Effects of Different LiDAR Intensity Normalization Methods on Scotch Pine Forest Leaf Area Index Estimation

doi:10.11947/j.AGCS.2018.20170515

Abstract

Abstract: The intensity data of airborne light detection and ranging (LiDAR) are affected by many factors during the acquisition process. It is of great significance for the normalization and application of LiDAR intensity data to study the effective quantification and normalization of the effect from each factor. In this paper, the LiDAR data were normalized with range, angel of incidence, range and angle of incidence based on radar equation, respectively. Then two metrics, including canopy intensity sum and ratio of intensity, were extracted and used to estimate forest LAI, which was aimed at quantifying the effects of intensity normalization on forest LAI estimation. It was found that the range intensity normalization could improve the accuracy of forest LAI estimation. While the angle of incidence intensity normalization did not improve the accuracy and made the results worse. Although the range and incidence angle normalized intensity data could improve the accuracy, the improvement was less than the result of range intensity normalization. Meanwhile, the differences between the results of forest LAI estimation from raw intensity data and normalized intensity data were relatively big for canopy intensity sum metrics. However, the differences were relatively small for the ratio of intensity metrics. The results demonstrated that the effects of intensity normalization on forest LAI estimation were depended on the choice of affecting factor, and the influential level is closely related to the characteristics of metrics used. Therefore, the appropriate method of intensity normalization should be chosen according to the characteristics of metrics used in the future research, which could avoid the waste of cost and the reduction of estimation accuracy caused by the introduction of inappropriate affecting factors into intensity normalization.

Key words: range, angle of incidence, canopy intensity sum, ratio of intensity, leaf area index

CLC Number:

P237

YOU Haotian, XING Yanqiu, PENG Tao, DING Jianhua. Effects of Different LiDAR Intensity Normalization Methods on Scotch Pine Forest Leaf Area Index Estimation[J]. Acta Geodaetica et Cartographica Sinica, 2018, 47(2): 170-179.

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