<abstract xmlns="http://www.w3.org/1999/xhtml">Leaf area index (LAI) characterizes the amount of photosynthetically active tissue in plant canopies. LAI is one of the key factors determining ecosystem net primary production and gas and energy exchange between the canopy and the atmosphere. The aim of the present study was to test different methods for LAI and effective plant area index (PAIe) estimation in mixed hemiboreal forests in Järvselja SMEAR Estonia (Station for Measuring Ecosystem-Atmosphere Relations) flux tower footprint. We used digital hemispherical images from sample plots, forest management inventory data, allometric foliage mass models, airborne discrete-return recording laser scanner (ALS) data and multispectral satellite images. The free ware program HemiSpherical Project Manager (HSP) was used to calculate canopy gap fraction from digital hemispherical photographs taken in 25 sample plots. PAIe was calculated from the gap fraction for up-scaling based on ALS point cloud metrics. The all ALS pulse returns-based canopy transmission was found to be the most suitable lidar metric to estimate PAIe in Järvselja forests. The 95-percentile (<italic>H95</italic>) of lidar point cloud height distribution correlates very well with allometric regression models based LAI and in birch stands the relationship was fitted with 0.7 m2 m−2 residual error. However, the relationship was specific to each allometric foliage mass model and systematic discrepancies were detected at large LAI values between the models. Relationships between the spectral reflectance and allometric LAI were not good enough to be used for LAI mapping. Therefore, airborne laser scanning data-based PAIe map was created for areas near SMEAR tower. We recommend to establish a network of permanent sample plots for forest growth and gap fraction measurements into the flux footprint of SMEAR Estonia flux tower in Järvselja to provide consistent up to date data for interpretation of the flux measurements.</abstract>

Leaf area index (LAI) characterizes the amount of photosynthetically active tissue in plant canopies. LAI is one of the key factors determining ecosystem net primary production and gas and energy exchange between the canopy and the atmosphere. The aim of the present study was to test different methods for LAI and effective plant area index (PAIe) estimation in mixed hemiboreal forests in Järvselja SMEAR Estonia (Station for Measuring Ecosystem-Atmosphere Relations) flux tower footprint. We used digital hemispherical images from sample plots, forest management inventory data, allometric foliage mass models, airborne discrete-return recording laser scanner (ALS) data and multispectral satellite images. The free ware program HemiSpherical Project Manager (HSP) was used to calculate canopy gap fraction from digital hemispherical photographs taken in 25 sample plots. PAIe was calculated from the gap fraction for up-scaling based on ALS point cloud metrics. The all ALS pulse returns-based canopy transmission was found to be the most suitable lidar metric to estimate PAIe in Järvselja forests. The 95-percentile (H95) of lidar point cloud height distribution correlates very well with allometric regression models based LAI and in birch stands the relationship was fitted with 0.7 m2 m−2 residual error. However, the relationship was specific to each allometric foliage mass model and systematic discrepancies were detected at large LAI values between the models. Relationships between the spectral reflectance and allometric LAI were not good enough to be used for LAI mapping. Therefore, airborne laser scanning data-based PAIe map was created for areas near SMEAR tower. We recommend to establish a network of permanent sample plots for forest growth and gap fraction measurements into the flux footprint of SMEAR Estonia flux tower in Järvselja to provide consistent up to date data for interpretation of the flux measurements.

Leaf area index mapping with optical methods and allometric models in SMEAR flux tower footprint at Järvselja, Estonia

Tartu Observatory, 61602 Tõravere, Tartumaa, Estonia

Institute of Forestry and Rural Engineering, Estonian University of Life Sciences, Kreutzwaldi 5, 51014 Tartu, Estonia

Forestry Studies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Leaf area index (LAI) characterizes the amount of photosynthetically active tissue in plant canopies. LAI is one of the key factors determining ecosystem net...

Leaf area index mapping with optical methods and allometric models in SMEAR flux tower footprint at Järvselja, Estonia

Online veröffentlicht: 08. Juni 2016

Seitenbereich: 85 - 99

Eingereicht: 16. Juli 2015

Akzeptiert: 11. Nov. 2015

DOI: https://doi.org/10.1515/fsmu-2015-0010

Schlüsselwörter
leaf area index, airborne lidar, hemispherical images, multispectral satellite images, SMEAR

© 2015 Marta Mõistus et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Leaf area index mapping with optical methods and allometric models in SMEAR flux tower footprint at Järvselja, Estonia

Online veröffentlicht: 08. Juni 2016

Seitenbereich: 85 - 99

Eingereicht: 16. Juli 2015

Akzeptiert: 11. Nov. 2015

DOI: https://doi.org/10.1515/fsmu-2015-0010

Schlüsselwörterleaf area index, airborne lidar, hemispherical images, multispectral satellite images, SMEAR

© 2015 Marta Mõistus et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
leaf area index, airborne lidar, hemispherical images, multispectral satellite images, SMEAR