van Aardt, Jan: Rochester Institute of Technology (Project Lead)
Project Funding:
2013 - 2016
NRA: 2011 NASA: HyspIRI Preparatory Airborne Activities and Associated Science and Applications
Funded by NASA
Abstract:
Overview: The estimation of vegetation structural parameters, specifically in forest environments, has evolved from a spectral (multispectral to hyperspectral) to structural (radar to lidar) sensing modality space. All of these remote sensing modalities have their respective advantages and disadvantages. The goal of this proposal therefore is to integrate fine scale airborne and ground-based lidar assessments with the relatively coarse scale, but global coverage, HyspIRI imagery to improve HyspIRI-based vegetation structure and function assessments. We hypothesize that: (1)fine-scale, sub-pixel structural assessments can be used to improve our understanding of HyspIRI-based estimates of leaf area, leaf area index (LAI), and vegetation biomass; (2) from a systems perspective, the spatially explicit sub-pixel structural variations are quantifiable when it comes to their impact on the HyspIRI system s response (point spread function); and (3)an improved understanding of (1) and (2) will lead to proper calibration of HyspIRI-based vegetation structure estimates. Our objectives thus are to (i) assess the structural variability within HyspIRI-scale hyperspectral pixels, (ii) link that variability to structural estimates, derived from HyspIRI, and (iii) improve those estimates via a defined calibration-validation effort.
Brief methods: We propose to achieve these objectives by making use of the FY2013 and FY2014 HyspIRI-like airborne measurements over the National Ecological Observatory Network (NEON) Pacific Southwest domain, coupled to NEON Airborne Observation Platform (AOP) flights with a next-generation AVIRIS and waveform lidar onboard, and ground-based lidar and field sampling. Spatially explicit field structural measurements will be used to assess the vegetation structure models (biomass, LAI, etc.) from the HyspIRI sensor. We propose to also use a simulation-based approach, whereby we will simulate HyspIRI and structural sensing over a virtual forest scene using the MODTRAN-based Digital Imaging and Remote Sensing Image Generation tool (DIRSIG) to address hypothesis (2). The impact of the spatial distribution and variability of structure on HyspIRI parameter estimates will be assessed via correlation, sensitivity, and linear and non-linear modeling methods, e.g., Markov modeling and neural networks.
Significance: While ecosystem composition and to some degree, function , have been addressed by hyperspectral remote sensing with relative efficacy, the structural detail of many vegetation systems still present challenges. Leaf-related structural assessment approaches often saturate at high biomass levels, while it remains difficult to relate volume scattering to sub-canopy structure. This proposal therefore is aimed at an evaluation of the synergies between structure and hyperspectral remote sensing at the HyspIRI resolution/scales, in context of the HyspIRI science questions and structure-function type relationships. Specifically, we aim to address whether or not such a fusion of structural-spectral sensing could be useful in terms of
- HyspIRI cq5: What is the composition of exposed terrestrial surface of the Earth and how does it respond to anthropogenic and non-anthropogenic drivers?
- HyspIRI vq1: What is the global spatial pattern of ecosystem and diversity distributions and how do ecosystems differ in their composition or biodiversity?
The improved LAI, vertically stratified LAI, and functional-type LAI, and its link to biodiversity in terms of "structural biodiversity" (cq 5 and vq1) are of specific interest. Finally, we believe that project outputs will contribute to the NASA Earth Science Research Program focus area of Carbon Cycle and Ecosystems and assessing (i) how these systems are changing, (ii) what causes such change, and (iii) what impact does global environmental change have on system structure.
Publications:
Kelbe, D., van Aardt, J., Romanczyk, P., van Leeuwen, M., Cawse-Nicholson, K. 2015. Single-Scan Stem Reconstruction Using Low-Resolution Terrestrial Laser Scanner Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 8(7), 3414-3427. DOI: 10.1109/jstars.2015.2416001
Kelbe, D., van Aardt, J., Romanczyk, P., van Leeuwen, M., Cawse-Nicholson, K. 2016. Marker-Free Registration of Forest Terrestrial Laser Scanner Data Pairs With Embedded Confidence Metrics. IEEE Transactions on Geoscience and Remote Sensing. 54(7), 4314-4330. DOI: 10.1109/tgrs.2016.2539219
Kelbe, D., van Aardt, J., Romanczyk, P., van Leeuwen, M., Cawse-Nicholson, K. 2017. Multiview Marker-Free Registration of Forest Terrestrial Laser Scanner Data With Embedded Confidence Metrics. IEEE Transactions on Geoscience and Remote Sensing. 55(2), 729-741. DOI: 10.1109/tgrs.2016.2614251
Wang, M., Yao, W., Brown, S., Goodenough, A., van Aardt, J. 2016. On validating remote sensing simulations using coincident real data. Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXII. DOI: 10.1117/12.2223455
Williams, M., Parody, R., Fafard, A., Kerekes, J., van Aardt, J. 2017. Validation of Abundance Map Reference Data for Spectral Unmixing. Remote Sensing. 9(5), 473. DOI: 10.3390/rs9050473
Yao, W., Kelbe, D., Leeuwen, M., Romanczyk, P., Aardt, J. 2016. Towards an Improved LAI Collection Protocol via Simulated and Field-Based PAR Sensing. Sensors. 16(7), 1092. DOI: 10.3390/s16071092
Yao, W., van Aardt, J., van Leeuwen, M., Kelbe, D., Romanczyk, P. 2018. A Simulation-Based Approach to Assess Subpixel Vegetation Structural Variation Impacts on Global Imaging Spectroscopy. IEEE Transactions on Geoscience and Remote Sensing. 56(7), 4149-4164. DOI: 10.1109/TGRS.2018.2827376
Yao, W., van Leeuwen, M., Romanczyk, P., Kelbe, D., Brown, S., Kerekes, J., van Aardt, J. 2015. Towards robust forest leaf area index assessment using an imaging spectroscopy simulation approach. 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). DOI: 10.1109/igarss.2015.7327057
Yao, W., van Leeuwen, M., Romanczyk, P., Kelbe, D., van Aardt, J. 2015. Assessing the impact of sub-pixel vegetation structure on imaging spectroscopy via simulation. Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXI. DOI: 10.1117/12.2176992
Yao, W., van Leeuwen, M., Romanczyk, P., Kelbe, D., Wang, M., Brown, S. D., Goodenough, A. A., van Aardt, J. 2016. Towards an improved understanding of the influence of subpixel vegetation structure on pixel-level spectra: a simulation approach. Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXII. DOI: 10.1117/12.2224141
More details may be found in the following project profile(s):