Roberts, Dar: UC Santa Barbara (Project Lead)
Hulley, Glynn: JPL (Institution Lead)
Project Funding:
2011 - 2013
NRA: 2011 NASA: HyspIRI Preparatory Airborne Activities and Associated Science
Funded by Other US Funding: , NASA
Abstract:
Imaging spectrometry has a demonstrated capability to discriminate plant species and functional types. However, this capability has been limited in three important ways: 1) Mapping of species and measurement of species-specific properties have been restricted to local scales with relatively uniform environmental conditions; 2) Limited data availability across time has allowed only a few studies to investigate impacts of vegetation phenology on species mapping; and 3) Species and functional type discrimination has largely been restricted to reflected solar radiance, neglecting the potential of emitted radiance in the mid-infrared and thermal infrared portions of the spectrum. In order to evaluate HyspIRI s capability to discriminate plant species and functional types globally, it is first necessary to evaluate how the spectral-biophysical properties of vegetation change both seasonally and along environmental gradients, such as the precipitation and temperature gradients that occur with elevation and distance from large bodies of water.
In this research, we propose to utilize HyspIRI-like data, collected using the Airborne Visible-Infrared Imaging Spectrometer (AVIRIS) and MODIS-ASTER Airborne Simulator (MASTER) in FY2013 and 2014 to address seasonal-environmental changes in species-specific properties, focusing on the proposed Santa Barbara - Southern Sierra Nevada transect. This transect provides an elevation gradient of over 2700 m, a factor of four change in annual precipitation, and average air temperature ranging from 17.6C to less than 2C. Specific objectives include:
1) Evaluation of species-level discrimination from the coast to the interior, focusing on broadly distributed species and functional types. Targeted plants include chamise and manzanita, the genus Ceanothus, and needleleaf evergreen trees as examples. Spectral separability will be evaluated using Multiple Endmember Spectral Mixture Analysis (MESMA) and a suite of endmember selection tools, such as Iterative Endmember Selection. We will leverage off of an extensive set of existing training and validation polygons, in addition to collecting new data in the field. Separability will be evaluated across multiple seasons, and potential improvements in classification of dominant species through incorporation of phenological data will be investigated.
2) Evaluation of synergies between Visible-Shortwave Infrared (VSWIR) and Thermal Infrared (TIR) data for improved accuracy in Temperature Emissivity Separation (TES). AVIRIS-derived column water vapor will be used to constrain atmospheric correction required for separating Land Surface Temperature (LST) from emissivity using MASTER TIR data. The large elevation gradient, seasonal variation in precipitable water vapor and the gradient from the coast to interior provides an ideal test-bed to evaluatepotential synergies between VSWIR and TIR. LST will be validated using several instrumented sites.
3) Evaluation of the relationship between species composition, cover fraction and LST. Canopy temperature varies considerably depending upon species-specific water use efficiency, fractional cover and water stress. We hypothesize that LST will vary depending upon dominant plant species and available soil moisture.
4) Evaluation of full spectral properties of a subset of species from the VSWIR to TIR including measurements of water content, chlorophyll, nitrogen and lignin-cellulose.
This research specifically addresses two HyspIRI science questions, VQ1 (Pattern and Spatial Distribution of Ecosystems and their Component) and combined question CQ4 (Ecosystem Function and Diversity) with strong linkages to VQ2 (Ecosystem Function, Physiology and Seasonal Activity). To address this research we have assembled a team consisting of experts in VSWIR data including vegetation image spectroscopy and species-level discrimination (Roberts & Dennison) and in TIR data using TES (Hulley).
