| Abstract | Various remote sensing sensors observe the Earth's surface at different spatial resolutions. In deriving surface parameters using remotely sensed data, the transportability of algorithms from one
resolution to another is often of great concern because of the surface heterogeneity. This article addresses this scaling issue through image degradation experiments using a Landsat TM image. It is shown theoretically that scaling problems in
deriving surface parameters exist not only because of the nonlinearity in the relationships between remote sensing measurements such as NDVI (normalized difference vegetation index) and SR (simple ratio) and the parameters of interest, but also
because of the discontinuity between contrasting cover types within a mixed pixel. To quantify the effects of the nonlinearity and discontinuity on scaling, it is found that contextual parameter are more effective than textural parameters.
Contexture-based functions are derived for the estimation of he scaling effects on leaf area index (LAI) calculations using algorithms based on NDVI and SR separately. Based on NDVI-LAI and SR-LAI relationships that were derived at the Landsat TM
scale (30 m) as part of the Boreal Ecosystem-Atmosphere Study (BOREAS), the effects of scaling on the retrieval of LAI were investigated using nine selected areas of the same size (990 m x 990 m) but different water area fractions. The following
conclusions are drawn from the investigation: 1) Negative biases in the estimation of LAI occur when either the NDVI or SR algorithm derived at a fine resolution (Landsat TM) is used for calculations at a coarse resolution (for example, AVHRR). 2)
The amount of the biases depends on the surface heterogeneity. For a pure forest pixel, the bias caused by the nonlinearity of the NDVI algorithm was smaller than 2% and the linear SR algorithm induces no error in scaling. Therefore, the scaling
problem for pure pixels may be ignored for many applications using either linear or non-linear algorithms. 3) Large negative biases occur when a pixel contains interfaces between two or more contrasting surfaces. In the case of two contrasting
surfaces between vegetation and open water, the biases can be up to 45% of the correct value depending of the water area fraction in the pixel. The biases in this case depend on contexture and little on texture. Simulations show that the most useful
contextural parameter for quantifying the scaling effects in vegetation-water mixed pixel is the water area fraction within each degraded pixel. Algorithms for remote sensing applications can be transported from one scale to another, if the
information on the water body size is available. This study shows the need for globol water masks at high resolutions for the purpose of accurate derivation of surface parameters maps at various resolutions. In boreal regions, this is particularly
important because of the large number of small water bodies.Degradation experiments using a Landsat TM image are carried out to address the spatial scaling of a remotely sensed surface parameter. Theoretically, it is demonstrated that scaling
problems in deriving surface parameter exist not only because of the nonlinearity in the relationships between remote sensing measurements but also because of the discontinuity between contrasting cover types within a mixed pixel. Overall, the need
for global water masks at high resolutions for the purpose of accurate derivation of surface parameters maps at various resolutions is emphasized. |