Michael Schultz Rasmussen and Karl-Erik Christensen

Michael Schultz Rasmussen and Karl-Erik Christensen

Case 2 Senegal

Michael Schultz Rasmussen and Karl-Erik Christensen

Introduction

Background

Senegal

Producing a biomass map

Danish research in Senegal

The light efficiency model

Exercises

Using LEOWorks:

Study the seasonal variation in vegetation in Senegal

Study a biomass map for a single year

Dessertification or improved conditions in Senegal?

Introduction

All over the world the living conditions of people changes from time to time. In some areas and in some countries the changes are fast and unpredictable, however, in other parts of the world, changes takes place slowly. In the areas south of the Sahara dessert in Africa, people are living in areas where serious changes take place from year to year because of changes in rainfall, fairly poor soil and lack of resources. In the 1980’ies they experience serious drought and people died and they lost their cattle. The international community reacts in the most cases with providing assistance in terms of food and medical care, which is good, however, providing help and assistance to people without any need or only a limited need can equally be a problem, because if food is provided free of charge, the local markets are destroyed and this will have a serious effect on the local society. It can be extremely difficult to study if people’s livelihood is changing, because of the substantial annual changes. Satellite data can help us to understand the long term development of living conditions by studying the changes in the vegetation cover. Vegetation includes crops as well as grass to be used to feed the animals.

In the following exercises we will try to answer the questions:

- is the daily lives and livelihood of people improving in Senegal?

- is the state of the environment deteriorating and causing what is known as desertification?

In Europe are the agricultural crops and the natural vegetation such as forests, wetlands, fallow land and protected areas growing steadily year after year. There may be minor differences, but the general picture is the same: our vegetation grows. In Senegal in West Africa, the rain is sparse and unpredictable and people here are very dependent on the growth of the vegetation. Either they are growing crops such as millet, groundnuts and corn, using the grass to feed the livestock or the forest for getting fuel wood and materials. The densities of people and cattle are high and the soils are sandy and not very fertile.

Therefore the condition of the vegetation is an excellent indicator of the livelihood of the people as well as the state of the environment in both Senegal and all the other semi-arid countries. The vegetation productivity decreases in years with low rainfall, in areas where the fields are not allowed to recover through fallow, where attacks from insects and maladies take place and where the density of grazing livestock is too high – just to mention the most important.

The condition and amount of vegetation can be estimated using satellite data. The principle is to keep track of the photosynthesis activities during the growing period. By the end of the season, we sum up the total photosynthesis or more correctly, we compute the net primary production (NPP) (also called biomass) for each pixel. If we know the NPP for some reference sites, we can compute a model that can estimate NPP for every pixel in the image. If we repeat this exercise every year, we get annual NPP or biomass maps and we can study the trend of the NPP for each pixel. This information can reveal if the environmental conditions and people’s livelihood are deteriorating or improving.

Keeping track of the vegetation development using Earth Observation is useful for many different purposes:

- The growth of the global vegetation can be estimated and this provides information about the global CO2 sink. This is an important contribution to the studies and modelling of global change

- Estimate crop production in developing countries. This is used to assess food security and evaluate export potentials (and obtain better prices)

- Support crop management for irrigation, pest management, water logging and insect attacks

- Estimate the availability and management of fodder resources for grazing animals

- Identify long term environmental development and trends. Is a certain area experiencing a positive or negative development, e.g. increased or reduced crop production, reduced fertility of the soils and desertification

Background

Senegal

Senegal is located at the most western point of Africa and just south of the dessert Sahara (see the map). In the north, it is dry with only a little rain between 100 and 200 mm, whereas in the south the rainfall exceeds 1400 mm a year. The area we are concerned with in this exercise is the northern half of Senegal where droughts occasionally happen and where the degradation of the environment takes place in some areas. The average temperature is around 27 degrees and the climate is tropical with a rainy season from June to August.

The size of the country is 196.000 km2 and the population is ca 11,5 mill. The most people are living doing agriculture or raising livestock. The central part of Senegal is the so called Peanut Basin, where the traditional cash crop Peanuts are grown, mixed with millet and some corn and vegetables. Recently Senegal has been successful exporting vegetables to Europe from the irrigated fields along the coast and around the Senegal River. Fishing is another important business. The official language is French because of the colonial heritage; however, the largest ethnic majority are the Wolof having their own language. There are at least four other important ethnic groups. The country is a democracy.

