Estimation of forest leaf water content through inversion of a radiative transfer model from LiDAR and hyperspectral data |
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| Institution: | 1. Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, P.O. Box 217, 7500 AE Enschede, the Netherlands;2. Department of Environmental Science, Macquarie University, NSW 2109, Australia;1. Department of Natural Resources, Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, P.O. Box 217, 7500 AE Enschede, The Netherlands;2. Department of Geography, University of Victoria, P.O. Box 1700 STN CSC, Victoria, BC V8W 2Y2, Canada;1. Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, P.O. Box 217, 7500 AE, Enschede, The Netherlands;2. Department of Environmental Science, Macquarie University, NSW, 2106, Australia;3. Department of Conservation and Research, Bavarian Forest National Park, 94481, Grafenau, Germany;4. Chair of Wildlife Ecology and Wildlife Management, University of Freiburg, Tennenbacher Straße 4, Freiburg, Germany;1. Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, P.O. Box 217, 7500 AE Enschede, The Netherlands;2. School of Mathematical and Geospatial Sciences, RMIT University, GPO Box 2476, Melbourne, Victoria 3001, Australia;1. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, Hubei 430079, China;2. Collaborative Innovation Center of Geospatial Technology, Wuhan, Hubei 430079, China;3. Faculty of Information Engineering, China University of Geosciences, Wuhan, Hubei 430074, China;4. International Institute for Earth System Science, Nanjing University, Nanjing, Jiangsu 210023, China;5. Laboratory of Critical Zone Evolution, School of Earth Sciences, China University of Geosciences, Wuhan 430074, China;1. Xinjiang Institute of Ecology and Geography Chinese Academy of Sciences, 818 Beijing South Road, Urumqi, Xinjiang 830011, PR China;2. Institute of Hydrology and Water Resources, Zhejiang University, Hangzhou, Zhejiang 310058, PR China;1. Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Hengelosestraat 99, P.O. Box 217, 7500 AE Enschede, The Netherlands;2. Department of Geography, University of Victoria, PO Box 1700 STN CSC, Victoria, BC, V8W 2Y2, Canada |
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| Abstract: | The accurate estimation of leaf water content (LWC) and knowledge about its spatial variation are important for forest and agricultural management since LWC provides key information for evaluating plant physiology. Hyperspectral data have been widely used to estimate LWC. However, the canopy reflectance can be affected by canopy structure, thereby introducing error to the retrieval of LWC from hyperspectral data alone. Radiative transfer models (RTM) provide a robust approach to combine LiDAR and hyperspectral data in order to address the confounding effects caused by the variation of canopy structure. In this study, the INFORM model was adjusted to retrieve LWC from airborne hyperspectral and LiDAR data. Two structural parameters (i.e. stem density and crown diameter) in the input of the INFORM model that affect canopy reflectance most were replaced by canopy cover which could be directly obtained from LiDAR data. The LiDAR-derived canopy cover was used to constrain in the inversion procedure to alleviate the ill-posed problem. The models were validated against field measurements obtained from 26 forest plots and then used to map LWC in the southern part of the Bavarian Forest National Park in Germany. The results show that with the introduction of prior information of canopy cover obtained from LiDAR data, LWC could be retrieved with a good accuracy (R2 = 0.87, RMSE = 0.0022 g/cm2, nRMSE = 0.13). The adjustment of the INFORM model facilitated the introduction of prior information over a large extent, as the estimation of canopy cover can be achieved from airborne LiDAR data. |
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| Keywords: | Leaf water content INFORM Prior information Canopy cover |
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