While the historic focus of vegetation condition is the bulk live and dead compositions, these variables provide no information on the structure of vegetation (density and orientation). Canopy structure information is critical for monitoring status and trends, and essential in climate, weather, and ecological studies.
The Science Issue and Relevance: While the historic focus of vegetation condition is the bulk live and dead compositions, these variables provide no information on the structure of vegetation (density and orientation). Canopy structure information is critical for monitoring status and trends, and essential in climate, weather, and ecological studies. A three-dimensional description of canopy structure improves water flow estimates, advances optical condition and change mapping, and fire burn dynamics and emission projections. A critical component in tracking vegetation change and function of grass canopies are quantitative and robust field techniques; unfortunately, they are lacking. The problem is that measurements of density and orientation are both needed, and one or the other must be estimated; most often an estimate of orientation is used to solve for density. However, the calculated density is only as good as the orientation estimate, and the estimate variability is unknown. If both density and orientation could be calculated solely from common field measurements without the need for user estimates, the information content and direct comparability over time and space for a given species would dramatically increase. Independent density and orientation measures would be directly amenable to remote sensing mapping, greatly increasing the effectiveness of monitoring status and trends.
Methodology for Addressing the Issue: Models can be developed to iteratively solve for leaf orientation and canopy density. One such model was developed that uses common measurements of light transmittance in grass canopies to calculate the grass (or marsh) canopy average orientation (Leaf Angle Distribution, LAD) and then use the LAD to calculate the canopy density (Leaf Area Index, LAI). A sequential iteration is used to calculate the LAI and LAD set that most closely reproduces the original light transmittance data without user supplied estimates.
Calculated LAI and LAD were then used to calibrate NASA UAVSAR polarimetric synthetic aperture radar (PolSAR) image data.
Derived empirical relationships were used to transform the 2009 to 2012 PolSAR images into marsh structure maps (Figure 4). The maps show marsh density (LAI) increased (increased image brightness) throughout; however, orientation (LAD) showed the density increase differed. Interior marsh increased vertical orientation (lower brightness) as density increased likely signifying new growth. In contrast, more bay-ward marshes (centered on box outline in 2010 LAI) increased their horizontal orientation, likely signifying continuous growth. These maps are original and they are a uniquely new perspective of the coastal resource.
Future Steps:
- Determine if spatial coincidence exists between high oiling and abnormal latent marsh structure changes.
- Extend marsh structure mapping from 2012 to 2015 in S. alterniflora marsh in coastal Louisiana.
- Build empirical relationships between collected field structure and PolSAR image data of S. patens marsh.
- Extend the structure mapping to J. romerianus marsh.
Additional Publications:
Ramsey III, E., Rangoonwala, A. and Jones, C. E., in review. Marsh Canopy Structure Changes and the Deepwater Horizon Oil Spill. Remote Sens. Environment.
- Overview
While the historic focus of vegetation condition is the bulk live and dead compositions, these variables provide no information on the structure of vegetation (density and orientation). Canopy structure information is critical for monitoring status and trends, and essential in climate, weather, and ecological studies.
Figure 1. NASA in coordination with USGS collected polarimetric synthetic aperture radar (PolSAR) image data of coastal Louisiana in response to the 2010 Deepwater Horizon oil spill and its possible long-term effects on coastal marshes. PolSAR and field data collections occurred in June 2010, 2011, and 2012 of Rockefeller Refuge, Golden Meadow and Barataria Bay, and prespill in June 2009 of Barataria Bay. The Science Issue and Relevance: While the historic focus of vegetation condition is the bulk live and dead compositions, these variables provide no information on the structure of vegetation (density and orientation). Canopy structure information is critical for monitoring status and trends, and essential in climate, weather, and ecological studies. A three-dimensional description of canopy structure improves water flow estimates, advances optical condition and change mapping, and fire burn dynamics and emission projections. A critical component in tracking vegetation change and function of grass canopies are quantitative and robust field techniques; unfortunately, they are lacking. The problem is that measurements of density and orientation are both needed, and one or the other must be estimated; most often an estimate of orientation is used to solve for density. However, the calculated density is only as good as the orientation estimate, and the estimate variability is unknown. If both density and orientation could be calculated solely from common field measurements without the need for user estimates, the information content and direct comparability over time and space for a given species would dramatically increase. Independent density and orientation measures would be directly amenable to remote sensing mapping, greatly increasing the effectiveness of monitoring status and trends.
Methodology for Addressing the Issue: Models can be developed to iteratively solve for leaf orientation and canopy density. One such model was developed that uses common measurements of light transmittance in grass canopies to calculate the grass (or marsh) canopy average orientation (Leaf Angle Distribution, LAD) and then use the LAD to calculate the canopy density (Leaf Area Index, LAI). A sequential iteration is used to calculate the LAI and LAD set that most closely reproduces the original light transmittance data without user supplied estimates.
Figure 2. Three Dimensional LAI Profiles. LAI (density) profiles calculated from field collected PAR profiles based on new model developments. The S. alterniflora marsh is highly lodged in 2010 as indicated by the LAD of 1.04 (dominantly horizontal orientation). The marsh underwent a severe dieback in 2011exhibited as decreased LAI, increased vertical orientation (LAD, 0.59), decreased live-total ratio, and loss of total biomass (vertical summed LAI). High live-dead ratio, increased fuel load, and largely vertical orientation (LAD, 0.40) accompanied regrowth. Calculated LAI and LAD were then used to calibrate NASA UAVSAR polarimetric synthetic aperture radar (PolSAR) image data.
Derived empirical relationships were used to transform the 2009 to 2012 PolSAR images into marsh structure maps (Figure 4). The maps show marsh density (LAI) increased (increased image brightness) throughout; however, orientation (LAD) showed the density increase differed. Interior marsh increased vertical orientation (lower brightness) as density increased likely signifying new growth. In contrast, more bay-ward marshes (centered on box outline in 2010 LAI) increased their horizontal orientation, likely signifying continuous growth. These maps are original and they are a uniquely new perspective of the coastal resource.
Future Steps:
- Determine if spatial coincidence exists between high oiling and abnormal latent marsh structure changes.
- Extend marsh structure mapping from 2012 to 2015 in S. alterniflora marsh in coastal Louisiana.
- Build empirical relationships between collected field structure and PolSAR image data of S. patens marsh.
- Extend the structure mapping to J. romerianus marsh.
Additional Publications:
Ramsey III, E., Rangoonwala, A. and Jones, C. E., in review. Marsh Canopy Structure Changes and the Deepwater Horizon Oil Spill. Remote Sens. Environment.
Figure 3. Empirical relationships derived between field-based LAI and LAD and PolSAR data (n=17 S. alterniflora marsh sites) produced mapping predictions of (a) LAI (density, R2=0.77) and (b) LAD (orientation, R2=0.74). A LAD of 0.5 is spherical, <0.5 more vertical and >0.5 more horizontal leaves and stems.
Figure 4. Maps of LAI (top) and LAD (bottom) for 2009 [prespill] and 2010 [postspill at oil spill cessation] - Publications