Senegal is a great country with the Senegalese full of warm humour – you always laugh when you are there! Occasional you get as well frustrated because things are not working as smoothly as at home. Dakar has a lot of shops, restaurants and cafes and you can enjoy yourself sit looking at the clear blue ocean. There are a number of excellent beaches. In the south east you can visit the major Nikolokoba National Park, where you may meet elephants and lions and for sure many other animals. Some of the greatest dancers and musicians in Africa live in Senegal; check out Youssou N’Dour for the lively mixture of modern music and the traditional Mbalax: or Baaba Maal:

Learn more about Senegal:

Producing a biomass map

The vegetation index, such as the NDVI, is a measure of the vegetation to conduct photosynthesis (link to the NDVI explanation). In other words, it is a measure of how much Active Photosyntetic Radiation the vegetation can catch. The NDVI is a measure of the efficiency, e.g. if the vegetation is stressed because of maladies or lack of water the value of NDVI decreases and the photosynthesis capacity of the vegetation goes down. Read more about the light efficiency model…

In practical terms we integrate (sums up) the NDVI for the growing period and we compare the integrated NDVI values with biomass samples collected on the ground. We can make a regression (computing the mathematical equation that relates integrated NDVI to biomass), that will allow us to compute the biomass for any pixel in the image (provided vegetation is growing here).

Figure 2 show the development of NDVI during the growing season, where each point on the curve is one observation (one image) from the satellite. Figure 3 is an example where biomass has been correlated with integrated NDVI and a regression equation has been computed. Take a minute or two to understand the principle.

Why all this fuzz with the light efficiency model when we just make a simple relationship between integrated NDVI and biomass collected on the ground anyway?

Yes, you may say so, but it is very important when you develop methods to estimate biomass (and other parameters) that you have a sound and good bio-physical principle. In this way you have an understanding of what is going on and it helps you to improve and further develop your model and ideas.

Danish research in Senegal

The Department of Geography at the University of Copenhagen has a long record working in Senegal with Senegalese partners. Where the most important is the Centre de Suivi Ecologique (CSE). The centre was established back in 1986 in order to study the serious droughts that happened in 1984 and the previous 10 years. New satellite technology was used for this purpose, since they could cover large areas and provide information how vegetation and soils changed year from year. The Department of Geography got involved through the United Nations and collaborated among others with the American organisation NASA and University of Maryland. Since then a number of topics have been studied: desertification, vegetation growth, bush fires or wild fires, rainfall, hydrological modelling of the Senegal River, land degradation, the behaviour of the tropical monsoon system, climatic parameters etc. Follow the links below to learn more:

Vegetation and water balance:

Carbon:

Hydrological modelling:

Land degradation and desertification:

Wild fires:

Biomass estimation:

The Department of Biological Science from University of Aarhus has equally worked for many years in Senegal.

Senegal_1_S.jpg

Figure 1. Map showing Senegal. The area around Bambey, Diourbel and Kaolack is the Peanut Basin, where the main agricultural crops are grown. The Peanut Basin has been delimited. The numbers show the places where biomass and crop yield samples have been made. Rasmussen, M.S. 1997. Click to see a larger image.

Senegal_2_S.jpg

Figure 2. NDVI during the rainy season for a typical try northern site close to the Sahara. The numbers starts on January 1. Below is shown the rainfall for the same period. Rasmussen, M.S. 1992. Click to see a larger image.

Senegal_3_S.jpg

Figure 3. A plot between crop yield or biomass, collected in the field and NDVI integrated during the rainy season. Once this relationship has been established, the cropy yield (or biomass) can be estimated for every pixel in the image (provided vegetation is growing there). Rasmussen, M.S. 1992. Click to see a larger image.

Senegal_4_S.jpg

Figure 4. Landsat ETM image from November 1999 showing Dakar (the most western point) and a good part of the Peanut Basin. You can see groups of fields and natural vegetation. Click to see a larger image.

The light efficiency model

The light efficiency model is a simple model that can be used to calculate the growth of the vegetation. This growth is measured in kg of dry vegetation matter per hectar (kg/ha), or so called biomass. Another word for Biomass is the Net Primary Production (NPP), that more correctly is the energy the plant is using to produce the biomass. The Gross Primary Production (GPP) is all energy used to produce biomass and to maintain the plant itself, e.g. keeping leaves, stem and roots alive. So the light efficiency model is used to calculate the energy available to the plant to produce biomass and was first formulated by Kumar and Monteith in 1972:

NPP = ε * ∫ APAR dt

where ε is the efficiency coefficient

APAR is the Active Photosynthetic Radiation and

dt is the integration step (time).

If we start looking at the APAR, this is the energy coming from the sun that the vegetation can use for their photosynthesis. So by integrating it over the growing season, we sum up all available sun energy that can be use by the vegetation to maintain itself and produce biomass. The term:

∫ APAR dt

simply sums up this amount of energy.

The ε term is the efficiency coefficient and it tells how “efficient” the vegetation is to convert sun energy into NPP. Thus by multiplying the two:

ε * ∫ APAR dt

we know the amount of energy available to produce the biomass. When we want to use the the light efficiency model to compute biomass, we could of cause measure the APAR and use laboratory studies to find out what ε was, but in reality, it is much more simple:

The vegetation index, such as the NDVI, is a measure of the vegetation to conduct photosynthesis (link to the NDVI explanation). In other words, it is a measure of how much APAR the vegetation can catch. Conveniently, the NDVI is also a measure of the efficiency, e.g. if the vegetation is stressed because of maladies or lack of water the value of NDVI decreases and the photosynthesis capacity of the vegetation goes down.

