Steephill and mountain slopes severely affect remote sensing of vegetation. The irradiation on a slope varies strongly with slope azimuth relative to the sun, and the reflectance of the slope varies with the angles of incidence and exitance relative to the slope normal. Topographic correction involves standardizing imagery for these two effects. We use an atmospheric model with a Digital Elevation Model (DEM) to calculate direct and diffuse illumination, and a simple function of incidence and exitance angles to calculate vegetation-canopy reflectance on terrain slope. The reflectance correction has been derived from the physics of visible direct radiation on a vegetation canopy, but has proved applicable to infrared wavelengths and only requires solar position, slope and aspect. We applied the reflectance and illumination correction to a SPOT 4 image of New Zealand to remove topographic variation. In all spectral bands, the algorithm markedly reduced the coefficients of variation of vegetation groups on rugged terrain. This produced clean spectral signatures, improving the capacity for automated classification. If illumination correction is performed alone, the coefficients of variation can be increased, and so should not be applied without a reflectance correction. The algorithm output is reflectance on a level surface, enabling the monitoring of vegetation in hilly and mountainous areas.

Optimisation of economic return from forests requires that comprehensive forest inventory data be available to support the design of harvesting strategies. Such inventory data can potentially be obtained by remote sensing. This study investigates the accuracy with which wood volume (m3 ha -1) in a plantation forest can be calculated from Landsat TM data at the pixel and foreststand spatial scales. Wood volumes were estimated from regression analysis, nonparametric line-fitting, and an N-dimensional K-nearest-neighbour classification scheme. At the pixel scale, relations between Landsat data and measured wood volume were found to be significant but weak, with r2 values of 0.3, and with correspondingly poor estimates of wood volume (root-mean-square errors rmses of 100 m3 ha -1). By averaging the pixel-scale estimates, wood volume estimates of acceptable accuracy were obtained for forest-stand areas of about 40 ha (rmses of 46 m3 ha -1). Parametric regression performed slightly better overall than non-parametric line fitting techniques for estimating wood volume. Estimates of similar accuracy to those obtained by regression were also given by NK-classification at the pixel-scale, provided K was large ( 15), although the classifier produced biased results at the forest-stand scale. It is concluded that Landsat TM only provides an acceptable data source for estimating wood volumes in plantation forests for areas of about 40 ha and larger. The very low dynamic range in the Landsat data is probably a significant factor limiting its use for inventory at more detailed scales.

Multispectral SPOT imagery is used to map percentage vegetation cover in the Mackenzie Basin of the South Island, New Zealand. Twenty ground measurements of quadrats 60 by 60 m² relate percentage vegetation cover to normalized vegetation index. The non-linear calibration curve is C = 50 tanh (6·1(NV1-0·22))+ 50 (1) where C is percentage vegetation cover and NVI is normalized vegetation index. The vertical spread of quadrat points about the calibration curve gives the accuracy of a percentage cover prediction from NVI. In the linear part of the calibration curve the upper and lower prediction limits (80 per cent confidence interval) are plus or minus 15 per cent cover.