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Optical instruments for measuring leaf area index in low vegetation: Application in Arctic ecosystems
Ansvarig organisation
2005 (Engelska)Ingår i: Ecological Applications, ISSN 1051-0761, E-ISSN 1939-5582, Vol. 15, nr 4, s. 1462-1470Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

Leaf area index (LAI) is a powerful diagnostic of plant productivity. Despite the fact that many methods have been developed to quantify LAI, both directly and indirectly, leaf area index remains difficult to quantify accurately, owing to large spatial and temporal variability. The gap-fraction technique is widely used to estimate the LAI indirectly. However, for low-stature vegetation, the gap-fraction sensor either cannot get totally underneath the plant canopy, thereby missing part of the leaf area present, or is too close to the individual leaves of the canopy, which leads to a large distortion of the LAI estimate. We set out to develop a methodology for easy and accurate nondestructive assessment of the variability of LAI in low-stature vegetation. We developed and tested the methodology in an arctic landscape close to Abisko, Sweden. The LAI of arctic vegetation could be estimated accurately and rapidly by combining field measurements of canopy reflectance (NDVI) and light penetration through,the canopy (gap-fraction analysis using a LI-COR LAI-2000). By combining the two methodologies, the limitations of each could be circumvented, and a significantly increased accuracy of the LAI estimates was obtained. The combination of an NDVI sensor for sparser vegetation and a LAI-2000 for denser vegetation could explain 81% of the variance of LAI measured by destructive harvest. We used the method to quantify the spatial variability and the associated uncertainty of leaf area index in a small catchment area.

Ort, förlag, år, upplaga, sidor
ECOLOGICAL SOC AMER , 2005. Vol. 15, nr 4, s. 1462-1470
Nyckelord [en]
arctic tundra; LAI; leaf area index; low-stature vegetation; normalized difference vegetation index; optical instruments; Sweden; uncertainty analysis
Nationell ämneskategori
Naturvetenskap
Identifikatorer
URN: urn:nbn:se:polar:diva-3669DOI: 10.1890/03-5354OAI: oai:DiVA.org:polar-3669DiVA, id: diva2:1103617
Tillgänglig från: 2017-05-30 Skapad: 2017-05-30 Senast uppdaterad: 2017-06-01

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