30-05-2012, 12:13 PM
Remote Sensing Technology for Vegetation Monitoring
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INTRODUCTION
Remote sensing (RS) technologies have the potential to be useful in monitoring
agroforestry projects when applied in a qualified manner. The size of the agroforestry project to be
monitored, coupled with the pixel size of the chosen medium of remote sensing, together are the
most important factors to consider for accurate RS assessment of project development (tree
growth). The frequency of needed observation as well as the investigator’s budget must also be
accounted for when undertaking the decision whether or not to use RS for monitoring.
REMOTELY SENSED DATA: WHAT IS IT?
Remotely sensed data is that which is measured from an object without touching it. This
term generally refers to data collected from space- or aircraft-based images. The basis of a
remotely sensed image is that it is a pixel-by-pixel measurement of the energy reflected back from
the earth’s surface (Figure 1).
TYPES OF REMOTELY SENSED DATA THAT ARE AVAILABLE
VIS-NIR REFLECTANCE DATA
Remotely sensed data that is acquired from visible-near infrared (VIS-NIR) detectors is
most commonly used in vegetation detection. The most common product from VIS-NIR sources is
the Normalized Difference Vegetation Index (NDVI), which is an index of the greenness of the
vegetation being monitored.
PANCHROMATIC DATA
Panchromatic (PAN) data is “black and white”. Certain detectors such as the French
satellite SPOT (and the upcoming Lewis and Clark satellites) have both VIS-NIR detectors as well
as a panchromatic detector. PAN data that are available tend to be of finer resolution (i.e.,
smaller pixels), and can approach the resolution of large-scale aerial photographs. For vegetation
classification, PAN data are most commonly used for boundary delimitation or edge detection of
forest types, in a manner similar to aerial photos.
RADAR DATA
Radar technology is an active remote sensing system, meaning that the detector sends out a
signal and measures the time and manner of the signal return to the detector (rather than relying
upon the passive reflectance of solar energy back to the detector). Different surface qualities result
in different signal returns, and therefore the radar detector is able to distinguish surface textures.
The main utility of radar data is that it can penetrate cloud cover (a common problem in remote
sensing of tropical regions) and even acquire data at night if necessary. Radar data has been used
to detect the amount of flooding occurring under a forest canopy in the Amazon basin (as certain
types of radar bands can penetrate forest canopies) (Hess, Simonett et al. 1990). The type of data
generated by radar technology is most frequently used for measuring the surface texture of objects.
Recently, researchers have shown that certain bands of radar data can be useful for estimating
forest canopy texture and roughness (Moghaddam, Durden et al. 1994). The Canadian satellite
RADARSAT was launched in November 1995, and is now providing frequent regional-scale
imagery commercially. In addition, NASA has also implemented the SIR-C radar detector on the
Space Shuttle.