Project Funding: 2014 - 2017

NRA: 2013 NASA: Terrestrial Ecology   

Funded by NASA

Abstract:
Dryland systems cover over 40% of the global terrestrial area and accommodate roughly 35% of the total human population. Changes in climate are likely to have significant impacts on these regions. Short and long-term impacts of population growth and climate variability are revealed in the changing vegetation composition and structure of dryland systems. Altered fire regimes are transforming native shrub-dominated landscapes to annual invasive grasses, thus perpetuating the fire cycle. Woody plant expansion can lead to changes in landscape ecohydrology, degradation of soil nutrients, and increased fire risk at the urban fringe. Consequently, techniques to quantify vegetation structure and predict changing vegetation structural patterns in dryland system under future scenarios are necessary to address numerous questions related to quantifying vulnerabilities to and responses of global environmental changes. For example, will warmer temperatures and higher intensity and frequency fires in California’s Mediterranean ecosystem cause an irreversible shift from a shrubland-dominated ecosystem to one of non-native annual grasses and exotic weeds? Similarly will the shrubland-dominated Great Basin be converted to annual grasses and how will the conversion rate vary with interannual precipitation and climate? Current and upcoming NASA missions with observations at multiple spectral, spatial, and temporal scales, coupled with robust calibrations and validations, have the potential to help solve these globally relevant questions. We address Subelement 2.2 of the NASA Terrestrial Ecology program by developing new approaches to combining LiDAR and hyperspectral datasets for the purpose of quantifying vegetation structure and function in dryland terrestrial ecosystems using ecosystem modeling. Our research approach is to collect new airborne LiDAR and hyperspectral datasets over a climate and elevation gradient of the Great Basin where the researchers have existing field, hyperspectral, and LiDAR datasets. The overall goal of the proposed investigation is to synthesize existing and new ground and airborne datasets to quantify metrics such as height, cover, biomass and LAI. Results will be used to parameterize ecosystem productivity modeling in dryland systems and will provide a basis for investigating feedback mechanisms related to changing climate conditions, fire regimes and patterns of non-native plant invasion. Specifically, we will initialize the ecosystem demography model with ecological information retrieved from the remotely sensed datasets in a way that explicitly accounts for the uncertainty in the retrieved information. The overarching questions we seek to answer in this study are: 1) What are the best data fusion approaches to quantify ecosystem structure and what are the errors associated with these approaches across multiple scales? 2) To what extent do LiDAR and hyperspectral sensors reveal the same plant structure information and to what extent are they complementary? 3) What are the compositional and spatial changes in vegetation structure with disturbance regimes of climate, fire and invasive species? We will develop regionally scaled LiDAR / hyperspectral vegetation products in anticipation of a need to determine how best to assimilate the growing number of these site-level collections (G-LiHT (GSFC), Carnegie Airborne Observatory, NEON AOP), which will ultimately be linked to satellite observations. This foundational work will provide a basis for evaluating the extent to which new and future satellite missions are suitable for characterizing dryland vegetation structure (i.e., LDCM, NPP-VIIRS, HyspIRI, LIST, Icesat-2). Importantly, the effect of changes in ecosystem composition and function on resource management and 3D vegetation structure have been identified by Science Study Groups (e.g., HyspIRI mission) and scientific panels (e.g., NRC Decadal Survey) as high science priorities.

Publications:

Dashti, H., Glenn, N. F., Ustin, S., Mitchell, J. J., Qi, Y., Ilangakoon, N. T., Flores, A. N., Silvan-Cardenas, J. L., Zhao, K., Spaete, L. P., de Graaff, M. 2019. Empirical Methods for Remote Sensing of Nitrogen in Drylands May Lead to Unreliable Interpretation of Ecosystem Function. IEEE Transactions on Geoscience and Remote Sensing. 57(6), 3993-4004. DOI: 10.1109/tgrs.2018.2889318

Glenn, N. F., Neuenschwander, A., Vierling, L. A., Spaete, L., Li, A., Shinneman, D. J., Pilliod, D. S., Arkle, R. S., McIlroy, S. K. 2016. Landsat 8 and ICESat-2: Performance and potential synergies for quantifying dryland ecosystem vegetation cover and biomass. Remote Sensing of Environment. 185, 233-242. DOI: 10.1016/j.rse.2016.02.039

Ilangakoon, N. T., Glenn, N. F., Dashti, H., Painter, T. H., Mikesell, T. D., Spaete, L. P., Mitchell, J. J., Shannon, K. 2018. Constraining plant functional types in a semi-arid ecosystem with waveform lidar. Remote Sensing of Environment. 209, 497-509. DOI: 10.1016/j.rse.2018.02.070

Li, A., Dhakal, S., Glenn, N., Spaete, L., Shinneman, D., Pilliod, D., Arkle, R., McIlroy, S. 2017. Lidar Aboveground Vegetation Biomass Estimates in Shrublands: Prediction, Uncertainties and Application to Coarser Scales. Remote Sensing. 9(9), 903. DOI: 10.3390/rs9090903

Li, A., Glenn, N. F., Olsoy, P. J., Mitchell, J. J., Shrestha, R. 2015. Aboveground biomass estimates of sagebrush using terrestrial and airborne LiDAR data in a dryland ecosystem. Agricultural and Forest Meteorology. 213, 138-147. DOI: 10.1016/j.agrformet.2015.06.005

Mitchell, J. J., Shrestha, R., Spaete, L. P., Glenn, N. F. 2015. Combining airborne hyperspectral and LiDAR data across local sites for upscaling shrubland structural information: Lessons for HyspIRI. Remote Sensing of Environment. 167, 98-110. DOI: 10.1016/j.rse.2015.04.015

Olsoy, P., Mitchell, J., Glenn, N., Flores, A. 2017. Assessing a Multi-Platform Data Fusion Technique in Capturing Spatiotemporal Dynamics of Heterogeneous Dryland Ecosystems in Topographically Complex Terrain. Remote Sensing. 9(10), 981. DOI: 10.3390/rs9100981

Pandit, K., Dashti, H., Glenn, N. F., Flores, A. N., Maguire, K. C., Shinneman, D. J., Flerchinger, G. N., Fellows, A. W. Optimizing shrub parameters to estimate gross primary production of the sagebrush ecosystem using the Ecosystem Demography (EDv2.2) model DOI: 10.5194/gmd-2018-264

Qi, Y., Ustin, S., Glenn, N. 2018. Imaging Spectroscopic Analysis of Biochemical Traits for Shrub Species in Great Basin, USA. Remote Sensing. 10(10), 1621. DOI: 10.3390/rs10101621

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

• A canopy level investigation of specific leaf area spectral responses from sagebrush shrublands in Reynolds Creek Experimental Watershed, Idaho, USA   --   (Jessica J Mitchell, Nancy F Glenn, Hamid Dashti, Alejandro Flores, Lucas Spaete, Nayani Ilangakoon)   [abstract]   [poster]
• Ecosystem modeling in the Great Basin, USA: derivation of remotely sensed vegetation parameters and parameterization of a shrub PFT.   --   (Rupesh Shrestha, Nancy F Glenn, Jessica Jean Mitchell, Hamid Dashti, Alejandro Flores, Aihua Li, Lucas Spaete, Susan L. Ustin, Yi Qi)   [abstract]

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

• NASA Terrestrial Ecology (TE) Project Profile