CHP toolkit: case study of LAIe sensitivity to discontinuity of canopy cover in fruit plantations

Fieber, K. D., Davenport, I. J., Ferryman, J. M., Gurney, R. J., Becerra, V. M., Walker, J. P. and Hacker, J. M. (2016) CHP toolkit: case study of LAIe sensitivity to discontinuity of canopy cover in fruit plantations. IEEE Transactions on Geoscience and Remote Sensing, 54 (9). pp. 5071-5080. ISSN 0196-2892 doi: 10.1109/TGRS.2016.2550623

Abstract/Summary

This paper presents an open-source canopy height profile (CHP) toolkit designed for processing small-footprint full-waveform LiDAR data to obtain the estimates of effective leaf area index (LAIe) and CHPs. The use of the toolkit is presented with a case study of LAIe estimation in discontinuous-canopy fruit plantations. The experiments are carried out in two study areas, namely, orange and almond plantations, with different percentages of canopy cover (48% and 40%, respectively). For comparison, two commonly used discrete-point LAIe estimation methods are also tested. The LiDAR LAIe values are first computed for each of the sites and each method as a whole, providing “apparent” site-level LAIe, which disregards the discontinuity of the plantations’ canopies. Since the toolkit allows for the calculation of the study area LAIe at different spatial scales, between-tree-level clumpingcan be easily accounted for and is then used to illustrate the impact of the discontinuity of canopy cover on LAIe retrieval. The LiDAR LAIe estimates are therefore computed at smaller scales as a mean of LAIe in various grid-cell sizes, providing estimates of “actual” site-level LAIe. Subsequently, the LiDAR LAIe results are compared with theoretical models of “apparent” LAIe versus “actual” LAIe, based on known percent canopy cover in each site. The comparison of those models to LiDAR LAIe derived from the smallest grid-cell sizes against the estimates of LAIe for the whole site has shown that the LAIe estimates obtained from the CHP toolkit provided values that are closest to those of theoretical models.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/65601
Identification Number/DOI	10.1109/TGRS.2016.2550623
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science Science > School of Archaeology, Geography and Environmental Science > Department of Geography and Environmental Science Science > School of Mathematical, Physical and Computational Sciences > Environmental Systems Science Centre Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
Publisher	IEEE Geoscience and Remote Sensing Society
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