Overview of ENDELEO tools
Image viewer
Statistics: graphs and tables
Focus area

Overview of monitoring tool
The ENDELEO web-based monitoring tool is intended to provide updated information on the status of natural ecosystems in Kenya. To determine overall trends in vegetation status, the monitoring tool uses low spatial resolution remote sensing data products updated every 10 days. The monitoring tool also enables focusing on specific areas (focus areas) to study patterns of change in more detail using high spatial resolution images.
The low spatial resolution products provide of two vegetation indices, the Normalized Difference Vegetation Index (NDVI), which is a rapid identification of the ‘Vegetation health and density’, and Dry Matter Productivity (DMP) which provide an estimate of the ’Vegetation growth rate’. The high resolution products consist of colour composite images of Landsat, ASTER, or SPOT HRVIR images, as well as land cover maps and land cover change maps.

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Image viewer
The image viewer allows you to study images representing the status of the vegetation for different locations of Kenya at different periods in time. Before you can explore your location of interest, you should select the image characteristics. Afterwards you can print your result.
Example on how to interpret the images are demonstrated in some case studies.

A) Select image of interest
First you should select the characteristics of the image you would like to view. These characteristics include the ‘sensor’, the indicator and the ‘type’. Thereafter you will determine the time period for which you would like to view the image by selecting the year, the month and the part. Once the selections have been made, the requested image will appear. The meaning of the colours displayed in the image is provided by the legend.

Selection of image characteristics and period in time

B) Select method to explore
There are various possibilities to explore the images. You can manually zoom to a certain image with the ‘click and zoom’ function, you can enter or mark the coordinates of a region of interest with ‘region of interest (ROI)’, or you can directly focus on predefined areas by selecting them. The coordinates of the current selection appear beneath the image (in UTM zone 37 south, Arc1960). Whichever method you select, you will receive some guidance on top of the image window.


Selection of method to explore the image

Click and zoom
From the zoom menu, a drop down box appears with the options ‘in’, ‘out’ and ‘pan’. To zoom in or out, click on the area where you want to zoom in or out. To pan, click on the image and keep the mouse button pressed. Move your mouse in the direction where you want to drag the image.

Selection of zooming mode

Select Region Of Interest
To select a Region Of Interest (ROI) you can either draw a box or enter the coordinates. To draw a box, click a first time on the image to define the upper left corner. A small square will appear. Clicking a second time will define the lower right corner of the ROI. The image will be centered to this box.

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Selection of region of interest (ROI) by drawing a box: Selection of upper left corner (left image), selection of lower right corner (middle image), ROI will appear (right image)

To enter the exact coordinates, use the text boxes on the right. The coordinates should be expressed in the following projection system: Universal Transverse Mercator zone 37 south, datum: Artc1960 and the ROI should have more or less the same shape as the window of the viewer. Then press ‘set ROI’

Select by name of spatial unit
You can directly zoom to a specific ‘district’, ‘NRT conservancy’, ‘Forest Protected Area, ‘Wildlife Protected Area’ or ‘Kenya Atlas hotspot’. If you select one of these spatial units, a drop down box will appear in which you can select the name of interest.
The Kenya Atlas hotspots are the hotspots identified and discussed in the Kenya: Atlas of our Changing Environment, produced by the Ministry for Environment and Mineral Resources and UNEP. The Atlas describes changes on the Kenya’s landscape based on a comparison of satellite images of the same place for different years. The image viewer allows a continued monitoring of the changes described in the atlas, by investigating the most recent image available for these areas. If you click on … you get a short description of the hotspots. For more information, please consult (http://www.unep.org/dewa/Africa/KenyaAtlas/).

Selection of region for a certain spatial unit

C) Borders
Additionally, you can toggle on three different vector layers: districts, protected areas or NRT conservancies. These vector layers appear as lines (borders) on the image.  


Selection of borders

D) Print or Save
 You can print what is displayed in the image viewer by clicking on the ‘print’ button beneath the image window.

To save what is displayed in the image viewer, click right with the mouse on the image and choose ‘save image as’. The image can be saved in the format you prefer.

