Introduction

The main objectives of this section are: introducing the citizen to remote sensing science, inform him of the importance of this technique nowadays in public institutions such as River Basin Districts and communicate the different works that are being carried-out by the Hydrologic Planning Office from the Guadalquivir River Basin District.

What is remote sensing?

Remote sensing is the technique that allows getting images from the earth surface from sensors installed in space platforms with no physical contact. The advantage lies on the different materials that have different performances with the received radiation, be it from a natural origin (solar radiation) or an artificial one (radar). Besides, a material can have different performances according to its state. It is particularly interesting to see the variations in the behaviour of vegetation in relation to its water contents. Details about this point are given in the ‘vegetation index’ section.

Different types of resolution. Most used sensors.

We call resolution of a system to its capacity to discriminate detailed information in a sensed object. In case of remote sensors on board satellites we have to take into account different variables that will impose condition on different resolutions, like spatial, spectral and temporal variables.

Each one of these variables has a different level of importance depending on the pursued objective. That is why before starting a remote sensing job, the needed precision for each type of resolution must be determined. This is because there is a certain antagonism among several types of resolution. For example, to achieve a higher temporal resolution we need a higher orbit, what damages the spatial resolution.

For the task we are dealing with, relating to the analysis of vegetation along the time at the Guadalquivir basin, it is important to take into account:

• Spectral resolution: The greater the sensor capacity to reproduce the spectral response from the watched object, the greater will be the possibility to discriminate coverages from one another. The analysis carried-out by CHG show that both MODIS and TM/ETM+ sensors have enough bands to execute this type of studies.

• Temporal resolution: For a continuous follow-up it is convenient to have an image temporal coverage of at least two images per month. For this matter the adequate sensors are those with high resolution (1-2 days) and medium resolution (10-20 days).

• Spatial resolution: This type of resolution is more or less important depending on the working scale. At CHG they are working at a great scale, even monitoring at plot level, besides regional works of a minor scale. For this purpose they are combining a medium spatial resolution of the LANDSAT satellite sensors with the adequate temporal resolution offered by MODIS sensors.

Nowadays there is a broad range of sensors which differentiate from one another mainly by features, resolutions and aims. The resolutions of the main sensors used are detailed hereafter. In following sections we describe in detail the features of sensors: MODIS, TM, LISS-IV, LISS-III and AWiFS, which are used by CHG.

The following table shows them organized from lesser to greater spatial resolution:

• Low: Pixel size: over 100 metres.
• Medium: Pixel size: between 15 and 100 metres.
• High: Pixel size: under 15 metres.

Vegetation indexes

The use of IVs (Vegetation Indexes) was the first useful tool to determine the properties of vegetation coverage as they are capable of increasing the vegetation signal whereas they minimize the influence of distortion factors like soil, solar irradiance, angle of the rising sun and the atmosphere.

Healthy vegetation shows a radiometric contrast between red and near infrared bands the spectrum. Therefore IVs are based mainly on arithmetic combinations between those bands. Thus three of the most used indexes are defined as follows:

Normalized Difference Vegetation Index (NDVI)

Among the most extended ones we see the NDVI (Vegetation Index) that is defined as the quotient between subtraction and addition of these two bands.

Where,
R: Reflectivity at the Red band
IRp: Reflectivity at the Near Infrared band

One of the greatest advantages of this index is its easy interpretation as it is delimited between ±1, with a critical threshold for vegetarian coverages around 0.1.

The works of CHG with images from the MODIS sensor began with the compilation of fortnightly images from NDVI. Currently CHG disposes of a temporal series since February 2000 till present time where new images are being added. These images are processed every fifteen days and they show vegetation in a very neat way due to the absence of atmospheric distortion like clouds.

Next there is a fortnight NDVI image of the Guadalquivir river basin:

Soil Adjusted Vegetation Index (SAVI)

SAVI was developed by Huete in 1988. It is a very adequate index for work in semi-arid zones where the soil contribution is very important. Thus for a study in a zone of these characteristics this kind of index will be more consistent than NDVI because of the greater distinction between soil and vegetation.

Where,
R: Reflectivity at the Red band
IRp: Reflectivity at the Near Infrared band
L: Correction factor which value can fluctuate between 1 and 0, although value 0.5 is most commonly used

Next there is a SAVI image corresponding to a LANDSAT scene of the Guadalquivir river basin:

Enhanced Vegetation Index (EVI)

This index obtains the response from structural variations of the vegetation layer including LAI (Leaf Area Index), type and architecture of the layer and features of the plant. It was developed to optimize the vegetation signal with enhanced sensibility for high biomass densities. This is achieved because the signal from vegetation and atmospheric influence are separated.

Where,
A, R, IRp: Reflectivity at the Blue, Red and Near Infrared bands
C₁ = 6.0, Atmospheric resistance coefficient
C₂ = 7.5, Atmospheric resistance coefficient
G = 2.5, Gain factor
L = 1, Correction factor

Next there is a fortnight EVI image of the Guadalquivir river basin: