Selecting Representative High Resolution Sample Images for Land Cover Studies. Part 2 Application to Estimating Land Cover Composition

We tested the effectiveness of the Purposive Selection Algorithm (PSA, described in the companion first article) to accurately estimate land cover composition over a large area. The knowledge of land cover distribution over large areas is increasingly more important for numerous scientific and policy purposes. Unless a complete detailed map is necessary, a sampling approach is the best strategy for determining the relative proportions of individual cover types because of its cost-effectiveness and speed of application. With coarse resolution land cover maps at continental or global scales increasingly becoming available, the possibility arises of using such maps synergistically with a sample of high resolution satellite coverage. The goal of such synergy would be to obtain accurate estimates of land cover composition over large areas as well as the knowledge of local spatial distribution. We evaluated PSA performance for sample selection over a 136,432 km2 area (domain) in the BOREAS Region of Saskatchewan and Manitoba, Canada. Two maps were prepared for the domain, one based on NOAA Advanced Very High Resolution Radiometer (AVHRR, 1 km pixels) and one on LANDSAT Thematic Mapper (TM, 30 m). After dividing the area into 134 tiles, a PSA sample was selected using the AVHRR tiles. A random sample was also selected for comparison. The domain AVHRR cover type fractions were then corrected using TM maps for the selected tiles, following the method of Walsh and Burk (1993). The land cover composition obtained through the combined “domain AVHRR/sample TM” data was then compared with the domain TM coverage. We found that PSA provided a representative sample to correct the AVHRR map, particularly for small sample sizes. Compared to the random selection, PSA yielded more accurate results at all tested sampling fractions (up to 30% of all tiles). With a PSA sample of 7% (18%), the average absolute difference per class between the correct and the estimated fraction was 0.058% (0.043%). For the same sample fractions, the average relative error per class was 16.1% (9.8%) for PSA and 24.5% (18.7%) for random selection. The difference between PSA and random selections was significant at the 0.001 probability level. It is concluded that the PSA strategy is an effective way to combine coarse and fine resolution satellite data to obtain expedient and cost-effective land cover information over large areas. An important benefit of the synergistic combination of the two maps is knowledge of land cover distribution at the landscape level. This is because the coarse resolution map provides the overall distribution patterns across the domain, while the fine resolution map supplies the average composition of the coarse resolution pixels in each cover type. Thus, each coarse pixel can be statistically divided into the component high resolution classes. Crown