Using First- and Second-Order Variograms for Characterizing Landscape Spatial Structures From Remote Sensing Imagery
| Autor: | Denis Allard, Frédéric Baret, Sébastien Garrigues |
|---|---|
| Přispěvatelé: | Atmospheric and Environmental Research, Inc. (AER), Biostatistique et Processus Spatiaux (BioSP), Institut National de la Recherche Agronomique (INRA), Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE) |
| Rok vydání: | 2007 |
| Předmět: |
010504 meteorology & atmospheric sciences
Stochastic modelling STOCHASTIC SIMULATION télédétection géostatistique variogramme 0211 other engineering and technologies modélisation spatiale Image processing 02 engineering and technology Geostatistics POISSON LINE MOSAIC MODEL 01 natural sciences Normalized Difference Vegetation Index REMOTE SENSING VARIOGRAM LANDSCAPE STRUCTURE MULTI-GAUSSIAN MODEL Ingénierie assistée par ordinateur paysage Electrical and Electronic Engineering végétation Variogram 021101 geological & geomatics engineering 0105 earth and related environmental sciences Remote sensing Vegetation [INFO.INFO-IA]Computer Science [cs]/Computer Aided Engineering 15. Life on land Computer Aided Engineering General Earth and Planetary Sciences Spatial variability Scale (map) Geology simulation stochastique |
| Zdroj: | IEEE Transactions on Geoscience and Remote Sensing 6 (45), 1823-1834. (2007) IEEE Transactions on Geoscience and Remote Sensing IEEE Transactions on Geoscience and Remote Sensing, Institute of Electrical and Electronics Engineers, 2007, 45 (6), pp.1823-1834. ⟨10.1109/TGRS.2007.894572⟩ |
| ISSN: | 0196-2892 |
| DOI: | 10.1109/tgrs.2007.894572 |
| Popis: | International audience; The spatial structures displayed by remote sensing imagery are essential information characterizing the nature and the scale of spatial variation of Earth surface processes. This paper provides a new approach to characterize the spatial structures within remote sensing imagery using stochastic models an geostatistic metrics. Up to now, the second-order variogram has been widely used to describe the spatial variations within an image. In this paper, we demonstrate its limitation to discriminate distinct image spatial structures. We introduce a different geostatistic metric, the first-order variogram, which used in combination with the second-order variogram, will prove its efficiency to describe the image spatial structures. We then develop a method based on the simultaneous use of both first- and second-order variogram metrics to model the image spatial structures as the weighted linear combination of two stochastic models: a Poisson line mosaic model and a multi-Gaussian model. The image spatial structures are characterized by the variance weight and the variogram range related to each model. This method is applied to several SPOT-HRV Normalized Difference Vegetation Index (NDVI) images from the VALERI database in order to characterize the nature of the processes structuring different types of landscape. The mosaic model is an indicator of strong NDVI discontinuities within the image mainly generated by anthropogenic processes such as the mosaic pattern of crop sites. The multi-Gaussian model shows, evidence of diffuse and continuous variation of NDVI generally engendered by ecological and environmental processes such as the fuzzy pattern observed over forest and natural vegetation sites. |
| Databáze: | OpenAIRE |
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