Use of airborne LiDAR for estimating leaf area index of tropical montane forests

Leaf area index (LAI) is one descriptor of forest canopy structure and can be linked to vegetation productivity, carbon cycling, and several other ecosystem services. Airborne lidar (light detection and ranging) provides proxies of canopy gap fraction (GF) in the near-vertical direction, which can be related to LAI using a logarithmic model derived from Beer’s Law. The approach has been successful in LAI mapping in boreal and temperate forests. In this study, we evaluated the logarithmic model and several GF proxies in tropical montane forests in southeastern Kenya. We used two discrete-return lidar datasets (max. scan angle ~16°) with different flying heights and pulse densities (5.4 and 2.6 pulses m–2). GF for the 0–15° zenith angle range (GF15) and effective LAI (Le) were estimated for 29 sample plots using digital hemispherical photography. Twenty-one plots were located in indigenous forests and eight plots in plantation forests. According to the results, GF15 was best approximated by the proxies that included all canopy and ground return types (all echo cover index, ACI, root mean square error, RMSE = 0.050, bias = –0.003; Solberg’s cover index, SCI, RMSE = 0.057, bias = 0.002) although some saturation occurred when using data from the higher flight altitude. The results of the Le modelling propose that the logarithmic model needs to be fit separately for indigenous forest and plantations. Furthermore, the slope parameters of the models based on SCI suggest planophile ( 1.6) and spherical ( 2) leaf angle distribution for indigenous forests and plantations, respectively. We conclude that lidar cover indices based on all returns can estimate GF15 in closed-canopy tropical forests but the detection of the smallest gaps can be limited by the scanner or scanning parameters. The application of the logarithmic model requires stratification in the structurally heterogeneous and multi-species forest areas as should be estimated separately for the different forest types

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DOI:

https://doi.org/10.1080/01431161.2015.1041177

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