual interpretation of NDVI (Normalised Difference Vegetation Index)
Learning objectives and requirements
Objectives
  • Displaying vegetation index and rainfall estimate images
  • Visualizing world country outlines
  • Loading another color table
  • Loading a Shape file
  • Loading a locations file
  • Visual interpretation of NDVI (Normalised Difference Vegetation Index) and RFE (Rainfall Estimate) images
  • Calculating average vegetation indices per province.
QuestionsThis 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 growing season.

The south of the country is characterized by a total annual precipitation of about 1200 mm, while the north of the country receives only around 400 mm each year. These climatic zones have an effect on the overall vegetation distribution.

Can you detect bio-climatic zones ? Vegetation index Burkina Faso
Remote Data Access

Zimbabwe case




Selecting data appropriate for a certain task

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:

  • The NDVI images indicate the amount of green vegetation on the earth surface, and;
  • The RFE images give us an estimate of mm rainfall per dekad.

  • Exercise Zimbabwe

    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:

    dods://iridl.ldeo.columbia.edu/SOURCES/.USGS/.ADDS/.NDVI/.NDVIg/.dekadal/.LTA/dods

    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/dods
    
    NDVI Dekadal Maximum 2004-now:
    dods://iridl.ldeo.columbia.edu/SOURCES/.USGS/.ADDS/.NDVI/.NDVIrg/.dekadal/.maximum/dods
    
    Note the dods:// prefix -instead of http://. This signals the OPeNDAP data server a DODS-data object is being requested instead of the metadata (text). If no error occurs in accessing and subsetting the data, the server responds by returning a data object to the client. Try the same url, but now with the http:// prefix and observe the difference

    Question 1
    Which of these remote data sources best describes bio-climatic zones, long-term average (LTA) or Dekadal Maximum (DM) NDVI?
    Long-term average (LTA)
    Dekadal Maximum (DM)
    None of the above



     


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    U09-NRM-127: The role of Distributed Data Access Technologies in NRM - for ITC-IDV version 2.7 > Thematic Expert Models > Food security > Remotely Sensed Indices > NDVI > Exercises > Indexed approaches to monitoring of Crops, Rangeland and Food Security at National Level