Sensitivity of direct canopy gap fraction retrieval from airborne waveform lidar to topography and survey characteristics

REMOTE SENSING OF ENVIRONMENT

卷:143

Article

Chen, X. T.¹  Disney, M. I.^2,3  Lewis, P.^2,3  Armston, J.^4,5  Han, J. T.¹  Li, J. C.¹ 

[1] Natl Univ Def Technol, Coll Elect Sci & Engn, Changsha 410073, Hunan, Peoples R China

[2] UCL, Dept Geog, London WC1E 6BT, England

[3] NERC Natl Ctr Earth Observat NCEO, Reading, Berks, England

[4] Ctr Remote Sensing, Dept Sci Informat Technol Innovat & Arts, Brisbane, Qld 4102, Australia

[5] Univ Queensland, Sch Geog Planning & Environm Management, Joint Remote Sensing Res Program, St Lucia, Qld 4072, Australia

关键词: Waveform lidar;    Canopy;    Gap fraction;    Airborne;    Forest;    Biophysical parameters;   

DOI  :  10.1016/j.rse.2013.12.010

来源: Elsevier

【 摘 要 】

Recently, Armston et al. (2013) have demonstrated that a new, physically-based method for direct retrieval of canopy gap probability P-gap from waveform lidar can improve the estimation of P-gap over discrete return lidar data. The success of the approach was demonstrated in a savanna woodland environment in Australia. The huge advantage of this method is that it uses the data themselves to solve for the canopy contrast term i.e. the ratio of the reflectance from crown and ground, rho(v)/rho(g). In this way the method avoids local calibration that is typically required to overcome differences in either rho(v) or rho(g). To be more generally useful the method must be demonstrated on different sites and in the presence of slope and different sensor and survey configurations. If it is robust to these things, slope in particular, then we would suggest it is likely to be widely useful. Here, we test the robustness of the retrieval of P-gap from waveform lidar using the Watershed Allied Telemetry Experimental Research dataset, over the Heihe River Basin region of China. The data contain significant canopy, terrain and survey variations, presenting a rather different set of conditions to those previously used. Results show that rho(v)/rho(g) is seen to be stable across all flights and for all levels of spatial aggregation. This strongly supports the robustness of the new P-gap retrieval method, which assumes that this relationship is stable. A comparison between P-gap estimated from hemiphotos and from the waveform lidar showed agreement with Pearson correlation coefficient R = 0.91. The waveform lidar-derived estimates of P-gap agreed to within 8% of values derived from hemiphotos, with a bias of 0.17%. The new waveform model was shown to be stable across different off-nadir scan angles and in the presence of slopes up to 26 degrees with R >= 0.85 in all cases. We also show that the waveform model can be used to calculate P-gap using just the mean value of canopy returns, assuming that their distribution is unimodal. Lastly, we show that the method can also be applied to discrete return lidar data, albeit with slightly lower accuracy and higher bias, allowing P-gap comparisons with previously-collected lidar datasets. Our results show the new method should be applicable for estimating P-gap robustly across large areas, and from lidar data collected at different times and using different systems; an increasingly iinportant requirement. (C) 2014 The Authors. Published by Elsevier Inc. All rights reserved.

【 授权许可】

Free