Image prosessing and supervised classification

The image was processed and analyzed using ERDAS Imagine software. Prior to image classification, all ground cover features not associated with undeveloped desert land were extracted from the scene. This includes all impervious and landscaped surfaces associated with urban areas and exposed soil related to industrial sites, clearings for new urban development, and major disturbances (Ward et al. 2000). A LANDISCOR color aerial photograph (2000) covering the study area was used as an interpretive guide for ground features in order to aid in extraction of urban features. A supervised classification procedure was used in order to assign vegetation classes to the image pixels. This method involves creation of spectral signatures for each candidate class based on training sites in the field, which contain vegetation indicative for each class. These class signatures are used as a reference tool for the assigning of community types to pixels according to maximum likelihood

Field data collection

A pilot study, which used field sampling locations used in the TWINSPAN classification as training sites, yielded poor results, so several corrective actions were instituted in order to increase accuracy. Given the area of pixels (900 m2), the original field sample plots were too small to use as training sites without significant risk of contamination by other community types. Therefore, a separate effort was made to collect training samples of each vegetation type over an area encompassing multiple pixels, which were recorded on GPS and designated during the training process. Since these samples formed the reference source for all vegetation in the study area, each training site was selected as an unambiguous representative of a given community type. Multiple training sites, scattered across the landscape as much as possible, were used for each reference signature. Whenever possible, a minimum set of pixels equal to ten times the number of bands used, 70 in this case, was utilized in order to create reference spectra, as recommended by Congalton (1991).

Auxiliary data

The larger-scale thermal band 6, which has a resolution of 120 m rather than 30 m inherent to the other bands, was dropped and replaced with a Soil Adjusted Vegetation Index (SAVI) layer calculated from the Landsat image. SAVI is a modification of the Normalized Difference Vegetation Index (NDVI), which is commonly used to detect photosynthetically active vegetation by virtue of relatively high reflectance of near-infrared and low reflectance of visible red light. SAVI includes a correction for soil reflectance, which is especially useful given deserts’ high exposed soil coverage. Efforts were made to make the soil substrate in each round of image classification as constant as practical so that the vegetation would be the dissimilar variable between pixels. Soil texture influences the scattering of incident light so that larger particle soils provide more surfaces off which light can reflect. GIS-based soil maps were obtained from the Natural Resources Conservation Service, a division of the US Department of Agriculture (Soil Survey Geographic [SSURGO] database 2002). These maps were used to divide the total study area into sections based on texture characteristics: sandy, loamy, clayey, and coarse particle soils. An additional unlabeled class, roughly coinciding with the shallow bedrock of mountainous areas, was divided further into sections consisting of continuous patches for individualized treatment. The intention of this step was to conglomerate sites with similar reflectance features for a common classification effort. Each separate patch was analyzed using unsupervised classification into eight classes. In unsupervised classification, reference spectra are not determined by the user; rather, the classifier groups pixels based on spectral similarity inherent to the image itself with only the total number of classes selected by the user. A GIS layer depicting local geology was utilized in order to visually ascertain correspondence between geological formations and the image classification. If there was a correlation, the candidate area was split into separate parts; if there was no correlation, the patch was retained whole. Next, separate patches judged to be relatively self-similar were combined into a common view, the classification was repeated, and similar areas were aggregated. This process resulted in seven different study sections, each of which was classified on its own with reference spectra derived from training sites located within each section, if possible. If a hypothesized vegetation type was not located during the training process, a signature from another section was used, though this was necessary only a few times.

Accuracy assessment

A random subset of these points was chosen to be surveyed in the field. Registration of Landsat pixels is not perfect; image rectification and restoration from raw data necessarily distorts actual positioning of pixels to a slight degree. For this reason, points were designated from clusters of similarly classed pixels. Coordinates were chosen from each image section, which allowed for a separate accuracy assessment for each section’s classification. Vegetation within a 20 m radius was surveyed to determine the appropriate community type. Since the pixel array represents a two-dimensional depiction of the landscape, training site radius was lengthened on slopes to allow for a horizontal distance of 20 m. Post-groundtruthing procedures were used in order to maximize accuracy, including refinement of training areas, deletion of classes found to be absent or rare in each study section, and aggregation of classes lacking strong discrimination according to groundtruthing results, followed by reclassification of the scene. Accuracy assessment was reported using an error matrix (Congalton and Green 1999). Overall accuracy is a holistic summary of how successful predicted class membership agreed with field observations from the groundtruthing effort, and is calculated as the sum of the diagonal cells divided by the total survey sites used to assess that particular classification. Producer’s accuracy demonstrates how well survey site pixels of a particular vegetation type are classified, and equals the number of correctly classified sites divided by the total number of survey sites for that type, the column total (Lillesand and Kiefer 2000). User’s accuracy represents the probability that a classified pixel indicates the correct vegetation type in the field, and equals the number of correctly classified sites divided by the total number of sites that actually belong to that class, the row total. Since accuracy assessment was not feasible for the more remote or inaccessible locations of the Sierra Estrella Mountains, the McDowell Mountains, and the sandy soil of the Hassayampa River, these areas were not conducted.