Problem Statement

Aerial orthophotography, airborne LiDAR, and subsequent data products, i.e. Structure from Motion (SfM), have difficulty penetrating dense foliage to measure surface height or sub-canopy vegetation. Determining net ecosystem productivity (NEP), e.g. grass and annual forb production beneath mesquite canopies is critical to establishing total utilization by cattle and other native grazers and browsers, e.g. white-tail deer, mule deer, and jack rabbits. Mesquites have the unique property of both increasing nutrient availability through nitrogen fixation (Rhoades et al. 1996, Scholes and Archer 1997) as well as lowering Evaporation/Transpiration (E/T) relative to exposed surfaces (Dugas and Mayeux 1991, Villegas et al. 2010). 

Photo credit: Noah Whiteman, Steve Smith (UA).

In order to evaluate NEP beneath mesquite:

• Use Tyson's CyVerse Solar Radiation modelling to establish variation in E/T (Hamon or Penman-Monteith)
• Use measured values (Naito et al. in prep., Villegas et al. 2010) that show the difference in NEP in open spaces vs beneath trees.
• Calculate total cover area of large mesquites from the ALS data. 
• Calculate the non-linear response of NEP for larger canopy footprint areas.
• Collect both oblique and vertical UAV imagery for SfM (James and Robson 2014, Markelin et al. 2014)
  • Barron-Gafford & Hendryx GoPro Hero 3 imagery from Santa Rita Flux Tower Site
  • Firefly6 Sony Alpha 6000 imagery TBD
• Use a UAV SfM & NDVI model to find grass growing beneath mesquites
  • Establish measurement precision.
  • Relate the locations to the solar radiation model to establish relationships of NEP. 

References 

Bahre, C. J., & Shelton, M. L. (1993). Historic vegetation change, mesquite increases, and climate in southeastern Arizona. Journal of Biogeography, 489-504.

Dugas, W. A., & Mayeux Jr, H. S. (1991). Evaporation from rangeland with and without honey mesquite. Journal of Range Management, 161-170.

James, M., & Robson, S. (2014). Systematic vertical error in UAV-derived topographic models: origins and solutions.

 James, M.R., & Robson S. "Mitigating systematic error in topographic models derived from UAV and ground?based image networks."Earth Surface Processes and Landforms 39, no. 10 (2014): 1413-1420.

 Markelin, L., Honkavaara, E., Näsi, R., Nurminen, K., & Hakala, T. (2014). GEOMETRIC PROCESSING WORKFLOW FOR VERTICAL AND OBLIQUE HYPERSPECTRAL FRAME IMAGES COLLECTED USING UAV. ISPRS-International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 1, 205-210. 

Rhoades, C. C. (1996). Single-tree influences on soil properties in agroforestry: lessons from natural forest and savanna ecosystems.Agroforestry systems, 35(1), 71-94.

Scholes, R. J., & Archer, S. R. (1997). Tree-grass interactions in savannas.Annual review of Ecology and Systematics, 517-544.

Villegas, J. C., Breshears, D. D., Zou, C. B., & Royer, P. D. (2010). Seasonally pulsed heterogeneity in microclimate: phenology and cover effects along deciduous grassland–forest continuum. UNKNOWN, 9(3), 537-547.