A novel approach to estimate canopy height using ICESat/GLAS data: A case study in the New Forest National Park,UK |
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| Affiliation: | 1. College of Forestry, Wildlife and Environment, Auburn University, Auburn, AL 36849, USA;2. Department of Ecology and Conservation Biology, Texas A&M University, College Station, TX 77843, USA;1. Boise Center Aerospace Laboratory, Department of Geosciences, Boise State University, 1910 University Drive, Boise, ID 83725-1535, USA;2. Geospatial Laser Applications and Measurements, Applied Research Laboratories, University of Texas at Austin, 10000 Burnet Rd, Austin, TX 78758, USA;3. Department of Natural Resources and Society, University of Idaho, 875 Perimeter Drive MS 1133, Moscow, ID 83844-1133, USA;4. US Geological Survey Forest and Rangeland Ecosystem Science Center, 970 Lusk Street, Boise, ID 83706, USA;1. State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China;2. University of Chinese Academy of Sciences, Beijing 100049, China;3. Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA;4. Institute of Geodesy and Geophysics, Chinese Academy of Sciences, Wuhan 430077, China |
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| Abstract: | The Geoscience Laser Altimeter System (GLAS) aboard Ice, Cloud and land Elevation Satellite (ICESat) is a spaceborne LiDAR sensor. It is the first LiDAR instrument which can digitize the backscattered waveform and offer near global coverage. Among others, scientific objectives of the mission include precise measurement of vegetation canopy heights. Existing approaches of waveform processing for canopy height estimation suggest Gaussian decomposition of the waveform which has the limitation to properly characterize significant peaks and results in discrepant information. Moreover, in most cases, Digital Terrain Models (DTMs) are required for canopy height estimation. This paper presents a new automated method of GLAS waveform processing for extracting vegetation canopy height in the absence of a DTM. Canopy heights retrieved from GLAS waveforms were validated with field measured heights. The newly proposed method was able to explain 79% of variation in canopy heights with an RMSE of 3.18 m, in the study area. The unexplained variation in canopy heights retrieved from GLAS data can be due to errors introduced by footprint eccentricity, decay of energy between emitted and received signals, uncertainty in the field measurements and limited number of sampled footprints.Results achieved with the newly proposed method were encouraging and demonstrated its potential of processing full-waveform LiDAR data for estimating forest canopy height. The study also had implications on future full-waveform spaceborne missions and their utility in vegetation studies. |
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| Keywords: | Canopy height LiDAR Remote sensing |
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