Estimation of FPAR and FPAR profile for maize canopies using airborne LiDAR. (December 2017)

• Record Type: Journal Article
• Authors: Qin, Haiming; Wang, Cheng; Pan, Feifei; Lin, Yi; Xi, Xiaohuan; Luo, Shezhou
• Is Part Of: Ecological indicators. Volume 83(2017)
• Page Start: 53
• Page End: 61
• Publication Date: 2017-12
• Subjects: Airborne LiDAR -- Full-waveform -- FPAR -- Maize -- FPAR profile; Environmental monitoring -- Periodicals; Environmental management -- Periodicals; Environmental impact analysis -- Periodicals; Environmental risk assessment -- Periodicals; Sustainable development -- Periodicals; 333.71405
• DOI: 10.1016/j.ecolind.2017.07.044
• Languages: English
• ISSNs: 1470-160X

Abstract

Highlights: Vegetation FPAR profile is estimated firstly. Maize FPAR and FPAR profile is first estimated by airborne full-waveform LiDAR data. The method of generating pseudo-waveform from intensity corrected small-footprint full-waveform LiDAR data is proposed. Abstract: FPAR (fraction of photosynthetically active radiation) and FPAR profile (vertical FPAR distribution) are important parameters for characterizing the vegetation growth status and studying global climate change. Few studies have been carried out to estimate FPAR and FPAR profile using waveform LiDAR data. This research explored the potential of airborne small-footprint full-waveform LiDAR in the estimation of FPAR and FPAR profile of the maize canopy in Huailai County of Hebei Province, China. First, the maize growing area was identified by a simple decision tree model. Second, raw waveform data were processed to extract LiDAR-derived energy ratio and energy ratio profile. Third, FPAR and FPAR profile were estimated from LiDAR-derived metrics. Finally, we analyzed the FPAR and FPAR profile estimation results and assessed the model validity using the leave-one-out cross-validation (LOOCV) method. The comparative analyses found that the LiDAR-derived energy ratio profile and field-measured FPAR profile had the same trend and similar change rate for all maize layers. The accuracy assessments indicated that the FPAR and FPAR profile were estimated well by the LiDAR waveform data, with the high R 2 (0.90 for the whole canopy, and 0.95, 0.90, 0.93, 0.92, and 0.97 for layers 1–5) and low RMSEs (0.042 for the whole canopy, and 0.033, 0.035, 0.039, 0.043, and 0.044 for layers 1–5). The spatial distribution map of FPAR was produced to describe the maize growth status of the whole study area, and the map showed that the FPAR distributed relatively uniformly. This study suggested that airborne small-footprint full-waveform LiDAR was useful in accurately measuring FPAR and FPAR profile of the maize canopy and in effectively mapping the maize FPAR spatial distribution.