In its rawest full-resolution form, known as High Resolution Picture Transmission (HRPT) or Large Area Coverage (LAC), a single swath of data from the AVHRR sensor spans about 2700km in the cross-track direction, and a single recording of 10 minutes covers more than 3600 kilometres in the along-track direction. So, for example, the whole of the Amazon Basin could be covered by only two such scenes. These would have a spatial resolution ranging from 1.1 by 1.1km at centre swath (near-nadir) to about 2.4 by 6.9 km at the edge of swath, i.e. at the AVHRR's most extreme view angles of 55.4° off-nadir (Belward, 1991). Assuming the spatial and spectral resolutions were detailed enough for the purpose at hand this would offer considerable time and cost advantages over the purchase and processing of over 200 Landsat TM or 1800 SPOT images to cover the same area.
Although the orbital characteristics of the NOAA series of satellites are very similar to those of the Landsat series (i.e. 14.1 orbits per day, sun-synchronous, near-polar orbit at 99.1° orbital inclination), the higher altitude (nominally 833km), the wider swath-width of the AVHRR sensor, and the fact that the NOAA series of satellites usually operate in complementary pairs, together permit a much higher theoretical frequency of image acquisition for the same area (albeit with different look angles). Potentially one image per day may be acquired if afternoon overpasses only are considered, or 4 images per 24-hour period if night-time and early morning overpasses are also suitable (for example in applications requiring thermal waveband detection only). Even considering just afternoon images that are reasonably near-nadir, at least one image every 5 days should be possible (Goward et. al., 1991).
This high image repeatability, or temporal resolution, of the data is very important in tropical areas where cloud cover is high and/or where intra-seasonal or phenological variations in time have to be monitored. For most land applications, the images acquired by early morning overpasses of the NOAA satellites are not very suitable since at that time (about 0730 local solar time), the light levels are too low and the thermal contrast between differing land cover types is not sufficiently developed. The light levels and thermal contrast are more suitable at the time of the NOAA satellite afternoon overpass time (about 1430 local solar time). However, this is normally also a time of intense local convection in tropical areas and there is therefore also an increased likelihood of cloud and haze contamination at that time. In sub-tropical or more extreme latitudes, frontal cloudiness and lower light levels for longer periods of the year provide other restrictions to suitable image acquisition (Kasischke and French, 1997).
In practice, therefore, the theoretical rate of daily image acquisition is rarely achieved so a large number of daily AVHRR images have to be examined and/or composited to obtain sufficiently cloud-free scenes for forest mapping and monitoring (Malingreau et. al., 1989). In the past, the potential daily acquisition for certain areas may also have been hampered by lack of data capture and archiving facilities, but the number and distribution of local and regional receiving and archiving facilities for NOAA data acquisition has continued to grow and improve. So have the facilities for exchange and transmission of data acquired all over the world, particularly with the IGBP-DIS project – a concerted global effort to collect full-resolution AVHRR data for all land surfaces on a daily basis for over 54 months in 1992 – 6 (Townshend, 1992; Belward et. al., 1999)
Another characteristics of the NOAA-AVHRR systems that should be noted, is the marked orbital drift which causes the Equator crossing time of the orbit to change by several hours during a three or four year period (Price, 1991; Privette et. al., 1995). This together the gradual degradation of the sensor in the visible channels over time (D’Souza, 1996; Rao and Chen, 1999) should be borne in mind when examining long quantitative time series of data from the AVHRR sensor.
All of the AVHRR sensors have had four to five channels, sensing data in the visible red, near-infrared, shortwave-infrared and thermal channels. In AVHRR land applications the first two channels of the radiometer, Channel 1 (0.58-0.68 µm ) sensitive to red reflected light, and Channel 2 (0.725-1.10µm), sensitive to infra-red reflected light, have been the most widely used. Photosynthetically-active vegetation will typically yield a low reflectance at red wavelengths (chlorophyll pigment absorption) and a high reflectance at near-infrared wavelengths (caused by scattering of leaf surface and internal structure). Because it is the only material on the Earth’s surface that will exhibit this contrast, the two channels are useful for discriminating and monitoring the condition of vegetation.
Often the reflectances from the two channels are combined mathematically to form a spectral vegetation index. Spectral vegetation indices attempt to discriminate between vegetated and non-vegetated areas by enhancing the spectral contribution of green vegetation whilst minimising the contribution of the background. Numerous spectral vegetation indices have been developed based upon combinations of the first two channels. The normalised difference vegetation index (NDVI) is the most commonly used AVHRR vegetation index and is defined as the difference between near-infrared and red reflectances divided by their sum (e.g. Curran, 1983). The NDVI has been demonstrated to be a robust and sensitive vegetation measure and is the only vegetation index that has been used widely for continental and global-scale vegetation dynamics examination (i.e. Tucker et. al., 1984; Justice et. al., 1985, etc.., and see box 4). The widespread use of the NDVI is, in part, due to its “ratioing” properties, which cancel out a large proportion of signal variations attributed to changing irradiance conditions.