Publications:
Bell, T. W., Cavanaugh, K. C., Siegel, D. A. 2015. Remote monitoring of giant kelp biomass and physiological condition: An evaluation of the potential for the Hyperspectral Infrared Imager (HyspIRI) mission. Remote Sensing of Environment. 167, 218-228. DOI: 10.1016/j.rse.2015.05.003
Calvin, W., Pace, E. 2016. Utilizing HyspIRI Prototype Data for Geological Exploration Applications: A Southern California Case Study. Geosciences. 6(1), 11. DOI: 10.3390/geosciences6010011
Clark, M. L. 2020. Comparison of multi-seasonal Landsat 8, Sentinel-2 and hyperspectral images for mapping forest alliances in Northern California. ISPRS Journal of Photogrammetry and Remote Sensing. 159, 26-40. DOI: 10.1016/j.isprsjprs.2019.11.007
Clark, M. L., Buck-Diaz, J., Evens, J. 2018. Mapping of forest alliances with simulated multi-seasonal hyperspectral satellite imagery. Remote Sensing of Environment. 210, 490-507. DOI: 10.1016/j.rse.2018.03.021
Clark, M. L., Kilham, N. E. 2016. Mapping of land cover in northern California with simulated hyperspectral satellite imagery. ISPRS Journal of Photogrammetry and Remote Sensing. 119, 228-245. DOI: 10.1016/j.isprsjprs.2016.06.007
Clasen, A., Somers, B., Pipkins, K., Tits, L., Segl, K., Brell, M., Kleinschmit, B., Spengler, D., Lausch, A., Forster, M. 2015. Spectral Unmixing of Forest Crown Components at Close Range, Airborne and Simulated Sentinel-2 and EnMAP Spectral Imaging Scale. Remote Sensing. 7(11), 15361-15387. DOI: 10.3390/rs71115361
Guidici, D., Clark, M. 2017. One-Dimensional Convolutional Neural Network Land-Cover Classification of Multi-Seasonal Hyperspectral Imagery in the San Francisco Bay Area, California. Remote Sensing. 9(6), 629. DOI: 10.3390/rs9060629
Meerdink, S. K., Hook, S. J., Roberts, D. A., Abbott, E. A. 2019. The ECOSTRESS spectral library version 1.0. Remote Sensing of Environment. 230, 111196. DOI: 10.1016/j.rse.2019.05.015
Meerdink, S. K., Roberts, D. A., King, J. Y., Roth, K. L., Dennison, P. E., Amaral, C. H., Hook, S. J. 2016. Linking seasonal foliar traits to VSWIR-TIR spectroscopy across California ecosystems. Remote Sensing of Environment. 186, 322-338. DOI: 10.1016/j.rse.2016.08.003
Miraglio, T., Adeline, K., Huesca, M., Ustin, S., Briottet, X. 2019. Monitoring LAI, Chlorophylls, and Carotenoids Content of a Woodland Savanna Using Hyperspectral Imagery and 3D Radiative Transfer Modeling. Remote Sensing. 12(1), 28. DOI: 10.3390/rs12010028
Miraglio, T., Adeline, K., Huesca, M., Ustin, S., Briottet, X. 2022. Assessing vegetation traits estimates accuracies from the future SBG and biodiversity hyperspectral missions over two Mediterranean Forests. International Journal of Remote Sensing. 43(10), 3537-3562. DOI: 10.1080/01431161.2022.2093143
Okujeni, A., Canters, F., Cooper, S. D., Degerickx, J., Heiden, U., Hostert, P., Priem, F., Roberts, D. A., Somers, B., van der Linden, S. 2018. Generalizing machine learning regression models using multi-site spectral libraries for mapping vegetation-impervious-soil fractions across multiple cities. Remote Sensing of Environment. 216, 482-496. DOI: 10.1016/j.rse.2018.07.011
Pureur, V., Dudley, J. M. 2010. Non-linear spectral broadening across multiple bandgaps of all solid photonic crystal fibers. Nonlinear Optics and Applications IV. DOI: 10.1117/12.854041
