## 2 Methods, Study Area and Data Analysis
### Satellite Image Data Analysis  
DMC images of the study area were supplied as quick-looks by DMC International Imaging, and nine images were identified with a cloud-free view of the study site (Table 1). Four of the images were from the UK-DMC and five from NigeriaSat-1. The nine images represented a subset of the possible imaging opportunities. As with most commercial remote sensing sensors, the DMC Constellation collects data on a tasking basis, and so, not all targets are covered on every overpass of the satellites. In addition, the first generation of DMC satellite had limited on-board storage, and this coupled to a then limited network of ground receiving stations reduced the volume of data collected on each overpass. These factors meant that while it was theoretically possible to have daily images of the study area, the actual imagery available was more limited. The image for June 27, 2005, appeared on visual inspection to have both little cloud cover and the least haze. This image was thus selected as the base for the atmospheric correction procedure applied to all of the images. The June 27 image was geometrically corrected to the UTM WGS84 coordinate system by selecting 30 ground control points (GCPs) from U.K. Ordnance Survey maps. The image was resampled to a 32-m pixel size using nearest neighbour resampling Thereafter, this image was used to geometrically correct the other eight DMC images, using image to image geometric correction (Figure 1). The June 27, 2005, image was atmospherically corrected using the COST (cosine of the solar zenith angle correction) model [55]. The COST model was applied to the image to convert it to estimated reflectance. The remaining eight DMC images were then cross-calibrated using this base image. Thirty homogeneous, flat, temporally- and spatially-stable objects, which were spectrally invariant, were used as targets in the calibration. The targets included dark objects, like black asphalt (major roads and airport runways) and water bodies, and brightly reflective objects, such as beaches. Antecedent surface conditions at each target were not explicitly accounted for, and so, some minor errors may have been introduced in to the corrections. The targets were used to derive regression equations of the cross-sensor reflectance calibration relationships [56; 57] based on the equation:  
\[y=ax+b \tag{1}\] \begin{table}
\begin{tabular}{l l c} \hline
**Satellite Sensor** & **Date** & **Day of Year (DOY 2005)** \\ \hline NigeriaSat-1 & March 3, 2005 & 62 \\ NigeriaSat-1 & April 19, 2005 & 109 \\ UK-DMC 150 & May 30, 2005 & 150 \\ NigeriaSat-1 & June 8, 2005 & 159 \\ UK-DMC & June 27, 2005 & 178 \\ UK-DMC & July 11, 2005 & 192 \\ NigeriaSat-1 & August 17, 2005 & 226 \\ NigeriaSat-1 & September 2, 2005 & 245 \\ UK-DMC & February 19, 2006 & 50 in 2006 \\ \hline \end{tabular}
\end{table} 
Table 1: List of the Disaster Monitoring Constellation (DMC) data sets. where \(y\) is estimated reflectance, \(x\) is the digital number for the image and a and b are regression coefficients. Table 2 shows the regression equations for the individual images. From each image, a single pixel was then identified corresponding to the centre of the Risley Moss sample plot and the green, red and near-infrared reflectance values extracted. The data analysis examined the linear regression relationships between the LAI of the test stand and a range of vegetation indices (VI) computed from the remotely sensed data. Results are reported here for three widely used VI, the normalised difference vegetation index (NDVI), the difference vegetation index (DVI) and the soil adjusted vegetation index (SAVI). After determining the strongest relationship, the  
Figure 1: Geometrically-corrected DMC images used in this study; the images are displayed as false colour composites (red = DMC Band 1; green = DMC Band 2; blue = DMC Band 3); UK = UK-DMC, Nigeria = NigeriaSat-1; the yellow rectangle on DOY 178 shows the study area.