Project Funding: 2013 - 2015

NRA: 2012 NASA: Terrestrial Ecology   

Funded by NASA

Abstract:
To date, many of the relationships between remote sensing observations and forest biophysical parameters are determined through empirical relationships. The functional forms of these relationships are created through a basic physical modeling of the remote sensing signal's interaction with the vegetated target and a comparison with measured ground validation. In this scenario, model coefficients are adjusted to better fit remote sensing observations to ground validation data with the resulting model applied to the wider area of data collected by the remote sensing instrument. While there has been a considerable degree of success in using these approaches, a limit is now being reached where such approaches are difficult to extend beyond the region where the empirical relationships were immediately formed. As a result, the global application of such techniques is limited and difficult to perform error analyses necessary for the formulation of spaceborne missions. The missing component in the application of remote sensing for the characterization of vegetation structure is a frame of reference of these estimates based on physical and environmental constraints (e.g. water resources, light availability and soil type) that are inherent to ecosystems. Models that take these types of constraints into account do exist, ranging from regional, climate-type models to individual tree-based models, known as IBM's. What has not been accomplished thus far, to a high degree of resolution, is the use of remote sensing and IBM's in a combined probabilistic model. Such a model would simulate possible trajectories of the forest environment, and, using an electromagnetic interaction model to simulate remote sensing observations. The actual remote sensing observations are then used to determine which trajectory of the ecosystem was most likely for a particular region. Once accomplished, forest properties of interest (biomass, vertical structure, species diversity, etc.) are calculated from the IBM and ecologically consistent estimates of these properties produced. By taking into account uncertainties in the IBM and observational models, with the use of Bayes' theorem, the probability density functions (pdf's) of these desired quantities can be determined, and metrics such as mean, mode and variance determined in a statistically and environmentally meaningful way. By varying the configuration of the simulation input, the model will also be used for determining the sensitivity of remote sensing observations to those parameters, and thereby serve as a method for testing observing strategies that can be used by NASA's DESDynI-R satellite mission.

Publications:

Cartus, O., Siqueira, P., Kellndorfer, J. 2018. An Error Model for Mapping Forest Cover and Forest Cover Change Using L-Band SAR. IEEE Geoscience and Remote Sensing Letters. 15(1), 107-111. DOI: 10.1109/LGRS.2017.2775659

2015 NASA Carbon Cycle & Ecosystems Joint Science Workshop Poster(s)

• Modeling of Polarimetric SAR and InSAR for Forest Structure Estimation   --   (Paul Robert Siqueira, Dustin Lagoy)   [abstract]
• UMass Airborne and Tower-based Assets for Remote Sensing Monitoring and Measurement of Vegetation Structure and Hydrological Cycles   --   (Paul Robert Siqueira, Tom Hartley, Gerard Ruiz Carregal, Thomas Millette, Mark Vanscoy)   [abstract]

More details may be found in the following project profile(s):

• NASA Terrestrial Ecology (TE) Project Profile