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The image viewer allows to examine precisely the spatial distribution of the vegetation status in Kenya. The changes over time can be studied by selecting different dates or by means of the ‘comparison images. However, if you want to study the changes over a whole year or between a number of years, the comparison becomes complicated or even confusing. Therefore, ENDELEO offers two statistical tools: the graph and the table. Another advantage of the graph and the table is that it allows you to study the vegetation status for a specific land cover class. These classes are derived from the AfriCover map. AfriCover classes that occur in small patches are merged into a larger class of similar vegetation. As such it is avoided that statistics are calculated from too few pixels, which could give unreliable results.


Landcover map used for the calculation of the statistics. Between brackets the original AfriCover classes.

Examples on how to interpret the graphs and tables are given in some case studies.

This tool plots the temporal evolution of the ‘vegetation health & density’ (NDVI) of the selected year and the average year.
To display the graph, select the geographic area, the land cover class and the year of your interest. You have to wait a few seconds after choosing a parameter untill the next one will appear. Then click on ‘analyze’.
Three graphs will be plotted. The red graph shows the temporal evolution of the NDVI (vegetation health & density) for the selected year. The blue graph is representing the same, but the dataset has been smoothed over time to remove possible dips related to bad values, e.g. due to undetected clouds. The smoothed value is calculated as the average value of the two previous decades, the decade itself and the two next decades. In this way, it removes all abrupt local minima (supposedly clouds), as far as they don’t persist longer than four decades. It is better to interpret the change of the blue graph than of the red graph as the overall trend of the values becomes more clear after removing small changes that are not due to the trend. The black graph shows the smoothed NDVI for the ‘average year’. The ‘average year’ corresponds to the ten year average (1999-2008) NDVI. Comparing the position of the blue graph with the black graph shows whether the vegetation conditions for the year of interest are better or inferior compared to the average situation. If the blue graph is above the black one, the conditions are better, if it is bellow, the conditions are worse.
You can change your selection to analyze different areas or years. Do not forget to click ‘Analyze’ each time you change a parameter.

If you want to save the displayed graph, click right with the mouse on the graph and choose ‘save image as’. If you want to use the graph values and combine for instance these values with another dataset, you can click on ‘export to txt-file’ or ‘export to csv-file (excel)’. If you open this file, the values on which the graph is based are given - separated by ; - ready to use for analysis’.

With this tool, you can create a table with the percentage increase or decrease of the ‘vegetation health & density (NDVI) for a certain month in the current year compared to the same month in a year of interest.

To display the table, select the geographic area, the month and the year of your interest. You have to wait a few seconds after choosing a parameter untill the next one will appear. Then click ‘Analyze’. The results are shown for the complete area as well as for the different land cover types.
You can change your selection to analyze different areas or years. Do not forget to click ‘Analyze’ each time you change a parameter.

If you want to save the displayed table, click right with the mouse on the table and choose ‘save image as’. If you want to use the table values and combine for instance these values with another dataset, you can click on ‘export to txt-file’ or ‘export to csv-file (excel)’. If you open this file, the values on which the table is based are given - separated by ; - ready to use to combine with other analysis’.

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Focus areas
The ‘focus areas’ menu allows to compare high spatial resolution images of different years for a number of selected areas. This makes it possible to investigate with more detail changes that have occurred in these areas. The high spatial resolution images include Landsat, SPOT HRVIR, and ASTER images.

To select a focus area, you can either click on one of the markers in the map of Kenya or select the area of interest from the list of focus areas.

For each focus area, you can select the type of image you want to view: either a false colour composite image or a SWIR colour composite image. Next, you can select the period for which you want to compare images. After making the selection, press the ‘Submit’ button and the requested images will be shown.

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Nov/Dec 2010
Final workshop in Nairobi
October 2010
Download Newsletter 6
May 2010
DEMO Endeleo in Nairobi
April 2010
Smoothed graphs available
March 2010
° Download Newsletter 5
° Case studies elaborated by users
February 2010
NEW in image viewer: toggle on/off borders
January 2010
Help desk available

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