Introduction

The Leaf Area Index (LAI) is a measurement of foliage in an area. LAI is a measurement with a scale of 0, or bare ground, to over 10, or dense conifer forest. This index is useful for measuring biosphere and atmosphere interactions and cycles and for use in climate models. While LAI can be measured via in situ methods, these practices tend to require a lot of time and resources to survey an area. In some landscapes, on the ground measuring is not a feasible option. In these cases, researchers can use satellite data products to measure LAI. NASA's Land Processes Distributed Active Archive Center (LP DAAC) distributes several LAI data products derived from Moderate Resolution Imaging Spectroradiometer (MODIS) data.

Instruments and Techniques Used

One example of how MODIS LAI can be used is in Cristiano and others’ (2014) study. The authors used MODIS LAI along with MODIS NDVI to evaluate seasonal changes in the relationship between canopy cover and photosynthesis in NE Argentina’s Atlantic Forest. 

Major Findings

The authors deduce the area has fairly high NDVI values throughout the year, indicating ongoing photosynthesis. During the cold season (July) the deciduous trees shed their leaves. The leaves on the evergreen trees, below the deciduous trees, absorb the sunlight and continue photosynthesis in the area. Thus, the forests can provide year-round carbon sequestration to aid in the global carbon balance.

With the recent release of MODIS Version 6 LAI data products, researchers have an improved data product to measure LAI. Version 6 products have a higher spatial resolution of 500 meters. These updated LAI data products also use a new surface reflectance algorithm based on daily Terra MODIS Surface Reflectance data (MOD09GA) and use improved multi-year land cover products inputs. With these updates, the LAI data products provide finer detail and offer a more accurate account of foliage in an area.

The image below is a recently captured image of the study area from the study mentioned above using MODIS Version 5 (left) and MODIS Version 6 (right). The higher resolution and better classification of LAI is evident in the MODIS Version 6 (right) photo. Using MODIS Version 6 data, researchers can now obtain more accurate results when studying LAI.

For a more detailed look at the new MODIS Version 6 LAI product, be sure to read the Product User’s Guide.

References

Article References

Chen, B., Wu, Z., Wang, J., Dong, J., Guan, L., Chen, J., Yang, K., and Xie, G., 2015, Spatio-temporal prediction of leaf area index of rubber plantation using HJ-1A/1B CCD images and recurrent neural network: ISPRS Journal of Photogrammetry and Remote Sensing, v. 102, p. 148-160, accessed January 4, 2016. https://doi.org/10.1016/j.isprsjprs.2014.12.011.

Cristiano, P., Madanes, N., Campanello, P., di Francescantonio, D., Rodriguez, S., Zhang, Y-J., Carrasco, L., and Goldstein, G., 2014, High NDVI and potential canopy photosynthesis of South American subtropical forests despite seasonal changes in leaf area index and air temperature: Forests, v. 5, no. 2, p. 287-308, accessed January 4, 2016. https://doi.org/10.3390/f5020287.

National Aeronautics and Space Administration, 2015, Leaf area index, accessed January 7, 2016.

Scurlock, J., Asner, G., and Gower, S., 2001, Global leaf area index data from field measurements, 1932-2000, accessed December 28, 2015. https://doi.org/10.3334/ORNLDAAC/584.

Weier, J., 2002, Seeing leaves in a new light, accessed January 8, 2016.

Image Reference

Granule IDs

• MCD15A2.A2015177.h12v11.005.2015188025607
• MCD15A2.A2015177.h13v11.005.2015188025600
• MCD15A2H.A2015177.h12v11.006.2015301223210
• MCD15A2H.A2015177.h13v11.006.2015301222816