This image shows evergreen and deciduous trees in the Alto Paraná ecoregion of the Atlantic Forest in Argentina. The leaves of the evergreen trees are located below the leaves of the deciduous trees and absorb more sunlight during the cold season when the deciduous trees shed their leaves.
This image shows evergreen and deciduous trees in the Alto Paraná ecoregion of the Atlantic Forest in Argentina. The leaves of the evergreen trees are located below the leaves of the deciduous trees and absorb more sunlight during the cold season when the deciduous trees shed their leaves.
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. The Land Processes Distributed Active Archive Center (LP DAAC) distributes several LAI data products derived from Moderate Resolution Imaging Spectroradiometer (MODIS) data.
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. 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.
Use the slider tool to compare these two images. Both images are 8-day composites of data from June 26 to July 3, 2015, of the Alto Paraná Atlantic Forest area in northeastern Argentina. The image on the left shows a Version 5 MODIS LAI data product (MCD15A2) with 1,000 meter resolution. The image on the right shows a Version 6 MODIS LAI data product (MCD15A2H) with 500 meter resolution. You can see the right image has a higher spatial resolution and shows the area in finer detail.
Granule IDs:
MCD15A2.A2015177.h12v11.005.2015188025607
MCD15A2.A2015177.h13v11.005.2015188025600
MCD15A2H.A2015177.h12v11.006.2015301223210
MCD15A2H.A2015177.h13v11.006.2015301222816
Use the slider tool to compare these two images. Both images are 8-day composites of data from June 26 to July 3, 2015, of the Alto Paraná Atlantic Forest area in northeastern Argentina. The image on the left shows a Version 5 MODIS LAI data product (MCD15A2) with 1,000 meter resolution. The image on the right shows a Version 6 MODIS LAI data product (MCD15A2H) with 500 meter resolution. You can see the right image has a higher spatial resolution and shows the area in finer detail.
Granule IDs:
MCD15A2.A2015177.h12v11.005.2015188025607
MCD15A2.A2015177.h13v11.005.2015188025600
MCD15A2H.A2015177.h12v11.006.2015301223210
MCD15A2H.A2015177.h13v11.006.2015301222816
References: (Author's Note 5/15/2020: At the time of publishing these references were available online, some resources may no longer be available.)
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, athttp://dx.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, at http://dx.doi.org/10.3390/f5020287.
National Aeronautics and Space Administration, 2015, Leaf area index, accessed January 7, 2016, at http://modis-land.gsfc.nasa.gov/lai.html.
Scurlock, J., Asner, G., and Gower, S., 2001, Global leaf area index data from field measurements, 1932-2000, accessed December 28, 2015, at https://daac.ornl.gov/VEGETATION/lai_des.html.
Weier, J., 2002, Seeing leaves in a new light, accessed January 8, 2016, at http://earthobservatory.nasa.gov/Features/LAI/LAI.php.
Material written by Danielle Golon1
1 Innovate!, Inc., contractor to the U.S. Geological Survey, Earth Resources Observation and Science (EROS) Center, Sioux Falls, South Dakota, USA. Work performed under USGS contract G15PD00766 for LP DAAC2.
2 LP DAAC Work performed under NASA contract NNG14HH33I.