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Estimation of forest leaf area index based on spectrally corrected airborne LiDAR pulse penetration index by intensity of point cloud

  • role： First author第一作者
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 100875, China

    Beijing Engineering Research Center for Global Land Remote Sensing Products, Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

  • Email：tianluo@mail.bnu.edu.cn
  • Introduction：田罗，1991年生,男,博士研究生, 研究方向为激光雷达植被遥感。E-mail: tianluo@mail.bnu.edu.cn
TIAN Luo12,  
  • role： Corresponding author通信作者
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 100875, China

    Beijing Engineering Research Center for Global Land Remote Sensing Products, Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

  • Email：qyh@bnu.edu.cn
  • Introduction：屈永华，1972年生,男,副教授, 研究方向为定量遥感。E-mail: qyh@bnu.edu.cn
QU Yonghua12*,  
  • Affiliation：

    University of Eastern Finland, School of Forest Sciences, 70210 Finland

KORHONEN Lauri3,  
  • Affiliation：

    Department of Geosciences and Geography, University of Helsinki, 00100 Finland

    Institute for Atmospheric and Earth System Research, Faculty of Science, University of Helsinki, 00100 Finland

KORPELA Ilkka45,  
  • Affiliation：

    Department of Geosciences and Geography, University of Helsinki, 00100 Finland

    Institute for Atmospheric and Earth System Research, Faculty of Science, University of Helsinki, 00100 Finland

HEISKANEN Janne45

resumen

Canopy gap fraction and extinction coefficient are two primary variables to retrieve Leaf Area Index (LAI) from light transmittance-based model. Currently, for the difficulty of calculating gap fraction from discrete LiDAR Point Cloud Data (PCD), LiDAR Penetration Index (LPI) is used as the alternative of gap fraction to estimate LAI. However, LPI ignores the target spectral difference which is an important factor affecting the number of canopy and background echoes. Therefore, the backscattering coefficient of the background and canopy, μ=ρg/ρv, is required to correct the LPI to GF. We extracted μ from intensity of the PCD data, which achieved by using a linear regression between the intensity of background and that of canopy in each pulse intensity groups, then the mean μ of all valid groups was used to transform LPI to gap. Given there was a dominant species of vegetation in study area, the light extinction coefficient (k) was extracted using constrained optimization method to obtain the ellipsoidal model parameter χ from multi-angle gap fraction at the large spatial scale (tile scale) under the assumption that the leaf angle distribution can be modeled by a ellipsoidal model and the leaf mean tilt angle is constant through study area. Finally, LiDAR LAI was estimated using retrieved gap fraction and extinction coefficient. Meanwhile, the impact of tile scale (Rxy_Tile), sample scale Rxy_Plot and height threshold (Ht) were also investigated. The results showed that the μ value was close to unit, and it is contributed by the extensive coverage of lichen vegetation in the area, which is consistent with the actual field characteristics. The gap fraction corrected by μ has a good ability to reflect the field measured data (R2=0.78, RMSE=0.09), and the leaf angle distribution parameter χ, is affected mainly by the large gap between the crowns for areas with dominant species. In terms of size of tile, the retrieval χ, the parameter of ellipsoidal model, was sensitive to the spatial size of tile, which means that attention should be paid to select tile size. An ill-suited tile size would result in a systematic underestimation of LAI. For the target parameter of LAI, the result revealed that it was highly consistent with the ground measurement (R2=0.84, RMSE=0.51) under the condition of Rxy_Tile, Rxy_Plot and Ht of 950 m, 10 m and 2.6 m respectively. It was concluded that the retrieved LAI was more sensitive to the choice of Ht, and it was noted that more attention would be paid to select appropriate Ht to ensuring the consistent result of LiDAR LAI and field measurements in the further work direction. We conclude that it is feasible to retrieve μ and further to produce LAI using ALS PCD data only. The significance of the proposed method is that it can produce reliable remotely sensed LAI from ALS PCD even with no ancillary spectral data.

palabra clave

remote sensing;leaf area index;LiDAR;gap fraction;extinction coefficient;target spectral property

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