The Comparison Map Profile Method: A Strategy for Multiscale Comparison of Quantitative and Qualitative Images
| Autor: | Christelle Hély, S. Alleaume, C. Gaucherel |
|---|---|
| Přispěvatelé: | INRA SAD Armor, Institut National de la Recherche Agronomique (INRA), Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), Centre de Bio-Archéologie et d’Écologie, Centre National de la Recherche Scientifique (CNRS), Centre de Bio-Archéologie et d'Ecologie (CBAE), Université Montpellier 2 - Sciences et Techniques (UM2)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS) |
| Jazyk: | angličtina |
| Rok vydání: | 2008 |
| Předmět: |
0106 biological sciences
010504 meteorology & atmospheric sciences Contextual image classification Vegetation Land cover 15. Life on land Dynamic global vegetation model 010603 evolutionary biology 01 natural sciences Normalized Difference Vegetation Index Cohen's kappa Similarity (network science) [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV] General Earth and Planetary Sciences Electrical and Electronic Engineering Leaf area index ComputingMilieux_MISCELLANEOUS 0105 earth and related environmental sciences Remote sensing Mathematics |
| Zdroj: | IEEE Transactions on Geoscience and Remote Sensing IEEE Transactions on Geoscience and Remote Sensing, Institute of Electrical and Electronics Engineers, 2008, 46 (9), pp.2708-2719. ⟨10.1109/TGRS.2008.919379⟩ |
| ISSN: | 0196-2892 |
| DOI: | 10.1109/TGRS.2008.919379⟩ |
| Popis: | The comparison map profile (CMP) method compares two spatially explicit data sets (original images) at each point and through several spatial scales simultaneously. The CMP combines the moving window concept with similarity indices for quantitative or qualitative data to visualize and quantify outputs: changes in mean similarity value and its variability through scales are reported on a profile, similarities between regions are estimated on monoscale maps, and their persistence through scales assessed on a mean multiscale map. The CMP method is first illustrated using two images with slight difference in the checkered pattern. Second, two sets of comparisons related to African vegetation are conducted using the CMP method. The first set deals with quantitative data of leaf area index (LAI): Remote-sensed LAI images extracted from the AVHRR-NVDI product are compared to simulated LAI output from a dynamic global vegetation model (DGVM) using the distance and the cross-correlational coefficient for quantitative comparison of values and structure patterns, respectively. The second set of images deals with qualitative data: the remote-sensed product of land cover type by IGBP-MODIS is compared to the DGVM classified LAI output into land cover types using the Kappa statistics as similarity index. Results show that taking spatial patterns into account using the CMP method decreases the mean correlation by 50%, and increases the distance by 50% as compared to the global pixel-to-pixel indices. Similarly, comparison of land cover maps costs only 35% of the global Kappa value. Equatorial gradients of vegetation from forests to grassland are the most persistent similar regions between both types of data sets. Potential limits and strengths of the CMP method are discussed. |
| Databáze: | OpenAIRE |
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