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Article

Satellite Leaf Area Index: Global Scale Analysis of the Tendencies Per Vegetation Type Over the Last 17 Years

1
CNRM, UMR 3589, Météo France, 42 av Gaspard Coriolis, 31000 Toulouse, France
2
EOLAB, Parc Cientific Universitat de Valencia, C/Catedratico Agustin Escardino, 9, E-46980 Paterna, Valencia, Spain
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(3), 424; https://doi.org/10.3390/rs10030424
Received: 23 January 2018 / Revised: 2 March 2018 / Accepted: 6 March 2018 / Published: 9 March 2018
The main objective of this study is to detect and quantify changes in the vegetation dynamics of each vegetation type at the global scale over the last 17 years. With recent advances in remote sensing techniques, it is now possible to study the Leaf Area Index (LAI) seasonal and interannual variability at the global scale and in a consistent way over the last decades. However, the coarse spatial resolution of these satellite-derived products does not permit distinguishing vegetation types within mixed pixels. Considering only the dominant type per pixel has two main drawbacks: the LAI of the dominant vegetation type is contaminated by spurious signal from other vegetation types and at the global scale, significant areas of individual vegetation types are neglected. In this study, we first developed a Kalman Filtering (KF) approach to disaggregate the satellite-derived LAI from GEOV1 over nine main vegetation types, including grasslands and crops as well as evergreen, broadleaf and coniferous forests. The KF approach permits the separation of distinct LAI values for individual vegetation types that coexist within a pixel. The disaggregated LAI product, called LAI-MC (Multi-Cover), consists of world-wide LAI maps provided every 10 days for each vegetation type over the 1999–2015 period. A trend analysis of the original GEOV1 LAI product and of the disaggregated LAI time series was conducted using the Mann-Kendall test. Resulting trends of the GEOV1 LAI (which accounts for all vegetation types) compare well with previous regional or global studies, showing a greening over a large part of the globe. When considering each vegetation type individually, the largest global trend from LAI-MC is found for coniferous forests (0.0419 m 2 m 2 yr 1 ) followed by summer crops (0.0394 m 2 m 2 yr 1 ), while winter crops and grasslands show the smallest global trends (0.0261 m 2 m 2 yr 1 and 0.0279 m 2 m 2 yr 1 , respectively). The LAI-MC presents contrasting trends among the various vegetation types within the same pixel. For instance, coniferous and broadleaf forests experience a marked greening in the North-East of Europe while crops and grasslands show a browning. In addition, trends from LAI-MC can significantly differ (by up to 50%) from trends obtained with GEOV1 by considering only the dominant vegetation type over each pixel. These results demonstrate the usefulness of the disaggregation method compared to simple ones. LAI-MC may provide a new tool to monitor and quantify tendencies of LAI per vegetation type all over the globe. View Full-Text
Keywords: leaf area index; remote sensing; trend; greening; vegetation type; forest; crops leaf area index; remote sensing; trend; greening; vegetation type; forest; crops
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MDPI and ACS Style

Munier, S.; Carrer, D.; Planque, C.; Camacho, F.; Albergel, C.; Calvet, J.-C. Satellite Leaf Area Index: Global Scale Analysis of the Tendencies Per Vegetation Type Over the Last 17 Years. Remote Sens. 2018, 10, 424. https://doi.org/10.3390/rs10030424

AMA Style

Munier S, Carrer D, Planque C, Camacho F, Albergel C, Calvet J-C. Satellite Leaf Area Index: Global Scale Analysis of the Tendencies Per Vegetation Type Over the Last 17 Years. Remote Sensing. 2018; 10(3):424. https://doi.org/10.3390/rs10030424

Chicago/Turabian Style

Munier, Simon, Dominique Carrer, Carole Planque, Fernando Camacho, Clément Albergel, and Jean-Christophe Calvet. 2018. "Satellite Leaf Area Index: Global Scale Analysis of the Tendencies Per Vegetation Type Over the Last 17 Years" Remote Sensing 10, no. 3: 424. https://doi.org/10.3390/rs10030424

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