Map comparison using spatial autocorrelation: an example using AVHRR derived land cover of Canada

摘要

Toute zone géographique donnée est souvent soumise à des efforts de cartographie multiples à travers le temps. Des produits finaux similaires peuvent être générés à partir de la même source de données avec des attributs semblables au niveau de la cible. Par exemple, deux cartes représentant le couvert du Canada ont été produites en 1995 et 1997 à l'aide des données du capteur AVHRR ( advanced very high resolution radiometer ) : le produit NBIOME ( Northern Biosphère Observation and Modeling Experiment ) généré par Ressources Naturelles Canada et le produit IGBP DISCover ( International Geosphere-Biosphere Programme Data and Information System ). Dans cette étude, on fait référence à la concordance thématique et spatiale entre les classes forestières de la zone de la carte représentant le Canada. Une image des différences a été générée pour chacun des deux scénarios où un produit a identifié la forêt et l'autre a identifié des zones non-forestières et vice versa. Des sommaires standards de la zone et des analyses par pixel ont été utilisés pour identifier et quantifier initialement les différences entre les deux produits cartographiques.%Any given geographic area is often subjected to numerous mapping efforts over the course of time. Similar end products may be generated from the same data source with similar target attributes. For instance, two maps representing the land cover of Canada were produced in 1995 and 1997 with data from the advanced very high resolution radiometer (AVHRR) satellite: the Northern Biosphere Observation and Modeling Experiment (NBIOME) product produced by Natural Resources Canada, and the International Geosphere-Biosphere Programme Data and Information System (IGBP DISCover) product. The thematic and spatial agreement of the forested classes of the map area representing Canada are considered in this study. A difference image was generated for each of two scenarios, where one product identified forest and the other identified non-forest and vice versa. Standard area summaries and per-pixel analyses were used to initially identify and quantify the differences between the two map products. To enable a more comprehensive comparison of the two map products, a 50 km x 50 km grid extending over the entire area of Canada was used as a framework for analyzing the spatial autocorrelation in the difference images. Differences that are not spatially autocorrelated are considered random; conversely, differences that are spatially autocorrelated may be systematic and reflect differences in classification legends and methodologies, and in image-processing methods. The total estimates of forest area from both maps are similar, varying by 6%, yet the area of agreement between the two maps (i.e., where both mapping processes have the same result in the same location) represents 62% of the total area classified as forest in both maps, or 35% of Canada. The spatial distribution of these classification differences is captured through the introduction of ancillary data (ecozones) and the consideration of spatial autocorrelation. Predominantly, spatially autocorrelated differences are found to occur within ecozones that are transition areas between forest and non-forest and at ecozonal interfaces. These differences appear related to the heterogeneous nature of the land cover and the small size of contiguous forest stands. In this research we demonstrate a range of approaches to map comparison.