In practical terms we integrate (sums up) the NDVI for the growing period and we compare the integrated NDVI values with biomass samples collected on the ground. We can make a regression (computing the mathematical equation that relates integrated NDVI to biomass), that will allow us to compute the biomass for any pixel in the image (provided vegetation is growing here).

Exercises

Using LEOWorks:

In the following exercises we will try to understand the variability in the distribution of vegetation in Senegal and try to answer the questions:

  • is the daily lives and livelihood of people improving in Senegal?
  • is the state of the environment deteriorating and causing what is known as desertification?

Study the seasonal variation in vegetation in Senegal

In this exercise the purpose is to investigate the variation of vegetation during the growing season from May to September in Senegal in the year 2000 on the bases of 17 NDVI maps shown as an animation.

Senegal_5_S.jpg

Click here to download the NDVI images for this exercise, 3 Mb.

Senegal_NDVI_2000.zip

The file names are:

20000501_ndvi_pfR.tif
20000511_ndvi_pfR.tif

20000521_ndvi_pfR.tif

20000601_ndvi_pfR.tif
20000611_ndvi_pfR.tif

20000621_ndvi_pfR.tif

20000701_ndvi_pfR.tif

20000711_ndvi_pfR.tif

20000721_ndvi_pfR.tif

20000801_ndvi_pfR.tif

20000811_ndvi_pfR.tif

20000821_ndvi_pfR.tif

20000901_ndvi_pfR.tif

20000911_ndvi_pfR.tif

20000921_ndvi_pfR.tif

20001001_ndvi_pfR.tif

Open LEOWorks and select TOOLS > IMAGE ANIMATION to see an animated film showing the development of the vegetation index (one map every 10 days) during the growing season from May to October for the year 2000. Select all the images.

Stop the animation and scroll through the images one by one to investigate the changes in vegetation during the growing season.

Do you see a pattern in the variation of vegetation during the growing season?

Can you relate this pattern to your knowledge of the climatic system in this part of Africa?

Study a biomass map for a single year

In this exercise the purpose is to study the total biomass production in the different parts of Senegal on the basis of the NDVI map from the year 2000.

Senegal_7_S.jpg
Average NDVI map showing the biomass for the year 2000.

Click here to download the image.

2000biomass.tif.zip

Open the integrated NDVI map (the biomass map) that has been computed for the year 2000. The file name is 2000biomass.tif.
What does a biomass image show?
How does the biomass production in year 2000 vary from south to north?
–and from west to east?
Can you relate this to your knowledge of the climatic system in this part of Africa? What else can you relate to the variations in biomass production? –any human factors?

Dessertification or improved conditions in Senegal?

In this exercise the purpose is to study the biomass maps from the years 1981 to 2000 as an animation to evaluate the long term tendency in biomass production and the variations from year to year.

Senegal_8_S.jpg

Click here to download the NDVI images for this exercise, 3 Mb.

Senegal_NDVI_1981-2000.zip

Open LEOWorks and select TOOLS > IMAGE ANIMATION to see an animated film showing 19 years (1981 – 2000) of biomass maps as an animated film, Select all the images.

The file names are

1981_m_pf8.tif

1982_m_pf8.tif

1983_m_pf8.tif

1984_m_pf8.tif

1985_m_pf8.tif

1986_m_pf8.tif

1987_m_pf8.tif

1988_m_pf8.tif

1989_m_pf8.tif

1990_m_pf8.tif
1991_m_pf8.tif

1992_m_pf8.tif

1993_m_pf8.tif

1994_m_pf8.tif

1995_m_pf8.tif

1996_m_pf8.tif

1997_m_pf8.tif

1998_m_pf8.tif

1999_m_pf8.tif

2000_m_pf8.tif

Stop the animation and scroll through the images one by one to investigate the biomass production year by year.
Compare the northern and the southern parts of the biomass maps.

Where do you see the largest variations in biomass production from year to year?
How can you explain that?
Can you identify local areas in the northern parts of Senegal with almost no variation in biomass production from year to year?
How can you explain the constant biomass production in these areas?
Can you identify local areas in the northern parts with large variations from year to year?
How can you explain these large variations?

In Leoworks you can investigate the pixelvalues (biomassproduction) using different methods. You can also do image arithmetic on images.
Apply some of these methods (remember to explain, what methods you have used) to find answers to important questions like:

Is the situation getting better or worse during the 19 year period?

Can you identify specific years with serious problems?

Can you identify specific years with a god biomass production?

Discuss where you find improved and deteriorated conditions and what the possible explanations can be.External links

Centre de Suivi Ecologique, Dakar, Senegal :

Centre Agrhymet, Niamey, Niger

Department of Geography, University of Copenhagen: