A method for determining suitable scales for vegetation remote sensing based on the spatial distribution of leaves

doi:10.6046/zrzyyg.2021148

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Abstract

High spatial resolution remote sensing data serve as a new data source for quantitative remote sensing of vegetation, bringing in both new challenges and opportunities. The traditional leaf area index (LAI) inversion method based on the radiative transfer theory takes Beer-Lambert Law as the primary theoretical basis. The prerequisite for its application is that the leaf distribution in pixels follows a Poisson distribution. This study explored the appropriate scale in the case that the spatial distribution of continuous vegetation leaves in pixels follows a Poisson distribution. Focusing on the wheat canopy, this study used the LESS (LargE-Scale remote sensing data and image Simulation framework) software to simulate the remote sensing images of continuous wheat canopy. Based on this, this study analyzed the appropriate scale on which continuous wheat canopy leaves follow a Poisson distribution through the three-dimensional simulation of leaf canopy. Moreover, this study constructed a method for calculating the appropriate scale of the LAI inversion of continuous vegetation. The results show that the appropriate scale is influenced by the LAI value and the aggregation effect. The UAV hyperspectral data and the LAI inversion results from Luohe City, Henan Province validated the feasibility of this method.

Keywords high spatial resolution LESS appropriate scale

Corresponding Authors: FAN Wenjie E-mail: wuhb@pku.edu.cn;fanwj@pku.edu.cn

Issue Date: 20 June 2022

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	Haobo WU
	Mengtong WU
	Siqi YANG
	Wenjie FAN
	Huazhong REN

Cite this article:

Haobo WU,Mengtong WU,Siqi YANG, et al. A method for determining suitable scales for vegetation remote sensing based on the spatial distribution of leaves[J]. Remote Sensing for Natural Resources, 2022, 34(2): 72-79.

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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2021148 OR https://www.gtzyyg.com/EN/Y2022/V34/I2/72

Tab.1 Main scene parameter

Fig.1 Wheat and leaf model at heading stage

Fig.2 Schematic diagram of wheat distribution in simulated scenario

Fig.3 Schematic diagram of the experimental site in Luohe City

Fig.4 Drone and imaging spectrometer

Fig.5 Schematic diagram of the statistical histogram of the spatial distribution of leaves in the scene with LAI of 4.18 (resolution of 0.1 m)

Fig.6 Statistical histogram of LAI distribution at 2 m resolution

Tab.2 2 m resolution LAI distribution goodness of fit calculation process

Fig.7 Euclidean distance similarity curve in different scenarios

Fig.8 UAV hyperspectral image and LAI inversion results

Fig.9 Real image similarity curve

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