Glenn, Nancy: Boise State University (Project Lead)
Flores, Alejandro: Boise State University (Co-Investigator)
Ustin, Susan: University of California Davis (Co-Investigator)
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):