Validating and linking the GIMMS leaf area index (LAI3g) with environmental controls in tropical Africa

Marion Pfeifer, Veronique Lefebvre, Alemu Gonsamo, Petri Pellikka, Robert Marchant, Dereje Denu, Philip John Platts

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The recent Global Inventory Modeling and Mapping Studies (GIMMS) LAI3g product provides a 30-year global times-series of remotely sensed leaf area index (LAI), an essential variable in models of ecosystem process and productivity. In this study, we use a new dataset of field-based LAITrue to indirectly validate the GIMMS LAI3g product, LAIavhrr, in East Africa, comparing the distribution properties of LAIavhrr across biomes and environmental gradients with those properties derived for LAITrue. We show that the increase in LAI with vegetation height in natural biomes is captured by both LAIavhrr and LAITrue, but that LAIavhrr overestimates LAI for all biomes except shrubland and cropland. Non-linear responses of LAI to precipitation and moisture indices, whereby leaf area peaks at intermediate values and declines thereafter, are apparent in both LAITrue and LAIavhrr, although LAITrue reaches its maximum at lower values of the respective environmental driver. Socio-economic variables such as governance (protected areas) and population affect both LAI responses, although cause and effect are not always obvious: a positive relationship with human population pressure was detected, but shown to be an artefact of both LAI and human settlement covarying with precipitation. Despite these complexities, targeted field measurements, stratified according to both environmental and socio-economic gradients, could provide crucial data for improving satellite-derived LAI estimates, especially in the human-modified landscapes of tropical Africa.
Original languageEnglish
Pages (from-to)1973-1990
Number of pages18
JournalRemote Sensing
Issue number3
Publication statusPublished - 4 Mar 2014

Bibliographical note

©2014 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (


  • hemispherical images
  • tropical landscapes
  • field assessments
  • Kenya
  • Ethiopia
  • Tanzania
  • East Africa
  • essential climate variables

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