Realmuto, V. J., Dennison, P. E., Foote, M., Ramsey, M. S., Wooster, M. J., Wright, R. 2015. Specifying the saturation temperature for the HyspIRI 4-mm channel. Remote Sensing of Environment. 167, 40-52. DOI: 10.1016/j.rse.2015.04.028
Roberts, D., Alonzo, M., Wetherley, E. B., Dudley, K. L., Dennison, P. E. 2017. 9. Multiscale Analysis of Urban Areas Using Mixing Models in: Integrating Scale in Remote Sensing and GIS. CRC Press, 247-282. DOI: 10.1201/9781315373720-10
Roth, K. L., Roberts, D. A., Dennison, P. E., Alonzo, M., Peterson, S. H., Beland, M. 2015. Differentiating plant species within and across diverse ecosystems with imaging spectroscopy. Remote Sensing of Environment. 167, 135-151. DOI: 10.1016/j.rse.2015.05.007
Roth, K. L., Roberts, D. A., Dennison, P. E., Peterson, S. H., Alonzo, M. 2015. The impact of spatial resolution on the classification of plant species and functional types within imaging spectrometer data. Remote Sensing of Environment. 171, 45-57. DOI: 10.1016/j.rse.2015.10.004
Somers, B., Tits, L., Roberts, D., Wetherley, E. 2016. Endmember Library Approaches to Resolve Spectral Mixing Problems in Remotely Sensed Data in: Data Handling in Science and Technology: Resolving Spectral Mixtures - With Applications from Ultrafast Time-Resolved Spectroscopy to Super-Resolution Imaging. Elsevier, 551-577. DOI: 10.1016/b978-0-444-63638-6.00017-6
Wetherley, E. B., Roberts, D. A., McFadden, J. P. 2017. Mapping spectrally similar urban materials at sub-pixel scales. Remote Sensing of Environment. 195, 170-183. DOI: 10.1016/j.rse.2017.04.013
Coates, A., Dennison, P., Roberts, D., Roth, K. 2015. Monitoring the Impacts of Severe Drought on Southern California Chaparral Species using Hyperspectral and Thermal Infrared Imagery. Remote Sensing. 7(11), 14276-14291. DOI: 10.3390/rs71114276
Dudley, K. L., Dennison, P. E., Roth, K. L., Roberts, D. A., Coates, A. R. 2015. A multi-temporal spectral library approach for mapping vegetation species across spatial and temporal phenological gradients. Remote Sensing of Environment. 167, 121-134. DOI: 10.1016/j.rse.2015.05.004
Grigsby, S. P., Hulley, G. C., Roberts, D. A., Scheele, C., Ustin, S. L., Alsina, M. M. 2015. Improved surface temperature estimates with MASTER/AVIRIS sensor fusion. Remote Sensing of Environment. 167, 53-63. DOI: 10.1016/j.rse.2015.05.019
Hochberg, E. J., Roberts, D. A., Dennison, P. E., Hulley, G. C. 2015. Special issue on the Hyperspectral Infrared Imager (HyspIRI): Emerging science in terrestrial and aquatic ecology, radiation balance and hazards. Remote Sensing of Environment. 167, 1-5. DOI: 10.1016/j.rse.2015.06.011
Roberts, D. A., Dennison, P. E., Roth, K. L., Dudley, K., Hulley, G. 2015. Relationships between dominant plant species, fractional cover and Land Surface Temperature in a Mediterranean ecosystem. Remote Sensing of Environment. 167, 152-167. DOI: 10.1016/j.rse.2015.01.026
Thompson, D. R., Gao, B., Green, R. O., Roberts, D. A., Dennison, P. E., Lundeen, S. R. 2015. Atmospheric correction for global mapping spectroscopy: ATREM advances for the HyspIRI preparatory campaign. Remote Sensing of Environment. 167, 64-77. DOI: 10.1016/j.rse.2015.02.010
Thompson, D. R., Roberts, D. A., Gao, B. C., Green, R. O., Guild, L., Hayashi, K., Kudela, R., Palacios, S. 2016. Atmospheric correction with the Bayesian empirical line. Optics Express. 24(3), 2134. DOI: 10.1364/OE.24.002134
More details may be found in the following project profile(s):