However, the uncalibrated wide-wavelength bands used for the first two channels of the AVHRR are particularly sensitive to the smoke, water vapour, aerosols and other atmospheric particles that are prevalent in the atmosphere in tropical regions in particular. Canopy structure and background (soil, snow, wetness, litter), thin and sub-pixel cloud, illumination and viewing geometry effects have also been shown to effect the AVHRR NDVI values. Furthermore, near infrared/red vegetation indices are often more sensitive to leaf-area than to standing biomass (Sellers, 1986; Goward et. al., 1991; Los et. al., 1994; Meyer et. al., 1995; Walter-Shea et. al., 1997). Therefore dense forest areas may yield similar (saturated) vegetation indices to recently regenerated or agricultural areas although in terms of standing biomass these two are vastly different (Tucker et. al., 1984b). Furthermore, the NDVI and other vegetation indices have been found to be particularly insensitive in dense forest areas or areas of complex canopies with several layers of shadow interactions and effects (Singh, 1987). Depending on the understorey, closed conifer cover often shows an inverse relationship with NDVI (Ripple, 1994; Ripple et. al., 1991, Spanner et. al., 1990).
On some single date NOAA-AVHRR images, channel 3 (3.5-3.9µm), sensitive to reflected and emitted shortwave-infrared light, has been found to be more useful for forest/ non-forest delineation (Kerber & Schutt, 1986). This channel has advantages (at least when used in conjunction with the visible and near-infrared channels) because:
Thus the presence of dense forest areas work to reduce both the reflected and the emitted parts of the signal in channel 3 while non-forested areas generally yield increased components of both. One of the disadvantages of channel 3 is that the signal obtained in this wavelength can be prone to noise, especially in earlier satellites (NOAA-7 and NOAA-9) (Warner, 1989) and the mixed emitted/reflected signal is also occasionally difficult to interpret.
Some work carried out on splitting the channel 3 response to its reflected and emitted parts (Holben and Simabukuro, 1993; Kaufman and Kendall, 1994) has showed some promise, but has been hampered by the need to know the thermal emissivity of the surface features being examined before reliable splits can be achieved.
Channel 3 is also very sensitive to the presence of very hot areas within pixels, either from active or recent fires (Gregoire et. al., 1988; Malingreau et. al., 1985). The occurrence of fire, especially within dense forested areas is a good indicator of human activities (e.g. forest clearing for cultivation), and is often used as "circumstantial evidence" of forest degradation activities (Malingreau, 1990).
The fully-calibrated channels 4 (10.5 – 11.3µm) and 5 (11.5 – 12.5µm) (the thermal sensitive bands) of the AVHRR have also been used for fire detection and characterisation (Matson, 1987; Matson & Holben, 1987). Although they show some sensitivity to forest/non-forest areas (Kerber, 1983; Kerber & Schutt, 1986), these channels are also often heavily contaminated by the water vapour which is ever present in the tropical atmosphere. They have therefore been little used for characterisation or classification of tropical forest areas on single-date AVHRR images. However, the seasonal variation of surface temperature as estimated from AVHRR channels 4 and 5 in multitemporal data sets has been shown to be closely related to the land cover type in West Africa (Achard & Blasco, 1989). It has also been used in conjunction with NDVI time series for land cover classification over Africa (Lambin and Ehrlich, 1996) and for forest classification over Europe (Roy et. al., 1997).
The other use for channels 4 and 5 in land applications has been in masking out areas of cloud or haze (Saunders & Kriebel, 1990).
Raw NOAA-AVHRR images are characterised by a very distorted geometry (Emery et. al., 1989). They have a nominal spatial resolution of approximately 1.1 by 1.1 km. However, because of the large swath width, the very off-nadir viewing angles, and the Earth's curvature, the spatial resolution decreases to about 2.4km (in the along-track direction) by 6.9 km at the most off-nadir viewing angle of the sensor. The varying pixel resolution compounded by the over sampling of the pixels in the across-track direction (Mannstein and Gessell, 1991; Breaker, 1990) makes it difficult to use the data in any detailed spatial or textural pattern analyses (Belward and Lambin, 1990), but if the use of the data is restricted to that acquired at less than 25° off-nadir the problem is considerably reduced (Goward et. al., 1991; Vogt, 1992).
Much literature refers to NOAA AVHRR data as 1.1km resolution, and several products are made at a nominal pixel cell size of 1km. In practice, because of the spatial characteristics of the imagery, the orbital characteristics of the NOAA satellite and the subsequent processing which may include automated remapping of the data, the actual spatial resolution of the data is somewhat coarser. Even with the most advanced geometric correction methods of the data, the locational accuracy of remapped data, especially if carried out over large areas, is probably closer to 1.5 – 2.0 km (D’Souza and Sandford, 1995). Where several images are composited (i.e. for multi-temporal analyses), the individual image mis-registration is compounded, and the effective pixel size of a multi-date composite could be as high as 5.1km, assuming a view zenith angle cutoff of 57 degrees and a misregistration root mean square of 1km (Cihlar et. al., 1996).
However, despite the geometrical problems and the somewhat poor spatial resolution compared with that of other Earth Resources satellites, the imagery has been found to be suitable for the detection of a number of features relevant to forest monitoring, especially where single-date , good quality images are used (see box 3). Many useful maps and datasets have been published at scales ranging from 1: 1 million to 1 : 5 million, and these will be described in detail later.
