Estimation of forest canopy structural parameters using kernel-driven bi-directional reflectance model based multi-angular vegetation indices |
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| Affiliation: | 1. Center for Environmental Measurement and Analysis, National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba, Ibaraki 305-8506, Japan;2. Department of Computer & Information Sciences, College of Engineering, Ibaraki University, 4-12-1 Nakanarusawa, Hitachi, Ibaraki 316-8511, Japan;1. Global Change Research Institute, Academy of Sciences of the Czech Republic, Bělidla 4a, 60300 Brno, Czech Republic;2. Department of Experimental Plant Biology, Faculty of Science, Charles University in Prague, Viničná 5, 12844 Prague, Czech Republic;1. State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Institute of Remote Sensing and Digital Earth of the Chinese Academy of Sciences, Beijing 100875, China;2. Beijing Engineering Research Center for Global Land Remote Sensing Products, Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China;3. VITROCISET, Bratustrasse 7, 64293 Darmstadt, Germany;4. School for the Environment, Earth and Ocean Sciences, University of Massachusetts, Boston, MA, USA;5. Laboratoire des Sciences du Climat et de l''Environnement, CEA-CNRS-UVSQ, 91191 Gif sur Yvette, France;6. College of Water Sciences, Beijing Normal University, Beijing 100875, China;7. Terrestrial Information Systems Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA;8. Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, USA;9. Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China;10. Institute of Environmental Physics, University of Bremen, Otto-Hahn-Allee 1, 28359 Bremen, Germany;1. SERCO SpA c/o European Space Agency ESA, European Space Research Institute (ESRIN), 00044 Frascati, Italy;2. NASA Goddard Space Flight Center Code 619, Greenbelt, MD 20771, USA;3. Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA;4. European Space Agency ESA, European Space Research Institute (ESRIN), 00044 Frascati, Italy;5. Planet Labs PBC, San Francisco, USA;6. VITO, Boeretang 200, 2400 Mol, Belgium;7. Centre National d''Etudes Spatiales CNES, 31401 Toulouse Cedex 9, France;8. Geography Department, Humboldt-Universität zu Berlin, 10099 Berlin, Germany;9. Earth Observation and Climate Processes, Trier University, 54286 Trier, Germany;10. KBR, Contractor to U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS), Sioux Falls, SD 57198, USA;11. Telespazio France, 31023 Toulouse, France;12. Maxar, Denver, CO, United States;13. DLR, German Aerospace Center, Berlin, Germany;14. Airbus Defence and Space, 5, rue des Satellites, 31400 Toulouse, France;15. HYGEOS, Euratechnologies, 165 avenue de Bretagne, 59000 Lille, France;p. DLR, German Aerospace Center, Wessling, Germany;q. Department of Geography, University College London, United Kingdom;r. National Centre for Earth Observation (NCEO), NERC, United Kingdom |
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| Abstract: | Near-surface bi-directional reflectance and high-spatial resolution true-color imagery of several forested canopies were acquired using an unmanned helicopter. The observed reflectance from multiple view-zenith angles were simulated with a kernel-driven bidirectional reflectance model, and the BRDF parameters were retrieved. Based on the retrieved BRDF parameters, kernel-derived multi-angular vegetation indices (KMVIs) were computed. The potential of KMVI for prediction of canopy structural parameters such as canopy fraction and canopy volume was assessed. The performance of each KMVI was tested by comparison to field measured canopy fraction and canopy volume. For the prediction of canopy fraction, the KMVI that included the nadir-based NDVI performed better than other KMVI emphasizing the importance of nadir observation for remote estimation of the canopy fraction. The Nadir BRDF-adjusted NDVI was found to be superior for the prediction of canopy fraction, which could explain 77% variation of the canopy fraction. However, none of the existing KMVI predicted the canopy volume better than Nadir BRDF-adjusted NDVI and Nadir-view NDVI. The Canopy structural index (CSI) was proposed with the combination of normalized difference between dark-spot near infrared reflectance and hot-spot red reflectance. The CSI could establish an improved relationship with the canopy volume over Nadir BRDF-adjusted NDVI and Nadir-view NDVI, explaining 72% variation in canopy volume. In addition, MODIS based KMVI were evaluated for the prediction of canopy fraction and canopy volume. MODIS based KMVI also showed similar results to the helicopter based KMVI. The promising results shown by the CSI suggest that it could be an appropriate candidate for remote estimation of three-dimensional canopy structure. |
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