Multivariate quantification of landscape spatial heterogeneity using variogram models

Autor: Jeffrey T. Morisette, Denis Allard, Sébastien Garrigues, Frédéric Baret
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)
Jazyk: angličtina
Rok vydání: 2008
Předmět:
[SPI.OTHER]Engineering Sciences [physics]/Other
Spatial correlation
VARIOGRAM
MULTIVARIATE VARIOGRAM MODEL
STATIONARITY
DIGITAL IMAGE
LANDSCAPE STRUCTURE
RED AND NEAR INFRARED
SPATIAL HETEROGENEITY
LINEAR MODEL
LENGTH SCALE
SPATIAL STRUCTURE
MODERATE SPATIAL RESOLUTION
NDVI
010504 meteorology & atmospheric sciences
télédétection
variogramme
0211 other engineering and technologies
Soil Science
modélisation spatiale
02 engineering and technology
Astrophysics::Cosmology and Extragalactic Astrophysics
hétérogénéité spatiale
01 natural sciences
Normalized Difference Vegetation Index
lumière rouge et infra rouge
modèle linéaire
paysage
Computers in Earth Sciences
végétation
Variogram
Astrophysics::Galaxy Astrophysics
021101 geological & geomatics engineering
0105 earth and related environmental sciences
Remote sensing
Geology
Vegetation
Spectral bands
15. Life on land
Spatial heterogeneity
échelle
Autre (Sciences de l'ingénieur)
image numérique
Environmental science
Spatial variability
Scale (map)
Zdroj: Remote Sensing of Environment
Remote Sensing of Environment, Elsevier, 2008, 112 (1), pp.216-230. ⟨10.1016/j.rse.2007.04.017⟩
Remote Sensing of Environment 1 (112), 216-230. (2008)
ISSN: 0034-4257
1879-0704
DOI: 10.1016/j.rse.2007.04.017⟩
Popis: International audience; The monitoring of earth surface processes at a global scale requires high temporal frequency remote sensing observations provided up to now by moderate spatial resolution sensors (from 250 m to 7 kin). Non-linear estimation processes of land surface variables derived from remote sensing data can be biased by the surface spatial heterogeneity within the moderate spatial resolution pixel. Quantifying this surface spatial heterogeneity is thus required to correct non-linear estimation processes of land surface variables. The first step in this process is to properly characterize the scale of spatial variation of the processes structuring the landscape. Since the description of land surface processes generally involves various spectral bands, a multivariate approach to characterize the surface spatial heterogeneity from multi-spectral remote sensing observations has to be established. This work aims at quantifying the landscape spatial heterogeneity captured by red and near infrared high spatial resolution images using direct and cross-variograms modeled together with the geostatistical linear model of coregionalization. This model quantifies the overall spatial variability and correlation of red and near infrared reflectances over the scene. In addition, it provides an explicit understanding of the landscape spatial structures captured by red and near infrared reflectances and is thus appropriate to describe landscapes composed of areas with contrasted red and near infrared spectral properties. The application of the linear model of coregionalization to 18 contrasted landscapes provides a spatial signature of red and near infrared spectral properties characterizing each type of landscape. Low vegetation cover sites are characterized by positive spatial correlation between red and near infrared. The mosaic pattern of vegetation fields and bare soil fields over crop sites generates high and negative spatial correlation between red and near infrared and increases the spatial variability of red and near infrared. On forest sites, the important amount of vegetation limits the spatial variability of red and the shadow effects mainly captured by near infrared induce a low and positive spatial correlation between red and near infrared. Finally, the linear model of coregionalization applied to red and near infrared is shown to be more powerful than the univariate variogram modeling applied to NDVI because the second order stationarity hypothesis on which variogram modeling relies is more frequently verified for red and near infrared than for NDVI.
Databáze: OpenAIRE
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